Error: 404 - Page Not Found
+ + diff --git a/doc/.gitignore b/doc/.gitignore new file mode 100644 index 00000000..fcb8f85a --- /dev/null +++ b/doc/.gitignore @@ -0,0 +1,15 @@ +# default ignored by sphinx-deployment +_deploy +_deploy_heroku + +# Ignore autogenerated +flotilla*.rst +modules.rst +_build +apidoc +generated +gallery_thumbs +gallery +tutorial/*files +tutorial/*.rst +tutorial/*.ipynb \ No newline at end of file diff --git a/doc/CHANGELOG_sphinx_deployment.md b/doc/CHANGELOG_sphinx_deployment.md new file mode 100644 index 00000000..fc25cc32 --- /dev/null +++ b/doc/CHANGELOG_sphinx_deployment.md @@ -0,0 +1,45 @@ +Change Log +========== + +[0.3.0][] (2013-11-26) +---------------------- + +- Improvement + + [SPXD-10] - Deploy vX.X.X tag to docs/X.X.X instead of docs/vX.X.X + +- New Feature + + [SPXD-9] - PaaS deployment: heroku + +- Migration (from v0.2.0 to v0.3.0) + + `REPO_URL` was changed to `REPO_URL_GITHUB` + + `DEPLOY_BRANCH` was changed to `DEPLOY_BRANCH_GITHUB` + + `$ make push` was changed to `$ make deploy_gh_pages` + + `$ make rsync` was changed to `$ make deploy_rsync` + + +[0.2.0][] (2013-09-26) +---------------------- + +- Improvement + + [SPXD-6] - remove duplication of git init when setup_gh_pages + +- New Feature + + [SPXD-5] - Rsync support + + +[0.1.0][] (2013-08-18) +---------------------- + +- Improvement + + [SPXD-2] - remove "make init_gh_pages" step + +- New Feature + + [SPXD-1] - make gen_deploy + + [SPXD-3] - installation bash script + + +[0.1.0]: https://issues.teracy.org/secure/ReleaseNote.jspa?version=10003&styleName=Text&projectId=10405&Create=Create&atl_token=BD5N-YNBS-EHHQ-478Z%7C87dd31199258f9de5ade180582481463461ded32%7Clin + +[0.2.0]: https://issues.teracy.org/secure/ReleaseNote.jspa?projectId=10405&version=10004 + +[0.3.0]: https://issues.teracy.org/secure/ReleaseNote.jspa?projectId=10405&version=10301 diff --git a/doc/LICENSE_sphinx_deployment b/doc/LICENSE_sphinx_deployment new file mode 100644 index 00000000..113810e5 --- /dev/null +++ b/doc/LICENSE_sphinx_deployment @@ -0,0 +1,27 @@ +Copyright (c) Teracy, Inc. and individual contributors. +All rights reserved. + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + + 1. Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + + 2. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + 3. Neither the name of Teracy, Inc. nor the names of its contributors may be used + to endorse or promote products derived from this software without + specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR +ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON +ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/doc/Makefile b/doc/Makefile new file mode 100644 index 00000000..aa67076a --- /dev/null +++ b/doc/Makefile @@ -0,0 +1,190 @@ +# Makefile for Sphinx documentation +# + +# You can set these variables from the command line. +SPHINXOPTS = +SPHINXBUILD = sphinx-build +PAPER = +BUILDDIR = _build + +# User-friendly check for sphinx-build +ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1) +$(error The '$(SPHINXBUILD)' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the '$(SPHINXBUILD)' executable. Alternatively you can add the directory with the executable to your PATH. If you don't have Sphinx installed, grab it from http://sphinx-doc.org/) +endif + +# Internal variables. +PAPEROPT_a4 = -D latex_paper_size=a4 +PAPEROPT_letter = -D latex_paper_size=letter +ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) . +# the i18n builder cannot share the environment and doctrees with the others +I18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) . + +.PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest gettext + +help: + @echo "Please use \`make
+ {{ super() }}
+ Olga B. Botvinnik is funded by the
+ NDSEG fellowship and is a
+ NumFOCUS
+ John
+ Hunter Technology Fellow.
+ Michael T. Lovci was partially funded by a fellowship from + Genentech. + Partially funded by NIH grants NS075449 + and HG004659 and CIRM grants RB4-06045 + and TR3-05676 to Gene + Yeo.
+ + {% endblock %} diff --git a/doc/api.rst b/doc/api.rst new file mode 100644 index 00000000..f1fc0e4e --- /dev/null +++ b/doc/api.rst @@ -0,0 +1,46 @@ +.. _api: + +.. currentmodule:: flotilla + +API Reference +============= + +General use plots +----------------- + +.. autosummary:: + :toctree: generated/ + + Study.plot_gene + Study.plot_event + +Interactive plots +----------------- + +.. autosummary:: + :toctree: generated/ + + Study.interactive_pca + Study.interactive_classifier + Study.interactive_graph + Study.interactive_lavalamp_pooled_inconsistent + Study.interactive_choose_outliers + Study.interactive_reset_outliers + +Machine learning plots +---------------------- + +.. autosummary:: + :toctree: generated/ + + Study.plot_pca + Study.plot_classifier + Study.plot_graph + +Splicing-specific plots +----------------------- + +.. autosummary:: + :toctree: generated/ + + Study.plot_lavalamp_pooled_inconsistent diff --git a/doc/conf.py b/doc/conf.py new file mode 100644 index 00000000..c0b8f05d --- /dev/null +++ b/doc/conf.py @@ -0,0 +1,372 @@ +# -*- coding: utf-8 -*- +# +# flotilla documentation build configuration file, created by +# sphinx-quickstart on Thu Jul 10 08:32:34 2014. +# +# This file is execfile()d with the current directory set to its +# containing dir. +# +# Note that not all possible configuration values are present in this +# autogenerated file. +# +# All configuration values have a default; values that are commented out +# serve to show the default. + +import sys +import os + +import matplotlib as mpl +import sphinx_bootstrap_theme + + +mpl.use('Agg') + + + + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +#sys.path.insert(0, os.path.abspath('.')) +sys.path.insert(0, os.path.abspath('..{}flotilla'.format(os.sep))) +sys.path.insert(0, os.path.abspath('sphinxext'.format(os.sep))) + +# -- General configuration ------------------------------------------------ + +# If your documentation needs a minimal Sphinx version, state it here. +#needs_sphinx = '1.0' + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + 'sphinx.ext.autodoc', + 'sphinx.ext.doctest', + 'sphinx.ext.intersphinx', + 'sphinx.ext.todo', + 'sphinx.ext.autosummary', + 'sphinx.ext.coverage', + 'sphinx.ext.mathjax', + 'sphinx.ext.ifconfig', + 'sphinx.ext.viewcode', + 'numpydoc', + 'plot_generator', + 'ipython_directive', + 'ipython_console_highlighting', +] + +## Show both the class docstring and the __init__docstring +autoclass_content = 'both' + +# Generate the API documentation when building +autosummary_generate = True +numpydoc_show_class_members = False + +# Add any paths that contain templates here, relative to this directory. +templates_path = ['_templates'] + +# The suffix of source filenames. +source_suffix = '.rst' + +# The encoding of source files. +#source_encoding = 'utf-8-sig' + +# The master toctree document. +master_doc = 'index' + +# General information about the project. +project = u'flotilla' +copyright = u'2014, Olga B. Botvinnik, Michael T. Lovci, Patrick Liu, ' \ + u'Yan Song, and Gene Yeo' + +# The version info for the project you're documenting, acts as replacement for +# |version| and |release|, also used in various other places throughout the +# built documents. +# +# The short X.Y version. +sys.path.insert(0, os.path.abspath(os.path.pardir)) +import flotilla + +version = flotilla.__version__ +# The full version, including alpha/beta/rc tags. +release = flotilla.__version__ + +# The language for content autogenerated by Sphinx. Refer to documentation +# for a list of supported languages. +#language = None + +# There are two options for replacing |today|: either, you set today to some +# non-false value, then it is used: +#today = '' +# Else, today_fmt is used as the format for a strftime call. +#today_fmt = '%B %d, %Y' + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +exclude_patterns = [] + +# The reST default role (used for this markup: `text`) to use for all +# documents. +#default_role = None + +# If true, '()' will be appended to :func: etc. cross-reference text. +#add_function_parentheses = True + +# If true, the current module name will be prepended to all description +# unit titles (such as .. function::). +#add_module_names = True + +# If true, sectionauthor and moduleauthor directives will be shown in the +# output. They are ignored by default. +#show_authors = False + +# The name of the Pygments (syntax highlighting) style to use. +pygments_style = 'sphinx' + +# A list of ignored prefixes for module index sorting. +#modindex_common_prefix = [] + +# If true, keep warnings as "system message" paragraphs in the built documents. +#keep_warnings = False + + +# -- Options for HTML output ---------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +#html_theme = 'default' +html_theme = 'bootstrap' +html_theme_path = sphinx_bootstrap_theme.get_html_theme_path() + +# Theme options are theme-specific and customize the look and feel of a theme +# further. For a list of options available for each theme, see the +# documentation. +#html_theme_options = {} +# (Optional) Logo. Should be small enough to fit the navbar (ideally 24x24). +# Path should be relative to the ``_static`` files directory. +#html_logo = "my_logo.png" + +# Theme options are theme-specific and customize the look and feel of a +# theme further. +html_theme_options = { + # Navigation bar title. (Default: ``project`` value) + 'navbar_title': "Flotilla", + + # Tab name for entire site. (Default: "Site") + 'navbar_site_name': "Site", + + # A list of tuples containing pages or urls to link to. + # Valid tuples should be in the following forms: + # (name, page) # a link to a page + # (name, "/aa/bb", 1) # a link to an arbitrary relative url + # (name, "http://example.com", True) # arbitrary absolute url + # Note the "1" or "True" value above as the third argument to indicate + # an arbitrary url. + 'navbar_links': [ + ("Tutorial", "tutorial"), + ("Gallery", "gallery/index.html", 1), + ("YeoLab@UCSD", "http://yeolab.ucsd.edu", True), + ], + + # Render the next and previous page links in navbar. (Default: true) + 'navbar_sidebarrel': False, + + # Render the current pages TOC in the navbar. (Default: true) + 'navbar_pagenav': True, + + # Global TOC depth for "site" navbar tab. (Default: 1) + # Switching to -1 shows all levels. + 'globaltoc_depth': 2, + + # Include hidden TOCs in Site navbar? + # + # Note: If this is "false", you cannot have mixed ``:hidden:`` and + # non-hidden ``toctree`` directives in the same page, or else the build + # will break. + # + # Values: "true" (default) or "false" + 'globaltoc_includehidden': "true", + + # HTML navbar class (Default: "navbar") to attach to
+ Michael T. Lovci was partially funded by a fellowship from + Genentech. + Partially funded by NIH grants NS075449 + and HG004659 and CIRM grants RB4-06045 + and TR3-05676 to Gene + Yeo.
+ + {% endblock %} diff --git a/doc/api.rst b/doc/api.rst new file mode 100644 index 00000000..f1fc0e4e --- /dev/null +++ b/doc/api.rst @@ -0,0 +1,46 @@ +.. _api: + +.. currentmodule:: flotilla + +API Reference +============= + +General use plots +----------------- + +.. autosummary:: + :toctree: generated/ + + Study.plot_gene + Study.plot_event + +Interactive plots +----------------- + +.. autosummary:: + :toctree: generated/ + + Study.interactive_pca + Study.interactive_classifier + Study.interactive_graph + Study.interactive_lavalamp_pooled_inconsistent + Study.interactive_choose_outliers + Study.interactive_reset_outliers + +Machine learning plots +---------------------- + +.. autosummary:: + :toctree: generated/ + + Study.plot_pca + Study.plot_classifier + Study.plot_graph + +Splicing-specific plots +----------------------- + +.. autosummary:: + :toctree: generated/ + + Study.plot_lavalamp_pooled_inconsistent diff --git a/doc/conf.py b/doc/conf.py new file mode 100644 index 00000000..c0b8f05d --- /dev/null +++ b/doc/conf.py @@ -0,0 +1,372 @@ +# -*- coding: utf-8 -*- +# +# flotilla documentation build configuration file, created by +# sphinx-quickstart on Thu Jul 10 08:32:34 2014. +# +# This file is execfile()d with the current directory set to its +# containing dir. +# +# Note that not all possible configuration values are present in this +# autogenerated file. +# +# All configuration values have a default; values that are commented out +# serve to show the default. + +import sys +import os + +import matplotlib as mpl +import sphinx_bootstrap_theme + + +mpl.use('Agg') + + + + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +#sys.path.insert(0, os.path.abspath('.')) +sys.path.insert(0, os.path.abspath('..{}flotilla'.format(os.sep))) +sys.path.insert(0, os.path.abspath('sphinxext'.format(os.sep))) + +# -- General configuration ------------------------------------------------ + +# If your documentation needs a minimal Sphinx version, state it here. +#needs_sphinx = '1.0' + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + 'sphinx.ext.autodoc', + 'sphinx.ext.doctest', + 'sphinx.ext.intersphinx', + 'sphinx.ext.todo', + 'sphinx.ext.autosummary', + 'sphinx.ext.coverage', + 'sphinx.ext.mathjax', + 'sphinx.ext.ifconfig', + 'sphinx.ext.viewcode', + 'numpydoc', + 'plot_generator', + 'ipython_directive', + 'ipython_console_highlighting', +] + +## Show both the class docstring and the __init__docstring +autoclass_content = 'both' + +# Generate the API documentation when building +autosummary_generate = True +numpydoc_show_class_members = False + +# Add any paths that contain templates here, relative to this directory. +templates_path = ['_templates'] + +# The suffix of source filenames. +source_suffix = '.rst' + +# The encoding of source files. +#source_encoding = 'utf-8-sig' + +# The master toctree document. +master_doc = 'index' + +# General information about the project. +project = u'flotilla' +copyright = u'2014, Olga B. Botvinnik, Michael T. Lovci, Patrick Liu, ' \ + u'Yan Song, and Gene Yeo' + +# The version info for the project you're documenting, acts as replacement for +# |version| and |release|, also used in various other places throughout the +# built documents. +# +# The short X.Y version. +sys.path.insert(0, os.path.abspath(os.path.pardir)) +import flotilla + +version = flotilla.__version__ +# The full version, including alpha/beta/rc tags. +release = flotilla.__version__ + +# The language for content autogenerated by Sphinx. Refer to documentation +# for a list of supported languages. +#language = None + +# There are two options for replacing |today|: either, you set today to some +# non-false value, then it is used: +#today = '' +# Else, today_fmt is used as the format for a strftime call. +#today_fmt = '%B %d, %Y' + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +exclude_patterns = [] + +# The reST default role (used for this markup: `text`) to use for all +# documents. +#default_role = None + +# If true, '()' will be appended to :func: etc. cross-reference text. +#add_function_parentheses = True + +# If true, the current module name will be prepended to all description +# unit titles (such as .. function::). +#add_module_names = True + +# If true, sectionauthor and moduleauthor directives will be shown in the +# output. They are ignored by default. +#show_authors = False + +# The name of the Pygments (syntax highlighting) style to use. +pygments_style = 'sphinx' + +# A list of ignored prefixes for module index sorting. +#modindex_common_prefix = [] + +# If true, keep warnings as "system message" paragraphs in the built documents. +#keep_warnings = False + + +# -- Options for HTML output ---------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +#html_theme = 'default' +html_theme = 'bootstrap' +html_theme_path = sphinx_bootstrap_theme.get_html_theme_path() + +# Theme options are theme-specific and customize the look and feel of a theme +# further. For a list of options available for each theme, see the +# documentation. +#html_theme_options = {} +# (Optional) Logo. Should be small enough to fit the navbar (ideally 24x24). +# Path should be relative to the ``_static`` files directory. +#html_logo = "my_logo.png" + +# Theme options are theme-specific and customize the look and feel of a +# theme further. +html_theme_options = { + # Navigation bar title. (Default: ``project`` value) + 'navbar_title': "Flotilla", + + # Tab name for entire site. (Default: "Site") + 'navbar_site_name': "Site", + + # A list of tuples containing pages or urls to link to. + # Valid tuples should be in the following forms: + # (name, page) # a link to a page + # (name, "/aa/bb", 1) # a link to an arbitrary relative url + # (name, "http://example.com", True) # arbitrary absolute url + # Note the "1" or "True" value above as the third argument to indicate + # an arbitrary url. + 'navbar_links': [ + ("Tutorial", "tutorial"), + ("Gallery", "gallery/index.html", 1), + ("YeoLab@UCSD", "http://yeolab.ucsd.edu", True), + ], + + # Render the next and previous page links in navbar. (Default: true) + 'navbar_sidebarrel': False, + + # Render the current pages TOC in the navbar. (Default: true) + 'navbar_pagenav': True, + + # Global TOC depth for "site" navbar tab. (Default: 1) + # Switching to -1 shows all levels. + 'globaltoc_depth': 2, + + # Include hidden TOCs in Site navbar? + # + # Note: If this is "false", you cannot have mixed ``:hidden:`` and + # non-hidden ``toctree`` directives in the same page, or else the build + # will break. + # + # Values: "true" (default) or "false" + 'globaltoc_includehidden': "true", + + # HTML navbar class (Default: "navbar") to attach to
element.
+ # For black navbar, do "navbar navbar-inverse"
+ 'navbar_class': "navbar",
+
+ # Fix navigation bar to top of page?
+ # Values: "true" (default) or "false"
+ 'navbar_fixed_top': "true",
+
+ # Location of link to source.
+ # Options are "nav" (default), "footer" or anything else to exclude.
+ 'source_link_position': "nav",
+
+ # Bootswatch (http://bootswatch.com/) theme.
+ #
+ # Options are nothing with "" (default) or the name of a valid theme
+ # such as "amelia" or "cosmo".
+ 'bootswatch_theme': "paper", # "united",
+
+ # Choose Bootstrap version.
+ # Values: "3" (default) or "2" (in quotes)
+ 'bootstrap_version': "3",
+}
+
+# Add any paths that contain custom themes here, relative to this directory.
+#html_theme_path = []
+
+# The name for this set of Sphinx documents. If None, it defaults to
+# " v documentation".
+#html_title = None
+
+# A shorter title for the navigation bar. Default is the same as html_title.
+#html_short_title = None
+
+# The name of an image file (relative to this directory) to place at the top
+# of the sidebar.
+#html_logo = None
+
+# The name of an image file (within the static path) to use as favicon of the
+# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32
+# pixels large.
+#html_favicon = None
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ['_static', 'gallery_thumbs']
+
+# Add any extra paths that contain custom files (such as robots.txt or
+# .htaccess) here, relative to this directory. These files are copied
+# directly to the root of the documentation.
+#html_extra_path = []
+
+# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
+# using the given strftime format.
+#html_last_updated_fmt = '%b %d, %Y'
+
+# If true, SmartyPants will be used to convert quotes and dashes to
+# typographically correct entities.
+#html_use_smartypants = True
+
+# Custom sidebar templates, maps document names to template names.
+#html_sidebars = {}
+# html_sidebars = {'**': ['localtoc.html', 'sourcelink.html', 'searchbox.html']}
+
+# Additional templates that should be rendered to pages, maps page names to
+# template names.
+#html_additional_pages = {}
+
+# If false, no module index is generated.
+#html_domain_indices = True
+
+# If false, no index is generated.
+#html_use_index = True
+
+# If true, the index is split into individual pages for each letter.
+#html_split_index = False
+
+# If true, links to the reST sources are added to the pages.
+#html_show_sourcelink = True
+
+# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
+#html_show_sphinx = True
+
+# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
+#html_show_copyright = True
+
+# If true, an OpenSearch description file will be output, and all pages will
+# contain a tag referring to it. The value of this option must be the
+# base URL from which the finished HTML is served.
+#html_use_opensearch = ''
+
+# This is the file name suffix for HTML files (e.g. ".xhtml").
+#html_file_suffix = None
+
+# Output file base name for HTML help builder.
+htmlhelp_basename = 'flotilladoc'
+
+
+# -- Options for LaTeX output ---------------------------------------------
+
+latex_elements = {
+ # The paper size ('letterpaper' or 'a4paper').
+ #'papersize': 'letterpaper',
+
+ # The font size ('10pt', '11pt' or '12pt').
+ #'pointsize': '10pt',
+
+ # Additional stuff for the LaTeX preamble.
+ #'preamble': '',
+}
+
+# Grouping the document tree into LaTeX files. List of tuples
+# (source start file, target name, title,
+# author, documentclass [howto, manual, or own class]).
+latex_documents = [
+ ('index', 'flotilla.tex', u'flotilla Documentation',
+ u'Olga B. Botvinnik, Michael T. Lovci', 'manual'),
+]
+
+# The name of an image file (relative to this directory) to place at the top of
+# the title page.
+#latex_logo = None
+
+# For "manual" documents, if this is true, then toplevel headings are parts,
+# not chapters.
+#latex_use_parts = False
+
+# If true, show page references after internal links.
+#latex_show_pagerefs = False
+
+# If true, show URL addresses after external links.
+#latex_show_urls = False
+
+# Documents to append as an appendix to all manuals.
+#latex_appendices = []
+
+# If false, no module index is generated.
+#latex_domain_indices = True
+
+
+# -- Options for manual page output ---------------------------------------
+
+# One entry per manual page. List of tuples
+# (source start file, name, description, authors, manual section).
+man_pages = [
+ ('index', 'flotilla', u'flotilla Documentation',
+ [u'Olga B. Botvinnik, Michael T. Lovci'], 1)
+]
+
+# If true, show URL addresses after external links.
+#man_show_urls = False
+
+
+# -- Options for Texinfo output -------------------------------------------
+
+# Grouping the document tree into Texinfo files. List of tuples
+# (source start file, target name, title, author,
+# dir menu entry, description, category)
+texinfo_documents = [
+ ('index', 'flotilla', u'flotilla Documentation',
+ u'Olga B. Botvinnik, Michael T. Lovci', 'flotilla',
+ 'One line description of project.',
+ 'Miscellaneous'),
+]
+
+# Documents to append as an appendix to all manuals.
+#texinfo_appendices = []
+
+# If false, no module index is generated.
+#texinfo_domain_indices = True
+
+# How to display URL addresses: 'footnote', 'no', or 'inline'.
+#texinfo_show_urls = 'footnote'
+
+# If true, do not generate a @detailmenu in the "Top" node's menu.
+#texinfo_no_detailmenu = False
+
+
+# Example configuration for intersphinx: refer to the Python standard library.
+intersphinx_mapping = {'http://docs.python.org/': None}
+
+
+# ----------- Don't skip __init__
diff --git a/doc/conf.py~ b/doc/conf.py~
new file mode 100644
index 00000000..58038754
--- /dev/null
+++ b/doc/conf.py~
@@ -0,0 +1,345 @@
+# -*- coding: utf-8 -*-
+#
+# flotilla documentation build configuration file, created by
+# sphinx-quickstart on Thu Jul 10 08:32:34 2014.
+#
+# This file is execfile()d with the current directory set to its
+# containing dir.
+#
+# Note that not all possible configuration values are present in this
+# autogenerated file.
+#
+# All configuration values have a default; values that are commented out
+# serve to show the default.
+
+import sys
+import os
+import sphinx_bootstrap_theme
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+#sys.path.insert(0, os.path.abspath('.'))
+sys.path.insert(0, os.path.abspath('..{}flotilla'.format(os.sep)))
+sys.path.insert(0, os.path.abspath('sphinxext'.format(os.sep)))
+
+# -- General configuration ------------------------------------------------
+
+# If your documentation needs a minimal Sphinx version, state it here.
+#needs_sphinx = '1.0'
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = [
+ 'sphinx.ext.autodoc',
+ 'sphinx.ext.doctest',
+ 'sphinx.ext.intersphinx',
+ 'sphinx.ext.todo',
+ 'sphinx.ext.coverage',
+ 'sphinx.ext.mathjax',
+ 'sphinx.ext.ifconfig',
+ 'sphinx.ext.viewcode',
+ 'numpydoc',
+ 'ipython_directive',
+ 'ipython_console_highlighting',
+]
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ['_templates']
+
+# The suffix of source filenames.
+source_suffix = '.rst'
+
+# The encoding of source files.
+#source_encoding = 'utf-8-sig'
+
+# The master toctree document.
+master_doc = 'index'
+
+# General information about the project.
+project = u'flotilla'
+copyright = u'2014, Olga Botvinnik, Michael Lovci, Patrick Liu, Yan Song'
+
+# The version info for the project you're documenting, acts as replacement for
+# |version| and |release|, also used in various other places throughout the
+# built documents.
+#
+# The short X.Y version.
+version = '0.1.0'
+# The full version, including alpha/beta/rc tags.
+release = '0.1.0'
+
+# The language for content autogenerated by Sphinx. Refer to documentation
+# for a list of supported languages.
+#language = None
+
+# There are two options for replacing |today|: either, you set today to some
+# non-false value, then it is used:
+#today = ''
+# Else, today_fmt is used as the format for a strftime call.
+#today_fmt = '%B %d, %Y'
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+exclude_patterns = []
+
+# The reST default role (used for this markup: `text`) to use for all
+# documents.
+#default_role = None
+
+# If true, '()' will be appended to :func: etc. cross-reference text.
+#add_function_parentheses = True
+
+# If true, the current module name will be prepended to all description
+# unit titles (such as .. function::).
+#add_module_names = True
+
+# If true, sectionauthor and moduleauthor directives will be shown in the
+# output. They are ignored by default.
+#show_authors = False
+
+# The name of the Pygments (syntax highlighting) style to use.
+pygments_style = 'sphinx'
+
+# A list of ignored prefixes for module index sorting.
+#modindex_common_prefix = []
+
+# If true, keep warnings as "system message" paragraphs in the built documents.
+#keep_warnings = False
+
+
+# -- Options for HTML output ----------------------------------------------
+
+# The theme to use for HTML and HTML Help pages. See the documentation for
+# a list of builtin themes.
+#html_theme = 'default'
+html_theme = 'bootstrap'
+html_theme_path = sphinx_bootstrap_theme.get_html_theme_path()
+
+# Theme options are theme-specific and customize the look and feel of a theme
+# further. For a list of options available for each theme, see the
+# documentation.
+#html_theme_options = {}
+# (Optional) Logo. Should be small enough to fit the navbar (ideally 24x24).
+# Path should be relative to the ``_static`` files directory.
+#html_logo = "my_logo.png"
+
+# Theme options are theme-specific and customize the look and feel of a
+# theme further.
+html_theme_options = {
+ # Navigation bar title. (Default: ``project`` value)
+ 'navbar_title': "Flotilla",
+
+ # Tab name for entire site. (Default: "Site")
+ 'navbar_site_name': "Site",
+
+ # A list of tuples containing pages or urls to link to.
+ # Valid tuples should be in the following forms:
+ # (name, page) # a link to a page
+ # (name, "/aa/bb", 1) # a link to an arbitrary relative url
+ # (name, "http://example.com", True) # arbitrary absolute url
+ # Note the "1" or "True" value above as the third argument to indicate
+ # an arbitrary url.
+ 'navbar_links': [
+ ("Examples", "examples"),
+ ("Link", "http://example.com", True),
+ ],
+
+ # Render the next and previous page links in navbar. (Default: true)
+ 'navbar_sidebarrel': True,
+
+ # Render the current pages TOC in the navbar. (Default: true)
+ 'navbar_pagenav': True,
+
+ # Global TOC depth for "site" navbar tab. (Default: 1)
+ # Switching to -1 shows all levels.
+ 'globaltoc_depth': 2,
+
+ # Include hidden TOCs in Site navbar?
+ #
+ # Note: If this is "false", you cannot have mixed ``:hidden:`` and
+ # non-hidden ``toctree`` directives in the same page, or else the build
+ # will break.
+ #
+ # Values: "true" (default) or "false"
+ 'globaltoc_includehidden': "true",
+
+ # HTML navbar class (Default: "navbar") to attach to
element.
+ # For black navbar, do "navbar navbar-inverse"
+ 'navbar_class': "navbar navbar-inverse",
+
+ # Fix navigation bar to top of page?
+ # Values: "true" (default) or "false"
+ 'navbar_fixed_top': "true",
+
+ # Location of link to source.
+ # Options are "nav" (default), "footer" or anything else to exclude.
+ 'source_link_position': "nav",
+
+ # Bootswatch (http://bootswatch.com/) theme.
+ #
+ # Options are nothing with "" (default) or the name of a valid theme
+ # such as "amelia" or "cosmo".
+ 'bootswatch_theme': "flatly", #"united",
+
+ # Choose Bootstrap version.
+ # Values: "3" (default) or "2" (in quotes)
+ 'bootstrap_version': "3",
+}
+
+# Add any paths that contain custom themes here, relative to this directory.
+#html_theme_path = []
+
+# The name for this set of Sphinx documents. If None, it defaults to
+# " v documentation".
+#html_title = None
+
+# A shorter title for the navigation bar. Default is the same as html_title.
+#html_short_title = None
+
+# The name of an image file (relative to this directory) to place at the top
+# of the sidebar.
+#html_logo = None
+
+# The name of an image file (within the static path) to use as favicon of the
+# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32
+# pixels large.
+#html_favicon = None
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ['_static']
+
+# Add any extra paths that contain custom files (such as robots.txt or
+# .htaccess) here, relative to this directory. These files are copied
+# directly to the root of the documentation.
+#html_extra_path = []
+
+# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
+# using the given strftime format.
+#html_last_updated_fmt = '%b %d, %Y'
+
+# If true, SmartyPants will be used to convert quotes and dashes to
+# typographically correct entities.
+#html_use_smartypants = True
+
+# Custom sidebar templates, maps document names to template names.
+#html_sidebars = {}
+
+# Additional templates that should be rendered to pages, maps page names to
+# template names.
+#html_additional_pages = {}
+
+# If false, no module index is generated.
+#html_domain_indices = True
+
+# If false, no index is generated.
+#html_use_index = True
+
+# If true, the index is split into individual pages for each letter.
+#html_split_index = False
+
+# If true, links to the reST sources are added to the pages.
+#html_show_sourcelink = True
+
+# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
+#html_show_sphinx = True
+
+# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
+#html_show_copyright = True
+
+# If true, an OpenSearch description file will be output, and all pages will
+# contain a tag referring to it. The value of this option must be the
+# base URL from which the finished HTML is served.
+#html_use_opensearch = ''
+
+# This is the file name suffix for HTML files (e.g. ".xhtml").
+#html_file_suffix = None
+
+# Output file base name for HTML help builder.
+htmlhelp_basename = 'flotilladoc'
+
+
+# -- Options for LaTeX output ---------------------------------------------
+
+latex_elements = {
+# The paper size ('letterpaper' or 'a4paper').
+#'papersize': 'letterpaper',
+
+# The font size ('10pt', '11pt' or '12pt').
+#'pointsize': '10pt',
+
+# Additional stuff for the LaTeX preamble.
+#'preamble': '',
+}
+
+# Grouping the document tree into LaTeX files. List of tuples
+# (source start file, target name, title,
+# author, documentclass [howto, manual, or own class]).
+latex_documents = [
+ ('index', 'flotilla.tex', u'flotilla Documentation',
+ u'Olga Botvinnik, Michael Lovci', 'manual'),
+]
+
+# The name of an image file (relative to this directory) to place at the top of
+# the title page.
+#latex_logo = None
+
+# For "manual" documents, if this is true, then toplevel headings are parts,
+# not chapters.
+#latex_use_parts = False
+
+# If true, show page references after internal links.
+#latex_show_pagerefs = False
+
+# If true, show URL addresses after external links.
+#latex_show_urls = False
+
+# Documents to append as an appendix to all manuals.
+#latex_appendices = []
+
+# If false, no module index is generated.
+#latex_domain_indices = True
+
+
+# -- Options for manual page output ---------------------------------------
+
+# One entry per manual page. List of tuples
+# (source start file, name, description, authors, manual section).
+man_pages = [
+ ('index', 'flotilla', u'flotilla Documentation',
+ [u'Olga Botvinnik, Michael Lovci'], 1)
+]
+
+# If true, show URL addresses after external links.
+#man_show_urls = False
+
+
+# -- Options for Texinfo output -------------------------------------------
+
+# Grouping the document tree into Texinfo files. List of tuples
+# (source start file, target name, title, author,
+# dir menu entry, description, category)
+texinfo_documents = [
+ ('index', 'flotilla', u'flotilla Documentation',
+ u'Olga Botvinnik, Michael Lovci', 'flotilla', 'One line description of project.',
+ 'Miscellaneous'),
+]
+
+# Documents to append as an appendix to all manuals.
+#texinfo_appendices = []
+
+# If false, no module index is generated.
+#texinfo_domain_indices = True
+
+# How to display URL addresses: 'footnote', 'no', or 'inline'.
+#texinfo_show_urls = 'footnote'
+
+# If true, do not generate a @detailmenu in the "Top" node's menu.
+#texinfo_no_detailmenu = False
+
+
+# Example configuration for intersphinx: refer to the Python standard library.
+intersphinx_mapping = {'http://docs.python.org/': None}
diff --git a/doc/create_datapackage.rst b/doc/create_datapackage.rst
new file mode 100644
index 00000000..93cd4386
--- /dev/null
+++ b/doc/create_datapackage.rst
@@ -0,0 +1,3 @@
+Create your very own datapackage!
+=================================
+
diff --git a/doc/docker_instructions.rst b/doc/docker_instructions.rst
new file mode 100644
index 00000000..713ee82e
--- /dev/null
+++ b/doc/docker_instructions.rst
@@ -0,0 +1,29 @@
+Docker Instructions
+===================
+
+
+`Docker `__.
+2. Then start flotilla on the command line (OS X ``Terminal``
+ application):
+
+ curl https://raw.githubusercontent.com/YeoLab/flotilla/master/docker/start_docker.py | python
+
+After the ipython notebook interface opens, test the installation with
+our test dataset by running the following commands in a new notebook:
+
+ import flotilla
+ study = flotilla.embark("http://sauron.ucsd.edu/flotilla_projects/neural_diff_chr22/datapackage.json")
+ study.interactive_pca()
diff --git a/doc/index.rst b/doc/index.rst
new file mode 100644
index 00000000..b2eb7319
--- /dev/null
+++ b/doc/index.rst
@@ -0,0 +1,40 @@
+
+Welcome to flotilla's documentation!
+====================================
+
+.. image:: _static/flotilla.png
+
+Flotilla is a Python package for reproducible machine learning analysis on
+gene expression and alternative splicing data.
+
+Check out `what's new`_
+
+Check out the `API Reference`_
+
+To check out the code, report a bug, or contribute a new feature, please visit
+the `github repository `_. You can also get
+in touch on `twitter `_.
+
+Contents:
+
+.. toctree::
+ :maxdepth: 1
+
+ modules
+ whatsnew
+ installation
+ api
+ create_datapackage
+ tutorial
+
+.. _what's new: whatsnew
+.. _API Reference: api
+
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`
+
diff --git a/doc/index.rst~ b/doc/index.rst~
new file mode 100644
index 00000000..812257dc
--- /dev/null
+++ b/doc/index.rst~
@@ -0,0 +1,23 @@
+.. flotilla documentation master file, created by
+ sphinx-quickstart on Thu Jul 10 10:03:26 2014.
+ You can adapt this file completely to your liking, but it should at least
+ contain the root `toctree` directive.
+
+Welcome to flotilla's documentation!
+====================================
+
+Contents:
+
+.. toctree::
+ :maxdepth: 4
+
+ flotilla
+
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`
+
diff --git a/doc/installation.rst b/doc/installation.rst
new file mode 100644
index 00000000..056020d0
--- /dev/null
+++ b/doc/installation.rst
@@ -0,0 +1,158 @@
+OS X Installation instructions
+==============================
+
+The following steps have been tested on a clean install of Mavericks
+10.9.4.
+
+All others must fend for themselves to install ``matplotlib``, ``scipy`` and
+their third-party dependencies.
+
+
+To install, first install the Anaconda_ Python Distribution, which comes
+pre-packaged with a bunch of the scientific packages we use all the time,
+pre-installed.
+
+Create a flotilla sandbox
+-------------------------
+
+We recommend creating a "sandbox" where you can install any and all packages
+without disturbing the rest of the Python distribution. This way, your
+flotilla environment is completely insulated from everything else, just in
+case something goes wrong with You can do this with
+
+.. code::
+
+ conda create --yes flotilla_env pip numpy scipy cython matplotlib nose six scikit-learn ipython networkx pandas tornado statsmodels setuptools pytest pyzmq jinja2 pyyaml
+
+You've now just created a "virtual environment" called ``flotilla_env`` (the first
+argument). Now activate that environment with,
+
+.. code::
+
+ source activate flotilla_env
+
+Now at the beginning of your terminal prompt, you should see:
+
+.. code::
+
+ (flotilla_env)
+
+Which indicates that you are now in the ``flotilla_env`` virtual environment. Now
+that you're in the environment, follow along with the non-sandbox
+installation instructions.
+
+Install and update all packages in your environment
+---------------------------------------------------
+
+To make sure you have the latest packages, on the command line in your
+terminal, enter this command:
+
+.. code::
+
+ conda update --yes pip numpy scipy cython matplotlib nose six scikit-learn ipython networkx pandas tornado statsmodels setuptools pytest pyzmq jinja2 pyyaml
+
+Not all packages are available using ``conda``, so we'll install the rest using
+``pip``, which is a Python package installer and installs from PyPI_, the
+Python Package Index.
+
+.. code::
+
+ pip install seaborn fastcluster brewer2mpl pymongo gspread husl semantic_version joblib
+
+Next, to install the latest release of ``flotilla``, do
+
+.. code::
+
+ pip install flotilla
+
+If you want the bleeding-edge master version because you're a developer or a
+super-user, (we work really hard to make sure it's always working but could be
+buggy!), then install the ``git`` master with,
+
+.. code::
+
+ pip install git+git://github.com/yeolab/flotilla.git
+
+Old OSX Installation instructions
+=================================
+
+*This part only needs to be done once*
+
+- Install Anaconda_
+- Install XCode_ (This can take an hour)
+- Install homebrew_, via
+
+.. code::
+
+ ruby -e "$(curl -fsSL https://raw.github.com/Homebrew/homebrew/go/install)"
+
+- Install freetype:
+
+.. code::
+
+ brew install freetype
+
+- Install heavy packages (this can take an hour or more)
+
+.. code::
+
+ conda install --yes pip numpy scipy cython matplotlib nose six scikit-learn ipython networkx pandas tornado statsmodels setuptools pytest pyzmq jinja2 pyyaml`
+
+- Create a virtual environment
+
+.. code::
+
+ conda create --yes -n flotilla_env pip numpy scipy cython matplotlib nose six scikit-learn ipython networkx pandas tornado statsmodels setuptools pytest pyzmq jinja2 pyyaml
+
+- Switch to virtual environment
+
+.. code::
+
+ source activate flotilla_env
+
+- Install flotilla and its dependencies (this can take a few minutes):
+
+.. code::
+
+ pip install git+https://github.com/YeoLab/flotilla.git
+
+- Create a scratch space for your work
+
+.. code::
+
+ mkdir ~/flotilla_scratch
+
+- Make a place to store flotilla projects
+
+.. code::
+
+ mkdir ~/flotilla_projects
+
+- Go back to the real world
+
+.. code::
+
+ source deactivate
+
+- switch to virtual environment
+
+.. code::
+
+ source activate flotilla_env
+
+- start an ipython notebook:
+
+.. code::
+
+ ipython notebook --notebook-dir=~/flotilla_scratch
+
+- create a new notebook by clicking ``New Notebook``
+- rename your notebook from "Untitled" to something more informative by
+ clicking the title panel.
+
+.. include:: docker_instructions.rst
+
+.. _Anaconda: http://continuum.io/downloads
+.. _XCode: https://itunes.apple.com/us/app/xcode/id497799835?mt=12
+.. _homebrew: http://brew.sh/
+.. _PyPI: https://pypi.python.org/
\ No newline at end of file
diff --git a/doc/make.bat b/doc/make.bat
new file mode 100644
index 00000000..e84354af
--- /dev/null
+++ b/doc/make.bat
@@ -0,0 +1,242 @@
+@ECHO OFF
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+ set SPHINXBUILD=sphinx-build
+)
+set BUILDDIR=_build
+set ALLSPHINXOPTS=-d %BUILDDIR%/doctrees %SPHINXOPTS% .
+set I18NSPHINXOPTS=%SPHINXOPTS% .
+if NOT "%PAPER%" == "" (
+ set ALLSPHINXOPTS=-D latex_paper_size=%PAPER% %ALLSPHINXOPTS%
+ set I18NSPHINXOPTS=-D latex_paper_size=%PAPER% %I18NSPHINXOPTS%
+)
+
+if "%1" == "" goto help
+
+if "%1" == "help" (
+ :help
+ echo.Please use `make ^` where ^ is one of
+ echo. html to make standalone HTML files
+ echo. dirhtml to make HTML files named index.html in directories
+ echo. singlehtml to make a single large HTML file
+ echo. pickle to make pickle files
+ echo. json to make JSON files
+ echo. htmlhelp to make HTML files and a HTML help project
+ echo. qthelp to make HTML files and a qthelp project
+ echo. devhelp to make HTML files and a Devhelp project
+ echo. epub to make an epub
+ echo. latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter
+ echo. text to make text files
+ echo. man to make manual pages
+ echo. texinfo to make Texinfo files
+ echo. gettext to make PO message catalogs
+ echo. changes to make an overview over all changed/added/deprecated items
+ echo. xml to make Docutils-native XML files
+ echo. pseudoxml to make pseudoxml-XML files for display purposes
+ echo. linkcheck to check all external links for integrity
+ echo. doctest to run all doctests embedded in the documentation if enabled
+ goto end
+)
+
+if "%1" == "clean" (
+ for /d %%i in (%BUILDDIR%\*) do rmdir /q /s %%i
+ del /q /s %BUILDDIR%\*
+ goto end
+)
+
+
+%SPHINXBUILD% 2> nul
+if errorlevel 9009 (
+ echo.
+ echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+ echo.installed, then set the SPHINXBUILD environment variable to point
+ echo.to the full path of the 'sphinx-build' executable. Alternatively you
+ echo.may add the Sphinx directory to PATH.
+ echo.
+ echo.If you don't have Sphinx installed, grab it from
+ echo.http://sphinx-doc.org/
+ exit /b 1
+)
+
+if "%1" == "html" (
+ %SPHINXBUILD% -b html %ALLSPHINXOPTS% %BUILDDIR%/html
+ if errorlevel 1 exit /b 1
+ echo.
+ echo.Build finished. The HTML pages are in %BUILDDIR%/html.
+ goto end
+)
+
+if "%1" == "dirhtml" (
+ %SPHINXBUILD% -b dirhtml %ALLSPHINXOPTS% %BUILDDIR%/dirhtml
+ if errorlevel 1 exit /b 1
+ echo.
+ echo.Build finished. The HTML pages are in %BUILDDIR%/dirhtml.
+ goto end
+)
+
+if "%1" == "singlehtml" (
+ %SPHINXBUILD% -b singlehtml %ALLSPHINXOPTS% %BUILDDIR%/singlehtml
+ if errorlevel 1 exit /b 1
+ echo.
+ echo.Build finished. The HTML pages are in %BUILDDIR%/singlehtml.
+ goto end
+)
+
+if "%1" == "pickle" (
+ %SPHINXBUILD% -b pickle %ALLSPHINXOPTS% %BUILDDIR%/pickle
+ if errorlevel 1 exit /b 1
+ echo.
+ echo.Build finished; now you can process the pickle files.
+ goto end
+)
+
+if "%1" == "json" (
+ %SPHINXBUILD% -b json %ALLSPHINXOPTS% %BUILDDIR%/json
+ if errorlevel 1 exit /b 1
+ echo.
+ echo.Build finished; now you can process the JSON files.
+ goto end
+)
+
+if "%1" == "htmlhelp" (
+ %SPHINXBUILD% -b htmlhelp %ALLSPHINXOPTS% %BUILDDIR%/htmlhelp
+ if errorlevel 1 exit /b 1
+ echo.
+ echo.Build finished; now you can run HTML Help Workshop with the ^
+.hhp project file in %BUILDDIR%/htmlhelp.
+ goto end
+)
+
+if "%1" == "qthelp" (
+ %SPHINXBUILD% -b qthelp %ALLSPHINXOPTS% %BUILDDIR%/qthelp
+ if errorlevel 1 exit /b 1
+ echo.
+ echo.Build finished; now you can run "qcollectiongenerator" with the ^
+.qhcp project file in %BUILDDIR%/qthelp, like this:
+ echo.^> qcollectiongenerator %BUILDDIR%\qthelp\flotilla.qhcp
+ echo.To view the help file:
+ echo.^> assistant -collectionFile %BUILDDIR%\qthelp\flotilla.ghc
+ goto end
+)
+
+if "%1" == "devhelp" (
+ %SPHINXBUILD% -b devhelp %ALLSPHINXOPTS% %BUILDDIR%/devhelp
+ if errorlevel 1 exit /b 1
+ echo.
+ echo.Build finished.
+ goto end
+)
+
+if "%1" == "epub" (
+ %SPHINXBUILD% -b epub %ALLSPHINXOPTS% %BUILDDIR%/epub
+ if errorlevel 1 exit /b 1
+ echo.
+ echo.Build finished. The epub file is in %BUILDDIR%/epub.
+ goto end
+)
+
+if "%1" == "latex" (
+ %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex
+ if errorlevel 1 exit /b 1
+ echo.
+ echo.Build finished; the LaTeX files are in %BUILDDIR%/latex.
+ goto end
+)
+
+if "%1" == "latexpdf" (
+ %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex
+ cd %BUILDDIR%/latex
+ make all-pdf
+ cd %BUILDDIR%/..
+ echo.
+ echo.Build finished; the PDF files are in %BUILDDIR%/latex.
+ goto end
+)
+
+if "%1" == "latexpdfja" (
+ %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex
+ cd %BUILDDIR%/latex
+ make all-pdf-ja
+ cd %BUILDDIR%/..
+ echo.
+ echo.Build finished; the PDF files are in %BUILDDIR%/latex.
+ goto end
+)
+
+if "%1" == "text" (
+ %SPHINXBUILD% -b text %ALLSPHINXOPTS% %BUILDDIR%/text
+ if errorlevel 1 exit /b 1
+ echo.
+ echo.Build finished. The text files are in %BUILDDIR%/text.
+ goto end
+)
+
+if "%1" == "man" (
+ %SPHINXBUILD% -b man %ALLSPHINXOPTS% %BUILDDIR%/man
+ if errorlevel 1 exit /b 1
+ echo.
+ echo.Build finished. The manual pages are in %BUILDDIR%/man.
+ goto end
+)
+
+if "%1" == "texinfo" (
+ %SPHINXBUILD% -b texinfo %ALLSPHINXOPTS% %BUILDDIR%/texinfo
+ if errorlevel 1 exit /b 1
+ echo.
+ echo.Build finished. The Texinfo files are in %BUILDDIR%/texinfo.
+ goto end
+)
+
+if "%1" == "gettext" (
+ %SPHINXBUILD% -b gettext %I18NSPHINXOPTS% %BUILDDIR%/locale
+ if errorlevel 1 exit /b 1
+ echo.
+ echo.Build finished. The message catalogs are in %BUILDDIR%/locale.
+ goto end
+)
+
+if "%1" == "changes" (
+ %SPHINXBUILD% -b changes %ALLSPHINXOPTS% %BUILDDIR%/changes
+ if errorlevel 1 exit /b 1
+ echo.
+ echo.The overview file is in %BUILDDIR%/changes.
+ goto end
+)
+
+if "%1" == "linkcheck" (
+ %SPHINXBUILD% -b linkcheck %ALLSPHINXOPTS% %BUILDDIR%/linkcheck
+ if errorlevel 1 exit /b 1
+ echo.
+ echo.Link check complete; look for any errors in the above output ^
+or in %BUILDDIR%/linkcheck/output.txt.
+ goto end
+)
+
+if "%1" == "doctest" (
+ %SPHINXBUILD% -b doctest %ALLSPHINXOPTS% %BUILDDIR%/doctest
+ if errorlevel 1 exit /b 1
+ echo.
+ echo.Testing of doctests in the sources finished, look at the ^
+results in %BUILDDIR%/doctest/output.txt.
+ goto end
+)
+
+if "%1" == "xml" (
+ %SPHINXBUILD% -b xml %ALLSPHINXOPTS% %BUILDDIR%/xml
+ if errorlevel 1 exit /b 1
+ echo.
+ echo.Build finished. The XML files are in %BUILDDIR%/xml.
+ goto end
+)
+
+if "%1" == "pseudoxml" (
+ %SPHINXBUILD% -b pseudoxml %ALLSPHINXOPTS% %BUILDDIR%/pseudoxml
+ if errorlevel 1 exit /b 1
+ echo.
+ echo.Build finished. The pseudo-XML files are in %BUILDDIR%/pseudoxml.
+ goto end
+)
+
+:end
diff --git a/doc/releases/v0.2.0.txt b/doc/releases/v0.2.0.txt
new file mode 100644
index 00000000..01cc0ebe
--- /dev/null
+++ b/doc/releases/v0.2.0.txt
@@ -0,0 +1,57 @@
+v0.2.0 (November 5th, 2014)
+---------------------------
+
+This is a minor release, with some breaking changes from v0.1.1.
+
+New features
+~~~~~~~~~~~~
+
+- Plot the expression or splicing of two *samples* with
+ :py:meth:`.Study.plot_two_samples`
+- Plot the expression or splicing of two *features* with
+ :py:meth:`.Study.plot_two_features`
+- Detect outliers with :py:meth:`Study.interactive_choose_outliers` which
+ performs a ``OneClassSVM`` on the PCA-reduced space of data (either
+ expression or splicing), using the first three components
+- :py:class:`.Study` doesn't filter out the pooled or outlier samples from the
+ data, only technical outliers with fewer reads than specified in the
+ argument ``mapping_stats_min_reads``.
+- To filter expression or splicing data on the number of samples that must
+ detect each feature, you can specify ``expression_thresh``, and
+ ``metadata_min_samples`` in the :py:class:`.Study` constructor.
+
+ * For example, if ``expression_thresh=1`` and ``metadata_min_samples=3``,
+ then we will only take genes which have expression values greater than
+ 1 in at least 3 samples. Additionally, we will also take splicing events
+ which were detected in at least three cells, since
+ ``metadata_min_samples`` applies to *all* data types.
+
+API changes
+~~~~~~~~~~~
+
+- The attribute ``data`` in :py:class:`.BaseData` (i.e.
+ :py:attr:`.BaseData.data`) now contains **all** the data, including pooled,
+ singles, and outliers
+- The attribute ``data_original`` in :py:class:`.BaseData` (i.e.
+ :py:attr:`.BaseData.data_original`) contains the original, unfiltered
+ data. For example, before removing features detected in fewer than 3 samples
+ with expression > 1.
+- :py:class:`.BaseData` now has the attributes
+ :py:attr:`.BaseData.singles`, :py:attr:`.BaseData.pooled`, and
+ :py:attr:`.BaseData.outliers` which are on-the-fly subsets of
+ :py:attr:`.BaseData.data`. This is to maintain data provenance, meaning if
+ "outliers" is changed, this is also changed.
+- In :py:class:`.Study`, you now must specify ``expression_feature_rename_col``,
+ ``splicing_feature_rename_col``, ``mapping_stats_number_mapped_col``
+ explicitly, they are no longer defaulting to,
+ ``{splicing,expression}_feature_rename_col="gene_name"`` and
+ ``mapping_stats_number_mapped_col="Uniquely mapped reads number"``
+
+Other Changes
+~~~~~~~~~~~~~
+
+- Status messages in :py:func:`embark` have been moved to ``stdout`` instead
+ of ``stderr`` to avoid confusion that something is going wrong
+- In :py:func:`embark`, user gets notified which samples are removed for having
+ too few reads (default minimum number of reads is :math:`5\times 10^5`, or
+ half a million reads).
diff --git a/doc/releases/v0.2.1.txt b/doc/releases/v0.2.1.txt
new file mode 100644
index 00000000..d63be982
--- /dev/null
+++ b/doc/releases/v0.2.1.txt
@@ -0,0 +1,13 @@
+v0.2.1 (November 6th, 2014)
+---------------------------
+
+This is a patch release, with non-breaking changes from v0.2.0.
+
+Documentation updates
+~~~~~~~~~~~~~~~~~~~~~
+
+- Update gallery_
+- Update tutorial for `advanced study making`_
+
+.. _gallery: ../gallery
+.. _advanced study making: ../tutorial/addvanced_study_making.ipynb
diff --git a/doc/releases/v0.2.2.txt b/doc/releases/v0.2.2.txt
new file mode 100644
index 00000000..b89e16f1
--- /dev/null
+++ b/doc/releases/v0.2.2.txt
@@ -0,0 +1,10 @@
+v0.2.2 (November 7th, 2014)
+---------------------------
+
+This is a patch release, with non-breaking changes from v0.2.0.
+
+Documentation updates
+~~~~~~~~~~~~~~~~~~~~~
+
+ - Update documentation
+ - Fixed `issue with pip install reported by @roryk `__
diff --git a/doc/releases/v0.2.3.txt b/doc/releases/v0.2.3.txt
new file mode 100644
index 00000000..dbae14c3
--- /dev/null
+++ b/doc/releases/v0.2.3.txt
@@ -0,0 +1,41 @@
+v0.2.3 (November 17th, 2014)
+---------------------------
+
+This is a patch release, with non-breaking changes from v0.2.2.
+
+Compute functions
+~~~~~~~~~~~~~~~~~
+
+- Restore :py:func:`.Study.detect_outliers`,
+ :py:func:`.Study.interactive_choose_outliers` and
+ :py:func:`.Study.interactive_reset_outliers`
+
+Plotting functions
+~~~~~~~~~~~~~~~~~~
+
+- Add :py:class:`.Study`-level NMF space transitions/positions
+
+Bug Fixes
+~~~~~~~~~
+
+- :py:func:`embark` wouldn't work if `metadata` didn't have a `pooled` column,
+ now it does
+- :py:func:`.BaseData.drop_outliers` would actually drop samples from the data,
+ but we never want to remove data, only mark it as something to be removed so
+ all the original data is there
+- For all :py:mod:`.compute` submodules, add a check to make sure the input
+ data is truly a probability distribution (non-negative, sums to 1)
+- :py:func:`.BaseData.plot_feature` now plots all features with the same name
+ (e.g. all splicing events within that gene) onto a single `fig` object
+
+Documentation
+~~~~~~~~~~~~~
+
+- Restore some lost documentation on :py:class:`.BaseData` and
+ :py:class:`.Study`
+
+Other
+~~~~~
+
+- Rename modalities that couldn't be assigned when `bootstrapped=True` in
+ :py:class:`.compute.splicing.Modalities`, from "unassigned" to "ambiguous"
\ No newline at end of file
diff --git a/doc/releases/v0.2.4.txt b/doc/releases/v0.2.4.txt
new file mode 100644
index 00000000..ef224b6e
--- /dev/null
+++ b/doc/releases/v0.2.4.txt
@@ -0,0 +1,18 @@
+v0.2.4 (November 23rd, 2014)
+---------------------------
+
+This is a patch release, with non-breaking changes from v0.2.3.
+
+Plotting functions
+~~~~~~~~~~~~~~~~~~
+
+- New clustered heatmap and :py:func:`.data_model.Study.plot_clustermap` and
+ :py:func:`.data_model.Study.plot_correlations`
+
+API changes
+~~~~~~~~~~~
+
+- :py:func:`.data_model.Study.save()` now saves relative instead of absolute
+ paths, which makes for more portable ``datapackages``
+- Underlying code for :py:class:`.visualize.DecompositionViz` and
+ :py:class:`.visualize.ClassifierViz` now plots via ``plot()``
\ No newline at end of file
diff --git a/doc/releases/v0.2.5.txt b/doc/releases/v0.2.5.txt
new file mode 100644
index 00000000..41e67ab4
--- /dev/null
+++ b/doc/releases/v0.2.5.txt
@@ -0,0 +1,72 @@
+v0.2.5 (March 3rd, 2015)
+------------------------
+
+This is a patch release, with non-breaking changes from v0.2.4. This includes
+many changes and bugfixes. Upgrading to this version is highly recommended.
+
+New features
+~~~~~~~~~~~~
+
+- Added data structure and functions for calculating gene ontology enrichment
+ in `.data_model.Study.go_enrichment`, using the data structure
+ `.gene_ontology.GeneOntologyData`
+
+Plotting functions
+~~~~~~~~~~~~~~~~~~
+
+- New function
+ :py:func:`.data_model.Study.plot_expression_vs_inconsistent_splicing()`
+ shows the percent of splicing events in single cells that are inconsistent
+ with the pooled samples. Has the option to choose an expression cutoff.
+- Add options to :py:func:`.data_model.Study.plot_pca` and
+ :py:func:`.data_model.Study.interactive_pca`
+ - Keyword argument ``color_samples_by`` will take a column name from the
+ ``metadata`` DataFrame, to color samples by different columns in the
+ metadata.
+ - Keyword argument ``scale_by_variance`` is a boolean which when ``True``
+ (default) will scale the :math:`x` and :math:`y` axes by the explained
+ variance of their individual principal components (PC1 for :math:`x` and
+ PC2 for :math:`y`). If ``False``, then the axes are the same scale, by the
+ variance in PC1. Often this will "squish" down the samples in the :math:`y`
+ -axis.
+
+API changes
+~~~~~~~~~~~
+
+- :py:func:`.data_model.Study.plot_classifier` returns a
+ :py:func:`.visualize.predict.ClassifierViz` object
+- Multi-index columns for data matrices are no longer supported
+- Modalities are now calculated using Bayesian methods
+- :py:func:`.data_model.Splicing._subset_and_standardize` now doesn't fill
+ ``NA``s with the mean Percent spliced-in/Psi/:math:`\Psi` score for the
+ event, but rather replaces ``NA`` with the value 0.5. Then, all values for
+ that event are transformed with :math:`\arccos`/:math:`\cos^{-1}`/arc cosine
+ so that all values range from :math:`-\pi` to :math:`+\pi` and are centered
+ around :math:`0`.
+
+Bug fixes
+~~~~~~~~~
+
+- Fixed issue with
+ :py:func:`.data_model.Study.tidy_splicing_with_expression` and
+ :py:func:`.data_model.Study.filter_splicing_on_expression` which
+ had an issue with when the index names are not `"miso_id"` or
+ `"sample_id"`.
+- Don't cache :py:func:`.data_model.BaseData.feature_renamer_series`, so you
+ can change the column used to rename features
+
+Miscellaneous
+~~~~~~~~~~~~~
+
+- Add link to PyData NYC talk
+- Add scrambled dataset with ~300 samples and both expression and splicing
+- Fix build status badge in README
+- Removed auto-call to ``%matplotlib inline`` call within
+ :py:mod:`flotilla.visualize` because it messes up the ``make lint`` call
+ and it's dishonest to the user to be messing with their IPython under the
+ hood. It's possible they don't want the plotting to be inline, but rather
+ in a separate X-window as specified by their ``$DISPLAY`` environment
+ variable.
+- Reformatted all code to pass ``make lint`` and ``make pep8``, and these
+ standards will be enforced for all future enhancements.
+- Add Gitter chat room badge to README
diff --git a/doc/releases/v0.2.6.txt b/doc/releases/v0.2.6.txt
new file mode 100644
index 00000000..9fb0a0fe
--- /dev/null
+++ b/doc/releases/v0.2.6.txt
@@ -0,0 +1,33 @@
+v0.2.6 (...)
+------------------------
+
+This is a patch release, with non-breaking changes from 0.2.5.
+
+New features
+~~~~~~~~~~~~
+
+- Add a :py:class:`.data_model.SupplementalData` data type, which allows the
+ user to store any ``pandas.DataFrame`` on the :py:class:`.data_model.Study`
+ object as `study.supplemental`. This is essentially user-driven caching.
+
+Plotting functions
+~~~~~~~~~~~~~~~~~~
+
+
+API changes
+~~~~~~~~~~~
+
+
+Bug fixes
+~~~~~~~~~
+
+- Fixed :py:func:`.data_model.Study.save()` to actually save:
+ - Gene Ontology Data
+ - Minimum number of mapped reads per sample
+
+
+Miscellaneous
+~~~~~~~~~~~~~
+
+- Streamlined test suite to test fewer things at a time, which shortened the
+ test suite from ~20 minutes to ~3 minutes, about 85% time savings.
\ No newline at end of file
diff --git a/doc/requirements.txt b/doc/requirements.txt
new file mode 100644
index 00000000..e0523dea
--- /dev/null
+++ b/doc/requirements.txt
@@ -0,0 +1,8 @@
+# pip requirements for docs generation
+# pip install -r docs/requirements.txt
+
+Sphinx
+fabric
+sphinx_bootstrap_theme
+runipy
+pandoc
\ No newline at end of file
diff --git a/doc/rsync_exclude b/doc/rsync_exclude
new file mode 100644
index 00000000..6b8710a7
--- /dev/null
+++ b/doc/rsync_exclude
@@ -0,0 +1 @@
+.git
diff --git a/doc/sphinx_deployment.mk b/doc/sphinx_deployment.mk
new file mode 100644
index 00000000..3528dbab
--- /dev/null
+++ b/doc/sphinx_deployment.mk
@@ -0,0 +1,203 @@
+# Copyright (c) Teracy, Inc. and individual contributors.
+# All rights reserved.
+
+# Redistribution and use in source and binary forms, with or without modification,
+# are permitted provided that the following conditions are met:
+
+# 1. Redistributions of source code must retain the above copyright notice,
+# this list of conditions and the following disclaimer.
+
+# 2. Redistributions in binary form must reproduce the above copyright
+# notice, this list of conditions and the following disclaimer in the
+# documentation and/or other materials provided with the distribution.
+
+# 3. Neither the name of Teracy, Inc. nor the names of its contributors may be used
+# to endorse or promote products derived from this software without
+# specific prior written permission.
+
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+# ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+# ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+# Deployment configurations from sphinx_deployment project
+
+# default deployment when $ make deploy
+# deploy_gh_pages : to $ make deploy_gh_pages
+# deploy_rsync : to $ make deploy_rsync
+# deploy_heroku : to $ make deploy_heroku
+# deploy_gh_pages deploy_rsync deploy_heroku : to $ make deploy_gh_pages then $ make deploy_rsync
+# and then $ make deploy_heroku
+# default value: deploy_gh_pages
+ifndef DEPLOY_DEFAULT
+DEPLOY_DEFAULT = deploy_gh_pages
+endif
+
+# The deployment directory to be deployed
+ifndef DEPLOY_DIR
+DEPLOY_DIR = _deploy
+endif
+
+# The heroku deployment directory to be deployed
+# we must create this separated dir to avoid any conflict with _deploy (rsync and gh_pages)
+ifndef DEPLOY_DIR_HEROKU
+DEPLOY_DIR_HEROKU = _deploy_heroku
+endif
+
+# Copy contents from $(BUILDDIR) to $(DEPLOY_DIR)/$(DEPLOY_HTML_DIR) directory
+ifndef DEPLOY_HTML_DIR
+DEPLOY_HTML_DIR =
+endif
+
+
+## -- Rsync Deploy config -- ##
+# Be sure your public key is listed in your server's ~/.ssh/authorized_keys file
+ifndef SSH_USER
+SSH_USER = user@domain.com
+endif
+
+ifndef SSH_PORT
+SSH_PORT = 22
+endif
+
+ifndef DOCUMENT_ROOT
+DOCUMENT_ROOT = ~/website.com/
+endif
+
+#If you choose to delete on sync, rsync will create a 1:1 match
+ifndef RSYNC_DELETE
+RSYNC_DELETE = false
+endif
+
+# Any extra arguments to pass to rsync
+ifndef RSYNC_ARGS
+RSYNC_ARGS =
+endif
+
+## -- Github Pages Deploy config -- ##
+
+# Configure the right deployment branch
+ifndef DEPLOY_BRANCH_GITHUB
+DEPLOY_BRANCH_GITHUB = gh-pages
+endif
+
+#if REPO_URL_GITHUB was NOT defined by travis-ci
+ifndef REPO_URL_GITHUB
+# Configure your right github project repo
+REPO_URL = git@github.com:YeoLab/flotilla.git
+endif
+
+## -- Heroku Deployment Config -- ##
+
+ifndef REPO_URL_HEROKU
+# Configure your right heroku repo
+# REPO_URL_HEROKU = git@heroku.com:spxd.git
+endif
+
+
+## end deployment configuration, don't edit anything below this line ##
+#######################################################################
+
+ifeq ($(RSYNC_DELETE), true)
+RSYNC_DELETE_OPT = --delete
+endif
+
+init_gh_pages:
+ echo "DEPLOY_DIR is" $(DEPLOY_DIR)
+ rm -rf $(DEPLOY_DIR)
+ mkdir -p $(DEPLOY_DIR)
+ cd $(DEPLOY_DIR); git init;\
+ echo 'sphinx docs coming soon...' > index.html;\
+ touch .nojekyll;\
+ git add .; git commit -m "sphinx docs init";\
+ git branch -m $(DEPLOY_BRANCH_GITHUB);\
+ git remote add origin $(REPO_URL_GITHUB);
+ cd $(DEPLOY_DIR);\
+ if ! git ls-remote origin $(DEPLOY_BRANCH_GITHUB) | grep $(DEPLOY_BRANCH_GITHUB) ; then \
+ echo "Preparing Github deployment branch: $(DEPLOY_BRANCH_GITHUB) for the first time only...";\
+ git push -u origin $(DEPLOY_BRANCH_GITHUB);\
+ fi
+
+setup_gh_pages: init_gh_pages
+ echo "Setting up gh-pages deployment..."
+ cd $(DEPLOY_DIR);\
+ git fetch origin;\
+ git reset --hard origin/$(DEPLOY_BRANCH_GITHUB);\
+ git branch --set-upstream-to origin/$(DEPLOY_BRANCH_GITHUB)
+ echo "Now you can deploy to Github Pages with 'make generate' and then 'make deploy_gh_pages'"
+
+init_heroku:
+ @rm -rf $(DEPLOY_DIR_HEROKU)
+ @mkdir -p $(DEPLOY_DIR_HEROKU)
+ @cd $(DEPLOY_DIR_HEROKU); git init;\
+ cp -r ../.deploy_heroku/* .;\
+ echo 'sphinx docs comming soon...' > public/index.html;\
+ git add .; git commit -m "sphinx docs init";\
+ git remote add origin $(REPO_URL_HEROKU);
+ @cd $(DEPLOY_DIR_HEROKU);\
+ if ! git ls-remote origin master | grep master ; then\
+ echo "Preparing Heroku deployment for the first time only...";\
+ git push -u origin master;\
+ fi
+
+setup_heroku: init_heroku
+ @echo "setting up heroku deployment..."
+ @cd $(DEPLOY_DIR_HEROKU);\
+ git fetch origin;\
+ git reset --hard origin/master;\
+ git branch --set-upstream master origin/master
+ @echo "Now you can deploy to Heroku with 'make generate' and then 'make deploy_heroku'"
+
+generate: html
+
+prepare_rsync_deployment:
+ @echo "Preparing rsync deployment..."
+ @mkdir -p $(DEPLOY_DIR)/$(DEPLOY_HTML_DIR)
+ @echo "Copying files from '$(BUILDDIR)/html/.' to '$(DEPLOY_DIR)/$(DEPLOY_HTML_DIR)'"
+ @cp -r $(BUILDDIR)/html/. $(DEPLOY_DIR)/
+
+deploy_rsync: prepare_rsync_deployment
+ @echo "Deploying on rsync now..."
+ rsync -avze 'ssh -p $(SSH_PORT)' --exclude-from $(realpath ./rsync_exclude) $(RSYNC_ARGS) $(RSYNC_DELETE_OPT) ${DEPLOY_DIR}/ $(SSH_USER):$(DOCUMENT_ROOT)
+
+prepare_gh_pages_deployment:
+ echo "Preparing gh_pages deployment..."
+ echo "Pulling any updates from Github Pages..."
+ cd $(DEPLOY_DIR); git pull;
+ mkdir -p $(DEPLOY_DIR)/
+ echo "Copying files from '$(BUILDDIR)/html/.' to '$(DEPLOY_DIR)/$(DEPLOY_HTML_DIR)'"
+ cp -r $(BUILDDIR)/html/. $(DEPLOY_DIR)/$(DEPLOY_HTML_DIR)
+
+deploy_gh_pages: prepare_gh_pages_deployment
+ echo "Deploying on github pages now..."
+ cd $(DEPLOY_DIR); git add -A; git commit -m "docs updated at `date -u`";\
+ git push origin $(DEPLOY_BRANCH) --quiet
+ echo "Github Pages deploy was completed at `date -u`"
+
+prepare_heroku_deployment:
+ @echo "Preparing heroku deployment..."
+ @echo "Pulling any updates from Heroku..."
+ @cd $(DEPLOY_DIR_HEROKU); git pull;
+ @mkdir -p $(DEPLOY_DIR_HEROKU)/public/$(DEPLOY_HTML_DIR)
+ @echo "Copying files from .deploy_heroku to $(DEPLOY_DIR_HEROKU)"
+ @cp -r .deploy_heroku/. $(DEPLOY_DIR_HEROKU)
+ @echo "Copying files from '$(BUILDDIR)/html/.' to '$(DEPLOY_DIR_HEROKU)/public/$(DEPLOY_HTML_DIR)'"
+ @cp -r $(BUILDDIR)/html/. $(DEPLOY_DIR_HEROKU)/public/$(DEPLOY_HTML_DIR)
+
+
+deploy_heroku: prepare_heroku_deployment
+ @echo "Deploying on heroku now..."
+ @cd $(DEPLOY_DIR_HEROKU); git add -A; git commit -m "docs updated at `date -u`";\
+ git push origin master --quiet
+ @echo "Heroku deployment was completed at `date -u`"
+
+
+deploy: $(DEPLOY_DEFAULT)
+
+gen_deploy: generate deploy
diff --git a/doc/sphinxext/ipython_console_highlighting.py b/doc/sphinxext/ipython_console_highlighting.py
new file mode 100644
index 00000000..dfb489e4
--- /dev/null
+++ b/doc/sphinxext/ipython_console_highlighting.py
@@ -0,0 +1,116 @@
+"""reST directive for syntax-highlighting ipython interactive sessions.
+
+XXX - See what improvements can be made based on the new (as of Sept 2009)
+'pycon' lexer for the python console. At the very least it will give better
+highlighted tracebacks.
+"""
+
+#-----------------------------------------------------------------------------
+# Needed modules
+
+# Standard library
+import re
+
+# Third party
+from pygments.lexer import Lexer, do_insertions
+from pygments.lexers.agile import (PythonConsoleLexer, PythonLexer,
+ PythonTracebackLexer)
+from pygments.token import Comment, Generic
+
+from sphinx import highlighting
+
+#-----------------------------------------------------------------------------
+# Global constants
+line_re = re.compile('.*?\n')
+
+#-----------------------------------------------------------------------------
+# Code begins - classes and functions
+
+
+class IPythonConsoleLexer(Lexer):
+
+ """
+ For IPython console output or doctests, such as:
+
+ .. sourcecode:: ipython
+
+ In [1]: a = 'foo'
+
+ In [2]: a
+ Out[2]: 'foo'
+
+ In [3]: print(a)
+ foo
+
+ In [4]: 1 / 0
+
+ Notes:
+
+ - Tracebacks are not currently supported.
+
+ - It assumes the default IPython prompts, not customized ones.
+ """
+
+ name = 'IPython console session'
+ aliases = ['ipython']
+ mimetypes = ['text/x-ipython-console']
+ input_prompt = re.compile("(In \[[0-9]+\]: )|( \.\.\.+:)")
+ output_prompt = re.compile("(Out\[[0-9]+\]: )|( \.\.\.+:)")
+ continue_prompt = re.compile(" \.\.\.+:")
+ tb_start = re.compile("\-+")
+
+ def get_tokens_unprocessed(self, text):
+ pylexer = PythonLexer(**self.options)
+ tblexer = PythonTracebackLexer(**self.options)
+
+ curcode = ''
+ insertions = []
+ for match in line_re.finditer(text):
+ line = match.group()
+ input_prompt = self.input_prompt.match(line)
+ continue_prompt = self.continue_prompt.match(line.rstrip())
+ output_prompt = self.output_prompt.match(line)
+ if line.startswith("#"):
+ insertions.append((len(curcode),
+ [(0, Comment, line)]))
+ elif input_prompt is not None:
+ insertions.append((len(curcode),
+ [(0, Generic.Prompt, input_prompt.group())]))
+ curcode += line[input_prompt.end():]
+ elif continue_prompt is not None:
+ insertions.append((len(curcode),
+ [(0, Generic.Prompt, continue_prompt.group())]))
+ curcode += line[continue_prompt.end():]
+ elif output_prompt is not None:
+ # Use the 'error' token for output. We should probably make
+ # our own token, but error is typicaly in a bright color like
+ # red, so it works fine for our output prompts.
+ insertions.append((len(curcode),
+ [(0, Generic.Error, output_prompt.group())]))
+ curcode += line[output_prompt.end():]
+ else:
+ if curcode:
+ for item in do_insertions(insertions,
+ pylexer.get_tokens_unprocessed(curcode)):
+ yield item
+ curcode = ''
+ insertions = []
+ yield match.start(), Generic.Output, line
+ if curcode:
+ for item in do_insertions(insertions,
+ pylexer.get_tokens_unprocessed(curcode)):
+ yield item
+
+
+def setup(app):
+ """Setup as a sphinx extension."""
+
+ # This is only a lexer, so adding it below to pygments appears sufficient.
+ # But if somebody knows that the right API usage should be to do that via
+ # sphinx, by all means fix it here. At least having this setup.py
+ # suppresses the sphinx warning we'd get without it.
+ pass
+
+#-----------------------------------------------------------------------------
+# Register the extension as a valid pygments lexer
+highlighting.lexers['ipython'] = IPythonConsoleLexer()
diff --git a/doc/sphinxext/ipython_directive.py b/doc/sphinxext/ipython_directive.py
new file mode 100644
index 00000000..2fb64c1f
--- /dev/null
+++ b/doc/sphinxext/ipython_directive.py
@@ -0,0 +1,1091 @@
+# -*- coding: utf-8 -*-
+"""
+Sphinx directive to support embedded IPython code.
+
+This directive allows pasting of entire interactive IPython sessions, prompts
+and all, and their code will actually get re-executed at doc build time, with
+all prompts renumbered sequentially. It also allows you to input code as a pure
+python input by giving the argument python to the directive. The output looks
+like an interactive ipython section.
+
+To enable this directive, simply list it in your Sphinx ``conf.py`` file
+(making sure the directory where you placed it is visible to sphinx, as is
+needed for all Sphinx directives). For example, to enable syntax highlighting
+and the IPython directive::
+
+ extensions = ['IPython.sphinxext.ipython_console_highlighting',
+ 'IPython.sphinxext.ipython_directive']
+
+The IPython directive outputs code-blocks with the language 'ipython'. So
+if you do not have the syntax highlighting extension enabled as well, then
+all rendered code-blocks will be uncolored. By default this directive assumes
+that your prompts are unchanged IPython ones, but this can be customized.
+The configurable options that can be placed in conf.py are:
+
+ipython_savefig_dir:
+ The directory in which to save the figures. This is relative to the
+ Sphinx source directory. The default is `html_static_path`.
+ipython_rgxin:
+ The compiled regular expression to denote the start of IPython input
+ lines. The default is re.compile('In \[(\d+)\]:\s?(.*)\s*'). You
+ shouldn't need to change this.
+ipython_rgxout:
+ The compiled regular expression to denote the start of IPython output
+ lines. The default is re.compile('Out\[(\d+)\]:\s?(.*)\s*'). You
+ shouldn't need to change this.
+ipython_promptin:
+ The string to represent the IPython input prompt in the generated ReST.
+ The default is 'In [%d]:'. This expects that the line numbers are used
+ in the prompt.
+ipython_promptout:
+ The string to represent the IPython prompt in the generated ReST. The
+ default is 'Out [%d]:'. This expects that the line numbers are used
+ in the prompt.
+ipython_mplbackend:
+ The string which specifies if the embedded Sphinx shell should import
+ Matplotlib and set the backend. The value specifies a backend that is
+ passed to `matplotlib.use()` before any lines in `ipython_execlines` are
+ executed. If not specified in conf.py, then the default value of 'agg' is
+ used. To use the IPython directive without matplotlib as a dependency, set
+ the value to `None`. It may end up that matplotlib is still imported
+ if the user specifies so in `ipython_execlines` or makes use of the
+ @savefig pseudo decorator.
+ipython_execlines:
+ A list of strings to be exec'd in the embedded Sphinx shell. Typical
+ usage is to make certain packages always available. Set this to an empty
+ list if you wish to have no imports always available. If specified in
+ conf.py as `None`, then it has the effect of making no imports available.
+ If omitted from conf.py altogether, then the default value of
+ ['import numpy as np', 'import matplotlib.pyplot as plt'] is used.
+ipython_holdcount
+ When the @suppress pseudo-decorator is used, the execution count can be
+ incremented or not. The default behavior is to hold the execution count,
+ corresponding to a value of `True`. Set this to `False` to increment
+ the execution count after each suppressed command.
+
+As an example, to use the IPython directive when `matplotlib` is not available,
+one sets the backend to `None`::
+
+ ipython_mplbackend = None
+
+An example usage of the directive is:
+
+.. code-block:: rst
+
+ .. ipython::
+
+ In [1]: x = 1
+
+ In [2]: y = x**2
+
+ In [3]: print(y)
+
+See http://matplotlib.org/sampledoc/ipython_directive.html for additional
+documentation.
+
+ToDo
+----
+
+- Turn the ad-hoc test() function into a real test suite.
+- Break up ipython-specific functionality from matplotlib stuff into better
+ separated code.
+
+Authors
+-------
+
+- John D Hunter: orignal author.
+- Fernando Perez: refactoring, documentation, cleanups, port to 0.11.
+- VáclavŠmilauer : Prompt generalizations.
+- Skipper Seabold, refactoring, cleanups, pure python addition
+"""
+from __future__ import print_function
+from __future__ import unicode_literals
+
+#-----------------------------------------------------------------------------
+# Imports
+#-----------------------------------------------------------------------------
+
+# Stdlib
+import os
+import re
+import sys
+import tempfile
+import ast
+from pandas.compat import range
+import warnings
+
+# To keep compatibility with various python versions
+try:
+ from hashlib import md5
+except ImportError:
+ from md5 import md5
+
+# Third-party
+from docutils.parsers.rst import directives
+from sphinx.util.compat import Directive
+
+# Our own
+from IPython import Config, InteractiveShell
+from IPython.core.profiledir import ProfileDir
+from IPython.utils import io
+from IPython.utils.py3compat import PY3
+
+if PY3:
+ from io import StringIO
+
+ text_type = str
+else:
+ from StringIO import StringIO
+
+ text_type = unicode
+
+#-----------------------------------------------------------------------------
+# Globals
+#-----------------------------------------------------------------------------
+# for tokenizing blocks
+COMMENT, INPUT, OUTPUT = range(3)
+
+#-----------------------------------------------------------------------------
+# Functions and class declarations
+#-----------------------------------------------------------------------------
+
+def block_parser(part, rgxin, rgxout, fmtin, fmtout):
+ """
+ part is a string of ipython text, comprised of at most one
+ input, one ouput, comments, and blank lines. The block parser
+ parses the text into a list of::
+
+ blocks = [ (TOKEN0, data0), (TOKEN1, data1), ...]
+
+ where TOKEN is one of [COMMENT | INPUT | OUTPUT ] and
+ dataset is, depending on the type of token::
+
+ COMMENT : the comment string
+
+ INPUT: the (DECORATOR, INPUT_LINE, REST) where
+ DECORATOR: the input decorator (or None)
+ INPUT_LINE: the input as string (possibly multi-line)
+ REST : any stdout generated by the input line (not OUTPUT)
+
+ OUTPUT: the output string, possibly multi-line
+
+ """
+ block = []
+ lines = part.split('\n')
+ N = len(lines)
+ i = 0
+ decorator = None
+ while 1:
+
+ if i == N:
+ # nothing left to parse -- the last line
+ break
+
+ line = lines[i]
+ i += 1
+ line_stripped = line.strip()
+ if line_stripped.startswith('#'):
+ block.append((COMMENT, line))
+ continue
+
+ if line_stripped.startswith('@'):
+ # we're assuming at most one decorator -- may need to
+ # rethink
+ decorator = line_stripped
+ continue
+
+ # does this look like an input line?
+ matchin = rgxin.match(line)
+ if matchin:
+ lineno, inputline = int(matchin.group(1)), matchin.group(2)
+
+ # the ....: continuation string
+ continuation = ' %s:' % ''.join(['.'] * (len(str(lineno)) + 2))
+ Nc = len(continuation)
+ # input lines can continue on for more than one line, if
+ # we have a '\' line continuation char or a function call
+ # echo line 'print'. The input line can only be
+ # terminated by the end of the block or an output line, so
+ # we parse out the rest of the input line if it is
+ # multiline as well as any echo text
+
+ rest = []
+ while i < N:
+
+ # look ahead; if the next line is blank, or a comment, or
+ # an output line, we're done
+
+ nextline = lines[i]
+ matchout = rgxout.match(nextline)
+ #print "nextline=%s, continuation=%s, starts=%s"%(nextline, continuation, nextline.startswith(continuation))
+ if matchout or nextline.startswith('#'):
+ break
+ elif nextline.startswith(continuation):
+ nextline = nextline[Nc:]
+ if nextline and nextline[0] == ' ':
+ nextline = nextline[1:]
+
+ inputline += '\n' + nextline
+
+ else:
+ rest.append(nextline)
+ i += 1
+
+ block.append((INPUT, (decorator, inputline, '\n'.join(rest))))
+ continue
+
+ # if it looks like an output line grab all the text to the end
+ # of the block
+ matchout = rgxout.match(line)
+ if matchout:
+ lineno, output = int(matchout.group(1)), matchout.group(2)
+ if i < N - 1:
+ output = '\n'.join([output] + lines[i:])
+
+ block.append((OUTPUT, output))
+ break
+
+ return block
+
+
+class DecodingStringIO(StringIO, object):
+ def __init__(self, buf='', encodings=('utf8',), *args, **kwds):
+ super(DecodingStringIO, self).__init__(buf, *args, **kwds)
+ self.set_encodings(encodings)
+
+ def set_encodings(self, encodings):
+ self.encodings = encodings
+
+ def write(self, data):
+ if isinstance(data, text_type):
+ return super(DecodingStringIO, self).write(data)
+ else:
+ for enc in self.encodings:
+ try:
+ data = data.decode(enc)
+ return super(DecodingStringIO, self).write(data)
+ except:
+ pass
+ # default to brute utf8 if no encoding succeded
+ return super(DecodingStringIO, self).write(
+ data.decode('utf8', 'replace'))
+
+
+class EmbeddedSphinxShell(object):
+ """An embedded IPython instance to run inside Sphinx"""
+
+ def __init__(self, exec_lines=None, state=None):
+
+ self.cout = DecodingStringIO(u'')
+
+ if exec_lines is None:
+ exec_lines = []
+
+ self.state = state
+
+ # Create config object for IPython
+ config = Config()
+ config.InteractiveShell.autocall = False
+ config.InteractiveShell.autoindent = False
+ config.InteractiveShell.colors = 'NoColor'
+
+ # create a profile so instance history isn't saved
+ tmp_profile_dir = tempfile.mkdtemp(prefix='profile_')
+ profname = 'auto_profile_sphinx_build'
+ pdir = os.path.join(tmp_profile_dir, profname)
+ profile = ProfileDir.create_profile_dir(pdir)
+
+ # Create and initialize global ipython, but don't start its mainloop.
+ # This will persist across different EmbededSphinxShell instances.
+ IP = InteractiveShell.instance(config=config, profile_dir=profile)
+
+ # io.stdout redirect must be done after instantiating InteractiveShell
+ io.stdout = self.cout
+ io.stderr = self.cout
+
+ # For debugging, so we can see normal output, use this:
+ #from IPython.utils.io import Tee
+ #io.stdout = Tee(self.cout, channel='stdout') # dbg
+ #io.stderr = Tee(self.cout, channel='stderr') # dbg
+
+ # Store a few parts of IPython we'll need.
+ self.IP = IP
+ self.user_ns = self.IP.user_ns
+ self.user_global_ns = self.IP.user_global_ns
+
+ self.input = ''
+ self.output = ''
+
+ self.is_verbatim = False
+ self.is_doctest = False
+ self.is_suppress = False
+
+ # Optionally, provide more detailed information to shell.
+ self.directive = None
+
+ # on the first call to the savefig decorator, we'll import
+ # pyplot as plt so we can make a call to the plt.gcf().savefig
+ self._pyplot_imported = False
+
+ # Prepopulate the namespace.
+ for line in exec_lines:
+ self.process_input_line(line, store_history=False)
+
+ def clear_cout(self):
+ self.cout.seek(0)
+ self.cout.truncate(0)
+
+ def process_input_line(self, line, store_history=True):
+ """process the input, capturing stdout"""
+
+ stdout = sys.stdout
+ splitter = self.IP.input_splitter
+ try:
+ sys.stdout = self.cout
+ splitter.push(line)
+ more = splitter.push_accepts_more()
+ if not more:
+ try:
+ source_raw = splitter.source_raw_reset()[1]
+ except:
+ # recent ipython #4504
+ source_raw = splitter.raw_reset()
+ self.IP.run_cell(source_raw, store_history=store_history)
+ finally:
+ sys.stdout = stdout
+
+ def process_image(self, decorator):
+ """
+ # build out an image directive like
+ # .. image:: somefile.png
+ # :width 4in
+ #
+ # from an input like
+ # savefig somefile.png width=4in
+ """
+ savefig_dir = self.savefig_dir
+ source_dir = self.source_dir
+ saveargs = decorator.split(' ')
+ filename = saveargs[1]
+ # insert relative path to image file in source
+ outfile = os.path.relpath(os.path.join(savefig_dir, filename),
+ source_dir)
+
+ imagerows = ['.. image:: %s' % outfile]
+
+ for kwarg in saveargs[2:]:
+ arg, val = kwarg.split('=')
+ arg = arg.strip()
+ val = val.strip()
+ imagerows.append(' :%s: %s' % (arg, val))
+
+ image_file = os.path.basename(outfile) # only return file name
+ image_directive = '\n'.join(imagerows)
+ return image_file, image_directive
+
+ # Callbacks for each type of token
+ def process_input(self, data, input_prompt, lineno):
+ """
+ Process dataset block for INPUT token.
+
+ """
+ decorator, input, rest = data
+ image_file = None
+ image_directive = None
+
+ is_verbatim = decorator == '@verbatim' or self.is_verbatim
+ is_doctest = (decorator is not None and \
+ decorator.startswith('@doctest')) or self.is_doctest
+ is_suppress = decorator == '@suppress' or self.is_suppress
+ is_okexcept = decorator == '@okexcept' or self.is_okexcept
+ is_okwarning = decorator == '@okwarning' or self.is_okwarning
+ is_savefig = decorator is not None and \
+ decorator.startswith('@savefig')
+
+ # set the encodings to be used by DecodingStringIO
+ # to convert the execution output into unicode if
+ # needed. this attrib is set by IpythonDirective.run()
+ # based on the specified block options, defaulting to ['ut
+ self.cout.set_encodings(self.output_encoding)
+
+ input_lines = input.split('\n')
+
+ if len(input_lines) > 1:
+ if input_lines[-1] != "":
+ input_lines.append('') # make sure there's a blank line
+ # so splitter buffer gets reset
+
+ continuation = ' %s:' % ''.join(['.'] * (len(str(lineno)) + 2))
+
+ if is_savefig:
+ image_file, image_directive = self.process_image(decorator)
+
+ ret = []
+ is_semicolon = False
+
+ # Hold the execution count, if requested to do so.
+ if is_suppress and self.hold_count:
+ store_history = False
+ else:
+ store_history = True
+
+ # Note: catch_warnings is not thread safe
+ with warnings.catch_warnings(record=True) as ws:
+ for i, line in enumerate(input_lines):
+ if line.endswith(';'):
+ is_semicolon = True
+
+ if i == 0:
+ # process the first input line
+ if is_verbatim:
+ self.process_input_line('')
+ self.IP.execution_count += 1 # increment it anyway
+ else:
+ # only submit the line in non-verbatim mode
+ self.process_input_line(line,
+ store_history=store_history)
+ formatted_line = '%s %s' % (input_prompt, line)
+ else:
+ # process a continuation line
+ if not is_verbatim:
+ self.process_input_line(line,
+ store_history=store_history)
+
+ formatted_line = '%s %s' % (continuation, line)
+
+ if not is_suppress:
+ ret.append(formatted_line)
+
+ if not is_suppress and len(rest.strip()) and is_verbatim:
+ # the "rest" is the standard output of the
+ # input, which needs to be added in
+ # verbatim mode
+ ret.append(rest)
+
+ self.cout.seek(0)
+ output = self.cout.read()
+ if not is_suppress and not is_semicolon:
+ ret.append(output)
+ elif is_semicolon: # get spacing right
+ ret.append('')
+
+ # context information
+ filename = self.state.document.current_source
+ lineno = self.state.document.current_line
+
+ # output any exceptions raised during execution to stdout
+ # unless :okexcept: has been specified.
+ if not is_okexcept and "Traceback" in output:
+ s = "\nException in %s at block ending on line %s\n" % (
+ filename, lineno)
+ s += "Specify :okexcept: as an option in the ipython:: block to suppress this message\n"
+ sys.stdout.write('\n\n>>>' + ('-' * 73))
+ sys.stdout.write(s)
+ sys.stdout.write(output)
+ sys.stdout.write('<<<' + ('-' * 73) + '\n\n')
+
+ # output any warning raised during execution to stdout
+ # unless :okwarning: has been specified.
+ if not is_okwarning:
+ for w in ws:
+ s = "\nWarning in %s at block ending on line %s\n" % (
+ filename, lineno)
+ s += "Specify :okwarning: as an option in the ipython:: block to suppress this message\n"
+ sys.stdout.write('\n\n>>>' + ('-' * 73))
+ sys.stdout.write(s)
+ sys.stdout.write('-' * 76 + '\n')
+ s = warnings.formatwarning(w.message, w.category,
+ w.filename, w.lineno, w.line)
+ sys.stdout.write(s)
+ sys.stdout.write('<<<' + ('-' * 73) + '\n')
+
+ self.cout.truncate(0)
+ return (ret, input_lines, output, is_doctest, decorator, image_file,
+ image_directive)
+
+
+ def process_output(self, data, output_prompt,
+ input_lines, output, is_doctest, decorator, image_file):
+ """
+ Process dataset block for OUTPUT token.
+
+ """
+ TAB = ' ' * 4
+
+ if is_doctest and output is not None:
+
+ found = output
+ found = found.strip()
+ submitted = data.strip()
+
+ if self.directive is None:
+ source = 'Unavailable'
+ content = 'Unavailable'
+ else:
+ source = self.directive.state.document.current_source
+ content = self.directive.content
+ # Add tabs and join into a single string.
+ content = '\n'.join([TAB + line for line in content])
+
+ # Make sure the output contains the output prompt.
+ ind = found.find(output_prompt)
+ if ind < 0:
+ e = ('output does not contain output prompt\n\n'
+ 'Document source: {0}\n\n'
+ 'Raw content: \n{1}\n\n'
+ 'Input line(s):\n{TAB}{2}\n\n'
+ 'Output line(s):\n{TAB}{3}\n\n')
+ e = e.format(source, content, '\n'.join(input_lines),
+ repr(found), TAB=TAB)
+ raise RuntimeError(e)
+ found = found[len(output_prompt):].strip()
+
+ # Handle the actual doctest comparison.
+ if decorator.strip() == '@doctest':
+ # Standard doctest
+ if found != submitted:
+ e = ('doctest failure\n\n'
+ 'Document source: {0}\n\n'
+ 'Raw content: \n{1}\n\n'
+ 'On input line(s):\n{TAB}{2}\n\n'
+ 'we found output:\n{TAB}{3}\n\n'
+ 'instead of the expected:\n{TAB}{4}\n\n')
+ e = e.format(source, content, '\n'.join(input_lines),
+ repr(found), repr(submitted), TAB=TAB)
+ raise RuntimeError(e)
+ else:
+ self.custom_doctest(decorator, input_lines, found, submitted)
+
+ def process_comment(self, data):
+ """Process dataset fPblock for COMMENT token."""
+ if not self.is_suppress:
+ return [data]
+
+ def save_image(self, image_file):
+ """
+ Saves the image file to disk.
+ """
+ self.ensure_pyplot()
+ command = ('plt.gcf().savefig("%s", bbox_inches="tight", '
+ 'dpi=100)' % image_file)
+
+ #print 'SAVEFIG', command # dbg
+ self.process_input_line('bookmark ipy_thisdir', store_history=False)
+ self.process_input_line('cd -b ipy_savedir', store_history=False)
+ self.process_input_line(command, store_history=False)
+ self.process_input_line('cd -b ipy_thisdir', store_history=False)
+ self.process_input_line('bookmark -d ipy_thisdir', store_history=False)
+ self.clear_cout()
+
+ def process_block(self, block):
+ """
+ process block from the block_parser and return a list of processed lines
+ """
+ ret = []
+ output = None
+ input_lines = None
+ lineno = self.IP.execution_count
+
+ input_prompt = self.promptin % lineno
+ output_prompt = self.promptout % lineno
+ image_file = None
+ image_directive = None
+
+ for token, data in block:
+ if token == COMMENT:
+ out_data = self.process_comment(data)
+ elif token == INPUT:
+ (out_data, input_lines, output, is_doctest, decorator,
+ image_file, image_directive) = \
+ self.process_input(data, input_prompt, lineno)
+ elif token == OUTPUT:
+ out_data = \
+ self.process_output(data, output_prompt,
+ input_lines, output, is_doctest,
+ decorator, image_file)
+ if out_data:
+ ret.extend(out_data)
+
+ # save the image files
+ if image_file is not None:
+ self.save_image(image_file)
+
+ return ret, image_directive
+
+ def ensure_pyplot(self):
+ """
+ Ensures that pyplot has been imported into the embedded IPython shell.
+
+ Also, makes sure to set the backend appropriately if not set already.
+
+ """
+ # We are here if the @figure pseudo decorator was used. Thus, it's
+ # possible that we could be here even if python_mplbackend were set to
+ # `None`. That's also strange and perhaps worthy of raising an
+ # exception, but for now, we just set the backend to 'agg'.
+
+ if not self._pyplot_imported:
+ if 'matplotlib.backends' not in sys.modules:
+ # Then ipython_matplotlib was set to None but there was a
+ # call to the @figure decorator (and ipython_execlines did
+ # not set a backend).
+ #raise Exception("No backend was set, but @figure was used!")
+ import matplotlib
+
+ matplotlib.use('agg')
+
+ # Always import pyplot into embedded shell.
+ self.process_input_line('import matplotlib.pyplot as plt',
+ store_history=False)
+ self._pyplot_imported = True
+
+ def process_pure_python(self, content):
+ """
+ content is a list of strings. it is unedited directive content
+
+ This runs it line by line in the InteractiveShell, prepends
+ prompts as needed capturing stderr and stdout, then returns
+ the content as a list as if it were ipython code
+ """
+ output = []
+ savefig = False # keep up with this to clear figure
+ multiline = False # to handle line continuation
+ multiline_start = None
+ fmtin = self.promptin
+
+ ct = 0
+
+ for lineno, line in enumerate(content):
+
+ line_stripped = line.strip()
+ if not len(line):
+ output.append(line)
+ continue
+
+ # handle decorators
+ if line_stripped.startswith('@'):
+ output.extend([line])
+ if 'savefig' in line:
+ savefig = True # and need to clear figure
+ continue
+
+ # handle comments
+ if line_stripped.startswith('#'):
+ output.extend([line])
+ continue
+
+ # deal with lines checking for multiline
+ continuation = u' %s:' % ''.join(['.'] * (len(str(ct)) + 2))
+ if not multiline:
+ modified = u"%s %s" % (fmtin % ct, line_stripped)
+ output.append(modified)
+ ct += 1
+ try:
+ ast.parse(line_stripped)
+ output.append(u'')
+ except Exception: # on a multiline
+ multiline = True
+ multiline_start = lineno
+ else: # still on a multiline
+ modified = u'%s %s' % (continuation, line)
+ output.append(modified)
+
+ # if the next line is indented, it should be part of multiline
+ if len(content) > lineno + 1:
+ nextline = content[lineno + 1]
+ if len(nextline) - len(nextline.lstrip()) > 3:
+ continue
+ try:
+ mod = ast.parse(
+ '\n'.join(content[multiline_start:lineno + 1]))
+ if isinstance(mod.body[0], ast.FunctionDef):
+ # check to see if we have the whole function
+ for element in mod.body[0].body:
+ if isinstance(element, ast.Return):
+ multiline = False
+ else:
+ output.append(u'')
+ multiline = False
+ except Exception:
+ pass
+
+ if savefig: # clear figure if plotted
+ self.ensure_pyplot()
+ self.process_input_line('plt.clf()', store_history=False)
+ self.clear_cout()
+ savefig = False
+
+ return output
+
+ def custom_doctest(self, decorator, input_lines, found, submitted):
+ """
+ Perform a specialized doctest.
+
+ """
+ from .custom_doctests import doctests
+
+ args = decorator.split()
+ doctest_type = args[1]
+ if doctest_type in doctests:
+ doctests[doctest_type](self, args, input_lines, found, submitted)
+ else:
+ e = "Invalid option to @doctest: {0}".format(doctest_type)
+ raise Exception(e)
+
+
+class IPythonDirective(Directive):
+ has_content = True
+ required_arguments = 0
+ optional_arguments = 4 # python, suppress, verbatim, doctest
+ final_argumuent_whitespace = True
+ option_spec = {'python': directives.unchanged,
+ 'suppress': directives.flag,
+ 'verbatim': directives.flag,
+ 'doctest': directives.flag,
+ 'okexcept': directives.flag,
+ 'okwarning': directives.flag,
+ 'output_encoding': directives.unchanged_required
+ }
+
+ shell = None
+
+ seen_docs = set()
+
+ def get_config_options(self):
+ # contains sphinx configuration variables
+ config = self.state.document.settings.env.config
+
+ # get config variables to set figure output directory
+ confdir = self.state.document.settings.env.app.confdir
+ savefig_dir = config.ipython_savefig_dir
+ source_dir = os.path.dirname(self.state.document.current_source)
+ if savefig_dir is None:
+ savefig_dir = config.html_static_path
+ if isinstance(savefig_dir, list):
+ savefig_dir = savefig_dir[0] # safe to assume only one path?
+ savefig_dir = os.path.join(confdir, savefig_dir)
+
+ # get regex and prompt stuff
+ rgxin = config.ipython_rgxin
+ rgxout = config.ipython_rgxout
+ promptin = config.ipython_promptin
+ promptout = config.ipython_promptout
+ mplbackend = config.ipython_mplbackend
+ exec_lines = config.ipython_execlines
+ hold_count = config.ipython_holdcount
+
+ return (savefig_dir, source_dir, rgxin, rgxout,
+ promptin, promptout, mplbackend, exec_lines, hold_count)
+
+ def setup(self):
+ # Get configuration values.
+ (savefig_dir, source_dir, rgxin, rgxout, promptin, promptout,
+ mplbackend, exec_lines, hold_count) = self.get_config_options()
+
+ if self.shell is None:
+ # We will be here many times. However, when the
+ # EmbeddedSphinxShell is created, its interactive shell member
+ # is the same for each instance.
+
+ if mplbackend:
+ import matplotlib
+ # Repeated calls to use() will not hurt us since `mplbackend`
+ # is the same each time.
+ matplotlib.use(mplbackend)
+
+ # Must be called after (potentially) importing matplotlib and
+ # setting its backend since exec_lines might import pylab.
+ self.shell = EmbeddedSphinxShell(exec_lines, self.state)
+
+ # Store IPython directive to enable better error messages
+ self.shell.directive = self
+
+ # reset the execution count if we haven't processed this doc
+ #NOTE: this may be borked if there are multiple seen_doc tmp files
+ #check time stamp?
+ if not self.state.document.current_source in self.seen_docs:
+ self.shell.IP.history_manager.reset()
+ self.shell.IP.execution_count = 1
+ self.shell.IP.prompt_manager.width = 0
+ self.seen_docs.add(self.state.document.current_source)
+
+ # and attach to shell so we don't have to pass them around
+ self.shell.rgxin = rgxin
+ self.shell.rgxout = rgxout
+ self.shell.promptin = promptin
+ self.shell.promptout = promptout
+ self.shell.savefig_dir = savefig_dir
+ self.shell.source_dir = source_dir
+ self.shell.hold_count = hold_count
+
+ # setup bookmark for saving figures directory
+ self.shell.process_input_line('bookmark ipy_savedir %s' % savefig_dir,
+ store_history=False)
+ self.shell.clear_cout()
+
+ return rgxin, rgxout, promptin, promptout
+
+ def teardown(self):
+ # delete last bookmark
+ self.shell.process_input_line('bookmark -d ipy_savedir',
+ store_history=False)
+ self.shell.clear_cout()
+
+ def run(self):
+ debug = False
+
+ #TODO, any reason block_parser can't be a method of embeddable shell
+ # then we wouldn't have to carry these around
+ rgxin, rgxout, promptin, promptout = self.setup()
+
+ options = self.options
+ self.shell.is_suppress = 'suppress' in options
+ self.shell.is_doctest = 'doctest' in options
+ self.shell.is_verbatim = 'verbatim' in options
+ self.shell.is_okexcept = 'okexcept' in options
+ self.shell.is_okwarning = 'okwarning' in options
+
+ self.shell.output_encoding = [options.get('output_encoding', 'utf8')]
+
+ # handle pure python code
+ if 'python' in self.arguments:
+ content = self.content
+ self.content = self.shell.process_pure_python(content)
+
+ parts = '\n'.join(self.content).split('\n\n')
+
+ lines = ['.. code-block:: ipython', '']
+ figures = []
+
+ for part in parts:
+ block = block_parser(part, rgxin, rgxout, promptin, promptout)
+ if len(block):
+ rows, figure = self.shell.process_block(block)
+ for row in rows:
+ lines.extend([' %s' % line for line in row.split('\n')])
+
+ if figure is not None:
+ figures.append(figure)
+
+ for figure in figures:
+ lines.append('')
+ lines.extend(figure.split('\n'))
+ lines.append('')
+
+ if len(lines) > 2:
+ if debug:
+ print('\n'.join(lines))
+ else:
+ # This has to do with input, not output. But if we comment
+ # these lines out, then no IPython code will appear in the
+ # final output.
+ self.state_machine.insert_input(
+ lines, self.state_machine.input_lines.source(0))
+
+ # cleanup
+ self.teardown()
+
+ return []
+
+
+# Enable as a proper Sphinx directive
+def setup(app):
+ setup.app = app
+
+ app.add_directive('ipython', IPythonDirective)
+ app.add_config_value('ipython_savefig_dir', None, 'env')
+ app.add_config_value('ipython_rgxin',
+ re.compile('In \[(\d+)\]:\s?(.*)\s*'), 'env')
+ app.add_config_value('ipython_rgxout',
+ re.compile('Out\[(\d+)\]:\s?(.*)\s*'), 'env')
+ app.add_config_value('ipython_promptin', 'In [%d]:', 'env')
+ app.add_config_value('ipython_promptout', 'Out[%d]:', 'env')
+
+ # We could just let matplotlib pick whatever is specified as the default
+ # backend in the matplotlibrc file, but this would cause issues if the
+ # backend didn't work in headless environments. For this reason, 'agg'
+ # is a good default backend choice.
+ app.add_config_value('ipython_mplbackend', 'agg', 'env')
+
+ # If the user sets this config value to `None`, then EmbeddedSphinxShell's
+ # __init__ method will treat it as [].
+ execlines = ['import numpy as np', 'import matplotlib.pyplot as plt']
+ app.add_config_value('ipython_execlines', execlines, 'env')
+
+ app.add_config_value('ipython_holdcount', True, 'env')
+
+
+# Simple smoke test, needs to be converted to a proper automatic test.
+def test():
+ examples = [
+ r"""
+In [9]: pwd
+Out[9]: '/home/jdhunter/py4science/book'
+
+In [10]: cd bookdata/
+/home/jdhunter/py4science/book/bookdata
+
+In [2]: from pylab import *
+
+In [2]: ion()
+
+In [3]: im = imread('stinkbug.png')
+
+@savefig mystinkbug.png width=4in
+In [4]: imshow(im)
+Out[4]:
+
+""",
+ r"""
+
+In [1]: x = 'hello world'
+
+# string methods can be
+# used to alter the string
+@doctest
+In [2]: x.upper()
+Out[2]: 'HELLO WORLD'
+
+@verbatim
+In [3]: x.st
+x.startswith x.strip
+""",
+ r"""
+
+ In [130]: url = 'http://ichart.finance.yahoo.com/table.csv?s=CROX\
+ .....: &d=9&e=22&f=2009&g=d&a=1&br=8&c=2006&ignore=.csv'
+
+ In [131]: print url.split('&')
+ ['http://ichart.finance.yahoo.com/table.csv?s=CROX', 'd=9', 'e=22', 'f=2009', 'g=d', 'a=1', 'b=8', 'c=2006', 'ignore=.csv']
+
+ In [60]: import urllib
+
+ """,
+ r"""\
+
+ In [133]: import numpy.random
+
+ @suppress
+ In [134]: numpy.random.seed(2358)
+
+ @doctest
+ In [135]: numpy.random.rand(10,2)
+ Out[135]:
+ array([[ 0.64524308, 0.59943846],
+ [ 0.47102322, 0.8715456 ],
+ [ 0.29370834, 0.74776844],
+ [ 0.99539577, 0.1313423 ],
+ [ 0.16250302, 0.21103583],
+ [ 0.81626524, 0.1312433 ],
+ [ 0.67338089, 0.72302393],
+ [ 0.7566368 , 0.07033696],
+ [ 0.22591016, 0.77731835],
+ [ 0.0072729 , 0.34273127]])
+
+ """,
+
+ r"""
+ In [106]: print x
+ jdh
+
+ In [109]: for i in range(10):
+ .....: print i
+ .....:
+ .....:
+ 0
+ 1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+ """,
+
+ r"""
+
+In [144]: from pylab import *
+
+In [145]: ion()
+
+# use a semicolon to suppress the output
+@savefig test_hist.png width=4in
+In [151]: hist(np.random.randn(10000), 100);
+
+
+@savefig test_plot.png width=4in
+In [151]: plot(np.random.randn(10000), 'o');
+ """,
+
+ r"""
+# use a semicolon to suppress the output
+In [151]: plt.clf()
+
+@savefig plot_simple.png width=4in
+In [151]: plot([1,2,3])
+
+@savefig hist_simple.png width=4in
+In [151]: hist(np.random.randn(10000), 100);
+
+""",
+ r"""
+ # update the current fig
+ In [151]: ylabel('number')
+
+ In [152]: title('normal distribution')
+
+
+ @savefig hist_with_text.png
+ In [153]: grid(True)
+
+ @doctest float
+ In [154]: 0.1 + 0.2
+ Out[154]: 0.3
+
+ @doctest float
+ In [155]: np.arange(16).reshape(4,4)
+ Out[155]:
+ array([[ 0, 1, 2, 3],
+ [ 4, 5, 6, 7],
+ [ 8, 9, 10, 11],
+ [12, 13, 14, 15]])
+
+ In [1]: x = np.arange(16, dtype=float).reshape(4,4)
+
+ In [2]: x[0,0] = np.inf
+
+ In [3]: x[0,1] = np.nan
+
+ @doctest float
+ In [4]: x
+ Out[4]:
+ array([[ inf, nan, 2., 3.],
+ [ 4., 5., 6., 7.],
+ [ 8., 9., 10., 11.],
+ [ 12., 13., 14., 15.]])
+
+
+ """,
+ ]
+ # skip local-file depending first example:
+ examples = examples[1:]
+
+ #ipython_directive.DEBUG = True # dbg
+ #options = dict(suppress=True) # dbg
+ options = dict()
+ for example in examples:
+ content = example.split('\n')
+ IPythonDirective('debug', arguments=None, options=options,
+ content=content, lineno=0,
+ content_offset=None, block_text=None,
+ state=None, state_machine=None,
+ )
+
+# Run test suite as a script
+if __name__ == '__main__':
+ if not os.path.isdir('_static'):
+ os.mkdir('_static')
+ test()
+ print('All OK? Check figures in _static/')
diff --git a/doc/sphinxext/plot_generator.py b/doc/sphinxext/plot_generator.py
new file mode 100644
index 00000000..43ef7faa
--- /dev/null
+++ b/doc/sphinxext/plot_generator.py
@@ -0,0 +1,360 @@
+"""
+Sphinx plugin to run example scripts and create a gallery page.
+
+Lightly modified from the seaborn project, which was modified from the mpld3
+project.
+
+"""
+from __future__ import division
+import os
+import re
+import glob
+import token
+import tokenize
+import shutil
+
+import matplotlib
+
+
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+
+from matplotlib import image
+
+
+RST_TEMPLATE = """
+.. _{sphinx_tag}:
+
+{docstring}
+
+.. image:: {img_file}
+
+**Python source code:** :download:`[download source: {fname}]<{fname}>`
+
+.. literalinclude:: {fname}
+ :lines: {end_line}-
+"""
+
+
+INDEX_TEMPLATE = """
+
+.. raw:: html
+
+
+
+.. _{sphinx_tag}:
+
+Example gallery
+===============
+
+{toctree}
+
+{contents}
+
+.. raw:: html
+
+
+"""
+
+
+def create_thumbnail(infile, thumbfile,
+ width=300, height=300,
+ cx=0.5, cy=0.5, border=4):
+ baseout, extout = os.path.splitext(thumbfile)
+
+ im = image.imread(infile)
+ rows, cols = im.shape[:2]
+ x0 = int(cx * cols - .5 * width)
+ y0 = int(cy * rows - .5 * height)
+ xslice = slice(x0, x0 + width)
+ yslice = slice(y0, y0 + height)
+ thumb = im[yslice, xslice]
+ thumb[:border, :, :3] = thumb[-border:, :, :3] = 0
+ thumb[:, :border, :3] = thumb[:, -border:, :3] = 0
+
+ dpi = 100
+ fig = plt.figure(figsize=(width / dpi, height / dpi), dpi=dpi)
+
+ ax = fig.add_axes([0, 0, 1, 1], aspect='auto',
+ frameon=False, xticks=[], yticks=[])
+ ax.imshow(thumb, aspect='auto', resample=True,
+ interpolation='bilinear')
+ fig.savefig(thumbfile, dpi=dpi, format='png')
+ return fig
+
+
+def indent(s, N=4):
+ """indent a string"""
+ return s.replace('\n', '\n' + N * ' ')
+
+
+class ExampleGenerator(object):
+ """Tools for generating an example page from a file"""
+ def __init__(self, filename, target_dir):
+ self.filename = filename
+ self.target_dir = target_dir
+ self.thumbloc = .5, .5
+ self.extract_docstring()
+ self.exec_file()
+ with open(filename, "r") as fid:
+ self.filetext = fid.read()
+
+ @property
+ def dirname(self):
+ return os.path.split(self.filename)[0]
+
+ @property
+ def fname(self):
+ return os.path.split(self.filename)[1]
+
+ @property
+ def modulename(self):
+ return os.path.splitext(self.fname)[0]
+
+ @property
+ def pyfilename(self):
+ return self.modulename + '.py'
+
+ @property
+ def rstfilename(self):
+ return self.modulename + ".rst"
+
+ @property
+ def htmlfilename(self):
+ return self.modulename + '.html'
+
+ @property
+ def pngfilename(self):
+ pngfile = self.modulename + '.png'
+ return "_images/" + pngfile
+
+ @property
+ def thumbfilename(self):
+ pngfile = self.modulename + '_thumb.png'
+ return pngfile
+
+ @property
+ def sphinxtag(self):
+ return self.modulename
+
+ @property
+ def pagetitle(self):
+ return self.docstring.strip().split('\n')[0].strip()
+
+ @property
+ def plotfunc(self):
+ match = re.search(r"flotilla.Study\.(plot.+)\(", self.filetext)
+ if match:
+ return match.group(1)
+ match = re.search(r"flotilla.Study\.(.+map)\(", self.filetext)
+ if match:
+ return match.group(1)
+ match = re.search(r"flotilla.Study\.(.+Grid)\(", self.filetext)
+ if match:
+ return match.group(1)
+ return ""
+
+ def extract_docstring(self):
+ """ Extract a module-level docstring
+ """
+ lines = open(self.filename).readlines()
+ start_row = 0
+ if lines[0].startswith('#!'):
+ lines.pop(0)
+ start_row = 1
+
+ docstring = ''
+ first_par = ''
+ tokens = tokenize.generate_tokens(lines.__iter__().next)
+ for tok_type, tok_content, _, (erow, _), _ in tokens:
+ tok_type = token.tok_name[tok_type]
+ if tok_type in ('NEWLINE', 'COMMENT', 'NL', 'INDENT', 'DEDENT'):
+ continue
+ elif tok_type == 'STRING':
+ docstring = eval(tok_content)
+ # If the docstring is formatted with several paragraphs,
+ # extract the first one:
+ paragraphs = '\n'.join(line.rstrip()
+ for line in docstring.split('\n')
+ ).split('\n\n')
+ if len(paragraphs) > 0:
+ first_par = paragraphs[0]
+ break
+
+ thumbloc = None
+ for i, line in enumerate(docstring.split("\n")):
+ m = re.match(r"^_thumb: (\.\d+),\s*(\.\d+)", line)
+ if m:
+ thumbloc = float(m.group(1)), float(m.group(2))
+ break
+ if thumbloc is not None:
+ self.thumbloc = thumbloc
+ docstring = "\n".join([l for l in docstring.split("\n")
+ if not l.startswith("_thumb")])
+
+ self.docstring = docstring
+ self.short_desc = first_par
+ self.end_line = erow + 1 + start_row
+
+ def exec_file(self):
+ print("running {0}".format(self.filename))
+
+ plt.close('all')
+ my_globals = {'pl': plt,
+ 'plt': plt}
+ execfile(self.filename, my_globals)
+
+ fig = plt.gcf()
+ fig.canvas.draw()
+ pngfile = os.path.join(self.target_dir,
+ self.pngfilename)
+ thumbfile = os.path.join("gallery_thumbs",
+ self.thumbfilename)
+ self.html = "
" % self.pngfilename
+ cluster_or_correls = 'plot_clustermap' in self.filename or \
+ 'plot_correlations' in self.filename
+ fig.savefig(pngfile, dpi=75, format="png", bbox_inches='tight')
+
+ cx, cy = self.thumbloc
+ create_thumbnail(pngfile, thumbfile, cx=cx, cy=cy)
+
+ def toctree_entry(self):
+ return " ./%s\n\n" % os.path.splitext(self.htmlfilename)[0]
+
+ def contents_entry(self):
+ return (".. raw:: html\n\n"
+ "
+
+
+RUN adduser --disabled-password --gecos '' --home=/home/flotilla flotilla
+
+WORKDIR /usr/bin
+
+ADD https://raw.githubusercontent.com/YeoLab/flotilla/dev/docker/scripts/run_notebook.sh /usr/bin/run_notebook.sh
+RUN chmod 755 run_notebook.sh
+
+RUN apt-get install -y r-base
+RUN pip install --upgrade rpy2
+
+WORKDIR /home/root
+ADD https://raw.githubusercontent.com/YeoLab/flotilla/dev/docker/scripts/monocole_deps_installer.R /home/root/monocle_deps_installer.R
+RUN Rscript /home/root/monocle_deps_installer.R
+
+ADD http://monocle-bio.sourceforge.net/downloads/monocle_0.99.0.tar.gz /home/root/monocle_0.99.0.tar.gz
+ADD http://monocle-bio.sourceforge.net/downloads/HSMMSingleCell_0.99.0.tar.gz /home/root/HSMMSingleCell_0.99.0.tar.gz
+
+RUN R CMD INSTALL HSMMSingleCell_0.99.0.tar.gz
+RUN R CMD INSTALL monocle_0.99.0.tar.gz
+
+WORKDIR /home/root/ipython
+
+
+#this part needs a solution to https://github.com/docker/docker/issues/5189 but it would be preferred if the notebook were run as a flotilla user
+#USER flotilla
+#ENV HOME /home/flotilla
+#VOLUME /home/flotilla/ipython
+#VOLUME /home/flotilla/flotilla_projects
+
+RUN pip install -e git://github.com/YeoLab/flotilla.git@dev#egg=flotilla
+
+ENV HOME /root
+#user should use -v option to mount a host directory here
+VOLUME /root/ipython
+#user should use -v option to mount ~/flotilla_projects here
+VOLUME /root/flotilla_projects
+
+EXPOSE 80
+ENTRYPOINT run_notebook.sh
diff --git a/docker/docker_instructions.md b/docker/docker_instructions.md
new file mode 100644
index 00000000..6b298250
--- /dev/null
+++ b/docker/docker_instructions.md
@@ -0,0 +1,20 @@
+Docker is an application that runs a virtual machine that runs software on your computer in an isolated environment.
+
+Here are instructions to get an active docker image. These instructions have not been tested on Windows or Linux.
+
+Note: On Mac OS X and Windows you will need to start docker through the “boot2docker” application before you can use docker.
+
+ 1. Install docker ( ≥ version 1.3) according to the [instructions appropriate for your system](https://docs.docker.com/installation/#installation).
+ 2. Then start flotilla on the command line (OS X `Terminal` application): + + +
+
+RUN pip install -e git://github.com/YeoLab/flotilla.git@master#egg=flotilla
+
+RUN adduser --disabled-password --gecos '' --home=/home/flotilla flotilla
+
+WORKDIR /usr/bin
+
+ADD https://gist.githubusercontent.com/mlovci/74c96dda49680419bcca/raw/15029fffa38585360502eee4d11a2a5ec20f372f/run_notebook.sh /usr/bin/run_notebook.sh
+RUN chmod 755 run_notebook.sh
+
+WORKDIR /home/root/ipython
+
+
+#this part needs a solution to https://github.com/docker/docker/issues/5189 but it would be preferred if the notebook were run as a flotilla user
+#USER flotilla
+#ENV HOME /home/flotilla
+#MOUNT /home/flotilla/ipython
+#MOUNT /home/flotilla/flotilla_packages
+
+ENV HOME /root
+#user should use -v option to mount a host directory here
+VOLUME /root/ipython
+#user should use -v option to mount ~/flotilla_packages here
+VOLUME /root/flotilla_packages
+
+EXPOSE 8888
+ENTRYPOINT run_notebook.sh
+RUN apt-get install -y r-base
+RUN pip install --upgrade rpy2
diff --git a/docker/scripts/monocole_deps_installer.R b/docker/scripts/monocole_deps_installer.R
new file mode 100644
index 00000000..a207a2c7
--- /dev/null
+++ b/docker/scripts/monocole_deps_installer.R
@@ -0,0 +1,5 @@
+install.packages(c("VGAM", "irlba", "matrixStats", "igraph",
+"combinat", "fastICA", "grid", "ggplot2", "Hmisc",
+"reshape2", "plyr", "parallel", "methods"), repos='http://cran.us.r-project.org')
+source("http://bioconductor.org/biocLite.R")
+biocLite()
diff --git a/docker/scripts/run_notebook.sh b/docker/scripts/run_notebook.sh
new file mode 100644
index 00000000..cce5a848
--- /dev/null
+++ b/docker/scripts/run_notebook.sh
@@ -0,0 +1,2 @@
+#!/bin/sh
+ipython notebook --notebook-dir="/root/ipython" --ip="*" --no-browser --port 80
\ No newline at end of file
diff --git a/docker/start_docker.py b/docker/start_docker.py
new file mode 100644
index 00000000..cc870e72
--- /dev/null
+++ b/docker/start_docker.py
@@ -0,0 +1,239 @@
+"""
+special tricks for opening flotilla with docker in OS X
+
+for linux, this needs to use sudo
+"""
+
+import time
+import subprocess
+import os
+import sys
+import signal
+import argparse
+import json
+
+DEFAULT_FLOTILLA_VERSION = "latest"
+DEFAULT_FLOTILLA_NOTEBOOK_DIR = "~/flotilla_notebooks"
+DEFAULT_FLOTILLA_PROJECTS_DIR = "~/flotilla_projects"
+DEFAULT_MEMORY_REQUIREMENT = 3500
+
+
+class CommandLine(object):
+
+ def __init__(self, opts=None):
+ self.parser = parser = argparse.ArgumentParser(
+ description='Start flotilla with docker.')
+
+ parser.add_argument('--branch', required=False,
+ type=str, action='store',
+ default=DEFAULT_FLOTILLA_VERSION,
+ help="branch of flotilla to "
+ "use from dockerhub, default:{}".format(DEFAULT_FLOTILLA_VERSION))
+
+ parser.add_argument('--notebook_dir', required=False,
+ type=str, action='store',
+ default=DEFAULT_FLOTILLA_NOTEBOOK_DIR,
+ help="local directory to place/read notebooks:{}".format(DEFAULT_FLOTILLA_NOTEBOOK_DIR))
+
+ parser.add_argument('--flotilla_packages', required=False,
+ type=str, action='store',
+ default=DEFAULT_FLOTILLA_PROJECTS_DIR,
+ help="local directory to place/read "
+ "flotilla packages:{}".format(DEFAULT_FLOTILLA_PROJECTS_DIR))
+
+ parser.add_argument('--memory_request', required=False,
+ type=int, action='store',
+ default=DEFAULT_MEMORY_REQUIREMENT,
+ help="memory request for docker VM:{}".format(DEFAULT_MEMORY_REQUIREMENT))
+
+ if opts is None:
+ self.args = vars(self.parser.parse_args())
+ else:
+ self.args = vars(self.parser.parse_args(opts))
+
+ def do_usage_and_die(self, str):
+ '''
+ If a critical error is encountered, where it is suspected that the
+ program is not being called with consistent parameters or data, this
+ method will write out an error string (str), then terminate execution
+ of the program.
+ '''
+ import sys
+
+ print >> sys.stderr, str
+ self.parser.print_usage()
+ return 2
+
+
+# Class: Usage
+class Usage(Exception):
+ '''
+ Used to signal a Usage error, evoking a usage statement and eventual
+ exit when raised
+ '''
+
+ def __init__(self, msg):
+ self.msg = msg
+
+
+
+def waiter(signum, frame):
+ pass
+
+def is_docker_running():
+ """
+ open a subprocess and check that `boot2docker status` returns 'running'
+ raises OSError if boot2docker command fails
+ """
+ with open("/dev/null", 'w') as junk:
+ try:
+ p = subprocess.Popen("boot2docker status", shell=True, stdout=subprocess.PIPE, stderr=junk)
+ except OSError:
+ print "There is a problem with the boot2docker installation"
+ raise
+ stdout, stderr = p.communicate()
+ if stdout == "running\n":
+ return True
+ else:
+ return False
+
+
+class Boot2DockerRunner(object):
+
+ def assert_minimum_memory_allocated(self, minimum=DEFAULT_MEMORY_REQUIREMENT):
+ """
+ check that the boot2docker app has started with sufficient memory
+ miniumim = minimum memory required, in MB
+ """
+
+
+ p = subprocess.Popen("boot2docker info", shell=True, stdout=subprocess.PIPE)
+ boot2docker_state = json.loads("".join(p.stdout.readlines()))
+ if boot2docker_state['Memory'] < minimum:
+ sys.stderr.write("WARNING: your boot2docker VM has been initialized with insufficient memory.\n")
+ return boot2docker_state['Memory'] >= minimum
+
+ def remove_boot2docker_image(self):
+ sys.stderr.write("removing existing boot2docker image\n")
+ p = subprocess.Popen("boot2docker delete", shell=True)
+ p.wait()
+
+ def initialize_boot2docker(self, memory=DEFAULT_MEMORY_REQUIREMENT):
+ """set docker VM with memory allocation"""
+
+ sys.stderr.write("initializing boot2docker VM with {}MB memory (yah, it needs a lot)\n"
+ "please wait...\n".format(memory))
+ p = subprocess.Popen("boot2docker init -m {}".format(memory), shell=True)
+ p.wait()
+
+ def reinitialize_boot2docker(self, memory=DEFAULT_MEMORY_REQUIREMENT):
+ """reset docker VM with memory allocation"""
+ self.remove_boot2docker_image()
+ self.initialize_boot2docker(memory)
+
+ def __init__(self, memory=DEFAULT_MEMORY_REQUIREMENT, keep_docker_running=False):
+ #keep docker open after exit
+ self.keep_docker_running = keep_docker_running
+ self.memory_req = memory
+
+ def __enter__(self):
+ try:
+ subprocess.check_call("boot2docker status", shell=True)
+ except:
+ self.initialize_boot2docker(self.memory_req)
+
+ if is_docker_running():
+ # if docker was already running, don't stop it on exit
+ self.keep_docker_running = True
+
+ if not self.assert_minimum_memory_allocated(self.memory_req):
+ self.reinitialize_boot2docker(self.memory_req)
+
+ boot2docker_cmd = "boot2docker -m {} up".format(self.memory_req)
+ sys.stderr.write("starting boot2docker with: {}".format(boot2docker_cmd))
+ p = subprocess.Popen(boot2docker_cmd, shell=True, stdout=subprocess.PIPE)
+ output = p.stdout.readlines()
+ for line in output:
+ line = line.strip()
+ print line
+ if "export" in line:
+ export_this = line.split("export ")[1]
+ variable, value = export_this.split("=")
+ os.environ[variable] = value
+ os.environ['DOCKER_IP'] = os.environ['DOCKER_HOST'].lstrip("tcp://").split(":")[0]
+
+ def __exit__(self, exc_type, exc_val, exc_tb):
+ if not self.keep_docker_running:
+ p = subprocess.Popen("boot2docker stop", shell=True)
+ try:
+ sys.stderr.write("Shutting down boot2docker. Be slightly patient.")
+ p.wait()
+ except KeyboardInterrupt:
+ signal.signal(signal.SIGTERM, waiter)
+
+
+class FlotillaRunner(object):
+ """Start docker flotilla, open the browser"""
+ def __init__(self, flotilla_version=DEFAULT_FLOTILLA_VERSION,
+ notebook_dir=DEFAULT_FLOTILLA_NOTEBOOK_DIR,
+ flotilla_packages_dir=DEFAULT_FLOTILLA_PROJECTS_DIR,
+ memory_request=DEFAULT_MEMORY_REQUIREMENT):
+ notebook_dir =os.path.abspath(os.path.expanduser(notebook_dir))
+ flotilla_packages_dir = os.path.abspath(os.path.expanduser(flotilla_packages_dir))
+ self.flotilla_version = flotilla_version
+ self.flotilla_process = None
+ self.flotilla_packages_dir = flotilla_packages_dir
+ self.notebook_dir = notebook_dir
+ self.memory_request = memory_request
+
+ subprocess.call("docker pull mlovci/flotilla:%s" % flotilla_version, shell=True)
+
+ def __enter__(self):
+ docker_runner = "docker run -m \"{3}m\" -v {0}:/root/flotilla_projects " \
+ "-v {1}:/root/ipython " \
+ "-d -P -p 8888 " \
+ "mlovci/flotilla:{2}".format(self.flotilla_packages_dir,
+ self.notebook_dir,
+ self.flotilla_version,
+ self.memory_request)
+ sys.stderr.write("running: {}\n".format(docker_runner))
+ self.flotilla_process = subprocess.Popen(docker_runner,
+ shell=True, stdout=subprocess.PIPE)
+ self.flotilla_container = self.flotilla_process.stdout.readlines()[0].strip()
+ docker_port = subprocess.Popen("docker port {}".format(self.flotilla_container),
+ shell=True,
+ stdout=subprocess.PIPE)
+ self.flotilla_port = docker_port.stdout.readlines()[0].split(":")[-1].strip()
+ flotilla_url = 'http://{}:{}'.format(os.environ['DOCKER_IP'], self.flotilla_port)
+ sys.stderr.write("flotilla is running at: {}\n".format(flotilla_url))
+ subprocess.call('open {}'.format(flotilla_url), shell=True)
+
+ def __exit__(self, exc_type, exc_val, exc_tb):
+ p = subprocess.Popen("docker stop {0} && docker rm {0}".format(self.flotilla_container), shell=True)
+ try:
+ sys.stderr.write("Shutting down notebook. Be slightly patient.")
+ p.wait()
+ except KeyboardInterrupt:
+ signal.signal(signal.SIGTERM, waiter)
+
+
+def main(flotilla_branch, flotilla_notebooks, flotilla_projects, memory_request):
+ with Boot2DockerRunner(memory_request) as bd:
+ with FlotillaRunner(flotilla_branch, flotilla_notebooks, flotilla_projects, memory_request) as fr:
+ print "Use Ctrl-C once, and only once, to exit"
+ while True:
+ try:
+ time.sleep(1)
+ except KeyboardInterrupt:
+ exit(0)
+
+
+
+if __name__ == '__main__':
+ try:
+ cl = CommandLine()
+ main(cl.args['branch'], cl.args['notebook_dir'],
+ cl.args['flotilla_packages'], cl.args['memory_request'])
+
+ except Usage, err:
+ cl.do_usage_and_die()
diff --git a/examples/Makefile b/examples/Makefile
new file mode 100644
index 00000000..a7b47629
--- /dev/null
+++ b/examples/Makefile
@@ -0,0 +1,15 @@
+test:
+
+ python ipnbdoctest.py *.ipynb
+
+pat = 'Figure at 0x[a-f0-9]*>'
+rep = 'Figure at 0xFFFFFFFFF>'
+sed = sed s/$(pat)/$(rep)/ | sed s/}\n\r/}/
+dir = .
+
+hexstrip:
+
+ for ipynb in $(ls $(dir)/*.ipynb) ; do
+ cat $ipynb | $(sed) > $ipynb.sed
+ mv $ipynb.sed $ipynb
+ done
diff --git a/examples/README.md b/examples/README.md
new file mode 100644
index 00000000..982d3069
--- /dev/null
+++ b/examples/README.md
@@ -0,0 +1,6 @@
+Example plots and notebooks
+===========================
+
+A listing of various plots and tutorials for flotilla.
+
+Please, no spaces in filenames!
\ No newline at end of file
diff --git a/examples/barebones_study_making.ipynb b/examples/barebones_study_making.ipynb
new file mode 100644
index 00000000..2b84cec1
--- /dev/null
+++ b/examples/barebones_study_making.ipynb
@@ -0,0 +1,3953 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:f62944fc610527e062d16d8b216c1580bbed564378de367ff8d3baa386238f50"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Create a barebones datapackage"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Before we begin, let's import everything we need."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "# Import the flotilla package for biological data analysis\n",
+ "import flotilla\n",
+ "\n",
+ "# Import \"numerical python\" library for number crunching\n",
+ "import numpy as np\n",
+ "\n",
+ "# Import \"panel data analysis\" library for tabular data\n",
+ "import pandas as pd"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Couldn't import dot_parser, loading of dot files will not be possible.\n"
+ ]
+ }
+ ],
+ "prompt_number": 1
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Shalek and Sujita, *et al* (2013)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "In the 2013 paper, [Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells](http://www.ncbi.nlm.nih.gov/pubmed/23685454) (Shalek and Sujita, *et al*. *Nature* (2013)), Regev and colleagues performed single-cell sequencing 18 bone marrow-derived dendritic cells (BMDCs), in addition to 3 pooled samples.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Expression data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "First, we will read in the expression data. These data were obtained using,\n",
+ "\n",
+ " wget ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE41nnn/GSE41265/suppl/GSE41265_allGenesTPM.txt.gz\n",
+ "\n",
+ "We will also compare to the supplementary table 2 data, obtained using\n",
+ "\n",
+ " wget http://www.nature.com/nature/journal/v498/n7453/extref/nature12172-s1.zip\n",
+ " unzip nature12172-s1.zip"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "expression = pd.read_table(\"GSE41265_allGenesTPM.txt.gz\", compression=\"gzip\", index_col=0)\n",
+ "expression.head()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "html": [
+ "\n",
+ "2014-11-04 14:19:33\tAdded ExtraTreesClassifier to default predictors\n",
+ "2014-11-04 14:19:33\tPredictor ExtraTreesRegressor is of type \n",
+ "2014-11-04 14:19:33\tAdded ExtraTreesRegressor to default predictors\n",
+ "2014-11-04 14:19:33\tPredictor GradientBoostingClassifier is of type \n",
+ "2014-11-04 14:19:33\tAdded GradientBoostingClassifier to default predictors\n",
+ "2014-11-04 14:19:33\tPredictor GradientBoostingRegressor is of type \n",
+ "2014-11-04 14:19:33\tAdded GradientBoostingRegressor to default predictors\n",
+ "2014-11-04 14:19:33\tLoading metadata\n",
+ "2014-11-04 14:19:33\tLoading expression data\n",
+ "2014-11-04 14:19:33\tInitializing expression\n",
+ "2014-11-04 14:19:33\tDone initializing expression\n",
+ "2014-11-04 14:19:33\tLoading splicing data\n",
+ "2014-11-04 14:19:33\tInitializing splicing\n",
+ "2014-11-04 14:19:33\tDone initializing splicing\n",
+ "2014-11-04 14:19:33\tSuccessfully initialized a Study object!\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stderr",
+ "text": [
+ "No phenotype to color mapping was provided, so coming up with reasonable defaults\n",
+ "No phenotype to marker (matplotlib plotting symbol) was provided, so each phenotype will be plotted as a circle in the PCA visualizations.\n",
+ "samples had too few mapped reads (<5.0e+05 reads):\n",
+ "\t\n",
+ "Removing technical outliers from consideration in expression:\n",
+ "\t\n",
+ "Removing technical outliers from consideration in splicing:\n",
+ "\t\n"
+ ]
+ }
+ ],
+ "prompt_number": 17
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As a side note, you can save this study to disk now, so you can \"`embark`\" later:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "study.save('shalek2013')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Wrote datapackage to /Users/olga/flotilla_projects/shalek2013/datapackage.json"
+ ]
+ }
+ ],
+ "prompt_number": 18
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Note that this is saved to my home directory, in `~/flotilla_projects//`. This will be saved in your home directory, too.\n",
+ "\n",
+ "The `datapackage.json` file is what holds all the information relative to the study, and loosely follows the [datapackage spec](http://data.okfn.org/doc/data-package) created by the Open Knowledge Foundation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "cat /Users/olga/flotilla_projects/shalek2013/datapackage.json"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "{\r\n",
+ " \"name\": \"shalek2013\", \r\n",
+ " \"title\": null, \r\n",
+ " \"datapackage_version\": \"0.1.1\", \r\n",
+ " \"sources\": null, \r\n",
+ " \"licenses\": null, \r\n",
+ " \"resources\": [\r\n",
+ " {\r\n",
+ " \"path\": \"/Users/olga/flotilla_projects/shalek2013/splicing.csv.gz\", \r\n",
+ " \"format\": \"csv\", \r\n",
+ " \"name\": \"splicing\", \r\n",
+ " \"compression\": \"gzip\"\r\n",
+ " }, \r\n",
+ " {\r\n",
+ " \"number_mapped_col\": \"PF_READS\", \r\n",
+ " \"path\": \"/Users/olga/flotilla_projects/shalek2013/mapping_stats.csv.gz\", \r\n",
+ " \"format\": \"csv\", \r\n",
+ " \"name\": \"mapping_stats\", \r\n",
+ " \"compression\": \"gzip\"\r\n",
+ " }, \r\n",
+ " {\r\n",
+ " \"name\": \"expression_feature\", \r\n",
+ " \"format\": \"csv\", \r\n",
+ " \"rename_col\": null, \r\n",
+ " \"ignore_subset_cols\": [], \r\n",
+ " \"path\": \"/Users/olga/flotilla_projects/shalek2013/expression_feature.csv.gz\", \r\n",
+ " \"compression\": \"gzip\"\r\n",
+ " }, \r\n",
+ " {\r\n",
+ " \"name\": \"expression\", \r\n",
+ " \"log_base\": null, \r\n",
+ " \"format\": \"csv\", \r\n",
+ " \"thresh\": -Infinity, \r\n",
+ " \"path\": \"/Users/olga/flotilla_projects/shalek2013/expression.csv.gz\", \r\n",
+ " \"compression\": \"gzip\"\r\n",
+ " }, \r\n",
+ " {\r\n",
+ " \"name\": \"splicing_feature\", \r\n",
+ " \"format\": \"csv\", \r\n",
+ " \"rename_col\": \"gene_name\", \r\n",
+ " \"ignore_subset_cols\": [], \r\n",
+ " \"path\": \"/Users/olga/flotilla_projects/shalek2013/splicing_feature.csv.gz\", \r\n",
+ " \"compression\": \"gzip\"\r\n",
+ " }, \r\n",
+ " {\r\n",
+ " \"pooled_col\": \"pooled\", \r\n",
+ " \"name\": \"metadata\", \r\n",
+ " \"phenotype_to_marker\": {\r\n",
+ " \"BDMC\": \"o\"\r\n",
+ " }, \r\n",
+ " \"format\": \"csv\", \r\n",
+ " \"minimum_samples\": 0, \r\n",
+ " \"phenotype_to_color\": {\r\n",
+ " \"BDMC\": \"#1b9e77\"\r\n",
+ " }, \r\n",
+ " \"path\": \"/Users/olga/flotilla_projects/shalek2013/metadata.csv.gz\", \r\n",
+ " \"phenotype_col\": \"phenotype\", \r\n",
+ " \"phenotype_order\": [\r\n",
+ " \"BDMC\"\r\n",
+ " ], \r\n",
+ " \"compression\": \"gzip\"\r\n",
+ " }\r\n",
+ " ]\r\n",
+ "}"
+ ]
+ }
+ ],
+ "prompt_number": 21
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "One thing to note is that when you save, the version number is bumped up. `study.version` (the one we just made) is `0.1.0`, but the one we saved is `0.1.1`, since we could have made some changes to the data."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "Let's look at what else is in this folder:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "ls /Users/olga/flotilla_projects/shalek2013"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "datapackage.json expression_feature.csv.gz metadata.csv.gz splicing_feature.csv.gz\r\n",
+ "expression.csv.gz mapping_stats.csv.gz splicing.csv.gz\r\n"
+ ]
+ }
+ ],
+ "prompt_number": 22
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "So this is where all the other files are. Good to know!\n",
+ "\n",
+ "We can \"embark\" on this newly-saved study now very painlessly, without having to open and process all those files again:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "study2 = flotilla.embark('shalek2013')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "2014-10-30 12:16:56\tReading datapackage from /Users/olga/flotilla_projects/shalek2013/datapackage.json\n",
+ "2014-10-30 12:16:56\tParsing datapackage to create a Study object\n",
+ "2014-10-30 12:16:56\tInitializing Study\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "2014-10-30 12:16:56\tInitializing Predictor configuration manager for Study\n",
+ "2014-10-30 12:16:56\tPredictor ExtraTreesClassifier is of type \n",
+ "2014-10-30 12:16:56\tAdded ExtraTreesClassifier to default predictors\n",
+ "2014-10-30 12:16:56\tPredictor ExtraTreesRegressor is of type \n",
+ "2014-10-30 12:16:56\tAdded ExtraTreesRegressor to default predictors\n",
+ "2014-10-30 12:16:56\tPredictor GradientBoostingClassifier is of type \n",
+ "2014-10-30 12:16:56\tAdded GradientBoostingClassifier to default predictors\n",
+ "2014-10-30 12:16:56\tPredictor GradientBoostingRegressor is of type \n",
+ "2014-10-30 12:16:56\tAdded GradientBoostingRegressor to default predictors\n",
+ "2014-10-30 12:16:56\tLoading metadata\n",
+ "2014-10-30 12:16:56\tLoading expression data\n",
+ "2014-10-30 12:16:56\tInitializing expression\n",
+ "2014-10-30 12:16:56\tDone initializing expression\n",
+ "2014-10-30 12:16:56\tLoading splicing data\n",
+ "2014-10-30 12:16:56\tInitializing splicing\n",
+ "2014-10-30 12:16:56\tDone initializing splicing\n",
+ "2014-10-30 12:16:56\tSuccessfully initialized a Study object!\n"
+ ]
+ }
+ ],
+ "prompt_number": 23
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+}
\ No newline at end of file
diff --git a/examples/plot_classifier.py b/examples/plot_classifier.py
new file mode 100644
index 00000000..d1c1ee1f
--- /dev/null
+++ b/examples/plot_classifier.py
@@ -0,0 +1,12 @@
+"""
+Perform classification on categorical traits
+============================================
+
+See also
+--------
+:py:func:`Study.interactive_classifier`
+
+"""
+import flotilla
+study = flotilla.embark(flotilla._brainspan)
+study.plot_classifier('structure_name: cerebellar cortex')
\ No newline at end of file
diff --git a/examples/plot_clustermap.py b/examples/plot_clustermap.py
new file mode 100644
index 00000000..58808773
--- /dev/null
+++ b/examples/plot_clustermap.py
@@ -0,0 +1,11 @@
+"""
+Visualize hierarchical relationships between samples and features
+
+See also
+--------
+:py:func:`Study.interactive_clustermap`
+
+"""
+import flotilla
+study = flotilla.embark(flotilla._shalek2013)
+study.plot_clustermap()
diff --git a/examples/plot_correlations.py b/examples/plot_correlations.py
new file mode 100644
index 00000000..e34b8cf3
--- /dev/null
+++ b/examples/plot_correlations.py
@@ -0,0 +1,11 @@
+"""
+Visualize global correlations between samples or features
+
+See also
+--------
+:py:func:`Study.interactive_correlations`
+
+"""
+import flotilla
+study = flotilla.embark(flotilla._shalek2013)
+study.plot_correlations()
\ No newline at end of file
diff --git a/examples/plot_event_modality_estimation.py b/examples/plot_event_modality_estimation.py
new file mode 100644
index 00000000..8b9e8e54
--- /dev/null
+++ b/examples/plot_event_modality_estimation.py
@@ -0,0 +1,12 @@
+"""
+Plot the modality log-likelihoods and barplots during estimation
+================================================================
+
+See also
+--------
+:py:func:`Study.plot_event_modality_estimation`
+
+"""
+import flotilla
+study = flotilla.embark('shalek2013')
+study.plot_event_modality_estimation('chr8:97356415:97356600:-@chr8:97355689:97355825:-@chr8:97353054:97353130:-@chr8:97352177:97352339:-')
\ No newline at end of file
diff --git a/examples/plot_expression_vs_inconsistent_splicing.py b/examples/plot_expression_vs_inconsistent_splicing.py
new file mode 100644
index 00000000..2cbc43e5
--- /dev/null
+++ b/examples/plot_expression_vs_inconsistent_splicing.py
@@ -0,0 +1,7 @@
+"""
+Show percentage of splicing events whose psi scores are inconsistent between pooled and single
+==============================================================================================
+"""
+import flotilla
+study = flotilla.embark(flotilla._shalek2013)
+study.plot_expression_vs_inconsistent_splicing()
\ No newline at end of file
diff --git a/examples/plot_gene.py b/examples/plot_gene.py
new file mode 100644
index 00000000..131b52ab
--- /dev/null
+++ b/examples/plot_gene.py
@@ -0,0 +1,11 @@
+"""
+Plot expression of a gene in all phenotypes
+==========================================
+
+In each column of the phenotype, the pooled samples are plotted as black dots
+and the outliers are plotted as grey shadows.
+
+"""
+import flotilla
+study = flotilla.embark(flotilla._shalek2013)
+study.plot_gene('IRF7')
\ No newline at end of file
diff --git a/examples/plot_graph.py b/examples/plot_graph.py
new file mode 100644
index 00000000..d340efea
--- /dev/null
+++ b/examples/plot_graph.py
@@ -0,0 +1,11 @@
+"""
+Visualize hierarchical relationships between samples and features
+
+See also
+--------
+:py:func:`Study.interactive_graph`
+
+"""
+import flotilla
+study = flotilla.embark(flotilla._shalek2013)
+study.plot_graph(cov_std_cut=0.5)
\ No newline at end of file
diff --git a/examples/plot_modalities_bars.py b/examples/plot_modalities_bars.py
new file mode 100644
index 00000000..8a95f9cc
--- /dev/null
+++ b/examples/plot_modalities_bars.py
@@ -0,0 +1,12 @@
+"""
+Plot bar graphs of percentage of splicing events in each modality
+=================================================================
+
+See also
+--------
+:py:func:`Study.plot_modalities_bars`
+
+"""
+import flotilla
+study = flotilla.embark('shalek2013')
+study.plot_modalities_bars()
\ No newline at end of file
diff --git a/examples/plot_modalities_lavalamps.py b/examples/plot_modalities_lavalamps.py
new file mode 100644
index 00000000..762608c0
--- /dev/null
+++ b/examples/plot_modalities_lavalamps.py
@@ -0,0 +1,12 @@
+"""
+Plot bar graphs of percentage of splicing events in each modality
+=================================================================
+
+See also
+--------
+:py:func:`Study.plot_modalities_bars`
+
+"""
+import flotilla
+study = flotilla.embark('shalek2013')
+study.plot_modalities_lavalamps()
\ No newline at end of file
diff --git a/examples/plot_modalities_reduced.py b/examples/plot_modalities_reduced.py
new file mode 100644
index 00000000..cd371fc2
--- /dev/null
+++ b/examples/plot_modalities_reduced.py
@@ -0,0 +1,12 @@
+"""
+Plot splicing events on NMF space, colored by their modality in each celltype
+=============================================================================
+
+See also
+--------
+:py:func:`Study.plot_modalities_reduced`
+
+"""
+import flotilla
+study = flotilla.embark('shalek2013')
+study.plot_modalities_reduced()
\ No newline at end of file
diff --git a/examples/plot_pca.py b/examples/plot_pca.py
new file mode 100644
index 00000000..0ff3b7be
--- /dev/null
+++ b/examples/plot_pca.py
@@ -0,0 +1,12 @@
+"""
+Visualize principal component analysis dimensionality reduction
+================================================================
+
+See also
+--------
+:py:func:`Study.interactive_pca`
+
+"""
+import flotilla
+study = flotilla.embark(flotilla._shalek2013)
+study.plot_pca(plot_violins=False)
\ No newline at end of file
diff --git a/examples/plot_pca_splicing.py b/examples/plot_pca_splicing.py
new file mode 100644
index 00000000..bdea7a50
--- /dev/null
+++ b/examples/plot_pca_splicing.py
@@ -0,0 +1,12 @@
+"""
+Visualize principal component analysis dimensionality reduction
+================================================================
+
+See also
+--------
+:py:func:`Study.interactive_pca`
+
+"""
+import flotilla
+study = flotilla.embark(flotilla._shalek2013)
+study.plot_pca(data_type='splicing')
\ No newline at end of file
diff --git a/examples/plot_two_features.py b/examples/plot_two_features.py
new file mode 100644
index 00000000..f6eacef1
--- /dev/null
+++ b/examples/plot_two_features.py
@@ -0,0 +1,7 @@
+"""
+Compare gene expression in two samples
+======================================
+"""
+import flotilla
+study = flotilla.embark(flotilla._shalek2013)
+study.plot_two_features('IRF7', 'STAT2')
\ No newline at end of file
diff --git a/examples/plot_two_samples.py b/examples/plot_two_samples.py
new file mode 100644
index 00000000..2131d77d
--- /dev/null
+++ b/examples/plot_two_samples.py
@@ -0,0 +1,7 @@
+"""
+Compare gene expression in two samples
+======================================
+"""
+import flotilla
+study = flotilla.embark(flotilla._shalek2013)
+study.plot_two_samples('S1', 'S2')
\ No newline at end of file
diff --git a/examples/shalek2013.ipynb b/examples/shalek2013.ipynb
new file mode 100644
index 00000000..622e1ec2
--- /dev/null
+++ b/examples/shalek2013.ipynb
@@ -0,0 +1,9236 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:2caf714617648c0ef5284317629cd40403afb2695ab75a035032ea1d48fc30d8"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In the interest of reproducibility, and to showcase our new package [`flotilla`](http://github.com/yeolab/flotilla), I've reproduced many figures from the landmark single-cell paper, [Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells](http://www.ncbi.nlm.nih.gov/pubmed/23685454) by Shalek and Sujita, *et al*. *Nature* (2013)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Before we begin, let's import everything we need."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "# Import the flotilla package for biological data analysis\n",
+ "import flotilla\n",
+ "\n",
+ "# Import \"numerical python\" library for number crunching\n",
+ "import numpy as np\n",
+ "\n",
+ "# Import \"panel data analysis\" library for tabular data\n",
+ "import pandas as pd\n",
+ "\n",
+ "# Import statistical data visualization package\n",
+ "# Note: As of November 6th, 2014, you will need the \"master\" version of \n",
+ "# seaborn on github (v0.5.dev), installed via \n",
+ "# \"pip install git+ssh://git@github.com/mwaskom/seaborn.git\n",
+ "import seaborn as sns\n",
+ "\n",
+ "# Turn on inline plots with IPython\n",
+ "%matplotlib inline"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Couldn't import dot_parser, loading of dot files will not be possible.\n"
+ ]
+ }
+ ],
+ "prompt_number": 1
+ },
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Shalek and Sujita, *et al* (2013)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "In the 2013 paper, [Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells](http://www.ncbi.nlm.nih.gov/pubmed/23685454) (Shalek and Sujita, *et al*. *Nature* (2013)), Regev and colleagues performed single-cell sequencing 18 bone marrow-derived dendritic cells (BMDCs), in addition to 3 pooled samples.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Expression data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "First, we will read in the expression data. These data were obtained using,"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "! wget ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE41nnn/GSE41265/suppl/GSE41265_allGenesTPM.txt.gz"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "--2014-11-10 12:35:20-- ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE41nnn/GSE41265/suppl/GSE41265_allGenesTPM.txt.gz\r\n",
+ " => 'GSE41265_allGenesTPM.txt.gz.1'\r\n",
+ "Resolving ftp.ncbi.nlm.nih.gov... "
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "130.14.250.7, 2607:f220:41e:250::11\r\n",
+ "Connecting to ftp.ncbi.nlm.nih.gov|130.14.250.7|:21... connected.\r\n",
+ "Logging in as anonymous ... "
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Logged in!\r\n",
+ "==> SYST ... done. ==> PWD ... done.\r\n",
+ "==> TYPE I ... "
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "done. ==> CWD (1) /geo/series/GSE41nnn/GSE41265/suppl ... done.\r\n",
+ "==> SIZE GSE41265_allGenesTPM.txt.gz ... 1099290\r\n",
+ "==> PASV ... done. ==> RETR GSE41265_allGenesTPM.txt.gz ... "
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "done.\r\n",
+ "Length: 1099290 (1.0M) (unauthoritative)\r\n",
+ "\r\n",
+ "\r",
+ " 0% [ ] 0 --.-K/s "
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "\r",
+ "100%[======================================>] 1,099,290 6.28MB/s in 0.2s \r\n",
+ "\r\n",
+ "2014-11-10 12:35:21 (6.28 MB/s) - 'GSE41265_allGenesTPM.txt.gz.1' saved [1099290]\r\n",
+ "\r\n"
+ ]
+ }
+ ],
+ "prompt_number": 4
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We will also compare to the supplementary table 2 data, obtained using"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "! wget http://www.nature.com/nature/journal/v498/n7453/extref/nature12172-s1.zip\n",
+ "! unzip nature12172-s1.zip"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "--2014-11-10 12:35:22-- http://www.nature.com/nature/journal/v498/n7453/extref/nature12172-s1.zip\r\n",
+ "Resolving www.nature.com... 23.62.97.233, 23.62.97.227\r\n",
+ "Connecting to www.nature.com|23.62.97.233|:80... connected.\r\n",
+ "HTTP request sent, awaiting response... "
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "200 OK\r\n",
+ "Length: 4634226 (4.4M) [application/zip]\r\n",
+ "Saving to: 'nature12172-s1.zip'\r\n",
+ "\r\n",
+ "\r",
+ " 0% [ ] 0 --.-K/s "
+ ]
+ },
+ {
+ "output_type": "stream",
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+ "text": [
+ "\r",
+ " 5% [=> ] 266,920 1.24MB/s "
+ ]
+ },
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+ "output_type": "stream",
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+ "\r",
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+ ]
+ },
+ {
+ "output_type": "stream",
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+ "text": [
+ "\r",
+ "16% [=====> ] 753,928 1.18MB/s "
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "\r",
+ "21% [=======> ] 994,208 1.17MB/s "
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "\r",
+ "27% [=========> ] 1,253,248 1.18MB/s "
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "\r",
+ "31% [===========> ] 1,459,896 1.06MB/s "
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "\r",
+ "39% [==============> ] 1,818,048 1.06MB/s "
+ ]
+ },
+ {
+ "output_type": "stream",
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+ "text": [
+ "\r",
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+ ]
+ },
+ {
+ "output_type": "stream",
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+ "\r",
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+ ]
+ },
+ {
+ "output_type": "stream",
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+ "text": [
+ "\r",
+ "59% [======================> ] 2,755,688 1.13MB/s "
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "\r",
+ "66% [========================> ] 3,059,488 1.11MB/s "
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "\r",
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+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "\r",
+ "83% [===============================> ] 3,882,096 1.13MB/s eta 1s "
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "\r",
+ "92% [==================================> ] 4,270,104 1.17MB/s eta 1s "
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "\r",
+ "96% [====================================> ] 4,463,712 1.13MB/s eta 1s "
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "\r",
+ "100%[======================================>] 4,634,226 1.17MB/s in 3.8s \r\n",
+ "\r\n",
+ "2014-11-10 12:35:26 (1.16 MB/s) - 'nature12172-s1.zip' saved [4634226/4634226]\r\n",
+ "\r\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Archive: nature12172-s1.zip\r\n",
+ " creating: nature12172-s1/\r\n",
+ " inflating: nature12172-s1/Supplementary_Table1.xls \r\n",
+ " inflating: nature12172-s1/Supplementary_Table2.xlsx \r\n",
+ " inflating: nature12172-s1/Supplementary_Table3.xls \r\n",
+ " inflating: nature12172-s1/Supplementary_Table4.xls \r\n",
+ " inflating: nature12172-s1/Supplementary_Table5.xls \r\n",
+ " inflating: nature12172-s1/Supplementary_Table6.xls \r\n",
+ " inflating: nature12172-s1/Supplementary_Table7.xlsx "
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "\r\n"
+ ]
+ }
+ ],
+ "prompt_number": 5
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "expression = pd.read_table(\"GSE41265_allGenesTPM.txt.gz\", compression=\"gzip\", index_col=0)\n",
+ "expression.head()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "html": [
+ "\n",
+ "2014-11-10 12:36:35\tAdded ExtraTreesClassifier to default predictors\n",
+ "2014-11-10 12:36:35\tPredictor ExtraTreesRegressor is of type \n",
+ "2014-11-10 12:36:35\tAdded ExtraTreesRegressor to default predictors\n",
+ "2014-11-10 12:36:35\tPredictor GradientBoostingClassifier is of type \n",
+ "2014-11-10 12:36:35\tAdded GradientBoostingClassifier to default predictors\n",
+ "2014-11-10 12:36:35\tPredictor GradientBoostingRegressor is of type \n",
+ "2014-11-10 12:36:35\tAdded GradientBoostingRegressor to default predictors\n",
+ "2014-11-10 12:36:35\tLoading metadata\n",
+ "2014-11-10 12:36:35\tLoading expression data\n",
+ "2014-11-10 12:36:35\tInitializing expression\n",
+ "2014-11-10 12:36:35\tDone initializing expression\n",
+ "2014-11-10 12:36:35\tLoading splicing data\n",
+ "2014-11-10 12:36:35\tInitializing splicing\n",
+ "2014-11-10 12:36:35\tDone initializing splicing\n",
+ "2014-11-10 12:36:35\tSuccessfully initialized a Study object!\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stderr",
+ "text": [
+ "No phenotype to color mapping was provided, so coming up with reasonable defaults\n",
+ "No phenotype to marker (matplotlib plotting symbol) was provided, so each phenotype will be plotted as a circle in the PCA visualizations.\n"
+ ]
+ }
+ ],
+ "prompt_number": 22
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As a side note, you can save this study to disk now, so you can \"`embark`\" later:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "study.save('shalek2013')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Wrote datapackage to /Users/olga/flotilla_projects/shalek2013/datapackage.json"
+ ]
+ }
+ ],
+ "prompt_number": 23
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Note that this is saved to my home directory, in `~/flotilla_projects//`. This will be saved in your home directory, too.\n",
+ "\n",
+ "The `datapackage.json` file is what holds all the information relative to the study, and loosely follows the [datapackage spec](http://data.okfn.org/doc/data-package) created by the Open Knowledge Foundation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "cat /Users/olga/flotilla_projects/shalek2013/datapackage.json"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "{\r\n",
+ " \"name\": \"shalek2013\", \r\n",
+ " \"title\": null, \r\n",
+ " \"datapackage_version\": \"0.1.1\", \r\n",
+ " \"sources\": null, \r\n",
+ " \"licenses\": null, \r\n",
+ " \"resources\": [\r\n",
+ " {\r\n",
+ " \"header\": [\r\n",
+ " 0, \r\n",
+ " 1\r\n",
+ " ], \r\n",
+ " \"format\": \"csv\", \r\n",
+ " \"compression\": \"gzip\", \r\n",
+ " \"name\": \"splicing\", \r\n",
+ " \"path\": \"/Users/olga/flotilla_projects/shalek2013/splicing.csv.gz\"\r\n",
+ " }, \r\n",
+ " {\r\n",
+ " \"number_mapped_col\": \"PF_READS\", \r\n",
+ " \"path\": \"/Users/olga/flotilla_projects/shalek2013/mapping_stats.csv.gz\", \r\n",
+ " \"format\": \"csv\", \r\n",
+ " \"name\": \"mapping_stats\", \r\n",
+ " \"compression\": \"gzip\"\r\n",
+ " }, \r\n",
+ " {\r\n",
+ " \"name\": \"expression_feature\", \r\n",
+ " \"format\": \"csv\", \r\n",
+ " \"rename_col\": null, \r\n",
+ " \"ignore_subset_cols\": [], \r\n",
+ " \"path\": \"/Users/olga/flotilla_projects/shalek2013/expression_feature.csv.gz\", \r\n",
+ " \"compression\": \"gzip\"\r\n",
+ " }, \r\n",
+ " {\r\n",
+ " \"name\": \"expression\", \r\n",
+ " \"log_base\": null, \r\n",
+ " \"format\": \"csv\", \r\n",
+ " \"thresh\": -Infinity, \r\n",
+ " \"path\": \"/Users/olga/flotilla_projects/shalek2013/expression.csv.gz\", \r\n",
+ " \"compression\": \"gzip\"\r\n",
+ " }, \r\n",
+ " {\r\n",
+ " \"name\": \"splicing_feature\", \r\n",
+ " \"format\": \"csv\", \r\n",
+ " \"rename_col\": \"gene_name\", \r\n",
+ " \"ignore_subset_cols\": [], \r\n",
+ " \"path\": \"/Users/olga/flotilla_projects/shalek2013/splicing_feature.csv.gz\", \r\n",
+ " \"compression\": \"gzip\"\r\n",
+ " }, \r\n",
+ " {\r\n",
+ " \"pooled_col\": \"pooled\", \r\n",
+ " \"name\": \"metadata\", \r\n",
+ " \"phenotype_to_marker\": {\r\n",
+ " \"BDMC\": \"o\"\r\n",
+ " }, \r\n",
+ " \"format\": \"csv\", \r\n",
+ " \"minimum_samples\": 0, \r\n",
+ " \"phenotype_to_color\": {\r\n",
+ " \"BDMC\": \"#1b9e77\"\r\n",
+ " }, \r\n",
+ " \"path\": \"/Users/olga/flotilla_projects/shalek2013/metadata.csv.gz\", \r\n",
+ " \"phenotype_col\": \"phenotype\", \r\n",
+ " \"phenotype_order\": [\r\n",
+ " \"BDMC\"\r\n",
+ " ], \r\n",
+ " \"compression\": \"gzip\"\r\n",
+ " }\r\n",
+ " ]\r\n",
+ "}"
+ ]
+ }
+ ],
+ "prompt_number": 24
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "One thing to note is that when you save, the version number is bumped up. `study.version` (the one we just made) is `0.1.0`, but the one we saved is `0.1.1`, since we could have made some changes to the data."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "Let's look at what else is in this folder:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "ls /Users/olga/flotilla_projects/shalek2013"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "datapackage.json expression_feature.csv.gz metadata.csv.gz splicing_feature.csv.gz\r\n",
+ "expression.csv.gz mapping_stats.csv.gz splicing.csv.gz\r\n"
+ ]
+ }
+ ],
+ "prompt_number": 25
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "So this is where all the other files are. Good to know!\n",
+ "\n",
+ "We can \"embark\" on this newly-saved study now very painlessly, without having to open and process all those files again:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "study2 = flotilla.embark('shalek2013')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "2014-11-10 12:36:38\tReading datapackage from /Users/olga/flotilla_projects/shalek2013/datapackage.json\n",
+ "2014-11-10 12:36:38\tParsing datapackage to create a Study object\n",
+ "2014-11-10 12:36:39\tInitializing Study\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "2014-11-10 12:36:39\tInitializing Predictor configuration manager for Study\n",
+ "2014-11-10 12:36:39\tPredictor ExtraTreesClassifier is of type \n",
+ "2014-11-10 12:36:39\tAdded ExtraTreesClassifier to default predictors\n",
+ "2014-11-10 12:36:39\tPredictor ExtraTreesRegressor is of type \n",
+ "2014-11-10 12:36:39\tAdded ExtraTreesRegressor to default predictors\n",
+ "2014-11-10 12:36:39\tPredictor GradientBoostingClassifier is of type \n",
+ "2014-11-10 12:36:39\tAdded GradientBoostingClassifier to default predictors\n",
+ "2014-11-10 12:36:39\tPredictor GradientBoostingRegressor is of type \n",
+ "2014-11-10 12:36:39\tAdded GradientBoostingRegressor to default predictors\n",
+ "2014-11-10 12:36:39\tLoading metadata\n",
+ "2014-11-10 12:36:39\tLoading expression data\n",
+ "2014-11-10 12:36:39\tInitializing expression\n",
+ "2014-11-10 12:36:39\tDone initializing expression\n",
+ "2014-11-10 12:36:39\tLoading splicing data\n",
+ "2014-11-10 12:36:39\tInitializing splicing\n",
+ "2014-11-10 12:36:39\tDone initializing splicing\n",
+ "2014-11-10 12:36:39\tSuccessfully initialized a Study object!\n"
+ ]
+ }
+ ],
+ "prompt_number": 26
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we can start creating figures!\n",
+ "\n",
+ "## Figure 1\n",
+ "Here, we will attempt to re-create the sub-panels in [Figure 1](http://www.nature.com/nature/journal/v498/n7453/fig_tab/nature12172_F1.html), where the original is:\n",
+ "\n",
+ "\n",
+ "\n",
+ "### Figure 1a"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "study.plot_two_samples('P1', 'P2')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stderr",
+ "text": [
+ "/usr/local/lib/python2.7/site-packages/matplotlib/font_manager.py:1279: UserWarning: findfont: Font family ['Helvetica'] not found. Falling back to Bitstream Vera Sans\n",
+ " (prop.get_family(), self.defaultFamily[fontext]))\n",
+ "/usr/local/lib/python2.7/site-packages/matplotlib/figure.py:1644: UserWarning: This figure includes Axes that are not compatible with tight_layout, so its results might be incorrect.\n",
+ " warnings.warn(\"This figure includes Axes that are not \"\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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2Z555Zlexw+bNm7n33nuH9Ge+9tprufbaawf0vZ/97Gf5zne+w4oVK5g5cyb33nsv06ZN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\nJQkoIYQQWUkCSgghRFaSgBJCCJGV/h8XSgaak8ZI4AAAAABJRU5ErkJggg==\n",
+ "text": [
+ ""
+ ]
+ }
+ ],
+ "prompt_number": 27
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Without flotilla, you would do"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "import seaborn as sns\n",
+ "sns.set_style('ticks')\n",
+ "\n",
+ "x = expression_filtered.ix['P1']\n",
+ "y = expression_filtered.ix['P2']\n",
+ "jointgrid = sns.jointplot(x, y, joint_kws=dict(alpha=0.5))\n",
+ "xmin, xmax, ymin, ymax = jointgrid.ax_joint.axis()\n",
+ "jointgrid.ax_joint.set_xlim(0, xmax)\n",
+ "jointgrid.ax_joint.set_ylim(0, ymax)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 28,
+ "text": [
+ "(0, 12.0)"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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f/dhJnHbiRFl/KmDJZPrZc76SgBIij+mGyW/++j5/2thAVyT91GlKZREPfGkp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\n9CsklH5JeBl6qhnCdhSaBlkBDzlhH4mEzcGjDUS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DAkoIIURakoASQgiRliSghBBCpCUJ\nKCGEEGlJAkoIIURakoASQgiRlobmjBMhBtH8+fM5ffp059umaVJYWMhDDz3ET37yky6jgTZt2sT6\n9euH/IaSQqQjCSghevDCCy/w6KOPAqmp7IcOHeLFF18kGAxSXV0NwKeffsorr7wi2yIIMUjkEp8Q\nPQiHw+Tn55Ofn09RURELFy6kqqqKv/71rwDU1NRQXV3NyJEjXa5UiMwlASVELxmG0TlVfteuXfzh\nD3/g/vvvH5JbiAgxFEhACdGDS0PHtm127tzJn/70JxYsWADAxo0bmTJlioSTEINI7kEJ0YNf//rX\n1NTUAJBIJDAMg0ceeYSnn37a5cqEuHlIQAnRg2XLlvHwww8DqW1cCgoKMAzD5aqEuLlIQAnRg2HD\nhjFixAi3yxDipib3oIQQQqQlCSghhBBpSQJKiAHQNK3LFuRCiOtHtnwXQgiRlmQFJYQQIi1JQAkh\nhEhLElBCCCHSkgSUEEKItCQBJYQQIi1JQAkhhEhLElBCCCHSkgSUEEKItPT/72Rh49uh+9sAAAAA\nSUVORK5CYII=\n",
+ "text": [
+ ""
+ ]
+ }
+ ],
+ "prompt_number": 28
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Figure 1b\n",
+ "\n",
+ "Paper: $r=0.54$. Not sure at all what's going on here."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "study.plot_two_samples('S1', 'S2')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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CE5quk6y+8zOf+rwZD1BNTU088cQTfPWrX+UrX/kKixcv5qtf/SqXXHLJTDdN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\nkzleur6LoQwwFJZlIJN7RyjDgMVjRWp1n0gIvIyphWGlDjidXoyUirA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lY9sUcw5hLCjl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\nAAAASUVORK5CYII=\n",
+ "text": [
+ ""
+ ]
+ }
+ ],
+ "prompt_number": 29
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Without `flotilla`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "import seaborn as sns\n",
+ "sns.set_style('ticks')\n",
+ "\n",
+ "x = expression_filtered.ix['S1']\n",
+ "y = expression_filtered.ix['S2']\n",
+ "jointgrid = sns.jointplot(x, y, joint_kws=dict(alpha=0.5))\n",
+ "\n",
+ "# Adjust xmin, ymin to 0\n",
+ "xmin, xmax, ymin, ymax = jointgrid.ax_joint.axis()\n",
+ "jointgrid.ax_joint.set_xlim(0, xmax)\n",
+ "jointgrid.ax_joint.set_ylim(0, ymax)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 30,
+ "text": [
+ "(0, 12.0)"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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7ktjO0FSh7dLSmZzQVF2xVgqZyeyTJMbMNNMeoAA2bNjAf/zHf0x3M8QcNVnV\nEnoSGUzLIRLy09OfxbBcFAU0VUFVFQbSJj99rZmtm5YWRg6245BMGVi2jWnZIzrokyVPjDUCWbWk\nvFDbb29jHw2t/dTXlY1738dPK55tc7FSyFQaGBiY7iZMqmnfByXEbLB6aYygT8N1vXWfqvIgfr+K\nooCigOOCXx9Z427N8nmF4zX2NvaN2mvk0zVuvnoFv3/tSjatreP3t5zDDVcuZ39TnD2HewAK1ykN\n+6mvK+PAUW8k15PI0JPIkMyYgDJplS/Guu9F1dHC8fOaprBwflSm6sSkKIoRlBAzzVhZerfdeD7f\nfeZdcqZNeWmMyLE4Dc1xb/pLUUhnLaorgoWRkq6plJUExlyLGn79NfWVrFs1f9y1pPyxGXsb++hJ\npGlo7QcXQkHdq623onLKptJ8usbN16zknMUVHGlLAArLascesQlxqiRACcGpbRgdLwsvHPTziZsv\nKFxnbX0lL+5qpr1nkEzGJBoN8Mo7HbR2p8Y8b8mwbP5rdyuHmuOksgZdfd55UD9/4xhXXVQHMGot\n6aevNRcesx2HxtZ+EgNZdF0lk7OoioUAZUqn0ny6Nux8KYu9jb00tCZkT5M4YxKgxJw3kQ2jwwOY\nZTvjZuENDwRvHehC1xTKIgECPh1F8daixjpvybBsfrO3ndKInz0NPcQHsiyoitA/aOC6LoZhEwro\nQ6ftjuz084cbxgey2LZLWTSAqiq4rsu8slChtt5Ukj1NYirIGpSY8052tPnwunRvHOjk2Zcb6OhJ\nYTvOuNc0LZuDzXH6BrL09mfpSWSGNtC+d0xGvlBsadhPW3eSaNiPrmlkDYtMzqKlYxDLdnBdGEyb\nhTTy4WtJV6+v5UhrP+09KXr7s+RMm9KIn3DQRzjoI+R/b73JtGz2HO4Zcaz7ZLIdh86+FJ19J/5s\nhJgoGUGJWW0yar0NP87icHOCnGHR058hnsxyzuJYYdNsPnFh9dKYd4yF6XDu0gq6+jIc7RggVhIA\nFPw+Fct2eP3dDnbubqUnkWUgk2MwaTCvPMRgysB2IGs4mFaO8tIgZdEAmqqycU0Nbd0pAK7buIiG\nlgGW1ZURH8jhOC49/RkqSgKoqkrAp3HbjecB3mhu5+42ohEfmqpOelmh+oWlPPHzg2RyFgCJwRx/\nsGXlpFxbzF2zOkBZtjOi05D58LllorXeJrphtCeRxbQcVFVlcU0EgFg0yHUbF404tv3AsT7q68oB\nb2/SgsqUPiQ0AAAgAElEQVQI88qDxKJBFtdEOXgszu7D3bzx2w664hlCfh1F9abF2nuSuK6LT1MI\n+FUMy0FXFeaVe2dENbb1Y5je6OSFV5qorysfOiojDFD4Ocvrygr3sONXDTS1D9DVl8bv01i1JDbp\nU3DDAyVArDRAQ8uATPGJMzKrA9RPf3OUYNT7BZFClHPPRNdFTrZhNB/AHMfFdV38Pm0oICgsryuj\noWVg1M+xHZv+wSxZ0yZWGiSTMVmzvIIjrQM0dyUBSOe8Y9wtx8WnqsRKAiiKQs5vEysNkDNsbNvh\n/OWVrF81H8v2MvXyCRFH2wdRFS993bC89PZoyMfWTUsL7R9elVxRFEzLW6+qLA9N+uc9PFAOL7Mk\nxOma1QEqm7OJlMqi7Ww1WUc1nOw6+QC2t6FnaJrMT35vUX46bzjbcfj1ng6iET+Zvgz7Dvdw3vIK\nnnqpgWTaxO9TMU0vOOWDHq53jtMffuAcfvzqUTI5i1BAJxTQ+fgN5xEO+guzAbbjcOBoHMO0cXFZ\nNL+ENcsr0DV13M+hsjxEb3+WrGHRm8jg0xTqF5ae1uc1FilbVBwsy5ruJkyqWR2gxOw1kem7iXSa\np3Lkg65pQ6WElBHB4Pifk99869c1dF1B11WOdXg19QI+lcRgjnTWwnUddF0F10XXFJbVlXLhyiou\nXFnFj189Smdfmg3nVhfakv85R9sHMUy7MJIzTKdwuOHxhrdtWV0Z+w73UBbzE434eeGVpkmbVZCy\nRWIqzOoAFRzaPQ/yjW62mcj03VidJjAqmeFk18kXRM1PzS2qjnLzNSsLHfDxP8eyHfY29gLgOC6p\njIlpOiiaQndfCneoPl80HGDZglIa2/qpqghRXhLkhVea2Hr5UhzXJRr2s/9ogqMdg4VActNV9byw\nswmX0UdvjGV42xpb+1l7TlWh2vlkzypI2aLpN9FTIGaK2XU3x7nufUsYNL05cflGN72m6+TU4Z3m\nWKOlfDLDiext6GX3oZ5CIOhJZPDpGisXxca8l1VLymloTXglh4ZSv4N+jWMdg9iOja6qKKpKWdRP\nxrCIlQapKo/g17VRm29h9D6rrZuWsuNX9tBa18lLFw3/DN4+ZJz6hyjENJnVAUrXVC5YIt/opttk\nnZw6PMjVLyw95TWPsUZL4HXwJ7rOkbZ++pM5VFUhGNDpjWd480AXqaxVKDf0witNJDMmvYksgTc1\n/njrKl56o43KshCrl8Q43JxAUUBXVXw+Dct2SGUtfD6NnOGtR3l7h5QJ3f/Wy5dy4Gi8UIl8ImSd\nSMw0szpAieIwGVUGxq1D1+JVb57IqbVj0bXx107yp9nuPtxNOusVXU0M5tA1hdhQDb38iKc/ZbDn\nUDeW7RIMaGx/YT+bLqxlIG0UMuhCAQ3HAU1TcB0X27ZJZQySWYvDLXGOdfSz4bz5VFcE2X2wi0BA\nR1EgHNBZXBMZcf97GrrpTqQxLZd4Mjuh0kKzeZ1oukboYmpJgBJFz7RsXtjZRFP7QGHdJX+mUX4d\nKV86aHhwGSugjTWCGGvtJP/+o+2DpDMmmuZtfM0aNpGgTnUhndqhpWuQtw92YVg2iqIQH3TImTbz\nK8L4fSqG6VAS9uPTNUqjfrr70gAkkgZmIgMoqIpCZVmAN97tZt+RPizToas1QVk0wEWrqtn+wn5C\nIR8DyRy243K0fQDH8dapEoM5ltWV8cLOpsL+p/HKNK1eGpt160STNUIXxUcClJhyZzK1NDxQdPWl\nSQzmWLUkBihYtjNuxzTeIX0THUHk36+qCqqqUlEaJOjXiIb9BP06oGBYNkda+wkHdDI5C8NycF0H\nywbHyfBfu9vYuKaGNcsrWLO8grrqMO82ximNBMiZ9lDtPAAXR3FJ5WxMK4fl+Ap1+1wXBpI5TMvh\nYHOCUEAnlTFIJA3KIn5URcEwbXYf6mLx/FIG0saIz2EudN5SB3D2klp8Ysrlp5YuWlnFRSurTqmD\nzHc+1RUh/D4Nw7Tp7EsPnW9kc7R9kJ5EBnBH1dAbqzZcPi08f+2T1aSrLA/i071fk9Kod5rt7Ted\nz0Urq4hFgyyrK8Pv11lQGUVVwHFAwcV1wbQc3j3SB8C6VfP5/S2ruOS8GpbVlREJ6aiKgpJfcnLB\nshwcvICUyZq4w5LzFEUZtjql4NMVNE3Bcb0sQVyGNsm6HG0f5IWdTYWR04nqDE6Fqa75J+YOGUGJ\ns+JMU5A1VWXVkhhdfRlWLCznuo2L+O4z++jsS6EoCr39GVYsei8jb7zacCcbUeQ7dct2CtNzKxaV\nk0yZbLqw1svQG1r3WlwTZaDRoLI8SG9/hlhpkO54GvACR2+/Fzh37m5jTf3IDDzLcujoTeNTVBzH\nBQXKowHig1lsxyVr2KgqzJ8XpjQaoLVjkNqqiFeZPBamuz/NvNIQqqIQCerUzfcSJYZv4N3xK3vc\n03SnynSM2CT5Y/aSACWK2sjOR2HJghK2blrK/qa4txnWp2FaDoZpk0yZhY5pvNpwlu3Q1D6AAuQH\nKHsbelm3qnpU5+rXlaEKDV6Hl84afHX7GxiWw7KFZUSD+oggpikKpmGRzFpeSroCuq56J+Y29BaO\nvbjufYtQFYVMxiLn2KhDx8I7DkTCfrI5i2zOojoWYt2qajr7MtTVlNDQnAAFzlkcY0FlmKBPR9NU\nrl5fy09/0zxqA2/+MztZluJkmo7pttmc/HGqpJKEEGfRiTqf/KiqJ5HBdlw2XVg7omM6vjacZdvs\n3N1OR29qKHC5VJQGh0Y489jb0OPVtxs6FsOwvCy/C1ZUks4a/D/feo2+gSwo0NmXon5RjHMWlnPO\ncm/kNjCYI50zcZUsqaxFRUmAJTVeOaGdu9soKwlgOw7//rMEpdEA0aifebrGlevqAJef72qhK56m\nJOwnEvJRUR7C79MLp+6eu6yCzr40pWEfpuWQGhod/vQ3zWy9fCk/fa151AZeXVPnROctm4RnJ1mD\nEmfN6a5N5Duf/EZV8DraUEADFCrLwyxdUMqa+nmF9+SfH352EihEIz4M0/ZGKYaNYTpEI75Cnb3O\nvhQdvSkOHI2PONPop681kzNtFFVBQaE/ZXDwaB9vHuxi5+52LNslEvFjWg5Bv04ooKHrKuWlQY61\nDZAxLMClN5GhtTvJ0fYBehIZOvq8ozPW1FeycH4UTfMKwWqqwqLq6Ig9Tl7AjaCpGobljlhXamgZ\nYOumpSxdUAooIzbwjvX5TZWxPneZbjt7pJKEEKdhstcmTjatM9bz+5vi2I5LJmth2g669l7awbGO\n5AmnDAHCQZ2caZPJWTiOMxTgbI51esEmZ9lDm3BdIiE/9bVltHYMEvBrdMfT9CdzuK5XIFZRvNRy\ny3Y41jHIulXV3Hz1Cs5ZVF7YfJsPuN5R6u9N0S2uKSmUUhrvM7FsB3DHTL+fytGUTLeJySQBSkyJ\n4zvCqVibONm0zvHP1y8s5XvP7sUYGr05jouuKyRTJheuqGKg0Rh3yvC6jYt480AnAK4Lju1QWxlB\nURRSGZOsaYHjEgr6CQV1+gYyNLb3Y1ku0YifnGGTypiURf1e9p3jMJg2iEZ8LK6JFtq7btV81q2a\nP+I+xqoneHzQyj+ez1Ic68sAcFYSGGS6TUwWCVBi0o1d887LJrMd7zwiZ2hN6GxqaBlgQWUEw3Io\nCXsFW4N+nU0X1rKmfl6h068sD+PXFWzHZscvGwqjmU/fso7/9z8PsmSBS86waO9N0dWXJpMz0TWV\n8miAyrIQrd2D5LI27lA2Xlc8zbyyEJqqoCveNF1+NOXT1KF9XeMbq8M/0ShlvC8D+f+X/UJippAA\nJSbd2DXvvKPOdx/qxrK84yEOHosX0q+nwliljqoqwvSnDEzLwXVdFsyLsGpJOfub4kNB1Gvzb5t6\neeqlBizL4bV9Kr9tmoemKoSCOt19GZIpA8OyGUhlUVUVx3Xo7s+AqpDOmKQNi6yl4joOlu2SSGZZ\nXltOSUkAXKiKeQcGxkqDIypi5Nt6ss9ERiliLpAAVWRma00xXVM5Z1GM5s5BNFWhsjyEYblT9g3+\nRKWOViwq94q6+ryirs++3EhzVxLXhYCusbA6SmuXd36Toij0J3P88o1mYuUhcKE7nsG2HXKGBYri\nHV+hQDZnMZD00tpdF0zT28ukawqRoJ95ZUFcvE238yu8I+Pz2YXjTb2dzr+HE+0Lkv1CYiaRAFVE\nZktZmvE6yP1N8aEsNG+UcqbHgo/Veecfa2ztJ5kxR5x9dHypo/qFpfz4laPsfKedgE+ltz+LYTk0\ntAZwXAj4NeIDOQbTORjK3NM1FV1XUVTvIELDcsgaplfeyAVj0CDo87LrLNtFVyAc8hErHbqmrmG7\nDq1dSVRVYeH8KKCMOfU23lrSiers+XTthIkKksAgZhIJUEVkttQUG6+DnOiO/4mMGsYbIeWPvTh8\nLMFg2uDi1dWjsvsuWFFZeP9bB7oKBVgN08ZxXQbSKpbtoOKVK1JQCId8ZLImmZyJZmkE/RqVsQjt\n3YOkszbDQ62XAu6iKaAqCqURPwuro6goRCI+DjXHSWdMFs0v9YZajB2oT/bv4URfaMabApSpQTGT\nSIASU2KsjnAiKcgTHUWO1Xn/9LVmLzg1J8hZNoNpgzf3d7Ju9XyiId+IYJh/f6w0SEdvikzOwrK9\n/UeW5aCq3mjHBcqCPgzLwT9UzdzFATQGkllqKsI0dw1iWqCqXi0+xwXVhZKID9t20VWFmlgI01Ho\nSWRwHQgFfKgKNHclUVHoG8hgWg6V5SFCfg3Ltr1j4h0HTT1xQduZ/oVGiPHM6gB14Gic+TULZsw0\nxlyoKTbeN/gTTc1NpNO1HYe2niSt3UkM0zu1dl55CL+uEosG2bpp6Zj/DuZXhOnrz+K6KVIZE11X\nsWwbzVWZVxkimTGHRk4WruMSiwYIBXX8Pg3bdQkEdVBUXBzsoX29CuDTFZIZi2hIoz9p8Mu32thw\nfg3gZQ+mMibxgSwlER/HOgYJB3XmlQYZSBr4y4PsbezDdhyOtPazrK4MTVWn/N/DbF3/nEuk1NEM\n8u6RXnrShzhncaxQT62Yf+nm0ibH40/HzScqxAey2K7L+cvmoanjFzoZHsytoQoV+fTtVMaksjxE\nwKexYlE5tVURXtjZBHj7mcJB/4j3n7Mkxvx54aE29ZHOuJiWQ0dvioBfJZ218Ps1VFUB1y0cuZE1\nLHyqSklIJ2EbhQAV8GsEfCqOY+G4CgFdJRzy0doxyKKaEvY2ZEhmLTQga1jMrwjjuqDr3ibhtp4U\ntZVRNFVjWV0ZsWhwxDlPYxW0hTP7QjNb1j/F7DKrAxTA7kM9HOsYRFVVfvlmC7fdeB7hoH+6mzWu\nubBGcHxn+Itdx2jvS+M4Lq7r0jeQpaMkTU1lZESne/w3/JuuqmdvQw87ftlA1rA41jmAo7iYtpe4\nsGppBX5d4ZmXG72MO+DNA518+pZ1HOtIFdLKdU1l9dIYext6cB2Fwy1xsoZFKmuRyYHfp+E4LuGA\nzkDaW4eqioWxHIfy0iDxwRyG6ZAzveoNPt07Q8rn09BVhaBfR1VgzdDfayjow3UhmTGxTW8Db0kk\nMOZnpakqy+vKxl13Or6g7UQDytnYSC3OPil1NIP0DWQxrRCt3UkCfh3XdfnuM/v4xM1rp/SboUyV\nnNjxnWFnPM1AMkdpJACKd5x6NOzjopVVheD01oFOfvV2K4bhoKoK7zb1cvPVKwCFvsEsqaxJJmtj\nWjbBgE4255BKmUQDQfr6M6iqSjioM5gxeOA7v6GmMkJleYhoyFcYKeiaRlVFiIFUjqPtA+iaQjjg\nw3IcTMsZmnr02nf1xXU0tg2QyVkc6xhAVRWiYa/On0/XvAri/RbokEwbmJbGlksW8tIbbQT9Gpms\nFzBty9u4rGsK5dEY4ZAPXBfD8hInjh8VHf/ZGZZbKGg7UWNvpC4/ybuEOPtmdYByXBfDdAj4NVRF\nwQGy5tR+M5SpkvdMNFCXlwRJpk2coRP6fD6NS9fUjMi2O9Laz2+bvMP/QgGdYx0D1NeV0tqVxqdr\nGKZTKGGECz5dpa07yZsHO0hnLEJBnXRWJWfY3r8HVaGlM8mCeRGefbmBlYsqWFwT4Ymf95POWeQs\nG9t2qJkXoSeRIZP11qeiIT+G7VUJv/nqFexvinP+snk8v/MI3fE04VgI03IIB30sqi4dOkwRzq2f\nx7GOFItrohhv2NiO47UpYxIJ60QjAVIZk49cd07hROCTfW6na+yN1O5ZPZZDiImY1QHq8gtqOdDu\n0NI1iOO6+HSVyvLQlP7MmTxVMpkjvxMF6uOTQeqqwuiaQm9/lrKIn8ULSllT731e+c9zIGWQM2xy\npk3OsPHpKi/8VxPXXbqY/mSucDKt64KmqQxmDNp6Uyiui+OCmTIJ+lVwIVYSGRpdO7T3JmloidO3\nJsv/91KKoE8jPpglVhIgPpijO572CrziJTfkC8keafUOLcwXdv3Lj17E9hf2kzVtKstDDAwatPel\n8Pu9z7CptZ9LVs9nTX0l9XXlHDwaJ2fYlET8VJaFqCoPUVbiVZW4YEVlYdrt+GKvU5VIo2tzZ/1T\nzByzOkCdv3weV76vmu8+s6/QcRyfbiw8kz3yO1GgPr7q9sHmOLHSILbjfYlYXltW6JjBy9AbSBnk\nTBvTcsB1CfgDBIM67d1pqmJhBpJZVFUpVBq3HFBcF13X8OkqlmXj1zVWL51HZ18ay3ZIpg0AcpbN\nr95qIxr2kTO8PU0KEPCpZLI2juuAC6bl4rom3X1wpL2fXQc6C6WQ1tTPY+OaGtq60yyuKSFnmjz+\n4wNkchZBn0ZJxI+3PqVxx01r+PbT+2hsS3gZh9rIL04n2990sorlJzNekJsL659iZpnVASqbM2lo\nGWDThbUMXwyfK8dPn8qI6GyP/PKd4Z7DPRimg1/3ToH9bVMfv3yrhfkVkcLm21+80ewdj6EquJqC\nz6dSEvZTPXQYYUVpEJ+msHRBGfHBLOmsBa5LPJkja3gbbUMBH4tqokRCOpGQzkBKQdc1VMUla3jX\nxnWxLIeMYaKrKpqm4vOpWLYCOICCqipYls07h7qJhP2UhP1YtsOv97XT1pNifkWE3zb1cOBonPiA\nt+cpZ9hEIr4R9z6/MkzOtGjpGsTFq6oRDekTSlg4WcXyk/2dz6VsUTGzzeoA9dgT77B2zYrCHpKt\nly9lb0PPiPN2JvucnGL55Z/utbDTCdQ9iSyW5Yw4iO/A0QR4p6dTURYkmTaIhv0srC4h5NfImhbx\nQe99pp2jOhZCVVVCQR+/2duOqiqE/D4Cfo1zFsewHciZDt19aRzHxrBdFMBWFZIZhapYiNZOC8eF\nsrCPbM7Cdm00FDRFIWs4ZB2XZNamP2VQXhIopMMrgO14o7FM1sK0XBQFfCpksxb5QrT7m+IYpkNd\ndQk1lRG6+jIn3Ks1lrGC2N6GHhpa+yf0dy6jJTETzOoA1dw1yIK+DHXVUZIZk2/t2OtN7wxNyxw8\nVsnN16wEJuecnPFqw+053DPisbPhVEdEkz3yywfqvQ29HOsYLJx5dKKf6wwdUT58uutYxyClJX5i\npUFMyyES8lES8vP+9y0CYG9jH+curaCrL8PR9n66FZdYNIiCwp/euIb2niSaqlFbFWH/0Th+XaEq\nFmT3IdsLIIA7tH4VDvrQNY3FC0pp706SShs4KAR8KrmcSzpnM7x8oO1AX3+OUFAjHNDpTmQ42jFA\nKmNiO961dVVF1zUWzS9F10bv69JUleqKMMvrys54nelYR3LGrn8KMZYTBqh9+/bx3HPPkUwmueyy\ny9i6deuI55PJJF/84hd56KGHprSRp8swbA41x6mpDNObyHqbQG0XTVWxHZfmrmShA21qH2B+RRhN\nVU/rF/tEteGKJaPPsp1xg+VUjfzyZyztbezj4LF4YdN0/cJSDhyNc6wjyZKaUu902+UVHDwWx7De\nOy68tirMoZY45SUBlKERyOaLa9E1jcbWfmzHGao64ZLMmDgu2DYoKrR0htE0Fdt2aO4cKJQNamwZ\nwHEcwkEdw3JxbIeATyOdMfFpCt0Jr+xQXjjo82rx5cY+v6qmIkIk5KM/mSNr2AT9OigKqbSBi0sk\nqLN4frQQZE4WgCbyd3H8Nfw+7z47+1KFf8di7onH41iWNWv2Q417Fy+99BKf+tSneN/73gfAvffe\nyw9+8AMeeeQRysu9PROZTIbnn3/+jANUR0cHX/ziF9m1axfRaJTbb7+dP/7jPz6jawKoQ5WnO/vS\nBH06JWE/zV2DKIqXquy6sHN3GznTpqsvTWIwN3R4nHLSax9vvNpw0/WNdlQHpiscbI4Xqg6MFSwn\ne9pn+GdiOw67D/XS0pViXnmQf3/xIKZtY1sOOdNmRV05t394DWvqK0dWmPivI/T0ZxhMGmiawiXn\nzaehpR/DcjEsm32He1hQGaFvMIthOqiKhYtXSujH8aO4Q0OeyooQAV2nfmEZluN9BtmcN5Wnawq2\n433JiHckC+336wquC4Zpe8VjFbCPq+sa8EFpxE95SQAUKBt63rIdgn6NkF/j0vMXsGLxyM3G3r4j\n94wqnNTXlXOsY5C66jANLf0MZAz6BrL09Wc5Z0lMEoLmoN/s6+C88xJUVs6OUfO4AerrX/86n/nM\nZ7j11lsB2L9/P3fffTfbtm1j+/btxGKT8w/fdV0++clPctlll/HYY49x5MgR/uiP/ogLLriAiy66\n6IyuvWh+lEULyli5MMbV62v56r++RTpr4jgumZzFgsow0YiPMtVPfDCLYdp09qVZuqB0xv9iH/8t\n3LK9Ucx0Tf/0JDKFrLOG5gRH2vpxXbBdFxWwLIfvPvMun7j5gkKb9hzuIZOzUFDIGV6l8Tf2dxIO\n+ImVBOkbzJI1LFq6B8lkLHKGRTrrlRxSgEhIx+/z/omn0xZVNWFKw35iUT/7czZmfkDkusRKfXTH\nMyPabFouuqZg2Q4BTUPXVWzTOe410J1I09ufxe9TWbKglNJogPhAljpdpao8SNZy2NvYy8FjfaAo\nI0oTjTWiPtn64fHPN+8ZIBrx49e1wnTnqa5pidkhWlI23U2YVOPOAzQ1NbFly5bCn1evXs2//uu/\nYhgGt912G4ODg5PSgN27d9Pd3c1nPvMZNE1jxYoV/Nu//RtLly4942svqy1jWV0pWzct9UrbLCzj\nvOXzWFxTyqrFFSypKUVTVTTVO3Z7fkWElQtjpzUNt3ppjFBAw3bem566buOiUY+dzcCXHxFdsKIS\nXZv6jiq/3rbncA+mZY/6TFTNq+Z9pL2f/pTJQNoklbFI56yhEkJWIaDmdcczdPWlsB1vCu9o+yBN\nbf3sPtxNY0uCdM4ilbZIZg3sofRyx/VGOqmcRSpr4LouGcOiP5UDXPqTJiURH+Gghk/3sgJ74plC\nLb08F+8LVKw0SNCvUxL2kV9GUhVv5KWokMpaDKYN+pNeyaNMxuTqi+tYVFVCW08acNFUhWOdSfYc\n7hnavOuOOIp9uOEjz3yyyPDXHf981rTpTWSBsde0hJipxg1QNTU1vPHGGyMeq66u5jvf+Q49PT3c\ncccdkxKk9u3bx8qVK/n7v/97rrjiCj74wQ+ye/fuwjTimRhI5dh6+XvfIjVVpbYyyuqlFdRURlhW\nW04ooGFYNl19GQI+L6iczi92fsRy0coqLlpZxU1X1RMO+kc9Nl2dxlgBdDKDZf5b/duHunn7UDdP\n/eIQbx/s8s5ACmpsXldH7bwoAykDx3FRh82i5k+fjSezQ6Os99qcyZjYtotpeUkUmupVBDFMC9Oy\nsW0XF8hkbSyvIMJ713W8kdlAyiCbNclZNrsPdzOQ8Q4gjAT9lEYC6LqCdVxwAtA1WFFXTnV5CNv2\nNuhqKuiqNzrzad6qmArouooLpDIm0YifX+/p5Ej7AF19aX57pI/W7kEOHu2jrz9DR2+KA0fj2ENT\njccH9lOVL4w7XV+EhJgq407x3X777XzhC1/g7bff5uMf/zhLliwBYNGiRXzve9/j4x//ONu2bUNR\nTn29Zrj+/n5ee+01Lr30Ul566SX27NnD7bffzsKFC9mwYcNJ3x+Px0kkEiMe6+joAOBQc4J9jX1c\ncl4N9QtL+eWbzSM27K6pn8eqJeV895l9uLhEIz5eeKXptAPJeGcgFUMW1VSnv+9vipMcOkLCcVy6\nEhl2vtNO0O9NjZm2y2UXLKCrL03WtMGF7NC0neN46zzNnYPsbewupP/7dI3f3bSM7T96l3TGK4Vk\n2y7zSoNkDItUxsRxHRKD5qgAowABv4qiKPg0hZKwn86eFOGQD13xfp7f5x3pbuRGRycFWLGonHTW\noj9uoCqQM228vVBgWd6mW1VVCAd1+lMGmurtz+pJZFBQqK4I0TsUkDp6U7i4OBYoaW9U16SpnL9s\nHk+9dHjE2mD+aPqxkihMy8aybfoHs0QjfjRVJRry8QdbVtLQMjAlf7eiuJyoz5ttxg1Q//2//3fK\ny8t56qmnRo2UVqxYwRNPPMFXvvIVXnzxxTNqgN/vp6ysjD/7sz8DYN26dXzgAx/gxRdfnFCAevzx\nx3n00UfHfM60bH786yZ0TeVgc5xoxE8ukSWZMvmDLSsLNc/KSoJUzIHU3KkMlpbtcPBoHNvxEhQS\ng1nKogE01Tu0r6UzyTmLyrlgZSXWQYdO28F2XUzTxsZL2c5mTX7y62MEdZ0PX7MC8KbRVtSVk8xa\nNLQmMHIWoYBOaTRAKmPQ0ZsuFFYdQfGuGYv6KC8N0tmbJmt4hxJGQ34WLYgSDfhxXZd9R3pHZT/4\nfCq6pmIYNvrQ8e2KohDwaVSWB1EUhfq6Ukqifna92+WllSsOPQMZ6uZFKB3aH1VRFqQ/mcPv09A1\nld7+DFnDJmdYaJrCS2+2EB/Mcu7SikIG6fFH0w/fspBfe4pG/CRTJpsurC0E9Nn4b1aMdqI+b7Y5\nYS7ipk2bsCyLZcuWFR7bvn07O3fupKKigjvvvPOMM/iWL1+Obds4joM6lBpr2xOf5ti2bRvXX3/9\niPJrgWoAACAASURBVMc6Ojq49dZbyWQtuuMp/uPnBxlMGSyu8faiRCO+Qs2z2eZMNhyf2WZld9zk\nR9d1iQ9mOdaR5IYrlrG8tpTX9nXS25emob2fZNoonIWUzBj89PWjtPUlqSoPYztQEvFzpH2AqvIQ\nKuDza/zOpUt4dW87x9pHTzOrindERjSkU10RJp21cIay+fK19FIpi8VVZbiug+uOfn9pxEc46KN+\nUZBDx/owh6YeHdelNBKgflE5F66o5PBQZuS8shAKkMlavG/NfI62DxbOtyqJ+Dl3aQVvHej2pil1\nhYBfJxjwMZjKYQ1VNJ8/VBkDxv4yMXLtSaOsxAuiMlqaW07U58024waotrY2tm3bRldXF88//zyR\nSIQHH3yQ733ve2zZsgXLsrjlllv4/ve/z9q1a0+7AZs2bSIYDPLoo49y1113sXv3bn72s5/x/e9/\nf0Lvj8ViozIKfT6vrIyqKgymLHoSXvmbjt4US2pK6U6kuXDol7+YShOdqTOpHnGmlSd0zavUEB/w\nOlwXh8GUQTpnYlkOtVUlxJNZnn25ERSFsmjAK/o6NLLInwXlumDZLu8c6kVVetl0US09/Rm6E2kC\nfp2K0iCOafPavg5+29A7Ku0bQFG8NaJo2Idf1yEEpYa/MJJBAU1XSGUN2ntTo6apdU2hOhbm/Zcs\n4vV9nYSCPny6Sm+/Vxk/a1g0tPTj1xX2NfZhmN5U5byyEK7r0tadAkVBQaE8GmQgbeACdVVRHNeh\nNBwgZ9moijf1mEgO0pvIECsNSGq4OKkT9XmzzQnTzJctW8Yzzzzz/7P3pjGSXeeZ5nPP3eLGHpFr\nZVXWXsUiVTIpy7ZsUpvt5kCmLVnSAEa7x54xjBEa0z+60f5h/+gWerrRaGOAwTTQ3RBGGEFwW0Z7\n4DHUljxNNUZjWaJJaaidZKlYC2vLrKzKJTL25W7nnPlxIqIyszJrISmyyIoXIFGReePek7Gc737f\n937vSz6fp16v86UvfYmnn36af//v/z0An//85/l3/+7f8YUvfOF1L8D3fb70pS/xr/7Vv+LJJ58k\nn8/z2c9+9g0FvRFcx2wm/dDMu8hYcXmlyfHF7bNOx/aXdpU/eqfhjejp7ZxZunazw7MvXL1nqvIo\n0Jtz1emHqSmppcp4caHZbA6ot0PK+Qzz01k0moxvE0Y2yZDz7TgWUSLRGrRS/O33lolTxSBMGUQp\nUZSSz7kMBglqj5TNtS0C38GxbRb3FdhsDghDycH5AtfXu9i2ReA5XLjWQMNYTDaV4DowW8mybzo3\nVLHwOTRXZG2zx+H5Eo4rsIVFmmpu1PpUihlWN3tIaUqbxbwHWMSJZN90DqkUuqYZhCkffGIf3/uJ\nIEoVm+0BaKh3IrIZl1Le21Z6vtNr/G64mZpggnvBngHq+eef53Of+xz5vJGo+bu/+zvSNOWTn/zk\n+JgPfehDfP7zn3/Dizh48OAbCnJ7oR8mCNJtP0slrNa6pFLeljVcWmly+tjUm76ON4K30vxQKsV6\nPWR5rYPrWGg0X3lO3lMmNSJhPPvCVYRlDZW/MSSEVHFjvUN/kKK1ptOLjX6eVGhpAsLifI6Xz9eI\nEkmcSCzLMsKtkRxnSTLRpDIhUYpCxiWVCsc27+lW5AIPrTSPHp0i8BwOzBaYrZoZqFzGZXWzx9Ka\nEWl1hEUUpzi2IOMZwsHRA2UOzhbpRSmeY7N/No8lwMJi33QOgJu13lCZwiIXuKRSUy74vPf4NEcW\nSpy5vIlUivPXGubvAf7rd65xcF+RpB2xMJVj31SOpbUus9VgrG5yp9Lzg6LzOMEEbxX2pJm3221m\nZmbGj1988UUcx+EXf/EXxz8rFAootQs/9wHBzqVZlvkvShXffuUGZy7V7jhv8nZjJ3X7K89duiMN\n+Y1QyQ/O53jpwgZnr2zSaA9odWOmy5k9X5PdqNGuY3N0fwmNJk5M30cICyWVmQ+KUxzbQmlodkI2\nWyFRInFsi2435cn3HaCY83FtYSjkSmPbO7IkDa6w8H2HKJbYQuAI0zdybQh8MTynoNWJiNOUs5dr\n/Oj8GnGcMl0J6EcpSmt81x5r61m2IOM7OI5gECYs7tAO3ErljlPzmblZM+w81xEoqXjPsSpHF4qA\nxnMs1ur9IVvQRgjBIEppdyP2TeeoFAM81/TI7keWaOts2yQ4TbATm7UNNjc3SdP07ge/A7BnBrWw\nsMDly5dZWFhASslzzz3H+9//fnK53PiY733vexw4cOAtWejrwdb2hLCG/wmLajFDnGiWtsjaPIi4\nl5LdzgzrXu+wtz7v2IEiX3r2vBk8RSM1FPIejXY8Fm7defxOjcFnnjzMpettwjhlECbjXpKlNIFn\noy3wHZtUauJEEvgOQghygamdh6nk2o0WhxeK/Oj8BtIw0W9j6Dm2he+75n0sZUgSwwbsDhJTJtRG\nqWGjOaCYdzl3tU67F6GU5j9vXmK6FFAu+GT6DkppPNdGaU25YEps/VjTjRL+7xcuU85nqBQz26jc\n5681eOGlG8b+w76lCpHLunzn5Zu8dLFGJZ9hEKZMlTxmygHz0zlqw0HarViYybF8pv2Gvcreyix7\nggcbvu/x3I+uMzU19a6QO9ozQH3qU5/iX//rf80//sf/mG9/+9vUajX++T//5+Pfv/zyy/zbf/tv\n+ft//++/JQt9IyjnjVncIErJBS5xothsDVj42QMkq/Itrem/Fa61d+s57Xzet354nUGUIoRgqpyl\n1uwTDplvgW+EXXcen8+52MIaShhpvvBXZ6iUAtbqPVxnSHzQI0adw1Qlw0a9j1QazxEIyxr2a0AI\nM1QdR5IbwzmfjGcRJ3obCcIRpnMY+A5Ka9rdmGLeJ+onaKmQykg6AaAtNpshWhmWnhhmyCsbXfpR\nakRdXcFM2cwqNTsxYZSQSuj2YrK+Q6kQcURpfNchlzE3Zo5tUypkSJp9hBBIZa4nY0PyWN/ss7LW\nRQhYWRfkcz7T5YBK0afZCcfGjJ5jcflGiyBwWd3s0+8n/JPffuJ1Kei/nbYqEzxY2Lf/II7rvd3L\neNNwx0HddrvNv/yX/xIhBH/wB3/Axz72MQD+zb/5N/zpn/4pTz/9NJ/5zGfessW+HliYKf98xsHz\nHALPIcg4CGHh2NZbWtO/383kbk3xvTKskendbn9TkkqefeEqV1baWIJxwx/LkAWSVFEtZigEHr/y\ncwfG4q1brxMlkn49od2LDTW8PxRyLWUA058RliEigCafc4ZSQmaWSAjB4YUSSSJxXJtzV4xp4b6Z\nHCvr8dDAzwSuUbwJfLPOIHD5mRPTXL7eotWJafcitDKiq1Fyq6yhLU2sFJ1+TJJqhAC0kUHabIZY\nFsSxRTHnc2hficvXG/QG5vdRoknSBGHBDy9GlLIe19bavHRxg9/4oBm5mC4HbLZC9JCj7tgW3YGk\nGyY4wgTX1Na4bsryaoePf/jotmHaVEpeeq3GlZUWiVTEqeRLz57n9z/x2H0N3L7VRpMTTPBWYs8A\n5TgOf/iHf8gf/uEf3va7T3/603zyk5/kscce+6ku7s2AxvQ7un2bYs6llPOoljJMlQMc2x7X9Hdm\nNnB3Z9L7xf1uJq+nKZ5Ktavtx6XrbVIpubDU4Npqh3NX61gWTJUCbFuwr5rl+GKZzWaI79r8/ice\nI5vZ/U5sqpxh6UabKE4ZREbpO+MbBQWlNKlUKA1Z3yGMJd1+YlxugVzgkqSSziDm137pEH/27Ku0\n+6YkePVmm3zgkoYm6xmpQ3iOIULYNiipeP6H18lmXQpZl14/IdUay9LbSrpKQq0RbXu8FVqb8uGN\nzS6dQUyYyG1afEpDvZOgAd8W5LMegyhlea1L4Nt0BwnFnIcjLAo5l/WG6anJVCEtE/hdVxiliazx\nmcpmvG1CuLWhrYewLKTW3Nzs8r9+6QccXDAakZNsaIKHHa/LNOTUqVNv9jp+qjAabZJmRxElConm\nwFxhm3zM1k397NVN0Hrc/zh7dZOTi5W3xDJ+J+6kELBbhjUSIR0Fwe4g4Ytf/QmlQob1ep/Nlpm3\nsSxDIumHKcW8x5OP78MfzlLs/Bt3XicfuDz9iwf5P/+f8wAUcqZ8NVPJcmOjQ8a3SVNNmEhcxyJJ\njPdSdyiomvEdrq+2+bOvnWOjeUukVUno9BOmSwFhIhmEybBHZCjnSsMgSkklNLoJWd8mk3FIYkmU\n3ApPu9li7IU0VfQHMVFyO9lndIpmLwbLWMvbQvCxDyzyha+cod2LKeV8hIDpUoapks/FpRaNTjgm\n3hSHJT64vY+X+aGN1hqpNY1OhGsLk9lJxSOHKveUDb1e6vmkbzXBOwHvDlere0CqwLE0rm2su08u\n3vpS7sxsrq910WgWpvNDH6MNltc6zFVzb+iu9qflWrt1o9nJuNtshmg0VWEhhLGO6PRipkoB/TCl\nWsxw9EAJ33XvuBHunBU7c2kT33fYbIUM4pRqKUM+cFmcLY4p5JutAWmqmZ3J0GhHWMJCp5reICWx\nLZrdhJ1xRGnMvNJaB7Q2M0pSEyUarTQZz6EbplgapDKU9TTdkT1teTDiAO4Vr1IJnmsNbTV2P0qm\n5jXzHJuPvn+B89ca3Njs0e0lNDsRCDg4k6fdT6gUjfjsIEw5cbDK/FSWfODe1sc7v1Tnd585xZee\nPc9qvUc575GkGtc1ZdZac8B0Obvrerbi9WTZb3ffahIcJ7hXPDQBCoy6dSI1pbyPM6Qyn7vaGDuz\n2uL2L0qtGZKm6jYq+uup8d+yQa+NN/utGK1n5Jt0L2Z2OzOsnUHQd23yOZMZTZczbDT75LMeqYwo\n5j2OHijtyh7bupatRoejWbFUKjabIZ1+DEAUS/ZP5cGCStGn1YmYm8rhWhaDRJLxHXqDhESbdcV7\npTjaDPo+enya1Y0uUSyZnQpYrXWJY4wCA2A7AtcR9MN0z+Cz5ZS7wsKMHTi2hXBcZBqTarZJH1mY\nbEwqxcH5Al//7jJaGTLE+DituZy0xzNRU6WAI4+VmC4GHN1fGt847CzvLq32+Ieffi/PvnCVi9cb\nTJczvLbcIk7kfY0J3K8O39vZt3q7g+ME7yw8XAEKowvne2LbHa1UiisrLY7sN2ZfriNAQzy0eHBs\nMS7TvBm4tNIa2qBvcmmlyW9++BgAX3nuEt1BwoVrDbDg5MHKHb/Au92J7ryj3k4Jt3j8xAwnF0eb\n3u5BMEklX/7bi2MtOak07zk6NRYzPXe1wdUbLTr9GCkVidSEkeSVixsgTAajtTH7m6vmmK/mqLX7\ntDqGnLBT+24rDLVcMhiY8llLR1jaKIf3+8ZsUmpAqbExoRiec7fTbv2ZNXwsLHAdC8cWOI7N/FSW\n3iAhilOsYSlxNG6mAVtA4Lt4rs0gkrQ70bYT9mOjbjFSZX/saBVb2NuC0143Qa5j88xTh/nyN1Ou\nr3Up5Xx8T/ChJw68o1VN9sKE1DHB/eChClBCmPLRUz+zwH/9zlV+fGGDSiHDbDXgyP4SxazHjY3u\nmP7c7cV85GcXxhbjb4bXzl5f0NG/RwEBoNGOsIXY9Qu8NYgAnL1S49O/fGIcpLYef78loDOXNnnp\nYm2sTN4PE2bKWWarAau1Hs/3Y9brXbRSpFKPA06rHzFXzdEPE1pd01OJE0mtFfLYwSprm30zJHsX\nLeBUaq6utCkXfDRweaWN0srcYDAcuB4eK8QwOA3Vy3fCtsB1LaL4VhnQFnBoX5Faw8gN1RoDemEy\n1BC8fcDbiMd6Q1q5AkuTCxyUMplj1nfYN53n2o0WYSxZrfU5vljm2IEiX/7ma1xf66KUpt2POXag\nhC3E7Z8jrdFoLAFz1exPNThNJJMmeKfgIQtQFq4tePVqne+9uka7E3Njo8u1VZfFuSLFrEepkBkH\nj1Ihg++6fPqXTzxwNfOtQURpzdJqG9ex+fiHjt62vvstAS2tdkilwh4O0vbDhHp7wEarT605oDy0\nNB/JGY0gLMuoJfRipAQLTbefkqSKy6tNwlhyL0L1WhlDwtVNU76zGLH4zPsiAEsYxXKtjUL5bsEJ\nQNgQxttzq3zGYWm1M2QcapQ2fahCzqXTv70vJhWEiSSXdbmy0uLgviL5rE9/kFCe8klSk4EnqaGL\nX7vZ5n/6b9/L+WsNXrq4gRyWMy0LilmXE4vVbZ+jc1cbxKnpeYJhF/40s4q3UzJpEhx/ulhfu4nj\n+mxu5iiXyzjOO3uLf2ev/j4hU83NWo96O8TzbFKpGAwkYZxiYeG5YuhhdOcN/o00ee/0BT2/VKdS\nzLDRGKDQpKmi1Yk4dqB423lGQcSyLOqtECkVPzy/jtJ6TCt/PesDODif58WzYryxzk5lWZjOcbPW\nH6uQg4XrWsNNfmiB7gj6UTLOAM3ck9GwAwij2zf/UdltK2ybMc1cWLfo5ltJDDJlaGIInm0hhSbe\nRd1FK5NFjZ4qLOgMUqQyZb4RoUJJTRSr27InMOKzWd8hiVOO7C9hC4upUgZhwRMnpnn50ia9MEFY\ngsB3yWddvvmDG2bNqRpLGY1KfA9COevt8o+a6An+dCHThHyh+K5Rk3ioAhRAqxcjO5qMayy6tVJI\nZZHLOsSJot2JqQwHTne7u3ujTd7dekSjf48Cy3uOVPn2KzdI0r1dfkdBpN2NzcZnCyoFfxutfK/1\n3W3m6/SxaV69WufsFaNO/tiRKo8enuL//e4SN6/1xtYYnusQxym+a5lh6KxLpRDQ6oas1/uk0gQa\nhWHu7ZbljEKOsBhHq6Gw+ZigsBcSabISqTV7KULuvKaRuzJBL5V6HCCVhjCSt51HWOD7zlCNwgi6\n/vj8BqlUBL7Nzc0+e9EwDs4XePEnYhywHVuMiTE7Kec7b1qOHSjyyms14N23iU/MFX962Lf/IFMz\n87Sa9bd7KW8KHqoApQEpFVJpBlrh2GL8i1bH0JUPzBY4fXRqz5mnN6PJu3U4eLdgd+5qg2oxe8dr\nnD42zYXlJq9crKG1plTwma1mWa8PxrTy3Z57t5mv0WCvLSzKBR8wQ6ePHCrzzR8uEcWmZOe6NhaQ\nz/qIIcNxqpShmPXZbA2wbTE2+esNJP3B4I5su1EmtDVLutPxAlCYACjV7X2jrdh2HguygUPcSbfR\n0XMZmzBWaKURwxmx0a8d28K2BU+cnOEvvn6B9YbxkApjl+myJBf45AOPOJXm8yU1T39gEdexubA0\nPe4TLs7mOX1satf3fWvWu5vW4YTpNsHDiIcqQKnh7AyYTTcfuPTCFK1Mp8NzbYp5D8cWb8kd3plL\nNa7d7CCEdUfl8N3gOjaf/uhxTi5WeOGlG0MqubWNVr4b7jTzBSagff3FZQaxxMKi0Q6pt0NTapOK\nOFXGnTaVOELg+4L+QBIqhdOxaLRCXNsU9WzrVnZyL3Ozu1q374Gt8UhrY1A48v0aQVjbZ6LA2Gt0\n++m2+SjXBmELPMd8IRzbojdI0doQKqJYsjiXZ2W9Q6zM62JhobSi3YuoFDM8+fg+Ll9vIZXmkx85\nPlbh2K1/+cprtdtucrbabOz2+wnTbYKHEQ9VgAKzIfmOTZAx0jOB79LqRlSLPicPbTcy3A1v1uQ+\nwAsv3WCtbu7GN1sDji+W7+sarmPzvkdmOX1sak+l8dfThJZKcu5anfXNPt1Bgtaa5bWOsdCQGoVG\nxiB8i1Y7Ih1Gge4gYWE6T6M9wPdsQxnfRaHhp4EkkRSyHr3QEDSygcNgcHtTKhwqTviuwPdsM3Mk\nFRbW0C3XmCW6jvkcZFzzdyyvdmh2Ivr91Pxt2vStXEewOJsnTjUnDlYJfJsnTt6yqZmUsyaY4PXj\noQlQo3kZ1xFUSxmiJKVS8CkXMtRaA8qFDGDddUN/syb3j+0vk895eK5t2F+JpNtLxue7n2vcD618\nZ/A7MJffVuILfJt903k2G1fHwUlKTX+QkKaG9CCEhWUZzb184BLFpncjpaLVNQSUfmRKgXeaedoN\no6zHBu5G+LMYCsoqiFJNNjBq4xnXEBLuFBpTqSi6HmkqsVwbKRWDVGFZgFRGeUSYgWKpNEmqjAJG\nKsnnPBwhEAJ+/amjPHFy5r4+D1vfA6kU3V48NtB0HXvCdJtggiEemgDl2EbOZqaSJRu4pG1FMWf6\nNgdmc5w8ONoArLEi+F4bzZsxub+02sEWgkcOVcaWFQvTuW3X3usad2MR7iWAu9sg76nDFZLUlPUA\nnnp8ns/9Xy+TKI2UcqgqfiurlNrQx11HDG0oPKQMSZUmThS9MOXkYoVmOzRGfkrfV+nOwgSGjGfT\nDe/OSR+RIBwB3UGM79gIYSFTTeAJ4kTtWmKUChqdkFzg4Qg9JFxo0lSTz3okYUycgi0kWsFGc8Bc\nNYvrCGSqOHqwxKH5Ak+cnLnvz8MtRZHNYXnW48zlOpdWWuNe04TpNsEED1mAyvgOlYJPKe8zVQoI\nMg65jMORhSKPHKq8rsZ0kkrOXNpkabXDwfk8p4/dm9Ppwfn8WFGiUsxwZaVFexDz44sbt137bmaB\nu63zTmzDrRtqksrx+eJU8tn//TuAptePkYrhf9ooiQ/Pbdh5iqwP7X6EsG0yDghhpB1+cH5trMQg\n2L0XtBOeY67l2RZT5Sxrjd5dX8Otp0wVCA2RVmQ8m1zgEsYpthbIPcqM+axHOefS6CZG1DYVCKHw\nPBupXHqDBFtYaMv8vQDz03ksoJD1+NgvHXrdQWTkmVUq+Lv2mialwQkmeEgCVMYVeJ5NIe8RJpLV\npQaVos/KuuTiUoN6e4q//rvLZHyHfdO5bZI+d9okRmoOL12skUrFi2cFF5abfPqjx+9YVgt8m9PH\npsdeS5dXWrAfvOFztl57N3PBfM7d9dituFevqHNXG3QHCbXmgIvX6rR6MWlq5ItG5bmRcsPWmSWp\noNmJyQUOUiqmKgH9QWpmuLZEDsWtJwUZgUoVCotkR1Y1mmEKlWaj3gNr98BmWeC5xua9F24PPCMm\n4Px0jjCWdPoJyR7ByRUwUwnodmNsy8wrZTwbKS18xyYSEs8VFLKeYfEJi0ohg9KmtNnpx/xv/+lH\nw7moW9YYwOu6YflpYyLQOsE7EQ9FgNJoSlmfw3NFWt0Yz0lptqMxDfq5H90gH7iA5sZGl8W5AlPl\ngFSqO86inLvaYHm9azIMYQZbr691dw0Yx/aXd920Rsf9+GK869p3MwsMm+mYdTfCbiSMnQjjhM9/\n+eWxxfj5pTqH5otcuNag3Yto92OiRKLUrcDg2uA51ljcddtgrWX+FyUp3V5CnMg9MyVHQMa2ULZL\nu5fsftDw3JG81V/aWZtzbQvPsY0DMCYAbl2T6wianYj90znCJKXVicbDxFsHdrUFy6tdnjg5zdpm\nn3onIhe4RInJJA/M5hhEKQdmi1RLGZaGXlUbzQGeaw8delMa7Yi5atZoK16qcWGpcdcblhHeql7T\nRKD14cH62k2iOKHX7ZCmC2/3ct4wHooAJZVmEKfUWiHR8FZdjSRypEIIAZam10/ph6a53+xEeI4Y\n9zhe75d65+ZwaaXF6WPbg9f9bFRT5QzdXjymy4+GOr/8txe5ttqh1Y2YrQb83q8/tu2cnmPxnVdW\nub7ewbIsmp2I44tlltfa40BjYY3t0dVYnsdCYYRVpZTbh2eHgrq+6+AIi2RLr2onUgWtvkRrOZYv\nulPVT3P7kO3o7JYFniNILImDkViKU43vGmWIMZFDWxQCj0QqemGCGgY+2x56RknJuWsNynmPYtal\nXMjQ7sf0Bwm2EHzgPft49LCZifvtp0/y9ReXsUSDuWqWWnNw25qXVrv3fMMCb52qwkSg9eGBTBNk\nGqPk3jeB7yQ8FAHKcwXuUMfNcQSDKEUqtSVAmeb4TDUgjKSxPM953Kz12TedA3b/Up86XOHslRr1\nlvE/chzBgbn8Pdmybz3PnTaq3cwCt1qHnzpc4cylTX50YYONxgCpFDdrPf4j5/gff/M92yzGr68b\nSruwLJJUsdEYMAhTKoUMlXyGJJFotLFIt4zWnS3A913mygHrzQHtbmjKf0NLdtsWZDyB6zlYFjTv\n8D7s9GmyYE+mnSPM+ePUBBzG5UYL33UoZi2k1HieQz9M6PZjlNbYQLefcH29wyCShInEsy0cC7Rj\nobS+NdyrIYpTGh2o5D26g5huP8bCzEHFqdo2E/fMU4f5ynNy3DdsdiIqRX8sInxwvsDF6/c2x7b1\nvX+7A8Wk/PfuwVYliXe6Dh88JAFqECpOnyiSdR008HOPzPL8mZuGnaYt4iTF9wWDMKWU9zlxsMzN\nWo9GJzJuqeWA3eajXMfm0798gpMHq2+457DXRrVX8Np67NJqh04vRimNsARaa9Yb/duGP6fKGTZb\nA9q9GKUUcZzyMydmWF7vgIbHH5nlWz9YRlgQZMzg7/xMll4/odUztuwZz2Gu4FMIPHphSqcf0Y8k\n7V53PGN0L1AYYkScbi+9gRmOBTOAu9Wew8JQ2ZNUMjeVp5zPkM+4KK14+bUaiVSkqUbKlEFshFvR\nmlRZVIsBB+bynLlUJ0klqTbnywcevitwXWecXdu20WXcbIa3vRfPPHl4zHj81D84ytJqb/y+AFxY\nqt/xhuXtwJ0y9En5b4IHGQ9FgNLAmYs1Du8r8bOPzHDsQIkfv1YDZXyLLEubDXdgxEwHseRmrYeU\niigxjf/Tx6qkUvLKa7Vtd5mjYdn3PTK767Vvn3lJSIeb7P3o993pLnthJkucSuJE4ro2trAo5bzb\n1nH26iYKo6OXSk0+b+M4gkcPV1mt9bi83GRhNketEaK0ZrYakHFsNsLBeCZKa00h58PQnbfeNkE8\n3UWo9U5wBCYLaUckOwT3XNuQKNId6ZUGsIxqRK3eY70+YK4S4LkO0+WAdj8hjlOUVqRS4XsOFhau\nY3p3l1baLMzm2GyGtLrxWCVC2BYffWyWpbUeKxtdPNdESN/dXmrdyngE+Pp3l2/bzN+sG5Y3E3fK\n0CflvwkeZDwUAQrMnXe9ORj68dgIy7if9gYxWsNUKcujR4zf0YUrdRzbwnUc4iGhYK3e50atpada\njwAAIABJREFUD2z3XtqJfhiP77Cf/sAi2Yw3dtF94aUbBIHL33x/iRdeWuH3P/GesSTO3bBbGcZQ\n3Gs8/9IKrm0Rp5JUKQ7NFzi4r7htc3Udm5OLFWNdX8milGat3me9PkAIi1YvxvMFUgoWZvL0BgmB\n5zJTzbHRHBAlRuJHCE2nHxNGKf3Q1LmFZaHv0FEaVehsYaSGpNK4rk2rY4LTzp7UbpnYSC094wrq\n7RApNbaw6PZjAt8hn/OIYpMBm7VK0KAtTbObgDYlvVY3Yq4aEKc2tmVh2xZZz2FhNs9GM2RhJodl\nWQSeze9/4rFt7/G9lmvvdMPyduFBKCVOMMH94qEJUKnUtAcxV1banDo8xclDFRrtkEZbkPHToeCp\nQAiB79ukUiMsC9+zaPdiOv2YwHfRWrO02sG1bT7+4e3eS/0w5n/50+8ziEw68f1za3ziQ0fJeOZl\nzuc8XltuDhUWNF/86ln+4aff+7qUKJ558jDPfvsqV1ZanL2yST9KcWwLhRly/fgHjwBsYyE6tmCu\nmsMWFlIpaq2QpdX2WOInyDjYwiKRZn2eI3j/qVkuLNVxHWFoFJbxYkIb4oTWkugupT1hYUwgtcZ2\nbJqd0GQuW2jkdxKYEpg+YjbjYAuBilJAISwzCJykinLOIz9bIIoSMm6B6+sdwjglToaqELaF51hE\nsaLRjliYzdNoReSzLq4r+M9/e4n3Hp8mShS+a4LTnW4epFKs1ftcXvFed9/mQej9TFQrJniQ8dAE\nqJHi9U8ubfLrHzzC+aU6thDjIdlRs9t3bY4eKI8N6LTWhLHCc83mUW+HpKniB+fXUOhtJZ6vv7jM\nIErNJqo1K+sd/upbr3Hq8BStTsggliSpQlgmkETJ7uWUnRvXbnfu5lpm1mck4Oq6Dr5joZTFTy5v\n8uKZ1TGl/JVL67i24MpKk8W5Ao5js6+aJUoVjm1RKfpcWGpSyvmsbHRwbIvF+QJLax1+/rFZvnd2\nHSk1lm2R9Yzyd8az6fTvzhaybQg8m5OHqiyvdYjilEGY4rk2akhrv6PSOZDNODiOTSEwZIxOPyGR\nCiWV8aVCc329Q6tj+j+9gcS2TT/JaAVqLEvge4Jy3scRgtlqgBCCKE4NPb0bM1sNWK8P+PqLyzzz\n1OFd59m6g4QL1xpgQaMb8pXnLt133+ZB6f1MVCsmeJDx0AQoo2igyQYul663t30pt7LiRkoNxxfL\n1JoDMq7Nzz02y1f/7orxXpIa2zZWFNdudnj2hau3bWSAUdZWpgxlC4t8zqPeaqOH3kWuI5gqZ25b\n5+66fSWT8TQjACpFf3x8Ke8jLBO4tNbYwqaQ9fjat42W3kiIdr3eJ+M5BBmHl1+r8d997BFsUeXM\n5fo48J08WGEQpjh2idlqABi18yMLBX731x7jxkaPfhzznZdukipNGKfGcl3uzsazbUBD4HucPjZF\nuZgFjGyQ0iZAu7ZFdBeZCdc2tPcga5NKhuKuQzNDDVJqbm706PRjhCVIUrnFut2c2/AlJBnP4ej+\nEoWsTz82WadUeuhfJfnBq2skqUJpTfKc3BY0Rpv5sy9cZaoUMFsN7jjUfacM6UHq/UzKfxM8qHho\nApQlwHUdap0BF5dv3zS2Sv8c219iabXL48dnOH1sCoBrq11eea2GUgrbESyvdXAdQb09YHmtze9/\n4j08/YFFfnh+jUGUopRhcR09UALAFoJfe+oIL55ZJUokU+UM+cC9rZyy28aVSmMpPiodNjshn/oH\nR/n6d5eZKmdYmMlxs9YjH7gUCz6eZ9HuS/phSi5w2aj3GYQpge8OXWPN+Y4vVml1IvI5F1sI8oHL\n48enOXO5DmjOX2sQD6nnShtDxS/81Rm6/YRumIxnyOQeKZAcDsfaQjNdzXJ9tUezazIcMOy8KNF7\nzkS5DjiOjVaaVJk1WGiEEGgSNBaeaxFGxrlQWNbQJsRAaRM884E97J9ZlPIe7X7CzFSO9WafI/tL\nSKVZWe9y4VqDOJU4tk2jHTJTCXbtMR3dX6LRDcezUJXivd1oTNhxE0xwf3goApSwIOPbzFYyrG70\n+O6rq1y63uRbP7y+rddw+1Btk0cOlTl/rYkrBO89WuU7P1kljhW9MCZONAuzOZbWOuN+0h/99z/H\n119cRipjJS+VNZ6TeeLkzB2Vr5NUcnmlxXq9z3Q5Q71tVBByGYcj+0s02ob2XClmWFrtjbPAx49P\nk0rNjY0e+2ezvPDyDeJEMohSBpHxNbIEJnBqjZKKb3z/Or1Iks+5dHsxTz2+wCOHKpy/1qTViegO\nIm7WuthCcOpwdVxWjKUi49v0otT0oyyN5wmiSA0fbx+wlUPr9u+dW6O2OUApjVKKfNYljtOxkOxO\n1XPHhplywGYrJJUapQx1Ph+4hLEkSTTa0nhC4DgmOMXDTGzrqYRlFM4/+nMHuHK9jUZz6nAVWwiO\n7C9RzLrc2OgxP5XjQi9GKShXfNq9mItLDR4/PsNOHDtQ5C+/cWHLDUPEb/3qiW3H3C1DmvR+buFB\n6MVN8GDioQhQgS84vK/IzY0eSSq5vtphoz6gWvT54ld/wj/89M/gOvZtm0p3kPCFvzrDar1PnEpa\n3QjftSnlPTr9GAtNFKXkAm9bP+k3P2I02XYy+nZma1sxCo7dQUKtNeDVq5tUCj6OY9PpRuRyHvNT\nWaNQMNyIdyvNvPJajVI+w0YjZKpk0RskTJcCWr2IZicaq0ZkA5fN5gAhBGmqeG25wTd/eJ1a0wz7\nXrvZwcIM637nlZs8+d59gAmOvUGKlHIcXDKu8U+ybQtHCKJYIvXQmsOy0FpzY62H7wosy6hV+K5A\naxvLMpYc4RbNPMeGE4tl2r14qPwgsIWxyBhEksAX9AbDuShLGUFbqYYKE2aAVwO+J4zpoG1Rb0XM\nT+XG2SKYrNYWNqVChkT2mSplqDUHrG32cYSFLeDCcoPTx6a2bZqXrreHNwy3Sq5bZ87uBZPej8Ek\n03xzMZI6MkoSB9/u5bxhPBQBSmvN8mqXcDyICY6jxkoDe9X+N5sh9faAOJWsbfaJEoltQbsXo7Vh\njzW7McWcf1s/qR/GfPGrZ8flvGe/fXXPL16SSp594SpXb7aZq2aZLmVodyN810FpTS9KaHRDGu2Q\nk4cqBJ49nsk6dqC4TVUC2GbjIZXmV3/uIKlU/NW3XsMWFoWcx3q9z/JaB8+12WgOeOXiGr1IYtsC\nC9MfymUcLMsiSSTXVjucPjbNSxfWqZZ8OoMIhoO0qYLpko/AIsh62I7FjbWuCUSeQz+Mh/I/FqW8\nbyj8tiCTd4hTRTHncWWlRapMv8kdElIyno0lBDamZ+fYAq00vdC8j5pbIrOGiGF6bGBIFKWcT6Xo\n0xsk5AOX/+HXHx3PMY18mHIZB6kU08NsLePZWBYUsj7ve2SGOFG7fj5sIZirmp6a3KWHduxAkW/9\ncHlMUtmtnDvp/TxYvbh3A2Sa0GnV+dgHT1Eul9/u5bxhPBQBKow0mgTfE0OKtSaOJYlj3FNHPZGd\nZRfftSkEHpdW1kkShRAWkdRYlkWlmKHbT8gFLpXi9n5Skkq++NWfsLRmdO8anZDji+U9G+lfee4S\n1252WK/3jXxOwScXuAhhkSQKy7Io5nw0kPMdFHDmch2pFH/5jQvbFLWfefLwmGkmlSbj2jxyqIzr\n2FxbbY8355X1LnFiWID9QTwcHjZuuiMTQEtY5LMucqgLdO5agyBwub7eoVII6PZjUqnxXIt2PyXr\nOxyp5nBsi3YnIo4lnV5EKo0hU2xJ2t2QcjHDU4/vZ2OzT394zKnDVa5vdEFr8oHHZjvCd8TYar43\nkASejSUg6m+nZAgLsq4NloXvOWhlGIblgkc/TLBtiydOTnPpeptj+8ukUvLimTXyOY92P+bKSosj\n+0scXyyzZAuCjMP8UNV+t+Bzt/LcaKA3n/OImiHdXsJv/eruc3MTTPBmYt/+gziux9TU1LtC6ki8\n3Qt4K6AYqmQnClsIPEegMXNJjU7IheXGWNnhNz98jCdOzPDEiRl+95lHWK33SBJFKo2zbMazyfgO\nU6WAX/n5RY4dKHNysbItOzp3tUGYyG26dztlc0YY3UHOVgM81x4rgge+QyHnoZSi0YmMynYiefVK\nfUhlt2i0I/phwuXrLWpNo/Zw6XqbZ548TLeXYGHYg89++ypgnHZPH50i55sPbqsb0etHaG2NTQnV\nkI5vYcR0fc8m4zksLhSxhcVMJQOWhdYK17WNXl6iSBJJGEvOXtnkyo0WlgWNTkyc6rFhoHkdFVEo\nWav1qJYyuLagnM8QxpLpYRbaHSQoqWh1IyrFzNDDyphN2kLcRqjQGiQatKbVGdAPUxZmctQ7EYNI\n4ns2z377Kj84v8aZIf1+ZFliC4t84DIIUx4/PsM/+e0nCHyX9brJnHfrDe38nOzMjEfvqefY7JvO\nUSr44yx3gu04dbhC4NtIpce92oe1FzfB7Xjnh9j7gBDgezaVgs9sJYvnOUyXMwyilGdfuMrR/SWO\nHSiOj7+80mb/TIE4kTQ6EZYFpZxH4LscPVBCCMGhfYVdaebT5YBmJzKUZaXojbOUWxJH/TDmuR8t\nc3G5yf7ZAicWy9SaIccPlHn6A4ucv9bkr//uEmBEUl1HEGRcas0BC9N5lFJstkKyGUmUSDaafR4/\nbjKF3YzwTh2ucGGpzvMv3WB1s0eamqwwlYpcxkY5kiTFOOUGDvnApZT3+Xs/v8hr19vEacqPz29g\nC5BaUCrYhJFNpx+jpKI3MDNRzU50m+2GxrDq4lThejYXlhqcvbzJI4enEMIM0ra7MY5tozHlwFRp\nWt0IqTRaWwSBSzdMsUWyjYihgX6o8B2F0ha2rbm80kYqReA5rG8qquUMjXbIXDVHmEiiZshsNRgz\nFS1hcWGpzoXlBvmcS9hM6fbi2zKfSUP/zcWkFzfBnfDQBCjfFUxXAqaLAY+fmGJpzYh8KqW5sNSg\nWszQ6IbbSmatTsh0OUOrF5DLevQHCXOVLP/kt5/YJhK68ws1KgEd2V/i0vUmrU7MiUNVzlze5NJK\nk9/88DGSVPLH//H7XF9r0xuk3Kz1OLxQ5GdPzY0D3kgu52++v4QtLKbLAVLpsd1GmhrzPKW1UfLW\nI43wW5BKsV431PqLy3VeeW3zlkTRMIB5nmC6miXnu2w0+uQCz5xfaqJIcuVGG1tovnd2g3bXaO/l\nApdCzufUYpal9Q4XrzXv6pqrAa2M/l0YmUHbs1c3yQ+HcIOMQ9JPEAiCwKYfpqRDRfEUqDcHVAo+\nAk2za4wVt9q5RynYlinBgiJONUmS4NgWvShh35RRpp8uB3R7icmSEonn2sxVsyyvd7Gw2DedY2E6\nj1R6G/nhXhv6e5UAJ8Ftd0x6cRPshYciQDm2cU+tFDM8drRKIhWbrQGpVFy90SQbeEOPn3BoQmfu\ntPM5j24v4fhimc1muE0C59Rhj3NXG+PMZOtmM1K9/uJXzyIsQ0pYutnmkUOVcTZzeaVFvTWAoSZg\nlKSEccrJxe3nOn1siksrzS12Gw6/9asnOH+tyZWVJjOVLFGckiSSEwcrY5FSzzEzQReWGmgNG80+\n/SghTgw5wKhkjLyTBEf3lfnF987xX164ymYrZKPRZ6SQ99LFDRZn8qbXk3GIEkWnn5BKTRil1Fsh\n8i7BaQTjxDtAaQvHthiECWGYorVmdsoop6dS0h3Y4wwvkZqpkk+lmCFwHR47OsWPz6+z3uijlSZO\n1TijkhosrRg6tJugqI09SL01MExMx+YznzrNX33rMq4jOL5YGjP77oR7bejvlhUAE7baBBPcJx6K\nADVdzLAwk+fXfukQtrA5c3mTR49UqTUH1JoDyvnMeINSWm+bN3rq8QWcof/DiDGXSsWF5QbxkBq9\n1e57tCmlUlEq+CRSsbrZI0kVteaA6XL2tvVZloXn2uQDb3ytEXZudqM1LK12WJwrEK+0cGyBUopX\nL2/inRS0Lxul7m4/QWBRLHhstkJcx6bZiszQ63C2VgBaK85crlFvDggCF5n2UMoM4bZ6MZutkH6U\nIlNNvR2ilKFx9wYpgS/oDu5dynwUB5TSeBnHZGlxgufA6mafVCpSCUoN3XmHkW+zOaBUyPDf/NJB\n1jZDPvzEfl65VGNlo0cUJ3T76S233KH6rKVN6VAIi2rRZ60+IJtxyPgO//P/8f/x84/No4EL15qc\nPFRhcTYPljV+X99IP2RnVvDKa7UJW22CCe4TD0WA0pZFxnW4tNLm5KKhXhqacM6w8XqG8VbMe7x6\nJaI/SNhshVSLGX7rV0+QzXjbyjtr9R71dsijw4FPY/e9uS3TGSk0TA89mOJEbmsCHztQ5LtnV2l1\nWyilCTIO7zla3XVDHG12u63hkYMV6u2IzdaA2bzpO63XeyyttsllPdJUcWOji+faRJGkkPNwXZve\nwNi7C2E28P4g4cL15lBUVaC1BRrSYaYVhimp0vQjOR6qtYWZf3KG9hh7YatSRKXgkSro9GIzLyVN\n9jOIAW4FulG5UAyHfy1h0e/HvPiTVapDyaRThyuU8z7fe3XNBFvLzDxlMw7FvEenawZvHcesr1rK\n4Dk23YG59tJqh0cPV1mvD6jkMzzz1GGAPctwI4PK5fUuAIuzb7/X0wQTvJvxQAWoWq3Gxz/+cf74\nj/+Yj370o2/KOS2gVPCptQY0uiFHF4oEvr2rQ+3FpQbVYkB32OxPleb8tSbve2R2W3nHFhZpqqg1\nw/EszNJqh+4gGQ9vlvMerW5EmmpKOQ/fc/jQE/u3DX2+52gV0PQGCYf2FfnkR47fseSzdQ1z1Sz1\nVkitGTJbzeLagiBwOH+tQbMT0e3HaCCbcYkSSW+QkMnYBK5DIe+RrBtWoBy649qe6d1EiSRVGmEZ\n5fFsxjUZjmujYjl+TUeW7EkisYRAsF2Pz9pynGUNH1sYVp8y1Hkp1Tiw7NW/UtrYuzu2IIwlg0hR\naxrbk2Lex/ccCoGLVBrPsXEci8B3mK/keP+pea5cb+E5ghOHy/zw1XW6A2MVojHrqA3ZlQfn83cc\npL71h1lYoz6fdScN9u2YKEdMMMH944EKUP/sn/0zWq3WsMn95sB3wLPNxmOyif6urKFThys8/9IK\nnX5MPushhhvo0mrnNm+f6XLARsPI9oyyov2zWV48exM5rDOtNXrMVQKzQQuLuWqwLTidu9pAKnjP\nUbMZ7mzI3w22EJw8VKGSz4zZh1/86lnixGyAti1wXZt+mCAsi9lqlulygG0JokSSDI+LElO6U1jE\nUYrjiCH5wOLgbJGZSsBKrYtrG9sRhrJJAhOgokTjCHmbWGzgGTr4qFzmOIJSziVMTMYk0CCM0kQi\n9e5ifEMIyzj5+r7D9bUWYNTiozhlbiqH4xh/L9cRaDSuI/i1J4+Q8Rx+4dF5Th2u0A9jvvn960Sx\nRGlNGEnqndBYwjuGVXg3c8FzV01Zd9+0IVvsNcS7GyZstQneCqyv3aRQLPFuUJGAByhA/fmf/znZ\nbJb5+fk39bxRCu1eRDZwcW3BwkxuV+O/kczQIEoJY8lUKYPjCA7O58fH3roDtnj8xDQnD1ZwbBPc\nzlza3LbJ9gcpSUFzYLYAmMzhfnsOO1lfu5WYnv7AIpeut7l0vc0HTs8TJqZMVmsO2GgYMdO5apZT\nh6uAxemjVZZWu1jCZAobjQEXrjXQWtFIJHGixhlLKiV/7xcOcnR/gT/5L2ept0Msy8xJ2QKGgg/b\nKN+uAMezyfo2carwPAfPFeR8h3LR48JSa9jDMgrljifwBOPMYidyvsBzHfJZl7lylvPLjfEAc6oU\nJ7MeaBjECWiNawuOLBR5z9HqNo3Fb/7gBkcXyrT6IZuNkGzGIRzOKj16pMryendPZfo3CxO22gQ/\nbXQ7TZ758GPvChUJeEAC1JUrV/iTP/kT/uIv/oJPfepTb+q5NWYgVQj4mffMcfmGUQXfbIZjsdhL\n143CwvxUlkY7pNWN8FyzmZw+ZjaUvcgKIzi24NhimWs3zM+qBWOJsVY3dPRi3ufySmv83FSqbUri\nu6kRfPlvL46D0dkrNT7+oaPbSkxSaf76+SvjLMVzBYuzebphyrWbbdJUkg3cba+HY9s8/YFFltfa\nrG4OaHcjFqbztLohrV6MpUBqTT5jaN+OLSjlAz78xAHiWHPB2qTVMy7Ejj1UEt8CBThCoBQszheZ\nKQ0tNlp9Wt3EZDDKkCU04Hk2R+byvLrUIIy3p1HzFR+EmVvTWnO91iUXmNcrFMbRd3Wzz+OPTFPK\n+6w3+sxXc1SKwVhaCuDLf3uRl1+r0RzOsqVSkcSKfNbDc21euVjDc43i+Z28nSZlugkedMzMLrxr\nVCTgAQhQaZryR3/0R3z2s5+lVCrd9/MbjQbNZnPbz1ZXV7c91pj+Srubks3ApS1mhF/86k946vEF\nwJTNHj1SZa3e58SByrhpvtWVdidZAQyL7+lfWOQvv9Ee6/1JZcRL48Rc59zVOr9wet+2WautSuI7\ny0tnLm3y0sXaWGqn3gpxbXtbielG7dbcDphrPXq4zLPPX0UpzUw1R6sbkaSStXqfw/uKHJzP8cWv\nnqXVjXjlcg2NRSXnEqV6WLI0ga8fphxauPV+OLbN/HSOmWqGH51bp9YKSaRxqxWCbdmjLSzmp3PM\nVczcVrMdobWhe1sWY6+mjGeTzzj0Ijn0tLp1ksAzun7vf2SOWnPA0mqbcsE3jMhEgWWRKkWUSi5c\nazI3FXD66BTNbjzuA5672iCVipcu1kikoh+aHqEQxsYjSUO00ghhkc96zFaDO7LrJmW6CR4E3Mue\n927B2x6gPve5z3Hq1Ck++MEPjn+md3ov3AF/9md/xn/4D//hjsd0+ymem3Cj1qXbj/G9oXU4ECam\nZHeLOGFxeF9xHJx2m13ZbR7mmz+4wcF9Ra7dMFlSNvCwLQvHEUY13Io5c3GDXOASJpJGO2KumqVU\nyODY9m0b3dJqh1SqMf09lYq1Rp98dm8bcoCV9T7ZrEs+Mn20SsHHc21OHKjw9AcW+eJXf8LV1TYr\n613CyNiu16XGstQ2ooIG+r1knCHcyh5gYaZAoxORcV20Mo7Do/DiOYLTx6aYrWRp92OUMXGiP0jw\nPIdsxqXTSxACXFdguzaeZ+O6Dk4Sm4CH8e6ysYYhyyKTcUFDGEnS1Cg/FHM+RxaKCMsIt37n5ZtI\nZWa1RqoaS6tdUqlwhEDYAssC27bJZhzzObMsDu8rMT+9XSl+L0zKdBO83biXPe/dgrc9QH3ta19j\nY2ODr33tawB0u13+6T/9p/yjf/SP+MxnPnPX5//O7/wOv/Ebv7HtZ6urq/ze7/3e+LHSIFPF0QMl\nXrqwYdStsx6uI5guBzi22PXO+Efn17l6sz1WcRjdXe8GqRSXlpvjDa7Z7bA4W2S6nOHqjRabrQjX\niREtC8e22TeVv+PfdXA+z4tnxZh04TiCn39slqs3DVtwsxni2TbTlcz4moFvc3C+QKMbjmWWAOar\nOZ556vBYIzCM5NCXCbQFoEikMQ70HLOJe67AdcVtbrLnrjbo9mPmqlmEEGZIVsZ4rgmy+axLp58Q\n+DGlvI/n2MxWA75/do0wTIcirH0cx2a+mmVuKkscK5JUsyIVgyjFcwXVYoa56Ty9XoIj4MZ6Fykl\nSapIlSbv2ZSHwb1S9Dl3pUGnn6C0ZhAlzFZzgHXb6+g6gplKgBAWSmk++rP7sYajAhMtuAneCbiX\nPe/dggciQG3Fr/zKr/Av/sW/4CMf+cg9Pb9SqVCp7BDzdLf3XVwHZqeytLsxj5+cZWW1Qzbrjm0Q\njh0o7kqceOGlG6zX+0PbdKNIPjpmZy9icS7P98+tja+ZDVx8T7BW75OkEkuA5zpobskT3WlDPH1s\nmgvLTa6vmR7Ugbk8j5+Y5bEjU3zxqz9BoykWPGxh8ejhW2QNMEaLWy3rf/8Tj40DTaWY4fzVOumQ\n4g2mBCqEGWwFU6LzPZu5ajAub27tuZlAaQRqsYyw7aGFImo432TkG0ArzVrdUMLfe3yKG+s9slmX\nx45MMRgkY5PEv37+Co1uSKXgY9uCasEjl3XZbPa5mSj6UcIgMsoVcphhJUNliLlK1thmBB5T5cBQ\nyDXGS8sWRn9w+DpWCxkurTTJDm1EAt/h1z94dOwFtvX93w0TqaIJHgTcy573bsHbHqDeCmR9F601\nteYA1xbbtPSOHSiOPYJgexkvn3PxXJskVcSJpNuLxxvTzozr3NUGJw9WtpnYPX58hqXVDs12ZEp7\nselHHZzLc3KxwtH9pT03Otex+fRHjw/7KKb0OPp3qZChOiovxpKl1S5H95fGz3vmycN8/cVlioHH\nwX15Ll1vj4eD/+JvzpOkhmoNw+FWYeMLhSXsIcXfzG7FqeQH59dYr/dZrfV47/FpHMcGJI12yCBK\nTCDCIp/xaA7Vxx85VCFOFa+8VsNxTImy6Tv8wT9437bXfcQ+/PgHj3ByscyVG22ur3WJUsl6vWfE\nYx2bVGqSoSo6mPgXuDaOsMhmbH721CzP//gGSSIJhkrtWd8Zv7ZbX8cw3s+PL2wwVw342C8dHjP9\n7la2mxjrTTDBW48HLkB94xvfeNPPqYbmdPPVPPmcx9e/uzzeXPaSoIGtxn9G3uepxxe2lby2bmqj\nrGrUMwp8m9PHpjh9bIowTnjpYg1r2JM6tFC6JzrzaD5r68bY6oTkcx62sJFKceFag6lSQLsfj/2g\n/vr5Kyzd7LC83iF7zuHUoSrnl+oc21+mkPXIZ32ELWh2QpxhX0Ypi6m8j1KaqUrA+07M0ItS/n/2\n3jTGzvM807y+9XxnX2pfWMXiLomyZEeJbCneYrgnkWM70aCTQZA9zkTIr2AQwH9mJhgMBjMGgkYa\nSAYd9MBIt9Od7hnDndiI7I46iTfJlq2NEkmJS1WRtS9n3771/d758Z5zWEUWKVKibEs89y+x6tTZ\n9d7f8zz3c9+XV+vUWz6tjs/3X9vk2FyB1R01HyvlkoN2oJCSiVKa8VISQ9ep1rskEgaHrBZpAAAg\nAElEQVSWoZPLJBgtJFnZ6txUZPLZjxzl9NFRzi5WeO7VDbxAYFoGXTccmNv2oWvKKimfTdD1Vdhj\nFEsMQ6fe9Jgez/IbT5za91ntfR8zKZtYcsv3P4wEZxfLrGy1mZvMAnJoVTTEED9i/MQR1N1GwtSI\nVN7eYBB+O4fL3jbeaCHZI5yb3/5WCq8nP36cE3OlgZHrmy2E7m0leUHI1c0Wuq4xWnAGBrb5rGof\notEjBQ3XF3zje1c4c2mXZjug7QZ03JCxvIuh66xsqftJJy00TcWkq4Naab77tkeOaXB4Os+3Xl7r\nzbEkbiAIRcyrl8r4YaRskIRksucQ/sBCiViqgzuIBJvlNoahEccqfmO0kBy8voNEJmcXyyyuN3B9\nQccLcYOIYiaB54egKVLSdRUfr2tqJpewTfSe+8VYIcn6bhvLMkg71r6LkJs95s2+A2Ek+Mo3L3Pm\n0i5RFPP8OZ2JkST5jIOhDyumIYb4UeE9T1BhJHEs5Re315qoj5vttrwVSfHNFF796Iy9jhQ3m2fs\nrS5EHPPqxV10Q8PQdSoNl2OHCgMD26V1m1rb2+fEvV11iXqkEoYxgRSsbreQwKn5Al4QUm14JGwD\nK1Dhi0nLwDB0um6oBBhNj2dfXQeh2qLKnUKioSPiGKQkRsf1IjZ22xyeznFoItczupWsbLV58Ngo\nr69U2dhtQ28m1I+cWFpvsFXpIKWk3Q3JpmzSjjEgEMvUyadtokgyNZLBjwRr2y1sS0dDEdL4SGpQ\n3QK0eurMhK3I6+1UOG9cqbG23UYIOVD2+UE8uDDY+z0ZYoifJFTKu0TR7Zs3/6TjPU9QMUq23nJD\nguBGpVafiM4uVgYVTh97TVrv1nC83zp69sxGr1Wn75tn7L3SL9d9DEMl8hq2jhdErGw0eejY6MBZ\nYi+ZtTshE6Uky5sNul5EECnV20alQyhiDB0+/PAMR2YKbOx2mB5TZP382W3a3YDV7SZBGCNiySsX\nd9VelIgJRQwoE9ZYgjAN0kkTL4xJJ01Gc0neuKren2TC4OhMnu16l9XNFn6gyO2F17f58//8Mo1u\nyPRoiteXy9RbAYYB2XQCy9Ao5FWFUswlOLdUwTZ1gkgZyn7k/TOs73TouOo1JhIGO1WXOFapv9l0\nAq/WxTQUiW5XO0RiZPC+v90lW13X9jnbD0USQ/wkIo7DN7/RuwjveYICiMIYIWJaXZ/TR+b3eeL1\n0XciP7tUZXG9MSCMuzkc79/X1c0W29UOtmXsy4g66Gpf0zRmJ7JowNWtJsmite85KnK9RngdPyIM\nBI5tADbNToChQaMd8NrlClEECzO5fa/hgSMj/M//5nsDV4iOGyKlSrVVcm2ot31Gi0k6Xki7E5J0\nbCxTEVQYK9leue4iYsn8ZI6Ly1UVNmjqhFFM24349ivrWKbO4ppJ148IhSQSoGsh3UBg9iqUct2j\nVHDwvIg4hlTK4uJKnURCmcGulzsYusbUaAbD0DgxV8AxTTrdAC8Q7FS76LrGd15ZAxh83gdVxAdd\nfJw6XOT8lQqVpjsITDw0njnwezPEED9JGBuffs+4SMA9QlCxlJjobNe6gLzhkLnVfOJOZhdvhv59\n6bqGpmkHZkTtvdIv5hLUWx6jhSTluksmZffmaNq+52EaBpm0PcixmhzN0Oz4VGruoC0mpURK5Ut4\ndbO1z3fu3FKFZscnEn2lnMTQry1MZ1M2TsJgbiLHI/eN890zG1zZaGBbKu49jGNqTa8XzS55/uwW\n81NZNiptwkgie/K7votE21WS8f7PXF+wvF7nf/zlB3Fsi6V1GxFJVjpN/FAQS4NsWslom22fKIYo\nUmT6/pPjnFuuYmhaL/o95NCkWiRe22nz3364wrNnNnpuHSP7Pre9Hox7ra9Sjs2THzvGiUOFgUji\ndslpKEUfYoi7h3uCoEAdtqahs7LV5v0nJ2777yJx7Yp8tOBwfaT6W8HNMqLgRrFFPwpkab1xw7zp\n2nNUar69Lgh+EKEZ2iDPKZW00HRodHz8UCCR/N23BU88dpi//+4Vmm2fvQYees9xQQjVPkw7Fr/1\nqftIOTamYfCPvceSsaoq4zgm5djk0jaZtMXRmRzfO7uFENeCBNMpS1k/9RaItT0xG34QsbTe4MmP\nH+fobI5/fnGF3ZqLRFVymqai4qNILfNGsSSdFFxaqbNT6ZJMmHihwPcjsml7sJi7vtPCtgy8FyIW\n1+s3CCfabsjFlRrNdoCUkn/7d2f5w//+od7ccOKOvitDKfoQQ9xd3BMEJWJI2DqmoTE9lr7h9zeb\nT4SRikzvx8Pv1rs8dHzsLQ/H9z7OsUMF2p2QR09PYhravuj468UWD/ZmTnsPv/0zFLmPNz0/ZGos\ni2UowUEQClwvwtCUS3kQCjRUJfPM86v4UUQcSwwDeoUJqaTNWDGJgcYHT0/xiZ+e7aUJCy6v1qg2\nPTrdEMtSYgkhYpIJa/A8qo2Ajz1yiHOLu2pnylPZU6mESTZl0+wGRKFASLAMjQeOjg4c3wGmRzNU\nGi5SapimRtCbZbl+RCwhFjGNto9l6Ugp8QJFpEEYs7bdYryUIowklqV2u66vOvvYrXbZrnSJpar+\nFtfrnF0s3xEx9XE3q+0hhhjiHiEoUHszKcfi0moN09AHLgN9QjhoPvHa5TJBJAfx8CKWnDj01ts2\nex9HLY1GfP25ZZIJi7FS8pZX3LdSFZqGsW9JOIoEaEoyPjueZWWnxXTOAQlrO22yKYvljSblhscH\njo+paHhdScI1TbX38mmbtGNx7FCBhek8z/xglbYb8sbVKrtVF88PCSOJbam4eiwDPxA0Wj7Nts9D\nx8ZodgMeuW+S7WqXcqNLyra473AJL4x4banC5k6bQEjmJ7NMj+23ftINNWNyfYGUklzGQkrwgzZS\nQtIxSJgGx2YKXFyt0e5GaJpGJmUyOZLGsUySjsVuvTuwtLoepw4X+S/fvIQQcY/EdGzTuOMqe4gh\nhnhncM8QlJMwGS2kOLtUYX23g6bBt15a5Xc/8wApx76lCWg/Hl7EcqDiuh0cNI/oD+G/8s3LPPvq\nOs1WgGHo1FopTswXb7mbc6so8r1LwlpPNdd3bA+imEdOjbNbc1lab9DoqPRZL4iIiEk55iAx1zYg\nn3OYn8ph6Dq7VZfnXl3HDQQaGu1OiIhjZd7qKVLQNI0gFOh27+sk4eR8gdevlPn+2a2e0EBjeiTD\nwkyOs0tVJgppEoZBrbcsvF3t7otQP79cptLwiOMQ09C473CJWjMgCGMaLQ9d13nkgUl++tQEErhw\ntYamaeTSNifmizx0bAyQA/FI3xB4b/VrmQa/8NgCX/r6eeIYko6JZei9xdw7xzCOY4gh7i7uGYKy\nTYNWJyAMBes7LRK22YvbOM8fPPngXc//udU8or9nEwul0oulpNkJqPR2eu7kvuDGyuw7L29gmXrP\nIigmn7GpNX1anQAnYWCbJgnbII5jLl+pD1y9DUPHMCDtmFRbHlEUU2/5JBMGjm0qwUNvUOVYBr4f\n4UeCIIqRcYxtGeQyNm4g+OLXzrNb7RKEsXKsMA2ubjWpNF38MEbGai7o+oJcBpVx1UtStkyDT3/4\nCNsVF0PTyGeV6ey//MQx/rd/+30kGtm0xfp2i1//705ycr7AF796Di8UA3/Fk/MFFteaPP7QDCAH\nXoXXf84PnxhjaX16Xwjk6aMjvBUM4ziGGOLu4p4hqKRtkE1Z1NvewIEgBvzwxjnB3mrliccOD0xS\n7+TAuZ15RNIxcf0IEatKJ2EdTIBnFysHuqrvva9+BfjaZZV9pOs62ZSyQ/LCSJGVUIKJfMam1vKI\nYxUkuFv3lHt5whw4Teiahp0wezlNSqAQ9shI17WB/ZClayRtgxhlwNrqBGxXlMGuF0T4gVCKRS9k\nfVcwP5nD9ZVnXscN8UOBjsZIIcHa9rVU28W1JsW8w2hRtebcQPBv/8tZsmlb7X0JODyVG/gMPv7Q\nzGCP7eR8cZ+/YjJh3LJ1+uTHj981UvlRxXEM1YJD3Au4JwjK1ODEoQIf/sAs33l5nbXdNrGUWKbO\nSMHZd9sfhRJr755NMZcgCAVHZ/P87mfuB/YHJIaR4GvfXmS71iWVtPa5qt8MIwWHWssbxG0sTOUw\nNINSNkkslQu4ZemISLmDIyV+GDOaT5B0VFZWIZvA0DW2Kt1B1aRpGuOlFKauEQmJrml4YUS1oR5L\niJh2NyCdtEgmTFw/RMSSOO5nwsdU6i7HD5UwTI1608XQoNbyeO5Ml1zGJpaS1a80mSgl2ap0MA1t\nILNX5KiIN5aSatMnEvs/r8X1BqDdkVjh3ZbxNFQLDnGv4J4gKE2Heifg9NFRTs4X+eJXz+OHgkIu\nQbsTEAlB1wsGcu62G2L3/md/q0qsW7UH+w7b1+/ZwP6AxPPLZTYrHbaqXVrdANePKOWdgav6QTg6\nm+NbL62RTyeI4xg/EMyOZen4EbZpMDma4o0rVTpuiJnU8XyBYegqIUNKDENjJO9gaBrptM1uzaXj\nhtiWQTZtc3K+yE5Vyb8nSilefH2bOJZkUzYdN0SXMcmESdIxkXU1D9N0NcdLO8paqRuERN2YTDpB\nTACoGVYQKpf07UhwYaWG60cUswl2ay4TI0kWZvMs70lDTlgGB5HRylbrpp/Le6HyGKoFh7gZhlZH\n70LompKXP/3sFY7MqErlwtX6YIB+5nKZv/3WIgszecp1l2rT477DpQN3jm4XbzaPOGjP5npn9ZXt\nNpeuVgmFEmeEkcC29H2u6nsRRoKvfXcZ14+otTy6fsT9h4s8++oGHS/kgw9OkbQtjs8VubLWYGWn\npRJvAU3XGMs75HMJcpkE1YZHfavFpz98hM1ym8X1Jrqm/AxLuQQdNwQ0ZsaziFhFuY+VklTqHs1O\nQNcLsW2DSAhMDUbyDpZl4jgGmaRqMbbaPsfnCrQ6ISDJpWxCIQeOFqWcg2Ob5DMJHntwmqtbrRty\nrvrt172Ym8wMjGdFz8m+fxFyULTKu5GkhhjiIAytjt6FyOccljaa1NvBIJbi6EyefK+NtV3tqkO9\nqcxkqw2PnarLeCl1x0qs66/QbydnqH97lft0DY2Wj2nqxCh/PEPXcUzzpq7qZxfLnLm0ixCSjhvS\ndgO+e2YTXVeR6996cY2P/tQh8mmbTzx6iH//96/T9UK1LCsl6+UObhSzU3NpdQKkhO+f3eSDp6f4\n7isbGIZS7NVb17KdoiMjfOeVdVa2m3i+IJ2y0KTECwVJ22C04LBV6dL1IqayDpqmMVZ0qDU9olhy\ndbNFNmNTyjtkUzbluotpKOm6pmkUcw6jhSSObR1I+AdVqv1K+RvPXeXsUplDE1nOLlV59symyvh6\nm9XxrT7vHwXZDdWCQ9wMQ6ujdyGiMKLdDTgxVxgsbK5stQ+8raHrnJgvUsw4twwUPOhgutPZwPW3\nt00N29IJQjWzGS+mEFIZtnY9tav0C48v3PT+VrbaRFGMoauMp64XoWmQdmxSSQtD1wkCwROfPMwX\nv3oO01BKvziWZNMWMbBbdQmjCNM0kFLy8hs77Na76D3T2tmJ7L5sJ1BOFpfXasRxTLMTEImYZMKg\n6gtyaQsNZTfV9UNSCYtyXdkijRSSJG2TXMbmo++fwTQMnj2zQTJpsrhaB00FP+51mL+eTA6qVAG+\n9p0lXr1cpt7y8QLBfQsl/FDg1SOmR/fvXL1VhJHgK/98aaAAPL9c5smPH3/HSWqoFhziXsE9QVBT\noxlELLm82uDkfBHQmB5LsXq2hR8KSrkE9ZZJMecgYkkmafHE44cB3jQSA/an8N7JbOD62weR5PSR\n0mDX6uhsjq99Z2mfBPrhE2P7yHFvFPv0WLrn5i1xEqayCKI3W9J1irkE06MqYTeZtOh6ykFCxJKu\nJzg0maJS66p9L1CLuzq4boRtmxi2jqHrN7Q+TUMjlbRY32krKyMk9VZALFUEvN6LhffciKRtUmt6\ngxnS8bkCIpZs7HaZm8zw6OlJNnY7vO9jI72F6oPl4XtxPXG9fGGHM5fKNDsBHS/ECwQj+SQjBTW/\n61tCvd3K4+xihTOXyoP7qzY8TsyV9sWqvFN4twk7hhjireA9T1D5jMkDR0e5cLWGH0RcuFJjvJTk\n0qokk7bw6hEdN7zWsurFq59drHBxtTaoZm4WiQH7U3jfCkQcD5wqTh/Zb2h6vQQa2Bex8eV/usjC\nTF65IFg6p4+WWNvp0Gj7fODUGOvbKmoj6ZikHYtPPnqIxbUm9abPWClJLGO6XoRpgIgED50YY3mj\niZT9PaWIqbEMzU5AEAri67wDQTlZFDMOa9st4li1I6P42uuLJfiRwI4MXC/iww9Ps131yKQtRCxZ\nXm8gpyTPn98ECSfmi4RCvOX50PJ6g0bbR9M0dE1DiJhay2N+KjvwNuy/n2+18ggjwffPbtJo+2RS\nNrqmEYmYla3Wj4SghhjiXsBbVwG8SzA1ksHQdY4fKhD0TFL9IObsYhVD15gezZDPOqxsdTh1uMji\neoOzSxX+8YUVzlzahZ5Z6e2Q0KnDRZIJA9GrSt7sCv3U4SK2qfH6cpXNcodq0+Piao0wujaL6l8p\nP3hs9AZyrDW93uzMx9A1gjBmYTqP50VoaBSzSeanc9y3MMJjD07x+d98hJRj9w5mDdeLyKUTTI9l\nWJgp8NEPzPLUk+/jZx+a5vB0jsPTeWbGMoyXUhw7VGBuIsvPPTK7L4rktctl2m6g4kq8CD+6Rk6m\nDkbPmy+OVYuv60ccni7wB08+yEPHxnC9iEzKptr0EUK9b+p1qfe7/xivXS7ve19uhjASrO226Poh\ntaZHEMWk0xYfODnGZz9ydPD6gcH93ykGLug9ZWW57hLFKlpkb57YEEMM8fbwnq+gai2fIBJU6h7p\npM3xuQJLa00abZ+dapepPfOIvYe/oSsn8HcyhdcylYfe2k5n4JYehPGbDu5VxeUNWmV7f/7M8yu0\ne0u0Pzi3RS5jMzmS7kW7X3suowWHy2saQkjSKYvTR0ZYmM6zuNbk0x8+wuJaEz8MubxS59J6g+Mz\nOU4/OIlpKGJ65eIOX3/2CnbCZH2nqZRykVQtxd5jOAlTXRREElCPFYmY7722yQNHSiyu1+l6Ebu1\nrloEFrFaoO61zCIR39ZM73qhSTalgiDRYuI4xtZ1fv5Dh/fNCdtuSLnu7rO7uhWufwzXF0yOpqm1\nfRotn4Rp8ODx0ZsKWIYYYog7x3ueoIIgIpeyKGYcCrkEl1cbBKGg64dcuFojjmFuKsupw8V9FdJo\nIcluzR3MaG4VibGXiO40hdc0DMZLqUG7cG9kxkE4Opvjy/90sefqLWl1Au5fsBGxpN0JlEODFw0S\ndV0votkOuLrZ4pWLO728pQZBJPnp+ycp191eLpXH2aUqoIjgkz9ziD/9Dy+xvtNGiJjzSxXmJsuc\nmi/x//7jBeotZZ0k4hiJRhTHGKaGhrJu0jQwdI1CzqHTDdB1jYSlnCpcP+Tf/f3rdLyQ0YLDbr1L\npe4SypiUbVJueMyOZ1Du5QfP9PrvbySU43wQSUQcs7LRxI8EhaxDMhEhpWR6PMviWnOQ79V2Qy6v\n1gf7VLeyu4IbZ46NlkcmbWObBvcdLrFTdTk2Wxjka90Mt/pOvBf2s4YY4m7jPU9QbS9kdafFb3/q\nfv7137xCremRcpSizTQ06m2POamqKLXkujrwdHvo+Cgn5oq3NagH9hyaMRdX1aLp9UF41+N2JMN7\nDy8vCMmk7J5lUYL7FhKUckmOzOSJhODM5TK1to/rh4OZUSpp0a2E/M1/vcCDx0ZZXG/Q7Ph84NQ4\nE6U0G+W2ipXfQwT/8b9epNrwkEAkJH4QsVnuYJsG1aaHFwo0XUMKjVjGvTRCiUSSckyKWYeTc0V0\nHdZ32zTaAbquk7ANNnY7NDo+9WbAhStVvFAQRKrySadsSlln8L7f7H3uE8ZOtUul4XJivsDl1QZe\noAg5EjGjhSS2ZTBW3O9k3iflW9ld7cX1M8dM2qbdS/8Fjfmp7G2R082qwaEzxBBDHIz3PEGJCDw/\n5ktPv0EyaUFNpbIWss7A2y6IJK9c3OX5s5u4gQAJ7U7Ir3zi+D5SOeiqHdSB8sRjhwdLoNvVDuWG\ni442SJn94lfP8QdPvu+GQ+fN2oJ7Dy8Rx7x6cRfD0NB1nVrLo5BLDG57cr7IxdU6cSyxTZ1mJAFV\nJURRjGHonLlUxrZ02t2Ql97Y4QOnJnAso+f4rdqE/b0w2ctI8kOBiJUf38p2C0MHxzaVSaypEUYa\ntqFh6hpCQiGb4NHTE9imQduLaHVDWp2ATMqi2Qop5ByEkFSaHq4XIVFKQAkqql5j4LBh6LBRvqZi\n7Fe6fcLQdY0wEpy5sEsoYpyExVghyW6ti2nqHDtUIJO0BqR/6nCRb720ipSSGA60u3ozGLpalu6r\nLW/n4uVHldo8xL2NoZPEuxCtToBhaEyOpGi0/V6AXjhYAhVxzNefW6bthoNwO3B45vnVwZXxQVft\n9y0ot4m2q1pWXS9itODQbAfsVrs4CYtcyiYGvFtcpd9KMtxvSdWa6nlrukYYSRI2uH7I869tkkyY\n/OC8xQNHRjh+qMjyWoPN3Ta2pRMJwW7VZWYiQ73lo2sayYSpqgtTqe9+5RPHefq5Kypd9moNNDgy\nnWN1q0UQRL28pN5sSUpCAVqo2mhRFFPIJNSelaENZlDphE3TDVheb2DbyrS244ZMj2fodMNedImG\nrqvbG7rWc6eAMIwZKyapXfZYXKuTS9nouj5wO9+LUi7BuaUyUqrZUKXhMTueJZ9N4JgGDyyM3BAI\n+bufeYD/52/PsVPrks8kSNpvLma5cRn49iLghxjiR4mhk8S7DImEzngxRS6rBucn54tsVbq4bsjc\ndA7QaHf8wewGYLuqZNqaDn/3bXHDjpOuK0lxue4yWkhy8WoNXdPww4g3rlbJZ2z8MMYPPdK9rKW9\ngXk322M66Eq8X60JIWl1fCpNj8mRFCO5FBdW69RbHoamFnO3Kh38MOKlCzvKJVwHIUAjot70egSa\nJJYS2zI4dqjAkZk8Kcfmsx85ytPPXmEkn2S8lMTQdX7mgQleeH0HwwhUMm8Uk0nZzE/l0NDIpCw+\neHoS0Di7VNk3RzMMnUpdmcgGgcCylMXRaD5Fx23i+iFIiW3pIDUiIZBSU36DRVXdlusufiAwcwYT\npdRAQLKXMMp1j7FiikI6wevLFTSg27v4mJnM8vzZLfLZBCKO+dZLazz+0DQn5wtMjKYIRU9ueADx\n7cX1Ve7R2dyglXurKI+9uFUrd+gMMcTdwtBJ4l2GpKXz0w+M8+LrO2TSiqQWpnP7YjT6s5tGJ6De\nUnJny9SZKKUOlJf3B/silmxXu6DBsbk8r1woE4YCz4+YHc8MTGf7baajszlevrDT8wC0APbtMV0/\newgjwfJGk3Y3wDR1Kk0PPxBs7HbY3O2iaZJYSDRDOYt33ZCvP3sFN1AHb9xTUAskpZzDw6fybO20\nSaWsQW7SXuHHkZk8zW4wIJpGO+TobIFG26fe8oi7IYahDRZ1P3h6kvefnCCMhJKZ7zlgP/noIZbX\nGz0XcoGpg20brG23SCUNcqkUjW5IuxPgBxFxrOEkTHJpm2pNWU3dzApxL2EsrTco5BIsrzdIp0yC\npiCKYo4dylOue/RrusurdYJQ4L0Q8eyZdTJpm6nRNMBtKSf3il/6KsB+tXlirvimc6M3E9YMnSGG\nGOJGvOcJquMrA9XjswXanZDHH5oetGf6B1LXC3j2zAb5TII4lug6PHxyDEPXB6q6/Ve5Gg8dH+PE\noSIrWy1qbQ/bNDg0kSWMBKWcw/G5IiKWA8ukuck0X/zq+UGVk7BNitnEHg/A9L7ZQ9cL1O2rHSzT\noFxT4X2WaSCEkk8LSc9GSLXgTENDkxJdU8uxfcRCDv49kk+iGxoPLJR4+MT4Dcm855fLrGy3lQ+g\noTE7kVWZUBJACSG2qx1MQ+fiSo3TR0dvCEwEjXNLVXbr3R75SAIp6W43lcNFR2d6LM2v/YtTbJY7\nnFtUru3ZtJqneUGXWtPj2FyBesunkLHZLHdIWAZHZ3PANcI4dbjIX37lVYJQkE7aBJEkl7Ep173B\nbK3cq+T67VsvFPh1b0BQIo5ZWm8M3oNbkUO/kq41vcF3Q+2h6bdNcnf6uyGGuFfxnicoKVHJsO2A\nYi7BylYL09D3+ec9/dwVMmkbP1Tts9FCCRFr++TlN7vKPX10ZDCbGik4NDtJFmbygEYmaQ4sk/7y\nK6+yst2i60V0vZBkwlKRF8b+9lIkYl6+sM3Xvr1E0w3QNY1mR8XCu4ESLmiaBprE1Hp2RBoYOqST\nFo5tKnm8uMZQEo22G/LNH65QyDmkHZOtSocHjtw4RxGxZG27SSgkmoTljQZjxRS6rmEaGpOjWRKW\nPhCX9A/lvnFr/71440qFzUqH6bGMOtBbLkjIplT8eq3ps7nb4cmfO86hiQxf+sbrdNyQpGMyXkrx\ngVNjHD9U4pc/eoQvPf0GEuX88fRzV25IE378oWn8F9bQdY0HjoxQrnscmy3wyUcP8fRzV6jU1b5Y\nv9WqJPlhb6E6Znm9ATPwyqVgqKAbYoifILz3CQrJVqXDSN7h4kqXUs4ZOJrvnS3ZpsHUaBoRS+47\nXBpInA/acdqL64nrlz96hG++uAHAJx89BMDTz15hq9IFVIpuue7iBRGFTIJuLDm1kEDEEtvUuLiq\n4uC3a128QMndi9kEhqER78Q03QAhZK9i0rGTOpZuMDme5hcfP8w3vneVWtMj7BGUoSvLoisbTVKO\nQaPt0+4GjBWTPPP8Kp/96NHBa3njSo3NcpeEbdLpKexiGbNbc5mdyDCSS2IYGhOlfuWxf2fr+kXn\nOJZ0vRA/iImiuBdiKPB6RPvq4i4//9g8SxtNUo5Fqx3gB4IPnp7k0x8+ShgJ/t3fv85WpcuxuTy2\naRyocDt9dHQQrwHsk31/9iNHObtYHkSr9C8c+pZHSz1yul2H834lXcw57NRcun7aM94AACAASURB\nVG5A1ItBGc6Nhhji7uI9T1BhBB035OxihenxDBO9pdhbWReZxs3bLQctVF4/n+gflF/77jJCCM4t\nVQe2OwnLIGHpOLbBkdmCUhJet8fUaPuAhpQqNiPlmEyNZDi9UOIfnl/Bi2IShkazG6ILjVzaxNJ1\nTs2X+OH5bXKZBKLhEsVKRi1i5fDghwIrhgDJdtUlCKJ96b19dNwQ148IhZrnaJrGdqVLsxNwdDpP\nEIlBJtPcZHpwH5G4ZsB3ZDbP+m6HzXJnoAD028qSSdf03sJuxNPPLrO23WYsn2IsnwQ07jtcIowE\nX/j3L1Cuq8DEnVqXxx6awtAPjm2/1Xzn/ScnOH109Ibf9z/jVy4Ft/FN2v9YZxcrNFsBfuQojYW8\n9YL1EEMMced4zxMUwMRIGs+PcL2Qct1jtKBcy5fWG8xNZvdFXNxKQbV3QH7QAu71+ywrmy1Wd5o4\ntonfW5o1HJN8NsEHTo1jmyYilhyZyfPgsVFevrDNxZUaURSrKgM1T5koJRktOGyUO4yVUoShIsBI\nQtIy0DWNWsvj//irH2LqKkOp40YQhmi6hm3oxIYgCCRCkz3xgeS1pTIrO21GCs5gl2tqMcWZSzsE\ngSCWEgmEUUxLBHhBRDCW5cpagyiWzE3n+Ff/8WUOT+WoNn0sU6OYs9koewBMlVJEsaDrRliGRhQJ\nolhSyKn8pziWfP+1LfRezpTV21syDYNvfO8q5bqr5kaGThAKLq80ePjk2IGfz5vNcG72+7eioLNM\nA9PQKeadfU70w92lIYa4u7gnCErXNCQS14vYLLfZ7gkPjs5CcynANrVezMWt5cJ7bXKCUNBxQ/70\nSy/yx7/xU6Qcm0ioHam+r16joxSBhq4zWkjR7gacnCspz73oIENZVWpomopd94KIR05OsDCT4+xS\nlYlSarDLFIQCxzIYKSSpNj2VFwVouk4+k2ByNMlmWWIZGmnHpNqUZNM6QSQxNMilbSoNDz9UTt/H\nDhVYXGty3+ESa9stFnskpFp0EbZjYRg6L1/aIZ+1MXWd3XMd0imbM5fKJGyDOI5ZXpdMj2XQdUUs\nYaAWjONYQ0qwLQMnocIIvSBiZjRLyw0Ioxg/iFjZbPLAQolXL+7ScRXB6ppGyjGZGc/c9fnQUEE3\nxBA/ubgnCKrR9tA0jQ+9b5JOV5mE5lL2YO4QRPKWbb29qNQ9glBQbXpEIma71uWLXz3H737mAS6u\n1Kg0XCIRs1vvMl5MEsXxwPw0n03w2PumDmw3gZopnZgvUmuqCsRxLM5dqXDuSoWJkTQZx+LkfJHt\napekbbC606bVCXC9CBHHaLqqNGpNj4RlMF5wyKQTdN2Q2SmbKIwRQuL6IZ1uyFgpha6pIMJK3es9\nB4OpsSxTYxl2qi5nLu2gYWKbhmrhSZX1lEyY+EFEvR0wWkipPTI/QkowexL9MIrwwljtYmlKCj/S\ni5TXgFLGYbyUZNpMs13tsrLdZCxh8u2XN3BDgWXqhELFdzhpm9/61H3vCHnsra767unXfzbXY7i7\nNMRPIoZOEu9CFHMOpqmzstni5HyRMIpptH1MU+st0N56UbMPZZOzRscNiXrO254fcXmtzt9/d5kg\nkty3UBpkOz3+vhmWNhqsbSurntmJzECWvZcM91oo2aZSDwZhzA/ObalFVuD15Sofe+QQSdvk8JTa\n4/rbby3yzRfXkL1l0X6SrWUa6BpMj+eYm8iyXe2yWW4zMZpiab1OEMZkkhbNdkAhq6TdCevaAds/\neEcKDoencmz0JN5Abz6mYJk6UsZIGSPimCCMMY1rbuRtN2K8kKTS9EFKko7JWFEtGYcippBLcGWz\nycJMXi3+Omo3qdxT3Z2YL9LqhMSx5LMfPfKmjuNvF3fiiTesvIb4ScTQSeJdhoQBM2MZ1nfb1HyP\n9Z02HS8kkjGb5Q67NZeHjo/e1tWvssm5nz/90otsVDvUGq5y8hYx//jDFR49PYVpXtsuNQ2dJz92\n7AYHAjg4Jl7EMYtrDXIpmyubDaKexZCQoEvJ5k6Lh09MMD2W4sLVOo5tcmw2z0a5Qygk7a6PYxmc\nmC8N0nABijmbxY2I7eUKoNpsUQz5jIVp6jimwSP3j3F2sYJp6Dzx2GEuXK3z7JkNFqbzBJGKnJ8Z\ny7JZbmMYGn4QYxo6JxdKbO92qLRcDB3abshOrcNIwWG04LBZbpNOmoRhTBjFlLIJctlr1evCTJ5c\nylKKwVh54vUXoTVN4/icyth6+IQKAXwnHcHv1BPvVnOvoTv5ED8ODJ0k3mWIJDx/dhPTNMg4JjtV\nlwePjWCaOuW6RxxLTszd/gGScmz++Dd+iv/133yPpu5j22pgnkxavLpYRghJwtKxLIOLqzVOHx3h\n1OEiZxcrfPGr5wcOEt96aZXHH5oGtN4Vu2RprU614Slvu4TFTtglCOL+fiwXrtaYGc8OkmeLOYd6\nJ+D9J8cp1z1WtprMTmQZL6V7FkISNxBcvFpHkyixQRQzXkiioSqghGlwaDLLV7+zPEizvbBS5ehM\nnnw2gdHbLepHSjz15Gm+9PQbeKEgl0mwtdOmmLcoNzoYhsF4ycbtRZw8sDDCdtWl2fZpioCEYbBd\n89iueQMfQ4CN3Q6ZtM3qtlp6PjpbYKKU4tB4loXp/GCx+t3iCP6T9FyGGOLdjPc8QYkYBBCKa9ET\n5brLzHiWiVKqZ1p6ZwdHyrF57KFp/vGHK+i6Ml8t110yKQshJKGmdnOCMObsYpnF9QbLG02ubDQw\nDZ1U0iKKBFvVLrapMzmWYWWzSb3l0XFDVrdbTJSSLG80iAFNKpm2RPKDc1tkMza+L9B7xFWue4yX\nUsxOZDhxqDhYRAYG/nojeYftnsmt60WkHJOEZTA3laPWS7MFqDU9DF1nZavde/9UVhTA3GSGlGPz\n+EPTXF6p89zZTUxD4+pWk7YXMjeZw9R1dE3D0A0c2+TkfJGltQaappF0TEp5h2rTY7vaZaKUpt0J\nB9lKJ+YLXFyp8fpShfuOjNBo+3ztO4tcXqmxMJtjfac7sI+Cu+8IfrfmSkN38iGGuDt4zxPUXhiG\nThxLXD8aLJnuPYTuxMT15z80z5lLu7h+RMcN0Q2NQxPZwWFebfqMFpKsbLVpdAIuXa1Q74SAxDJ0\nErZJMmEipZK7jxQcnIRFq6NynFpuwOx4hkrTJYqUC4KmgRAxO5UuhqE85jIpm4XpHMcPFQ9seYEi\nttGCQ63lU8o52JbB1EiaR09P8cbVg3fB5iazXFytcebS7iCq41svrvOdVzbIZWwurdRotH3GiimS\njkWzG7BV6ZBPJ8ikLOYmVQjka4u7NDs+XS9E00BD4/hsYU+GVczZpQoijrm82qDRUjtJl1fr7Na6\nhKHgwooy452dyAAa9++pvvZiL5kWc3cWoQGqZffEY4d55vlVQC1aD6ueIYb48eGeISgN5apgmga/\n8NgCjq1e+s1mQbcycQVVRX3+Nx/hmedX2Si3sW0D29RptINBUGAyYTA9lua//fAq9XZIEAniGELj\n2kKrH0Rouka9HZBxTMZKKVwvxDJ1PvjgJD98fZtmK0A3dGV7KmOCSJC2bFKOha5rLEznbrg637uz\nVW16VBseRw8VcN1o4EcIcHWrSTGXYLfeHbQNkwmDk/MFltcb6GiMF1NUmx6X1hSZ9Q9/5RQRKaKN\npXp9UhntnpwvEEaC15ereGFEs6McLGzLoNnx+dVPnsAyDc4ulmm0PLp+RBAKTEMjEpL17ZZSDWoq\nh8rQdXaqLqBcNEo5h+nRFJFQqru5yTRf/qcGrq8UTPWWz6984vgdfUf6tleDRevvLN1RYGUfQ4Xf\nEEPcHdwTBKUBTkIn7Vgcmc7zwJHSDYqwvW2Zct2/qYnrXqQcm89+9Og+cjt2qECj7fXmJwUiIfB9\ntXRrW2pXqP+s2m6Apuk4pk7XDbFNHSehkc8kSDs26YTNr/2Lk/zD91dI2CrJttr0VBVkqgiKkULy\nwBblXgunfiz5aC7JE7+wP/m1r0R76NgooGEaOkdnczz93BWubDYJIsHajsqW0pRlAmEUM5pPsl3t\nIoRgs+JimDojWZUxdWQ6P7AR8oIIEUHCUtViLGIWZvJcuFofOKBn0jbVpsdILkm947NT7eCFEUJI\nTFO1DNF6xrgxdN2QYsbh/HKV9R21d2aZGnNTOepNn0bbJ5O0uHC1xvtPTtz292Tvd0DEMWcuVVjb\n6TBeSt3RHGmo8BtiiLuDnwiCeuGFF/jCF77A8vIyxWKRz33uc/zqr/7qXbv/heksuUyCXDpBIZe4\nwXD0VhBxzE7VZWm9cUvVWD++IxIxr1+psLTRZGmjiW0YHJkpcG65jJQapmmSSVpEQtDs9EkLnISB\nZemMFZOU6y5XN5s02wGhGOGPf/2neOb5VS6t1bhvocTl1QZBqFzD90Zm3AyGrjNeSnFkJn9gou/1\nxPva5TKuLwaLwR03JIwE+UwCiaTR8vGCkKlSGlPXMC0d2zSwDINYSqpNv/feqQDBIIx72UtqBqhm\nXK09vn1qFray0USImFIuia7ptLo+jm3Q9QSaBral4wWCQxM5QLKx26HeCkgnLTw/ZGokw1a1QyQk\nXT/k2TMbA1n/naJcV/tses9XcO9Fyu0o9Ibu5EMM8fbxYyeoRqPBH/7hH/Inf/InfOpTn+L8+fP8\nzu/8DnNzc3zoQx962/evAY89OEUguOXQem9bpphLUG955DIJXr9SBQkjBYe/+/biDaqxtqsWf52X\nVFLrhat1zi5WiERM14vQNDg8lWN2Ikej5RNFqsp65L5x/vMzl1TgoK6jSQ1dQrnhqorF1EknBWcu\nlTkxV9qX1dRf1j02WxiYosKNM7S322a6PuBxZjLLhatVGp2AUMSUdZdC1kHTJIFUjh1wbafKC6Le\nAQ9CCCWUsE0aLY+krbNV7qDr/QxejfuPlFjeaLK+26aYS5DL2EghmZu0ySQt5cwRS8ZLSS6tqGh7\nTVOPa5o6F1ZqSKkySILQIJm07kicsPc7IGJVvY1eFwc/VOjdPoZS+yHeLn7sBLW5ucnHP/5xPvWp\nTwFw//338+ijj/LSSy/dFYKSQLnhk8uohVQRx2xXuyyt27cMjfuVTxznmedXqeU8JkopAK5sNnn6\n2Ss88fjhfbZHYRQjpeSLXz3P9FiaMBLUWuowlbGk2fX5zIcXeOb7KySTFtl0gpde36WQTgzsfNRB\nqFGtuwRhhNb7aCIRs7LV4onHD+/Lozo8lbuBnPb6BCZeMviNJ06ystUB7uyAuD77qh/w+Mzzqyzq\nqtUnY0kQS7aqHQxdI+1YdN2QmYkMv/HESSxTqfh+5vQUL5zbwjBUlbW628Y0NL69UqXjCeJYmdFO\njKSZGk2SsM2eIEQjmVBhjw8dG8M0dCIRc3G1RhDGZFO22uFKmMRSEkQx2ZRF14twEiYpxxw4ctwu\n9udajQwea68l1VChd3sYEvkQdwM/doI6deoUX/jCFwb/bjQavPDCC/zSL/3SXXuMMIyxLR3XjwYp\nqLv1Dv/nX/2A00dG+fnH5kk59g1tmbnJLJfWamxXu1QbytpIQ+Pvvr3I0ZkC5bpLGClHiRjlFg7K\nBSISMVGkxOFhGPPKxTJz0zkMXePC1RodL2Sz3CaK1GFbrXtqR6oXDOgFEatbLcaKSWbGU2861zjI\nJ/Bf/c3L/OLjRwaCkNvFzR5rbjLLP714tddehEhIIiFJ2sqZ3DQNHNPkS0+/wQcfnERK2Nppk8/Y\n6Lp6/1sdn0vrDWIhCaOIhG2RTJiM5pOIWGN2LEPQUx9qqFYbMPhcTh8dUQRypMTMeIqNchcpYWMn\nQjd1pBfh+hHJhIFj3XnVuPc70H+sg97vIW6NIZH/eDC0OnoH0Wq1eOqppzh9+jQ/93M/d1fu07E1\nCvnEIP12JJ+kmLP53mtbhKFgu9rlzOVd/qdfe/++agPg4mqNct1lp9oliGJmxzOMl5KDxVrHMtTg\nH7X0WsolQGoUMgnKDRcplSHsZlndb8eNyKZs3CBkbbuNEDGhkERNn6Rj0HZDpsbSRLGk0fYRmnIT\nX1xr8NDx8Teda+zzCezZOX3p6+f5wKmJO76CPciO6eJqjTiWSCnxw3iQMBGKmHwygZSS5c0m+YzN\nxRV1IZB0TGpNn0zK7qX/SgzoLR8r5UM6afVafbAwkyMUqrUZiRjT1AcLz3ujTYCBp+HSeoNS3mF5\nvYGha3TckGzK5nc/88DbIpWD3u+hQm+In2QMrY7eIayurvLUU08xPz/Pn/3Zn93239VqNer1+r6f\nbW1tDf7bMmC3pq7C+3OcS71IC03T0HWNrhfyr//mFRZmCwADJwXXj9BQbbYgjOh41z5801Azpy9+\n9Tx+KCjlEixtNEg7FpWmatPZpoEOBFHMTqWDF0la3YCOF6rWlq5h9E5509BVLIcvSCYMwsgim7Y5\nPJXHDQRPP3uFIzN5Th0uEkZi365OyrFv8AlUC8hq76tSdynX3UF7sj9Du5Pq4I0rqt31yH2TfPeV\ndXbrLlovWl6DnlVR3COIiFZvXlapu7i+UHtQuoYQktFCknrLx7YMUgkDw9Ao5hIkEwanjypCWNvp\nDFzhVW7Utdd/0HPtz+b6PoifeGTult59b3U+cicKvTASnF0ss7LVZm4yOyDZewFDIn/ncKszb2h1\n9A7g3Llz/P7v/z6f/exn+fznP39Hf/vXf/3X/Pmf//lNf99yJatbTS6u1vj0zy5wYaWqco6kxDB0\nUo412M/Z245Y2WqzW+2yU3N7S6EatYbH+m6HjGMSCYFlGvzBkw/yxpUal1Zr+KFgfadNo+0TRaqF\npetAFOMFGmnHwNSViKDvIq5pSkSgazpHZwp4fkQYxyQdC9tUFdpLF7aZHcvS7Aa8cmmbF9/YIQxj\nUo7JSxe2+fxvPkLKsQc+gds1ld7rBQInYXJ1q4kQknrLxwsjPv2zC/v2fe5UQj05kqbjhSQdi1io\nSs0PBIah0eqGeL7AsgxCERPFKn/KNHWyKRtNlzi2yanDGSxdZ2Y8jWFoPUuj0V7WksF4L1hSxDEX\nV2o3JCED+/bWltcbLMzke87qxmDP6yC83fnI7Sj0wkjwlW9eHiw6P39O5+LKKE9+/Pg9QVJDqf07\nhzc7895L+LETVLlc5nOf+xy/93u/x+c+97k7/vtf//Vf5xd/8Rf3/Wxra4vf/u3fBkDT1EG9stni\nwtUan/3IUQ5PZfkP37iArkO7GxDHkvnp/L77mJvMcubSDkIoEsmmbGxTo1zrMnlslLNLVRbXG3z2\nI0d58NgoS+sNOt0QPxQ9zzuBFjMQFOi6amkVckkKmQSr2y0kUG2oK/5SPkHTDTh9ZITjh4o8+8oG\nixt1tisdYimpOR4acHaxQhgJEgmzN/NiEN3e9wn84lfP4/ohlaZHxwtpdwL0XhTHmUu7WIZ+w3yg\nbxQL+w+Ta07r8SDYMZdOkHIsijmHrhvQDQTphEkyYdDohNiGRjAgXw0pdXLpBOmkxWghyYlDxYFT\nRcePKNddNnY7nJxXj7v36nun6oLkwCTkvTL1hZk8xYxzyyqrjx/FfOSNK7VeG1flgYlYsrrTvqfm\nMEOp/TuDNzvz3kv4sRPUl7/8ZWq1Gn/xF3/BX/zFXwx+/lu/9Vv80R/90Zv+fbFYpFjc3zqwLOva\nP6SKMF/ebPDsGdVCeuS+KQ5P5fi//uoFLFPnvoUR1rdbA+eI/hV4JGK+9PXzxLGapfhBxFghSa23\n51PMJQYHztxkBsNUoXxhFKvwwF4UuG0bCBErPz0pyaVt/venPsT//f+9im3rhL15zrHZPJvlLo5t\nIjWVI6XpGsSwXVHVnBuECCHRdZ0QScfafxCnHHtQ1UVC8NyZTc5fqZBJ2eiaRhTFbJQ7A8FCPpOg\nkHN49swG+V70xkFVCjAIdjy9R6BQaWj4oYqyz6RsUkkbS9fwQoFtG3Q6isCchKpQ5yYyfPLRQzzz\n/Cormy1qLSU+aXcD/pe//B7/wydP8PCJ8cHVd98G6iBro70wdH2QTDzEEO9lvOmZ9x7Cj52gnnrq\nKZ566ql37P4l4IcRna422Is5dbjIf/qHS+iGRsI0We2R0/VX4A+fGOPS6iTnlqoATJZy7NZdpFQE\ntVvv9hwY1MD+gw9M8c8vruIHEaalITUNTYJlaNimiWWoAL6JUopvv7zO3HSObNNmq9JFSsmrl8rY\nlkG16dJy1QJqJmWzvtsmCCISCVPtEXUC2m6AaWiYrs7Hfmp632vef+WqsV5uI2JJLCW6rhGEEReu\nVomiGF1vk0vbPHR87IaKov/fIAeznRNzJd5/cpzTR0c5u1jhO6+sE8eS3ZqL60eMlpIYpsGDc0Wq\nTZ/pkTQ/ff8EO1WPucksR2ayfPGr59mqdKg2XbxAkV/HDTHaPl/6xussbTR58mPHePCYikH5yj9f\nYnVHmdceGs/ckFsFdzbj+FHMR04dLnL+SqXnpaiiSfY+9yGGGOLN8WMnqB8FYgmlYpJ6r/J544qa\nF2maNpgF1ZoeP3Pf5MAp4LXLSkUGGqVcEoAbLuJ7knBQpPAvP3ECxzJ54cI2OvRcFTziGExDo9Jw\n8YKI75/bJIpiUkmbk/MFKg2PasPFD2OyKRgrJPFDQdCTx0upKqlM0qJc91CdOA3bNPjQ+6ZZ2erw\n4LHkvqe2VwRw+ugI67sdGi0f09AJI0kp5+D6AimVu0O16TM1qr4OIo5ZWm8AEESC5fXGYNdLuTOM\n9GZFOsWcQzGXYKfqUmt5TI2kSPfSiqdGTUQsySQTfOijM4SR4C+/8ior2y3COB64NWia1tt7MokF\nrG1f1wrTNLR+qKR27f1+q8aud3M+cjOxhWUaPPmxY5w4VLgnRRJDDHE3cE8QlGloNFsB1oTymfvG\n965QaXbx/JBEb0eovzOzd4C+Xe1QrruMFZLouk4UxRSzDh1XOW7PT+cHcxtQh9KnP3KEUAhWd9pI\nqdRthqGzXe0SCYmMIjZ2O0yPZei6IeW6x8JMnkrdJWEb2JZBrUckI9kEVzbrGLrG9HgW1xUYukSi\nk0yYZHupuNfjehGAoUtsS6eQSyAlrG23SNgGmaRFLCXFrEPCMggiwW7VZbPc5oFjoxi6xtnLZUxD\nQ9d1bMsgk77RncHQdaZG04yXUqQdg+WNVu83kjiGS446lCMR44UCISWbOx2iOEaN0SS2KfFDwWhi\n/wHeVw9OjaYBCMJ4UAXvFXrciX1V/7N6u+3ANxNbWKbB+09O3JEf4BBDDHEN9wRBIUFHUszb/O23\nFnn1cpmdqnLvlhKOHSoOdmb6PnT9dtdOtUu7G5JOWghiWi0f0zBwEgZnL5fJ9Xz19nm+9a74NQ1m\nJrKsbDYJophISGIJhBE71S6nj45yfFa1fH7mgUkWe5VKJGImSklWd5SvnKZBraHiO6otA6IIz4/w\nfYFttTg6mxu81DBSkvQrm82esEBno9xBQ2N6NIOIY6pNj64bkLBNTENnbiLDz39oni89/Qb1todu\naCyvNzg5X2R6LEOj7TOSTzJacPj/23vzILuu6v73c8Y79u2+PUitVmuWLNmWbYxHLBubEIMhMSHg\nBN575lVCEYfhEYpMrlQ9QiqQVJliSoWAXzkhBFMxFI5/NhgTfraJB+QBC9uyJFtTT+p5uPNw5rPf\nH/veq27NkoduSedT5Srrqu/tdY669zp77e/6riAUrd3VkXZK0ivPZ65kMZ2rtXZ+I9NlDo6WqDeU\nf5WKgx8EzO8ndH1IxhU8L6B/+bFLYfN9Ef0gWPRG0KgZNSLizeW8SFChgM6OBFM5i1LVQQjoySap\nWS5tKZMb3rbymD0zyoLPEMzk6qQS8jBytmChqgpPvDjGSwdm2T9a5EM3bWT3QI7R6QqaKnt7Xh3K\nM1uUQwIbfa34vhRLxEyV1b1tHJqqoLT6eOSU35iu4LoBiqoQ+jLx1B0PTQHTlKUzQ5NJZ2Cs3CpN\nPvTUACOTFWbydYoVh81rsggBxWrT9kfQ2Rbnis09GLreKj3tHS7Q3hbHC0KmcnU8X85W6skmSJg6\n7W0xXD9gz8E5lncl2X+oQCKmt+yU/CBgaLzMockyCrTEHa4XEgYuuwbmMDSVRFLH9gJ8XzYhNwzS\n0XVImDoXrM5ywarDyal5XlS1PNn82/BFHN1ZJp0y0dSoZBYR0eRcc5I4sTTqHEAB0kmDmuVRrBz2\nZlMVhVTCJJuJLyjTbVmbJRHTpI8esKwrydq+DAlTI5Uw8PywZWXkegFqo/l0bLrK7oEc23dOMJWr\nMTBW4tmdE5SrDomYhqbJWBTkYpxti0MIuwdzzJUtXtk/y9RcnWwmRv/yNDPFOnPFOuWai+X42E5A\nts2kPR1DUcDQZMlNUQ+n0eYT/bLOBKah4XoBU7k65aqD54fsGZxjz2COfMUmCAXv37aWyzcvW1AW\ny2ZiOK5Pte7i+yHphMHHP3ARW9d3Mt5IpPsPFdk7kmdkqsS9j+yVwx3HSwxMlHhtOM/odIXAD6k7\nsnnXDwRCyCTvOgHJmN5K1qIxLVgRoKgqqaTJ7sEcDz01gOcHrbMmy/ZRUbhgTQemrpFOmVRrHkEo\nFnjlvZXM/1lZrBgiIuYTOUmcZQigbvtoisKG/g6WdybZPZhboKza0J9piCKkOeqGle2Awtb1hw1D\nhYDx2SoCBcf1cNyAREwnGT8s7zw0VSGR0ClXXVw/wPUChIBsW4z2tCIX/TAkbkjJ+nTRoqM9JgcD\nagrFqk0ipnHxui6GxksoqoLjys/A0Ag8SCXMBROBY/rRi2LThXwmb5GM6/RkE8zk68yVpKNGZ1sM\n1xfsHphrzZLa0J9h18AsL7zanKCrEDNV3n/dWpJxE13TSCYNZooWYcP9wnJ8bE+6WlhOgNoQO6gq\n+KFAU5DlTgU0TSbypKGhKtCRNshX5C+Tpshpx+v62jB1jSAMGZms8Mj2YW6+ZhWPPDNMzZYDHw+O\nytKjpqpsu6wPEAyNlwnCkJ8+PcC6vo63TIwQNaNGLDUiJ4mzENcNUNImX5FqTQAAIABJREFU3R0x\nPnjjBi5c29lSVm1e08EjzwwvKCFdsCZLOmHw/uvWAoJDU1Xiukq+bBMEITXLo6QodKQNhBComoKu\nKwRhQL5k0542KVZcNFUlbqqomorqBsRMjTgaXe0J+pe3MVuoMzhWxvNDNFWlMxPH9gJ2vDaNqip0\nZeLMlWxQoKstRtX2SMR1tqztpFJzSSdNbrh8ZWtRPNKFfM2KNtb0ZnjixTEGRgv4foCqqIzPVunu\nSDR6n+Q4iVeHc0znawSNGUhtSZNMOsbAWJkta7MMjpcIfIHj+jiuj6Gr6JpKd0eCIAiZztcoV106\nMjFs28fQFeJxA11VCEPBXMHCTOjEY3JnF4SCmCHP6jRdZV1vBkPXCcKQfSMFmdwRjE5XSKeM1mwq\n15PNu2tWyH+7nz49yMsH5pjOSb/D5V2pkzo2vJFjIM7mZtRoHEbEUue8SFDppEG2Lc6rQ0XixiCm\nYbSS038/O8zL+2el517D/aA5puG7P3m11bxaqjhsXNVBuSql6pl0jGw6RhCE7BqYYzpfx7J8hsZL\n1BwPXVfRVJV00uTKLcvRNIXJ2Tp1x6e3W47vKFTslvmqrqnkGo7pPR0J6WWnKSRjOqqq4IWClCYn\n7yoorSS6eU0Huw7OAXKRmf9Ev6E/w09/NcTYdAXbC3DckGRcwdBVRqcrrF6RaR3wj01XyZftVkNv\nEApyRRvH8/j/HniFquVxYKxIEMiJwK4XsrwzhaYKhiZL7BvK4Qu56Jm6RjwWo2a5qIrKyu402UwC\nQ5Oy/rrjU6m5+IG0PVLVxriNnpRs3i3bGI1ZTLmig1306etOHzUHa+9wgdGZKpWa0yoZVmruCR0b\nojEQkug+RJwNnBcJqrchUT40VWZipkJPZxJjj/TDK9UdqlVXig50uSMAOeZBQWkt4KmE9LTzA0F7\nKkY6YXDLO9bwrw/uZmy6iu35uF6IoSvSQFWB7vZEQ+WlcusN64GFzgyXbephfV+G53dPYbkBs4U6\npqHR252iJ0ySium8NpzH9nw8LyRm6lyysZu5ok02HW+Vv45cZJoL866Dc7heyOreDMGEIJ0QdLTF\nyGbirF/RTs1ZeJjano5RrrmtnidDV3lm5xRjsxXqto8IBcmGR6CuqVQsl9/srVEs29Rt6Ymnawox\nQ8P3BWEAFctBQeGKC5chy6Zy96qpKgJBte7Slopx49v72bymg6/e+xsADENj/0iR9pSJ4wa4vryP\nR87BEgJsJ8DzQgz95EeqkfJOEt2HiLOBcz5BKcDUXA0UBdvxiZkahbIcJW67ATFDbRxyh4SewHJ8\nfF9g2R6retsAKW/eN1qgUnex7YDpXI3e7gT7RopMFepUbZcwhCCQn5Nti5OI6VRqLqmUwW/2TTMx\nV+XjH7j4qDMLzw8Yna6yZyhHVyZO37I0IBNktj/bGvd+cKzIss7EgvHtA2PlU1pklnUmKFTkKI5s\nJs7aFRnefXU/9z6yF9sL6O5I0L88TRCEjM/UKFRtDF1jRVeC4Unp4GA3SnvNhtpUwkBXVSo1l3qz\n30pTQVFkCS8mXStQZP/TXNGmuyOBrmm8f9tavIZMHGhZS+0dLrC6L4PtB5QqDqWqQ7Y9ztsuWEa1\n5rHtsr4F50sb+jOUq3IwpOv5eL7C8s5k5NgQEXGOcM4nKAFYjgcoGLqc1BqEIbYnn74rdQ9VVXA8\ngWkodKRiKCqs7G1jeLLcaKK1qdU9LMsnFGB7AU+9NI6hKlQtlyCQir9QiEapMKDsB+iaSlAV6BmV\nQ9MVvvuTV/nTD13SSiB12+Wu7+/AcnyEEJRrLtmOBMPjJVCgULV55Jlh3n/d4QX9yOmuJ2L+mZQs\nT7r096RZvSLNfz87Qjpl4hRtqjWP379xPf/97EhrV5OMGwxPVZkp1pjO1fGbfVyhS7YthqGrtKdj\nzBUtaqovTXWR5cO4KV3YE3GjISbRCOfFfVJxgQDHDVr309RVzLYYuqYuMLF99PlRMukYF6VMSlWH\nELhy83Jufef645aqojEQkug+RJwNnPMJCmTfkaoJdE3aGnlewLKuFLqqUKwG1G1Z6hJCUKw5bFjd\nTqHskEoYZJImqZjGvuEcVUtmNQGEtZD/eWkCs+Gy0FTsgUx6uqYQCoGuaZi6lINP5qr89OlBNq3K\nsmVtlv9+doS5ouynSsYN0kmDAyMFDEMhmz5sSjswVj7mgn6yRWbhCPOA/Yeke/iTL06QK1lcuK6T\nFd0pglDwxG8mcH2ZEGq2T832aU/GmG70RAWhQFMVLtzQBYFgdZ9sDi5WEySTOlNzdRCClT0pLtnQ\nTb7k4AUhW9Z2YlnN3c/hZub54oLD1lIB5YqLH4TEG1L0mKkzV7To7ki2rmt+v1ezLLp5bSegsGn1\niQ/7l6ry7rBrvBS46Jr6psa2VO9DRMR8zosEFSL7bExDoz0dw/NDbnzbStb2tfO/njzIdL5OwpQL\nou8HbH95grDhMlGuyCfzWqMXCSBmqmiaiqYo9C9vo1xzCcMAFCmlbn5Pz5deds5cjZihUrNcKnWX\nQtXhf3YcYniqTNWSozDqtocIoS1lkiu5TOcsOtvjLUPaY6nFTmWRab5v18E53MYI+lJVls+m5mro\nuixxpmI6rh+wa2COat0FFF6xZskkTVzfRVVAUWG2UOf/fM8WOQYD+Mv/az2D4+WW1FvOdupg85oO\nBsbKx42ryZGH9SjQ3ZFAVRXyDdGI54eUKjZ+ELQW8ma/V7N0OZ2vs3ZF5pR2AWeivHszFW/Ne3Ck\nkvTNEi5E6r2Is4XzIkEBiFBOXZVD7RJsWdvFlrVZnt892VLvKQqMzdiyzyiQu4mq7WKoCn09bYwG\nFSzHR9UU4qZGKEIGx4qkkiaW6yNCiMf11kgMXZOJilBIHzonoKtdZdeBWTxfjtNwPUHMEFieLAn2\n9aTYf8gjCEMs28dImTQ9LY61sJzq8LzB8RJTuRqFslz067bHKwfnaEuZGJpCb2eS4YmiLDc2vp/j\n+dQdBSFko23ghxTLNs/tniCbSZAr2oxOV2Qj74ZuHnpqgJodsHswx8B48ZQW1yMP6zNpE7UG7W1x\nujsSlCsuKJBOma0ZXBtWysnH8/u9msq+k32/M1mc32zFW/Me5Ip2w1tRusdrqvqGCxci9V7E2cR5\nk6AUFVIp2VSbThitxWn+2PZQCGaLFq4bECpIlwg/BE2hWHHozMSYzPkgZMLJlx1ipkbCFMQNHRRp\njqopKkIJUVWVhKlhOx4o0g19Jl+nLRVDVRQ0TWHlshSBLxAIVnSnMA2drvY4ddujMxNjfX9Hw4H8\nzBaW+U/nY9NlKnWPro4EqZRJoSR3H+lEnFeH88R0eYZkGip+EBIGcvhiTFEaE3Olm8Zs0SZXclpq\nv+/+ZA/bLus7qWDjVJJDswG32UDsBwG7B/MLPhfkedb8fq9TTU5ncg/fCsVbEIaMTleoWR4Cwdh0\npaUobcb+Rux6IvXeuU1ubpZcLkdHR8c50bB79l/BKRKEMFew2LZ1RWtR8vyAgbFyy5Hg0FSVwA/Z\nsXeaMBTSqBUAQc1yGxNsFRIxk2rdQdeVRpNtwMXru5nMV0knDNJxk5HpMoYmy2d+EJIwNfxA4Poh\nYRjS0Rjh4QchK5an6V+WAkXBcnxmi3UyKZmcEjEdPwh5ZLtsJjYbC9OpLizNBcnUNVb3ZhgcL5Ew\ndUxdpVp1ScQMtIZT+4rONIl4TfYVCVA1ld7OJONztcaCJihVPdrbYvghrXEdlhtwaKrauM9ha3aU\nHxweu3685DD/HC0IQ6o1KWhpLsLNHq/56NqpnZ8cuaifzuI8/73yXKj5cxQyna8zOG6+YeWxLWuz\nPPniGIauNEa6qOi6SrXmHuWwD9GuJ+L4xGImT700RldXF93dZ/9Dx3mToACqNY+JhuPA/F96uTC6\nXHtJL0PjJVIJnXxZjrGQ6jzobDeZK7koQOAH+IFAE3Kku+sFVCyXbZeu5IJVWXRNjvXYN1LgmVcm\nOTRTJl+0ScQ1hAhx3JD2lEl3Z3KBgABkQpFDEBU5EXfXhBwPL2Rj74VrOxdMlz2dJ+tlnUlyZZtM\n2iQMBdOFOnFTpVJ3UVWFy7f0MJOrc8Dy0FToaIujqgqZpEG55hGGAlNXKJZs3EDaP2mqSq5s0dez\nktqow3O7pwgCQVvaZP9ooSULn58c5lsZvX/bWn7vnRvYPTDH9p0TjVLe4RLh8YQgJyttHmtRlxZW\nx2f+ePumxRXIScKmoWI5PvtHCi2F5Y8f30/c1NFUlZuvWXVMw+FTwdA1tl3Wh+35LO9KtWZfbbus\n75gO+69n1xOp985tVqxcjW6c2c/hUuS8SlC6Bo4bHjUt9uBoEdcLcBrNnumUSaXm0jBNAAEzeQdN\nkw4KnkJLsZdKGAShoK87zYdu2rggQWzd0M3+QwX2HSq0lHCqqtDZHqNclxNzP/6BixcsbE1X8t0D\nc/x8+zBlS1omaaqCAGbyFss6kyRiGhv6Myd9sj7S/uiyTT0tt/AV3Umef3Uay/FJxFQe2T5MqeoS\nBiGuKzB0mZRqloehqyTjBsm4jqYqzBUtYqZOIq6jNmxw54o2fsNMV7OkW8Qj24dZ3dvGwFiBfcN5\n2tMm+bJNueZSKNvYns+HbtqIrmm0t8WPuQifidrsWLslufvVjrk4HzkHLF8+/DDg+qLRYFyhqz3B\nss4EQRjyy9+MEtNl2fPFfdPc+X9fecZJauuGLgbGi0f0hr3xT8CnIqyJRBQRS4XzKkGpmrT4sV2P\nuGkQhCH7R/KMzlQxdY2ujPSVKxRt/IDWOIhQgOsFKIF0QRcCDF2hO5tEV1XSKYNtl65Y8IvcnMs0\nNlNj1bI0I6HAcn1ihoauaXR3JGhvi7dGZcx/X1NCPV2oY7uykTYIBdm2GMm4TiZpcvM1q07aqNtc\naKSoQEremwuO5wfsG55rzLdSCUMYmSyDIp0wQiFkkg5CQgG+76MokEmZdLTF6Mwk0HX5fbOZOBOz\nNRxXJvlQCMpVh6deGuOi9d08t2eSmXyNUMDwpHSFSCdMkomAnQdmuWBVR+vM6Vg0d0uvd+HUNbW1\nW2t6MTaZn9A0VcFvjBtZ3plsvXf9ynbKdRdNVThwqIjvhSQaOyjL8Xn0+VF+78YNpxXT/Gs8XuJ4\no3c9J9p9RuXEiKXEeZOgVEUuMoWyzc9+NcT/84eXceB/F9g/WsLzAhRgR80lndAxDBVsKU1vTrMQ\nDZl6GEjvvPa2ODFdo687jWkqDE2UAYWtG+S5y0NPDTA8WWYmX0fXVDJpE1GR2675lkpH0lwoVVUh\nlTCwHDkAMKbLXqve7hTlussjzwy31GzH4siFJhHTFpy9/fjx/fzi2REqdRfD0HF9KedWFIW4qWM5\nPgoC09DpzMQoVx10XaOjLcaa3raGY0TY+uzVvWn2juRwvAC/cc5m6BrVmkOl5hCGcrdZ8kMQkIzr\n6I2zr0NTVd6/be0JF+G67bbELF0d8ZMunCda1AfGS1jOQrXhfLo7EswWLHw/ZHy2imV5bFmT5eL1\nna3PDBu74flu9q+X4yWOt7JnKRJRRCwlzosEpakQi+mNsxvBwHiJ//fuZ9A0BRXpOecFIY4bgAiJ\nxQzakzqOF4KioKnyFzXwArmrUlV6snE62xLUbZ9C1Wdyrs6O16bZf6ibC1Z3YjnBAgfuno4kvZ1J\nwkDghYLpfP2EljzdHXFmC/XWrCTfD2lLqa0n/KPVbAsX4d0DOYYny2iqQndHYsFCs3tgjud3T0qp\nuwDX89FUadqqayq6pmDoCrqmETNUYqbOOy7rYjZvk04aXLA6y+Y1WQbGyq3GUj8ImJyr4rjSVQJF\nIak1JCZC4HoBmqbQlpQO8NBsZJZDG0+0CHt+wHd/sodD0xVp5lux2biq44QLZ3OO1KPPjwJw8zWr\nFpznSCm3NOvdPTDH1g3dC0qhWzd0MlOwGBovYRoa/+uJgwyOd3PrDeulw/uaDn7y9CCOK0fYJ2I6\nN1+z6vX/sHLm7QQREeca53yCUoG2pNGYYRRSsQJUBJW6K5Vqiiz9gSznpVMxPC/ADaGzPY7nCzno\nUIDbOJMylZBcwW4dZldqHq4XYOgKLzem6xqaiqbJwypNU9jQ384t71jTcBeXijc5TnYhG/ozPPni\nKLYX0J4yqVoea3ozCISc6dQoOwWh3Hkcr3y3fecEM/k6iqKQK8kFvcmhqSp+IDBNDT+UySMMBVds\nWY6uq8wVLdpSMeKGiqooZBqO7lXLpacz0epHev91a1ujSg6OFqnU5VmVqqpoCgRCkEqYTObrjYZb\nmeBX9baRbQgw+hpGvrsOzrFlbfaYpby9wwVsL5CzphSpnMwV7SNv3QI8P1hgpPvIM8OtnVIQhhwc\nLbZk8tt3TrB1w8KzLplwx4jHjJa7++hMdUFJ9uL1Xa0EeNMVfafUmHwyXm+J7fWWQSMRRcRS4pxP\nUCHyLMk0NPJF6QoRzhM5hAIIZeZRFciXbGKGQiymS7GAH8o0pMhkJwSESOXeTMHCdX0cTy50fhAy\nXajT05GgWHEJw5BEXMcwNGzXb8w5ClnRWJRdLzzqzOiRZ4ZbHnmOF3DZBT2YuspM3sJ2A3xfnuEM\njZdgJZQH3QXlO5BlmnTKwDS0xgTgoCVZBljd20YmHWt4AIZoqkI6abJyWYrf2baOJ34zARzedewd\nLjDY+H7zZe6PPj/aSk7FioNl+7h+QMyUP1aZVAzfD+jtTJLub28NUPzd69eRTsRa9ku7B3OAXIxv\nvnrVAhPbpvquuyPR2o3WLI9UXGdDf+a4C/KRO8iq5TUEG2nKFRfblc4guqaQSBitf4f5TvAnIxk3\n+b0bN7yupPJ6pPDH+qzXe34UWSBFLCXO+QQFUK170sC08edmcgLp0dD8Y2NILZYrMHRBLQzww5AQ\n6UIRNnZcyZhOWypGJm0yMlnGa5T+wlD+ve8LVFX2XrleiK5pjE5XSZ1E4TW/Z2lFdwrXj1OqOkzn\npVlrMq5jmiqZpMm6le0n7Ilquiw0y1hNyTJIxdj+Q93sVARKTiGd0Ll8yzJcX3DvI3tbQwybu47m\n5/5mn810vg7I0fBBGHLwUJFi1ZFqvkrjXgqImRq2F2B5AZ4XMDhukW2TNlO/fnWKbZf28cKrM1Tq\nLiu6U2iqStXy+KcfvkzV8lAU2Rwtd34K6YTBupXt7DwwIxucl7fx06cHF5yFNRdkYMEOcrZgESLo\nbk9QrrsEIsRxpf+iaegMjBZ5+wXLFvxbbOjPYPxGwXY8TEOW2I5Xkj3TpHImUvgTcapxnGyXFZUT\nI5YK50WCEgJqtlwEVOVwIoLDyelIKnWfxqR1afMjQNcUTENjWVeSRMygPRVjWJRa30NVpbmpoiiI\nUOB4Unjg+SF7Bud45+V9R42ZOFH5RFNV+rpSzOTrxE2N9f3taKrWkJ0ff/bR/DJNd0fiKMmyoWt8\n6F2biJvGgjEek3M1BILOYyxwG/oz3P/L/ViNGVL5ss0Fq9up1F2qdRfL8enIJGjzA+KmQSKhUSw7\nqCjyPngBddsjk4oxMVfn3kf2NiyXfMZnKqzqzUjrp4ZQo1nKmytaLfXdI9uHWb08w/LOJJqqMjpT\nRUFp7Uib8QILdpClqoOmKXSvyTJXtJgrWgikCAVoOEkd/klo7mQz6Rj9yzNYlsf7tq3jbRf0vKG7\nidOVwr8RRCq9c5uZ6Ul0I0Yulzon3CTO7uhPkfkJydAVXE8cNzE1mf/3ui7FFZ2ZBO98ex9zRZv2\ndJyp2Rq2I13MQ+nDShAEpJNJcmWLhhagpfgam66ycVWWQ1MVVvemF7h7w+HEUrU8ckVbOlEImeB8\nReHgaImNqzpY3ZtuKdHgxC7mzc89UgK/d7jA6t40tuthuQHT+Sr1usfKedLr+ewZzOEFIZ4XsqJH\nNpNO5y3evmUZL+2bwfNDVi1ro2q5pOMGA+MlvCDA8XwsW440aU/H6MzEGRwvoSgyQeRKFo7r4weC\nmKFx0fouhifLrfOhuHH4bG2+zPtE+IH0tcu2xaEhgkknTQ6OlvD8kGrdRdEUlmWTqKpCNhNbIHOf\nv5Nd2ZMmCOXkX2DB9OI3QwbeTMZnUmI7lTgild65TeB7pNsy54ybxHmRoJrETbUx7dUjOFmGQmoY\n2ttishFVga6OBJNzFn3dKS5c28XodIV00pDTZhuKOEVVUYRsgp3NyyRlGCqJmM5rQ3nsRjlqYLx0\nVCNmU3n23Z/sQSBw/IBcWe4ggoaYoVpz2bqhm60buk+7TCMbgHMNxwajsRODctVFQWFVbxuDEyXa\nkiaqqrRKWnXb5b5f7CNftkGBmu2xYWU7mirnM11x4XKm5uqyBGmozBYtdE0hCFWmclIgIYBi1aFU\nkzuunmwSy/GlE4Ou0tWeYHVvGxMzVdrTMVkmNDSuvaSXvcMFtqzNHrUAr1omk8fknHQH6V+eZkN/\nhgefPMjIVEk6WqRMLt3YzVzBJl+yUBSZKMNGnbe7I9HyHnxp3wwty6swRFMP31M/OP7O43gPBCcr\npZ2pS8bxiM6PIlasXE1XTy+lYn6xQ3lDOG8SlKFB3NTpbI9hOz6Bf/IMJQTU7YDONpOK5csprxWH\n4cky+bJDR8okCKXTt6KApkAqrhOLa7i1UJrNhgLfF4hQsLK3rfXk2jy0X7+yfcFCMjBWJp0y8coO\nhbJN4If0dKVQVfWos6Qj5ykBrSfmYy2WDz01wNBEmeGJEoaucfnmHqbzcrT9ss4EM3mLUsUhCEO6\nMkmChgT7ud1TKKrSGs3hegGWG3BBb5qJuSphKFWRALPFOq4XkEoYlOs1PC/ED0JihsbUXA1FlRW1\ng6NF2tMGfiDIpE3W9LUxMFoi2yaNdA1DpbsjzmvDReBwQjg83yokCAOe2TnZSjbTczV+9qtBdg3k\niZk6dcsjV7QwVJV3XLqCJ18KWqKJIBRkkgYgd7ZVy2NgtAgKbOrvaA2r1FSVREwDDkv7m16Duwdy\nXL5Znl0dmVROpZT2ZiSUkyW3SKUXcTZx3iQoL5CTWVNxg5ip44feYSujE2DZPkHKQNcUarZHte7h\neiE1y2NZZ7LVlySE9GvLZuIoinytf3mGYtlGVeDGK/vx5fFNw8GiQFd7gkLV5skXR1t+fE1lWxAI\nQiEoVB2WZZMs6zz6LAmOXghfHc6BEI3ZT4cXxr3DBaqWx/hMharl4QcOz+ycYEN/B4oi2DdSoFhx\n5CwoBTrSIc/tnmR8pkapKpNld4fs+6rbHl2ZGKCgoFCuOvhCJmRFUTAMjVLFIWzUPXVdxQ+FTOYN\n39VQQKHqkUkZGLrKzv2zJOMGvQ3BxPhMhVcH83R1JOjuiC8oRW1Zm5XJdrzM8GQJXVNIJgyCQDA4\nUaJcc+lsj1OuSYPfp3eN44Uhq5alW/fF1GVimi7IhOr5IaYhJy4Xqy7rVraTTcdbDxB7hwsL5Olh\nGPLTpwewXZ/JuSqaqi3w4zvVUtpbLUiIdlkRZxPnTYICcIOQuWK91X40XzBxpHiiiYCG75wmZd6N\nBbZac0iYGsu7UlRqDkEgSCUNylV5BnPx+k5qdsDKnjTZTIwtqztb50bT+TqhEIRhyMv7ZjF0KSQY\nGC+xpjfTOgBTFYXu9gRrV2QaPVUKL++fRdeUVt/TkSasuw7MIRBsafjIzRcO5Io2mq7K5tIwpCIE\nE3NV1qzI4HpydyCA6XydmYKFqSuoisqlm7qZKtSoWh52Yx5W3QmYHMxx4dpOVBUm52r4fkCxbOGH\nkO2ItWZaAXhhePisDhruffK/mKERBIKm7iMIQ8ZmKnh+SLHqMDQBK3vSpOI6fiCd00s1l/HZKjVb\n9qBpZZsV3WnSCZNSzWFsuoLjhggE+aLNroEcH7ppA3HTwA8Cnn5pgrHZKjXLo1xzMA2duBmSTsoE\no6nS2qiZPKTj+GjjPkGx6hKEgu/81050TaWrPX7afnyL5XkXqfQizhbOqwRVrLhUGvLvJs3z9mMl\npyZeAJ4VLHjN9qQbeKLmcsnGHnRNYf9IHl1TUTTY8doMG/vb0TSNRExn/coMQ+NlqnWX/mUp5koW\nI1MVqg0z2N6uFJYTMDFb44I1WQpl2YiaSceYzNWYyssn/eKzTmPgYmdDliwbcF3f5+V9c1TqDjFT\nZ99Igc1rsjSHHW5Zm+V/doxKZZ0ChqmTSZms7GljzfIMQSgIgpAdr03jeiGKAq6r0J1VKFVdrr14\nBftGCpi6yqWbeiiUbUoVhwOHiqzvzzCZq7FnKIeiQBAIpmZ8LMdryc6TCQOFEEtWAhHIEmql7jE0\nUaa9LUYYCl4bytOWNKnaPvW6K3vIHJ9C2SFfcdixd5r2pMnBsSK6Lp01FJRW6fGi9Z2UarInS1EE\npqGhayoz+Ro7XpvhE7+3lb3DBbzGuZjd8A/0AxfX01o+fKahHiU82XZZH86OMUpVh2wb5EsWfhCi\naSq2G6Ioh/34TlZKOxM1XWTiGnG+cV4lKDicnLTG47umQqMl5rRxPYGhyVbg2aJN3QmImRBDCgkG\nxopsXN2J6/l89QcvNuZJwb6RgLipI4TA80ICVbTOUVb3pvHGg5aMvFSxcdyQIBA4boAfhFTrHoWy\n07JuMg2V7a9MNs6PwPNdkjH9qDHo3dl467oNQ6M9HaOnM8HqFWnGZirsGskTBDI5xQyNUAgmZisE\nfkhbyqAnm2CuaHFwtIhAYDk+uZKF6wd4ri9LbXGzsQOSwgUFKb/v7UpSrthYeeeof4+67aOpCiuX\npQlEyO6BOUIRSvspPyRmqCAEuYJFps1kNl+nXHdRAU3XSMY1YjGDlT1pNFVjU3+WdNJgbLrSMnI1\ndNln9dBTA2xY2UFXR5zRaTmmPhnXMXQpoEmnTJRmRzYLk8LmNVkGxkuMTIKd91FVFUM/ttz/ZKW0\n01XTRfLwiPOR8y5BzUfh1JKTgkxk/jHOrFw/ZNfBOTmM0POxXZVcyUJVpCijVHUoVmyKFYdMSloG\neb5HIt74TCHQAsHoVJnVvZmjFHp+EPDLHWOAXDM9L8BS/cb5jhzjYuRKAAAXTElEQVTed8GqLDsP\nzFIo2Q25tHQTv+7SFdx6w4aWB10QwnVv6+PlfXN4fkC2LUYiprP/UIGJuRqFkkUjh2K7AaGQMeZK\nFhXLYd3K9taYDIDerhTZtpjsR1Jko7Lt+tQs7/B902RP2GzBpqPNJBXzqDkLb6SqKsRMnbmiheMG\nDVcOpDDElx6JiqlQtz1KNRdDV4gbmhzgqIChG2TTMd51ZT8xw2BDf0Y28aIwnathGCqrlrfRk40z\nPFkGIShXXOJxHbUmS3XdHQl8P6SnI8HyzhSuL9jx2jSPPjeC64es629n36E8779uLftGCmzfOcHG\n1Vl+vXuSMBTETfUoP743spQWycMjzkfO2wQVNPqWToQu13pUFQTykOrIs6ogEHKhNFRMQ8cLQoIA\n0KCrPY7nH3YtaJKI69huQNXyiRsaYRgSoqAKWpLq+Qq9/aNFZot1apaLHwpCETJbqtO/PN06h8qm\n40wbdcJQniPF4zrr+jqOesI2dZ0rLlzGdL7O+r4MCIVdB+ao1F1MU8f1/daQRpDXHgpBteYzNFZm\n89ose4fzKIrScKqwiBkqpq5RLLt4fkDYuLEC8Bt6fsvxScUNvCOUKZpKQ2QisF0fhHR7NwyFui1H\nfGiaSszUqDs+nh9g6AYCac+kN5zh1/d3EDOMlpffBas7MTSNyVyVuhPQk42zf6RIsWKzfzhPRyZO\nzfao1FwScZ3R6SoxU2XLuk6m8zUcL2T7y2PYbgAKzBTqXHPJCgbGyly+eXnrIeLyTT2MTpePEkmc\njEhNFxFxcs7bBHUyFJojITTaUiappE6hZFOqSmVYGMoEZhg6vu9j6hp9PWmmcnUsxaM9HZOOEkKw\nZkWGfEl664VCEASCtSvS7HPlE3EiJncPLx+cxfbDBZY9uwdyGJpKb3cCy/ZY0S1FF4qicMEqWTZq\njgzPZmItf7lLNvWga4fLT0cOLuzrTrVUbIWyTaUu7YU0DZoTzmWfsXxiD8KAmXyV9rYYyzoTdGbi\nzOQtcuU6+bJNV3uCnk7pl9fbneS1weKC/O8HAeOz1aPO+kIBMUPBsj1CoLc7yXRONu+qihQrdDZG\nzBuaQqjLER0xU8NxfTJpk/Z0jLmi1TKknV8Ka0rKdw/mODQtR6LomsLkXBXT0EklDQxNluocP+S5\nVyZpb4tRrjjUXXkvVUWV/odjJa6+sBdYuDu66qLe1vWc6jnR6arpooQWcSpUyiV0w6RWLS92KG8I\nUYI6DgKoWT7JGFTrLm+7oAdlNfx61yRVSwoAdF0lYWqoca1Vv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+ "text": [
+ ""
+ ]
+ }
+ ],
+ "prompt_number": 30
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "By the way, you can do other kinds of plots with `flotilla`, like a kernel density estimate (\"`kde`\") plot:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "study.plot_two_samples('S1', 'S2', kind='kde')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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j7EEREZGk6LSu5pvnoIiISFIMeg0AoKScAUVERBISHKQDAJSUWUWuJHAYUERE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\nzEyxS/LZwYMHcffddyMtLQ3jx4/Hxx9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+ "text": [
+ ""
+ ]
+ }
+ ],
+ "prompt_number": 31
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Or a binned hexagon plot (\"`hexbin\"`):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "study.plot_two_samples('S1', 'S2', kind='hexbin')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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xQFBV2j/6oIEsO7gjZ/GpJ0Ss58cu/GoHk/8pcj/F5r6twrJ5yvFkZ0bZGbAn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\nrkJggg==\n",
+ "text": [
+ ""
+ ]
+ }
+ ],
+ "prompt_number": 32
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Any inputs that are valid to `seaborn`'s [`jointplot`](http://web.stanford.edu/~mwaskom/software/seaborn/generated/seaborn.jointplot.html#seaborn.jointplot) are valid."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Figure 1c"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "x = study.expression.data.ix['P1']\n",
+ "y = study.expression.singles.mean()\n",
+ "y.name = \"Average singles\"\n",
+ "\n",
+ "jointgrid = sns.jointplot(x, y, joint_kws=dict(alpha=0.5))\n",
+ "\n",
+ "# Adjust xmin, ymin to 0\n",
+ "xmin, xmax, ymin, ymax = jointgrid.ax_joint.axis()\n",
+ "jointgrid.ax_joint.set_xlim(0, xmax)\n",
+ "jointgrid.ax_joint.set_ylim(0, ymax)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 33,
+ "text": [
+ "(0, 12.0)"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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dd3E6nZSUlOB2u/F6vVxyySX89Kc/5cCBA8Tjcb773e+yaNEiFi1aNG6fgc/nY82aNdxz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gen+CYvNpNNEj96/C3SSZeaF1Kq+LQkXaq1EClVC/v/+8Judg0V1B6fSJAv+moFZcIRx9/OcQRq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\nrmOxrK+Va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+ "text": [
+ ""
+ ]
+ }
+ ],
+ "prompt_number": 33
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Figure 2\n",
+ "\n",
+ "Next, we will attempt to recreate the figures from [Figure 2](http://www.nature.com/nature/journal/v498/n7453/fig_tab/nature12172_F2.html):\n",
+ "\n",
+ "\n",
+ "\n",
+ "### Figure 2a\n",
+ "\n",
+ "For this figure, we will need the \"LPS Response\" and \"Housekeeping\" gene annotations, which weren't very trivial to obtain, so I've moved them to the Appendix."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "# Get colors for plotting the gene categories\n",
+ "dark2 = sns.color_palette('Dark2')\n",
+ "\n",
+ "singles = study.expression.singles\n",
+ "# Get only gene categories for genes in the singles data\n",
+ "singles, gene_categories = singles.align(study.expression.feature_data.gene_category, join='left', axis=1)\n",
+ "\n",
+ "mean = singles.mean()\n",
+ "std = singles.std()\n",
+ "\n",
+ "jointgrid = sns.jointplot(mean, std, color='#262626', joint_kws=dict(alpha=0.5))\n",
+ "\n",
+ "for i, (category, s) in enumerate(gene_categories.groupby(gene_categories)):\n",
+ " jointgrid.ax_joint.plot(mean[s.index], std[s.index], 'o', color=dark2[i], markersize=5)\n",
+ "\n",
+ "jointgrid.ax_joint.set_xlabel('Standard deviation in single cells $\\mu$')\n",
+ "jointgrid.ax_joint.set_ylabel('Average expression in single cells $\\sigma$')\n",
+ "\n",
+ "xmin, xmax, ymin, ymax = jointgrid.ax_joint.axis()\n",
+ "vmax = max(xmax, ymax)\n",
+ "vmin = min(xmin, ymin)\n",
+ "jointgrid.ax_joint.plot([0, vmax], [0, vmax], color='steelblue')\n",
+ "jointgrid.ax_joint.plot([0, vmax], [0, .25*vmax], color='grey')\n",
+ "jointgrid.ax_joint.set_xlim(0, xmax)\n",
+ "jointgrid.ax_joint.set_ylim(0, ymax)\n",
+ "\n",
+ "jointgrid.ax_joint.fill_betweenx((ymin, ymax), 0, np.log(250), alpha=0.5, zorder=-1)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 34,
+ "text": [
+ ""
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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48cUXGzSA1atXM3bsWAYMGMC4ceNqLWtfuXIlY8aMYcCAAUyfPp2CgoIGvV+7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nwgFyASidDW1lYITlarFbvdPqlMfjokiY6FeZ/8ezq2/By7YvpLZS0lM1p6MzhjMK0AtXnzZv7x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\n9u0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+ "text": [
+ ""
+ ]
+ }
+ ],
+ "prompt_number": 34
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "I couldn't find the data for the `hESC`s for the right-side panel of Fig. 2a, so I couldn't remake the figure.\n",
+ "\n",
+ "### Figure 2b\n",
+ "\n",
+ "In the paper, they use *\"522 most highly expressed genes (single-cell average TPM > 250)\"*, but I wasn't able to replicate their numbers. If I use the pre-filtered expression data that I fed into flotilla, then I get 297 genes:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "mean = study.expression.singles.mean()\n",
+ "highly_expressed_genes = mean.index[mean > np.log(250 + 1)]\n",
+ "len(highly_expressed_genes)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 35,
+ "text": [
+ "297"
+ ]
+ }
+ ],
+ "prompt_number": 35
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Which is much less. If I use the original, unfiltered data, then I get the *\"522\"* number, but this seems strange because they did the filtering step of *\"discarded genes not appreciably expressed (transcripts per million (TPM) > 1) in at least three individual cells, retaining 6,313 genes for further analysis\"*, and yet they went back to the original data to get this new subset."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "expression.ix[:, expression.ix[singles_ids].mean() > 250].shape"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 36,
+ "text": [
+ "(21, 522)"
+ ]
+ }
+ ],
+ "prompt_number": 36
+ },
+ {
+ "cell_type": "code",
+ "collapsed": true,
+ "input": [
+ "expression_highly_expressed = np.log(expression.ix[singles_ids, expression.ix[singles_ids].mean() > 250] + 1)\n",
+ "\n",
+ "mean = expression_highly_expressed.mean()\n",
+ "\n",
+ "std = expression_highly_expressed.std()\n",
+ "\n",
+ "mean_bins = pd.cut(mean, bins=np.arange(0, 11, 1))\n",
+ "\n",
+ "# Coefficient of variation\n",
+ "cv = std/mean\n",
+ "cv.sort()\n",
+ "\n",
+ "genes = mean.index\n",
+ "\n",
+ "\n",
+ "# for name, df in shalek2013.expression.singles.groupby(dict(zip(genes, mean_bins)), axis=1):\n",
+ "def calculate_cells_per_tpm_per_cv(df, cv):\n",
+ " df = df[df > 1]\n",
+ " df_aligned, cv_aligned = df.align(cv, join='inner', axis=1)\n",
+ " cv_aligned.sort()\n",
+ " n_cells = pd.Series(0, index=cv.index)\n",
+ " n_cells[cv_aligned.index] = df_aligned.ix[:, cv_aligned.index].count()\n",
+ " return n_cells\n",
+ "\n",
+ "grouped = expression_highly_expressed.groupby(dict(zip(genes, mean_bins)), axis=1)\n",
+ "cells_per_tpm_per_cv = grouped.apply(calculate_cells_per_tpm_per_cv, cv=cv)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 37
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here's how you would make the original figure from the paper:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "fig, ax = plt.subplots(figsize=(10, 10))\n",
+ "sns.heatmap(cells_per_tpm_per_cv, linewidth=0, ax=ax, yticklabels=False)\n",
+ "ax.set_yticks([])\n",
+ "ax.set_xlabel('ln(TPM, binned)')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 38,
+ "text": [
+ ""
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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A6s3uoiSOUwIAoCAqnkBVbKbq/WymAupN8ASqUrbjo5LyhW1BDOqnqdlUeyJ4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\nww3z17/+tdNtqXjWyasLX2l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+ "text": [
+ ""
+ ]
+ }
+ ],
+ "prompt_number": 38
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Doesn't quite look the same. Maybe the y-axis labels were opposite, and higher up on the y-axis was less variant? Because I see a blob of color for (1,2] TPM (by the way, the figure in the paper is not TPM+1 as previous figures were)\n",
+ "\n",
+ "This is how you would make a modified version of the figure, which also plots the coefficient of variation on a side-plot, which I like because it shows the CV changes directly on the heatmap. Also, technically this is $\\ln$(TPM+1)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "from matplotlib import gridspec\n",
+ "\n",
+ "fig = plt.figure(figsize=(12, 10))\n",
+ "\n",
+ "gs = gridspec.GridSpec(1, 2, wspace=0.01, hspace=0.01, width_ratios=[.2, 1])\n",
+ "cv_ax = fig.add_subplot(gs[0, 0])\n",
+ "heatmap_ax = fig.add_subplot(gs[0, 1])\n",
+ "\n",
+ "sns.heatmap(cells_per_tpm_per_cv, linewidth=0, ax=heatmap_ax)\n",
+ "heatmap_ax.set_yticks([])\n",
+ "heatmap_ax.set_xlabel('$\\ln$(TPM+1), binned')\n",
+ "\n",
+ "y = np.arange(cv.shape[0])\n",
+ "cv_ax.set_xscale('log')\n",
+ "cv_ax.plot(cv, y, color='#262626')\n",
+ "cv_ax.fill_betweenx(cv, np.zeros(cv.shape), y, color='#262626', alpha=0.5)\n",
+ "cv_ax.set_ylim(0, y.max())\n",
+ "cv_ax.set_xlabel('CV = $\\mu/\\sigma$')\n",
+ "cv_ax.set_yticks([])\n",
+ "sns.despine(ax=cv_ax, left=True, right=False)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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vXqy5c+e2eH/GjBmaOXNmq7++srJS3bt317JlyyLvjRgxQkOGDIm6FisCZ3hK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\nLmjbUibbatb2OYvFwocffihV4dKUqVOn4vz587h//z7c3d3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+ "text": [
+ ""
+ ]
+ }
+ ],
+ "prompt_number": 39
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Figure 3\n",
+ "\n",
+ "We will attempt to re-create the sub-panel figures from [Figure 3](http://www.nature.com/nature/journal/v498/n7453/fig_tab/nature12172_F3.html):\n",
+ "\n",
+ "\n",
+ "\n",
+ "Since we can't re-do the microscopy (Figure 3a) or the RNA-FISH counts (Figure 3c), we will make Figures 3b. These histograms are simple to do outside of `flotilla`, so we do not have them within flotilla."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Figure 3b, top panel"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "fig, ax = plt.subplots()\n",
+ "sns.distplot(study.splicing.singles.values.flat, bins=np.arange(0, 1.05, 0.05), ax=ax)\n",
+ "ax.set_xlim(0, 1)\n",
+ "sns.despine()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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gddRbHXjvy7P4vqgcLy7Q8PQBUTeqNlvw1cFr+KawrGUVCKU8AHOnD8GTqYM9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\nVD1mk8Frp9O8xVuLqQI8NUf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+ "text": [
+ ""
+ ]
+ }
+ ],
+ "prompt_number": 40
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Figure 3b, bottom panel"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "fig, ax = plt.subplots()\n",
+ "sns.distplot(study.splicing.pooled.values.flat, bins=np.arange(0, 1.05, 0.05), ax=ax, color='grey')\n",
+ "ax.set_xlim(0, 1)\n",
+ "sns.despine()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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Sb28vsrOzTfebAgICEBERAanUfk++u7q6kJOTAz8/vzEfiw8ik6VkMhlWr16N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\nxIAiIiKbxIAiIiKb9L9cvkWRuSH15wAAAABJRU5ErkJggg==\n",
+ "text": [
+ ""
+ ]
+ }
+ ],
+ "prompt_number": 41
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Figure 4\n",
+ "\n",
+ "We will attempt to re-create the sub-panel figures from [Figure 4](http://www.nature.com/nature/journal/v498/n7453/fig_tab/nature12172_F4.html):\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Figure 4a\n",
+ "\n",
+ "Here, we can use the \"`interactive_pca`\" function we have to explore different dimensionality reductions on the data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "study.interactive_pca()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "savefile : data/last.pca.pdf\n",
+ "y_pc : 2\n",
+ "data_type : expression\n",
+ "featurewise : False\n",
+ "show_point_labels : False\n",
+ "sample_subset : pooled\n",
+ "feature_subset : variant\n",
+ "plot_violins : True\n",
+ "x_pc : 1\n",
+ "list_link : \n",
+ "1100001G20RIK"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " (18,)\n",
+ "verified_color [u'#1b9e77']\n",
+ "verified_order ['BDMC']\n",
+ "['BDMC']\n",
+ "\n",
+ "1110002B05RIK (18,)\n",
+ "verified_color [u'#1b9e77']\n",
+ "verified_order ['BDMC']\n",
+ "['BDMC']\n",
+ "\n",
+ "1600029D21RIK"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " (18,)\n",
+ "verified_color [u'#1b9e77']\n",
+ "verified_order ['BDMC']\n",
+ "['BDMC']\n",
+ "\n",
+ "2310016C08RIK (18,)\n",
+ "verified_color [u'#1b9e77']\n",
+ "verified_order ['BDMC']\n",
+ "['BDMC']\n",
+ "\n",
+ "ACSL1"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " (18,)\n",
+ "verified_color [u'#1b9e77']\n",
+ "verified_order ['BDMC']\n",
+ "['BDMC']\n",
+ "\n",
+ "AK041746 (18,)\n",
+ "verified_color [u'#1b9e77']\n",
+ "verified_order ['BDMC']\n",
+ "['BDMC']\n",
+ "\n",
+ "ALDOC"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " (18,)\n",
+ "verified_color [u'#1b9e77']\n",
+ "verified_order ['BDMC']\n",
+ "['BDMC']\n",
+ "\n",
+ "AMICA1 (18,)\n",
+ "verified_color [u'#1b9e77']\n",
+ "verified_order ['BDMC']\n",
+ "['BDMC']\n",
+ "\n",
+ "APOOL"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " (18,)\n",
+ "verified_color [u'#1b9e77']\n",
+ "verified_order ['BDMC']\n",
+ "['BDMC']\n",
+ "\n",
+ "ARG2 (18,)\n",
+ "verified_color [u'#1b9e77']\n",
+ "verified_order ['BDMC']\n",
+ "['BDMC']\n",
+ "\n",
+ "ASF1B"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " (18,)\n",
+ "verified_color [u'#1b9e77']\n",
+ "verified_order ['BDMC']\n",
+ "['BDMC']\n",
+ "\n",
+ "ATP6V0D2 (18,)\n",
+ "verified_color [u'#1b9e77']\n",
+ "verified_order ['BDMC']\n",
+ "['BDMC']\n",
+ "\n",
+ "AW112010"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " (18,)\n",
+ "verified_color [u'#1b9e77']\n",
+ "verified_order ['BDMC']\n",
+ "['BDMC']\n",
+ "\n",
+ "BANF1 (18,)\n",
+ "verified_color [u'#1b9e77']\n",
+ "verified_order ['BDMC']\n",
+ "['BDMC']\n",
+ "\n",
+ "C1QB"
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+ "verified_color [u'#1b9e77']\n",
+ "verified_order ['BDMC']\n",
+ "['BDMC']\n",
+ "\n",
+ "TMEM176B (18,)\n",
+ "verified_color [u'#1b9e77']\n",
+ "verified_order ['BDMC']\n",
+ "['BDMC']\n",
+ "\n",
+ "TMEM39A"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " (18,)\n",
+ "verified_color [u'#1b9e77']\n",
+ "verified_order ['BDMC']\n",
+ "['BDMC']\n",
+ "\n",
+ "TNFRSF9 (18,)\n",
+ "verified_color [u'#1b9e77']\n",
+ "verified_order ['BDMC']\n",
+ "['BDMC']\n",
+ "\n",
+ "TNFSF4"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " (18,)\n",
+ "verified_color [u'#1b9e77']\n",
+ "verified_order ['BDMC']\n",
+ "['BDMC']\n",
+ "\n",
+ "TRIM13 (18,)\n",
+ "verified_color [u'#1b9e77']\n",
+ "verified_order ['BDMC']\n",
+ "['BDMC']\n",
+ "\n",
+ "TUBA1A"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " (18,)\n",
+ "verified_color [u'#1b9e77']\n",
+ "verified_order ['BDMC']\n",
+ "['BDMC']\n",
+ "\n",
+ "TUBA1B (18,)\n",
+ "verified_color [u'#1b9e77']\n",
+ "verified_order ['BDMC']\n",
+ "['BDMC']\n",
+ "\n",
+ "TUBB2C"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " (18,)\n",
+ "verified_color [u'#1b9e77']\n",
+ "verified_order ['BDMC']\n",
+ "['BDMC']\n",
+ "\n",
+ "UPP1 (18,)\n",
+ "verified_color [u'#1b9e77']\n",
+ "verified_order ['BDMC']\n",
+ "['BDMC']\n",
+ "\n",
+ "VAMP4"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " (18,)\n",
+ "verified_color [u'#1b9e77']\n",
+ "verified_order ['BDMC']\n",
+ "['BDMC']\n",
+ "\n",
+ "VPS25 (18,)\n",
+ "verified_color [u'#1b9e77']\n",
+ "verified_order ['BDMC']\n",
+ "['BDMC']\n",
+ "\n",
+ "WDR61"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " (18,)\n",
+ "verified_color [u'#1b9e77']\n",
+ "verified_order ['BDMC']\n",
+ "['BDMC']\n",
+ "\n",
+ "ZFP263 (18,)\n",
+ "verified_color [u'#1b9e77']\n",
+ "verified_order ['BDMC']\n",
+ "['BDMC']\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 42,
+ "text": [
+ ""
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
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ho6ODNWvWQEnpPbuiU51W3tjNNZiJiKjWk5OT+2jJZSKi6kxBQQGDBg1CcnIy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+ "text": [
+ ""
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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OoKAgbrwRGxuL27dvw9fXF5WVlbw2DQwMYGBgoPKzVPThTE9PD3PmzEFERASO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\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M+Sq3x03V7mq27axi284d7N67B7fXG84ffyhIEKNlJJDThi0xHnuybkXtKOHmTV09Ibc3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\nbNu2jUmTJhESEkJwcPBdr8/OziYnJ8fqWGZmZmWFWyM4Ozk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www5w+u9HVC2FQoGDiwYH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NHU\nJ57igadHou2YjNc1/K05nU7yf9pL+oixLoxOVCcp8Ihq9UD/AWS8v4SA5DiXtWnOL6BxeH0ZMixE\nDWU0GOh/W0/639YTgNLSUr79aSuZm77mxO/HKbRacJi80UcEo/f3rbY1H+wWq0vOcRWn04n5XD7W\n42fQlJTiq/chOiSM2+74J+1bXCc5rhbZtWsXaWlpfPPNN8D5HW3Gjx/Pli1b8PX1JS0tjf79+ysc\npfKKiov4Zf8+du3fw/6sgxQVF2Ox27DYSrGVF3GM+DT0R62thxaQv4KaQ6VSoTMZ0ZkuHjFgcTr5\nvbiEn3d+y1vffgElpeg12vICUHhoGM3jE2gZn0RUg0i8ZB1Fl4mKisJms3H8+HEiIs5vc52VlUVc\nXNXvvVNTU+nVq1eFY7m5uQwaNMiVoQoX8vfzY9yjw5i+eB4B7Ztd9d9U/o79DO7TnyaNYlwcoagu\nUuAR1apjq9aErnqXQhcNIXQ6nVh3H+Lp6bNdEJ0QojrodDpu7XgDt3Y8P+rO6XSyc89uPv1mI1m7\nD1NotWDTeqEOC8QYEuSxN/5lDgdFJ87gOJWHt92Jr96H1tGN6Xn/v0iIjZPFTWsh2dHm8vIKCtj4\nw3d8u+0HzhbkU2KzYsMJvj5o/E0Y6vuj1gXJSBwPoVKp8DYZ8b5M8WdfYTE7f/yKpRs/R2W24qPR\nYvTWExsVQ7eUG2mRkOSxud/dTCYTXbp0YdasWUydOpX9+/ezdu3a8kWYqyIwMJDAwIoL9MtDhpqv\nbXJzhg8czJy33sC/beIVjeRxOp3k79hP/87d6H1LNzdGKdxNCjyi2v1n9NM8+PRIdCnNr/kLTMGe\nLAb941/4meR2UIjaSqVS0SopmVZJyeXHjuYc4+Ov17Nrz24KzWbMqjLU4YEYQ+td09DjP9PovSsd\noaNx4SLRZQ4HRbmnKDuRh49Kg5+PgRuTW3DHvbcSHhLqsn6EcmRHm/8pKCpiwVtvknX0MMVWK2Yc\neNXzxRgRgiY6SIo4dZhKpULvZ0LvV/Ffgc3pZPu502x55794FVkx6rwJMPlye6dbue1GWey1Mn++\np54yZQqTJk2iU6dOGAwGxowZQ4sWLRSMTlSXTu06YvQx8p9X5+LfoVmV7pmcTif5P+5lUK9/0Ktz\nl2qIUriTFHhEtfP39eXRAffy6qer8G926bUtTu85wIGPvwIgtmenS66BYT6bT5SPvyQiITxQw/oN\n+PeAe8tfnz57ljXrv2DbzzvIN5dgVYMmIghjSL2rLhQ36duFPSs+qfScq1VWVkbRyTM4cs/iU+aF\nv8HIrde1oedDXQn0l318PJHsaPM/Ga+9zD5nEaakhuhVKmTFKFGZS631k2ezsfCdZdzYuh3GK1hD\npq7p0KED33//fflrf39/XnzxRQUjEkpqm9yciY89wXPzXyCgkjV5zhd39vBw339JIdVD1IgCT25u\nLpMmTWLbtm2YTCYeeugh7r333sovFLVWt+tv4qMvPyevxIzOUHF/i79uW7xnxSc0uqXDRdsV23/L\nZlq6fHiJq7N+/Xpmz55NTk4OoaGhDB069KL55qLmCA4K4oH+A3ig/wAAjuUe58N1n7Nr96/kmYtx\nBBjwjaqPWlf1IeTBibE0uqXDZXfSanRLhyteYNlmsVJ0OAdtgYUAg4luzVtyZ2o3woIv3ulEeB53\n7GhTW3ezOXr0KHZ1GYUOB8awYNSyy5u4QpbCYizHT2G3WNn001a6y5dPIaqsZUIS4x4dSvqSRQS0\nTrjseQV7sxh4W28p7ngQxT9tnU4njz32GCkpKSxYsICsrCwGDhxI8+bNadWqldLhCTd6YvAjPPVy\nBrqWTcuP/bW4c8GFYxeKPCV5+STHNcVb57rpE6LuMJvNDB8+nFmzZtG9e3e2bdvGoEGDaN26NfXr\n11c6PFEFDcIjSBs4CDg/UubrrbxJ0KQAACAASURBVJtZnfk5J/POYtF54RNdH30V1vm6kFP+mnei\nbulAo78UlS/Hkl+I+XAuPjYn4fWCGdLnHjq0bC1r6Ajg2ne0qa272byeMYfTZ8/y2Tcb+GHXdvKL\niyi226CeCUNYMFofvfyNiHIOux3zuQLsJ8+iMdsxeeuJC4/g9jvvoW1yCzRSIBTiirVLbkmbxvHs\nOnEGY1i9i963FpVQX2OkX7ceCkQn3EXxbLlz505OnTrFqFGjUKlUxMXFsWLFiosW9hKeJ6ZhQ1Sl\n9vLXp/ccuOyTdDj/BcwYVo/gxFjsxWZimiRWR5jCA6lUKoxGI3a7HafTiUqlQqvVyhautZSXlxed\nO1xP5w7XA7Dv4AGWrVnJgT17sAUYMDVu8LejB6I6t8cYVq98Wmhcr87US2j8t306bHYKDxzFu8hK\nfEws9z02kujIRq77pYTHuNYdbWrzbjbBQUGk9rmL1D53AeeL6xt/+J7vtm/j7OEcrHbbHz92yrRe\nOI16NL4GfAL80eh1CkcvXMlZVoa1sBhzXgGqYgvOEis6lRpvjQa9RofJW0+rRo24vddAmkY3luKf\nEC7y2MBBPDhlHFyiwGM+cpyHBz6qQFTCnRQv8OzevZsmTZrw/PPP89FHH2E0GhkyZAh9+/ZVOjTh\nZus3bwLf/03PuvDl6u8c+PgrghNj0Qf68cOOn0i98x/uDFF4KL1eT3p6OsOGDWP06NGUlZUxffp0\nwsLClA5NuEB841imPPEUTqeT9Zs38e4nazhrLkYbWx9D4KXXvglOjK3SdKyS0+ewZR0nxOjHQ30H\ncEPrdq4OX3iYa93RxpN2s/Hx8aFHp1vp0enWCsedTidn8/LYd/B3dh/4jQOHD1FQdBKr3V5eBHJq\n1WDwBh9v9H4mdEYDXhopytcETqcTh7UUS0ERpUUleJlLwWJFgxfef2yN7q3VERcWRmKb60iIiaVx\nwyh0OiniCeFuBr0elaPsku85bQ6CAgIu+Z6ovRQv8OTn57NlyxY6duzIxo0b+fnnn3nooYeIjIyk\nbdu2lV5fW+em13Wnz57l5beW4JeSXPnJl6AzGDhuz+WDLz+VYYXiimVnZzNixAimTp1Kjx492LRp\nEyNHjiQxMZGEhMvPUwbJObWJSqWiS8qNdEm5kfyCAjJee5k9W3/FJzEab9OVLdZpyS/CuvcwLZvE\nM2LqKAw+PpVfJOo02dGm6lQqFfUCA7m+TTuub3Nx0dTpdHIuL4+s7KMcOHqIg9lHyT10EmuplVKH\n/fyP3YZdBfh4o/LRofU14u1rROMtRYRr4Swro7TEjDW/iLISC06zFZXVjk6tRqfWotNo0Ko1+Jl8\nadQghtjWUcQ2bETDiAZSwBGiBnj307Wogi/9cEtbvx7/XbmCZx57opqjEu6keIFHp9Ph7+/PI488\nAsB1111H9+7dWbduXZUKPLV1bnpd9tXWzcxd+l+MrZvi5eVVfjy0ZTzZ3/70t9eGtowv/99+iTG8\ntf4z9h08wJhH0mQ4r6iyzMxMkpKS6N27NwCdOnWic+fOrF69utICj+Sc2snfz48pT47h9NmzTF3w\nItlF2fg1j610+1CH3U7hrt9pXC+cp59Lx9/Pr5oiFrWZ7GjjWiqViqDAQIICA2nT/PKFMbPZzOGc\nbA5mH+HAkcMcPZ5DUfEpbA47VrudUocNu9MJBh0Y9Oj8zheB1LV0RNS1cjqd2EosWPILKSs2Q4kV\nVakdnVqDTqNBp9birdUSVS+YmLg44hpF0TgyipB69SrcvwkhaqYde3ezasMXBLRPuuT7ppB67Nz1\nG2s3rpNdiT2I4gWexo0b43A4KCsrK/+wcDgcVb6+Ns9Nr2ssVgtT5r3I/tM5+HVMvujmIHfb7krb\nyN22m5huNwDnb/j8WzZhR/YJBo0ezjOPP0GTqL9fN0MIOD9F66+LnqrV6iot4ig5p3YLDgrixWcm\ns2XXDmYuWoDxuiZoDZcejWMpKML680EmDx9FUlzTS54jhKg5fHx8SIhtQkJsk8ueY7VaOXTsKL8f\nPsT+w4c4ln2MEov5jwLQn0YCmfSofY0Y6gW4ZRTQ7rc/5uzegwDUS2xM0oCeLu/D6XRizS/Cci4f\nZ5EZlaX0fPFGrUGn0aJTa2gQFETj6GSaRscQ1yiakHrB8sBMCA/w6rvL+Xzzt/i3Tfjbv2m/5nG8\n+flqft77K2MfHSp//x5A8QLPDTfcgF6vZ968eaSlpbFz504yMzN54403qnS9J81N92TvfPoRKz/7\nGE18JP4tL/1FyW4trbSdS51jigzDERrEuHmzSIhoxIShT8juWuJvde7cmYyMDFatWkW/fv3YunUr\nmZmZLFmypNJrJed4hg4tWvHK5HSGPfc0jqRG6P1MFd43n81Hl3WSl2e8gK+x8t24hBC1g7e3N/GN\n44hvHMflSirFJcXszzrIjn2/svf33ykoOonZVorFXopN5URl9EHtb8QQFIBad+X5/4cX3sCaV1j+\n+syeg/zwwhu0f3LQFbfldDqxFhSfL+IUluBlseGt0aLXaPHR6oiLiKBFSnuaN02gQVi4bCYghIc7\ncOQw0xe8SJG/N4Htm1V6/vkH5k3ZlXOSe0cM5YkHH6Vtskwhrs0UL/B4e3uzdOlSJk+ezPXXX4/J\nZGLChAkyN91D7M86yPQFL1Hi741vx2Z/WxXWeOuwW6yXff/COZei1mkJaJNI1tk87h09nLtu78m/\nevS+ptiF5woPD2fhwoWkp6czffp0IiIiSE9Pp1mzyj8IheeoFxjIK9Nn8sCYJ9G2T0KtPf+RaLOW\n4tyXzSsZL6HTyhoSQtQ1RoOR65o157pmzS96r7ikmN2/7Wfnvj3sO/g7BUVFmG1WShw2CDRhjAhB\nd5lRgXBxcecCa15hpUWeMofj/ELvp86hMdsxeuvx0XkTHRpK87bXcV18Eg0bNJDpU0LUQafOnmHa\nghfJLjiHqVljfK9w5KGxfiiO0CBmvPUa9bx0jH30cWIayu6gtZHK6XQ6lQ7C1bKzs+nSpQvr1q0j\nMjJS6XDqJKfTSfqr89l2YC++ybFVesJ1es8B9qz45G/PSRxwR6U73TidTgoPHsW30M7s8c/KmhnC\n7STn1G77sg7w9MsvEND6/PpL+Vt/5cXRE2gQHqFwZEJcmuScmsdsNvPNjz/w1Q/fc+LMaYqtFkq1\nXmjCgjCF1UOlUvHrio85s+fg37bz5+ladmspRUeOQ34Jhj+2Ek+OT6T79TcRGxUtUylEtZK8UzPl\nnjzJ84sWcOTcSXwSrnwTiUuxWUop/vUgoT4mRjzwKHFRMS6IVFQXxUfwCM9TbC5h2HPPUBRsLP/C\nVBXBibE0uqUDRzZsueT7jW7pUKVtjFUqFX6xjbAWlfDg0yMZ8+jjtJOhhkKIy4iPiSXc4EehpRSH\nzUZMWH0p7gghroiPjw/db+xE9xs7lR/LOZHLB19+xg/bt1PkVVZpcQfOT9cqOHwMTuQRHhjM4Nv+\nwfXXtZWpwEKICk6eOc30l+eQnX8Wn/hGBMReeiHlq6HV6whonUCxtZSxL79AsNaH0Q8/RmyjKJf1\nIdxHCjzC5Sa+MBNLdDC+AVc+ciaqc3uAi4o8Ubd0oNEf71WVt8mAtmMyM19dwLJZc2SqhRDish4d\ncC/PLn8Vlc3OkyOeUTocIYQHqB8WTlrqINIYRHZODl0++KJK1/VvnsI/Rt5RpYX/hRB1S35hIdNe\nfomDJ3LwSYwmIC7UbX1pvXUEtGqK2VrKmPkziTD4M37IMCJCw9zWp7h2MklXuJTZbCbrVA4+V1Hc\nuSCqc3sSB9yBzteIztdI0v/1vOLizgVeajWqqBBee3/FVccjhPB8yfEJ6Mw29GVecuMihHC5yPr1\n6d69e6Xnde/enbvvuFOKO0KIi6zbvIkHx4/kmL8XAe2SXDIdqyq03joCrksgPzKAodMn8tbaD6ul\nX3F15NNDuJSPjw869bUPIw5OjK3SdKyqsJdYSWoc55K2hBCeSaVSYfTWo1XLx6IQwj3mzp3Lrbfe\nyrFjxy75foMGDZg7d241RyWEqA0WrlhK5s8/4peSrNhC6jqjD7qOzfnwp038fuQQEx97QpE4xN+T\nETzC5VrHJ1F4MFvpMACwFBThc7aY61u3UzoUIUQNp8ELo6F6noYJIeqm9evX06BBg4uOR0ZGsn79\negUiEkLUdHkF+azb8h0BzeNqxC55fk2j2Hnod/Zm/a50KOISlP8XIjzOUw8/RjP/MAr2HkLJTdpK\nTp2FPUd5ZfpMWZxQCFEplZeKQD9/pcMQQni49evXV5iulZDcjHXr1ikYkRCiJjtx5jRlPjVrLVFV\ngJH9WZUvHC+qnxR4hFtMTHuSu1NuIX/zL9gslmrt2+l0kv/L78SU6nhj5ksYfeSJvBCiclqNBm/v\nmnUDJYTwTBmzMmjVqyvtRgym5c0pSocjhKjB4mNiMVjLsBaVKB0KAHZrKRw7Q89OXZQORVyCFHiE\n2/S/rSfznpmCek82RUeOV0ufloIiCr7/mUd6/INpI8fKIoVCiCrTqNUuWUNMCCEqM+u/r+IVFYbe\n38TeQwfJLyxUOiQhRA02b9I01PuOUZx7StE4zGfzsf60nxefmYxarVY0FnFpUuARbhUeEsprM17g\npvpNyP9pL2VlZW7rq+hwDn7H8nlt+my63XCz2/oRQngmlUqldAhCiDpg3rI32HHsIMbQIAC8m8Xw\nyPhRHD95QuHIhBA1lb+fH/9Nf4EW+mDyf9iNpaCoWvu3mS3kbdtDZDG8MfMlIiPqV2v/oupkeINw\nO5VKRVrqIFr91IzZbyzCr30SahePrCncd4hW4dGMGzPUpe0KIeoWLynyCCHc5Fjucaa/PIfTWgd+\nzf63U6i3yYBXm3iGTZtIj5tvZfA/7paCsxDiIl5eXoz79+PkFeQzdcFLHNq/B5+ERnibjG7r02ax\nUrQnizC9L9NHPUOD8Ai39SVco0YUeF577TVeeOGFCgvhLl68mDZt2igYlXC1G1q3w9dgZPIbCwlo\n1dRl7ZacPkd8QBjjHpXijqiaNWvWMGnSpArHzGYzd999N5MnT1YoKqE0lUoGtQohXO/U2TOkvzqf\nQ2dyMSbF4Ovjc9E5Wr0O/5TmfH5gJ5kjvuIft93BXbf1lEKPuGrr169n9uzZ5OTkEBoaytChQ+nV\nq5fSYQkXCPDzJ2PsRE6cPsXMxS9zeM9hdE0j8fH3c1kfpcUllOw9TLjRn0lpT9G4USOXtS3cq0YU\nePbs2cPIkSMZPHiw0qEIN2uRkEQAGpxOp8tuWuxZxxk/fbZL2hJ1w5133smdd95Z/vq7775j7Nix\npKWlKRiVUJqXSoVy+/4JITyJ2WJm2ZoP+P6nrRSU2dDHRRIQnVTpdb6N6uNsGME727/l/S8/pX69\nEO7r159WicnVELXwFGazmeHDhzNr1iy6d+/Otm3bGDRoEK1bt6Z+fZla4ynCgkPIGDuRvIJ8Zi56\nmf37fkUXf22FnguFnUj/evxnpIzYqY1qTIHnrrvuUjoMUU20Oi02Fz6RUqlU6L31LmtP1C3FxcWM\nHTuWSZMmERYWpnQ4QkFOpxMV8rRcCHF1CgoLeP/zT9i8/UfOWYrxahCMqWVjAq7wnkelUuEfEwkx\ncMZiZcpbr+FjcRAZGs5dt91B2+YtZWSP+FsqlQqj0Yjdbi9/qKrVamVRXA8V4OfPtJFjyS8oIH3R\nfPbv/xVjcmO0PlX/fuQotVH4ywEi/YKYMXoi9UPlnri2UrzAYzabycrK4s0332T06NH4+fnx4IMP\nSsHHQ508c5rTRQX4u7BNZ5AvS1a/z319+ruwVVFXLF68mISEBLp0ka0e6zqn0wkqGcMjhKi6X3/b\nx4qP13AkN4ciRyle4UGYmjXE38s1Uz61em8C/1ivJ6fETPrKJWjetOJvMHJT2w7069YDo8Hgkr6E\n59Dr9aSnpzNs2DBGjx5NWVkZ06dPlwdZHs7fz4/pI8dx/OQJnps7mzMqG74J0XhVko8KDhzFkG9l\n2pAniY+J/dtzRc2neIHnzJkztGnThnvuuYfrr7+eHTt2MGTIEEJCQrj55sp3Qjp37hx5eXkVjuXm\n5rorXHEN7HY7o6Y/h6G5axOHX+NI1mzI5Prr2hLXKNqlbQvPVlxczPLly1m8eHGVr5Gc49mcTinw\nCCEuz1pq5eON61i36RvOFRdS6qNF3ygcfatYAtzct87ggy6xMQA2h4M1v23nw2/W46v1JiayIQPu\n6EPTmMZujkLUBtnZ2YwYMYKpU6fSo0cPNm3axMiRI0lMTCQhIaHS6+Vep3aLCA1j4ZR0vtj0Na+8\nswxTmwROb95FRKe25ecc/2obYTdcR/5Pe+nTqSv39ZHBFZ5C8QJPZGQkS5cuLX/dtm1b+vTpQ2Zm\nZpUKPMuWLWPevHnuDFG4gN1uZ8jEsdijQzFcwXDBqvJtk8CY56cxc8wzNG4Y5fL2hWfKzMykQYMG\ntGjRosrXSM7xXE6nk7IypaMQQtQ0RSXFLH5/BT/v2U2BzQohfpiahGHQNECpsTNeajV+keEQGQ7A\n/vwixi1+CW9rGcF+/txz5z/o2Kq1QtEJpWVmZpKUlETv3r0B6NSpE507d2b16tVVKvDIvY5n6H7D\nzTSLbcLI6c/isNkqvFfmKKNgyy88N2wUyU3iFYpQuIPiBZ5ffvmFTZs28eijj5Yfs1gsGKo43DQ1\nNfWiFeFzc3MZNGiQK8MU1+BCcac4wg9DSKBb+lBrNfh1SOKp9Kn856nxNGkU45Z+hGfZsGEDPXr0\nuKJrJOd4rjJnGcgyy0IIzhd8N2z5jnc+Xs3ZkiLUUWGYWjZ26RRzV9L7m9A3bwJAoc1GxgfL8F72\nXxIax/HY/91PvUD33H+Jmkmv12O1WiscU6vVaDRV++on9zqeo0F4BK9MnclDT4/CYbOj1mooKyvD\noPfh+dHj5cG4B1K8wGMymViwYAHR0dF069aNLVu28Mknn7B8+fIqXR8YGEjgXz60/rzdulCW0+nk\nsYljKanvjyHYvTcXaq0W3w7NGPv8dGaPn0RU/Ui39idqv507d3LPPfdc0TWSczyXo8yJvcyhdBhC\nCIWdOnuGJ6dMxBJowLdpA/y1it8uXxG1VkvAH1O59pzL55Ep4+l0XTuG3fuAwpGJ6tK5c2cyMjJY\ntWoV/fr1Y+vWrWRmZrJkyZIqXS/3Op7F38+PCUOfZOS0Z8n99TfK7HYG3nevFHc8lGtWgLsG0dHR\nzJkzh/nz59OmTRumTJlCeno6iYmJSocmXGDiSxkUhpjwcXNx5wK1VoNv+yTGpE/FWmqt/AJRZzkc\nDk6cOEFISIjSoYgawm63U1pqq/xEIYTHyjp2lMcmjUXdMoaAplGoa1lx568Mgf4Etm/GpiP7mDQn\nQ+lwRDUJDw9n4cKFvP3227Rr1678+1WzZs2UDk0o5Osv13Fkyw5KC4uxm628+cpimYbnoRQv8MD5\neaFr1qxh+/btfPLJJ3Tr1k3pkIQLHDxyhO+/+QZjRHD5seNfbatwjjtea3RavJpG8vyil686duH5\n1Go1v/76KzExMp1PnOekjLMFeZWfKITwWPkF+TgDfdHqXb9eoJJMjSM5ejxH6TBENWrbti3vvfce\n27Zt46OPPqJr165KhyQUMm/ePObOnXvR8blz50qRxwPViAKP8EyL3l2OJtBXkb4N9QLYf/igIn0L\nIWonh9NJcUmx0mEIIRTUKjEZTV4xNkup0qG4VOHBbK5v007pMIQQ1SwzM/OSxZ0L5s6dS2ZmZjVG\nJNxNCjzCbWwOGxE3t61w7M/b87n7tZeX/PMWQlSd2VZKiUztFKLOe3HCFKw/7SP7y+8rHK+OUcju\neF2w9xDtw2N4qP//IYSoW5599lmXnCNqD/kGLNzmlg43UJR1TJG+rUUl+BuVGT0khKh98gsKMDvt\nFJdasdlkHR7heuvXr6dXr160bt2a22+/nbVr1yodkriM8JBQXp0+C8fpfOzW2j2Sp/D3I3SKS2bU\ng/9WOhQhhAJKSyvPYVU5R9QeUuARbtOzcxcCS1VYCoqqtd8yh4PiHft5btjIau1XCFF7LV2zCnVk\nCIT68+G6z5UOR3gYs9nM8OHDGTZsGD/99BNTp05l7Nix5OTImig1lZ/JxOsLX6V452/lx6pzFLIr\nXge2iicCPWmpgxBC1E1VeWglD7Y8ixR4hFu98MxzeO07hvlM9Sxc6rDZydv8C+MefZxA/4Bq6VMI\nUfv9sPMnTCH18I0M59ON65QOR3gYlUqF0WjEbrfjdDpRqVRotVrUarXSoYm/0SA8AlUtnu6t9tah\n97DFooUQV8ZisbjkHFF71N5PLVErGH0MvDZjNoGnzRT+ftStfZnP5lP8w6+kjxxHu+Yt3dqXEMJz\nfP7NRsy+3qhUKrzUago0Trb9skvpsIQH0ev1pKenM27cOJKTk0lNTWXixImEhYUpHZq4jPyCAoZM\nGIOmQXDlJ9dQWr03B3KyeWvtaqVDEUIoxGQyueQcUXtIgUe4nVarZd6kafRMbkvell+wWVy7iKnT\n6ST/14OE59lZOmsuTaIbu7R9UX3uv/9+Nm7cqHQYog6xWC3897238Y1rWH7MNz6aWYtexm63KxiZ\n8CTZ2dmMGDGCqVOnsnPnThYuXMi0adPYu3dvpdeeO3eOrKysCj9Hj7r3gUld5nA4WLp6JQ8+M5ri\n6HoYIkKUDuma+LdP4sPtm/j3hKfIOnpE6XAEsHv3btLT05kwYQKffPLJRe8XFRUxcqQsMyBc4z//\n+Y9LzhG1hxR4RLW5r+8/mTP2WTR7j1FwwDU3p+az+RR+/wsPdOvNrPGT8NZ5u6RdoYwtW7aQlpbG\nxIkTOXfunNLhiDpgzPPT0CRFVdh1T63VQGwEk17KUDAy4UkyMzNJSkqid+/eaDQaOnXqROfOnVm9\nuvKRFcuWLeP222+v8DNo0CD3B13H5BcWMnXBSwwc/Thr9/2If0oy3iaj0mG5hF+TRpgbhzBq/kwe\nHDeCDZu/UzqkOmvjxo3861//Yt++fRw7dozRo0dz7733kpf3v6UMzGYzH3/8sYJRCk/StWtXHn/8\n8cu+//jjj9O1a9dqjEi4m0bpAETdUj8snNdmzGbJ6vdZs/5LfFrE4m00XHE7ZQ4HBbsPEuMXzJSZ\nL6H3ljnmnmLRokXMnDmT7t27M3DgQFJTUwkOrr1D5EXNNX3hHHI1dnwD/C56zxgaxO+/HWb+8jdI\nGzio2mMTnkWv12O1Vhy9qlar0Wgqvw1LTU2lV69eFY7l5uZKkccFnE4nX23dzDtrP+R0cSHaxvUx\ntU9SOiy30Pr4ENgqnjK7gwWfr2Lx+2/TLK4pQ/7vXlmzsBq99NJLjBo1qvzvd+/evTz++OOkpqay\ndOlSAgMDlQ1QeKShQ4cCMHfu3ArHhw0bRlpamhIhCTeSAo9QxH19+tPz5i48Pes/nPNR4RvbsPKL\n/mA+m4997xGeuP9BbmzT3o1RCiXEx8ezcuVK1qxZwyuvvMLixYu58cYb6datG+3bt6dhw6r/WxHi\ncl54YxE7T2Xj26TRZc/xbRLFxt278Fm5ggfuGlCN0QlP07lzZzIyMli1ahX9+vVj69atZGZmsmTJ\nkkqvDQwMvOhLn1ardVeodUJ2Tg6vvrec348cxuqnx69JJP7auvHZ4qVR4x8fDcAvZ/J4eNrTBGj0\n3N7pFvrcepv823KzQ4cO0aVLl/LXCQkJLF++nNTUVB544IEq5QQhrsbQoUMp06hYuGAhPjpv0mfM\nkJE7HqpGTdE6ffo0KSkpsgZHHVEvMJCFU5/n9sTWnPthN44qrHdReOAowWcsLMmYI8UdD+bl5UXf\nvn359NNPWbRoESEhIcyZM4du3brRsmVLbrrppmvuIzc3l0cffZQ2bdrQqVMnli5d6oLIRW3w7JwM\nNmf/9rfFnQv8mzXm010/kPHfhdUQmVDCunXrGDZsGA888ACLFi26aDeRvLw8Bgy4tgJfeHg4Cxcu\n5O2336Zdu3ZMmTKF9PR0mjVrdk3tiqrLPXmSaS/P4f4xT/DE3Bkc1DswtEsgMD76/LTMOshQL4CA\n1omUNWvEu9s3MXDscIZMHMPaDZmyBpmbhIeH8+OPP1Y4Fhoaymuvvcbp06d5+OGHKSwsVCg64emO\n5p+l7RP30ea2W6S448H+9hNtxIgRqFQq4Pww1stRqVTMmjXrmoN5+umnyc/PL+9T1A2D//EvOrS4\njkkvZWBok4BWr7voHKfTScHO/dze9noe7P9/CkQplJKSkkJKSgpwviizf/9+zpw5c01tOp1OHnvs\nMVJSUliwYAFZWVkMHDiQ5s2b06pVK1eELWogu93OiOnPctIHfOMqL+5c4J/YmK0HDzEuYzrTR46T\nzygPsnLlSp577jn69OmDv78/L7/8MqtWreLVV18tHy1os9nYsWPHNffVtm1b3nvvvWtuR1RNWVkZ\nW3b+xEfrvyTn5AmKcKCLDsdwXRwyIakiL7Uav+gGEN0Aq93Bm5szWfLRKgKNJto0b0m/LrcTUq+e\n0mF6hIceeoiJEyeyY8cOBg8eTFRUFAANGzbk9ddfZ/DgwaSmpsrnjHA5m83GkRPH8Y1K4lxpCafO\nniEkSP6uPdHfFnhiY2OZP38+UVFRtGrV6qIij0qlwul0uiQJvf322xgMBsLDw6+5LVH7JMU1Ze7E\nqQyb/Ay0iUerr7hYcsH2fdx7+53ceWt3hSIU1aF+/foVFrv9q/DwcJfkiJ07d3Lq1ClGjRqFSqUi\nLi6OFStWyNx3D5ZfWMjjz47HHhOKKfjK/3/2bRzJoZyTPDJ+FHOfnSbrfnmIxYsXM3nyZPr27Quc\nX2wyLS2NgQMHsnz5cpkSWos4nU72HfidD9Z9zoHDWRRaLTj8DRgiQ9E1kKJOVXlp1PjHREIM2J1O\n1uXs54uZm/FxehFo8uXm9incdkMnfGVb5aty1113ERAQwKpVqy4aqRMXF8f777/P9OnTWbdunUIR\nCk81ZcGLqGPO30Pr4xvxUII8FwAAIABJREFU7EsZzH9Ods/yRH9b4ElLSyMiIoLJkyczb948YmNj\n3RJEVlYWb7zxBu+++y79+vVzSx+i5gsPCWXOxKkMnToB/47J5YXDgv2H6XtzFynu1AHr16+vln52\n795NkyZNeP755/noo48wGo0MGTKk/Eue8CzHco/z5LRn8W4Zi8F05Yu6X2CsH0qJsYDBTz3B/Odm\nEBQgXxlru9zcXNq0aVP+OjQ0tPwp+v33389bb72FWq1WMEJxKTabje2//szXP27l4OFDlJRaKCkt\nxWHQ4V0/GEOLGHyVDtID/D979x0eVZU+cPw7vc8kpBdSSGihS8eCIGURXGyr64qKP0EE1rIrigqI\nYEGwgixWFBRFXEVdFBQBKyACUkMLEFKAQALJJJPpM/f3RwSNCSGQzEzK+TzPPG5uOffNJtyc+95z\n3iOTyTDFRkFsxVLxZR4vy7avZ+naVWiRo1drCDOZ6dmpC5d170V8jHhJWxtXXXVVpTo8fxQTE8Pc\nuXPFFDmhXv3062b2HM8jrGsbADRGA4WKIpZ+8Tm3jBgZ4uiE+nbeScfXX389v/zyCzNnzmTx4sX1\nHoDX62Xy5MlMmzYNi8VywecXFxdXWloQKjpsQuMUGxXN6OtuYvH3q7C0TcFlsxPlV3LrCJH4E+qP\n1Wpl06ZN9OnTh++++45du3YxZswYEhMT6dGjR43nintO47Iv+yBTX5iNsWd7lJqq0z8vlM5ixtMl\njXFTH+LFx56gZXxCPUQphEpKSgpr166ttCKV0WjkjTfeYNSoUdxxxx3MmTMndAE2cy6Xi72HDrJ9\nXyZ7D2VhLS2l3O3E4fOA2YA60oKuXTxKmYyqa+EJ9U2hUmJJjofkeAAkoNDl5r+Zm1j201pUHh96\nlQa9RktifDxd2ranS9sM4qJjxJSjP1i7di2ff/45NpuNvn37ctttt6HV/j4q1Gq1Mm7cOD788MMQ\nRik0FfuzD/HSojex9OlYabu5TTKffL+auJhoruzZN0TRCYFQq6pyM2bMoKioKCABLFiwgHbt2nHZ\nZZed3VZTvZ8/W7JkCfPnzw9EaEKIjLjyKj764nP8fj+OA7nMmfR4qEMSmhi1Wo3FYuHuu+8GoFu3\nbgwZMoS1a9eeN8Ej7jmNR0HhSaa8MBtznw4oarEcdW2p9DoMvTL496wZvPXMC1hMYqxAY3Xfffdx\n7733sn79eiZNmkTbtm2BipWr3n77bcaMGcPtt98uHk4DyOPxkHXkMNv37yUzaz/FJcU4PG5cHg9u\n/GDUITfp0UeFoWxpQQNoztuqECxKjRpLyzj4w2xGh9/P7tJSNq//GtlXnyN3e9EqVWiUKnRqDSkt\nk+jStj2d27YnskVEs/r3VZu6X263u17qfgnCzv17mfHKi5h7d6i2BIK5W1te+eBdvF4fg/peVk0L\nQmNUY4/31KlTREREoNFoSEgIzFvKVatWUVhYyKpVqwCw2Wz861//YsKECYwdO/a8548aNYoRI0ZU\n2lZQUFDpbZzQ+PTv04+vczIJU2qIi44OdThCkFx++eW1TvD+9NNPF32dVq1a4fP58Pv9Z//g+Xy+\nWp0r7jmNg8fj4d9PTcfYo129JnfOUKpVaLukc/+MKbzz3Nxm9YDSlAwYMIBly5bx2WefVZmKFRMT\nw7Jly5g/fz5fffVViCJs/CRJ4nRJCXsO7ifzUBYHj2RTbi/H6fHg8nlx+31g0CIz6dBHhKGKS0QB\n6H/7CI2PTC5HG2ZCG1Y5+S0BNp+PLdaTrP/2IPzvvyi8PjQKJRqVCq1STUxUNBnpbchIa01aUjJq\ndd1HXjYkou6XECzrf93Mi4srRu7IldVPNZbL5YT1yuC15UspKS3lxqFXBzlKIRBq7PVeccUV9OvX\nj5EjRzJo0KBKwwfry5nEzhkDBw5k+vTp9O/fv1bnh4eHVymMqlKp6i0+ITRuGHI1nz76Nb279wl1\nKEIQvfjii9x7773ExsZyxx13nDPZU9eH6UsvvRStVsv8+fOZOHEiO3bsYM2aNSxatOi854p7TuOw\n6NP/4kuKQK8N3Lt+jVGPNVzHim+/ETXCGrEOHTqcc7lyvV7Pgw8+yPjx44McVeMiSRLHCo7z695M\ntu/N5EThSZzeilE4Lp8Xv1KOZNCishjQxVtQqFuIJE4zJVco0LcIQ9+iag0zhySxv6ycHTt+Qlq/\nBpndiVqmQPPb6B+DTk/rVmn0yOhEhzZt0Gl1IfgO6kbU/RKC4fO1q3l35WdYenescfESqOhTh3Vv\nz7Ifvqaw+BTj/35bkKIUAqXGBM+rr77KypUrmTFjBtOmTWPo0KGMHDny7JLFghAoYWYzPpuDvl0u\nCXUoQhD17NmThQsXcuuttxIWFsaAAQMCch2NRsN7773HzJkz6devH0ajkWnTptG5c+eAXE8Ivh+3\nbMLYLT3g1zGlJvD5mq9FgqcJW79+PePGjWPv3r2hDiXk3G43ew5lsTVzJ3sPZlFWbsPhceP0ePBr\nlWDSo21hQdM2DplMJqZTCRdEJpOhNRvRmquu0OUFTrs9fHfiIGsO7IAyO2qZAq1ShU6tJi4mjksy\nOnJJRkdiIqMa7KhKUfdLCLTP1n7Nkq9XENaj/QX9O7B0as23+7bDUonxt9wewAiFQDvvCJ4rrrgC\nt9vNDz/8wJdffsmECRMwm80MHz6ckSNHnp2vXl+CtYqO0PBJHi9tUgOzcpvQcHXo0IH777+fd999\nN2AJHoCkpCTeeuutgLUvhJhcFpQOvlwux99AHySE+nMhtQGbEofDwadrv+bHXzZS6nTgknwVNXEs\nBgzxYSjULVADTWsSjdBQKdQqjDGREBNZabtTkthXVs72jatZ+PVnKN0+dEo1iTGx/P3qkXRs2y5E\nEVcl6n4JgbRl907eW/n5BSd3zjC3S2Vd5jZivoni+sHDAhChEAy1KkygVqsZNGgQgwYNwm638+23\n3/Lll19y0003kZKSwl//+lfuuuuuQMcqNDc+ifCLWFlNaPzuvPNO7rzzzlCHITRicqnioTzQnWS/\nz4cYTC80JeUOO3MXv8XBnCOUeVwQE4apbTw6hYLGNyFGaA7ONfInx1bO9CWvo3Z4aGE0c93Qq0Ne\nSDaYdb8KCgqYPn06W7ZswWg0MmbMGG67TUy/aaokSeKlha9h6dG2Tn0fc0YrPvziM4ZdfmWjnAYp\nQM2T8qqh1+sZPnw4CxYsYOHChcjlcp577rlAxCY0czIQ85CFKnw+HzabLdRhCA3c8AGDKMs+GvDr\nlB7I5dZrbwz4dQQhWHw+P1u2b0Nq3xJLj/ZYWsYhF3+LhUZIYzQQ3iENQ492FCq9bNuzO9QhARUj\nladMmUJ6etVpxGfqfn3++ed1uoYkSUyYMIH09HR++eUXFi5cyPz588XqXE1Ydl4OToO6zvdrmUyG\nLyaMdT9vqKfIhGC74ATPjh07mD17NgMHDmTs2LG0atWK119/PRCxCYIgVLF+/Xp69uwZ6jCEBu7G\nocPRFTtw2ewBu4bTWkakT8GVPUUx+MbK7Xaf91PbFfaaCrPRyIvTZmLfso/iX/dRcjgPd3ng/h0J\nQiD4fT5sJ4oo3nOI0s17aWOM5KG77gl1WLWyfv16evXqVac2duzYQWFhIZMmTUKhUJCens6HH35I\nSkpK/QQpNDhHTxQgqetn0Q+FXsvRE8frpS0h+Go1RevXX3/l66+/ZvXq1Zw8eZI+ffpw3333MXjw\nYAwGQ6BjFJopMftYOJfmWg9DqD2ZTMa8x59i7GMPoujRDqWmfquEuMsd+Pfk8vKcl+q1XSG4RGH1\n6qUktGTZvNcpLStlw/atbNi6hRPZudg9Lhx+D8X5J1DqNCjUqkpTAeL696i2vePfb6l2uzheHF8f\nx/vcHsqLivGdLkXp8KBXazBq9fRs04Yrh/elbau0864k1NDUtZ+TmZlJ69atmTNnDitWrMBgMDB+\n/Pizy7MLTU+frt1RLn23XtryHyviutvvq5e2hOCrMcHz1FNP8c0333DixAkyMjK44447uPrqq4mO\njg5WfEKzJlI8giBcPIvJxMtTZ/LAU4+j6ZKOxlg/CzI7rKX49+SyYOZstBptvbQphMbixYtrdVxz\nLXhqNpn5y+UD+Mvlvxe8dzgcTH1iOscKT2AvsePz+fFJFZ+SrXtArwW9Bm2YGY3JgKyRPVgLDY/f\n68PnduNze5F5vOCXUMjk+Hdmo1GqCNMbuKJ1Bv3/1pfUxKRm++/1j6xWK5s2baJPnz5899137Nq1\nizFjxpCYmEiPHtUn0oTGTaVS0aN9R37NPYoxKe6i23EUFpMSHk1Ui4h6jE4IphoTPOvWrePaa6/l\nr3/9K2lpVVczcrvdrFu3juXLl/PGG28ELEihuRKjNARBqJv4mFjefPp5Jjz+CM42CWhb1K1we3nh\nabR5p/jPsy+h14nig41d7969z7nP6/WiVNZqoHOzotPpeGF29cs4u1wuDuflsvfwQQ4cOczxQwU4\n3S6iwlvg9npw+XzIdGrQa1AadXidLhQadZUH8nON5DgXcXzjPd7v8+G22XGW2ijenw12NwqfH41S\niVqhQqNU0aVNe1olJ9MuJY12qWlERUaKJM55qNVqLBYLd999NwDdunVjyJAhrF27tlYJnuLiYkpK\nSiptKygoCEisQv15aMx4Jk5/FGtRMbrI8As+31VWjjz7BM/OeTkA0QnBUmPPZe3atdXeQPfs2cPy\n5ctZsWIFVquV1NTUgAUoCELz4Xa7z3tMc6uHIdSdxWzm7Tkvcd+MqZQ6XBgSLm4UalnOMeI8Cp5/\n5gXx4N+E7Nu3jxdffJEpU6aQnJx8dvukSZMoKyvjscceq/Yll1CVRqOhfXpr2qe3rna/1+vlWMFx\nDubmkJV3hCP5eZSWncLt8+LxenF5PXj8PtCp8evUqIx6tGYjSq1GPNA3Qj6vF4/NgbO0DBxupHIn\nCr+EWqlCLVegVirRqjXEREWT3rET6UnJpLZMwmIyN9mfd7D6Oa1atcLn8+H3+89OT7uQdpcsWcL8\n+fPrHIcQXDKZjLnTnmTC449gl0AXVfskj7OsHH9mDq8//Zzo4zRyNf70/nhzPX36NCtWrGD58uXs\n378fgCuuuILRo0fTr1+/wEYpNE9N9I+7cG6iHoYQKBq1hteemsPjc5/jwME8TOktL+j80n1H6JHQ\niofHTghQhEIo7Nu3j1tvvZW2bdvi9/sr7RsxYgQLFy7klltuYenSpSLJUw+USiVJiS1JSmzJQKpf\nrtrn83Hs5AkO5+ZwIDebnPw8iq3HcP+WAHJ6PfjkMjBqURj16MLNqHRiqmQo+H0+XKXlOK2lyMqd\nSHY3GoUSjVKJRqnCqNYSFxND6y5dSEtKJjUxCbPJFOqwQypY/ZxLL70UrVbL/PnzmThxIjt27GDN\nmjUsWrSoVuePGjWKESNGVNpWUFDA6NGj6z9YoV6pVCpefXI24x9/BLuMWo3kOZPceeOZ5zDqRX3d\nxq7GBI/P5+OHH35g+fLlfPvtt0iSRI8ePZg2bRpPP/00Dz30EK1bV/+WRhAE4UKJehhCIMlkMp58\n4GFeWvQmG/ftw9yudqNPrTuz+Ev3vtx1w98DHKEQbPPmzeOqq65izpyqU44GDRrEgAEDuPfee5k7\ndy7z5s0LQYTNj0KhoGVcPC3j4unfu2+1x5SUWjmQfYjMQ1lkZWdTUnoUl9eN67ckkF+tRGYxYIiN\nQqWt3wLrzY0kSTiKrbhOngabE5VMjlZZMXXKoNbQNjaO9r16ktEqneSERFSq+lnFp6kKVj9Ho9Hw\n3nvvMXPmTPr164fRaGTatGm1TjCFh4cTHl45MSB+to2HUqnk1ZnPcs/Uh3GoFOgs5nMe63E68e8+\nwhuznhfJnSaixgRP//79cTqd9O7dmxkzZjBgwABatGgBwDPPPCMesgRBqFeiHoYQDP8aPRbjRx/w\nTeZWzO1rTvJYd2Zx0xWD+dtfRtR4nNA4bdu2jXfeeeec+xUKBePGjWPCBDFyqyEJM1vo1eUSenW5\npMo+SZI4WVTExh1b2bB1M6dLj1HuduFWypBHWjBGR6BQib8l5+IsteE8cQqs5eiVagxqLe1TUhl4\n41/JSG+DVitGS9VFMPs5SUlJvPXWW/XWntC4KJVK5s94hjseuh9fz/bV3vf8fj/lWw/w6oxZIrnT\nhJz3LqLValGpVLjdbrxebzBiEgShGRP1MIRgGHvTPyh5y8rW7FyMqQnVHlN6IIfBXXuJ5E4T5vF4\n0J2nWHZYWBgOhyNIEQl1JZPJiImK4tpBf+HaQX85u/1owXHW/byerbt3cqzwJLpu6ahEsuKs0qwc\nLE6JNvEJXPmXG+jdpRsatSbUYTVJop8jBItWo+XR8ffy5PtvEd4xvcr+spxj3HT1CLFiVhNT49qV\nP/zwA3PmzKlYMeGFF+jfvz9///vfeeutt5Ck+lvhaOXKlQwbNoxu3boxYsQI1qxZU29tC4LQeJyp\nh2Gz2aqth2G327nllls4dOhQna+1cOFCOnbsSLdu3c5+tm7dWud2hcbjoTHjiXBWvLH+M/upElK1\nFu6+6dYQRCYES5s2bdi4cWONx2zcuJGUlJTgBCQETEJsHLddeyMvT53JgunPUL55Hz6PJ9RhNQhl\n2Ue5JDqZN59+nscn/osrevYRyZ0ACWY/RxAAurbviMFT/XO7oqiMG4YMD3JEQqDVmOCRy+X069eP\nWbNmsX79el588UVatGjB3Llz8fv9TJ06lc8++wyXy3XRAWRnZzNlyhRmzZrFtm3bmDJlCv/617+q\nLM0nCELTd6YexgcffFBldb5BgwaxZMkSevTowdy5c+t8rb179/Lggw+ybdu2s5/u3bvXuV2hcXlm\n0mM4M7MrbZMkCe/+fGY+8FCIohKCZdSoUbz00kts3ry52v2bNm3i+eef5+abbw5yZEIgnDp9mjlv\nLmDS7JlIJh31+K6yUZNr1Gw7sJcJ0x/lm/U/1utLXKGyYPZzBOEMtar6WmRKlQqFQhHkaIRAq/VE\nT41Gw7Bhwxg2bBhWq5WvvvqKFStW8Mgjj/DMM8/wyy+/XFQAqampbNiwAZ1Oh9frpbCwEKPRKAp5\nCUIzFMx6GHv37uWGG26ocztC42Yxmeie0ZFdp0rQR4QBYDtWyODLrhBvsJuBq6++mn379nH77bfT\nuXNnOnXqhMlkwmq1snPnTjIzM7nttttEgqcR8ng8ZGYd4Jdd29iTlUWJrRSb34MqKQZDt3Rqv3hw\n02eIj4L4KBweL298t4KFn3xImMFITFQU3Tt2oVenLsRGRYc6zCZB1P0Sgs3ucFDqKKe6MstOyUfu\nsaMkxVc/VV1onM6b4PH5fKxevZrLL78co9EIgMViAeCWW25hzpw5fPHFF3UKQqfTkZeXx9ChQ5Ek\niRkzZmAwiEJPgtDcBKsehsPhIDs7m8WLF/PQQw9hNpu56667RMKnmbr7b7cy7tnHzyZ4/MdPcds/\nrw9xVEKw/Pvf/2bgwIEsX76cHTt2UFpaSnh4ON26deOJJ56gY8eOoQ5RqIHH42HPwQP8smsHe7L2\nY3PYcXjcOH3eiqXUw03ok8NRqqIIC3WwDZxCpcSSlgRp4JEkssvtZP6yjsXfrEDp8aNTqdGp1MRG\nR9O9Qyd6dexKVGSkWHTlAoi6X0KwPfv6fJSpcdXuM7RL4pkFc3ntqaorSQqNV40JHrvdzvjx49m8\neTPvvfdepekLmZmZLF++nL59+/LKK6/UOZD4+Hh27drF5s2bGT9+PElJSfTp0+e85xUXF1eZzlVQ\nUFDneARBCL4z9TD+WHTwz+qjHsapU6fo3r07//jHP+jXrx/bt29n/PjxREVFccUVV9R4rrjnND0R\nLVqglf0+RNmg0qDViOKrzYXP5+P48eM8/PDDZ19kASxbtoycnBw6dOggHmBDyO/3k19wnL2Hsthz\nKIvco/k4XE5cXk/Fsuh+LzKjriKRkxSOQhWFFhD/gutGJpOhNhpQGyu/cHVLEodsdnZtWss7q1eg\n8PjQKFVolEo0ChXhYWG0SU2jQ1ob2rZqhdFgPMcVmqdg9XMEAeDTb75i76ljWDpUX7RbpdNxWi9n\n3rtvc9/t/xfk6IRAqTHB8/rrr1NQUMAXX3xBq1atKu2bOXMmo0aN4u677+att97in//8Z50COTP/\nr0+fPgwdOpQ1a9bUKsGzZMkS5s+fX6drC4LQMIwaNYoZM2aQlpZGz549q+w/Uw/joYfqVhslMTGR\n99577+zXPXr0YOTIkaxZs+a8CR5xz2malHJ5tf9baNpq8yLrs88+Y/78+Wg0YspeIEiSRGFREXsP\nH2TP4Syyc3Ow2ct/S+B4cPm8+DUqMGrRWExoksNRKJUoAP1vHyF4ZDIZapMBtanqSHunJJHrcHHg\n0A7+t/NnJJsTlR80KhUahQqNSkV0VBTtUtPJSEsnLSml2S27Hqx+jiCs+vFb3v/6f1i6t6/xOHOr\nRH7K3IVx+TL+73oxHbkpqDHBs3LlSqZOnVoluXNGmzZtePjhh5k7d+5FJ3i+//57Fi1aVGk+qtvt\nPjsN7HxGjRrFiBGVl7AtKChg9OjRFxWPIAihE6x6GLt372b9+vWMGzfu7Dan04lef/5HBXHPaZrc\nPt/ZP4huny+ksQjBU9sXWW+++WadX2Q1V5Ikcaq4mH2Hs9h7+BAHc7Ips5X9PgLH68GvViIzalCa\njGhjTSg14chAjMRpZGQyGWq9FrW+6k/ND9glif1l5ezYvQE2fQvlTlTI0ChVqJUqNCo1sdExtE9N\no31aa1q1TGpyiVVR90sIhpU/rGPh/z4mrHv7Wo1ANXdI46ttP+P3+xlz4y1BiFAIpBoTPCdPniQ9\nPb3GBjp16lSn6QkdOnRg9+7dfP7551xzzTX8+OOP/PDDD9x77721Oj88PJzw8Mql8kSBZkFovIJR\nD8NoNLJgwQJSUlIYPHgwmzZtYuXKlbz//vvnPVfcc5qe4ydP4pZJZ0cCOHxuysptmMTUgiYvGC+y\nmguH08Geg1lszdzJvoNZv9fC8XrwqxRg0KI0G9BFmVG2tIgETjMkk8nQmo1ozVXvrX7A7vezt8zG\nth0/wYY1YHeiQoFWpUKnUhMTGU3XjI70yOhIfGxco506Kep+CYF0ocmdM8wd0li9o2LRJJHkadxq\nTPDExsaSk5NDQsK5K2vn5+cTERFx0QFERkby6quvMmvWLGbOnElqaioLFiyosnSg0AyJVTqbpWDU\nw0hJSWHevHm88MILPPLII8TFxTF79mzat695GKvQNL3y3kK06Ylnv1Ymx/LqB+/y8FixiklTF4wX\nWWcUFBQwffp0tmzZgtFoZMyYMdx22211bjcU9h8+yKdrvyb/6NGKJI7HjUvygUmH0mJE91stHA3Q\ntMZfCIEkk8vRWkxoLaYq+1xnav9s/pb31n6J3O1Fq1ShU2kwG41c0qEz1w4agk5bcwHjhkDU/RIC\nJSs3m4XLlxHWu+NF/Q6ZO6Tx1ZYNtEluxRU9ewcgQiEYakzwDBkyhPnz59OjRw/UanWV/S6Xi7lz\n59K/f/86BdGjRw8++eSTOrUhNEUiw9PcBLMeRv/+/et87xIav+MnT5B1NBdLQoez2wyR4Wz+eSfW\nsjIspqoPGkLTEYwXWVAxTWnChAn07duXBQsWkJ2dza233kqnTp3o2rVrndoOlm17dvPBik85XlSI\nQy1H2zIabduKURQ6oOE/VguN2blq//iAIreH5Xt/Yfl3qzGrNPTqcgm3jBiJ2djw7t+i7pcQSDPn\nvoi5e7s6JQjNnVsz/92FXNa9J3JRk7BRqvGnNm7cOEpKSrj++uv58MMP2bNnD3l5eezevZv333+f\na6+9ltOnT4thy0JAiPRO8/PHehh/7PRART2M5cuXk5WVxZtvvhmiCIWmRJIkHnt+FvrOVUdwaDuk\n8sicp0IQlRBMZ15kud3uavfX14usHTt2UFhYyKRJk1AoFKSnp/Phhx82mpVybrjrdp5c8gYno3Vo\nu7chvFM6ujCzGGUgNAgKtQpLyzgsPdpD51S+PZ7F38b9H5+uXhXq0KoQ/RwhUI4czcOulqGoY9kA\nuVyOFBvO2o3r6ykyIdhqTPAYjUY+/PBDunfvznPPPcf111/P4MGDufHGG5k3bx6XXnopy5Ytq/Ob\nLUEQBKioh/HYY4+dtx7GihUrghyZ0BQ98vwzOGPNqLRV35JqTAaKjQqefnVeCCITgiVYL7IyMzNp\n3bo1c+bM4bLLLmPo0KHs2LGDsLCwevpOAuvlJ2ehKXfjspYh+f2hDkcQzsnn8uA9WcJVAwZw7eC/\nhDqcKkQ/RwiUYycKQF8/o75kBi05x/PrpS0h+M477spsNjNjxgw2btzIl19+yQcffMCqVavYsGED\nU6dOrVJsVBDqix8Jr9cb6jCEIApmPQyheXtqwcvkeG0Y4qPPeYwxOZ4dRXnMe/ftIEYmBFOwXmRZ\nrVY2bdpEeHg43333Hc8++yxPPvkkW7ZsOe+5xcXFZGdnV/rk5eXVKZ4L1TI+gcXPz6NfiyT0WSfw\n78zGtnU/xdv3U5KdT/43G5Gk38fdHv++8vclvhZf1/fXPq8X24kiSvYexrp5D65tWcgzc4kpdPDv\nm27n4TETGuQIM9HPEQLlkg6d4HRZvbTlKzjNgF796qUtIfhqrMHzR2q1mrS0tEDGIgiVyBQKTp4q\nIj4mNtShCEESrHoYQvN1ZlrWYW8ZptRz/56dYW6dzE9ZeyldMJepE+4PQoRCsJ15kTVlyhTy8vKw\nWq2Eh4eTlJSEQqGol2uo1WosFgt33303AN26dWPIkCGsXbuWHj161HjukiVLmD9/fr3EURdqlZr7\nbr+r0rZTxafZtHM7i/e/g3rvUexuFw6vG+fJ05Tsy0bSqdGYjfh9YtSPcGEkScLjcOEsLcNZUkrx\nzixkLjdquRKKSjEfOUXv9Db0HXIJGeltGs1qlqKfIwSKVqOlW+v2ZBYUYYiNvOh2HNZSEg1hpCUl\n12N0QjDJpD++cmnRxwW+AAAgAElEQVQi8vPzueqqq1i7di2JiYnnP0FokIaN/gcPjZvAwL6XhToU\nIUheeOEFtm7dyqJFi85Z2P2OO+6gffv2TJ8+PQQRVk/ccxoHt8fNA08+TnGYusaRO9UpyzlGok/D\nnMlT6+2hX2g+1q1bx6OPPsrGjRvPFq18+OGHiYiIYPLkyTWeW1xcTElJSaVtBQUFjB49usHec8rt\n5RzJzyMrN4eDuUc4dvw4DpcTt89b8fF68cokZAYt6NRozCbURj0KVa3fOwqNmCRJeJ0unNYyvDYH\nMrsLXB7UCgVqhQq1UolaoSQsLIzUxCRaJ6WQnpRMbHRMoy/62lj7OSD6Oo2Bz+dj7GMP4kqOQtfC\ncsHnu2x2vLsO89azL2LQ6QMQoRAM4i+p0CCdKDyJymJg4/ZfRYKnGRk3bhw33XQT119/PaNGjaJz\n586YTCasVis7duxgyZIl+Hw+UdhduGDF1hLufWIKUus4DC0uvO6JKTme4ydPMfbRB3llxjOi4yNc\nkEsvvRStVsv8+fOZOHEiO3bsYM2aNSxatOi854aHh1eZDt/QRysY9AY6tGlHhzbtznlMub2c7Lw8\nDuYd4WDOEY7lFuB0u/D8lgTy+Lx4/D78SgUynQZJq0Jj0KM2GVCoVQ1y+k1zJ/n9uO0OXKXl+OxO\nZC4POF0oJDkqhQK1Qonqt+SNxWwmpWU7Wrf8PXnTHJLnop8jBJJCoeC1p+Zwz9SHcfj96CJrX0rF\nWVaOb/cRXnvqOdHHaeREgkdokD76+gv0rRI4lHsk1KEIQXSmHsYLL7zAc889R3l5+dl9FouFa665\nhokTJ4raX8IFyTmWz6RZM9F1bY3acPGLOeujI3BqNdw1+V/Mm/400REXPwRaaF40Gg3vvfceM2fO\npF+/fhiNRqZNm0bnzp1DHVrIGPQGOrZtR8e2504CSZJEsbWE3GPHOHIsn5xj+Rw7UYCtvBCPz/db\nMqjivz4ZyHRqJJ0aldGA1mRAoVGLRFA9kPx+XGV2XDYbfrsLHC5kLg8quQKVQolKUZG0UatURLSI\nICklheT4RFISWhIfHSOW+/4D0c8RAk2tUvPG089z38ypFLs9tRqx7DhVgiL7BK8/+4JI7jQBYoqW\n0CCNevCfaHq0xbp9P8/fN5mUhJahDkkIMrfbHbB6GPVN3HMart1Z+5k+93nMvTJQqOtn1IPH6aR8\ny36ee2QaqYlJ9dKmIFwIcc+pyu6wk3vsKEeO5XM4L5e8Y8cotZXi8fpw+yumhXm8XvxqOeg0oNOg\nNRmb9dSwM3VuXGU2vDYHOF3gcKOSyX9P2vyWuImOiiIlviWtWiaRmtCS6MjIRj9dKtQaUz8HxH2n\nsZEkiUeee5ocHBiT4s55nP3EKYwnyvjPjFkNfnSoUDvN8y+a0KD996svcIcb0MpkGNol8+xr83nt\nydmhDksIMlHYXairowXHmT73eSx9OiJX1l+nWaXVYuqdwcOzn+LNp58jzHzh89wFQahfep2edmmt\naZfW+pzHSJJESamVI/l5ZOfncTg/l+N5J7C7HLi9XlxeDy6vB79GiWTQojYb0YWZUDTShx5JkvCU\n27EXl4LNgWR3oUaORqlErVShViiJCwsjOb4NaYlJpLZMIiEmttraMEL9E/0cIZBkMhmzH57K4y8/\nx4Gc4xiTqyZ57CdPE3bKwStPzm7QyUXhwogEj9CgFJ0+zbJVK7D06QiASqfjtMrPslUruHnYNSGO\nThCExsLj8fDgrBkYe7av1+TOGQqVCm3XdO6bMZXFz88T00AEoRGQyWSEW8IIt4TRrUOnao+RJIkT\nRYXsPZhF5qEDZOfmUu6wVyR/fqsPhF6NzGzAEBWBUhvaZIgkSTiKrbhOWcHmQOGT0P6WvNGq1LSM\niKBdRm8y0tJpnZyKTnfx01QFQWh8Zj7wEPc/OY2ThacxRLU4u91ZakOTf4pXZr0okjtNTINI8GzZ\nsoXZs2eTnZ1NeHg4Y8aM4eabbw51WEKQ2R0O7p3xGIaurSs9LJlaJ/HRNytJiounb9fuIYxQaGqK\nioq45pprmDVrFldeeWWowxHq0cKPlyIlR6HSBO7hS2PQY43Q8+mar7h+8LCAXUcQhOCRyWTERkUT\nGxXNgL6XVtnv8XjIOZrPr3t2s2XXDorLjmN3u3BKPmRhBrRRLdCYDAFJ+vo8XsoLT+MvLkXu8KBX\nadCrNbRPSqbPsCFkpLWmhajdIvyJ6OsILzw6ndsn3Yc/3IJcqUCSJFyZ2bwmkjtNUsgTPFarlQkT\nJjB9+nSGDx/Onj17uPPOO0lKSqJv376hDk8IErvDwbgpDyHPSEal01baJ5PJsPRoz/PvvM4jYybS\ns1OXEEUpNDVTpkzBarWK0RdN0IZtWzFdcu6pGvXFlJLAl+u+EQkeQWgmVCoV6SmppKekctPVv48s\ntpWX88uu7Wz4dQtH9x7F5nJgl0vokuPQhpku6lp+r4+yvAIosmLW6LDoDVzRPoPLr+9NWnKK+Nsl\n1Iro6whKpZJJd4/nmSVvEda5NWWH87hl+EiMekOoQxMCIOQJnuPHjzNgwACGDx8OQEZGBr179+bX\nX38VCZ5mwlpayvhpk5F3SEZnNlZ7jFwux9KrA7MWLuC+f4zmyl7id0Oom6VLl6LX64mNjQ11KEIg\nyIPTkZXL5fiDdC1BEBouo8HAwD6XMrDP76N+co8d5d3PPubg9kPY/G6UidEYoyNqbMfn8VCWfQyl\n1U4Lo5m/Xn4lw/sPFMVPhYsi+jrCGZdkdMLok+H3+1GetnOdeDHVZIU8wdOuXTtmz/69gK7VamXL\nli1ce+21IYxKCJZjJwp44KnH0XRJR2OseVk+uUJBeO+OzP/oPYqtVq4b/JcgRSk0NdnZ2SxatIiP\nPvqI6667LtThCAEglypqUwT6jaXf50OBSPAIglBVUnwCUyfcD0Cx1crCj5eyadN2NBmpaE2V35xL\nkkTZwTwMNjf3/+0WLuveS4y4EOpE9HWEP7u0ey9WHd5F27h4cX9pwkKe4PmjsrIy7rnnHjp27MjA\ngQNrdU5xcTElJSWVthUUFAQiPKGeHT95gvuenIahZ/ta18mQyWSE9cjg/bUrcXu93DxsRICjFJoa\nr9fL5MmTmTZtGhbLha9+JO45jcOIgYP5aMsPmNNaBvQ6pftzuP+6WwJ6DUEQGr9wi4VJd92DtbSU\n6fOep+DEaYzpFfcnj8tN+dZ9/GPEtVw/5OoQRyo0BaKvI1Rn2OVX8vHqlVx157hQhyIEUINJ8OTl\n5XHPPfeQnJzMyy+/XOvzlixZwvz58wMYmRAINns5Dzw5DUOP2id3/iisaxv+u3YV0S1aMKB3vwBE\nKDRVCxYsoF27dlx22WVnt0mSVOvzxT2ncbhhyNV8uXY1rrJyNKbAzDF3FJcShZrLe/QMSPuCIDQ9\nFrOZl6fO5Il5z7P/2En0cVHYtu5j3mMzSIituoyxIFwM0dcRqpMQG4evzE6nNu1CHYoQQDLpQv61\nB0hmZiZjx45l5MiRTJ48+YLOPVeGefTo0axdu5bExMT6DFWoJ5Ofe5p8kwxdmPmi25AkifKfM1ny\nwnyUygaTqxQauGHDhlFYWHh2aKrNZkOr1TJhwgTGjh173vPFPafxKLXZGPPov9F1b4eqnpcydpXb\n8e86wttzXkKj1tRr24JwPvn5+Vx11VXintOISZLEbZPuxRVjZmSb7oy69oZQhyQ0IaKvI5zLoL9d\ny1dLPxbPTk1YyH+yRUVFjBkzhrvuuosxY8Zc8Pnh4eGE/2lJSFGIrmHz+/0cOppHWJ+OdWpHJpPh\niw/nk9Urufnqv9ZTdEJTt2rVqkpfDxw4kOnTp9O/f/9anS/uOY2H2Whk7rQnuf/Jx/F3STtvna/a\nclhLkfbk8eqTs0VyRxCEiyKTyWgZF8/+vCPceP/wUIcjNDGiryMIzZc81AF8/PHHFBcX85///Idu\n3bqd/VzINC2hcSm3l+NX109uUW0xceRofr20JQhC0xMXHcObTz+Hf/cRHMWldW7PXnQa9aETLJz9\nEhbzxY9AFARBiImKAa8frUYb6lAEQWgGJEkChZwThSdDHYoQQCEfwXPPPfdwzz33hDoMIYh0Wh0q\nj79e2nIVnKLP8NoV5BaE6qxbty7UIQgBZjGbeXvOS/zziSnYnC4McVEX1Y4tr4BoB7z4zAtiaLMg\nCHVWbC1GkoPP50OhUIQ6HKEJE30dAeDXzJ2oosL4bvPP3PrX60MdjhAgIR/BIzQ/SqWSbu07YDtW\nt+yx1+VGW+aif88+9RSZIAhNlUat4Y2nnyPJo6Is++gFn1+WlUsHfSTzHn9KJHcEQagXecePIY8O\n56ctv4Q6FEEQmoH3P/+UyC5tWbv+x1CHIgSQSPAIITF57ARMheU4Si5uyoTf56Ns6z6enfRYPUcm\nCEJTJZPJmP3wVDqaYyg7XPupnaX7sumX1IZpEx8IYHSCIDQnq9f/QJlSwpwSz5sfLrmgFY4EQRAu\n1La9u8ktLUJjNFCmV/DZmq9CHZIQICLBI4SETCZj3vSnUWQdu+C6GH6vj5JNmTxy9z9JjIsPUISC\nIDRV0yY+QOewOGw5x897bNnBXC5P78j9d1z4IgCCIAjV2XVgH28sW4I5oxUKlQpvywgmzZqJz+cL\ndWiCIDRBX3y3lqdeewVzp9YAmNsks2T1Fyz85MMQRyYEgkjwCCGj02p569kX0Rw5iaOouFbn+Dxe\nrJt2M/PeSfTs2DmwAQqC0GRNmXA/4TYvTmvZOY+xF50mSWnk3lF3BjEyQRCasrc/+ZAnXp+HuVeH\ns0tYG+OjOWaA0Q/dT4EofioIQj05kp/L/U9NY/GaFYT16YhcWVHrSyaTEXZJO1bv/ZW7pzzEzv17\nQxypUJ9EIQEhpNQqNW88/TwTpz9Kqc+HISbynMd6XW5sm/cy5+GppCUlBzFKQRCaopemzuCOyf9C\n26dDlX2SJCEdPM6s518JQWSCIDQlkiSxbOUKvli3GneEkfCeGVWOMcRE4LEY+eezT5AYHsmk/xtH\nYnxCCKIVBKEx8/v9fPfLRt7/fDklkhtj2yQsuphqjzWlJ+F2e5ix+DVMXhg5+C+MuHIQKpUqyFEL\n9UkkeISQUyqVLJj5LOOnTaZcpULXwlLlGL/PR9nmvbw8ZQYtxbQsQRDqgVajpXfnbmwuOIYhtnJy\n2ZZ3nBEDBomCyoIgXLSTp4p4+5Nl7NibiS8mDFOPtmh/G7VTHZVWQ1jPDE7bHTzw8rNEqHVcO+Rq\nhlx6uVhlSxCEc8o5ms9/v/6SfQcPUOpyIln0mDokEa48/31DqVYR3rk1fr+fD7Z8zwdffYFZoyUl\noSU3Dh1O+/TWQfgOhPokeq5Cg6BQKJg/4xnumHQ/vh4GFKrKv5ql27OY+s/7RXJHEIR6Ne7vt7Lh\niYfhTwke/4kSRj0klhAVBOHCFJ4+xTufLGPPwQPYJC/q5FgMvauO2KmJWq9DfUlb3B4vb/+4ikWf\nf0Sk0cLIwcMY1O8y5HJRYUEQmqtiq5VNO7fx8/atnCg8SanDjkslR50Qha5TCuYaksg1kcvlWFIT\nIbXi6/3WMqYtXoDK4cGk1RHZIoJenbvRr2t3oiPPPeNCCD2R4BEaDLVKzSPj7+XJxa8R1qXN2e32\nUyV0aJlCt3YdQxidIAhNkUFvwHowD0dZeaXt3pMlZ+tjCIIgnIskSWzbs4uPv/qSY4Unsfk9qJNj\n0XdLJ6yObStUSixpLSEN7B4vb37/JQs/XUaYzkDvrpdww5CrsZjN9fJ9CILQsLhcLg4cyWbTzl/Z\ntX8vNrsDh9eNS+ZHFmZAH9kCdUZLdIAuANfXWkxoLSYA/EC+3cmBLd+xZM0XKL0SBo0GvUZHu7R0\n+nS5hPZp6eh1+gBEIlwokeARGpQu7TIwyyr/WrqOHGfyzOdCFJEgCE1ZsdWKv5o8jk/y4fF4xDx0\nQRCqcDgdLF+9ih+3bMJqL8djUKNPjkWdkFbnpM65/DHZ4/H7+Tp3Dyuf+gmDXEVCdAz/GHEtHdu0\nC9DVBUEIhNPFxWQe3M+urAMczj2CzV6Oy+PB6fXgwQcGHfIwI4bkCBQqJXogVCkUtV6LOqVyXbBy\nn48fT+Wy7pNdYHOiQoZWqUajVKLX6EhJSqJT67Z0TG9LVGSkeHEWJCLBIzQ4KrmSPy4UqvBL6LSB\nyE0LgtDcfbRqBZH9umBKqFyA0Ho4jxXrvuH6oVeHKDJBEBqS0yXFvP/Fp2zPzKTU44SYMExt4zGE\noDaOXC7HFB8N8dEA5NvsTH/vdbROL1GWcK4fejWX9+gtHqYEIYR8Ph9HTxRwKOcIWXlHyM7LpbSs\nFLfXi8tbkcTxKeVg0KKyGNDFW1CoW6AADKEOvpbkCgXG6BYQ3aLSdh9Q4vHys/UY36/bj+x/ThQe\nHxqlCq1ShUqhxGQwkpSQQOvkVNKTUmgZFy9eqtWTBpng2blzJxMnTuTHH38MdShCkPl8PkrsNkx/\n2CZZDHz78wYG9r00ZHEJTc/KlSt55ZVXKCgoICEhgQceeIBBgwaFOiwhiKylpazZ+COWvp2q7DOn\nJvLhl58zfMBVaNSaEEQnCEJD8MPmTbz98VJskhdlYiSGzilYGljiRG3Uo+6QBkCJy80rX33Cf5a+\nS9ukVCaNGY/ZaAxxhILQtHi9Xo6dPEF2Xi4HcrI5cjSPEqsVt9eDx+fD5fXg9vtAqwK9BpVRj7aF\nEUV8PDKZDDWgDvU3EWAKlRJDZDiGyPBK2yXADZxwuck5ncvanL3I7C5wulHL5KiVKtQKJWqFEpPJ\nRHJCIq2TUmjVMpnE2DjU6qb+/1zdNagEjyRJfPLJJzz77LMig9cMeb1e/jn9URSpcZW2G1slsOD9\nRSQnJIrl0YV6kZ2dzZQpU3jnnXfo2rUrGzdu5O677+bHH38kLCxQA+yFhsTldnHfzKloOraqdr9M\nJkPVLon7ZkzlPzNmidW0BKGZ2bjjV15bsphyvQJz51TCGskqVkqNGkvrir7SwWIrdz3+EO2SUpk2\n8QHUKvFgJAg1kSSJU8XFZOfnkp2fy+GjeZw4eRKn24Xb68Xj8+L2efFIfmQaNZJOjdKkRxtmRBkb\nh0wmQ0nFA3ZjGYUTKkqNGmNUC4hqUWWfBLiAcqebI4XZrDmciczpAqcHpSRDpaxIAKkUCrRqNZER\nUaQkJJLWMpnUhJZER0Y262L0DarH+tprr/HVV18xfvx43nzzzVCHIwRRZtZ+Zi2Yh5QWi/5PmV6F\nUompdwcefv5pbhxyNX8fPlIMOxbqJDU1lQ0bNqDT6fB6vRQWFmI0GkViuZkod9i5Z8pD+NskoDOd\nuwuma2GhzO/j7imTeO2pOeLhSBCakdc+eBdF11aNJrFTHX24BXpZ2J11hLUb1zPsigGhDkkQQsbj\n8ZB3/BiH83LIyj1C7tGjlNrKzo66cfsq/iuplaBTg06D1mxElRyOQqlEBs1i5E1DotSqMWojIDqi\n2v1eoMzvp8hmZ2fWr/h3bERyuJC5vKgUirMjgVQKJUaDgZZx8aQnpZCWlExSXAJarTa431CQNKgE\nz4033sj48ePZtGlTqEMRgmTvoQO89PabnPa7MV2SjuIcD9gKlZKwvp34bPcmVqxdzXVDhnHjX0aI\nRI9w0XQ6HXl5eQwdOhRJkpgxYwYGg3jf0tSt+3k9r33wLuqOqejM55+2oI9sgUOh5LYH7+PfY+6h\nd+euQYhSEIRQc9jK0ThcaIyNe1UYSZKQebwcPVkQ6lAEIWA8Hg9HjuZzIPsgWbm55B8/it1ux/1b\n4ubsqBudGkmrQWXSo400okioPOpGaHxkcjlasxHtOfp0Z6aEnXS5ybMe5fsNB2CtC8nhRiXJKhJB\nv00L02m1xMfEkp6UTJuUVqQnpzbKKWEN6nc5Kirqgs8pLi6mpKSk0raCAvFHrCHLPLCPJf/7lPyT\nBThUYGybQpj6/CMnZDIZppQEpOR4/rt9PZ+s+YoWRjPDr7yKoZdfKaZQCBcsPj6eXbt2sXnzZsaP\nH09SUhJ9+vSp8Rxxz2mcbPZynpj7PDnlJZj6dLigobu6cDP+3u15bunbtF0Tz7SJD6DVNM23PkLg\nFRUVcc011zBr1iyuvPLKUIcjnMOrT81hxvwXOGbPx9ShFYpG2MewFxbjPXiUvw0exi0jRoY6HEGo\nE4fDwf7sw+w8sJd9hw5SbC3B5fVUfHxe0KtBr0VtMqCJN6FQV0z9EaNuBKiYEqbUqOFPM0XO8ABO\nj5cC2yl+/jUH6advkJU7UckUaJUqNCoVFqOJNq3S6NS6LRnpbTAaGmZ9s8b31+pPlixZwvz580Md\nhlCDYycK+GbDj2zZtYNTpVbcOgX65Hg0ia25mNKlMpkMc2oipILD42XRxjUs/uJTwnR6WqemMbjv\nZXRul9Gs514KtaP4beh9nz59GDp0KGvWrDlvgkfccxoXu8PBnLcWkJl9EHWbllhS0y6qHblCQViX\nNmSfKub2R/7FJe078q/RY0QBZuGCTZkyBavVKkagNnAR4eHMm/YU2/bu5o2lSzhdXoY/woixZRwK\nVcPtPjtKSnEdOY5eUtCpVToPPj9ZTC8VGp0TRYWs/GEdm3dsJztzHz7Jj1+SkNRKZColSo2GxEG9\nkQO63z5nHP9+S7VtxvXvUe12cbw4/gyFSol1Z1albQ6g9LfjTzjdZOfvY1XmVqRyJyo/WI/kk5LR\njk7t2nPNlYNoGZ9Qpd1ga7h/oWpp1KhRjBgxotK2goICRo8eHZqAmjmny8nG7VtZu3E9J4oKKXe5\ncKtAHhmGoVUkemUs9TnYWaFSYmmVCK3AJ0lsKznFpmVvI7e5MKg1WAxG+nTtzlV9LyMqovr5m0Lz\n8/3337No0SLeeeeds9vcbjcWi+W854p7TuNwuqSYee8uJDP7EKrWCVh6daiXdvUR4RARzo6Tp7ht\n8gN0a5fBhH+MxmIynf9kodlbunQper2e2NjYUIci1FK39h15deazeL1evtnwA1+sW0NRWQk+iwFD\nUiwqbWiTvJIkUV54Cu/RIgwoaZ+cyp33P0pCXHxI4xKEC1FstbL0y8/YuW8PNqcDh1xCGR2OoU0s\n8pPHEa9shYZAqVVjjo2C2N9nHZWV23C3T+CHwmzWzZ+D2uPHqNHSJjWNW6+5jrjomODHGfQr1rPw\n8HDCwysPtRKFUgPP7rCzfU8mmzN3cignG7vTicPjxiV5wWJEHxeJOjYZPdRrQqcmMpkMfbiloqjg\nb6weD8v3bea/P61B5ZPQqdToVGpio2Po3qETPTp0JiYqOkgRCg1Fhw4d2L17N59//jnXXHMNP/74\nIz/88AP33nvvec8V95yG7fvNP/PB559w2mVH1SoeS+/6Sez8mSG6BUS3YEdhMXc9MZkog5n/u/EW\nenbuEpDrCY1fdnY2ixYt4qOPPuK6664LdTjCBVIqlQy7YiDDrhiI3+9n47YtfPrNV5w8nUu53I8q\nIQpDZHhQRmb53B7Kco8jL7Zh1ui4LKMjN936T6IjIgN+bUEIhDvvGYumd3uM7RPRymQUf7+FuK5t\ngYqRE8e/31JpxMWfR2X8cb84XhwfsuOjK+7B+d9txhZvYc+Ls3n72RcJtgab4BFDl0PP7XZzKPcI\nmYcOsOfgQU4UnsTpcWF3e3DjQ2bWow43o20VhVyhqDJEsiFQqFSYE2Mh8fe3pS5J4oDNzs6Na3h7\n9f9Qun1nEz8Ws4XWKa3omN6G9mmtMYu38k1SZGQkr776KrNmzWLmzJmkpqayYMECUlNTQx2acBFy\njx1l4cdLOZSbg9OkwdQuEUuQ6mUYo8IhKhynx8Psjxeje89Du9R0/u/Gv4fkrY3QMHm9XiZPnsy0\nadNqNVLwj0Tdr4ZHLpdzafdeXNq9F1AxnWTpl/9j185MrB4X8vgIjHFR9dqX9TjdlGfno7a5iA6P\n4NaB13Blr76i/qDQJNjL7SiKy/BaTKh0osad0LhJPgn3aSu2EycqCt0HOa8hkyRJCuoVgyA/P5+r\nrrqKtWvXkpiYGOpwGixJkjhZVMSegwfYfegAR3JzKXfYcfk8OD0e3JIPmUELRi26MAtqo77JJ968\nThf2klJ8ZXYoc6DwS+iUKjRKNRqViriYWDLS29AhvQ0pCYli5IYAiHtOKBw9foyFy5dxMOcIdoWE\nNjX+nCsoBJu9xIr7SAEGv4L2aa35vxtvFm/Wm7l58+ZRVFTEzJkzARg4cCCPP/54rYosv/LKK+es\n+yXuOQ1PWbmND774jF+2/0qpx4UqNQ59RNhFteX3+Sg7mIeq1EFsRBT/uOY6unfs3OT7YkLDFMi+\njiRJbNm1g/9+9QUFRYXYFRLKuAj0LcKQKxX1ei1BqG9+nw+ntQz30SK0bj+RljD+OmgoV/bqG5Ka\nsCLB04Q5HA6OHM1j/5HDHDhymGMFJ3C6Xbh/qzjv9nnxq5Vg1KIym9CFmRp04cBQk/x+XGXlOEtK\nodyJVO5CLVegUSpRK1VolCoiWrQgLSmFtimppCelEh4WJjpizYC45wSe3+9nw6+b+Wzt15w8fQq7\nzI82JR5tWMMeZWc/VYI7pwCDTEl8VDTXD7maHp26iPtCMzNs2DAKCwvP/txtNhtarZYJEyYwduzY\nGs891wie0aNHi3tOA1dWbuPlRW+x8+B+VGnx6M+xesuf+b0+Sg/kYHT6GX3jzfTv2UfcM4QLtmXL\nFmbPnk12djbh4eGMGTOGm2+++aLbC2Zf5+jxY3y2bjUHsg/9XgbC60EyapGbDegjwytWRBKEIPJ5\nPNhPleCzlkOZA41ciU6lQqfWkNoyiREDBtMmtVWow2y4U7SEmvl8Po4eP8bB3Bz25xwm52g+ZTZb\npeSNBwmZXgN6DRqLCXVKOAqlstqK88L5yeRytBYTWkvVB0o/YJckrHYHew5u57OdPyMrdyL3+lEr\nlKiVSjQKFTqNhvjYONKTU2mbkkpKQkt0OvGTEITqnDxVxMdff8n2zF2UOB34LToMSXGok1s0miVP\n9RFhZ9/eH68LTWYAACAASURBVLU7mL38PZSL38SsM9CrazduGHw14Rc4ZUdofFatWlXp64EDBzJ9\n+nT69+9/3nNF3a/Gy2QwMm3iA9gdDl5453V2bT+AuWubGs9xWMvwZ+bwwB13cVn3nkGKVGhqrFYr\nEyZMYPr06QwfPpw9e/Zw5513kpSURN++fUMd3nklxMUz8dbRlbZ5PB6yjhxmS+ZOdu/fh9VWgMNT\n8dzjlUkVsw4MWrQWExqTQSRFhQsmSRKecgcOaylSuRPJ5kThl9AolBWlPPRG+qW3pcfgzmSkt0aj\naZgrqYoETwN0ZupUVk42WbnZHM7NobikBLfXi9tXkbxx+3ygVYNBg9KoRxtpQploBkD920cILplM\nhtqgR22ovqy0B3B6vBTYTvHz1hz48WtwuFBKMtRKFWqFArVChUGvJyk+gdYpqaS3TCFZTAUTmomj\nBcdZ8d0adu7NpNThwCH3o4yLwNgxGXMT6Kip9TrU7SrqPPkkiW/y9rLq6fXoZQpMWj09OndheP+r\niImMOk9LgiA0JnqdjmkTHuDdzz7myx0/Y2qbUu1xPo8H7+4jvDPnZfTi5Y9QB8ePH2fAgAEMHz4c\ngIyMDHr37v3/7N17XFR1/j/w19yHmQHkGl4AuchwE8QLWpqaa15SWzctL9lqpobubm6139T2t2Vb\nbqtb36XEtTYtM+xe2Ka22cWvqQlKKpqCF0QFUQS5D8NcP78/yEkCFRRmGHg9H48ey5zzOWc+Z2Rf\nHN6cz+eDAwcOuEWBpzkKhQKxffSI7aNvsq/OWIeTZwpw/Ew+jhecxqUTF2GymGGyWmGyWWARdkCj\nBrQqqL08ofTUQMb5q7ocx2iM6hqgth6iruH3MJVcDpVcAbVcgWA/P0T2SUJMeB/0CQ1zy/lY+Z3t\nAkIIXCy9hLzTp5B7Oh8FhWdRW2eAyWqByWqF2WqBXaUANCrIdB5Qe+mguK07JBIJ5Gj4R3PWylTU\ntmQKeZOVvq5mBmA0W1BYeR7/t/s4JHUmCKMZSokMKrkCKrkcamXDU0Ax4ZGIiejDuYDILQkhkJd/\nEl/u/g65+SdQU2+ESS6BPMgXuuieUEsk6MzTLEokEnh2DwS6N6ziV2+z4ctzx7DtpT1Q2yXQqT3Q\nVx+DMUNHIDK0N/8S2cl8++23ru4CucDUsRPw+XfX/re3miwIDQ5mcYduWXR0NFauXOl4XVVVhezs\nbEyePNmFvWo/Gg8NEmPikBjT/MqZJpMJpwvPIff0KZw8W4ALZy6i3mT66Y/mVpisVtjkEkg0Kkg0\naqi9PKHy1EDigvlT6OY0PH1Th/qqGtjqTIChHlKzDUr5zyMpVEolwgMC0ScuDtHhkYgM7Q2NR+f7\nrZoFnnYihEBB4VmseukllJZfhtlshk0I2Ox22Ox2dIvpDaFVQ+Glg0d3T8iUPpACqLyyDFsdgIqG\nLw1Ao6XarvbLZduuYHv3bS9TKhwr81xpX39VW7td4HKABvv274Bk53+BunooJA1zAZXln4NcJkM3\nL2/8/ne/R0J0DJRKPs9FrldSVopvMvdgf85BVBlqUWuqh02rhDLAF5rYYGgkki5duJbKZI0KPhYh\nsKv0DHasPwSZ0QKtUg1fTy8MSRqIUYPvgK9Py+byIKKOwWQ2YenKFyAPC7pmG5VOg4JjBdj9w34O\nz6I2U1NTg5SUFMTHx2PUqFEtOqazrd6nUqkQE9kHMZF9mt0vhEBVTTXyz53FybNncOrcGZTmlzqe\nAjJfmbtUJYdEo4LMUwsPb0/IVEr+AcZJrCYz6qtrYak2AHX1kNRbGqbBkMkaz4UamYQ+oWGIDOkN\nPx+fLvnvwwLPLRJCoPjiBWQdPoQfjh3B5fJy1FnMqLeaYVMrUFVeArmHGjJPFSSA4wmcbgnXH4NN\ndC1SqQTqbp7NTi4rKitgsgkUm2rwYsZGSGqMUMsU8FAoofPQIDZKjyEJ/REdHsGnfqjdlFdWYveB\n/cg69AMulV+GwVQPsxyQ+nlDG+IHmSIAXq7uZAcnkUigC/QHAn9efeuy2YIPDu/B+zv+C5WQQKtU\n4Tb/ANzRfxCGJg2Alyc/VaKOaNOWDHz21ZeQR/WE1u/6xVnPAdF4JeNdpG/+GM/+4Ql0D7zNSb2k\nzqiwsBApKSkIDQ1Fampqi49LT0+/5up9nZFEIkE3L28MiE/AgPiEZttcGYHRMBTsNAoKz6Gquuyn\nJ4AsMFutsPw0F5DQqODh7QWVF+cCaokrT9/UVVRD1NUDBhPkdvHTFBYNw6d8tVr07tUb+gFhiAqL\nQI/A2yCTcYW15nAVrZsghMDeg9l49/MMlFZXwqZWAN5aaAN8ofDozIMKyJ3ZLFbUXa6ArbIWqDHC\nS6HCnclDMHPiZKiUHXOSMHfRlVfRunCpBDv3ZyH7yCFU1lSjzmKGWWIHfDyhvc2PmdjOzIY61F26\nDFQYoIIUGqUKPl7eGJLYH8MGJCPQn8uzd0ZdOXPcxQ8/5uDdzzfjwuVS2Pw84RnWs1XHW4xG1OWe\nhadMiYF9E/HgpPvg7YZzQZDrHD16FPPnz8evf/1rLFmypFXHcvW+m2M0GnG68CyOF5xG7ulTuHCp\nBCaLGfUWM0w2K2xyKaDzgMJLC49uXpApu84fW20Wa8NS4lUN899IzVaoFcqGoVMKOQL8/BEd3gfR\nYRGICAmBp455d7P4BE8r/WP9WhzMPQqzpxqeYT3hqezh6i4RtYhMIYdnUAAQ1DCBqxAC/y34EduW\n7YS/xhMvP72c4/7pmmw2G/LyT2LXgf04dvI4ao1G1JlNsCikkPl5QdPdF/LevtCAc4Q5k1KrgTJM\nAzTM3QwBoKTejPcO7ca7O76AwgZoFEp4arToGx2DoUkDERUWASnnFSBqU8Z6I77N3Iuvv/8Olyou\no16jgC6sJzRhfjd1PoWHB7z7R0MIge9KCvDtX5fCS6ZCfJQek0aN4dxcdF1lZWWYN28eHnnkEcyb\nN6/Vx3P1vpvj4eGBuKhoxEVFN9knhEB5ZSWOnTqOIydPIP/cGRgMDXOwGi1mWGQAvDTw8PeBykvn\nlv//FkLAXFsH4+UKiMo6yK12qBUNkxd7qj3QOzgE8f2jENdHj9v8A9zyGt0BCzytFBQQiNL/bkfo\nlF85tl3Ymd1oDhW+5mt3eC2RSODZ8zacO3wS/qFe8FDzSQtqUFNbi6zDB/H9wWwUl1xEndkMo8UM\nu04Fha8XNGEBkMpl0Lm6o9QshVoJ79AeQOjP26osVnxZlIcvcvZBajTDQ6GERqFEcI9euCNpAJIT\n+nXKiQaJ2kt1TTX+u/s7fP/DPlTU1sBgNQN+Ouh63gaPcH+01Z9LJBIJdEH+QJA/hBDIvnwJ369/\nBXKjFZ4qD/TuFYwJI0YhMSaOvyyRw8cff4yKigqsWbMGa9ascWyfPXs2/vjHP7qwZ12XRCKBn48P\n7hw0BHcOGtJkf3lFBQ4cO4J9R3JwPq8YdRYTjGYzLFIA3lp49AqEtAMNSRJCwHi+BKLSALnVDg+F\nEh4KFUICAjFg8K8wKD4RtwUEurqbXRILPK300L1T8N32b2DJOQ2DuR52Lw9YTWYIIfiDldyCzWpD\nZUERUF4LD6kMnlYJ0p5dwe/fLur8hWLsOpCNH37MQUV1FYxmM+ol9oabiQAfqGJ6QSmRgFN1uzeZ\nQg6vq57gAwCzEDhWXYsfvtoMfLwJaoms0RCvof0H8uaMCIDZbEb2kRzs2L8XhefPw2Cuh1HYIPH3\ngi40EApFILo5oR8SiQQafx9o/BuerBAAcqtqcPCDNyGrNUGrUsNTo8GA+ATcNXgogrvzKfOuKiUl\nBSkpKa7uBrWCr48PRg8djtFDhzfaXl5ZiQNHD8PztgCoO9Cwd5vVipIzhRgQ2xeBAQE3PoCcpkPM\nwXPs2DE888wzyM/PR2hoKJ577jkkJibe9PmcNTbdarXiUO5RfPX9LpwpOgeDyQQjbICXBgrvn8ZW\n8nFGchFht8NUbUB9ZTVETR3k9Vbo1Gr4e/tiePLtGDFoMHRarau76TLZ2dlYuXIlCgoK4OPjg3nz\n5mHatGk3dS53mA/jyrLkO/ZlIvfUCRjqfxpipZRB5usJTYAv5CqWcQiw1JtQV3IZ9opaKKx2aJQq\neGo0iI+Kwcjk2zk0pANwh8xxV2azGT8cPYLvsrNwpvBcQzHHaoHopoE6wK/DT5pqs1hhuFQGe3kN\n5CYbtEoVvHWeGNg3EXcOHIxeQd07dP+p42LuELkHlz/BYzKZkJKSgkWLFuH+++/H5s2bsXDhQnz9\n9dfQaDr24+JyuRwD+yZiYN+fi1GV1dU4nHcMP576aWxlXcPYSpPVApPdBolWDeg8oPHxgkKr4Q9Z\nuiVWkxnGiipYa+qAGiPkdtEwYZlcAbVSiaigHohLHoiEqGgE9+zF77efVFVVYdGiRXj22WcxYcIE\nHDt2DA8//DBCQkJw++23u7p7baKkrBTf7N2NfTkHUV1ncCxLrvDzhiacQ6zo2hRqVZMhXtUWK74q\nPo4v1+9zLNvuo/PE4KSBGD3kDvj6+Lquw0Q3qbyyEnsPZWPvwR9QWl6OOnM9jDYrhLcG6gAfqGN6\nut0TjDKFHF49g4CePy/HXmGxIOPED/g0cydkJiu0ShW0Kg/E9OmDYf0HITYyCnK5y38lICKiNuDy\nNM/MzIRMJsP06dMBAFOmTMGGDRuwc+dOjB8/3sW9a71uXl4YnjwEw5Objq00m83IP3cGP548gdzT\nJ3Gp6ELDsno2K8xWK8x2GyQeSggPJRSeGqi9PCFTKflLeRdls1hhqqmFucYAGM1AXT1kdjiWDFTK\n5fDTeSIqLBpxkXrEhEfC24vLJLfEhQsXcNddd2HChAkAgNjYWAwePBgHDhxwywKPEALZR3Lw2bdf\noqSsrPGy5KH+kCkCuSw53ZLmhniVmy346Mj3+PD/voTKLoFWpULP24IwefQ4JETH8mcXdRhCCBSe\nL8LOH/bh4NEjqDbUXrXinw6aQD8ouwdDBaAzrikpUyjg3TMIuGohL4PNhu8un8W37+VAUlsPD4UK\nGqUSwT16YljSQCQnJMGDCy8QEbkdlxd4CgoKEBER0WhbWFgYTp8+7aIetR+lUomYyCjEREY1u99q\ntaL44gXkF57DycIzOFNUiKrqy7DYrDBfKQTZbIBaCXgoINVpoPbUQaFR80bazVhNZtRXG2CtNQBG\nE2A0Qw4JlDI5FDI5VHI5NGoP9OzeAxHRvdEnJBShPXtxEtQ2Eh0djZUrVzpeV1VVITs7G5MnT3Zh\nr1rn0uUyfPzlNhw6dgTVxjpYvTyg6RUIZVAoV7Iip5ApFfAO6QGENLy2AzhZXYu/vrceyjozvD00\nGJTYH/fdPR4+3t4u7St1HXa7HUdPHsd3+7OQm3+yYTiqxQyrSgapjyd0vfwgU/p3+ZyUymTQBfgC\nAT8/fWcRArnVtTiwPQOSq+bl8vPxxZB+A3DngGT4dnPGbENERHSzXF7gqaura/IXAg8PD9TX17uo\nR64jl8sR0isYIb2CcdftQ5ttY7PZcOFSCU4XnkN+0VkUFBWioujizwUgqxVmmxV2uRTwUAEaFdSe\nOih1GsgULv/n7vSEELAY6lBfZYC1zgiJ0QSJydLwxM1PxRuFTA4/nQ7B3XshIiEUYb2C0btnMNRc\nxcolampqkJKSgvj4eIwaNcrV3bmu6tpa/O+bryH/fCGMsEPWww+6uBDoWOClDkLtpYM6tmHwn9lu\nx/Zzx/DFit3QSGSICYvEH+fMg4eaTwVQ2xBC4OSZ0/h67x4cO5GHWlNDMceuVUHu1w2ayEDIZDJ4\nurqjbkIikUDt7Qm198+fmB1AsbEe7+zfgY3b/wO1XQqNSgV/H18MHzgYQwcMgpeOnzARUUfh8t/4\nNRpNk2KO0WiEtoWTv1ZUVKCysrLRtosXL7ZZ/zoamUyGXt17oFf3Hs0OAwMabniqa2pQUFSI/MKz\nOF14FhfOlcBoqofZZoHZaoXJZoVNKgG0Kkg0aqi9PaHSaSCRSp18Re5DCAFrvQn1VTWw1tYBdSZI\nTFaofireKOVyKOUK+Pn6IjyiLyKCeyOsVzCCAgIh5efaIRUWFiIlJQWhoaFITU1t0TGuyJyKqkqs\nfONfyC8ugqJPT2iS+nTKYQTUuUilUnj2CAR6NKzEdaSsHLOXPYHYsAg8+UgKPLWcBYpap9ZgwPbd\nO/Hd/kxUGWphMJtg1yoh9/eGps9tkMtkHI7aDhQeanTr3RPo3fBaoKHos/777Vi/9VOoIYVGqULv\nXiG49667ERel55PlREQu4vICT3h4ONLT0xttKygowL333tui49PT05GWltYeXXNbEokE3l5e6Bcb\nh36xcddsV11Tg/xzZ3D8TAFOnS1AycmSxnMC2SyAWgWhUUHl0/AXHalM5sQrca4rT9/UVVRB1NYD\ndSYoIGko4MgVUMnl8PbyRnhINPShYejTOxy3+QfwJsZNHT16FPPnz8evf/1rLFmypMXHOTtzzl8o\nxkMLHkGPiSPgnRwLALiwMxvdRwx0tOFrvnaH1xp/X8DfF99/uQcHH/8dNv3zX116JT+6seqaanyx\n6/+w54f9qDTUos5uAfw84RkayLnFXEzhoUa3sF5AWMNrO4BjlTX4YdO/oagzw1OlRnCPXpg0cjT6\nxcbxXomIyElcXuAZMmQIzGYz0tPTMW3aNHz22WcoLy/HsGHDWnT8rFmzMHHixEbbLl68iDlz5rRD\nbzsXL09PJMX1RVJc32b322w2FF8qQW7+SRw+kYdzpwthNJlQb7Wg3mKGXSUHPD2g7uYNtbfOLZ7+\nEULAUlePuvJKiJo6wGCCWiZ3rDwV7O+PuPg7EN8nCmG9QjhsqpMqKyvDvHnz8Mgjj2DevHmtOtbZ\nmbP52+2Q+3pB7c2nHahzUKhVUPbuji9378SUsfe4ujvUwdjtdmz++r/Y8u3XqLaZIQnwhq53AJSK\n29xqNauuSN3NE+puDcO1BIATVbVY8eGbkNfUo3ePXvjDg3PQs3sP13aSiKiTkwghhKs7cfz4cTz7\n7LM4ceIEevfujeXLlyMhIeGmz1dUVIRf/epX+Oabb9CrV6827CldIYTAhUslOHIiDznHj6GwuBgW\nYYd++OAO+1ea87knYbhYBj9fX8RF6pGoj0FkaG8oFApXd42c7LXXXkNqamqT+b9mz56NP/7xj60+\nX3tmzkdfbsUH//0cmvhwKLVdeUpQ6izqq2tRf7QAj06bhbvvuNPV3XFLnfU+53/f+jf2HcmBPdAL\nupDuHN7ciZhqDTCeKoIXFFjy6O+gD4u48UHUoXTW3CHqbFz+BA8A6PV6vP/++67uBrWCRCJBj9uC\n0OO2IIy9c6Sru9MyI13dAeooUlJSkJKS4uputMj9Yydg5KAheDb1H7hkvwDPqBDIlCxKkvux1Jth\nOH4WvbTeeO75f8DbiwNsqLGcE3nQJce4uhvUDlQ6LVT99KguvoSsnAMs8BARtZMOUeAhIqJrC/D1\nw7/++ndk5hzAB1s+Q1lVJepkAqpegdD4ccla6piEEDCUlsN6vgwaIUWgrx+enP0oEqJjXd016qAC\nu/ngTHYupD38oOvOOe46E0u9CYaC85BWGKD/1W9c3R0iok6LBR4iIjcxJLE/hiT2B9Aw+fJ72/6D\nvMMnUWUyQnhroA70g8pbx1+KyCWEEDBWVMFUWgFZdT281R64IzoW02Yuwm3+Aa7uHrmBfyz5C2oM\ntXhv62fIOnSgIdsCvaEN9IPCg3PiuRO7zYb6imqYSsqhNlrR3T8Af5g6GwPiE/gzioioHbHAQ0Tk\nhnp274E/PdIwzMxiseCHo0fwf/v24kzuOdSZTDDaLD8VfXyh8mLRh9qWEAL1VTUwXaqApLoOHnIl\ndCoVYkLDcNeUe5EYHQu5nLcY1HqeWh0WPPAgFjzwIIz1Rny5eyf2H8nB5dOFqLOYYLSYIbQqSL21\n0Ab4cciqiwkhYKquhbGsAqiqg1JIoFEqoVV6IKF3GO4ecz/ioqJd3U0ioi6Dd19ERG5OoVBgSL/+\nGNKvv2ObyWxC9pEc/N/+TBQeK4LBXA+j1QKhU0PWTQetvw9knGCcWsBmtsBQehn2SgMkdSZ4KJTQ\nKFWICg7FXZPHIykmnpPVU7vwUHtg8uhxmDx6nGOb3W5H/rmzyDz0A3LyjqG6tgZGixn1VgvsKjkk\nOg8ou3lC5aWDjEXGNiGEgMVogrGiEqLGCGGohwJSqOVyaJRqRPboicGj7sSA+AR4e3JuLSIiV+JP\nPiKiTkilVGHogGQMHZDs2GaxWHCiIB9ZRw7h2InjqDbUwmgxw2i1ADo1pD6e0Pr5QKbgj4auyGa2\nwFBWAXtlLWCoh4dcAQ+lCt20nrgrOhHJffuhT+9wyGQyV3eVujCpVIo+vcPQp3cYHrpquxACF0sv\n4ejJ4zhy8jjOnilEnakeJosF9VYLrFJAovOA1EsDTTdvyNVcdP1qwm6HqcaA+opqiNp6SIwmqOQK\nqOUKqOQKdO/WDdF9ktC3TzSieoc3WYWSiIg6Bt7FExF1EQqFAnFR0U0el7dYLMjNP4msw4eQe+oE\nauoMqLdaUP/TUAh4aqD19+EcGJ2Euc4IY1kFRLUREmPDEzlquQLdtDoM18dhSEIS+vQO5xArcisS\niQTdA29D98DbMHro8Cb7a2prkHc6Hz+ePI7jp/NRVXMJ9VYzTFYrTDYroFVBovOAh683lFpNpxzW\narNYYayshrWqBsJQD5nF7ijgeCiUiAzqjrjkgejbR4+QHj1ZzCUickO8eyMi6uIUCgUSomObrG5k\nt9tx+txZ/HDsCA4fz0X5mSIYzWbUW8ywyCWApwZqX2+ovXWQSKUu6j01x26zNcyRU14NVNdBIQAP\nuRJqhRI9/PzQL3EY+sf2RUjPnpDy3466AE+dJwYl9MOghH5N9lksFpw6ewY/njyOY6dO4FJRMUwW\nK+qtZsi8dfCPdc8lvc11RpQezINaroBaoYCX2gP9Q0KRkByNuD5R8PPx7ZSFLCKirowFHiIiapZU\nKkVk7zBE9g7DtHvubbSvvKICh/KO4oejR3DuZBHqzCbUW8ww2a0Qnh6Qe+ug8evGeX7amWNYVVUt\nUFsPlUwBD4UCOpUa8cEhGDhmNBJjYjkvBtF1KBQKxET2QUxkH1d3pe3NdHUHiIjImVjgISKiVvP1\n8cGo24dh1O3DGm03mUw4duoEso8eQd6pk6gxGmA0mxomQPVQQdpNC42/DxRqlYt67p4sxnoYLl0G\naoyQGi1QKxqGVHTTemJ4VBwGxPZFTEQfTnZMRERE1IWxwENERG1GpVIhKa4vkuL6Ntput9tx5nwh\n9h/JwcGjP6KiugR1ZhMneP4FxxM5FTU/r1ilUCHQxwcD+g3DoPhEBPfoyWEVRERERNRE176TJiIi\np5BKpQgPDkV4cGij4V5WqxV5p09h76EDOHby+E9P/Jgh9+8G78hgF/bYeSryCiCqDPBQKOGr0WFE\ndDyG/DoJUWERnB+HiIiIiFqsQxZ4XnjhBSgUCixZssTVXSGiLuDw4cP43e9+h127drm6K12OXC5H\nfFQ04n+xspcQoss8pdKVrrUry87OxsqVK1FQUAAfHx/MmzcP06ZNc3W3iKiTOnbsGJ555hnk5+cj\nNDQUzz33HBITE13dLSJqZx3qT4MVFRVYunQp0tPTebNLRO1OCIGPP/4Yc+fOhdVqdXV36Cpd6WdA\nV7rWrqqqqgqLFi3CnDlzkJ2djVdeeQX/+7//i71797q6a0TUCZlMJqSkpGDq1KnIzs7GQw89hIUL\nF6Kurs7VXSOidtahCjwPPvggFAoFxowZAyGEq7tDRJ3ca6+9hnfeeQcLFy5k5hBRu7lw4QLuuusu\nTJgwAQAQGxuLwYMH48CBAy7uGRF1RpmZmZDJZJg+fTpkMhmmTJkCPz8/7Ny509VdI6J25tQCj81m\nQ3V1dZP/amtrAQBvv/02nn/+eWi1Wmd2i4i6qKlTp+Kzzz5DfHy8q7tCRJ1YdHQ0Vq5c6XhdVVWF\n7OxsxMTEuLBXRNRZFRQUICIiotG2sLAwnD592kU9IiJnceocPFlZWZg7d26T7T179sQ333yDgICA\nVp+zoqIClZWVjbYVFxcDAC5evHhzHSWiNhcUFAS5vGNN+8XMIeq8OmLmAEBNTQ1SUlIQHx+PUaNG\n3bA9M4fIPXSkzKmrq4OHh0ejbR4eHqivr2/R8cwdIvfQXO44NYXuuOMO5OXltek509PTkZaW1uy+\nBx98sE3fi4hu3jfffINevXq5uhu3jJlD5B46YuYUFhYiJSUFoaGhSE1NbdExzBwi99CRMkej0TQp\n5hiNxhaPkmDuELmH5nKnY5SZb8GsWbMwceLERtvMZjOKi4sRHh4OmUzmop7RrSosLMScOXOwYcMG\nBAd3jeWSO7OgoCBXd6FNMHM6L2ZO59LRMufo0aOYP38+fv3rX7dqlVBmTufFzOlcOlLmhIeHIz09\nvdG2goIC3HvvvS06nrnTeTF3OpfmcqdDFnhaM9mpj48PfHx8mmzX6/Vt2SVyAYvFAqDhG7ej/EWE\niJnTeTFzqL2UlZVh3rx5eOSRRzBv3rxWHcvM6byYOdRehgwZArPZjPT0dEybNg2fffYZysvLMWzY\nsBYdz9zpvJg7nV+HWkXrColEwmVjicipmDlE1F4+/vhjVFRUYM2aNUhKSnL819JhWkREraFUKvHG\nG29gy5YtGDx4MN59912sXbsWarXa1V0jonbWIZ/gefHFF13dBSLqQgYPHoy9e/e6uhtE1EmlpKQg\nJSXF1d0goi5Er9fj/fffd3U3iMjJOuQTPERERERERERE1HKy5cuXL3d1J4iuRa1WIzk5uclSj0RE\n7YGZQ0TOxMwhImdj7nRuEtGaGY2JiIiIiIiIiKjD4RAtIiIiIiIiIiI3xwIPEREREREREZGbY4GH\niIiIiIiIiMjNscBDREREREREROTmWOAhIiIiIiIiInJzLPAQEREREREREbk5FniIiIiIiIiIiNwc\nCzxEhfU52AAAIABJREFURERERERERG5O7uoOUOcTHR0NtVoNiUQCAOjWrRumT5+ORx99FACQlZWF\n2bNnw8PDAwAghEBQUBDuu+8+zJ8/33HcqFGjUFxcjO3btyMkJKTRe0yaNAknT55EXl6eY9t3332H\n9evXO7bFx8fj8ccfR3x8fLtfMxG5FnOHiJyJmUNEzsTMoZZigYfaxccff4zIyEgAwNmzZzFjxgxE\nRERg9OjRABpCKTMz09H+yJEj+NOf/oTq6mr86U9/cmz38fHB1q1bsXDhQse248ePo7i42BFUAPDh\nhx/i1VdfxYoVKzBs2DDYbDZs2rQJs2fPxgcffODoCxF1XswdInImZg4RORMzh1qCQ7So3YWGhmLg\nwIHIzc29Zpu+ffvihRdewIYNG1BdXe3YPmbMGGzdurVR288//xxjxoyBEAIAYDQasXLlSqxYsQIj\nRoyATCaDUqnEww8/jJkzZ+L06dPtc2FE1GExd4jImZg5RORMzBy6FhZ4qF1cCQcAyM3NxeHDhzF8\n+PDrHjNo0CDI5XLk5OQ4tt15550oKyvD8ePHHef94osvMHHiREebAwcOwGaz4c4772xyzieffBJj\nxoy51cshIjfA3CEiZ2LmEJEzMXOoJThEi9rF9OnTIZVKYbFYUF9fj+HDhyMqKuqGx3l5eaGqqsrx\nWi6XY9y4cdi2bRv0ej3279+P3r17IzAw0NGmoqICXl5ekEpZryTqypg7RORMzBwiciZmDrUE/8Wo\nXXzwwQfYv38/Dh06hN27dwMAnnjiieseY7PZUF1dDR8fH8c2iUSCiRMnOh4j/PzzzzFp0qRGFWx/\nf39UVVXBZrM1OWdNTU2z24mo82HuEJEzMXOIyJmYOdQSLPBQu/P398eMGTOwd+/e67bbv38/7HY7\nEhMTG20fOHAg7HY79u/fj++++w5jx45ttD8pKQkKhQI7d+5scs6nn34af/7zn2/9IojIrTB3iMiZ\nmDlE5EzMHLoWDtGidnF1Bbi6uhqffPIJ+vfvf822Bw8exPLly7FgwQLodLombSZMmIDly5dj0KBB\njuX/rlCpVHjiiSfwzDPPQCaTYejQoaivr8eGDRuwd+9evP/++217cUTUITF3iMiZmDlE5EzMHGoJ\nFnioXdx///2QSCSQSCRQKBS44447sGrVKgANjwVWVlYiKSkJQMM40O7du+Ohhx7Cgw8+2Oz5Jk2a\nhHXr1mHJkiWObVcv4zdz5kx4eXkhLS0N//M//wOJRIJ+/frhnXfe4RJ+RF0Ec4eInImZQ0TOxMyh\nlpCIq0uBRERERERERETkdjgHDxERERERERGRm2OBh4iIiIiIiIjIzbHAQ0RERERERETk5ljgISIi\nIiIiIiJycyzwkNv46quvMHXq1EbbDh48iPvvvx8DBw7EqFGj8Pbbb7uod0TU2TBziMiZmDlE5GzM\nnc6HBR7q8CwWC9544w08+eSTTfY9/vjjmDBhArKzs/HGG28gLS0N2dnZLuglEXUWzBwiciZmDhE5\nG3On85K7ugPUNRQVFWHy5Ml49NFH8fbbb8Nut2PSpElYtmwZkpKSmj3miy++QFBQEJ577jmcPXsW\nDz/8MHbv3t2ojU6ng8Vigc1mg91uh1QqhVKpdMYlEVEHxswhImdi5hCRszF3qDks8JDT1NbW4vz5\n89ixYweOHTuGWbNmYfz48Th48OB1j3vssccQGBiITz/9tEkAvfjii3jkkUeQmpoKm82G3//+90hI\nSGjPyyAiN8HMISJnYuYQkbMxd+iXOESLnGr+/PlQKBRITExEeHg4zp49e8NjAgMDm91eW1uLhQsX\nYv78+Th06BDef/99bNq0Cd99911bd5uI3BQzh4iciZlDRM7G3KGr8QkecipfX1/H13K5HHa7HYMG\nDWrSTiKR4D//+Q+CgoKuea7MzEwoFArMnz8fANCvXz888MAD+PjjjzF8+PC27zwRuR1mDhE5EzOH\niJyNuUNXY4GHXEoikWD//v03daxSqYTZbG60TSaTQS7ntzURNY+ZQ0TOxMwhImdj7nRtHKJFbmvg\nwIGQy+X417/+Bbvdjry8PHz44Ye45557XN01IuqEmDlE5EzMHCJyNuaO+2OBh5xGIpHc8vFXn0Oj\n0WDdunXIzMzE4MGD8dhjj+EPf/gDRo8efatdJaJOgJlDRM7EzCEiZ2Pu0C9JhBDC1Z0gIiIiIiIi\nIqKbxyd4iIiIiIiIiIjcHAs8RERERERERERujgUeIiIiIiIiIiI3xwIPEREREREREZGbY4GHiIiI\niIiIiMjNscBDREREREREROTmWOAhIiIiIiIiInJzLPDQTYuOjsbu3btd9v5ZWVk4fvy4y96fiJyL\nmUNEzsbcISJnYubQrWKBh9zW7NmzUVpa6upuEFEXwcwhImdj7hCRMzFz3B8LPOTWhBCu7gIRdSHM\nHCJyNuYOETkTM8e9scBD1xQdHY1PP/0UY8eORVJSEhYuXIiysrJGbQ4dOoT77rsPCQkJuO+++5Cb\nm+vYV1JSgsceewz9+/fH8OHD8dxzz6Gurg4AUFRUhOjoaHz11VcYO3YsEhIS8OCDD+Ls2bOO48+c\nOYOUlBQMGjQId9xxB1asWAGz2QwAGDVqFABg/vz5SEtLw4QJE5CWltaob4899hheeOEFx3tt27YN\nI0aMwIABA7B06VJHXwAgPz8fc+fORb9+/fCrX/0Kr7zyCqxWa9t+oER0XcwcZg6RszF3mDtEzsTM\nYea0O0F0DXq9XgwbNkx88803Ijc3V8ycOVNMmzatyf5du3aJ06dPi1mzZonf/OY3Qggh7Ha7mDp1\nqvjTn/4kTp06JXJycsS0adPE4sWLhRBCFBYWCr1eL+69916RnZ0t8vLyxLhx48Qf/vAHIYQQFRUV\n4vbbb3cc//3334tRo0aJ5cuXCyGEuHz5stDr9WLr1q3CYDCItWvXinvuucfRt5qaGpGQkCBycnIc\n7zVu3Dixb98+cejQIXHPPfeIxx9/XAghRH19vRg5cqT4+9//Ls6cOSMyMzPFuHHjxKpVq5zyORNR\nA2YOM4fI2Zg7zB0iZ2LmMHPaGws8dE16vV6kp6c7Xp87d07o9XqRm5vr2P/OO+849n/11VciJiZG\nCCHE999/LwYOHCgsFotj/+nTp4VerxcXL150hMKXX37p2L9x40YxcuRIx9fDhg0TZrPZsX/nzp0i\nNjZWVFdXO95/165djfqWl5cnhBAiIyNDjBkzRgjxc9jt2LHDca69e/eKmJgYUV5eLj766CMxYcKE\nRte+a9cu0bdvX2G322/y0yOi1mLmMHOInI25w9whciZmDjOnvcld/QQRdWwDBgxwfB0cHAxvb2+c\nOHEC0dHRjm1XeHp6wm63w2KxID8/H7W1tRg0aFCj80kkEhQUFKBXr14AgN69ezv2abVaWCwWAA2P\n9MXExEChUDj29+/fHzabDQUFBUhISGh03uDgYCQlJWHbtm3Q6/XYunUrJk6c2KjNwIEDHV/Hx8fD\nbrcjPz8f+fn5KCgoQFJSUqP2FosFRUVFja6RiNoXM4eZQ+RszB3mDpEzMXOYOe2JBR66Lrm88beI\n3W6HTCZzvL766yuEELBarQgJCcG6deua7AsICMDly5cBoFHAXE2lUjWZ4MtmszX631+69957sWHD\nBsydOxd79+7F008/3Wj/1X212+2O67PZbOjfvz/+9re/NelrUFBQs+9FRO2DmcPMIXI25g5zh8iZ\nmDnMnPbESZbpun788UfH1wUFBaipqXFUl68nIiICFy9ehFarRXBwMIKDg2GxWPDiiy/CYDDc8Pjw\n8HDk5uY6Jv0CgIMHD0IqlSI0NLTZY8aNG4fz58/j7bffhl6vR1hY2DWv5fDhw5DL5YiMjERERATO\nnj2L2267zdHXCxcu4OWXX+Ys8kROxsxh5hA5G3OHuUPkTMwcZk57YoGHris1NRV79+7FsWPHsGzZ\nMgwdOhQRERE3PG7YsGGIiIjAk08+iWPHjuHo0aN46qmnUFlZCX9//xsef++990IqleLpp59Gfn4+\nvv/+e/z1r3/F+PHj4evrCwDQaDQ4efIkamtrAQA+Pj4YNmwY1q9fj0mTJjU55/PPP4/Dhw/jhx9+\nwAsvvID77rsPOp0O9957LwBg2bJlOHXqFLKzs/HnP/8ZcrkcSqWyNR8XEd0iZg4zh8jZmDvMHSJn\nYuYwc9oTCzx0XVOnTsVf/vIXPPTQQwgJCcErr7xy3fYSicTxv//617+g0+kwa9YszJ07F6GhoViz\nZk2Tts299vDwwPr161FWVob77rsPTz31FMaNG4cXX3zR0WbOnDlITU3Fq6++6tg2YcIEWCwW3HPP\nPU36NmnSJCxatAiLFi3C8OHD8Ze//KXRe1VUVGDq1Kl47LHHMHToUKxYsaIVnxQRtQVmDhE5G3OH\niJyJmUPtSSL4jBRdQ3R0NN55550mE3l1ZG+99RZ27dqFN99807GtqKgIo0ePxrfffosePXq4sHdE\ndD3MHCJyNuYOETkTM4faG5/goU7h5MmT+M9//oP169dj+vTpru4OEXVyzBwicjbmDhE5EzPHPbHA\nQ51Cbm4unnnmGYwcORJjxoxpsv+XjysSEd0KZg4RORtzh4iciZnjnjhEi4iIiIiIiIjIzfEJHiIi\nIiIiIiIiN8cCDxERERERERGRm2OBh4iIiIiIiIjIzbHAQ0RERERERETk5ljgISIiIiIiIiJycyzw\nEBERERERERG5ORZ4iIiIiIiIiIjcHAs8RERERERERERujgUeIiIiIiIiIiI3J3d1B6jz+vzzz/HB\nBx/gxIkTkEgkiIqKwvz58zF8+PBG7Q4ePIi33noLBw4cgMFgQEhICB544AFMnz4dMpkMAPDpp5/i\n6aefxuHDh6FUKpu8V1FREUaPHn3NvuzYsQPdu3dv2wskog6lLTMnKysLs2fPbvIeWq0W4eHhePTR\nRxtlTllZGV5++WXs2bMHRqMRcXFxWLp0KaKjo9v3oonIpdoqd250HwMAycnJ2LhxI4QQWLduHd5/\n/32Ul5cjPj4ey5YtQ2xsbHteKhG5wI0yZvXq1VizZo2jvVQqhU6nQ0xMDObOnYsRI0Y0Ot+5c+ew\natUqHDhwAHa7HQMGDMDSpUsRHBzsaHP06FGsWLECubm5CAgIwIIFCzB16tRG59m6dSvS0tJQXFyM\nqKgoPP3000hKSmrHT4JaSiKEEK7uBHUuQggsWbIEX331FWbNmoXBgwfDZrPh888/x5YtW/Dcc89h\n2rRpAIAPPvgAzz//PMaOHYvx48dDq9UiKysLb775JsaMGYOXXnoJwI0LPGazGXl5eY22mUwmLF68\nGDExMVi/fn37XzgRuUR7ZM6VAs8///lP9OzZ0/E+ly5dwsaNG3HgwAF89NFHiI2Nhc1mw/Tp01Fd\nXY3FixdDp9Nhw4YNyMnJwdatWxEUFOSyz4aI2kdb584v72O2b9+OdevW4cMPP3Rs02q1iIiIwL//\n/W+sWbMGTz75JPr06YP09HTs27cPW7duRWBgoNM/CyJqey3NmNWrVyM9PR1vvPEGAMBms6GiogJb\ntmzBtm3bsGLFCkyZMgUAYDAYMGnSJPj6+iIlJQUSiQRr1qxxtNdqtSgtLcXEiRPRv39/zJw5E5mZ\nmVi/fj3S0tIcRejdu3djwYIFeOSRR5CcnIxNmzZh//792LJlC/+g3hEIojb23nvviZiYGJGVldVk\n37Jly0RCQoIoLy8XeXl5Ii4uTqxZs6ZJu4yMDKHX68WOHTuEEEJ88sknQq/XC5PJ1OJ+vPTSSyI5\nOVlcvnz5pq+FiDq+9siczMxModfrxenTp5u0NRgMom/fvmLVqlVCCCGysrKEXq8Xubm5jjZGo1EM\nGTJErF69uo2ukog6kvbInau9++67Qq/XN9lus9nE0KFDRWpqqmObyWQSiYmJ4q233rqlayKijqMl\nGXP58mXx6quviqFDhzZ7jqVLl4p+/fqJiooKIYQQn376qYiPjxdlZWWONqWlpSI2NlZkZGQIIYRI\nTU0Vw4cPFxaLpdF5pkyZ4ng9c+ZMsXjxYsdrq9Uq7r77brFy5cpbu2hqExyiRW3u7bffxujRo5Gc\nnNxk3+9//3t4eXmhtrYWmzZtgo+PDxYsWNCk3eTJk5GXlwetVntTfSgsLMSGDRuwdOlS+Pr63tQ5\niMg9ODtzFAoFFAqF47VKpcL06dMbDcdSq9UICgpCcXHxTV4VEXVkrrrXkUql2LBhQ6N7G5lMBolE\nAovFcnMXQ0QdTksyxmAwXPccixYtQkZGBr744gvMmDEDvr6+mDt3Lvz8/Bxt/P39odPpcP78eQDA\n3r17ceedd0Iu/7lMcNdddyEjIwPV1dVQKpXIycnB3/72N8d+mUyG4cOHY8+ePbd62dQGWOChNlVS\nUoKCggLMnTu32f09evTA0qVLAQCZmZkYMmRIowC52pV2N2PNmjXo3r07ZsyYcdPnIKKOr70zx2az\nwWq1AgDsdjtKSkrw2muvob6+HmPHjgUAJCYmIjExsdFx58+fx6lTpzBhwoSbvjYi6phcfa8TGRkJ\noGEIR3FxMVavXg2ZTIZ77rmn1ecioo6nNRlzPcHBwejZsycOHz6MGTNmYMSIEU3m5Dl48CCqqqoQ\nHh4OADh79ixGjRrVqE2vXr0ANMzfo1KpYLVaERoa2qTNJ5980uJrpPbDAg+1qZKSEgANwXMjpaWl\nLWrXWuXl5di2bRuWLFkCqZQLxRF1Zu2dORMnTmyyLSoqCmlpaUhISGj2GKvVimeeeQYajcYx7p2I\nOo+OcK8DABs3bsSLL74IAHjiiScc84URkXtrTcbciK+vLy5fvtzsPoPBgGeffRYhISG4++67AQC1\ntbVNniq88tpgMDieFGyujdFovOX+0q1jgYfa1JUVaGw22w3bSqXSFrVrrc2bN0OhUPAXK6IuoL0z\nZ/Xq1ejRowcMBgPWrl2LM2fO4KWXXkJUVFSz7a1WK5566ilkZWUhLS0NPj4+rXo/Iur4OsK9DgAM\nGzYM6enp2LNnD1JTU6FQKPDwww+3y3sRkfO0JmNulsFgQEpKCoqKivDOO+84njIUQkAikTR7jEQi\ngd1ud3zd3H5yPT7eQG3qyszpFy9evGabq6vSFy5cuGG71vr6668xcuRIqNXqmzqeiNxHe2dOZGQk\n4uLikJycjNdffx06nQ7z5s1DRUVFk7ZGoxELFy7E9u3bsXLlSowcObKVV0NE7qAj3OsAQEREBAYO\nHIjFixdjypQpXDGUqJNoScZcb9/VSktLERAQ0GhbeXk5fvvb3+Lo0aNYu3Yt4uLiHPt0Oh3q6uoa\ntb8y14+npyc8PT0bbbu6jUajaVGfqH2xwENtytfXF9HR0di9e3ez+8+fP48RI0Zg06ZNuP3225GV\nleWY3+KXZs6cicWLF7fq/Wtra5GTk+N4zJCIOjdnZo5SqcTy5ctx6dIlx3LqV9TW1mLOnDnYt28f\nUlNTOfcOUSfmynud2tpabN68ucmQC71ef81hGETkXlqSMSNHjsSmTZuue57CwkJcvHgRSUlJjm0l\nJSWYMWMGioqK8Oabb2Lw4MGNjunduzfOnTvXaFtRUREkEglCQkIQHBwMqVSKoqKiJm3CwsJac5nU\nTljgoTb34IMP4uuvv0Z2dnaTfa+++ioUCgXGjBmD6dOno7KyEuvWrWvS7pNPPsH58+dbPWFgbm4u\nbDYb+vXrd9P9JyL34szMGTBgAMaPH4+MjAwcP34cQMPjzIsXL8aJEyfw2muvYfTo0W1zYUTUYbny\nXuf//b//12Qy08zMTMfky0Tk/lqaMdfz+uuvw8vLC+PGjQMAmEwmzJs3D9XV1di4cWOzvy8NHjwY\nu3fvbrQq37fffov4+HhotVp4eHggMTER33zzjWO/1WrFzp07mxSLyDU4Bw+1ualTp+Lbb7/F/Pnz\n8dvf/hbJyckwGAzIyMjAjh07sGLFCgQEBCAgIACPP/44/vGPfyA/Px/jx4+HXC7H7t278e6772Ly\n5MmOVWquSE9PbzJxcnx8PAYOHAgAOHnyJFQqFYKCgpx2vUTkWu2ZOc154okn8NVXX2HVqlVYv349\ntm7dij179mDWrFnw8PDAoUOHHG19fX0REhLSnpdPRC7g7Ny5QqfTYcaMGVi7di08PDwQHh6OL7/8\nEt988w3S0tLa8YqJyJlamjEAYLFYkJOTAyEE7Ha7Y8GZL774An//+9+h0+kAABs2bMDJkyfx+OOP\nw2g0NrpfCQoKQlBQEGbOnIn09HQsXLgQDz30EPbt24fPPvsMq1evdrSdP38+Fi1aBH9/f9xxxx14\n7733UFlZiVmzZjn3Q6JmSYQQwtWdoM7HZrMhPT0dmzdvRmFhIWQyGWJiYvDoo4/i9ttvb9T222+/\nxcaNG3HixAkYjUaEhYVh2rRpuP/++x3FnIyMDCxbtqzJ+0gkEjz88MN46qmnADQsj/7+++9j165d\n7X+RRNRhtHXmZGVlYc6cOdi2bVuzjxy/8MIL2LRpE9avX4+PP/4YX3zxBZr7cTphwgS8/PLL7XPR\nRORSbZ07V3vvvffw17/+Fbm5uU32Wa1WvP7668jIyMClS5cQGRmJxx57jPN+EXUyLcmYtLS0RsVd\nmUwGf39/9OnTB3PnzsUdd9zh2Ddt2jQcPny42fuVBQsW4IknngAAHDp0CCtWrMDx48fRvXt3pKSk\n4De/+U2j9p988gnWrl2L0tJSxMTEYNmyZUhMTGyPj4FaiQUeIiIiIiIiIiI3xzl4iIiIiIiIiIjc\nHAs8RERERERERERujgUeIiIiIiIiIiI3xwIPEREREREREZGbY4GHiIiIiIiIiMjNyV3dAeq8Pv/8\nc3zwwQc4ceIEJBIJoqKiMH/+fAwfPrxRu4MHD+Ktt97CgQMHYDAYEBISggceeADTp0+HTCYDAHz6\n6ad4+umncfjwYSiVyibvVVRUhNGjR1+zLzt27ED37t3b9gKJqENpy8zJysrC7Nmzm7yHVqtFeHg4\nHn300UaZU1ZWhpdffhl79uyB0WhEXFwcli5diujo6Pa9aCJyqbbKnRvdxwBAcnIyNm7cCCEE1q1b\nh/fffx/l5eWIj4/HsmXLEBsb256XSkQucKOMWb16NdasWeNoL5VKodPpEBMTg7lz52LEiBGNznfu\n3DmsWrUKBw4cgN1ux4ABA7B06VIEBwc72hw9ehQrVqxAbm4uAgICsGDBAkydOrXRebZu3Yq0tDQU\nFxcjKioKTz/9NJKSktrxk6CW4jLp1OaEEFiyZAm++uorzJo1C4MHD4bNZsPnn3+OLVu24LnnnsO0\nadMAAB988AGef/55jB07FuPHj4dWq0VWVhbefPNNjBkzBi+99BKAGxd4zGYz8vLyGm0zmUxYvHgx\nYmJisH79+va/cCJyifbInCsFnn/+85/o2bOn430uXbqEjRs34sCBA/joo48QGxsLm82G6dOno7q6\nGosXL4ZOp8OGDRuQk5ODrVu3IigoyGWfDRG1j7bOnV/ex2zfvh3r1q3Dhx9+6Nim1WoRERGBf//7\n31izZg2efPJJ9OnTB+np6di3bx+2bt2KwMBAp38WRNT2Wpoxq1evRnp6Ot544w0AgM1mQ0VFBbZs\n2YJt27ZhxYoVmDJlCgDAYDBg0qRJ8PX1RUpKCiQSCdasWeNor9VqUVpaiokTJ6J///6YOXMmMjMz\nsX79eqSlpTmK0Lt378aCBQvwyCOPIDk5GZs2bcL+/fuxZcsW/kG9IxBEbey9994TMTExIisrq8m+\nZcuWiYSEBFFeXi7y8vJEXFycWLNmTZN2GRkZQq/Xix07dgghhPjkk0+EXq8XJpOpxf146aWXRHJy\nsrh8+fJNXwsRdXztkTmZmZlCr9eL06dPN2lrMBhE3759xapVq4QQQmRlZQm9Xi9yc3MdbYxGoxgy\nZIhYvXp1G10lEXUk7ZE7V3v33XeFXq9vst1ms4mhQ4eK1NRUxzaTySQSExPFW2+9dUvXREQdR0sy\n5vLly+LVV18VQ4cObfYcS5cuFf369RMVFRVCCCE+/fRTER8fL8rKyhxtSktLRWxsrMjIyBBCCJGa\nmiqGDx8uLBZLo/NMmTLF8XrmzJli8eLFjtdWq1XcfffdYuXKlbd20dQmOESL2tzbb7+N0aNHIzk5\nucm+3//+9/Dy8kJtbS02bdoEHx8fLFiwoEm7yZMnIy8vD1qt9qb6UFhYiA0bNmDp0qXw9fW9qXMQ\nkXtwduYoFAooFArHa5VKhenTpzcajqVWqxEUFITi4uKbvCoi6shcda8jlUqxYcOGRvc2MpkMEokE\nFovl5i6GiDqclmSMwWC47jkWLVqEjIwMfPHFF5gxYwZ8fX0xd+5c+Pn5Odr4+/tDp9Ph/PnzAIC9\ne/fizjvvhFz+c5ngrrvuQkZGBqqrq6FUKpGTk4O//e1vjv0ymQzDhw/Hnj17bvWyqQ2wwENtqqSk\nBAUFBZg7d26z+3v06IGlS5cCADIzMzFkyJBGAXK1K+1uxpo1a9C9e3fMmDHjps9BRB1fe2eOzWaD\n1WoFANjtdpSUlOC1115DfX09xo4dCwBITExEYmJio+POnz+PU6dOYcKECTd9bUTUMbn6XicyMhJA\nwxCO4uJirF69GjKZDPfcc0+rz0VEHU9rMuZ6goOD0bNnTxw+fBgzZszAiBEjmszJc/DgQVRVVSE8\nPBwAcPbsWYwaNapRm169egFomL9HpVLBarUiNDS0SZtPPvmkxddI7YcFHmpTJSUlABqC50ZKS0tb\n1K61ysvLsW3bNixZsgRSKReKI+rM2jtzJk6c2GRbVFQU0tLSkJCQ0OwxVqsVzzzzDDQajWPcOxF1\nHh3hXgcANm7ciBdffBEA8MQTTzjmCyMi99aajLkRX19fXL58udl9BoMBzz77LEJCQnD33XcDAGpr\na5s8VXjltcFgcDwp2Fwbo9F4y/2lW8cCD7WpKyvQ2Gy2G7aVSqUtatdamzdvhkKh4C9WRF1Ae2fh\nIogrAAAgAElEQVTO6tWr0aNHDxgMBqxduxZnzpzBSy+9hKioqGbbW61WPPXUU8jKykJaWhp8fHxa\n9X5E1PF1hHsdABg2bBjS09OxZ88epKamQqFQ4OGHH26X9yIi52lNxtwsg8GAlJQUFBUV4Z133nE8\nZSiEgEQiafYYiUQCu93u+Lq5/eR6fLyB2tSVmdMvXrx4zTZXV6UvXLhww3at9fXXX2PkyJFQq9U3\ndTwRuY/2zpzIyEjExcUhOTkZr7/+OnQ6HebNm4eKioombY1GIxYuXIjt27dj5cqVGDlyZCuvhojc\nQUe41wGAiIgIDBw4EIsXL8aUKVO4YihRJ9GSjLnevquVlpYiICCg0bby8nL89re/xdGjR7F27VrE\nxcU59ul0OtTV1TVqf2WuH09PT3h6ejbadnUbjUbToj5R+2KBh9qUr68voqOjsXv37mb3nz9/HiNG\njMCmTZtw++23IysryzG/xS/NnDkTixcvbtX719bWIicnx/GYIRF1bs7MHKVSieXLl+PSpUuO5dSv\nqK2txZw5c7Bv3z6kpqZy7h2iTsyV9zq1tbXYvHlzkyEXer3+msMwiMi9tCRjRo4ciU2bNl33PIWF\nhbh48SKSkpIc20pKSjBjxgwUFRXhzTffxODBgxsd07t3b5w7d67RtqKiIkgkEoSEhCA4OBhSqRRF\nRUVN2oSFhbXmMqmdsMBDbe7BBx/E119/jezs7Cb7Xn31VSgUCowZMwbTp09HZWUl1v1/9u4zPqoy\n7eP4bzKZlkwqkAQIoQVI6L2JgiJNUWTFBmEXlR6KAiJFepFqIyIqKAgILosKsiBSBGkCoQSEhBo6\ngQDpddrzwjXPZilpkznJ5Pp+PnkxZ865zx+FOzPXucvSpfedt379eq5fv17gBQOjo6OxWCw0bty4\n0PmFEKWLI/ucZs2a0a1bN3744QfOnDkD/DmceeTIkZw9e5YlS5bw9NNP2+cPJoQosZT8rPPee+/d\nt5jp77//nrP4shCi9MtvH/Mon3/+OZ6ennTt2hWArKws+vfvT3JyMt98880Dvy+1atWKvXv35tqV\nb+fOndSvXx93d3cMBgONGjVix44dOe+bzWZ27959X7FIKEPW4BF216tXL3bu3MmAAQP4+9//TsuW\nLUlLS+OHH37g119/ZdasWVSoUIEKFSrw9ttvM3/+fC5cuEC3bt1wdXVl7969fPvtt7zwwgs5u9T8\nZdWqVfctnFy/fn2aN28OwLlz59DpdAQEBDjszyuEUFZx9jkPMmrUKLZt28a8efNYtmwZ//73v9m3\nbx9hYWEYDAaOHz+ec66vry9BQUHF+ccXQijA0f3OX4xGI6+99hqfffYZBoOBGjVqsHXrVnbs2EFE\nREQx/omFEI6U3z4GwGQyERUVhc1mw2q15mw4s2XLFubMmYPRaARg+fLlnDt3jrfffpuMjIxcn1cC\nAgIICAigd+/erFq1iiFDhtC3b18OHTrEhg0bWLRoUc65AwYMYOjQoZQvX562bduyZs0aEhMTCQsL\nc+x/JPFAKpvNZnPkDU+cOEF4eDh79uwB/pw/OH36dI4cOYJGo6Fr166MHTsWrVbryFjCziwWC6tW\nreLHH3/k6tWrqNVqQkNDGTRoEG3atMl17s6dO/nmm284e/YsGRkZVK9enVdeeYWXXnopp5jzww8/\nMH78+Pvuo1KpeP311xk7dizw5/boa9euzfn7JcTD7Ny5kw8++IAbN27g5+fHsGHDHrhjkigd7N3n\nHDx4kH79+rF58+YHDjmeOXMmq1evZtmyZfzrX/9iy5YtPOjX6bPPPsvChQuL5w8tSo2NGzcyZcqU\nXMcyMjJ4+eWXmT59ukKpRFHZu9/5b2vWrGH69OlER0ff957ZbObzzz/nhx9+4Pbt2wQHBzNixAhZ\n90vkkD7HOeSnj4mIiMhV3FWr1ZQvX55atWrxxhtv0LZt25z3XnnlFU6cOPHAzysDBw5k1KhRABw/\nfpxZs2Zx5swZKlasyODBg+nZs2eu89evX89nn31GfHw8oaGhjB8/nkaNGhXHfwZRQA4r8NhsNtav\nX8+cOXPQaDQcOHAAgL59+1KnTh3Gjh1LcnIy4eHhtGnThrfeessRsYQQZVBGRgYtW7Zk4cKFdO7c\nmcjISPr168cvv/xSbNvZCiHEX/bv38+4ceNYt24d/v7+SscRQjg56XOEKDsctgbPkiVLWLlyJUOG\nDMmpGmZnZ+Pu7s6QIUPQarWUL1+e5557jmPHjjkqlhCiDFKpVLi7u2M2m3O2g9RoNDnbUgohRHFJ\nS0tj3LhxTJkyRb5oCSGKnfQ5QpQtDluDp1evXgwZMoSDBw/mHNNqtSxZsiTXeTt37iQ0NNRRsYQQ\nZZBer2fu3LmMGDGCd955B6vVyuzZs+WDjxCi2C1dupSQkBA6duyodBQhRBkgfY4QZYvDCjx/LQL1\nMDabjVmzZnHp0qX7tp99lISEBBITE3Mds1gsZGVlUadOHVxdZR1pIURu165dY9SoUcycOZNu3bqx\nb98+Ro8eTWhoKCEhIY+8VvocIURhpaWlsXr16gfuqPQw0ucIIQqrMH0OSL8jRGlWIv51ZmZmMnbs\nWM6dO8fKlSvx9fXN97WrVq166K4BO3bsIDAw0F4xhRBOYvv27dStW5fnnnsOgPbt29OhQwc2bNiQ\nZ4FH+hwhRGFt376dypUr07Bhw3xfI32OEKKwCtPngPQ7QpRmihd4EhMT6d+/P0ajke+++w5PT88C\nXR8WFnbfzjdxcXH069fPjimFEM5Er9eTlZWV65harc7XEynpc4QQhfXrr7/SrVu3Al0jfY4QorAK\n0+eA9DtClGaKFnhsNhvDhw+nQoUKLFq0qFDD/Xx8fPDx8cl1TKPR2CuiEMIJdejQgQULFvD999/T\ns2dPDh8+zPbt2/nmm2/yvFb6HCFEYUVFRdG7d+8CXSN9jhCisArT54D0O0KUZooUeFQqFQDHjh3j\n8OHD6PV6WrRokfN+/fr1WblypRLRhBBlQEBAAEuWLGHu3LnMnj2bihUrMnfuXOrVq6d0NCGEk7JY\nLNy6dSvPNQmFEMIepM8RomxyeIGnVatWHDhwAICmTZsSExPj6AhCCEHz5s1Zt26d0jGEEGWEWq3m\n9OnTSscQQpQR0ucIUTa5KB1ACCGEEEIIIYQQQhSN4ossCyGEEEIIUdrYbDbOXrqIm7cnLi6Of2aa\nkphMJd9yeHoUbIMSIYQQzksKPEIIIYQQQuQhMyuTvUcOs2P/Xm7fu0NqViYWdy1utYNy1pd0pOyE\nZExXbmHAFaPeQIOQUJ55/CmqVani8CxCCCFKBinwCCGEKHN2HjpAql6FKo+n7maTiYoubrRu1MRB\nyYQQJYHJZOJEzGn2HI3kfOwFUjIzSDdnY/Nxx72SH5rA6ngonFFT3hfK+wKQbbPx2+1Ydi5ZiCbb\nglGrx9fLm5aNmtCuaXP8K/gpnFYIIYQjSIFHCCFEmfLFP1ez7dhBjA2C83zqbrPZSDl2hl7Xu/Dq\nM887KKEQwpGSU5L5PeoY+49FEhd/m/TsLDLMJqweenTlvDHUDkDj4oKX0kEfQaVSYfQvD/7lAbAB\ncZlZrI3ay5pdP+NqtuKu1WHUG6hfpy7tmragTo2aikwtE0IIUXykwCOEEKLMmPflYiJvXMSrUe38\nXaBS4d0slPX7fyUxOZnBr4YVb0AhRLG5l5jIsdMnOXzqBFevXSPDlE2GKZtslQ2VtzuGCr7o6lZB\nC2iVDmsHGr0Or6BKEPT/x5LNZrbfPMvP3xzGJS0LvasGg1aHp7uRBnVCadmgEbWq1cDVVb4iCCFE\naSS9txBCiDJhzhefcuz2FTzrVCvwtV71a7Iz5jjWby0M7f0P+4cTQtiFzWbj6s0bHI85TVT0KW7e\nissp5JjVYPN0x1DOG11IJVxUKtwBd6VDO5Da1RWP/xrpA2AF7mab+HfsSX46fgBSMzG4atBrtHgY\n3KldsyZNQ+tRv3YIbgY35cILIYTIkxR4hBBCOL3Nv+3kyNXzeNWrWeg2PEOqsfN4JE3rNZQ1eYRQ\nWEpqKifPRnM85jRnL14kPTOdTLOJDJMJm84VPN3Q+3qhC62MWqXCqHTgEk6t1eBZyQ8q/f9aPTYg\nwWRi160LbD97AltKOlpc0Gs06F21VChfjoZ16tI0tB7VAoNkupcQQpQAUuARQgjh9Fb86594tqlX\n5HY8GwSzaMVSWn/wqR1SCSEexWQycf7yJY7FnOKPMzEkJCWSacom02wiW2UFDzc03h4Yqvqg1lRw\nmqlVJYlao8m1ts9fsm02LqVncDpqH2v2bMMlIxv9f0b9GLQ6qgVWoXFoPRqF1KOcj49C6YUQouyR\nAo8QQginduX6NSweOrtsY+yiVpPlCqlpaRjdy9LEDiGKh81mI/7OHY6cPsmxmFPcuHmTDFM2mSYT\nWRYTuOvB04B7OR80lQNRQ5mbVlUSqVQqtO5uaN1zT9myAakWC5FJ8ezb+ROqDd+hNttyij8e7u7U\nqRlM87oNqVerNlqtlOSEKE6zv4zArXbQQ9+/d+oCUwaNQK1WOzCVKE5S4BFClDkbN25kypQpuY5l\nZGTw8ssvM336dIVSieKSkZ2FzQ7FnRwqFZlZWVLgEaIALBYLF65c4sipk0TFnCYxOek/hZxsLBr1\n/0+pqlMRlUqFATAoHVoUiotajZuvN26+3rmOW/hzrZ9t18+w9fRRSMlA56LG8J9RP1WrBNG0bn2a\nhNbHx6sk71kmROkwc/HH/JFyC+PNh5+Tbk5m1OypfPTedLs8CBPKkwKPEKLMef7553n++f/f8nr/\n/v2MGzeO8PBwBVOJ4lKnek206Sa7tGW1WtFboLyvr13aE8IZXY+7yb5jkUSejCIhOYlMUzYZZhO4\n68DTDffyvmgCq6ABNEqHFQ6l1mrwDKgAARVyHU+3WjmaGM/vOzbAD2twtYLBVYubTkf1KlV5rGlz\nmtStj16nVyi5EKXLJ98s4+Td63jUevjoHQA3v3Lcyo7jvQ/nMmvUOAelE8VJCjxCiDItLS2NcePG\nMWXKFPz9/ZWOI4rJC526sf7wbjxrVy1SOymnLzK412t2SiVE6WY2mzl++hR7jx7m3KWLpGdnkp6d\njVnrgou3ETe/8miCvNEBOqXDihJN5eLywFE/mTYbRxPucGDTd6hWf4VercFNq8PH04sWDRrxePNW\n+Jev8JBWhSibVvy4jr3nT+FZt0a+zjcGBnAh9jrvfx7B+EHDijmdKG5S4BFClGlLly4lJCSEjh07\nKh1FFKNXunXn4LFI4u7cw6184UbfpN24TWiFQDq2fszO6URZEBcXx5QpU4iMjMRoNNK/f3/69u2r\ndKwCMZlM/Hb4INv27+b2vXukZmdi8dCjK+eNvpY/arUaD6VDCqeiUqnuK/xYgbjMLL47sZ+1u7ai\ns6rw0BloEBLKcx2eJqhyoHKBSxBn6HNEwe06dICf9u/Gu2lIga4zVq/MsZhYVm5cT9/nXyymdMIR\npMAjhCiz0tLSWL16NUuXLs33NQkJCSQmJuY6FhcXZ+9oohjMG/seb44bRZZej87olvcF/yUjKRmP\nuxlMmzmtmNIJZ2az2Rg6dCht2rRh8eLFxMbG0qdPHxo0aEDjxo2VjvdIt+/eIWLl11y5dZM0UxY2\nH3fcK/mhCayOp9LhRJml0evwCqoE/5l9YrLZ2BN/mV8Xz0ebbcOo0/FM+470eLpLmVxXpDT3OaLw\nEpOTiFj1NZ6t6xfqes+Q6vy4azutGzelVlB1O6cTjiIFHiFEmbV9+3YqV65Mw4YN833NqlWriIiI\nKMZUori4urryyZRZvDlhNJo29XFxccnXdVazBdMfl/hkwSdl8ouCKLqoqCji4+MZM2YMKpWK4OBg\n1q5di08J3T7aZrOxbd9v/HPzRhLNWehrBqJvVANZ9laUVCqVCqNfOfArB4DZamV15C6++/knalet\nzvCwN8rU2mmlrc8R9jFhwfsYGgbn+/PNg3g2qc30jxay8gP5rFtaFf7/vhBClHK//vor3bp1K9A1\nYWFh/Pzzz7l+li9fXjwBhd15eXgw4JU+pJy/ku9rkqMvMnbQMFncUxTaqVOnqFWrFvPmzaNdu3Z0\n6dKFqKgovL29875YAZM/XsAXO3/CWjcI76Yh6L2MSkcSokBcXFzwqh6Ie4tQzuuyeXPCaG7fvaN0\nLIcpbX2OKLo/zsVw25xe4BHK/0ut0ZDl68aGHVvtlEw4mozgEYqxWCz8cng/7hXKFfjatJQUGlWs\nSiX/gGJIJsqKqKgoevfuXaBrfHx87nsCptHIPjClSefHnmD5j+vyfb6bCZrXz/8oLyH+V1JSEgcP\nHqR169bs2rWLkydP0r9/fwIDA2nevPkjr3X0tND4e3f549J5yrVuUGz3EMKRDF6eqFuEMvXjBSye\nPkfpOA5RlD4HZDp6afTpyuV4hNhnWpVHzSr8sHULPTp2sUt7wrFkBI9QxPW4m/xjzAhWHNzJVwe3\nFfhnzbE9DJ89hZ9+3ab0H0WUUhaLhVu3blGhguy+UdaoVCpUFGCqlczKEkWk1Wrx8vJi4MCBuLq6\n0qRJEzp37syOHTvyvHbVqlV07do110+/fv2KLau3hyfVKgSQfOEqN3dH5npPXsvr0vjalJlNWtR5\nXu7eg7KiKH0OOL7fEUWTmZXJndRk1Fr7PHBUqVSkuli4duOGXdoTjiUjeITDRcWcZlrEB3i0qIte\npy1cIwY1mtb1WbFjMxeuXOatf/S3b0jh9NRqNadPn1Y6hlDAz3t2YXLPf9+TroZDJ47TsqEsTCkK\np0aNGlgsFqxWa87aCBaLJV/XhoWF0b1791zH4uLiiu3Llkaj4cOJ0/j8u1X8KzKapHOXMVarjFoj\nHxlF6WLKzCLxaAxeai0R782gop+/0pEcpih9Dji+3xFFs/z7f6KuYt8HloaagSxes4LZo8fbtV1R\n/FQ2m83myBueOHGC8PBw9uzZA/w5hHDChAkcPHgQDw8PwsPD6dWrV5Huce3aNTp27MiOHTsIDJSt\nEkuSmIvnmfjRPLxa1cNFrbZLmylnLvN4cD2G9elnl/aEKCjpc0qPXYcPsOjbFXi3rJfvBZOtFgtJ\nh04xachIGofUK+aEwhllZWXRuXNnXnzxRcLDw4mKiqJ///4sX768QIu8/8VRfY7NZuPXg/tZ//O/\nuZOShLW8B8ZAf9QyLVWUUBmJyWRdjsPd6kJozVq82etVKvgWfCmA0s7efQ7IZ52Symaz0WdUOIaW\noXbfCCLl4ClWzPlQ1iAsZRz2OMZms7F+/XrmzJmTa72KSZMmYTQa2b9/PzExMQwYMIBatWrRqFEj\nR0UTDvTBsiV4tqhrt+IOgEedquw+dIh+PV/C6OZut3aFEM7DYrEw54tPOR57Dq8WdQv0IchFrcar\nZT1mfvkp7Ro0YeQ/+stuWqJAdDodK1euZPr06bRt2xaj0cikSZMK/UXLUVQqFU+1foynWj+G2Wxm\ny2+/svPAXu6lJJNmyUZV3gtjJT8Z3SMUk5mUQsb122jSTXjoDDSoWo1Xw8OoFhikdDRFldY+RxTc\nF9+twlLJp1g+l6hrVWbmpx8zc9S7dm9bFB+H/UZesmQJP//8M0OGDOHLL78EIC0tjR07drB161a0\nWi0NGzbkueee48cff5QCjxOy2WzcS0/Fuxg+CNoq+rBx5zZ6d3/B7m0LIUq3vUcO8+nKr6CaP15N\n6xSqDRe1Gu8Wdfn9+hUiRw9jVP/BNK0ri9CK/AsKCmLp0qVKxyg0V1dXnnuqE8891QmAtPR0tu7b\nzW8HD3AvNZl0swl83XGv6IfGIE97hf1ZrVbS79zDdDsR1wwTHnoDdatUpUdYT+rWqiOF9/9R2vsc\nkbej0Sf55dB+fFoWz+hiN19vzpw8z6Zft9P9yaeL5R7C/hxW4OnVqxdDhgzh4MGDOccuX76Mq6tr\nrmF+1apVY9s2WTjXGalUKlzVxfNXzpqZTaCf7KglhPh/p86d4YNln5OsteHRIsQuIweNlf2x+Jdj\n9uqllFPpeHfgMGoEle0nxaJscndz42+duvG3Tt2APxf53HvkMDsO7CX+XhwpmRmYDRo0ft64lffN\nWQdEiPwyZWaRduM2JKTi5qLBqDfQvE4oXV5oT40qQVLQEWXaH+fOMGvxIrxaF+/Ucc/6NVn+03oM\nBgMdWz9WrPcS9uGwAs+DdqpJT09Hr8/9lEev15OZmZnvdmUbv9IlwNuXu6np6Ixudm1XczeFNk2a\n2bVNIUTpdD3uJrOXfMKtrDQ86lXHy87rhahdXfFuUIvMrGzGRswj0NOHSeGjKOfjY9f7CFGa6HV6\nnm77OE+3fRz4c9TuxSuX+XnvLk6diSElI4N0LLiU98RYUaZ1iftlJqWQefMOLimZeOj0+Hv78GS7\nLrRv0Ro3g30/NwpRmu069DuLVn9l1zVNH0alUuHVoi6L16/h1p14mS1RCij629VgMJCVlZXrWGZm\nJm5u+e/EV61aRUREhL2jiWIycehIhsx8D50dhxKmXLnJ022fyLW2kxCi7MnMymRaxIecu3kN93rV\n8DZULtb7aXRavJuGcDc1jUEzJlC/ejATB4+QvkgI/vxSULNqNcKr9ss5lpCUxLb9e9h/5BCJqSmk\nmbOgghfGyv6oXaXgU9ZkJqWScTUOXZYFo05PSOUqPNPrWRrXrS8jvoR4iIVffc6Bs6fwauW4fycq\nlQqfZiH8GLmHU2djmD7yHdTFXFgShafob9OqVatiMpm4efMmFStWBCA2Npbg4OB8tyHb+JUuFXzL\n0SQ4lD/i7+FewbfI7dlsNlxvJtL/ndfskE4IUVodPX2SuUsicA2tinfzUIfeW2d0R9eyHufiE+g7\nZjjT3nqHOtVrOjSDEKWBj5cXL3frzsvd/vzclpmVyZbffmXHvj3cS00h26BGXyUAvadR4aSiOFgt\nFlJu3IZbiRg1OmoHVuGVN4dLfylEPqRlpDN61lQSPTV4N66tSAbPkOpcvHWXfu+MZOHEqfiVK69I\nDvFoihZ4jEYjHTt2ZOHChcycOZOzZ8+yadOmnEWY88PHxwef/xkWL09PS7aR/3iTflPHgh0KPGnx\nd3m8WQuZhy1EGfbvXTtYtmk93q3su0NfQRkq+GDx9mD8x/MY84+BtJVpo0I8kl6np2enbvT8zzo+\n0efP8d2WjZw7HEO2jxse1Ssr+m9a2EdmcipZ567iq3fnb60eo8ewzrgZDErHEqLU+OPcWaZ/sgBt\ngxoYFS6Au/uXw+TlztBpExjW93U6tGijaB5xP0UKPP/9ZXzGjBlMmTKF9u3b4+bmxrvvvitb+Dk5\nrVaDymKzS1vmtEwqhvjbpS0hROmTlZ3Fiu//iU+b+iWi0KvWuOLdoi6LViylVcPGMoRZiAIIDa7F\n1OGjsdlsbN69k3VbfiJFq8Kzbo0S8e9bFExmQjLZ564RXLkKb4+fTgXfckpHEqLU2X8skoXLv/xz\nvR3XkvGZQqPX49m6PhH/XMXdxARe7PSM0pHEf3F4gadVq1YcOHAg57WXlxcfffSRo2MIBX28Yhnq\noPsX3S4Mz6CKfP/zJp5/qhOuMn9fiDInMTkZi0FTor78uajVmNQqsrOzMchTaiEKTKVS8WyHjjzb\noSM/79nF0k3/wrtJiNKxRAFkJqWivhDHN+9/gF6nz/sCIcR9zsReYMHyL/B24Ho7+eXi4oJ3s1C+\n3b4ZL6MnT7dpp3Qk8R8l62+KcGo2m40pHy/g0JVzGAPsU+BxUauxVPPn9bFvcSfhnl3aFGVDXFwc\ngwYNolmzZrRv356VK1cqHUkUgn/5CuhNNszZJqWj5DBlZuLhqpPijhB20PXxDnRu2obkyzeUjiIK\nwHT6Ep/NmCvFHSGKYPbiT/BqHlriijv/zatRbb5cswqz2ax0FPEfJfdvi3AqeyIP03f0cM5akvCq\nW8Oubbv7+eLSoCpDpk3gk5VfYbFY7Nq+cD42m42hQ4cSHBzMoUOHWLZsGRERERw/flzpaKIQ5oyZ\nQMbhGDKTU5WOQsa9JEzHLzD/3UlKRxHCafiXL48F+0ztFo6hdzPIOjtCFFGa1YS6hK8t6+Ligtmo\nIy7+ttJRxH9IgUcUqxNnonlz3Cg+2fgt2ma1cK9UPOvlaAwGvFrXZ//tS/QZPYyVG9Zjs8mHQfFg\nUVFRxMfHM2bMGNRqNcHBwaxdu5Zq1aopHU0UQpVKlflq3od4Xk8k8Y/zWBUo8lpMZhKjzuJ3L5vl\n8z+mvG/RF5EXQvzplz27MfrLbi2lSRoWYmLPKx1DiNLNYlU6Qb7YTCYMehmtV1JIgUcUi99PHOXN\ncW8z/ZslWOtVwatuTYfshGGs7Id7q7r8FHOE3qPC+XLdtzKiR9zn1KlT1KpVi3nz5tGuXTu6dOlC\nVFQU3t7eSkcTheRucGPxtDm8/bcwso+cI/nCVYcUea1WK0lnL2M9Ect7fx/Eh+9NQ6vRFvt9hSgr\nLly5TFxmChq9TukoogA8Qqvz4bIvlI4hRKnWqFYI6bfuKh3jkTKTU6ni6Us5H3mwVVLIqrTCrmKv\nXmHWpx+RqLHiUb8a3gosfKxSqfCsWgmqVmLH5dPsGBXOq91f4IVOXR2eRZRMSUlJHDx4kNatW7Nr\n1y5OnjxJ//79CQwMpHnz5krHE0XwWNPmtG3SjHU/b+LHX7ZgruCJR/XKdl+E2Wq1knLhKrrEDPo9\n15NnO3S0a/tCiD99t3kDhmqVlI4hCkit1ZCQkYbVai3R64cIUZKNHzSMwe+NJQ0b7iVwFGNGQjK2\nmKvMmDVf6Sjiv0iPK+zCZrMxbdEHvLNoHubQQLzrBaMuAbtaGQMDcG9dj28P7OD1d98m/l7JroIL\nx9BqtXh5eTFw4EBcXV1p0qQJnTt3ZseOHXlem5CQQGxsbK6fq1evOiC1yC+VSsXL3Z5j9Qef8lLT\ndmQciibl6k27tG2z2Ui+eI3syDP0e7wrqxZGSHFH5MuyZcuoX78+TZo0yfk5cuSI0rFKPAb7I1MA\nACAASURBVJPZjE0W7yyVLGYTmVmZSscos6TPKf1cXV35fNZ8AjNcSD53Rek4uaReuYnb1Xt8Pf8j\nPI1GpeOI/6L8N3DhFGYu/ojozHt4Ny1525iqVCo8a1fFlJnJyGnv8fX8j9BpZah3WVajRg0sFkuu\nJ4v5ncq3atUqIiIiijOesBOVSsVLXbvTq8uzfLV+Lb/s3Y1LjUq4+xVuGHHazXhsl2/zt85deaXb\n8yVqa3ZR8kVHRzN69Ghef/11paOUKuMHDaPvqOFk6bXojO5KxxH5YLPZSIyJpVObJ3AzuCkdp8yS\nPsc5qNVq5o+bzOpNP/DD9q24N66NRq/cVHCLyUxy1Fkeb9CUke+8KZ+FSiAp8IgiM5lMHI05TbnH\nGikd5ZE0ej1plbz5duMPvN7rVaXjCAU99thj6PV6IiIiCA8PJyoqiu3bt7N8+fI8rw0LC6N79+65\njsXFxdGvX7/iCSuKTKVS8Wav1+jb40Vmf7aIP46fxbNB/tcFs5jNpESdo2XteoxeOBm1A9YTE84n\nOjqaF198UekYpY5WoyVi2mzeX7KIE5v34errcd+uMhXbP3hq7c3dkQ88LucXz/k2m42UyzdxvZXI\ny5268sozzz/wfOEY0uc4lz7de/JkizaMnz+b1IpeGCsXz8Y1j5Ien4D1/HVmjhhFaM3aDr+/yB+Z\noiWKTKPR4KbRYikFQ6gttxPp1PYJpWMIhel0OlauXMmJEydo27Yt77zzDpMmTaJhw4Z5Xuvj40P1\n6tVz/VSpUsUBqUVRaTVapo4YzZjXXifl91OYs7LzvCY7LYP0g9FMHTiCsQOGSnFHFEpGRgaxsbGs\nWLGCdu3a8cwzz7B+/XqlY5UaFXzL8cGEqXRo3hp9mpnsW/fISkjGapZNFJRms9lIjb9L4tEYTMfO\n81xoM1Z/8KkUdxQmfY5zquQfwPL5H9PUqyKJx85gtTpul63k0xeplqVm5YJFUtwp4WQEj7CL8UNG\nMGPRB7iGBGHw9VI6zn0sJhPJx87Sve0TBFaSxRoFBAUFsXTpUqVjCAW0btSEDydMZeTsKXi1rv/Q\nkTwWk5mM4+f4fMY8fGWHNVEEd+/epVmzZvTu3Zu2bdty/PhxhgwZQoUKFXjiCXnokF/vTZgA/FlU\n2HvkED/8soVbCfdIOHEOXRU/DN6euaYLPGwkysPI+fk732Iyk3rjNrbbiXhpDTTwDKTPuGFU8C1X\noPZE8ZE+x3mpVCrG9h/K7sO/88nKr/BsEXrfqEZ7slosJB2N4eWnn+WVbt3zvkAoTgo8wi7q16rD\nyoWLmPTRAi5cOI2udiAGL0+lY2ExmUiJuYyHCea8NZZaQdWVjiSEKAECK1bizV6vsWzPz/gEBz3w\nnOSzl5k4eJgUd0SRBQYGsnLlypzXzZs3p0ePHmzfvj3PL1sJCQkkJibmOhYXF1csOUsLlUrF481b\n8XjzVgCcjb3I99u3cPHUJVIyM8nWuaDx98G9QjlZH8IOTJlZpN24jepeKm6uWnyMHjzb4jG6d+iI\nXqdXOp54gKL0OSD9TmnQvkVrqgRU5J15Mx/5sKoobDYbSZHRjH19MK0aNrZ7+6J4SIFH2I1Wo2Xu\nOxNITE7ig6++IObMaVSB5TFWrODwD1iZyalknr9OeZ0bb/UdQKOQeg69vxCi5OvU9nGWbVz30Pdd\n07NoUreBAxMJZ/XHH3+wb98+Bg0alHMsMzMTN7e8F6CVhd3zVrt6DcYNCM95ffn6NTbt2s4f0TGk\nZKaTgRUXXw+MlfxQa4vvSbczsNlsZCQmkxV3F3VqFkadDn8vHzo+3o32LVtLQaeUKEqfA9LvlBY1\nqlRl3KDhzF3xOd7NQu3efkp0LGHPviDFnVJGCjzC7rw9vZj+1jtkm7JZ8cO/2Bt5kDRXG+61q6LR\nF9/uVVaLhZTLN1DfSaVmlSCGjZlERT+/YrufEKJ0i/zjBCqPh3/YNes0XLxymZpVqzkulHBKRqOR\nxYsXU61aNTp16sTBgwfZvHkzq1evzvNaWdi94KpWDiS8T7+c1wlJSfz6+z72HT1MQkoyqVmZWIw6\ndP6+GHy8yvQoH0u2idSb8VjvJmPABaPOQO2gqnR5pQcNQ+rm7DQpSpei9Dkg/U5p0qJ+Q+pXqcHZ\ne4m4+dpvxLEpIxM/Fz09n+5qtzaFY6hsNptN6RD2du3aNTp27MiOHTsIDAxUOo4AYi6cY8malVy/\nF49rUADuAfabp52dlk7auat44UqPTl15/qnOZfrDmnA86XNKH4vFwutjR6JuVPOhT/SzUtPRX7jF\nF7MXSJ8iimz37t0sXLiQq1evUrFiRd5++206depUqLakzykam83GH2di2LpvN+diL5KSlUGWqwq1\nnzdGv3LFMtWhpMhOzyTt+m1cElMxavT4eHrStlkLOrZuh49XyVtDURSePfsckH6nJEvPyKDfxNF4\ntqxrtzYTj8bw0dsTCKwoa5eWNjKCRzhESM1afPTedDIyM/j8u9Ucjowi29uAZ80qhf7ilHEnAdPF\nGwT5VWTY8LFUqyw7GQkh8maz2XhnznTMVf3QPmK6hs7oRko5NyZ/PJ8Zb411YELhjNq3b0/79u2V\njiH4cw2fBiGhNAj5/ykNN27F8e/fdnL81EmSM9LJsFlQVy6P0a90r+Njysom7cpNXBLT8dDpCSxX\ngS6dX6Bt0xZoinFhVqE86XPKDjeDgQBvX5KzsnHVaYvcntVqxdNFK8WdUkoKPMKhDHoDb/2jPwAb\nd/7C2p82YPH3wli1Yr4/QKUnJmE6e40mtUIZPWcsOm3xTfsSQjiXrOwsRkx7jyRfPUa/Cnmebwz0\n59zlG7w1cxILxk3B1VV+bQrhjCr5BzDgpd7w0p+v7967x9otGzn+xx8kZWZgLWfEo0pF1JqS3wek\nJySRdeUWBgv4eZfjjS4v8njzljLdSggn9ubLrzF91Zf41A8uclupV+J4qcNTdkgllFDyf0sJp/X8\nU5157slOrNr4PRt2/oKxWQiueSx+mHzqItU9fHhvxnw83I0OSiqEcAax164yft4s1KFBGH3yv8uf\nsWolbt+5R793RrJg/GQC/PyLMaUQoiQo5+ubs46PyWRi+4G9/HvnNm6lJKKpUQm3ckVb6+JO9AUu\n/Hs3ADWfbU/50JpFas9qsZB8/ir6lCwa1g6h9/B+BFWqXKQ2hRClR6OQerhlWrBaLEWeZqqKS+Bv\nnbrZKZlwNCnwCEWpVCr69niRDi1a887cGWga1EBnfPCip4mHT/Na1+682OkZB6cUQpR2azdvZN0v\nm/FsXqdQu+i4lffFZHQnfOYk3njxVZ5tL0+2hCgrNBoN3Z54km5PPElyaioff7OUUwdPo64egMHP\nt8DtXd51iCu/Hsx5Hb12M0FPtqJqh5YFbstqsZB86iKeVhfCe77Ek63aFrgNIYRzGPBKGJ9sXIt3\nvcIXjFMu3aDrEx1QO/FaZM5OxmqKEqFKpcosnjaHzNOXHvh+yqUbPPf4k1LcEUIUiNls5t15M/n+\n0G/4tK5fpC2SNXod3m0asHzbT0xb9AFOuEeBECIPnkYjk4a+xar5n1Ah2Uzq9VsFuv5/izt/ufLr\nQS7vOlSgtqwWC0mHTjHqpb/z1ZwPpbgjRBn3RItWVNF5kHEvqVDXZ6Vl4HYvnX49X7ZzMuFIUuAR\nJYavtzfl3B4y7So+kb/36OXYQEKIUi0xOYnXx77FFb0Vz5BqdmlTpVLhVb8mMeZE3hz3NukZGXZp\nVwhRuri6uvLhxGl43EnHlJGZr2vuRF94YHHnL1d+Pcid6Av5zpB84jwTBw+nbZNm+b5GCOHc5r77\nHpYzVzFlZt/33p3oCxxc8BUHF3x1X19jNVvIOH6WhROmluqF5YUUeEQJY7JYHnjcwp+LowohRH5c\nu3mDgRPfgXpBuBdiCkVe3Cv6kV0jgDfHvc3dxAS7ty+EKPlUKhX1Qurmu8Dz15o7RT3nLxorNA1t\nkO/zhRDOT6vR8uHEaaQdicZiNuccv7zrENFrN5OdkkZ2ShrRazfnjBq02WwkRZ5m6ogx+HoXbX0x\nobwSUeDZuXMn3bt3p2nTpnTt2pVNmzYpHUkoYPOeX0lytT7wPV2tQMYveN/BiYQzW7ZsGfXr16dJ\nkyY5P0eOHFE6lrCDpJQU3p49FbfmIejcH7ymlz3ovYxoGwczbMp4MrPy9wVPCOFczlw4h8at+PqZ\nRzG5qjh17owi9xZClFwV/fyZ8dY7JB8+jdVqzXNqaNLRGMJf/Tv1a9VRIK2wN8ULPBkZGYwcOZIR\nI0Zw9OhRZs6cybhx47hx44bS0YQD/R51jK/Wr8Wrbo0Hvm/w9uSGLZN5Xy52cDLhrKKjoxk9ejTH\njh3L+WnWTIa5O4O3Z05G3ygYV5222O+lMehxqRvEqFlTi/1eQoiS5fDJKOLNGWj0+etr/Brl/eUp\nP+f8xaNudeZ/IZ+LhBD3C61ZmzGvD+Lqv/fkOTW0QYVAnmr9mAPTieKkeIFHpVLh7u6O2WzGZrOh\nUqnQaDSycncZ8v22Lcxf8QWeLevlzPm8uG0fe6ZGsGdqBLHb9gHgEVyFI3euMHbuDCwPmcolRH5F\nR0cTEhKidAxhZ+u3bibVU/vQ3fiKg8HLk9tk8dvhh3+AEkI4l2xTNgu+/AzPeg9+MPUgt6PyHm2T\nn3P+otZoyCxv5LM13+T7GiFE2dGmcTMSzl7K87xtG/9d/GGEwyhe4NHr9cydO5fx48dTv359wsLC\nmDx5Mv7+/kpHE8XMYrEwYeH7rNmzDe8WdXFx+fOv44mvv+f63qNgs4HNxrW9Rznx9fcAeFQP5Jre\nxj/GjODGrTgl44tSLCMjg9jYWFasWEG7du145plnWL9+vdKxhB1s2P4zHjUCHX5fzzrVWP79dw6/\nrxBCGQu/+hx1rUq4KPxA0li1IjsP7ZdpokKIB9LrdEpHEA6meIHn2rVrjBo1ipkzZxIVFcWSJUuY\nNWsWMTEx+bo+ISGB2NjYXD9Xr14t5tSiqO4k3KPfOyO55JqNV90aOSN3Tnz9PUmXrt93ftKl6zlF\nHjd/X9SNajJ81mR2/L7PobmFc7h79y7NmjWjd+/e7Nq1i+nTpzNnzhx+++23PK+VPqfkSkhKIl1l\nUWT3Bxe1mpTsTEwmk8PvLYRwvJNnY3CrULAF3Gs+294u5/wvdZA/X62XArMQ4n5Tp061yzmi9HBV\nOsD27dupW7cuzz33HADt27enQ4cObNiwIV/TJ1atWkVERERxxxR2dPr8WSZ/PB/3prXRGQw5x2O3\n7XtgcecvSZeuE7ttH9U7PYZGr8W7TQM++/E7zl+OZdArYY6ILpxEYGAgK1euzHndvHlzevTowfbt\n23niiSceea30OSXXsVMnsXm5F+raO9EXcnavqflse8qH1ixwGzajnvOXYwkNrl2oDEKIUqQQI3fK\nh9Yk6MlWD10PI+jJVoXqe1zd9CSnphb4OiGEEM5H8QKPXq8nKyv39tdqtRpX1/xFCwsLo3v37rmO\nxcXF0a9fP3tFFHZ0J+EeUz6ej0ereqj/5//xtb1H87z+2t6jVO/05yJgKpUK78a12X4ikoDyfvTo\n2LlYMgvn88cff7Bv3z4GDRqUcywzMxO3fOyEIn1OyXU57jpqd0PeJ/7vdf+zu0T02s0EPdmKqh1a\nFqgdm17DlZs3pMAjRBlgcHXFlJWNpoCLuf/Vr/xvkafqk60IKmCf85fMa7d47IWOhbpWCOHcJkyY\nkK9znn76aQekEY6g+BStDh06cPHiRb7//ntsNhuHDh1i+/btdO3aNV/X+/j4UL169Vw/VapUKebU\norBmRHyIW9Pa9xV3isKzfk1Wb/zebu0J52c0Glm8eDFbt27FarVy4MABNm/eTM+ePfO8VvqckkuF\nCqy2Al2T19ahBWGzgdpF8V+rooS7c+cObdq0YdeuXUpHEUUwY+RYUo/EYC3Epg9VO7Qk9NVn0Hq4\no/Vwp+5rzxa6uJN+6y7BnhV4vFnhrhfOT/qcsi0lJcUu54jSQ/ERPAEBASxZsoS5c+cye/ZsKlas\nyNy5c6lXr57S0UQxuJeajM7w4AW0XTSuWE3mR17vorn/r6xKpcLipuXK9WsEVXb84qqi9KlWrRqf\nfPIJCxcuZNy4cTn9TmhoqNLRRBFU8C2H9Wz+18C5E30hz61D3f3L5XvKhMpkxsfTO9/3F2XTxIkT\nSUpKUmStKGE/lfwDGNt/KPO//BRDo2B0xoJNDy0fWrNQ07H+W/K5y1RRuzNz7LtFakc4N+lzyjaj\n0UhycnKe5wjnUSIeNTZv3px169YRGRnJTz/9JEPEnJi7Vo/lIUWcOi/mPcXqYeeos8xU9JOd10T+\ntW/fno0bN3Ls2DE2b95Mp06dlI4kiqhOtRokHo3Odezm7siHvj7344482zzzr635bo+0LGpVq57f\nuKIMWrNmDW5ubgQEBCgdRdhBq4aNWTb7A/QX40k6fwWbLf8jCO9EX+Dggq84uOAr7kRfKNB9s9LS\nSfz9D55v1JqFE6bme1kDUfZInyPef/99u5wjSo8SUeARZceAV8NIPnHuge/9tfjgwzxs8cG067dp\nEFwbjUZjt5xCiNKnRlBVVGZrvs83Z2XneY7VnP/pF1qbCk8Pj3yfL8qW2NhYli9fLruVOBkvDw++\nnL2Al5s/QcqBU6TfScjzmsu7DhG9djPZKWlkp6QRvXZzvqaEWi0WEv84j+eVBJZMnk3Y8y/a448g\nnJT0OQLg6aefZvjw4Q99f/jw4TK4wslIyV84VLN6DXjh8afYeHgvXvXuL9YUdPHBjDsJeCdm8d6o\nkcUTWAhRari4uFC5du5+pWL75g997eKqLvC00Ie1Z7PZcNfoCpxZlA1ms5l3332XSZMm4eXlVaBr\nExISSExMzHUsLi7OnvGEHbzUtTvPP9WJ2UsWcerwadzrV0djuH/R90et+wU8dHH3lMs3cI1L5K2w\n12nXrIV9wwunU5Q+B6TfcTbDhg0DYNGiRbmOjxgxgvDwcCUiiWIkBR7hcH2ff5H0jAy2nzyCV/0H\nF3nc/cvlbFkc3L0D5UJq3Hde+q17GG8ls2j6HJlXLIQAoGqlylxITkXvmfd8chd1Pgo8+dwKOS3+\nLm1CZA0n8WCLFy8mJCSEdu3a5RzL73SeVatWERERUVzRhB3ptDqmjRjDjdu3mPXpR9wyp+NZt0ZO\nP1KYdb8y7iVhOnOVrk88yetjX5bPOyJfitLngPQ7zmjYsGGEhIQwYuRIyvn6MmXKFBm546RUtoL8\nay8lrl27RseOHdmxYweBgbLobkn11fq1/HzyMJ4hBV+zIiMhGber91gyYy7qfH4BE6K4SJ9Tcly/\nFcfIhTPxbhqS57kHF3xFdkraI8/RerjTaswbebaVeOg0y6bNxUumaIkH6NatG/Hx8TlfzlNTU9Hr\n9QwdOpQBAwY88tqHPUnv16+f9Dkl3P5jR1i0Yim2KhUwVvYrUJ9jMZlJOXGO2gGBTB72Nnqd3kGp\nhTMoSp8D0u84s26v/I0t38nuw85MRvAIxbzx4qtcu3mT6Ju3ca/ol+/rLCYz1pgrRMz/WIo7Qohc\nKvsHEODmSVJqOjqj2yPPrflse6LXbs7znLxkJCVTM6CyFHfEQ23ZsiXX66eeeoopU6bQvn3ef798\nfHzw8fHJdUzWnCsd2jZpRquGjflw+Zf8Hnki3yMo0m/dhdhbzBzxNiE1ahVzSuGMitLngPQ7zkyF\njAJ0drLIslDUxCEjsF29U6BrUmOvM+C1vui0st6FEOJ+M0e9S8bJC3l+mSrswu7/zWq1Yjp1mSnD\nRxUqqxDCuanVasa8OZjJA4ZTOTjvEcuVGoZQw6pn5cJFUtwRQghRYDKCRyhKrVbjrr9/EcJHsSal\n0a6pLDAohHgwb08vBr7chy82/DPPqVoFXdj9v9lsNpIOn2bsm0Nwe8BiqkI8zM6dO5WOIBysYZ1Q\nNnzzLc+89Deun3nwtuj+oTXp9+IrvPZsDwenE45mtVrZt28fx48fJy4ujuzsbAwGA/7+/jRt2pQ2\nbdrY9X7S5whRdsgIHqGozKxMUrMyC3SNytfIL3t3F1MiIYQz6PzYE/Tu9CwJR2PyHMlTtUNLQl99\nBq2HO1oPd+q+9myexR2rxULCoVMMfaUvrRo1tmd0IYST0uv0bPnXD1Sqdf/GEQH1ajF88FAp7pQB\nV69e5fnnn2fkyJEcOnSI9PR0VCoVycnJ7N+/n/DwcJ5//nmuX7+udFThjGShdqcnI3iEosa8Px3X\n2pULdI1n9UBW/LCOx5u3wsvTs5iSCSWlp6eTkpKCv7//fe9ZrVYuX75M9eoFX5xblC1/69QNLw9P\nFq9ZgWfzuqg1D/+VVz60Zp7Tsf5iysomNTKacQPDadlAijtCiPzTaXWs+2Y1rw3tT9z5SwBUebwZ\nTQJr8FLX7sqGEw4xefJkatSowbp16zA8YPRneno648aNY/LkySxbtkyBhMKpOd/+SuJ/yAgeoZjp\nER9wxwAGr4IVaVxcXNA3CmbI5HGkpj96NwpRuqSkpDBs2DCaNWtG+/bteeaZZ9i3b1+uc+7evcsz\nzzyjUEJR2nRs/RhzR08g/XA0GQnJRW4vLf4e5mPnWfTeDCnuOInU1FQiIyNJTU0FICoqivHjxzN4\n8GAWLFhAfHy8wgmFsynv68uLPV6gXu/utBrzBh5omDpijNKxhIMcO3aMkSNHPrC4A+Dm5sbw4cM5\nevSog5OJMkFG8Di9hxZ4nn76aVavXu3ILKIMmfLJAk6nxGOsWqlQ1+uMbrjUq8rA8WNISZMij7OY\nM2cON27cYPXq1Xz77bcEBwczYMAAvv3221zn5XcnkrzcuXOHNm3asGvXLru0J0qm4KBqfLPgE8rf\nySDlwtVCt5McE0tQppoVCz6hkn+AHRMKpZw4cYKnnnqKsLAwunTpwubNm+nTpw83btwgMDCQyMhI\nunXrxokTJ5SOKpzMm71ew3b9DhmJydSrWUt2KCpDAgICOHLkyCPPOXToEOXKlXNQIiGEM3noePVr\n166xYMECdu7cyYQJE6hZM39D14XIy/ufLyIm7Q4e1QOL1I7ew53M+tUY8t5Yls5ZiF6nt1NCoZTd\nu3ezePFiGjZsCEDTpk354osvmD59Omq1mldeecWu95s4cSJJSUmo5GmG09NpdXw8aQZfrV/Lv/ft\nxrNpHdSu+ZulbMk2kXzsDK91fZ5eXWT0mDN5//336dmzJ8OHD+err75izJgxDBw4kLfeeivnnPnz\n5zN79mzWrl2rYFLhbDQaDT5uHsRfvUWfgW8rHUc40OjRoxkzZgwHDhygRYsW+Pn5odPpyM7O5vbt\n20RGRrJ161bmzZundFQhRCn0yCla3377LTqdjh49evDOO+9w+vRpR+USTuq3yENEXj1f5OLOX/Qe\n7thCKvPeh/JL0BmYzWb0+tyFuoEDBzJ8+HCmTp3Kxo0b7VaMWbNmDW5ubgQEyEiMsuSNF19l+pC3\nSP39FFmp6Xmen5GUQkZkDAtGT5TijhM6ffo0YWFhGI1GBgwYgNVqpWvXrrnO6dWrl3z+EcWido2a\nWBNSqVm1mtJRhAN16tSJNWvWoNfr+eabbxg7diyDBg1izJgxfPPNN2i1WtauXSvT0YUQhfLIx5d+\nfn4sXryYyMhIPvvsM1588UWCg4Pp1KkTLVq0oFatWnh5ecmwUpFvX69bg1fD+3ePKAqDlyeXzp4m\nMTkZb1l0uVRr2bIl8+bNY+7cubmGJoeHh5OUlMT48ePp379/ke8TGxvL8uXL+ec//0nPnj2L3J4o\nXerVqsPS2QsZNnU8mcGV0fs+uN/IuH0P3fV7RMz9SLZBd1Lly5fnzJkzVKlSBYPBwCeffIKPj0+u\nc44dO0blygXbDECI/GhYO4Rde/coHUMooG7durz//vtKxxBCOKF8jU9v3rw5y5Yt4/Lly2zZsoXd\nu3ezbNkysrKyUKlUREdHF3dO4SRUahdc1Gq7t2tTu6B2kWk2pd2ECRMIDw/nscceY+nSpbRr1y7X\ne56ennz66adFuofZbObdd99l0qRJeHl5Ffj6hIQEEhMTcx2Li4srUibheF6eniyb+yEDxo8h09UF\nvacx1/sZd5Nwj0vms9kLURdDnyVKhtdff52xY8cyevRo+vTpQ+fOnXPeu3DhAl988QWbNm1i1qxZ\nCqYUzqpKxUpYTWalYwgFpKamsmnTJo4fP86tW7fIzs5Gr9fj7+9PkyZNePbZZ3Fzc1M6phCiFCrQ\nNulVq1Zl8ODBDB48GLPZzOXLl7l7925xZRNOqGL5Cly6k4ChvE/eJ+eT1WzBNdOEh9HDbm0KZQQE\nBLBu3TrOnDlDpUr3L8A9bNgwOnXqxLZt2wp9j8WLFxMSEpKreFSQRZtXrVpFREREoe8vSg6tRstn\nM+byxrtvYWkRmrONuikzG9X5G0TM+1CKO04uLCwMX19fkpKS7nvvzp073Llzh8WLF9O+fXsF0gln\nV97HF6vZonQM4WCnT59mwIABuLm50axZM+rWrYtWqyU7O5v4+Hg+//xzPv74Y5YuXUpISIjScYUQ\npcxDCzwtWrTA9RELULq6ulKzZk1ZfFkUyNTho/n7mBFkG/Ro3Ys+5cFqtZJw5DRTho6wQzpREri4\nuBAaGvrQ94OCgjCZTIVuf8uWLcTHx7Nlyxbgz6dob7/9NkOHDmXAgAF5Xh8WFkb37t1zHYuLi6Nf\nv36FziSU42YwMHnEaCZ/8QneTf/8IJ164hwfj52EVqNVOJ0obocPH6ZTp04PnGreqlUrWrVqpUAq\nUVa4G9zAalU6hnCwKVOm0KVLFyZPnvzA9202GzNmzGDq1KmyuLsQosAeusjyypUr85y+YLFYSE1N\ntXso4bw0Gg2fzZiD7dRlMu4kFKkti8lE0u+nGPlaPxqH1LNTQqGk1NRUJkyYQMuWLWnbti3Tpk0j\nOzs75/3NmzfTrVs3li1bVuh7bNmyhcjISA4fPszhw4epWLEiH330Ub6KOwA+Pj5UnbQ5oQAAIABJ\nREFUr14910+VKlUKnUcor25wbYK8ymPKyPhzy+KqNQiseP8IMuF8+vbtS3JystIxRBn1qAepwnmd\nPXuWsLCwh76vUqno06ePLIEhhCiUR+6ilZd9+/bRokULe2URZYS3pxdfz/uIgGQrSWcuFaqNjPgE\nMiPPsGDsRDq0bGPfgEIxs2bNYufOnbzxxhu88cYb7Nq1iwULFpCWlkZ4eDijRo2iWrVqbNiwQemo\nwsmE932dtLNXyb54g5F931Q6jhCiDFCr1VCAKcLCOdSoUYOtW7c+8pxNmzYRFBTkoERCCGdS5EcH\nBVm7Qoi/uLq6snDCFL7bsol1WzdhbFoHV23eu7HZbDaSYy5Rw+jLzAWLZAc3J/Pbb78xY8YMOnXq\nBECbNm3o378/Fy5cIDo6mg8++MDu24bu3LnTru2J0qlmUFX0FhWuLhrK+foqHUcIUQaoVLI5RFk0\nceJEBg4cyK+//kqLFi3w8/NDp9ORnZ3N7du3iYyM5MyZMyxevFjpqEKIUqhEjA2Ni4tjypQpREZG\nYjQa6d+/P3379lU6lnCAV7p1p22jJrw7bxam4IqPXHzZnG0i5WgMYc/2pGenrg5MKRwlMTGRBg0a\n5LyuV68eKSkpJCYm8tNPP+XaOl0Ie3PTaNFK0bjMGThwYJ5TZVQqVZHXwti8eTOLFi0iLi6OypUr\n89Zbb/H0008XqU3hBKTIU+Y0b96cLVu2sG7dOo4dO8Zvv/1GZmZmzi5a7dq14+OPP8bf379I95E+\nR4iySfECj81mY+jQobRp04bFixcTGxtLnz59aNCgAY0bN1Y6nnCAKpUqs2L+x4yaPZWY33+javcn\nct67uTuSiu2b/7k2xpFzLBg3ieqBst6Js7JYLPeNytJoNEyYMEGKO6LYuapU+Hrbb4c/UTq0bNky\nz+2IizrSIjY2lokTJ/L111/TuHFjDhw4wMCBA9mzZw/e3t5FalsIUfr4+/szbNiwYmtf+hwhyq6H\nFnj+e2HTh7FYir61Y1RUFPHx8YwZMwaVSkVwcDBr167Fx0c+ZJclGo2GTybPpFff3qRdi8M9MCDn\nPVNmFpnHzrNk5jx85ZdSmeTn56d0BFEGuLi64uHmrnQM4WD9+/cv9gJy9erV2b9/PwaDAbPZTHx8\nPEajUaYZC1FG7dixgw0bNpCamkqbNm3o27cver0+5/3ExEQGDx5c6JGD0ucIUXY9tMDTsGFDhwQ4\ndeoUtWrVYt68efz000+4u7szZMgQXnjhBYfcX5QcKpWKf638loETxpDplYbOw52AJ5qReOAkn06a\nKcWdMuLy5cs5u9r8tcbX1atXMZvNuc6rXr26w7MJ5+bqov5z0VMhioHBYODq1at06dIFm83GtGnT\ncHeXgqIQZc369euZNm0aPXr0wMvLi88++4zvv/+eL774ImdXTpPJxPHjx4t0H+lzhCibHlrgWbFi\nRb4aKOqw5aSkJA4ePEjr1q3ZtWsXJ0+epH///gQGBtK8efM8r09ISCAxMTHXsbi4uCJlEspRqVQs\nmDCVAVPGomtZj5SL13i123MEVJARHGVF79697zv2xhtv5HqtUqlk+1BhdyoXlSx6WsZUqlQJF5ci\nbSha4PudPHmSw4cPM2TIEIKCgmjduvUjr5HPOUI4l6VLlzJ9+vSch9nDhw8nPDycPn36sHr16pwi\njz0Ups8B6XeEKM0eWuBp1arVQy8ym815LkiYX1qtFi8vLwYOHAhAkyZN6Ny5Mzt27MhXgWfVqlVE\nRETYJYsoGbw8PKhZqQpXU9LQ3EujV9fuSkcSDrJ9+3alI4gyzGKxYLEVfeqxKD0etYuePT/r/OWv\nEWKtW7emS5cubN++Pc8vW/I5RwjnEhcXR7NmzXJe+/n58fXXX/P666/zj3/8g2+//dZuo0kL0+eA\n9DtClGaPfGwVExPDwIEDuXz5cq7jY8b8H3v3GR5XdS18/H+m99Go925LluRuY5sSMCWEUMIFEsjF\nhA42BAKhmNDhDSF0bExJIITiUBLg0kILJphmA8K9yEWSZcmS3NRGmj5z3g+yDcLdGs2orN/zzIc5\n58yZxYO8Z5+19177ei6++GKqq6t7HUBhYSHhcJhIJLLr2MHU9pk2bRoffPBBj9dzzz3X67hEfJ1z\n8i/o3LCJjJRUGVEfQrKzs/f6ysjI6PFeiGgLhcIEA6H9XygGlVj0debPn8+FF17Y41ggEMDpdO73\ns9LPEWJwyc/PZ968eT2O2Ww2/vrXv2I2mzn//PNpbGzs1Xf0ps0BaXeEGMj2muCpqqri3HPPpbOz\ns0fyBeCUU07B4/Hw61//utcdnyOOOAKTycScOXMIh8MsWrSIjz/+mJNOOumAPu9yuSgoKOjxiubU\nRhEfo0rL8DdvZ+JI2UltqNnbw9Z1110XtYctIfZEVSNsa2uJdxgihmLV1ykvL2fFihW89dZbRCIR\n5s+fz2effcYpp+x/hqr0c4QYXK6++moefPBBLr30UtasWbPruMvl4tlnn8VgMPCb3/ymVwOcvWlz\ndsYi7Y4QA9NeEzyzZ8/muOOO46WXXtqtmOnxxx/P3LlzmTBhArNmzepVAEajkRdffJFly5Zx+OGH\nc8MNN3DbbbfFrMiz6J8URUEJRchOz4h3KCKGYvWwJcSe+CNh3F2d8Q5DxFCs+jrJyck8+eSTvPDC\nC0ycOJHHHnuMJ554QorFCzEETZ06lVdffZX8/PzdlmKlpaXx6quvcu6555KRceh9YGlzhBi69rq4\nfPHixfz973/f6we1Wi2XX345V1xxRa+DyM3N5Zlnnun1fcTgomoUQgexXE8MfDsftu6///7dzh1/\n/PFMnTqVq666ilmzZjF79uw4RCgGq1AoRKffh05RUFVVloYOEbHs60yYMIHXX3+91/cRQgx85eXl\nlJeX7/GcxWLhxhtv5MYbb+zVd0ibI8TQtNcZPMFgELPZvM8PJyQk4PV6ox6UEMFgEI1eR039hniH\nImJo8eLFu+2Y9UM7H7a+++67Xn/Xe++9x0knncTYsWM55ZRTpMDzEPfP99+FtASCDjP/XfhVvMMR\nMSJ9HSFEvMybN4+ZM2dyxhlncOKJJ3LmmWfyhz/8gfnz58c7NCHEALbXBM/w4cNZsGDBPj+8YMEC\n8vPzox2TEHy9dBGmjBQWr1wR71BEDMXqYau2tpZbbrmFe++9l8WLF3PLLbdw7bXX7rYlqBgaVFXl\n3598hD0nHVthFs+/8c94hyRiRPo6QohY83g8XHTRRVx99dU0NzczZswYfvaznzF27FgaGhqYMWMG\nl1xyCX6/P96hCiEGoL0u0Zo2bRp33XUXRUVFTJw4cbfzX3/9NQ8++CA33HBDnwYohqZX/v0WjqJs\ntixd3ydb1Yr+aefDVl5e3l6vicbDVkFBAV999RVms5lQKMTWrVux2Wzo9fpe3VcMTLNf+BvhrEQU\nRUGr09HlMvPi269z3mlnxjs00cekryOEiLXZs2dTX1/P22+/TVFR0W7na2pquPTSS3n22WeZMWNG\nHCIUQgxke31q/vnPf05VVRW/+c1vGDVqFCNHjsRut9Pe3s6yZctYuXIl5513HmeffXYs4xVDQFXt\nejZ7OnAaMlFyknn473/lxkt7X/9A9H+xfNgym83U19dz4oknoqoqd911F1artdf3FQPLvIVf8vmq\npSSMLdl1zFGYzZuffsyIwuFMqBgZx+hEX5O+jhAi1j788ENuv/32PSZ3AAoLC5k5cyazZs2SBI8Q\n4qDtc1rE73//e4499ljeeOMNli5dSkdHBy6Xi7Fjx3LnnXdSUVERqzjFEOHz+7jr0QexTSgFwJqe\nwjffraRyxXJ50BoCYv2wlZmZyfLly/n222+ZMWMGubm5TJ48eZ+faW1t3W0pV3Nzc1TiEbG1YOki\nnnjlBRIm7f5b5hxfyr1/fYy7rrqeimHD4xCdiBXp6wghYmnr1q2UlJTs85ry8nIaGxtjFJEQYjDZ\nZ4InHA7T1NTEjTfeiM1m23X81Vdfpa6ujvLyctlpRESNP+Dn8ltuQFuWh1b//Z+mY8xw7v3rY9x+\nxbWMLh0RxwhFLMTyYWvn9qSTJ0/mxBNP5OOPP95vgmfu3LnMmTMnajGI+Pjoy8/4y2sv4Txsz79j\nGq0W52Hl3DHnQa6/aDpTRo+LQ5QiFqSvI4SIpVAohNFo3Oc1BoNBirsLIQ7JXoss7ywAdt1117Fm\nzZoe51auXMnMmTO57LLLpACYiIrGzc1ceMM1hIsyMDntPc7tfNC6+6lZvP3JR3GKUMTKDx+2/vWv\nf/Hhhx/yyiuvUFxcTF1dHaqq9vo75s+fz4UXXtjjWCAQwOl07vez06ZN44MPPujxeu6553odk4id\nZ/71Mk+/8y8SDitHo9nrzyAanRbnpAoefOEZXn3/3RhGKGJF+jpCiP5IkspCiEO11xk8f/nLX2hu\nbubdd9+lsLCwx7m7776badOmcdlll/H000/z29/+ts8DFYPXf778jL+8Ohfr+FL0RsMer9HotCRM\nKufFT95nyaqV3HblNfLjNwh5PB5mzJjBt99+y4svvsj48eN3nVu5ciVvvPEGb775JnPmzNnv6Ne+\nlJeXs2LFCt566y1OPfVUPv/8cz777DOuuuqq/X7W5XLhcrl6HJPizAPHnY89xKq2Zpxj9j09fieN\nRoNrYhmvfTWPTU2b+P1Fl/dxhCKWpK8jhIiHyy67bJ8biASDwRhGI4QYTPY6dPnee+9x880379bh\n2Wn48OHceOONvPPOO30WnBjcQqEQtz78Z/763hs4p4zca3JnJ0VRcFYUsTrYxvnXX8Wm5qYYRSpi\n5YcPWz9M7kD3w9Ybb7zBunXrePrpp3v1PcnJyTz55JO88MILTJw4kccee4wnnniCgoKCXt1X9F/h\ncJhr/3g7Vb5WHMP2vkvb3jjLi/i6uZZbH7kvKrPIRP8gfR0hRKxdeeWVHHPMMRx11FEceeSRPV5H\nHXUURx11FFOnTpWkshDikOw1dbxlyxaKi4v3+eGRI0dKcVFxSDZv28p199xJJD8N58h9/539mDUj\nhaDLye/uvZNLf/m/nHjk0X0UpYi19957j1tvvXW/D1uzZs3qdcdnwoQJvP766726hxg4brzvjzTb\nNNjSUw/5HvbCbNbXN3PXYw9x59XXRzE6ES/S1xFxJwnjIeeqq64iHA7z4Ycf8pOf/GS32l82m42T\nTjppn0uIhRBib/bacqSnp1NXV7fPDzc0NJCUlBT1oMTgtmLdWq6462a0owuwpCUe0j30JgPOyRU8\n8/4bPP7S81GOUMSLPGyJvvDY3GdpULxY05N7fS9bTjqr2jbz4tuSHBwMpK8j4klmAw5NO2t/XX/9\n9Xut/XX55ZdL7S8hxCHZa4Lnpz/9KXPmzCEQCOzxvN/vZ9asWRx9tMyeEAeueesW7pz9IM5JFehN\npl7dS1EUnCOHMX/1Ev71gRRAHQzkYUtEm8fr5bNF32IvyI7aPR3D83j3k48JhUJRu6eID+nriHgK\nh8Mg9QSHnFgtRxdCDE17TfBcfvnltLW1ccYZZ/DKK6+watUq6uvrWbFiBf/4xz84/fTTaWlpkfWh\n4qDc/9cnsI4vQaPTRu2ejvIi3vjovajdT8SPPGyJaPvbay+jzU+L+n0jGS7e+I+0OwOd9HVEPAWD\nQUnwDEFS+0sI0Zf2WoPHZrPxyiuv8NBDD/HAAw/Q1dW165zT6eTUU0/lyiuv3G03GSH2pcXdjt7U\n+2USP+bXQofbjcNu3//Fot+6/PLL+dWvfsUZZ5zBtGnTGDVqFHa7nfb2dpYuXcrcuXMJh8PysCUO\n2Jrq9ViHp0f9vta0ZCqXL+NXJ50W9XuL2IllX6eyspL77ruP2tpaXC4Xl1xyCWeffXav7ysGrk6P\nBzSS4BlqYrUcXdocIYamve/PBzgcDu666y5uueUW6uvraW9vx+VykZubi1YbvRkYYujISc+gpr0D\ns9MR1fuaIxpJ7gwCklgW0RZWIyh9UKhSZzLg9W6P+n1F7MWir9Pe3s4VV1zBHXfcwcknn8yqVau4\n8MILyc3NZcqUKVH5DjHwbGvdjmYfW2WLwWnncvSsrKy9XtPb5ejS5ggxdB3Qr4rBYKCoqKivYxFD\nwHUXXc6lt9yAbnwpetO+t0U/UG2L13D+qadH5V4i/iSxLKJJoW9Gx9VIBK1WdjgZTPqyr9PU1MTU\nqVM5+eSTASgrK2PSpEksWrRIHraGsLqmTVFdsi4Ghp3L0SdMmIDBsHtfOBrL0aXNEWLokt6piKkE\nh5PZt/0/vJVV+Nxd+//APkTCYVq/XcWvjz+JU485PkoRiv5i58PWuHHjKCgokOSOOCRajaZPdqoJ\n+wNYLNao31cMTqWlpdx333273re3t1NZWcmIESPiGJWIt8WrVqKN0mCXGDhiUftL2hwhhi6ZFypi\nLiM1jWf+/DDX/ekO3AkmbLkZB30Pn7sL/7JqbrrsSiaOHN0HUQohBgO9To8aiaBEOUEYDocxGXu3\nE6AYmtxuN9OnT6eiooJjjz12v9e3trbS1tbW41hva3OI/qG2vg6Ny86qdWsoG1YS73BEjMR6OfrB\ntjkg7Y4QA5kkeERcOGw2nv7TQzzy3NN8uWgpjtHD0BzgA1jnhkYc7iBP3PswDputjyMVQgxkoXCo\nT2rwKIqGUFi2SRcHp76+nunTp5OXl8ejjz56QJ+ZO3cuc+bM6ePIRKz5A37avB4spbnMfef/+NPv\nb4p3SCKGYrUc/VDaHJB2R4iBTBI8Iq6uveBSfrJyOfc99RjGUUUY7Xtf8hAJh+lYspapYw/jyv89\nP4ZRCiEGqnAkgtIH2xBrDXq6NrdH/b5i8Fq5ciWXXnopv/jFL5g5c+YBf27atGmccsopPY41Nzdz\nwQUXRDlCEUuPPPcMuvw0jHYr61evxOvzYjaZ4x2WiLG+rP11qG0OSLsjxEDWr2rwbNu2jSlTpvDp\np5/GOxQRQ+PLR/LMvQ+jXduIZ0vLHq8J+QO0L1zB9dMukeSOiIrKykp++ctfMmHCBE444QReffXV\neIck+kAkEumT+2r1OgLBQJ/cWww+27Zt45JLLuGiiy466Actl8tFQUFBj1dOTk4fRSpiobZhI5VV\nK7CkJAKgL87mlofu28+nhDhwvWlzQNodIQayfpXgueWWW2hvb++T0VbRvzlsNv7254dxbu3Cu621\nx7lwKETnt6uZfcvdTB49Nk4RisFk5/ahF1xwAZWVlcyaNYuHH36YBQsWxDs0EWV99XOiqqr8VokD\n9tprr9Ha2srjjz/O2LFjd70OZsmEGBzcXV384f57sI8ZvuuYOdHJpoiHv7wyN46RicFE2hyxV32w\n8YToX/rNEq2XX34Zi8VCenp6vEMRcaLVannsjnu4aOY1BG1m9KbuAqadi9Zwz3U3kZV+8MWYhdgT\n2T506EhPTWNteycmZ3TrdXVu2c6EYcP3f6EQwPTp05k+fXq8wxBxtnnbVn53963oRxaiNeh7nLMP\ny+XjVYvoes7L7y+4NE4RisFC2hwhhq5+MYOntraW5557jjvvvDPeoYg40+l03HvDLXQtrwHA3dDM\n1AlTKCnom/XJYmiS7UOHjrNPOhVvbWPU7xtu2MoZJ5wU9fsKIQaneQu/5Mq7bsY4bvhe6w06ywr5\nurGaq+++FY/XG+MIhRBCDAZxT/CEQiFmzpzJbbfdhtPpjHc4oh/ISs8gPzmNoNeHtqmNGb8+L94h\niUHsULYPFQNHSWExWRYHvo7OqN3T29JGSXoOackpUbunEGJw8gf83PTgn3jynX/hmDISvcm4z+vt\nhdlsT7Vw/o2/47NvF8YoSiHEUCELtAa/uCd4nnjiCUpLSznyyCN3HVMPYm1ga2srtbW1PV719fV9\nEaqIofP/51d0rKkjOy1d6lyIPlNfX88555yDy+U64O1Apc0ZeP7ftTMJLq8h6PP3+l7+Tg/qmkZu\nv+raKEQmhBjMXn3/Hc674Wo2msMkjCxGozmwbrc5wYF9cjmz33qFK26/ia0t2/s4UiGEEINF3Gvw\nvP/++2zdupX3338fgM7OTq699lquuOIKLr10/2uQ586de8APZmLgqCgpxbd5O0f+9LR4hyIGqUPd\nPlTanIHHabcz+457+O1dt2AZNxy92XRI9/G7uwgur+Ev9zyI0bDvUXghxNC1bM1qHnrmKbxOI/bJ\nFYc0UKXRakmoKKazy8OMP97KuGEjuP6S6Rj0hj6IWAgxVBzMRAoxMPWLBM8PHXvssdxxxx0cffTR\nB/T5adOmccopp/Q41tzczAUXXBCtEEUcKIqCGggyprQs3qGIQWjn9qEXX3wxl1xyyUF9VtqcgSkt\nOYUn7/ozV911C+GyXExO+0F93rutFW3tFv76p4dw2KJbsFkIMThsbdnO3Y89TJOvA/uoIhz63nez\nDVYLhsPKWb6lhfOuv5pTj/sp5576PzK7WQhxSCJqRHYCHeTinuDpLZfLhcvl6nFMr9fv5WoxkKjB\nMOlS40L0gR9uH/r444/vOn7++edzzTXX7POz0uYMXMmJifz9/kf47R0305Xuw5p+YO1LV0Mzie4w\nj/75Yfl/LYTYTTgc5tHnn2HB8sWYywtJsEV/109raiJqiou3V37DR/P/y81XXk1p4bCof48QYnCL\nKAruzk4c9oMb6BIDR79L8HzyySfxDkH0ExoFDAaZiiyiT7YPHbpMRhNP3/sQNz1wDxtqG7AXZO/z\n+o51dVS4MrntumtktEsIsZsV69Zyz+OPoOamkDCpok+/S1EUHAXZhLND3PLULCqy87n9ymvRarV9\n+r1CiMFhW8t2NBYTy9es5ogJh8U7HNFH4l5kWYi9k4cpIUT0KYrCfTfeykhnBu6ahr1e11FVy5F5\nI7j9t9dKckcIsZt/vPt/3PHkI5gmlGDLTI3Z92r1OlzjSlmrdnLxzGtpbW+P2XcLIQau+d9+jTk3\njfmVskPfYCYJHtFvyeOUEKIv3XrF7yh3ptFZ37zbOXdNA0cWlnH1by6KQ2RCiP7u1fff5Y2F83Ed\nVo5WF58J8da0ZMIjsplx20wCwUBcYhBCDBzzvvqcxOH5rNtQG+9QRB+SBI8QQogh67YrrsHR6sPX\n0bnrmLelnYyInqt/c3EcIxNC9FfbWlr414fv4KoojncoGK0WtCXZ3D7rwXiHIoToxzo9XWx1t6PV\n6XATYsOm+niHJPqIJHiEEEIMWYqi8ODNd+Kvqtt1LLimnvtn3hbHqIQQ/dnHC76ArOR4h7GLOSmB\nTdu2xDsMIUQ/9sAzT6IvzgTAWprPA399Is4Rib4iCR4hhBBDmsNmY1RRCZ6WNtyNW/jJxEkY9FLg\nXQixZw3NjRis5niH0UMwFIp3CEKIfmptbQ0r62uxJCYAoDcZ2Rz2MW/hl3GOTPQFSfCIfkuNdwBC\niCHj8nOmEahrJtK4nYvPPCfe4Qgh+rFjJk7Bv7U13mH0YDEY4x2CEKIf8gf83P7IfdhH9VxS6igr\n4Ml/PM+21pY4RSb6iiR4RL+lohKSESkhRAykJiVjUXTY9EbM5v41Mi8Gp2XLlnHUUUfFOwxxCMZV\njETb4Yt3GLv4O7vISIndLl5iYJI2Z+hRVZXf3X0b2rI8tHp9j3MajQbruOFcc/dtUqR9kJEEj+i/\ntFqat8qaciFEbJj0BswyCi76mKqqvPbaa1x00UUyiDFAaTQaHKb+kwj2Nm7l1GOOj3cYop+SNmfo\nuvmhe2lPNGFOcOzxvN5sQh2WyVV33oKqytqJwUISPKJfikQiKGYjXy6ujHcoQoghQqto0Gm18Q5D\nDHJPPfUUL774IjNmzJAO9UCmxDuA76laDV5f/5lRJPoXaXOGpoee/Qs1ATfWzH3P7jMnOulItjLz\n/ntiFJnoa5LgEf3Sax+8i7Uwk08WfBHvUIQQQ4SqqtL5FX3urLPO4q233qKioiLeoYhD9P7n/6Uj\nHIx3GLtY0pJ57vVXZZmF2CNpc4ael//9Jl9vWIO9MOuArrdmJFMX6eLBZ5/q48hELEiCR/Q7Pr+P\nNz74N87CHLYHvXy7fGm8QxJCDAGBcAi/PCCJPpaSknJQ17e2tlJbW9vjVV9f30fRiX0Jh8M898Y/\neebNf+5WsDSejDYLocI0LrnpOjZskr8N0dPBtjkg7c5AVlW7ntfmfYijrPCgPmcvyOLrdatlZ61B\nQBfvAIT4oUAwwPRbZ6Itz0NRFBwji/nzXx/nj9dcz4ii4fEOTwxSy5Yt48orr+Tzzz+PdygiTrxe\nL50BH0RUwuEwWlmqJfqJuXPnMmfOnHiHMaT5A36eeOkFvlm6mEhGAgkTylCUfrRGi+5lFkGzketn\n30+izsSMc3/D2LKR8Q5LDFDS7gxM4XCYu2Y9hGN8ySF93jGqmCf/8TyTRo3BZrFGOToRK5LgEf3G\nS+++yZv/+QBDSS5mZ3cxMI1Wi2NSGbc9OYuSzBxumfE7LLLDjYgSVVV5/fXX+fOf/4z+R7sLiKFl\n9ovPos1OIRII8rfXX+GyX50b75CEAGDatGmccsopPY41NzdzwQUXxCegIaK1vZ3XPvo3lUuX0Ort\nRJuThnVSWbzD2ie92UTC2BKCwSD3vPQ3LEFIT0rmf074GZPHjO93SSnRf0m7MzA9/NzTkJeKVn9o\nj/iKomCsyOfO2Q/x4E23Rzk6ESuS4BFx1d7RwYtvv8HXiyvxJ9mwTy7frQOi1elImDCCDS3tXHjz\n7ynKzuWCM85meMHBTT0U4seeeuopPvjgA2bMmMHTTz8d73BEnCxatZxv168mYVwpAB8t/IKpkw9n\nWG5BnCMTAlwuFy6Xq8cxSUhHn8/vY+GSRXzw+X9p2rqVLjWELiMJa3kOjgGWGNHq9SRUdC8h2+z1\n8eDbL6Ob+3ecZgtjy0dy0pFTycvOloSP2Ctpdwaeto52vl65lITDynt1H7PTwYaaNVTVrqe0oP8s\nRRUHThI8IuZaWlv554fv8s2SxbjDfrQ5KVgnlGDcT0fDnOiESU7qOz384enbt+euAAAgAElEQVRZ\nWEIqeRlZTDv1DEqKiqWjIg7aWWedxYwZM/j666/jHYqIk6f/9RIfLvwcx9jSXcfsY4dz0yP38auf\nnszZJ50ax+jEYCe/W/ERDodZXrWajxd+QXVdLV1+P55wEDXBijUzBUN2MQnxDjJK9GYTrpLuZHVY\nVfl0czXznlqEzh/CajDicjiZPGYcUycdTpIrMc7Rir4mbc7g9YcH/4RpRH5U7mWvKOSPsx/h+Qdn\ny5L1AUgSPKJPBYNBFq9azkdffcHGTQ14Aj58ioouPRHr6AKch/BDY7RZMI4aBsAGdxe3PP8EOm8A\nm8FEojOBoyZO5piJk3DYHdH+zxGDzKEWHmxra+txrLm5OVohiRj5avG3/O3Vl+i0G0iY2HO0S6vX\n45pUwWvfzOeTLz9j+q9/w9hyqWUhomvSpEksWLAg3mEMeh3uDipXLGPh0sU0NDXiCfjoCvhR7WYM\nqS7MI7IxKAqGeAcaA4qiYE9PgfTvf/u2B4K8uuwrXv70Q0xhBYvBiMNqo6K0lCmjxzG8oAiNRvZk\nGQykzRm8HvjbU7RYtNjt0ambo9Xr8eenMfP+e3jgptskMTjASIJHRIWqqjQ2N/Hd6pUsWb2C5i2b\n8QT8dAUDqA4LprQkTBW5mABTFL/XZLdiKi/a9X6LL8AL33zCCx+8hVnRYjGYcNpsVJSUMr5sJMML\nimSKqegVKTw4cPn8Pp59/VW+WlSJz2bAXp6HfR/r1J0l+QQCQe555W+YvWGmTjmS8047Q9oQIfoh\nn9/HijVrWLD0O9bW1uDx+/AEAgSUCEqCBVOSC2NpJjpFwRnvYPsRrUGPMzcTcrvfq0BLIMj7G1by\n7yULUboCmPV6zHojSS4XE8pHcdioMWSlZ8hDnxBxtr21lZsf/BNtFg32opyo3tuSlkjDpi2cf8PV\n3H3tjeRnRff+ou9IgkccFI/Xw9raWhZXrWDl2rW4uzrxBv34gkEiJh3YLViSEtCXZsWlE6U3GUjI\nz4L87vdhYIs/wLs1y3l78QLo8mHS6jHrDZiNRgpy8hhfNpKy4uEkuVzSWRH7JYUHB45IJMLXS7/j\n7U8+pnnbVjqDfjSZSdgnlBxwollr0JNQXoyqqnxYu5z3/zAfu8FEdmoGpx33U8ZXjJJ2Q4gYamlt\nZfHqFVSuXE79pga8wQDeYICAGga7GZ3LiaUoBY1WixWQfWAOntagx5GZCpmpu46FgU1eH2uXfsFL\n8z9EEwhh0nX3p5w2OxUlpUwoH8Ww/EJJggvRx6rrNvD0P1+ipqkeY3kBdqulT77HlpVKKMXFDY/c\nS7YrhfPP+CVjRlT0yXeJ6JEEj+jB4/WwdkMtVTXrWVNbzdZt2/CHQwRCQfyhIEFFRbGY0DhtWDMT\n0BpcGKBfT23WGQ27dVRUoDMc5tv2zXz50Tr4Py9KMIJRp8Oo1WPU6bDb7BTl5TOioIjSwmKSE5Pk\nQU5I4cF+bFvLdr5asohvli6ieesWOvw+Ik4LluxUDFmFvUo4K4qCPTsdstMBqO3s4s+vv4DueT92\no4nMtHQmjx7HpNHjSEwYLNU7hIgPVVXZ2NDAd6uWs3j1Cra1tOANBvCFAgS1CtjNmJISMJV0zyKx\nAH3zeCN+SG82kfCD2T6w74E0i9FIUV4BE8pHMXpEGVbZdlmIQ9a8dQsvvv0Gy6tW06UHS2EWjpze\nFVQ+EDqDHueEMlp8Af748t8weUIU5+Vzwem/Ij9HZvX0R5LgGUKCwSANzU1Ub9zAuo0b2LhpEx2d\nHQRCOxM4oe4Ejs2MYjVhTrCj39F50gOD7RFWo9ViSUzAkrj7w1gA2OwLUNe0jg/XLkXp8qEEQhh1\neow6PQadDrPRRGZ6BsW5eRTl5FOYkyOdlwFMkncDRzAYZFnVKr5a+h1rq6vp8vvwBPyEdAq4bFhS\nEjFkFNCXVbiMNivG0u7CpSrdCZ+VX33EM++9gSGiYNYbsJnMlBYP58hxExhRNEwSgUL8SDAYZHX1\nOr5buZyV69bQ0dWJb8eMHNVsAIcFS7ILQ3o2OsAW74DFHu1tIM0dCrOgrZHP3qtCfdWLQVUwGwyY\ndAay0jMYV1bB+LKRpB5CPTwhBrNgMEjliiV89OUXbGpupMvvx6cDfWYKtvHD4jKwrjcZSCjrLoux\nvt3N9U89hM4fwmY0kZacwrGTjuCI8RMwGaNZjEMcCknwDBKBQICNjZuorq9j/cYN1G1qoLOri2A4\nRGDHDJygGkE1GcBsRG+3YEq2oc3KHLQJnN7SmQzYTMmQtvu5IOALhmju3MbC7zbA5x+hevzoVDDo\ndBi0evRaLSaDkYy0dIbl5lOUm0dhTi52mz3W/yliP6TwYP/j9XpZu6GG5euqqKpeT0tbK/5QEF8o\nhD8cRLWb0Sc6sRSnotFq4/7gZ7BZMdh6Jng7Q2E+21bDJy8vgU4fph0JYqNOT3JSEmWFwygfVsLw\n/AKMRmOcIhei7z3yyCOcftaZfLX4O5asXsHqyiUkFGbjCwXBZqKttoGc46egNSRiALbPryRjYtmu\nzzfNryTj6AnyfoC91+i0WJNdNK2s7nF+w6ff0pHpZPFXH/G3D96kfc0GskuKMRuM5GZls7W6jrvv\nuEMGzcSgp6oqm5qbWLSrhukWPH5fdw1TpxlTejKmijzMgDnewf6AyWnHNPL755l6j4cn//s2T77x\nMhatHqvRRHJiIqNHlDFuxEjys3OkWHsM9YsET2VlJffddx+1tbW4XC4uueQSzj777HiH1W94vV42\nbGqgpn4D6zZupKFpEx6PpztxEw4RCIUIEkGxGImYDBjsVkypNnSm7pkpkrzpG1q9DovLicW154Uf\nIaA9FGJrZxvfLP0CZcE8VK8fbVjFoNV9/9LrSUtNozgnj+LcfApycnA5E2RGiRjUgsEg9U2bWFe3\ngbUbaqit30iX14s/HMQfDBBQVRSbCcVuxuxKQJ+ehaIo/a6Tsy8anRZbajKkJvc47ldVNni8rFr9\nDf/6dj6Kx4ceLUadHpNej81ipTA3n+F5+QzLLSArIwOdrl/8XAuxX6FQiMrlS/nPgs/Z1NyENxCg\ncW01X2yvQ5Ngw5qeiJpsxzR22K5aWN4tLWgN0lMZKhRFweS0YXJ2p+a9bR1oRxfiV1WWt7fTvLGa\n8++8EaOqxWwwYDNbGFcxkhOPOJq0lNT93F2I/qfL00VVdTWLVq+gav063N4ufMFAjxqmpkTngC0E\nb7BYMBR9v3YzoKrUeXysXvoVL38+D43Xj0mnx6Q3YDVZGFZQwLiyCsqKhuN0yK7H0aaoqqrGM4D2\n9nZOOOEE7rjjDk4++WRWrVrFhRdeyKOPPsqUKVMO6Z4NDQ0cd9xxzJs3j+zs7ChHHF2qqrKtZTtV\nNeupqq2hum4DHe4O/OFg99KpcIgQgMWIYjFisFkwOmzSERpE1HAEf2cXvo5O8PpRPT40wcj3CSCd\nHovRRE5WFiMKiyktLCYnI1Me+PqRgdTmxEI4HGZTUyPrN9axdmMtdQ0NdLg7fpCUDhJQIyhmA6rZ\niN5uw5xgR7uPHa2GknAgiLfNTdDdBd4Aii+AXlEw6PS72gSnw0FBdi7D8wooys0jMy1dRseGkP7U\n5rS0tfHxgs/5alEl7Z1uOgM+VKcVU1oSRodVBitEr4WDITq3bEfd1oYhBDaDifycHE484mjGlo+U\nti9G+lO705+oqkprWxtrNtSwpnY96+s20NrWRiAcwr+jBEZIUcFmQpdgx5LoRDuEl2yHQyG8rR0E\n29zQ6UUbVrtnN2t1GHQ6HHYHRbn5lBYUUlJQREpSsvyOHKS496abmpqYOnUqJ598MgBlZWVMmjSJ\nRYsWHXKCp79xd3aytGoVq2rWUV23AXdn545/8N0v1aBDtRjROayYU+zocrJRAOOOlxjcFK2me6qj\nc89Lt8JAWyjE5o4tfPVVDerHb6N4Ahi0uu6i0Do9JoORnMzuBNCYEeVkpKZJYyj6hM/no66xgZr6\njVTX19HQ1IS7q5NAOEQwFCQQDhMIh1BNerDsXA5qR5edBXw/o1Am3u+d1qDHlpoIqYm7nVMBn6rS\n6fNT3bSWD9YuQfH4wR/EoNXuSgDptTocdjs5GVkU5+RSkJNHbkamLAUTUbGpqZEnXnqBDU2b8GtU\nlGQnttxktPqUATfyLPo/rV6HMysNsrrXzIdVlVUdnXz3+gtonvNh1xv42dFTOeOEn8vgl4g6j9dD\n3aYG1m/sLoOxqamRLq+XQLg7eRMIhQjrNWA1obWaMSU40Kd3l8Do7xvRxINWp8OWkggpu/dxAkCz\nz09t4xo+WLMYxeNHCYQw7NgAx6jTYTaayUxPpzAnj+LcPAqyc6T8xY/EvRUsLS3lvvvu2/W+vb2d\nyspKTj/99DhGdWi8Xi+3330Xm7dtpd3dQTAcJhQJEwYSxgxH77RiSnWgy3GiACagdX4leIA2oHEr\nnTvu9cO1yj/UNL9yj8fl+sF//Z4KQu+8PhJWWVW9jvc+/g9KMEhSXnb3VvB6I2kpKYwvH8WkkWOk\nkKHYK1VVaW1vo3pjXferYSNbtm0hEAj2WA4aUtQdM28M6G1WTCk2dDnd02t1O16ym03fUhQFvdmE\n3mzaY42wCN1JoC5/gNptG/h4w0o0vgB4A+hQ0O8YJTNodBgNRtJTUynMyaUoJ49heQU47NJREns2\n9+03+PjLz3ArYcxF2ZizSgbMkkkxeHQv8fp+YCwSifDasgW8/p8PyEpK5XcXXkJepswwEfvX4XZT\nU7+R9fUbqN5YR9PmZnwBP8Edg1WBUJCQAorFiGo2YHTYMGY50BqSdj3LSUnh6NKZjNjTUyB993M7\na6Bu7mxh4ZKN8NXHqJ4d5S9035e/MBoMpKak7ip/kZ+dQ2LC0Cl/EfcEzw+53W6mT59ORUUFxx57\n7AF9prW1lba2th7Hmpub+yK83Wxvaenerm7taryhIH41THt9ExqzEa3TgkbzffFi17C8mMQkhiaN\nVkGjNaI3dY/O2yeMALrXwNZ0eVjxzSc8/5930AYjmPV60pNS+N9T/odRpSPiGbaIIZ/PR23DRtbW\n1bJuQy2bmprwBfz4Q8FdyZuwXoNiNoLViMluw1iQgqLVyIzCAUhRFHQmIzaTcY8zgWBHRykUYltn\nB5UrFqJ+8yl4vq8TZtyxJMxiMpOdmcmw3HyGFxSSl5ktM4GGoOVrV/Pa5x+TNG4EriHSSRYDg0aj\nwZGXBXlZbPf5uWv2Qzz750fiHZaIM4/XQ/XGjazfWMv6ug00NjfjDfh2lcAIhEKEtQqK1YRqNmBy\n2DDkJ6LV6aTf049p9TrMLgdm155r94SAYCTCdncXi1d/Dd99vqP8RbhH38ZoMJCemkpxbgHD8goo\nyh08G+HEvQbPTvX19UyfPp28vDweffRRDIYDm9D22GOPMWfOnD2e64s1ottaW3jipeep3lhHpxpC\nn5OCNTlxyGQExeAQ8Hjoqm3C6A2SmpDIuaefycSK0fEOa8DqL+vSt7VsZ9HK5Xy3agUNTY1sWFlF\nWI0QjkRQAfRa0GlJO3IcBptlt5o3A2lGm1wfu+sj4QjhYJBwIAjBMK68THQoGHU7dgvUG8nLymZc\nWQVjy0aSmJCwx/uJ6IlHm3PbI/ez2rOdhKKcmHyfEIci6POz9b+VvPn08wf8LCEOTH/p6+zk8XpY\nvX49S9euoqp6ffdy8VDoBzNvVLCYUCxGjA47BrsFrSzhEztEQmECXR58HW7ULn/3AFdE3VEL6PsB\nruKCAsaUllFeXDJgZjj3i7/ylStXcumll/KLX/yCmTNnHtRnp02bximnnNLjWHNzMxdccEEUI/ze\nHx9/lK1JJsxji3H1yTcI0fcMFguG8iIA2vwBHnj6Sf4566k4RyUORCQSoba+jsqVy1latYrWtla8\nwQDeYJCwXuneiSEpAWNJBmxuRAtof3SPvY16CLEnGq2mxwzBhPE9Z/55IxGWtLey8L/vwFuvoo+A\nWWfAqNeTkpjE2BEVjC8bSW52tgyGDGB3X3MDf/+/f/LRZ5+i5iRjy0yV/5+i3wj5A3SuriXVaOXO\nP94nyZ1BYmcSZ8maVaypXkdHV3cdU18wQFBRwWZG47BiSXOiMybIzBtxwDQ67T5roAaBlmCQ+Vtr\nmLd+Oarbi16lezcwnQGr2UJxQQGjS0ZQMay0XyV/4j6DZ9u2bZx66qlcfPHFXHLJJVG5Z19mmC+8\n4Xd0Jllw5GVKx0YMCp6WNtq/Xc3Ls5/EYZcH/0MRi1Gt1evX8cw/X2LTts0ELXo0ThuWZFd3HRYh\n+qlAlxfP9lbU9i703hB56Rlcds55FOXKsuXeiOdIejgc5pnXXua75Utx+3wEjRp0aS6sKUnSLxIx\nE/T56WrcjNLShVVnIMFmZ/q5v6G0oDjeoQ1asWh3OtwdvPPpPL6q/IY2Txc+wij2HUkclxOdURJ3\non8IB4N4WzsItXeiur0YIwoOi4XxI0fzP8f9jOTEPS+Pj4W4z+B57bXXaG1t5fHHH+fxxx/fdfz8\n88/nmmuuiWNke/a3+x7hn++/w3ufzsNj0mItzJIHLDHghEMhujZtRtPcTlnxMH43+6l+lXkW3/vw\ni/k8+MCDOCqKsBVnYysso2l+JRkVw3Zd0zS/sscyG3kv7/vLe4PVzPbKlbveN3V6uPz3V5OSlclV\nF1zC4WPHM1SsWrWK22+/nerqavLy8rjrrrsYPXrgLY3VarVcfvY0OHsaABs3NfDu/HksX70at8+D\nlwiKw4wxyYUpwS5JH9FrIX8Az7YWIu0eNB4/Nr2RNFcix/3k5xw9cTImo/TD92QgtTlnX3AeHW43\nQTWCYjHib3Njz+muspsxfgRN8ytx/Oh35oe6GjZjze7edSDj6Al7/F2S6+X6vrzedfQEApEIHzes\n4dVLX0GHQvn4sTxw0+1oNBpiKbbftgfTp0+nqqqKxYsX93j1x+QOdBdyO+fkX/DCA7O5+ZyLyGoN\noS6vpe6fH9G6ugZvWweqqu72hyDv5X083zfM+5q2mnraK1cTXLIee+02zhp1BHMfmM3tV16LU5I7\n/dakUWMwm4woGgWtjFyJAU5nNoICGalpjCopjXc4MeP3+5k+fTpnnXUWlZWVnHfeecyYMQOPxxPv\n0HotNyubK/73fJ68+8/MvX82f7/tXq484XRG6xIwrWkisqyWru/W0Lqoirbqjbv6SUL8WDgQpGPT\nZtpWVdP+7Sr8i9ehWbmR1OYuTi0ey58uvJJX7n+MZ+99mPtvvJUTjzxGkjt7MdDanI5ON5rUBIxp\niRjsVjSSGBYDkEajwZ6RgjE1EW2qi00t2+IyyBH3GTwD2bjykYwrHwnAI488wrE/+ynvffZfalfW\nEdzSStt3q4k4LJhTXNKZETET9Prwd3TSumQt+lAEi96A1RPikqNO4uiJkzCbZFNbGDgjWwkOJ2+/\n+hr/+fIzXn73TbzhEGa7jbaajZiSEjE6rLsVyZX38r4/vFdVFX97J6asVNq+q8KoaDHpdNx98+1D\nauYOwMKFC9FqtZxzzjkAnHnmmTz33HPMnz+fk046Kc7RRZfDbmfqpCOYOumIHsfb3R18t2IZ3yxf\nSv2aBnzBIL5gEH84CBYD2MyYXE6MdqvM+hnEwoEg3rZ2gu1d4Paii3TXtDDq9SRYbRxTMorDRo5h\neEEhWu2PK8iJAzXQ2pyfn/4Lvlm2BC8hlCQnqYeP6bERxP5+d35Mrpfr43F9JBTGvXlbd22fsEJJ\nfkFcfs/iXoOnL/SXKu8er4fKFctYsGQRdQ31eAI+PIEAEbMeJWFH/QyTlAEThyYcDOFtbSPY4kbj\n9mLS6bEYTCS5XBw2agyTR48jPSU13mH2S36/nxNOOIErrriCX/7yl7z55ps89NBDfPzxx1gsloO+\nX6zbHHdnJ9+uWMqCJd+xqbERT9CPJxggYjag2s2Ynfbuh6QYTwkVQ1MkFMbf2YWv3Y3i9qLxBbEa\njJj1RvKyc5gyZhwTKkZhMR/8v63B4rnnnuOLL77gmWee2XXs6quvpqSkhCuvvPKg79df+jnREA6H\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dIcQPxT3B4/F4MJvNPY6ZzWZ8Pl+cIhJCDBZff/01F1100W7Hs7KymDdvHikpKXGISggx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TIX1lR5d2Hbi7bXsmzHibXbsOYq8b/be9eXw+H2k7DtCqdl1eeGG8DMUqh7xe7xUNUr1798bj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\norBtz24WrfyBk0mnSXO70IVVwq9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BflirBGOIDUOn0SCLwYnioNFoMPpZMfrl/5jKAS66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7BdvoGXwciTHbvycPde8h13jxQv8Gi1WlavXs3MmTOJiorCZDIxdepUmjVrpnQ0IYQQlcy1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CoVBg+/btGDRoEEJDQzF27FicP3/e0P/cuXN48cUXERUVhV69emHhwoVQq9UAgP79+wMA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\nWVHxDv71y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jYVp93B+X3P5vy+Zx/cpqoqO0t3s2DpEn5Zs5KqulrqfF4CegVNO2d90UdW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\nEUKE0oXkHJC8E6k+3vA5zuwU9u4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+YeMHvPDWCuyqiX5xCUyfdCVjhg6XxCx6tOaWZrbs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2vHOZit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\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dyZpL7rrrLuzduxcmk8m5Lj8/H3q9HlFRUS77fvPNNxg0aBBiYmK6/NyEhASsXr0aQUFB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Waj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5IXZLlqC/X0hOSrJncuO/HTdjwb1r/qci2nw0H2nsPcd/ud/O3eByo1diFE7VPP15eXJ00h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\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UrlIh/3wqJg0aznQUQsgb7Dp8AJJGLkzH0Bk2hwOZKQf/PHjAdBSiJcnJycjKykJ4eDg4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f78+cyfP793+7e//W1uvvnmE54vzd3jlyYjeOKeZPBBTtM0mpo/YcuO7Wyu2XakiOPHG/Tj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\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7Zvi2ffShS6fTkXL2CsFVazL+jeGGCS5KlVqtfmKBR6VSodPpZPJJYRANatXmj7NnMDMr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YmDZtGtOmTSMcDvPUU0/x4IMPcv/991NZWXnS512xYgVPPPEEjz/+eEdbKBQi/jhWLVu6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mYyhcXFLHjvLbJ2H6XEZUcRZMYQFoxS/JKtdpIkYTlXgOPXPHQu8NUbGTNoBC3iyu+NJtS8jIwM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YvbFekwRJSTvBObAoEAT7z6AtdMuxPjsH579B+09+/N4q0b+evNk1hXWRGhKEU0kpwjAHY2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QZ/ii2KkQQvzd0qVLy0pgeL1e3G43y5cvJzw8vNx+d999txLhCSFqsfMmeIYMGUJ+fj7j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\nqh++Y8vuHeh83PCoXqXEDZkzE5IwxsVTJzSMF2bNw8tMvaGE5Zi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lCnbs3U17MEDQAubM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m233XbsL0JEkoryjojEk3KOiMSTco4cjoZoSY/4fAW4paWFF154gYkTJx523xUrVnDHHXfw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\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p1bkb3dp2kDXlQoh7uhl/iy17dvH7oQMkpqaQYslAXSkQz8Y10OZyrNbDgLZ+Dex2O9tj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hbIo4wrsfDSLi4t9KxxJCZMPG/27FJai40jGeShvkz5pfflA6hhCFXkJSEoMmjuGvO5eluFMA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+ "text": [
+ ""
+ ]
+ }
+ ],
+ "prompt_number": 42
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A \"sequences shortened\" version of this is available as a gif:\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Equivalently, I could have written out the plotting command by hand, instead of using `study.interactive_pca`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "study.plot_pca(feature_subset='gene_category: LPS Response', sample_subset='~pooled', plot_violins=False)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 43,
+ "text": [
+ ""
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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16sXnn3/OgQMHaNeu3WPrK3ZLVuhuXkREcpWFhYUmlx9D24qIyN9lY2ODt7c3\nkZGRmfLCw8Pp0KFDltu6ffs2w4YN44cffuDLL7+kWrVqWa7bt29ftmzZYvYKDQ3Ncn3JP/y71szt\nIYiI5Gn+/v4sXbrU9L5EiRL06dOHRo0amQ4I9/X1Ze3atX+rn02bNrF582aztNTUVAoVKpSl+ord\nBVNW4vSTxHLd0YuIiORxVcpWYIZba/r8ww1P+9I4pIJDKnjal6bPP9yY4dZa24qIiMnFixdJSEgw\nva5cuQJAYGAgX375JZ999hnXr1/nzp07REZGsmDBAkaNGpXl9idPnkxiYiJhYWGmPSKzysnJiZde\nesnsVa5cuSdqQ/KHejWc6d3K5aH5j8oTEXketG7dmk8//ZQdO3Zw584dbt++zZ49ezh48CCNGjUC\n4K233uL8+fMEBARw8uRJAJKTkwkLCyMiIoIXXnjhsf2kpqYSHBzMiRMnuHPnDkuXLuX27dv4+Phk\naZyK3QVTVuJ0VrfHAG2RISIiBZDRaCQuLo4jR45w5MgRjh07BoCbmxvu7u64u7tTrlw5DAZDLo80\na7StiIg8iYEDB5q9r127NmFhYVSrVo3Q0FDef/99lixZQmpqKpUqVWLmzJm0atUKuPv75lG/G8+f\nP88333xDoUKFzG5Mixcvzvbt23PmgiTfunf6/F8PEerT2oWeLbJ+Mr2ISEHUuXNnLCwsWLx4MePG\njSM9PZ0qVaowZ84c3NzcAChWrBhr1qzh448/ZsSIEVy4cAFLS0tq1KhBcHDwAw/vfVA/Fy9eZNCg\nQSQlJeHq6srHH39M4cKFc/oSJY/LzjhtMBqNxmwbWT4RHx9Ps2bN2L59O2XLls3t4YiISDYyGo2M\nHj2aH44e4cad21y/k8r1tFQA7K1ssLe2wc66ELVrurNw4cJ8M8ksIlJQ6bt5wRf147n/HiZkYFg3\nN+q6PtnKdxERyVsUuwuW7IjTWsEsIiIFSlxcHD8cPcKJ65cpUfUVXqhUllcr3X2EK+lkHEkn4zn3\nyx9w9Ajx8fF6vEtERCSH1avh/ESP2YqIiMizkx1xWhPMIiJSoBw5cnflcomqr1Ctt/mpyHalivNi\n3Zr88vkGbpw4T0xMTLZNMLu4uFC4cGHTimhHR0d69uzJ0KFDAThw4AB+fn7Y2toCd1dalylThq5d\nuzJ48GBTPV9fX86ePUtkZCTly5c366NDhw78/vvvpkM/APbs2cMnn3xiSnN1dWXMmDG4urpmy3Vl\nxYQJE3D9f4/RAAAgAElEQVRycmL8+PFPXLdGjRps3bqVF198MQdGJiIiIiIiIjlNmzeKiEiBcuTI\nEa7fScWx0sMf1XKsVI7rd1I5evRotvYdERFBTEwMMTExhIaG8tlnn7Ft27b/9evoaMo/cuQIc+bM\nYc2aNcydO9esHScnJzZu3GiWdvz4cc6ePWu2pcfq1asJCgrijTfe4Pvvv2fv3r34+Pjg5+fHH3/8\nka3X9iiP27P1cXVFJOf069ePsLAw1q5dS9WqVfHw8DC9ateujZ+fH6dOnTKrc/jwYfz9/alfvz4e\nHh60aNGC9957j1u3bmVqPy4uDi8vL27evPmsLklERKRAc3Fxwd3dnevXr5ul37lzB29vb3x9fTPV\n6d27N3Xr1iU1NdUsPTAwkNdee420tDRT2rVr12jTpg2fffZZzlyAPJc0wSwiIgXKsWPHuJ6WimOl\nh69MdqxUjutp2T/BfL8KFSrg6enJr7/++tAyNWrUYMaMGYSGhpKSkmJKb9myZaYJ5vXr19OyZUvu\nHZ1w8+ZNZs+eTXBwMI0bN8bS0hIbGxsGDhxI7969TadM3y8+Ph5fX1/ee+89PD09adSoEStXrjTl\n//nnnwwdOpQ6derQvHlzli5dasq7dOkSgYGB1K1blyZNmjBnzhyzL7D3xpWenk5ISAi+vr7Ur1+f\noKAgrl27Ziq3YsUKGjVqRJ06dXj//fez+nGKyN9kMBioVq2a6Y9cMTEx7Nq1i2LFijFhwgRTucjI\nSPz9/WnUqBE7duwgOjqaJUuWEBsby//93/+Ztblt2zZ69+5t9jMu8iBRP57Fb9oW/KZtIerHc7k9\nHBGRPM/W1jbT4bl79+4lLS0t0wKNEydOkJCQQPXq1Vm/fr1Z3ttvv01KSgrz5s0zpQUFBVG5cmX8\n/Pxy7gIkX8mOOK0JZhERkWxy/7m5v/76K8eOHaNRo0aPrOPl5YWVlZXZZHfDhg25dOkSx48fN7W7\nefNm2rdvbyoTHR1Neno6DRs2zNRmYGDgQ0+UPnv2LFeuXOH7778nJCSEhQsXsnfvXlJTUxk4cCCv\nvvoq3333HR999BHh4eF88cUXAIwcORILCwt27NhBeHg4Bw8eNE0QG41G0xfdZcuWsX37dlatWsW3\n337LrVu3mDFjBnB3O4+QkBA+/PBD9u3bx+XLlzOtshCRZ6do0aJ07dqV3377Dbi7MmratGmMHz+e\n3r17m7b9efnll5k7dy4vv/wy6enpAKxbt45Zs2YxcuRInsMzw+UJrIo8zszQQ1xJuc2VlNvMDD3I\nqsjjuT0sEZE8rVWrVo9dcHJPeHg4LVq0oEuXLoSFhZnl2dvbM2/ePFauXMm+ffv49NNP+f3335k5\nc2aOX4PkD9kVpzXBLCIiBYqbmxv2VjYknYx7aJmkk3HYW9lQs2bNbO27Z8+eeHl54e7uTpcuXahc\nuTKVK1d+bD0HBweSk5NN762srGjdujWbNm0C4NChQ1SsWJFSpUqZyiQmJuLg4ICFxZOFcktLSyZO\nnIiNjQ1ubm507tyZjRs38sMPP3Dt2jUCAgKwtramUqVKDBo0iK+++oq4uDiOHDnCxIkTsbOzo3Tp\n0owePZqvvvoqU/sRERGMGDGC0qVLY29vT2BgIOvWrSM1NZWNGzfSpUsXqlatio2NDePGjcPS0vKJ\nxi8i2efixYuEhoZSv3594O4WQykpKXTq1ClT2WLFijFmzBjTz6yPjw+RkZE0aNDgmY5Z8pdVkcf5\nfGtspvTPt8ZqkllE5BHatGnDgQMHSEpKAu5ua3H48GGaNm1qVi41NZV169bRrVs3WrZsyblz54iO\njjYr4+rqSkBAAOPGjWPJkiUsWrQIe3v7Z3YtkndlZ5zWBLOIiBQo7u7u2FvbkHQy/qFlkk7GYW+d\n/RPM4eHhHDp0iCNHjrBv3z4AAgICHlknPT2dlJQUnJycTGkGg4H27dubVi2sX7+eDh06mK1WKFmy\nJMnJyabVhPe7evXqA9Ph7v7O93+hLFOmDBcvXuTKlSuULl3abMLa2dmZhIQELl++jK2tLY6OjmZ5\nly5dMtvPDeDcuXOMGzcOLy8vvLy86Ny5M9bW1pw9e5ZLly5RpkwZU1k7Ozuz6xaRnBUbG4uXlxe1\natXC1dWV119/napVqzJ79mwALly4gKOjIzY2NqY6gYGBpp/nmjVrcvjwYQCKFy/+xH/gkudL1I/n\nHnjTes+j8kREnnfFixfHy8uLyMhIAL799luaNm1qFqMBtm7dSoUKFahcuTI2NjYPXMUM0L59e65f\nv06lSpV45ZVXnsk1SN6WlTj9JNtl6FuhiIgUKO7u7thZF+LyL3/wy+cbOLv/KDcuXOHGhSuc3X+U\nXz7fwOVfTmBnXQgPD48cG0fJkiXp1asXUVFRjyx36NAhMjIyMk12e3p6kpGRwaFDh9izZw+tWrUy\ny/fw8MDa2prdu3dnajMoKIiJEyc+sL+kpCSzbSnOnj3Liy++iLOzMxcuXDCbmI6Pj6dkyZI4Oztz\n8+ZN0wqKe3mOjo5YWVmZtV+qVCkWL17MoUOHOHToEPv372fdunWUL1+eUqVKER//v4n/1NRUszZF\nJGe5uLhw6NAhoqOjmTNnDjdv3qRu3boUKVIEuHszm5ycbPaHo7lz55p+nm1tbf/2dhiJiYmcOnXK\n7BUX9/AnTiT/WrI25845EBEp6O4tONmwYQNwd8FJx44dM8Xh1atX89tvv+Hj44OPjw8RERFs3bqV\nixcvmsqkp6cTGBhI8+bNOXPmDIsXL36isSh2F0xZidNPEsutHl9EREQk/yhXrhy1a7rD0SPcOHGe\ni7Fx/L+0uxOq9lY22Fvb8HKREtSu6U7ZsmWzte/7v/ClpKSwZs0aatWq9dCyMTExTJ06lSFDhpgm\neO7Xrl07pk6dipeXF7a2tmZ5hQoVIiAggMmTJ2NpaUmDBg24desWoaGhREVFmfZO/qu0tDTmz59P\nQEAAv/zyC+vWrWPx4sW4ublRokQJFixYwKhRo4iLi+PTTz+lb9++lC5dmnr16jFz5kymTp3K1atX\nWbRoER06dMh07Z07dyYkJIRKlSrh6OjIggUL2Lp1K5GRkXTp0oURI0bQsWNHXFxcmDdvXqYV0CLy\nbLRp04ZLly4REBDAl19+SaVKlahduzZ2dnasW7eOrl275ki/K1euJCQkJEfaFhERKUiaN2/OtGnT\n+Pnnn4mLi8PT05OdO3ea8k+dOsXRo0fZuHEjdnZ2wN3v5CNHjiQ8PJyRI0cCsHDhQi5cuMCSJUs4\ncuQIgwcPpk6dOnh6emZpHIrdkhWaYBYRkQLFYDCwcOFC4uPjiYmJ4ejRo6YD9GrWrEnNmjXx8PCg\nbNmymU5g/rtef/11DAYDBoMBa2tr6tevz7vvvmsaV1JSkmnVtJWVFc7OzvTr148+ffo8sL0OHTqw\ndOlSxo8fb3Z99/Tu3RsHBwdCQkIYO3YsBoMBd3d3VqxY8dBH3ywtLTEYDDRq1IiiRYsyadIk05fL\nJUuWMGPGDHx8fChcuDB9+vQxnS793nvvERwcTLNmzQDo1KmTafuPe9cMMHToUO7cuUOPHj1ISUmh\nevXqfPjhh1hYWFCnTh2CgoL45z//SXJyMl26dKF06dJP/XmLyN/Tr18/tm3bRlBQEKtWrcLGxobp\n06cTFBTEzZs36dChAw4ODvzxxx989NFH3LhxAwcHh7/VZ9++fc0OLAVISEhgwIABf6tdyXv8u9Zk\nZujB3B6GiEi+ZW9vT5MmTRg3bhxt27bNlL969WoaNmxIuXLlzNK7du3KokWL8Pf3Z+/evSxfvpzw\n8HBsbW2pV68eAwcOJDAwkK+//jpL29UpdhdMWYnT/l2zvqWkwfgcHvscHx9Ps2bN2L59e7avXhMR\nEcmr4uPjadWqFT///HNuD0VEnpF+/frRunVr7OzsCAsLIyIiwiz/9OnTdOzYkYCAAPr37w/ATz/9\nxNKlSzl8+DDXr1/HyckJHx8f3nzzTSpUqGBWPz4+nhYtWhAdHZ3pSYus0nfzguthhwcB9G7lQq+W\nVZ7xiERE8r6qVauyfv16XnnlFXbu3Mnw4cPZsGEDL7/8Mjt37mTGjBls2bKFRo0aMWnSpEyTzykp\nKTRq1IipU6fyzjvvEBgYSPfu3U356enp9O7dG0dHRz788MOnGqNid8GQnXFaE8z6QRARkeeEJphF\nJC/Sd/OC7UE3r31au9CzhSaXRUTyK8XugiO74rS2yBAREXmOZPe2ICIiIo/Sq2UVKjo7/PegIAPD\nurlR19U5t4clIiIiZF+c1gSziIjIc6Js2bL89NNPuT0MERF5ztSr4Uy9GppUFhERyYuyI05bZNNY\nREREREREREREROQ5oxXMIiIiIiIFyJ49e/jkk0+Ijb27n56rqytjxozB1dWVCRMmsGHDBqytrQGw\ntLSkatWq/POf/6R27dqZ2goNDSU6OppFixZlyrt06RIdOnTgnXfeoUmTJjl6TZK/Rf14liVrjwF3\nT6TXamYRkYe7d0DvK6+8gp+fX6ZDdAsXLkxUVBTx8fE0b96c6Oholi9fbjqwLy0tjfT0dAoVKgTc\nfYpx/fr1pvpxcXF07dqVPXv2PPUBvVIwZGd81gSziIiIiEgBsXr1ahYtWkRwcDA+Pj6kp6cTFhaG\nn58f4eHhGAwG+vfvz7hx4wBITU0lIiKCQYMGERYWRrVq1QC4ceMGISEhLFu2jJYtWz6wr4kTJ5Kc\nnKy93eWR/np40MzQg098Mr2IyPPIYDDg6OjI/v37H1vO398ff39/AMLCwti6dSvLly/PVHbbtm1M\nmzaNa9eu5ciYJf/I7visLTJERERERAqAmzdvMnv2bIKDg2ncuDGWlpbY2NgwcOBA+vTpw4kTJwAw\nGo2mOjY2NvTu3ZvWrVuzZMkSU/qoUaOIi4ujR48eZuXvWbVqFXZ2dpQpUybnL0zyrQedTA/w+dZY\nVkUez4URiYjkHw+Kv1mt96C669atY9asWYwcOfKp25aCISfisyaYRUREREQKgOjoaNLT02nYsGGm\nvICAAFq1avXQug0bNuSHH34wvZ81axbvv/8+JUqUyFT21KlThIaGMnXq1GwZtxRMUT+ee+DN6z2P\nyhMRkezn4+NDZGQkDRo0yO2hSC7KSnyO+vHcE7erLTJERERERAqAxMREHBwcsLB48jUkxYoVIzk5\n2fT+hRdeeGC5tLQ0xo8fz6RJkyhWrNhTjTEpKcksLSEh4YnbkbxvydqjuT0EEZF8zWAwkJycjJeX\nl1n6ggULnmqSuHjx4k81DsXugiUr8XnJ2qNPvB+zJphFRERERAqAkiVLkpycTHp6OpaWlmZ5V69e\nfeRBPomJiTg6Oj62jw8++AAXFxd8fHxMaU/ymO3KlSsJCQnJcnkREZHnldFopFixYo/dgzmnKXZL\nVmiCWURERESkAPDw8MDa2prdu3fj6+trlhcUFIS9vT0Gg+GBh/Lt3bsXb2/vx/axefNmLl68yObN\nmwG4du0aY8aMYfjw4QwePPix9fv27Uv79u3N0hISEhgwYMBj60r+4t+1JjNDD+b2MERE5G9S7C5Y\nshKf/bvWfOJ2NcEsIiIiIlIAFCpUiICAACZPnoylpSUNGjTg1q1bhIaGEhUVxRdffMHSpUvNVhzf\nvHmTL7/8ku3bt7Nq1arH9nFvYvkeX19fpkyZQuPGjbM0RicnJ5ycnMzSrK2ts1RX8pd6NZzp3crl\nofs89m7l8oxHJCIiT0Oxu2DJSnx+0u0xQBPMIiIiIiIFRu/evXFwcCAkJISxY8diMBhwd3dnxYoV\nvPLKKxgMBlasWMEXX3wBgJ2dHTVq1OCzzz7j1VdfzdTew1Y8i2RFr5ZVgMwH+vVp7ULPFlVyY0gi\nIvlGVmLwg/Kftp48P3IiPhuMT7JpWgERHx9Ps2bN2L59O2XLls3t4YiIiIiIPLf03bzgi/rx3H8P\nFTIwrJsbdV2ffGWUiIjkHYrdBUN2xmetYBYREREREZEcU6+G81M9bisiIiI5Jzvjs0W2tCIiIiIi\nIiIiIiIizx1NMIuIiIiIiIiIiIjIU9EEs4iIiIhIHuDi4sIff/zx0PykpCSCg4Np0aIFtWrVwtvb\nmxEjRhAbm/kU8D///JOqVasybdq0THm+vr7s2rXrgX2kpaUxb948fH198fDwoFGjRkyZMoWUlBRT\nmaioKDp37kytWrXo2bMnx44de/KLledG1I9n8Zu2Bb9pW4j68VxuD0dE5ImMHTsWV1dXLly4YEpb\nu3Yt3bp1MysXGxtL/fr1mT17NgDJycmMGDECT09PmjZtSkREhKns9evXGTduHPXq1cPb25vRo0eT\nmJhoyj98+DBdunTBw8ODDh06sH//frO8119/HU9PT1q0aEF4eLgp78SJE/Tv3x8vLy98fHyYN28e\n945de1yf8nzKzhitCWYRERERkTwuOTmZbt26cenSJUJDQ4mOjmbLli14eXnRr18/EhISzMqvXr2a\nLl26sH79eq5du5apvYedHv/BBx9w8OBBwsLCiImJISIignPnzjFu3Djg7qE+w4cPp2/fvhw+fJiB\nAwcyaNAgLl26lP0XLfneqsjjzAw9xJWU21xJuc3M0IOsijye28MSEcmS5ORk9uzZQ5s2bfjiiy8e\nWu6nn37Cz8+P/v37M378eAAmTZpEkSJF+P7771m4cCFz5szh6NGjACxdupT4+Hi+/fZbdu3aRXp6\nOnPmzAHg/PnzDB8+nOHDhxMTE4O/vz+jRo0iNTWV5ORkhg8fzoABAzh8+DALFy5k3rx5REVFATB5\n8mSqVavGgQMHWLNmDZs2beKbb755bJ/yfMruGK0JZhERERGRPO6DDz7A2dmZ+fPn849//AMAJycn\nBgwYwOjRo0lOTjaVvXPnDl9//TX9+/enZs2afPXVV1nu56effqJ+/fo4O9898KVUqVL861//4sUX\nXwRgz549VKlShddeew0LCwtatWpF5cqV2bJlSzZerRQEqyKP8/nWzKvrP98aq0lmEckXvv76a7y8\nvOjduzerV68mLS0tU5mYmBjefPNN/vnPf+Lv7w/cXS28fft2Ro0ahY2NDW5ubnTo0IGvv/4agCJF\nipCRkUFaWhpGoxGDwYCtrS0A33zzDQ0aNKBFixYAtGvXjuXLlwNw7tw5mjZtSrt27QCoVq0a3t7e\nxMTEmNpNS0sjPT0do9GIhYWFqd1H9SnPn5yI0ZpgFhERERHJ47Zv357pcdx7+vbtS5UqVUzvt23b\nRunSpXFxcaFHjx6EhYVluZ82bdqwdOlSgoKC2LRpEwkJCbz00ktMnjwZAKPRSKFChczqGAwG/t//\n+39PflFSYEX9eO6BN673fL41VttliEieFxERQbdu3fDw8MDJyYnNmzeb5R88eJA33ngDf39/evXq\nZUr/888/sbKyomzZsqa0ihUrcvLkSQD69+9P4cKFqVu3Lp6enpw+fZqAgAAAfvnlF0qVKsXIkSPx\n9vamZ8+e3LlzBxsbG1xcXExbcMDdFdaHDx/GxcUFuLtqevv27bi7u9OkSRNq165Nq1atAPDz88vU\n55gxY3Lmg5M8LaditCaYRURERETyuAsXLlC6dGnT+127duHl5YWXlxceHh5MmjTJlPfll1/SvXt3\n4O5+yzdu3GDfvn1Z6qdLly589NFH3L59mxkzZtCkSRM6depk2v/Rx8eHY8eOsXXrVtLS0ti2bRtH\njhwhNTU1S+0nJiZy6tQps1dcXFxWPwbJJ5asPZotZUREckt0dDQpKSk0btwYgJ49e5r9wfbMmTOM\nGjWKGjVqsH79erM4eOPGDQoXLmzWXuHChbl16xYA77zzDqmpqezbt4/vv/8eZ2dn0x9yk5KSWL16\nNb179+b777+nY8eODB061OwsBICrV6/i7++Pq6srvr6+ZGRkMHz4cJo1a0Z0dDQbN27k8OHDpj2a\nZ86cmanPKVOmZOmzUOwuWHIqRls9zWBEREREROTZKVGihNkBQ02aNOHQoUMAzJ49m6SkJADi4uKI\nioril19+ISQkBICUlBRWrlyJj49PlvqqW7cudevWBeDkyZOsWrWKoUOHsm3bNipUqMD8+fOZN28e\nU6ZMoUmTJjRr1gwHB4cstb1y5UrTuERERPKq1atXk5iYSKNGjYC7h+AmJyfz888/A3D79m0+/vhj\nqlWrRpcuXZgxYwZvv/02ALa2tty+fdusvVu3bmFvbw/A+vXrCQkJoWTJkgD861//onXr1kybNg0b\nGxuaNGlC/fr1AejduzeffPIJ0dHRNGnSBLgb6/39/alQoQILFiwA4Pjx45w8eZI1a9ZgbW3Nyy+/\nzJAhQ1i1ahU9evR4aJ9vv/22aVwPo9gtWaEJZhERERGRPM7X15e1a9fSuXPnR5b78ssvad68OVOn\nTjWlnTlzhp49exIfH2/2uO5fpaenU69ePZYuXYqbmxsAlSpVYuLEiWzYsIFTp05hZ2eHs7Mz69at\nM9Xr0KEDLVu2zNJ19O3bl/bt25ulJSQkMGDAgCzVl/zBv2tNZoYefGwZEZG86OrVq2zZsoXPPvuM\n8uXLA3e3iAoODmblypXUqVOHSpUq4enpCcC8efPo2bMnnp6edOzYkQoVKnDnzh3OnTtnOtPg1KlT\nvPzyywAUKlTIbALawsICg8GApaUllSpV4vTp02bjycjIMP37559/ZvDgwXTq1Ml0oCCAjY0NRqOR\nO3fuYG1tbWr33r8f1efjKHYXLDkVo7VFhoiIiIhIHnHx4kUSEhJMrytXrgDw1ltvcf78eQICAkx7\nOCYnJxMWFkZERAQvvPACaWlprF27lk6dOlGiRAnTy83NDTc3N7NHexMTE836uXjxIpaWljRv3pwZ\nM2aYVmglJycTGhqKlZUVNWrUIDExkZ49e/Lrr7+SmppKaGgoycnJ+Pr6Zun6nJyceOmll8xe5cqV\ny+ZPUXJbvRrO9G7l8tD83q1cqFfD+RmOSEQk67755hsqVqyIh4eHKZaWLFmS1157jY0bN5KYmGhW\nvnr16gQEBDBlyhROnDhBkSJFaNasGXPnzuXWrVscO3aMDRs20KFDBwDatm3LokWLuHLlCteuXWPu\n3Lk0bdoUW1tbOnXqxL59+9i9ezcZGRmsWLGC1NRUvL29uXTpEoMGDeKNN94wm1yGu38QrlKlCrNm\nzSI1NZX4+HiWLVtG27ZtH9nnX7fyeBDF7oIlp2K0wWg0Gv/OwPKj+Ph4mjVrxvbt2x+5ikNERERE\n5Fm5d0jP/WrXrm2aGL527Roff/wxkZGRXLhwAUtLS2rUqEGPHj1o2bIl3377LRMnTuS7774zrVi6\nJzw8nHnz5rF7927atm3L2bNnzfLLlCnDrl27uHPnDkuWLGHjxo2cP38eKysrvL29+b//+z8qVqwI\nwLp161i4cCFJSUlUr16dKVOmmFZlPQ19Ny+4HnRKfZ/WLvRsUeUhNUREcl+nTp1o3749gwcPNkvP\nyMigadOm9OnTh8jISCIiIszyBw8ezLlz54iIiOD27dtMmTKFqKgo7OzsGDVqFF27dgXubq/x3nvv\nsWXLFtLT02nUqBFBQUGm7aa+++473nvvPf78809eeuklpkyZgpubG0uWLGHBggXY2tqa9evn58c/\n//lPzp07x/Tp0/nhhx+wt7fn9ddfx9/fH4PB8Ng+n5Rid/6X3TFaE8z6QRARERERyTX6bl6wRf14\n7r+HBRkY1s2Nuq5auSwikt8pdhcM2RmjtQeziIiIiIiI5Ih6NZy1HYaIiEgelJ0xWnswi4iIiIiI\niIiIiMhT0QpmEREREZE8Ys+ePXzyySfExt7dE8/V1ZUxY8bg6urKhAkTcHJyynSwD8DatWuZOHHi\nAw/ref/99/Hx8QHg5MmThISEcODAAW7fvk358uUZNGiQ6RCg+0VERPDee++xf/9+4O7jsM2bN8+0\n9yP8b/9HEREReXr9+vWjdevWvPLKK/j5+WWKuYULFyYqKsoUk6Ojo1m+fDkffvghAGlpaaSnp1Oo\nUCEAypYty/r169m/fz+zZ8/mzz//pHjx4gwZMoTu3bs/8+uTgksTzCIiIiIiecDq1atZtGgRwcHB\n+Pj4kJ6eTlhYGH5+foSHh2MwGDAYDA+tX7169UwHDt0vNjaWfv36MWLECGbMmIGdnR379u0jMDCQ\n1NRUOnfubCobFxfHrFmzMh0WCPD9998/cJJZ5EGifjzLkrXHAPDvWlPbZYiIZIHBYMDR0dH0R95H\nlfP398ff3x+AsLAwtm7dyvLly01lrl69yrBhw5gzZw7Nmzfnt99+o3v37ri7u1O5cuUcvQ7Ju7I7\nPmuCWUREREQkl928eZPZs2czb948GjduDIClpSUDBw4kMTGREydOAPCo87kfd3b3O++8w+uvv86A\nAQNMaT4+PkycOJHTp0+b0tLT0xk3bhw9e/Z85IS1yOP89YT6maEH6d3KhV4tn+6EehGR58XjYvqj\n6v21btGiRfnuu++ws7MjIyODy5cvY2lpiZ2dXXYMVfKhnIjPmmAWEREREcll0dHRpKen07Bhw0x5\nAQEBAOzateup209NTeXgwYOMGTMmU17Hjh3N3n/00UdUrlyZRo0aPXCC+WlveuX58teb13vupWmS\nWUTk2bGzsyMtLQ13d3fS0tIYOnQoZcuWze1hSS7IqfisCWYRERERkVyWmJiIg4MDFhZPfwZ3bGws\nXl5eZmn29vbs2rWLpKQkjEYjxYsXf2QbP/30Exs2bCAiIoJjx449sMy9Fdb3W7FiBS4uLk89dilY\non4898Cb13s+3xpLRWcHbZchIvIQBoOB5OTkTHF9wYIFNGjQ4KnatLKyIiYmhj/++IPBgwdTsWJF\nunTpkh3DlXwiJ+OzJphFRERERHJZyZIlSU5OJj09HUtLS7O8q1evZmnPYxcXF9asWfPAPEdHR6ys\nrLh06RLly5c3y0tNTSU9PR2DwcCECROYPn36I/vbs2fPU+/BnJiYSFJSkllaQkLCU7UledeStUez\nVGwkES8AACAASURBVEYTzCIiD2Y0GilWrNhj92B+UtbW1lStWpUePXoQGRmZpQlmxe6CIyfjsyaY\nRURERERymYeHB9bW1uzevRtfX1+zvKCgIOzt7R95wN/j2NjY4O3tTWRkJLVq1TLLCw8PJzQ0lNmz\nZxMfH8/QoUOBuyfR37p1izp16rBu3bqn7vt+K1euJCQkJFvaEhERkceLjY1l7NixrFu3zvRdIjU1\nlWLFimWpvmK3ZMX/Z+/e42JM+z+Af6Y0Ig9NxLabddiWcYp00mKjKIcSW+vQQbLYsojIcR06qCfL\nrkOLddin3QqRFskhYsUWQghb7I/dFQrbCJ0P8/vD0zzGlAad+7xfr3k9O9d93df9vef1vHzn/nbN\ndbHATERERERUy5o2bQovLy8sW7YMqqqq6N+/P/Lz8xESEoLExETs2rUL27ZtQ25ursKsobZt2yp1\njblz58LFxQU6OjpwcHCAUCjEyZMnsXbtWixduhRGRka4fPmyrP/58+cxa9Ys2eyp9PR0AO+2BrOz\nszNsbGzk2jIyMuQ2HqT6z/2z3ggIOV9pHyIiqn6dO3dGTk4OtmzZgilTpuDatWvYs2cP1q9fr9T5\nzN0NR3XmZxaYiYiIiIjqAEdHR7Rs2RLBwcHw9vaGQCBAnz59EBoaCj09PQgEAkRERCAiIkJ2jkAg\nwNGjRyEQCPD777/DwMBAYdxp06bBw8MD3bt3R0hICDZs2IDNmzejsLAQnTt3RkBAAKytrRXOk0ql\n5c6aLm/tx759+2L79u2V3qNIJIJIJJJrU1NTq/Q8ql/MeunA0Vpc4TqPjtZiLo9BRPQaAoGg0l8u\nlXe8vPOEQiF++OEH+Pr6YuvWrdDR0YGPjw9MTEyUioW5u+GozvwskDbCbaDT09NhaWmJuLg47ppJ\nRERERFSL+N284Spvp3qnYWKMH/p2O9QTEVHdwNxdv1VHfuYMZiIiIiIiIqpyE6y6oqNOy/9uKiSA\nh70++vXkzGUiIqLaVB35mQVmIiIiIiIiqhZmvXS4HAYREVEdU9X5WaXKRiIiIiIiIiIiIiKiRoUz\nmImIiIiI6gEXFxcMGzYMenp6cHV1RbNmzeSOq6urIzExEenp6RgyZAguXbqEn3/+GT/88AMAoLi4\nGCUlJWjatCkAQFdXF9HR0bCwsMD9+/cRGxuLDz/8UG5MW1tb3Lp1C6mpL9bpO3ToEDZs2ICMjAx8\n8MEHmD17NoYMGVIDd0/1UWLKfWyOugrgxa70nMlMRI2NWCyGuro6fvvtN2hoaMjai4qKMGDAAGho\naODEiROy3F2W2wUCAZo0aQJTU1N8/fXXaNeuHQDg+fPnGDhwIIyNjbFlyxa5a2VkZMDX1xcXL16E\nmpoahg0bhvnz50MoFGLDhg24desW1q9fX3M3T3VaVedozmAmIiIiIqpHBAIBNDU1kZycLPdKTExU\n6Ofu7i47vnDhQhgZGcneR0dHy/qKRCLExMTInZ+Wlob79+/LdqO/c+cOlixZgsDAQCQnJ2PJkiWY\nM2cOnjx5Uv03TfXOztg0BIQkIetpAbKeFiAg5Dx2xqbVdlhERDWuWbNmiIuLk2s7ffo0iouLZTm2\nTEJCApKTk3Hp0iXEx8dDKBTC09NTdvzAgQMwNzdHcnIy7t69K3eut7c33n//fZw+fRr79u1DSkoK\nNm7cWH03RvVWdeToOllgvnr1KgYOHCh7n52dja+++gpGRkYYPHgwIiMjazE6IiIiIqLaI5VK3/q8\nis61srJSKDBHR0fDyspKdk6nTp2QkJCAPn36oLi4GI8ePUKLFi2gpqb2VvFQw1Xe7vQAsONoKovM\nRNToWFtbV5pjy6Ourg5bW1ukpf3v383IyEjY2tpi+PDhCA8Pl7UXFhZCQ0MDHh4eEAqFaNOmDWxs\nbJCcnFz1N0T1WnXl6DpVYJZKpYiMjMTkyZNRXFwsa1+6dClatGiBhIQErFu3Dt988w2uXLlSi5ES\nERERETUcAwcOxOPHj2UPsVKpFIcPH4aNjY1cv2bNmuHu3bvQ19fHggULMGfOHLmf/BIlpjwo98G1\nzI6jqUhMeVCDERER1a7hw4fj3Llzsl/8PH/+HBcuXMDgwYMV+r5ccH748CF27dqFfv36AXgxGfPh\nw4cYNGgQxo0bh6ioKOTl5QEAhEIhNm/ejNatW8vOP3nyJLp161adt0b1THXm6Dq1BvPmzZtx5MgR\neHh4YOvWrQCAnJwcxMXF4ejRoxAKhdDX14etrS327duH3r1713LEREREREQ1SyAQIDs7G8bGxnLt\na9euRf/+/d9qzCZNmmDYsGE4dOgQunbtiqSkJHTs2BFt27ZV6Pv+++8jJSUFSUlJ8PDwwIcffih7\n+K2MRCJRWFIjIyPjrWKmumlzVOUTgTZHXeF6zETUaGhpacHY2BixsbEYO3Ysjh07hsGDB0MoFCr0\nNTc3B/Ci0Ny8eXOYmJhg8eLFAIA9e/ZgzJgxUFVVRY8ePdChQwccOHAA48aNkxtDKpVi5cqV+PPP\nP7F69ep3jp+5u+GozhxdpwrMDg4O8PDwwLlz52Rtf/31F5o0aQJdXV1ZW8eOHXHs2LHaCJGIiIiI\nqFZJpVK0atUKZ8+erbIxBQIBbGxssHDhQsyZMwfR0dGwtbUt96e7qqqqAIB+/frB2toax48fV7rA\nHBYWhuDg4CqLm4iIqK4ry7F79+7F2LFjER0djenTp+PZs2cKfePj4xU28QVeTL48ePAg1NTU8Msv\nv8jawsLC5ArM+fn5mD9/Pm7duoXQ0FBoaWm9c/zM3aSMOlVg1tbWVmjLzc2Furq6XJu6ujry8/OV\nGpN/aSEiIiIiqpyRkRFKS0uRlJSE+Ph4LF68WG4DoVOnTiEkJAT/+c9/ZG2FhYVo1aqV0tdwdnZW\nWHYjIyMDkyZNeuf4qW5w/6w3AkLOV9qHiKgxGTJkCHx8fHD9+nXcvXsXRkZGOHnypNLnHzx4EB99\n9BF++OEHWVtubi5sbW1x/vx5mJiY4MmTJ5gyZQpatGiBiIgItGzZskpiZ+5uOKozR9epAnN5mjVr\nhoKCArm2/Px8NG/eXKnz+ZcWIiIiIiLljBw5EitWrICxsbHCDKru3bvj2rVr2L9/P2xtbXH69GnE\nx8dj5syZSo8vEokgEonk2rhJYMNi1ksHjtbiCtd4dLQWc3kMImp0NDQ0MGjQIMyfPx8jRox44/Mj\nIiJgZ2cnt8Zy69atYWlpibCwMJiYmGDmzJnQ1tbGhg0b0KSJYrmvoKAAmZmZcr9O0tLSKnepjpcx\ndzcc1Zmj63yBuUOHDigqKsKDBw+go/PiJu/cuQM9PT2lzudfWoiIiIioIREIBBAIBJX2eZvzbG1t\nsW3bNixYsEBhLG1tbWzatAmBgYHw9fVFp06dsHHjRnTq1Okt7oIasglWXQFA4QHWaZgY44d2rY2Q\niIhqxct519bWFocPH8aoUaPKPV5Rjr5x4wbS0tLKLUyPGTMG7u7uSEpKQlJSEtTV1eX2aOjZsydC\nQ0MhEAhw6tQp2RrPZdf78ccfYWZm9k73SPVLdeVogbS8hdVq2blz5+Dp6SlbV27WrFkQCoXw9/fH\nzZs3MXXqVGzduhX6+vpvNX56ejosLS0RFxcnt7YzERERERHVLH43b7gSUx78d0MhATzs9dGvJ2cu\nExE1BMzd9V9V5+g6O4P55b/c+Pn5Yfny5TA3N0fz5s2xYMGCty4uExERERERUfUz66XD5TCIiIjq\noKrO0XWywGxqaorExETZ+1atWmHt2rW1GBERERERERERERERvUqltgMgIiIiIiIiIiIiovqJBWYi\nIiIiogbMxcUF4eHhiIqKgr29faX9jx07BgcHh3KPrV27FmKxGFevXq3qMKmBSUy5D1efI3D1OYLE\nlAe1HQ4RUZ2UmJgIV1dXGBoawsTEBM7OzoiLi5Mdt7CwQO/evWFgYIC+ffuib9++cHR0xIULFwAA\nI0eOhIGBAQwMDNC9e3fo6+vL3m/ZsgXnzp2DWCzGoUOH5K6bnp4OsViMvLy8Gr1fqn3VlZ9ZYCYi\nIiIiagQq2p2+TFFREbZu3Yq5c+eWe7ykpARRUVH4/PPPER4eXh0hUgOxMzYNASFJyHpagKynBQgI\nOY+dsWm1HRYRUZ0SHR2N2bNnw9bWFvHx8UhMTMSkSZOwbNky/PTTT7J+69evR3JyMi5duoRLly7B\n2toa06ZNw5MnTxATE4Pk5GQkJyejW7du8PX1lb2fNm2abAwfHx88fPiwNm6T6pDqzM8sMBMRERER\nEXx8fBAfHw83NzdIpVKF4ydPnkTr1q3x1VdfITY2FllZWbUQJdV1O2PTsONoqkL7jqOpLDITEf1X\nfn4+/P394efnBwcHB2hoaEBVVRVDhgzBt99+i9WrV1eYZz///HPk5ubi3r17Sl1LU1MTpqamWLx4\ncVXeAtUz1Z2fWWAmIiIiIiLMmjULoaGh6NChQ7nHd+/eDXt7e7z33nswNTXF7t27azhCqusSUx6U\n+/BaZsfRVC6XQUQEIDk5GXl5ebC0tFQ4ZmpqCm1tbZw6dQoA5P7om5OTgx9//BFt2rSBnp6e0tdb\nsWIFfv/9d+zYsePdg6d6pybyc5N3OpuIiIiIiBqEtm3bVnjswYMHSEpKwurVqwEAEyZMwPLlyzF1\n6lSoqqoqfQ2JRIInT57ItWVkZLxdwFTnbI66olQfs146NRANEVHd9fjxY2hqalaYQ7W1tfHo0SMA\nwJw5c9CkyYvynaqqKrp3745NmzahadOmSl9PS0sLfn5+mDt3Lvr378/c3cjURH5mgZmIiIiIiF4r\nMjISRUVFGDFiBIAXs6mysrJw/PhxWFtbKz1OWFgYgoODqytMIiKieqFNmzb4559/UFxcLCsev+ze\nvXvQ1tYG8GKDXXNz83e+poWFBUaMGIEFCxZg1apVSp/H3E3KYIGZiIiIiIgqVLa536pVq2Bqagrg\nRYF5+/btCAsLe6MCs7OzM2xsbOTaMjIyMGnSpKoMmWqJ+2e9ERByvtI+RESNnaGhIVq2bIkDBw7g\ns88+kzt2+vRpZGdn49NPP8WGDRuq9LqLFy/GqFGj8MMPPyh9DnN3/VcT+ZkFZiIiIiKiRqK4uBiZ\nmZly6zlqampCXV29wnPi4+ORl5cHa2truZ/Ujhs3Dv/5z39w8+ZNdOnSRanri0QiiEQiuTY1NbU3\nvAuqq8x66cDRWlzhOo+O1mIuj0FEBEAoFGL58uVYtmwZSktLMWzYMKiqqiI+Ph6+vr7w8vJC69at\nq/y6Ghoa+Pe//42JEydCIBAodQ5zd/1XE/mZm/wRERERETUSaWlpMDc3x6BBg2SvgwcPyvURCARy\nD5179uyRPfi+rGPHjujTpw/Cw8NrJHaqHyZYdYWjtVih3WmYGBOsutZCREREddOwYcPw/fff4/Dh\nw7CwsMDAgQMRFhYGHx8fuLm5Vdl1Xi0kGxsbc/ZxI1Td+VkgfXn6QiORnp4OS0tLxMXFQVdXt7bD\nISIiIiJqtPjdvGFKTHnw302FBPCw10e/npy5TETUUDB311/VlZ+5RAYRERERERFVKbNeOlwOg4iI\nqI6prvzMJTKIiIiIiIiIiIiI6K2wwExEREREREREREREb4UFZiIiIiKiesrFxQXh4eGIioqCvb19\npf2PHTsGBwcHuTYLCwv07t0bBgYGspeFhQU2b94s67Nw4UJ8+umnyM7Oljt3w4YNmDVrVtXcDDUo\niSn34epzBK4+R5CY8qC2wyEieive3t7o2bMnHj58KGsrL+empqbik08+QVBQkFy7RCKBpaUl/vjj\nj3LHX7duncJYu3fvhrW1NQwNDeHg4IALFy7IjmVkZODLL7+EoaEhzM3NERoaCgC4f/++XB43MDBA\njx49YG1tDQDIycnB/PnzYWZmBlNTU3h6ekIikbz9B0P1UnXmZhaYiYiIiIjquVd3iH9VUVERtm7d\nirlz55Z7fP369UhOTpa9Vq5ciY0bN+L06dOyPg8fPoSPj88bXZcap52xaQgISULW0wJkPS1AQMh5\n7IxNq+2wiIjeSHZ2NuLj4zF8+HDs2rWrwn7Xrl2Dq6srJk6ciAULFsjaL1y4AEdHR9y/f7/c8y5f\nvoxt27bJ5dKzZ8/iu+++w7p163Dx4kU4OzvDw8MD2dnZkEqlmD59OvT09HD+/Hls374dwcHBuHz5\nMt5//325PH7s2DFoaWlh6dKlAIBt27YhPT0dx44dw6+//oqSkhJ88803VfRJUX1Q3bmZBWYiIiIi\nogbOx8cH8fHxcHNzg1QqrbS/mZkZunTpIptxJRAIMHz4cJw+fRoxMTGyfsqMRY3Lztg07DiaqtC+\n42gqi8xEVK/s27cPxsbGcHR0xO7du1FcXKzQJzk5GV988QVmz54Nd3d3WfuFCxdkbeXlypycHCxZ\nsgSOjo5yxzMzMzFlyhSIxWIAwOjRo6GiooJbt27hypUrePToEebNmwdVVVXo6elh165d6Nixo8L4\ny5Ytw4gRIzBgwAAAQIsWLVBaWori4mJIpVIIBAI0a9bsXT8iqidqIjezwExERERE1MDNmjULoaGh\n6NChQ7nHX364LSkpwaFDh3Dz5k2YmJjI2t977z0sWbIEvr6+yMzMrPaYqf5JTHlQ7gNsmR1HU7lc\nBhHVG5GRkbC3t4eBgQFEIhEOHz4sd/z8+fOYPHky3N3dMWHCBLljXbp0wYkTJ2BnZ1fu2IGBgbCz\ns5MVksvY2dnhiy++kL2/ePEicnJyoKenh+vXr+Pjjz/GqlWrMGDAAFhbW+PKlSvQ1NSUGyMxMRHJ\nycmYPXu2rM3V1RXq6uro168fjIyM8Pfff2POnDlv9blQ/VJTuZkFZiIiIiKiBq5t27avPT5nzhwY\nGxujT58+0NfXx969e/H999+jR48esj4CgQCjR4+GqakpFi9e/FZxSCQS3LlzR+519+7dtxqL6p7N\nUVeqpA8RUW27dOkSnj59CnNzcwDA+PHjER4eLjt+7949zJw5E7169UJ0dDQKCwvlzm/ZsiWEQmG5\nY8fFxeH27duYOnXqa38J9Mcff8DT0xOenp7Q1NREdnY2zp07B5FIhF9//RX//ve/4efnJ7dGMwBs\n2bIFkydPlpuhHBAQgMLCQpw5cwYJCQnQ0dHB8uXLlfosmLvrt5rKzU3eeQQiIiIiIqrX1q5dC3Nz\nc2RlZWHevHlQUVFBv3795PqUPQT7+PjA1tYW4eHhb7wGc1hYGIKDg6ssbiIiouqwe/duSCQSfPrp\npwCA4uJiZGdn4/r16wCAgoICbN26Fd27d8eYMWPg7+8PX1/fSsd9/PgxVq5ciZCQkNfm0DNnzsDL\nywuTJ0/G1KlTAQBCoRCtWrXCtGnTAAAGBgawsrJCXFwcjIyMAAAPHjxAUlISvvvuO7nxoqOjERwc\njDZt2gAAFi1ahGHDhsHX1xcaGhqvjZm5m5TBAjMREREREQEAtLS0sH79etjZ2cHX1xd+fn4KfUQi\nEfz8/ODl5QULC4s3Gt/Z2Rk2NjZybRkZGZg0adK7hE11hPtnvREQcr7SPkREddmzZ89w5MgR/PTT\nT/jwww8BvPgj68qVKxEWFgYTExN07txZVtT99ttvMX78eBgZGWHUqFGvHfu3336DRCKBvb09gBeb\n8BYVFcHExATnz7/493Pv3r0ICAiAn58fRowYITu3c+fOKCkpQWlpKVRUXixIUFJSIjf+yZMnYWpq\nqrBsRtOmTVFQUCB7r6KiAoFAAFVV1Uo/D+bu+q2mcjOXyCAiIiIiagCKi4uRmZmJjIwM2Ss/P/+N\nx2nRogUCAgIQGRmJ+Ph4AIqb+Q0ePBgjRoxATEzMG81iFolE6NSpk9yrffv2bxwj1U1mvXTgaC2u\n8LijtRhmvXRqMCIioje3f/9+dOzYEQYGBmjdujVat26NNm3awMHBATExMZBIJHL9e/ToAS8vLyxf\nvhz/93//99qx7ezskJycjKSkJCQlJWH58uUQi8Wy4nJiYiJ8fX2xZcsWueIyAPTv3x/q6uoIDg5G\nSUkJLl26hOPHj2P48OGyPleuXIGBgYHCdUeMGIH169cjKysLz58/x5o1azB48GCoq6tX+nkwd9dv\nNZWbWWAmIiIiImoA0tLSYG5ujkGDBsleBw8elOsjEAiUKgibmprCwcEBK1asQE5OTrnnLV68GB98\n8EGV3gPVfxOsupb7IOs0TIwJVl1rISIiojezZ88ejBw5UqHdzMwMIpEIxcXFCjnRzc0NRkZG8PT0\nVPjjbmV59+Xj27ZtQ3FxMaZMmQIDAwPZ68yZM2jatClCQ0Nx9epVfPLJJ/D29sbSpUuhr68vO//+\n/fvQ1tZWuMbcuXNhYGAAOzs7WFlZQV1dHYGBgUp9HlT/1URuFkhft6J4A5Weng5LS0vExcVBV1e3\ntsMhIiIiImq0+N28YUpMefDfTYME8LDXR7+enLlMRNRQMHfXT9WZm7kGMxEREREREVUps146XA6D\niIioDqnO3MwlMoiIiIiIiIiIiIjorXAGMxERERFRAzdlyhRcvHgRAFBYWAiBQAA1NTUAgKGhIc6c\nOQMvLy9MmzZN7jyxWIyDBw9CT08PCxcuxMGDB2XnlRk7diwWLVpUMzdC9UZiyn1sjroK4MXu9JzN\nTESN0ct59FUuLi64fPkymjT5X2lOQ0MDw4cPx6JFi6CiooINGzZg06ZNaNq0qaxPkyZNYGxsDF9f\nX7Rp0wbnzp2Dp6cnzp49K+vz4MEDTJo0CT169MCqVauQm5uLlStX4syZMygtLcXAgQPx9ddfo2XL\nltX7AVCdUp25mQVmIiIiIqIGbtu2bbL/njVrFrp06YIZM2YAAO7duwdLS0t8//33MDc3R9eu5W/2\nIhAIMHHiRMyfP79GYqb6a2dsGnYcTZW9Dwg5D0drbvJHRPSqhQsXwsnJSfb+999/x+TJk/HRRx9h\n/PjxAIChQ4di3bp1sj6PHj2Cp6cnAgIC8O233yqMeffuXbi6umLgwIHw8fEBAAQEBCAvLw+xsbGQ\nSqXw9vaGn58fvvnmm2q+Q6orqjs3c4kMIiIiIqJGrGzPbzs7O3h7e6OwsLCWI6L67NUH2DI7jqZi\nZ2xaLURERFR/dOvWDcbGxvjjjz9kbWV5uoy2tjZGjhyJW7duKZx/584duLi4wMbGRlZcBoDS0lJM\nnz4dGhoaaNGiBT7//HMkJydX341QnVITuZkFZiIiIiIiwpw5c1BaWio3S+pVrz7kEr0sMeVBuQ+w\nZXYcTUViyoMajIiIqP6QSqVITEzE2bNn0a9fvwr7/fXXX9izZw/MzMzk2m/dugUXFxcMGTIEXl5e\ncsdWrVoFsVgse3/ixAl069atam+A6qSays1cIoOIiIiIiKCuro6goCBMmDABFhYWMDQ0lDsulUoR\nHh6OyMhIWZtIJEJsbKzS15BIJHjy5IlcW0ZGxrsFTnXG5qgrSvXhesxERC988803WLt2LYqKilBY\nWIg+ffpg6dKlGDJkiKzPiRMnYGxsjOLiYhQVFeGDDz7AqFGj5PZNyMvLg5ubG7p27Yrjx49jxowZ\n0NTULPeaP/74I2JjYxEREaFUjMzd9VtN5WYWmImIiIiICADQo0cPuLu7Y8GCBdi3b5/cMYFAAGdn\n53dagzksLAzBwcHvGiYREVGD4O3tDScnJzx//hy+vr74v//7PwwaNEiuj6WlJdatW4fS0lKEh4dj\n8+bNGDRokNymu8XFxViyZAmsra3h4uICb29vbN26VW6ckpISBAQE4OjRowgJCUGnTp2UipG5m5TB\nJTKIiIiIiEjG3d0dWlpaCAwMVDj2rktkODs748iRI3KvkJCQdxqT6g73z3pXSR8iosamRYsWCAgI\ngKqqKmbPni13rCz3qqiowMXFBba2tvDw8EBWVpasz7/+9S8MHz4cKioqWL16Na5evYpNmzbJjhcU\nFMDDwwMXL17Enj170L17d6VjY+6u32oqN7PATEREREREMioqKggKCkJMTIxce1WsvywSidCpUye5\nV/v27d95XKobzHrpwNFaXOFxR2sxl8cgokbn0aNHyMjIkL1eLgy/rEmTJggKCkJSUhJ27txZ4Xhe\nXl7Q0NCAn59fucd1dHTg5+eH4OBgnD17FgCwbNkySCQShIeHQ0fnzf4dZu6u32oqN3OJDCIiIiKi\nRk4gEMi979SpE7y9veHv7y/X59V+RK+aYNUVABQ2FHIaJsb4oV1rIyQiolrl5uYm997Q0BDh4eHl\n9u3UqROmT5+ONWvWYPDgweXmXqFQCH9/fzg7O8PGxgb/+te/FPpYWVnB3t4e8+bNw969e7F//340\nbdoUAwYMkPXR0tJCXFxcFd0l1WU1kZsF0ka4FXR6ejosLS0RFxcHXV3d2g6HiIhqWWlpKYAXs/aI\niKhm8bt5w5SY8uC/GwsJ4GGvj349OXOZiKihYO6un6ozN3MGMxERNTpSqRRp6X8hVZKBVEkmbj3J\nBAB8rNkOYlE7iEXvoatuB87UIyIiektmvXS4HAYREVEdUp25mQVmIiJqdNLS/8LXV49AUDZjWfji\nfy7mZuJibiakdy/DH8Mgbt+x1mIkIiIiIiIiqg/4W2AiImp0UiUZ/ysul0OgooJUSUYNRkRERERE\nRERUP7HATEREjU6qJLPSPmlPKu9DRFQd4uPj4erqClNTU5iamuKLL77AtWvXUFpaCicnJ3z11Vdy\n/TMzM9G/f38cP3680jHKLFy4EEFBQZXG4u/vr9AvISEBNjY2MDAwgJOTE/788893u2FqcBJT7sPV\n5whcfY4gMeVBbYdDRFQviMVi/PHHH7L3hYWF8PDwgK2tLR4+fIioqCjY29u/9vw+ffrAwMBA7lWW\n/2/dugUnJycYGBhg6NChOHToULXfE9Ut1ZmfWWAmIqJGpbS0VLbm8uvclGTKNv8jIqopu3fvxuLF\nizF58mQkJCTg9OnTGDBgAFxdXXH79m18++23uHDhgmz3+cLCQsyaNQt2dnYYMmRIpWOUPbiWYqEq\nNgAAIABJREFUtyv9yyQSCRYuXIiwsDC5fo8fP8bMmTMxb948JCUlwczMDDNmzKjGT4Tqm52xaQgI\nSULW0wJkPS1AQMh57IxNq+2wiIjqlfz8fHh4eCArKwvh4eFo27atUudFRkYiOTlZ7tWzZ0/k5eVh\n6tSpGD58OJKTkxEYGIjFixcjI4O/2mwsqjs/s8BMRERERFQH5OXlISgoCCtXroS5uTlUVVUhFArh\n5uYGR0dH3L59G+3atUNgYCBWrVqFtLQ0BAUFQU1NDfPmzVN6jDJSqbTCWJycnKCmpgYrKyu5frGx\nsejevTsGDRqEJk2aYPr06Xj48CGuXr1afR8M1Rs7Y9Ow42iqQvuOo6ksMhMRKSk3NxfTpk2DVCpF\nSEgIWrZs+c5jnjhxAm3btoWzszMAwMjICJGRkfjXv/71zmNT3VcT+Zmb/BERUaOioqKCjzXb4WLu\n62cxdxG1g8pr1mkmIqpqly5dQklJCQYOHKhwbO7cubL/trCwwNixYzFt2jSUlJTgl19+kf17pewY\nlfnpp5+gra2NRYsWybXfvn0bH330key9iooK2rdvj9u3b0NfX1/p8anhSUx5UO7Da5kdR1PRUadl\nte1eT0TUEDx79gxffPEFCgoKEBERATU1tTc6v6I/Hl+/fh0dOnTAokWLcPLkSbRt2xbz5s2Dnp5e\nVYRNdVhN5Wc+ORMRUaMjFrWrtE9Xzcr7EBFVJYlEgpYtWyr1xy17e3tkZmbCyMgI2trabzXG67w8\n5svy8/Ohrq4u19asWTMUFBQoNa5EIsGdO3fkXnfv3n2nWKlu2Bx1pUr6EBE1Zl5eXmjevDlu3bqF\nlJSUNz5//PjxMDY2lr3Wr18PAMjOzsbhw4dhZmaG3377DbNmzYKnpyf+/vvvSsdk7q7faio/KzWD\nWSqV4saNG0hJSUFWVhZUVFTQpk0b9OzZE2Kx+J2DICIiqkli0XuQ3r0MQQUFGGlpKcSi92o4KiJq\n7Nq0aYPs7GyUlJRAVVVV7tizZ8/QvHlzqKqqIjc3F3PnzsXYsWMRHR2N/fv3w87O7o3GeFvq6urI\nz8+Xa8vLy0Pz5s2VOj8sLAzBwcFvfX0iIqKGzNLSEl9//TW+/fZbzJkzB7/88gu0tLSUPj8iIqLc\nWclCoRDdu3fHqFGjAABDhgxBr169cPr0aTg5Ob12TOZuUsZrC8zZ2dkIDw/Hzp07kZWVBV1dXYhE\nIpSUlEAikeD+/fvQ1tbG+PHj4eTkVCXrwhAREVW3rrod4I9hSJVkIO1JJm5KXiyX0UXUDl0120Es\neg9ddTvUcpRE1NgYGBhATU0Np06dgoWFhdyxxYsXo0WLFggMDMTSpUuhpaUFHx8f9O7dGytWrIC+\nvj46deqk9BgAXrvJX0U++ugjHDlyRPa+pKQEf//9t9I/sXV2doaNjY1cW0ZGBiZNmvTGsVDd4v5Z\nbwSEnK+0DxERVWz8+PEAAE9PT5w7dw7e3t7Ytm3bW+Xsl3Xu3BmXLl2SayspKVHqXObu+q2m8nOF\nBeZ9+/Zhw4YN6N+/P/z9/dGvXz80bdpUrs/z589x8eJFHDhwADY2Npg9ezY+++yzdw6KiIioOgkE\nAojbd4S4fUcAQGlpKQBwzWUiqlVNmzaFl5cXli1bBlVVVfTv3x/5+fkICQlBYmIidu3ahfDwcCQk\nJGD//v0QCASwt7dHQkICZs+ejT179ig1BvDiF4o5OTkKu8e3bdtW7t/CV9dyHDp0KFavXo1jx47B\n3NwcW7ZswXvvvYdu3bopdY8ikQgikUiu7U3Xl6S6yayXDhytxRWu8+hoLeb6y0RESlJVVcWaNWsw\nevRofP/995gxYwYAoLi4GJmZmXL5WVNTU2H5qldZW1vju+++w549e2Bvb48TJ07gxo0bWL16daWx\nMHfXbzWVnyssMP/++++IjIxU+D/Ry1q0aAFzc3OYm5vj8ePH2Lx5MwvMRERU77CwTER1haOjI1q2\nbIng4GB4e3tDIBCgT58+CA0NRV5eHlatWoXg4GC0bdtWdo6Pjw9Gjx6NgIAArFix4rVjlM00FggE\niIiIQEREhGwcgUCA2NhYtG/fXq7t5VlTbdq0wcaNGxEQEIAFCxage/fu/NksyUyw6goACg+xTsPE\nGD+0a22ERERUb7w6S1lXVxc+Pj6YP38+DA0NIRAIkJaWBnNzc7l+/v7+cHBweO3Ybdu2xc8//4yV\nK1ciKCgI7dq1w9q1a6Gjwz/8NQY1kZ8F0oq2mGzA0tPTYWlpibi4OOjq6tZ2OEREREREjRa/mzc8\niSkP/rthkAAe9vro15MFDCKihoS5u36qzvys1CZ/APD48WPs2bMHf/75J7y9vXH+/Hno6emhS5cu\nVRYMERERERER1W9mvXS4HAYREVEdU535WanfBN+4cQPW1tY4deoUYmJikJubi4SEBDg4OCAhIaFa\nAiMiIiIiIiIiIiKiuk2pAnNgYCBcXV2xa9cuqKmpQSAQwN/fH5MmTcKaNWuqO0YiIiIiIiIiIiIi\nqoOUKjBfv34ddnZ2Cu0ODg74448/qjwoIiIiIqKGxtvbGz179sTDhw9lbVFRUbC3t5frl5qaik8+\n+QRBQUEAgOzsbHz11VcwMjLC4MGDERkZWe7469atUxhr9+7dsLa2hqGhIRwcHHDhwgXZsRs3bsDB\nwQEGBgYYPXo0rly5ojCmRCKBpaWlwnf+NWvWwMzMDCYmJli5ciVKS0vf7MOgBisx5T5cfY7A1ecI\nElMe1HY4RERVysLCAr1794aBgYHs1bdvX8TGxkIsFqNPnz5yx6ytreXytouLC8RiMRITExXGdnd3\nh1gsxv3798u9lpmZGebOnYuMjIxyYyvvewAAlJaWwsLCAjY2NlX0KVB9VZ05WqkCc6tWrXDv3j2F\n9hs3bkBLS6tKAyIiIiIiamiys7MRHx+P4cOHY9euXRX2u3btGlxdXTFx4kQsWLAAALB06VK0aNEC\nCQkJWLduHb755huFYvDly5exbds2uR3oz549i++++w7r1q3DxYsX4ezsDA8PD2RnZ6OgoADu7u6y\norOLiws8PDyQm5srO//ChQtwdHSUPeiWCQsLw6lTpxAdHY1Dhw7h0qVL+PHHH6viY6J6bmdsGgJC\nkpD1tABZTwsQEHIeO2PTajssIqIqtX79eiQnJ8tely5dgpWVFQAgMjJSrn3GjBlYtmwZbt++LTtf\nU1MTMTExcmNKJBIkJyfL5fFXr3Xo0CGoq6vDxcUFeXl5cv3K+x5Q5vTp0/jggw9QVFSEs2fPVtXH\nQPVMdedopQrMEyZMwLJly3D06FFIpVKkpaUhPDwcy5cvx7hx46osGCIiIiKihmjfvn0wNjaGo6Mj\ndu/ejeLiYoU+ycnJ+OKLLzB79my4u7sDAHJychAXF4eZM2dCKBRCX18ftra22Ldvn+y8nJwcLFmy\nBI6OjpBKpbL2zMxMTJkyBWKxGAAwevRoqKio4NatWzh79ixUVVUxfvx4qKqqwt7eHq1bt8apU6cA\nvCgul8Xx8pgAsH//fkyaNAlt2rRBmzZt8OWXX+KXX36p8s+M6pedsWnYcTRVoX3H0VQWmYmoURII\nBLC1tUWrVq3kfglkbW2NY8eOoaioSNZ25MgRWFhYKOTcl4lEIvj5+UEgEGDv3r2y9oq+B5TZvXs3\nhg4dis8++wzh4eFVdHdUn9REjlaqwDxt2jS4ubkhMDAQ+fn5mDFjBjZt2gQPDw98+eWXVRIIERER\nEVFDFRkZCXt7exgYGEAkEuHw4cNyx8+fP4/JkyfD3d0dEyZMkLX/9ddfaNKkCXR1dWVtHTt2lJsJ\nFRgYCDs7O1khuYydnR2++OIL2fuLFy8iJycHenp6uHPnDj766CO5/p06dZKN26VLF5w4caLcZfLu\n3LkDPT09uXju3LnzJh8HNTCJKQ/KfXAts+NoKpfLIKIG43VF4JePFRYW4ueff0ZBQQF69+4ta+/S\npQvee+89nD59WtYWHR2NUaNGVXptFRUVfPLJJ7h48aKsraLvAQDw8OFDJCQkYNSoUbC3t0d8fDwe\nPOC/x41JTeXoJsp2dHJygpOTE3JyclBSUgIVFRW0aNHinQMgIiIiImrILl26hKdPn8Lc3BwAMH78\neISHh8PW1hYAcO/ePcycORO9evVCdHQ0nJycIBQKAQC5ublQV1eXG09dXR35+fkAgLi4ONy+fRt+\nfn6vnUX8xx9/wNPTE56entDU1ERubi6aNWsm16dZs2aycVu2bFnhWHl5eXIxNWvWDKWlpSgsLJTF\nXRGJRIInT57ItVW0liTVH5ujFNfvLq+PWS+dGoiGiKh6zZkzB02a/K+cNmTIEAQGBgJ4keNVVFRQ\nWFgIqVSKgQMHIiQkBO3atZMbw8bGBjExMbCwsEB6ejqysrLkitCv06pVK/z9998AKv8eEBUVhcGD\nB0NTUxMAMGjQIOzcuRNeXl5K3y9zd/1WUzlaqQJzVlYWFi1ahB49emDWrFkAgAEDBkBfXx+BgYFo\n1arVOwVBRERERNRQ7d69GxKJBJ9++ikAoLi4GNnZ2bh+/ToAoKCgAFu3bkX37t0xZswY+Pv7w9fX\nF8CL4m1BQYHcePn5+dDQ0MA///yDlStXIiQkpNw1F8ucOXMGXl5emDx5MqZOnQoAaN68uayYXCYv\nLw8aGhqV3s/LBe6y85o0aVJpcRl4sX5zcHBwpf2IiIjqqrVr18r+aPyqiIgI6OnpIT09HTNmzIBI\nJIK+vr5CPxsbG2zatAn5+fk4ePAgbG1tXzsz+mUSiQQikQiPHz9+7fcAqVSKPXv24MmTJxgwYACA\nFzn7/PnzmDFjhlJ5G2DuJuUoVWBesWIFcnJyMHLkSFnb9u3b4efnB39/f3zzzTfVFiARERERUX31\n7NkzHDlyBD/99BM+/PBDAC8e+FauXImwsDCYmJigc+fOMDIyAgB8++23GD9+PIyMjDBq1Ch06NAB\nRUVFePDgAXR0XswsKVve4rfffoNEIpHtGF9UVISioiKYmJjg/PnzAIC9e/ciICAAfn5+GDFihCyu\nzp07IywsTC7WO3fuKPXz3I8++gi3b9+WPTCXt9xGRZydnRV2sc/IyMCkSZOUOp/qJvfPeiMg5Hyl\nfYiIGgtdXV1s3LgRo0ePhq6urmxvhTI6Ojro3r074uLiEBMTg40bNyo1bmlpKX777Td8+eWXSEhI\neO33gN9++w0FBQU4evSorAAtlUrh4OCAmJgYjBkzRqlrMnfXbzWVo5UqMCckJGDXrl1yXxy7du2K\npUuXwsXF5Z2DICIiIiJqiPbv34+OHTvCwMBArt3BwQEeHh74+OOP5dp79OgBLy8vLF++HD169MBH\nH30ES0tLrFmzBv7+/rh58yYOHjyIrVu3Ql9fX64g/MsvvyAsLEy28U9iYiJ8fX3x448/wtDQUO46\n/fr1Q2FhIcLCwjBu3Djs378fWVlZshlOrzNq1Chs374dZmZmUFVVxQ8//FDuWs3lEYlEEIlEcm1q\nampKnUt1l1kvHThaiytc49HRWszlMYio0Xn//fexaNEiLF26FIMHD0bXrl3ljtvY2GDjxo1o2bIl\n2rdvj5ycHIUxXp7V/PjxY6xZswbq6uqws7ND06ZNX/s9YPfu3RgxYgTatGkjN6adnR3CwsKULjAz\nd9dvNZWjldrkr2nTpsjKylJoz8nJee3P8YiIiIiIGrM9e/bI/QqwjJmZGUQiEYqLixW+T7u5ucHI\nyAienp7Iz8+Hn58fiouLYW5uDk9PTyxYsKDcn9sCkBtr27ZtKC4uxpQpU2BgYCB7nTlzBkKhEFu3\nbsXBgwdhamqKHTt2YNOmTQrrPb86JgA4OjrC0tISDg4OGDlyJIyMjODm5vY2Hw81IBOsusLRWnGD\nKadhYkyw6lrOGUREDUt59bExY8bA1NQUS5YsQWlpqdwxa2tr/PXXX3JF4lfH8PT0hIGBAfr27QsH\nBweoqqoiNDQUTZs2fW0M//zzD06cOKEw8xgARo8ejRs3buDKlcrX5qWGoSZytECqxCIvK1euRHx8\nPJYsWYJevXoBAG7cuIHAwED07dtXtkZcfZGeng5LS0vExcXJ7chNREREREQ1i9/NG5bElAf/3VBI\nAA97ffTryZnLREQNDXN3/VSdOVqpJTLmzZuHp0+fYvr06SguLgYAqKqqwsHBAQsXLqyyYIiIiIiI\niKj+Muulw+UwiIiI6qDqzNFKFZibNm2KoKAgfP3117hz5w6EQiF0dXXRokWLagmKiIiIiIiIiIiI\niOo+pdZgBoAnT57gxo0bePbsGR4/fozLly/jzJkzOHPmTHXGR0REREREL7lz5w48PDxgYmKCvn37\nws7ODpGRkQCAqKgo2W7y5RGLxejTp4/cmswGBga4du0a0tPTIRaLsWXLlnLP++OPPwC82Dl++vTp\nMDU1xYABA+Dv74/CwsLquVmqVxJT7sPV5whcfY4gMeVBbYdDRFStEhMT4erqCkNDQ5iYmMDZ2Rlx\ncXGy4xYWFvj111/LPXfDhg3o3r27Qj6eOXMmAGDhwoXo2bOn3DEzMzPMnz8feXl5CuP5+/sjKChI\nof3YsWNwcHBQaL979y6MjY3LHYsarurM00rNYI6KisLy5ctRVFRU7vHU1PJ3IiQiIiIioqpTWlqK\nKVOmwMHBAevWrYNQKERSUhJmzJiBli1bKrUBd2RkJPT09BTa09PTAQDff/89zM3NFXa7L+Pt7Y2u\nXbti7dq1ePr0Kb766its3LgRs2fPfrebo3ptZ2ya3A71ASHn4WjNDf6IqGGKjo6Gv78/vL29sXHj\nRqirq+PkyZNYtmwZ0tPT4erqCqD8jf/K2ocOHYp169ZVeHzixImYP3++rO3vv//GtGnTsHHjRsyd\nOxcAIJFIEBQUhH379mHy5MmyvkVFRQgJCcGGDRvQpUsXubGPHz8OHx8fPH/+/J0+A6pfqjtPKzWD\nef369Rg3bhwuXLiA1NRUhRcREREREVU/iUSCe/fuwcbGBkKhEABgbGwMb2/vCieDvCk7Ozt4e3uX\nOyu5sLAQGhoa8PDwgFAoRJs2bWBra4vk5OQquTbVT68+tJbZcTQVO2PTaiEiIqLqk5+fD39/f/j5\n+cHBwQEaGhpQVVXFkCFD8O2332L16tXIysp67RhSqRRSqfSNrvvhhx9i8ODBuHXrlqzNyckJampq\nsLKykhvPx8cH8fHxcHNzk2s/cOAA/v3vf2PGjBlvfH2qv2oiTytVYJZIJJg0aRLXXCYiIiIiqkWt\nW7eGiYkJJk+ejA0bNuDs2bPIzc2Fg4MDRo4cqdTDYmV95syZg9LS0nJnVQmFQmzevBmtW7eWtZ04\ncQLdunV785uhBiEx5UG5D61ldhxN5XIZRNSgJCcnIy8vD5aWlgrHTE1Noa2tjVOnTr3zdV7N19ev\nX8fRo0dhZmYma/vpp5/g5+cHDQ0Nub6zZs1CaGgoOnToINc+YMAAxMbGon///u8cH9UPNZWnlVoi\nw8zMDL/99hvGjRv3zhckIiIiIqK3t23bNuzcuRPHjh2TrZdsZWWFpUuXKnX++PHjoaLyv3kmLi4u\nmDVrluy9uro6goKCMGHCBFhYWMDQ0LDccaRSKVauXIk///wTq1evVuraEokET548kWvLyMhQ6lyq\nmzZHXVGqT3XtWk9EVNMeP34MTU1NqKqqlntcW1sbjx49qnScEydOwNjYWPZeIBAgPj4e6urqkEql\nCA8PR2RkJIqLi1FYWIiPP/4Ybm5ucHZ2lrtWedq2bVtuu5aWVqVxvYq5u36rqTytVIG5Z8+eWLly\nJU6ePIlOnTpBTU0NwIsvlQKBAF5eXu8UBBERERERKUcoFMLV1RWurq4oLCzExYsX8c0332Dx4sUY\nOnRopedHRESUuwbzy3r06AF3d3csWLAA+/btUzien5+P+fPn49atWwgNDVX6gTUsLAzBwcFK9SUi\nIqqL2rRpg3/++QfFxcVo0kSxrHbv3r0KC78vs7S0fO0azM7Ozpg/fz4KCwuxfv16HD16FJaWlkrt\nt1CVmLtJGUotkXHu3Dn07t0bOTk5uHbtGpKTk5GcnIzLly9zvTUiIiIiohpy6NAhjBo1SvZeKBTC\nzMwMM2fOrPK9Udzd3aGlpYXAwEC59idPnsDZ2RlPnz5FREQEPvjgA6XHdHZ2xpEjR+ReISEhVRo3\n1Sz3z3pXSR8iovrC0NAQLVu2xIEDBxSOnT59GtnZ2fj0008rHaeyJavKjguFQsybNw9isRju7u7l\n7pFQnZi767eaytNKzWAODQ195wsREREREdG7+eSTT+Dn54c1a9bAzc0NIpEIf/31F0JDQ2FhYQEA\nKC4uRmZmptyDq6amJtTV1d/oWioqKggKCsKYMWNkbVKpFDNnzoS2tjY2bNhQ7syt1xGJRBCJRHJt\nZb+OpPrJrJcOHK3FFa7v6Ggt5vIYRNSgCIVCLF++HMuWLUNpaSmGDRsGVVVVxMfHw9fXF15eXrK9\nCiQSidxyEqqqqkrNbi6v+Ozr64sRI0Zg/fr1mDdvXqX9qwpzd/1WU3la6W+EmZmZuH37NkpKSgC8\n+D9vYWEhrl+/LrdmGxERERERVQ9NTU3s2LEDa9euhY2NDXJzc6GlpQU7OztMnz4dBw8eRFpaGszN\nzeXO8/f3h4ODQ6Xjv/qz206dOsHb2xv+/v4AXmxslJSUBHV1dbl1I3v27MlJKY3YBKuuAKDw8Oo0\nTIzxQ7vWRkhERNVq2LBhaN26NTZv3oxVq1ahtLQU3bp1g4+PD4YMGSLrt3DhQrnz3nvvPfz6668Q\nCASvXeqivOMikQiLFy/GokWLMHz4cPTo0eO1/V/XXnaMGoeayNMCqRJ/5ggPD0dAQICsuFymSZMm\n6Nu3L37++ecqCaampKenw9LSEnFxcdDV1a3tcIiIiIiIGi1+N284ElMe/HczIQE87PXRrydnLhMR\nNUTM3fVTdeZppWYwb9++He7u7nB3d8fgwYOxZ88e5OTkwNvbG1OmTKmyYIiIiIiIiKh+Muulw+Uw\niIiI6qjqzNNKbfL38OFDjB49GmpqaujWrRuuXLkCPT09LFq0CGvXrq2WwIiIiIiIiIiIiIioblOq\nwKypqYmnT58CADp27Ii0tDQAwPvvv49bt25VX3REREREREREREREVGcpVWAePHgwli9fjtTUVPTr\n1w/79u3DxYsXERoaivfff7+6YyQiIiIiokqIxWL06dMHOTk5cu1FRUUwNTWFhYUFgBfrJorFYhgY\nGMDAwAB9+/aFiYkJZs6ciczMTABAVFQUunXrJtdn4MCBCAgIQHFxsdz4BQUFGDt2LH799dcauU+q\nexJT7sPV5whcfY4gMeVBbYdDRFQjLCws0Lt3b1muLHsdO3YMALBnzx6IxWIcPnxY7ryyPDx48GCF\nMf/55x/06NEDLi4uCscSExPRrVs35OXlydoyMjLw5ZdfwtDQEObm5uVuuFtRHNS4VHeuVqrAvGDB\nAnTt2hW///47LC0tYWJiAicnJ+zZswfz58+v8qCIiIgamtLSUpSWltZ2GETUwDVr1gxxcXFybadP\nn0ZxcbHCbvEJCQlITk7GpUuXEB8fD6FQCE9PT9nxHj16IDk5WdYnMjISZ86cwfr162V9bt68iYkT\nJ+Lq1avcjb6R2hmbhoCQJGQ9LUDW0wIEhJzHzti02g6LiKhGrF+/XpYry15Dhw4FAOzevRuff/45\nwsPDyz03Pz8fFy9elGs7dOgQ1NXVFXJqdnY2Fi9eLNcmlUoxffp06Onp4fz589i+fTuCg4Nx+fJl\nuX6VxUENX03kaqUKzC1atMDKlSsxZswYAEBQUBASExNx7tw5WFpaVmlAREREDYFUKkXq3T+x7+pZ\n/PvUfkyN3oap0dvw71P7se/qWaTe/RNSqbS2wySiBsba2hoxMTFybdHR0bCysnrtvznq6uqwtbWV\nLYUHQKF/u3btYG5ujps3bwIA7t27h4kTJ2L48OH8VWMjtTM2DTuOpiq07ziayiIzETVqqampuHv3\nLhYsWIC0tDS5/FrmTXL2ihUrMHLkSLn2K1eu4NGjR5g3bx5UVVWhp6eHXbt2oWPHjm8UBzVsNZWr\nKywwR0REoKCgAACwa9cuREREyL1iY2MRFRWFiIiIKguGiIiooUhL/wtfXz2C8HtXcTE3E0+FwFMh\ncDE3E+H3ruLrq0eQlv5XbYdJRA3M8OHDce7cOTx58gQA8Pz5c1y4cKHcn+G+/JD68OFD7Nq1C/36\n9St33NLSUty8eRPHjx+X9dHS0sLx48cxadKkqr8RqvMSUx6U+8BaZsfRVC6XQUQNXkV/vI2IiMCY\nMWPQokUL2NnZISwsTKGPjY0Njhw5Ihvjr7/+wvPnz9GzZ0+5fgcOHMDz588xYcIEufbr16/j448/\nxqpVqzBgwABYW1vjypUr0NTUfKM4qOGqyVzdpKIDP/zwA6ysrNC0aVNs2bLltYOMGzeuSoJ5ne3b\nt+O7776DmpqarG3btm0wNDSs9msTERG9qVRJBgQqFf9QSKCiglRJBsTtO9ZcUETU4GlpacHY2Bix\nsbEYO3Ysjh07hsGDB0MoFCr0NTc3B/Di4bh58+YwMTGR+/ltamoqjI2NZX1at26NESNGwNXVFcCL\n5TjelEQikRW/y2RkZLzxOFT7NkddUaqPWS+dGoiGiKh2zJkzB02a/K+0NmTIECxbtgwxMTHYtWsX\ngBc1s7Fjx8Lb2xstW7aU9e3evTs0NTWRkJCA/v37Izo6GnZ2dnLj379/H+vXr8fOnTtlk0DLZGdn\n49y5c+jXrx9+/fVXpKSkYMqUKdDV1YWRkRHy8vKUiqMyzN31V03m6goLzCdOnJD9d2hoKD744IN3\nvti7+P333zF37ly4ubnVahxERETKSJVkVton7UnlfYiI3oRAIICNjQ327t2LsWPHIjo6GtOnT8ez\nZ88U+sbHx7+2SCwWi7F3794qjS8sLAzBwcFVOiYREVFtWbt2rewPtmWioqLw7NkzTJxLtsyeAAAg\nAElEQVQ4UdZWUFCAyMhITJ48Wa6vjY0NDh48iP79+yMmJgbbt2+X1eOkUikWLFiAOXPmQFtbG3fv\n3pW1A4BQKESrVq0wbdo0AICBgQGsrKwQFxcHIyMjHD58WOk4Xoe5m5RRYYH5ZePGjcOmTZvQq1ev\n6o6nQr///jvs7e1r7fpERETKKi0txa0nmYDihEE5NyWZKC0thcprZjoTEb2pIUOGwMfHB9evX8fd\nu3dhZGSEkydP1nZYAABnZ2fY2NjItWVkZHCZjXrI/bPeCAg5X2kfIqLGZvfu3fD29pabjRwTE4Of\nf/5ZrrBb9kdhe3t7ODg4oHXr1nJ7GmRkZODq1atITU3FihUrZBuGDxo0CJs3b0bnzp1RUlIi9zxR\nUlKiVBxubm5Kb87L3F1/1WSuVuqJVkNDA7m5uVVywbeRl5eHO3fu4KeffsKAAQMwYsSIKp9NQURE\nRETUEGhoaGDQoEGYP38+RowYUdvhyBGJROjUqZPcq3379rUdFr0Fs146cLQWV3jc0VrM5TGIqNG5\nefMmrl27hjFjxqB169ay15gxY/Do0SOFP/h++OGH6Ny5M5YvX66wPIaOjg6uXLmCpKQkJCUl4cCB\nAwCAU6dOoW/fvujfvz/U1dURHByMkpISXLp0CcePH8fw4cMrjePXX39V+p6Yu+uvmszVSs1g7t+/\nP6ZOnYr+/fujffv2UFdXB/BiWr5AIICXl1eVBFORf/75B4aGhnB0dMQnn3yCy5cvw8PDA9ra2vj0\n009fey7XiiEiopqmoqKCjzXb4WLu65fA6CJqx9nLRFRlXp6JZGtri8OHD2PUqFHlHq9s1pJAIFB6\nZhM1XhOsugKAwgZCTsPEGD/0/9m787ias/+B469baaWNsQzGTrbI2CKSopQw2aOsY7JTlsY69iG7\nxtIwkwlZKgYZJCQksmQZGYyZKUtfRqJN6+8PX/c391vRzKSF9/PxuI+Hzjmfz32fi973cz7nc06D\n4ghJCCGK1Z49ezA3N8fIyEilvFy5ctjY2LB9+3bmzZuXK2d7eXlhZ2cH5J+DX4/BvaalpYWfnx/z\n58+nXbt26OnpMXv2bExNTVm0aNFb48hrA2Dx/imqXK3IyW/Ly79wcXF5Y72fn1+hBVRQCxcuJD09\nnfnz57+x3bp16/JdKyY0NJRq1aq9i/CEKHKvH5cp7sGqyMhIdu3axcqVK5Vly5cvp06dOvzyyy9c\nvnyZzMxM+vfvT9++fXMd/8cff/D111/z9OlTFAoFJiYmTJkyBT09PRYvXszNmzcBePz4MQYGBuza\ntavI+ibE37Hv6jm237/6xjaDqprSy7RtEUUkhBAlU1xcHNbW1vLdvBSLuPbwvxsJKRjd25S2TWTm\nshBCvM8kd5c+7zpXF2gGc3EMIP/V9evXOXPmDF988YWyLC0tDV1d3bceK2vFiPdVTk4Ot+J+Jybh\nETEJ8a/WewXqGVbCxKgSJkaVaVCtRpHPPsrr/RQKBTk5OcTFxbFz507S09Pp3r07dnZ2lCtXTtku\nJSWF0aNHs2TJEkxNTQHYt28fHh4ebNy4UbmzfWZmJs7OzixcuLBoOiXEP2BiVJmc2Cso8rnpk5Od\njYlR5SKOSgghhCh85k2ryHIYQgghRAn2rnN1gQaYAa5cucLt27eVsyRzcnJIT0/nxo0bLF269J0F\nCFC2bFnWr19PzZo16dKlC5GRkRw6dIjt27e/9VgjI6NcjwSUKVPmXYUqRJG5Ffc7s64e/v/Bq/9u\nJnYxJZ6LKfHkxF5hIXaYVK9ZpHHl9VDE698XixcvVpZlZWWhoaH6K+j48eOYm5srB5cBevXqhb+/\nP3Fxcco7o35+flhYWFCvXr131Ash/r0G1WqwEDtiEh5x61k8vyS8uglU36gSDQz//yaQEEIIIYQQ\nQghRmhVogHn16tVs2rSJihUrEh8fT+XKlXny5Ilyx8t3rWbNmqxdu5YVK1bg6elJlSpVWLp0KQ0b\nNnzn7y1ESRWT8CjfmZEACjU1YhIeFfkAM8C5c+dUltaJi4tj4sSJlCtXjoyMDDw9Penfvz86Ojoq\nxz148CDPx2uqVq3K/fv3qVatGunp6ezatYuAgIB33g8h/g2FQoFJ9ZrK/4MlZRkbIYQQQgghhBCi\nMBVogDkwMJC5c+cyYMAArKys+OGHHzAwMGDChAm0bVs0a0daWlpiaWlZJO8lRGkQk/DmzcMAbj17\ne5t3oW3btiprMK9YsYKcnBwSExOZOHEibdq0YdSoUQC4ubmRnJxMgwYNaNasGdHR0bnO99tvv1Gx\nYkUAIiIiaN26NWXLli2azghRSGRgWQhRVE6dOsWWLVuIiXm1mUuTJk2YPHkyTZo0ASA+Ph5vb29O\nnTpFUlISlStXxtnZmUGDBgGv9lOYOHEi586dy/P8UVFRLF26lHv37mFkZMTIkSPp379/0XROlDgR\n1x6wMejVngNuTs1kqQwhhCgkJiYmaGtrc+bMGfT09JTlGRkZWFhYoKenx/HjxwkPD2fMmDFs375d\n5WngVatWcfbsWfz9/cnOzsbLy4uffvqJjIwMzMzMmDt3LlWqyO/sD0FR5OoCXe0mJCTQsWNH4NU/\n8OjoaPT19XF3d2fTpk2FHpQQ4s2ys7OVay6/yS8J8cpZk8Xt4cOHWFpakpGRwc8//6ws37hxI/fu\n3WPWrFkkJyfj7++Pk5MTrq6utGrVis8//xxdXV3mzJlD3759mTNnDpcvX2b48OH85z//AWD06NEM\nHDgQFxcX5cD1unXrsLW1xcXFBWdnZ0aNGsWLFy+Kpe9CCCFEUdm9ezczZsxg+PDhnD17lvDwcCws\nLBgyZAh37twhPj4eJycnjIyM+PHHH7l48SJLlixhy5Yt+W6M/VeJiYmMGTOGoUOHEhUVxZo1a1i5\nciURERFF0DtR0vgfvcVi3ws8ff6Sp89fstj3PP5HbxV3WEII8d7Q0dEhNDRUpSw8PJzMzEzl/kcd\nOnTA1dUVDw8PkpKSAAgNDWXXrl2sXbsWDQ0NNm3axI0bN9i/fz/h4eFUqlQJDw+PIu+PKHpFlasL\nNMD80Ucf8ejRIwBq1arFzZs3ATA0NCQuLq7QgxJClF4KhSLPjf7S09NJT0/nt99+4/jx43Tr1i3X\n7w8tLS369u1LlSpVyMjI4OOPPyYyMhI9PT3S09NZtmwZJiYmeHl50aVLF7777jsA/vjjD/z9/fHz\n88PHx0cZx/Dhw/Hz82PHjh00bNiQPXv2vPsPQAghhCgmqampLF26lEWLFmFpaYm6ujqampoMGzaM\nQYMGcffuXdasWUPLli1xd3fH0NAQAFNTUxYtWsSTJ0/e+h4PHz7EysoKBwcHABo1akSbNm24dOnS\nO+2bKHn8j95ix5GYXOU7jsTIILMQQhQSW1tbgoODVcoOHDhA165dVfY/mjx5MsbGxsydO5fY2Fhm\nzJiBl5eXcoZyamoqY8aMwdjYGE1NTZydnbl69WqR9kUUvaLM1QUaYLa3t2fq1KlERUXRsWNHAgIC\n2L9/P6tXr6Z27dqFGpAQ4u3U1NSoZ1jpre3qG1Uq8sfyW7duzYoVK1TKPDw86NOnD02bNqVjx47M\nmzePjIyMXBtu5uTkUK5cOb755hv8/f3x9vamSpUqzJo1C4VCQU5ODps2bcLExIRnz56hp6fHn3/+\nyfPnz3Fzc8PZ2ZmTJ0+qnO+1Z8+eUb58+XfadyGEEKI4Xbp0iaysLDp06JCrzt3dHVtbW06fPk3X\nrl1z1Zubm/PVV1+99T1MTExUNvhOTEwkKipK9kb5wERce5jnBetrO47EEHHtYRFGJIQQ76du3boR\nGRnJs2fPAEhKSiIqKgorKyuVdhoaGqxYsYJTp07h4uLCoEGDVL4PTJs2DQsLC+XPx48fp379+kXT\nCVEsijpX57sG8/Xr15XrtE2ePBk9PT2ePXuGjY0NgwYNYu7cuVSqVIklS5YUWjBCiIIzMarExZQ3\nL5PRoACD0MWhUqVKTJw4kRkzZrBlyxaVuoMHD3LlyhXU1NTQ0dHBy8uL6tWrU6ZMGaZPn462tjZq\namrUrl2bqVOn8vz5c0aMGIGrqyvPnj1j4MCBmJqakpOTw/fff09wcDCJiYk8f/6cMWPGFFOPhRBC\niHcvISEBfX39N95cTkhIwNjYuFDe78WLF7i5udGkSRM6d+5c4BhfXyS/9vpJSVF6bAzKvWdGXm1k\nPWYhhPh3jI2NadWqFUePHqVfv36EhIRgZWWFpqZmrrbVqlWjdevWnDhxgm7duuV7zkOHDuHj48O3\n335boBgkd5dORZ2r8x1g7tOnD7Vr16Znz544OjqqDMxMmDCBCRMmFEoAQoh/xsSoMjmxV1DkcxGZ\nk52NiVHlIo6q4BwdHQkJCWHHjh25yt3d3fM8ZtmyZdSqVUulTFNTk/79+6OmpoaxsTE1a9Zk06ZN\nlCtXjuHDhys3HQoMDMTT05Pvv//+3XRICCGEKGYVKlQgMTGRrKws1NXVVepevHiBjo4OH330EY8f\nP851bHZ2Ni9evMDAwKBA7xUbG4ubmxs1atRg9erVBY5x27ZtBVrrWQghhBCvln7s3r07gYGB9OvX\njwMHDjBmzJg89xcKDAwkOjoae3t73N3dCQgIQEtLS6WNj48PPj4+rFu3jpYtWxYoBsndoiDynd4Q\nHBxMt27d2LdvH9bW1ri4uLBnzx7lguFCiOLVoFoNFpraMaiqKS31KqGfDvrp0FKvEoOqmrLQ1I4G\n1WoUd5hv9NVXX/Hdd9+RnJysLPvrshb/K6+6s2fPMnHiRACSk5P57bff8PX1JSwsTKV95cqVyczM\nLMTohRBCiJLFzMyMMmXKEBYWlqtuxowZzJo1CwsLC0JCQnLVnzx5EisrK1JSUt76Pjdu3KB///50\n7NiR9evX5zmLKj+DBw/m8OHDKi9fX98CHy9KBjenZoXSRgghxNvZ2Nhw/fp1bty4QWxsbJ4DwzEx\nMSxcuJClS5eyYMECsrKyWLBggbI+OzubWbNmsXPnTrZv366yXMbbSO4unYo6V+c7g7lOnTqMHz+e\n8ePHc/PmTYKDg9mwYQMLFizAysqKnj17KjcPEUIUPYVCgUn1mphUrwm8ShhAka+5XFB/3fjv9Z+N\njY358ssvGTdunLI8rw0C8zrHax07diQiIkI5i3n69Ok0atQIKysrfv31V4KDg9HQ0CA1NZVZs2YV\ncq+EEEKIkkNLSwt3d3fmzJmDuro67du3Jy0tDV9fXyIiIti5cyflypWjZ8+erFq1iuHDh1O2bFnO\nnz/P3LlzGTlyJLq6usCrm7rx8fEqN2vLli1LWloaI0eOZMSIEYwcOfJvx2hkZISRkZFK2f/uySBK\nPvOmVXC2Ncl3bUdnWxNZHkMIIQqJnp4enTp1Ytq0adjb2+eqT0pKYsKECbi6utK+fXsAVq5cSb9+\n/TA3N8fBwQFvb2/OnTvH7t27qVChwt96f8ndpVNR52pFzpumC+bhypUrBAcHc+zYMdLS0ujWrRtz\n5swptICKQlxcHNbW1oSGhlKtWrXiDkcI8Q48fvyYLl26YGNjg5eX1xsHroUQQoj3ycGDB9m6dSu/\n//47CoWC5s2bM2nSJOVGfL///jurVq3iwoULpKamUrVqVQYNGsSAAQMAOH/+PK6urrnO6+bmho6O\nDqtWrUJHR0elbsiQIUyaNOkfxSvfzUuvvHanH2RnwoAuDYopIiGEeH80bNiQAwcOULduXU6cOMGY\nMWM4ePAgderU4cSJEyxcuJDQ0FDGjx9PQkICP/zwg8qEs23btrF69WoCAwPp1asXmZmZaGj8/zxT\nhULB2bNn0dbW/tuxSe4uPYoqV//tAWaA1NRUwsPD2bBhAzdv3iQmJv9dCUsi+Y8gxIfh6dOn2Nra\n0rZtW9asWVNiZ3eL3Er6jHwhhBCFR76bl24R1x7+dyMhBaN7m9K2icxcFkKI953k7tKlKHJ1vktk\n/K+UlBROnjzJ4cOHOXXqFMbGxjg6OrJ8+fJCD0oIIQqDsbExx44do1u3bri5ubFx40YZsCyhcnJy\nuBX3OzEJj4hJiOf2s3gA6hlWwsSoEiZGlWlQrYbMRBdCCCFKGPOmVWQ5DCGEEKIEK4pc/cYB5qSk\nJE6cOMGRI0cIDw9HS0sLOzs7Nm/ezKeffioX+kKIEs/AwIAjR47g6OjIsGHD+O6773KtHf/nn39S\nvnz5YopQANyK+51ZVw+jeH0D4L/7RV1MiediSjw5sVdYiJ1yzXEhhBBCCCGEEEKUDPlO5Rs9ejTt\n2rVj5syZKBQKli9fzunTp5k/fz4tW7aUwWUhRKlRrlw5Dh06xIMHDxg8eDAZGRnKuhcvXtCwYUNC\nQ0OLMUIRk/Do/weX86BQUyMm4VERRiSEEEXn3r17jB49mtatW9OiRQt69uxJQECASpukpCTMzMwY\nNWpUruNdXFwwMTEhIiIiV52bmxsmJiY8ePAAgM6dO9OsWTPMzMwwMzPD3NwcDw8PHj36/9+xKSkp\nzJ07l3bt2mFhYcHy5cvJysoCIDIykrZt2xZm94UQQoj3yqlTpxgyZAht2rShTZs2jBgxguvXrwMQ\nFBRE7969ARg5cqQyHzdu3JgmTZoof/7qq6+4f/8+JiYmyrK/vlavXg3AunXraNSokbK8RYsW9OvX\nL9f17bFjx3B0dOTTTz+le/fuHDt2rGg/FPHey3cG8/Pnz5k1axa2trYYGBgUZUxCCFHodHV1OXDg\nAL1796Z///7s3LkTTU1NypUrx549e+jTpw/79u1T7rorilZMQvxb29x69vY2QghR2mRnZzNy5Ej6\n9OnDmjVr0NTU5MKFC4wbNw59fX26du0KwP79+7G0tOTMmTPExsZSvXp1lfMYGhoSHByMubm5siwh\nIYHLly/nmhiydu1aLC0tlW2WL1+Oi4sL+/fvR0dHh2XLlnHjxg327duHpqYm48ePZ+XKlUydOvUd\nfxqiNIq49oCNQVcBcHNqJstlCCE+aLt372bt2rUsWrQICwsLsrKy2L59O0OGDGHXrl0qbTdv3qz8\n84QJE6hfvz7jxo1TlsXFxQFw9uzZXJvrvqZQKOjSpQtr1qwBICsri5CQEKZMmcLq1auxtLTk3r17\nTJ8+nfXr19OmTRvOnDnDuHHjCAwMpHbt2oX9EYgSqChydb7TxbZv306/fv1kcFkI8d7Q1tYmKCiI\nnJwcnJycSEtLA8DS0pJt27bx2WefcfHixWKO8sOTnZ2tXHP5TX5JiFdu/ieEEO+LhIQE7t+/T/fu\n3dHUfLU+UKtWrZgyZQqZmZnKdgEBATg6OtKtWze2b9+e6zy2traEhISoPKVz+PBhOnfuzJv29DYy\nMmLBggUoFAoCAwMBCAkJYdKkSVSsWBFDQ0PGjh1LUFBQYXVZvEf8j95ise8Fnj5/ydPnL1nsex7/\no7eKOywhhCgWqampLF26lEWLFmFpaYm6ujqampoMGzYMZ2dn7t69W+irAeTk5KjkeXV1dezs7Bgx\nYoRy0PnBgwf069ePNm3aANC+fXtq1arFtWvXCjUWUTIVVa6W3a6EEB8ULS0tdu/ejZ6eHj169CAl\nJQV4dWHu4+ODg4OD8vElIYQQ4l0rX748rVu3Zvjw4axbt45z586RkpJC3759sbe3B+Dq1av85z//\noVOnTvTv35+goCBSU1NVzlO/fn0qV65MeHi4suzAgQP06NHjrTGoqanRrl075U3WrKwstLW1lfUK\nhYKEhASeP39eGF0W7wn/o7fYcSQmV/mOIzEyyCyE+CBdunSJrKwsOnTokKvOw8MDW1vbN970zc8/\nOaZDhw7cvHmTtLQ02rdvz/Tp05V1sbGx3LlzBxMTk799XlG6FGWulgFmIcQHp0yZMmzfvp3KlStj\nb29PUlISAL169WLVqlXY2tpy+/btYo7yw6GmpkY9w0pvbVffqBJqb1inWQghSqvNmzczePBgIiMj\n+fzzz2nTpg0eHh48e/YMgD179vDZZ5+hrq5O48aNqVGjBvv37891nu7duxMcHAy8eqz26dOnNGvW\nrEAxGBgYKAeQO3fujLe3N0+fPuXp06f4+PgA8PLly8LorngPRFx7mOcF62s7jsQQce1hEUYkhBDF\nLyEhAX19/UK/ZrG0tKRVq1Yqr5iY/H8Hw6u8npOTk+vmcHx8PJ9//jlOTk40aNCgUOMUJUtR5+p8\n12AOCwujbdu2aGlpFdqbCSFESaGhocH333+Pm5sbtra2HDp0CAMDAwYOHEhKSgo2NjacOnWKGjVq\nFHeoHwQTo0pcTHnzMhkNCjAILYQQpZGmpiZDhgxhyJAhpKenc/HiRby8vJgxYwbLly/n4MGDlClT\nhr179wKQnJzMtm3b6N+/v8p5unfvzoYNG0hLS+PgwYM4OjoWeNZTQkIChoaGAMyYMYNFixbh6OiI\nsbExzs7OnDlzBn19/X/d14SEBOXA+Wt/3WBQlA4bg6IL1EbWYxZCfEgqVKhAYmIiWVlZqKurq9S9\nePEi33WU3+bUqVN/+9iEhATU1NRUlr39+eefcXNzo3Pnznz11Vd/61ySu0ufos7V+Q4wT5gwgcOH\nD1OlShWsra0JCAjAyMioUN5UCCFKAnV1dTZt2sSECRPo0qULR44cwcjIiBEjRpCcnIy1tTWnTp3i\n448/Lu5Q33smRpXJib2CIp+7/TnZ2ZgYVS7iqIQQ4t07dOgQGzduVM5I1tTUxNzcnPHjx7NgwQIO\nHjxI7dq1lbOIAVJSUnB0dOT8+fO0bt1aWV6lShUaNWpEaGgowcHBrF+/vkAxZGdnc+bMGb744gsA\n/vOf/+Dp6cmSJUsAOHLkCLVq1SqUiSfbtm3D29v7X59HCCGEKGnMzMwoU6YMYWFhdO7cWaVuxowZ\n6OnpKddBftfCw8MxNTVV5u5Tp07h7u7OuHHjGDp06N86l+RuURD5DjCXL1+eefPm0bhxY+7fv8+3\n336Lrq5unm3/usulEEKUJmpqaqxbt44pU6bQuXNnQkJCqFChAhMmTCA5ORkbGxvCwsL46KOPijvU\n91qDajVYiB0xCY+49SyeXxJezWaub1SJBoaVMDGqTINqMptcCPH+adeuHQsWLGDFihUMGzYMIyMj\nfv/9d7Zt24aVlRW7du2iR48elC9fXnlM+fLlsba2Ztu2bSoDzPBqFvP69evR19enevXqJCcn53rP\nv85qfvLkCStWrEBbW5uePXsC8N1335Gens6iRYt49OgR3t7eDBgwQOX4+Ph4lfOULVuWsmXLvrW/\ngwcPpnv37ipljx49+tsXu6J4uTk1Y7Hv+be2EUKID4mWlhbu7u7MmTMHdXV12rdvT1paGr6+vkRE\nRLBz506uXr36t8/7d9ZgTk9P58iRI/zwww+sXbsWgNu3bzNhwgQWL16s3N/h75DcXToVda7Od4D5\n66+/ZtOmTZw+fRqAc+fOUaZMGZU2OTk5KBQKGWAWQpRqCoWC5cuXM2vWLDp16kRoaCiVKlXiyy+/\nJCkpia5du3LixAnlo8Oi8CkUCkyq18Skek3g1Ww6QNZcFkK89wwNDdmxYwerV6+me/fupKSkYGxs\nTM+ePbGysmLnzp1s3Lgx13GfffYZbm5uuR5RtbW1ZeHChcycOVNZ9r871k+cOBE1NTUUCgX6+vpY\nWFjg5+ennOU0ZcoUZsyYQbt27dDR0cHZ2ZkhQ4Yoz5WYmIilpaXKOUePHs3EiRPf2l8jI6NcT0X+\n7zWGKPnMm1bB2dYk37UdnW1NZHkMIcQHydnZGX19fby9vZk6dSoKhYLmzZvj5+dH3bp1uXbtWq68\n/Dbt27fPVdaiRQu2bNmCQqEgNDQUMzMz4NUgd/369Vm3bh3m5uYA+Pn5kZ6ezsyZM1W+H8yYMYO+\nffu+9f0ld5dORZ2rFTkFuBXSuXNnAgICMDY2LrQ3Lk5xcXFYW1sTGhpKtWrVijscIUQJkZOTw4IF\nC9ixYwehoaFUrVqVnJwcJk+eTGRkJEePHqVcuXLFHaYQQgjxXpHv5qVXXrvTD7IzYUAX2ThKCCHe\nZ5K7S4+iytX5zmD+q7179/Lzzz8rN/9o1KiRzOQTQrx3FAoFc+bMQUtLC0tLS0JDQ6lRowarVq1i\n1KhR9OjRg0OHDv3jzRmEEEIIId4nA7s2oGYV/f9uJKRgdG9T2jaRmctCCCFESVFUufqNA8x//vkn\nixcv5siRI2RmZv7/QRoa2NraMnPmzPdmVrMQQrw2ffp0tLW1lYPMderUYePGjbi6utK7d2/27t1b\nKBsdCSGEEEKUduZNq8hyGEIIIUQJVhS5Ot/FLZ8/f87gwYO5e/cuy5cvJzw8nKtXrxISEsKyZcuI\niYnB2dmZpKSkdxqgEEIUh4kTJ+Lp6UmnTp24desW6urqbN26FW1tbZydnVVuugkhhBBCCCGEEEJ8\nqPIdYPbx8cHAwIDdu3djZ2fHRx99hKamJtWrV8fe3p69e/dibGzMpk2bijJeIYQoMm5ubsyfP5/O\nnTtz48YNNDQ08Pf3JyUlhWHDhik3ohNCCCFKOhcXF7Zv305QUBC9e/d+a/uQkBD69OmjUta5c2ea\nNWuGmZkZzZs3x8LCgiVLlpCVlfWuwhalQMS1BwyZd5gh8w4Tce1hcYcjhBCl0qlTpxgyZAht2rSh\nTZs2jBgxguvXrwPg6elJkyZNMDMzw8zMjJYtW+Li4sLFixdVzrFp0yY6depEy5YtGThwIDdu3FDW\nPXr0iC+++IJPP/0US0tL/Pz8irR/ongVRa7Od4D5yJEjjBs3Dk1NzTzrNTU1GT9+PIcPH34ngQkh\nREkwbNgwvLy8sLGxITo6Gi0tLQIDA4mNjWX06NEUYJ9UIYQQosR42871GRkZfPvtt3h4eORZv3bt\nWi5fvsyVK1fYt28fp0+flovUD5j/0Vss9r3A0+cvefr8JYt9z+N/9FZxhyWEEKXK7t27mTFjBsOH\nD+fs2bOEh4djYWHBkCFDuHPnDgqFAldXVy5fvszly5c5e/Ys3bp1Y+TIkfz882n+JSsAACAASURB\nVM8ARERE8N1337F161aioqKwsrJi4sSJwKvN7MeMGUPdunU5f/48W7ZswdvbmytXrhRnt0URKapc\nne8Ac3x8PHXq1HnjwTVq1CA+Pr7QgxJCiJLE2dmZdevWYWtrS1RUFLq6uhw4cIDo6Gjc3d1lkFkI\nIcR7Y968eZw6dYphw4a9Nb9VqFCBjh07cvPmzSKKTpQkee1KD7DjSIwMMgshRAGlpqaydOlSFi1a\nhKWlJerq6mhqajJs2DAGDRrE3bt3AVRysqamJs7OztjZ2bFx40YAdHV1AcjMzCQrKws1NTXl5vTR\n0dE8fvyYKVOmoK6uTt26ddm5cyc1a9Ys2s6KIleUuTrfTf4qVKjAb7/9RpUq+S8C/ccff1CpUqVC\nDUgIUbLExcXh7u5OnTp1uHHjBgYGBgA8e/aMYcOG4eTkRFBQEGvXrqV69erK44YPH07btm1xd3fn\nxYsXlClTBi8vLypUqFBcXflX+vTpg5aWFg4ODuzbtw9zc3N++uknOnfuzJw5c1iwYEFxhyiEEEL8\naxMmTKBixYoEBQVx+vTpXPV/vcCNjY3l9OnTjB07tihDFCVAxLWHeV6wvrbjSAw1q+jL5n9CCPEW\nly5dIisriw4dOuSqc3d3B+DkyZN5HtuhQwcWLVoEQLNmzXB2dsbBwQF1dXX09PT44YcfALhx4wb1\n6tVj2bJlHDhwAD09PUaPHk2vXr3eTadEiVDUuTrfAebOnTvzzTff0Lp1a9TV1XPVZ2RksG7dOrp2\n7VoogQghSiaFQqF8nHbatGlYWFgAkJiYiIODA05OTigUCnr06KFMgK/5+/tTp04dpkyZwp49e9iy\nZQvTp08v8j4UFkdHR7Zu3UrPnj3Zs2cPlpaWHD16FEtLS/T09PD09CzuEIUQQoh/pWLFim+snzx5\nMhoaGmRmZpKamkrjxo1p3bp1gc+fkJDAs2fPVMoePXr0j2IVxWdjUHSB2sgAsxBCvFlCQgL6+vqo\nqeW7wEC+DAwMSExMBODw4cPs3r2bwMBA6tWrh4+PD+PGjSM4OJjExEQiIyNp27YtJ0+e5Nq1a4wc\nOZJq1arRsmXLAsUoubv0Kepcne8A85gxY+jbty8uLi6MGjWKZs2aYWBgwOPHj4mOjmbdunVkZmby\nxRdfFEogQoiS6a8zlf7658ePH6Otra0sz+sxWm1tbWUiej2LubSzs7PD39+fPn364O/vj42NDSEh\nIcpB5vHjxxd3iEIIIcQ7s3r1aiwtLYFXuX3x4sUMHz6cffv2Fej4bdu24e3t/S5DFEIIIUqNChUq\nkJiYSFZWVq7JnS9evFAuc5GXhIQEjIyMANi/fz8DBgygcePGAIwbN449e/Zw9uxZNDU1MTAwYNSo\nUQCYmZnRtWtXQkNDCzTALLlbFES+A8zGxsb4+/uzcOFCxowZQ3Z2trJOXV0dOzs7Zs6cib6+fpEE\nKoQofl5eXmzcuJEHDx5Qp04d1qxZo6w7ePCgcpMAY2Nj1qxZQ5cuXfDx8cHBwYHExES2b99eXKEX\nKmtra4KCgujduze+vr7Y29tz7NgxOnbsiJ6eHsOHDy/uEIUQQoh3rly5cgwbNowePXrw9OlTjI2N\n33rM4MGD6d69u0rZo0ePGDp06DuKUrwLbk7NWOx7/q1thBBCvJmZmRllypQhLCyMzp07q9TNmDED\nPT09laeK/yo8PFz5FJG2tjYvX75UqVdXV0dDQ4PatWuTlZVFdna2cqZ0VlZWgWOU3F06FXWuzneA\nGV49Ird27Vr+/PNPrl+/TmJiIgYGBjRt2rRAXyCFECVbZGQkkyZNom7duigUCl6+fImjoyODBw9m\n165dHDhwgPT0dO7cuUO5cuWUS2Rs2LABHx8f5s+fj6amJllZWdja2uLp6Ymnpyc///wzLi4u3Lt3\nj7Jly7J582YuX76Mvb09LVq0AGDgwIHY29sX8yfwz3Xo0IH9+/fTs2dPNm3aRK9evTh27BhWVlbo\n6uoyYMCA4g5RCCGEyFNmZibx8fEqTx8ZGhoqn0x6k78ek5qaqtwkqKDXBkZGRsrZVq+9D084fWjM\nm1bB2dYk37UdnW1NZHkMIYQoAC0tLdzd3ZkzZw7q6uq0b9+etLQ0fH19iYiIYOfOnWzevDlX/t2z\nZw+hoaH4+/sDYG9vz8yZM7G3t6d+/fr4+fmRnZ3Np59+ipqaGtra2nh7ezN27Fiio6M5duwYvr6+\nBYpRcnfpVNS5+o0DzK+VL19e+SicEOL9oVAoaNeuHStWrAAgPT0dOzs7jI2NOXv2LFu3buXhw4eM\nGzeOqKgoevfuTUxMDCEhIfTt25f4+HjWrFmDu7u7cvayQqFQDkR7eHhgbW1NlSpV+Omnn9DV1cXP\nz684u1yo2rZty6FDh3BwcODly5f079+fw4cP06VLF3R0dOjZs2dxhyiEEELkcuvWrVzf7RcuXEif\nPn2UP+c3W2rixImoqamhUChQV1fHzMyM9evXv/OYRckzsGsDgFwXroPsTBjQpUFxhCSEEKWSs7Mz\n+vr6eHt7M3XqVBQKBc2bN8fPz085GczPz4+dO3cCoKurS9OmTdm6dSv16tUDwMbGhidPnjBp0iSe\nPXtGw4YN2bx5M7q6ugD4+fkxf/582rVrR9myZZk9ezampqbF1mdRNIoyVyty8lo49T0XFxeHtbU1\noaGhVKtWrbjDEaLYREZGsnnzZtTU1EhNTeX58+c8ePAAExMTPv/8cwICAnj06BG//PILH330ETNm\nzODEiRMEBwcTERHBZ599xtSpU3n69ClXr17l3r173L59G2NjY9auXYumpiZubm7cv3+fly9fUrZs\nWerWrUvNmjWVj/u8D65evYqdnR1Lly7FxcWFqKgo7O3t2bZtm2yEKoQQQryFfDcv3SKuPfzvRkIK\nRvc2pW0TmbkshBDvO8ndpUtR5OoCzWAWQryfUlJSOHPmDE2aNEFHR4fy5cujra3NnTt3WL58OQsW\nLFDe1Vy0aBGXLl3i/v37aGhoUKZMGTZs2MCMGTMAuHv3LgsWLCAkJIQLFy4wcOBA6tatS3x8PBER\nEQQHB3Po0CGcnZ355Zdf8Pb2Zvr06cXZ/UJjampKaGgoXbp0IT09nREjRhAUFMRnn31GYGAgHTt2\nLO4QhRBCCCHeCfOmVWQ5DCGEEKIEK4pcLQPMQnzALl68SPXq1dm9e7eyLCUlBVdXV3R1dVUemWnX\nrh3169dn48aNREdHA1C9enX8/PxIT0+na9euWFpacuzYMXr37s2vv/6Kl5cXT58+RUtLiy5duhAW\nFoa2tjY2NjYsXLiwyPv7LjVs2JATJ05gY2PDy5cvGTNmDP7+/vTp04eDBw8qN18QQgghhBBCCCGE\neJ+oFXcAQojik5iYqFyT6TVdXV2aN29OfHy8cmfZe/fuMW/ePHR0dBgwYAApKSk8fvxYeUxERAQa\nGhpoaGjw4sULdu/ezbhx41AoFJQvXx6Anj17Eh8fT7t27YiIiKBJkyZF19EiUq9ePU6ePMny5ctZ\ntWoVNjY2fPfddzg6OnL16tXiDk8IIYQQQgghhBCi0P2tGcz3799nwYIFXLp0iZycHMzMzJg5cyY1\natR4V/EJId6hChUqkJaWplIWGxuLra0t58+fx9nZmTJlypCVlcXcuXOJjo7GysoKPT09PD09ycjI\nIDU1lcqVK7N27VrOnTvHuXPnmDx5MjVr1gQgOzsbLy8vqlatSkZGBi4uLlSsWJH58+cDcPr0aWrV\nqkXVqlWLuvvvRK1atQgLC6Nz586kpaXx5Zdfsm7dOuzs7Dh+/DgmJibFHaIQQogS6tSpU2zZsoWY\nmFcbsTRp0oTJkyfTpEkTPD09MTIyynN5qaCgIGbOnIm2tnauunXr1mFhYQHAr7/+ire3N5GRkbx8\n+ZJPPvmEkSNHYm9vn+u4gIAAli9fzrlz54BXay3a2Nigo6MDQE5ODsbGxtjb2zNhwgQ0NTUL7XMQ\npUfEtQdsDHp1E93NqZkslSGE+OCZmJhw8OBB6tatC0B6ejoTJ04kLi6OLVu2cPr06Vw5u3r16ri4\nuNC3b1+V82hra3PmzBmVvYsyMjKwsLBAT0+P48ePq7z3nTt3cHJyIigoSPn+r61evZqNGzeye/du\n2dzvA1QU+fpvDTBPnz4dGxsbpk6dSmZmJvv27WPy5MkEBQUVemBCiHdvxIgRHDt2jNjYWKpXr05G\nRgZLly6lXbt2aGhoMHPmTExNTcnJyWHhwoXo6OhgZWWFpqYmW7ZsUTnXuXPnWLx4MQcOHKBKlf//\nZTVnzhy0tLT44Ycf8tyNPiIigh49etCjRw+mTJnyXsxsrl69OmFhYVhbW5OWlsZXX31FamoqXbt2\nJSwsjFq1ahV3iEIIIUqY3bt3s3btWhYtWoSFhQVZWVls376dIUOGsGvXLhQKRZ559LXGjRsTEBCQ\nb31MTAwuLi6MHTuWhQsXoqury+nTp/Hw8CA9PZ1evXop28bGxvL1119TpkyZXOc5e/ascpD5119/\nxdPTk+nTp7Nq1ap/0XtRGvkfvaWyK/1i3/M425ood6wXQogPXVpaGmPHjiUpKYnt27ejr68P5M7Z\nERERuLu7k5mZycCBA5XlOjo6hIaG0qNHD2VZeHg4mZmZub4TpKenM23aNDIyMnLFkZWVRVBQEH37\n9mX79u0ywPyBKap8ne8SGV9//TVPnz5VKXv06BH29vbUqVOHBg0aYG1tzf379ws1ICFE0Slbtixf\nf/01s2bNwsXFhf79+2NiYoKzszNr1qzB29sbFxcX+vTpg0KhYNKkSQA8e/aM3r17K1/BwcEsWbKE\nzMxMpk2bhouLC3PnzuXnn38mMDCQX375BVdXV1xcXDh27JhKDFOnTuXOnTvUq1cPGxsbHBwcOHny\nJDk5OcXxkRSajz/+mLCwMPbu3cuXX36Jq6srnp6eWFtbExcXV9zhCSGEKEFSU1NZunQpixYtwtLS\nEnV1dTQ1NRk2bBiDBg3i7t27AG/MjW/Lm0uWLKFv374MHTpUuTyWhYUFM2fOVMlLWVlZTJs2jQED\nBrz1nLVr12blypUcOXKEX375paDdFe+B/71YfW3HkRj8j94qhoiEEKJkSUlJYdSoUeTk5ODr66sc\nXIbcOdvc3Jzp06ezbt06lXJbW1uCg4NVyg4cOEDXrl1znWPt2rW0a9cuz9x94sQJypcvz9ixYzl6\n9GiusT7x/irKfJ3vDOaPP/6YAQMGYGtry8iRIzEwMGDkyJE4ODhQq1YtsrOzuXPnDpMnTy7UgIQQ\nRatx48Zs3bo1V3n16tXx8fHJ85jr16/nKnNwcMiz7c2bN98ag7GxMTNnzsTDwwM/Pz+++OIL9PX1\nmTp1Kk5OTmholM79SCtWrMiJEyfo2rUraWlprFq1iuTkZGxsbAgLC6NSpUrFHaIQQogS4NKlS2Rl\nZdGhQ4dcde7u7gCcPHnyH58/PT2d8+fP5/m9/a+zogB8fHyoX78+HTt2fOOM6NeqVatGzZo1uXjx\nIvXr1//HMYrSI+LawzwvVl/bcSSGmlX0ZbkMIcQH68WLF4wYMYKXL1+ya9euPJ8I+l8WFhZ4enry\n66+/Urt2bQC6devGF198wbNnzzA0NCQpKYmoqChmz55NZGSk8tioqCjOnj3Lrl272Lx5c65z7969\nm969e1O5cmXatGnD7t27cXNzK7wOixKpqPN1vjOYXV1d2b9/P4aGhvTp04c1a9bg4ODAwYMHGTly\nJG5ubgQHBzNkyJBCCUQIIR4/fkz79u25efMmM2fOZM2aNdSvXx9vb2+Sk5OLO7x/pHz58oSGhnLu\n3DnGjBmDh4cHAwYMoEuXLnLnWAghBAAJCQno6+ujpvbP99+OiYmhVatWKq9OnToBr548er1m8ptc\nv36dgwcP4unp+beeJDIwMOD58+cFapuQkMC9e/dUXrGxsQV+L1H8NgZFF0obIYR4X7m7u6Orq8vt\n27e5du1agY4xMDAAIDExUVlmbGxMq1atOHr0KAAhISHKJStfS0pKYtasWfkubfXw4UMuXLigvKE8\ncOBAdu7cSVZWVoH7I7m7dCrqfP3GaYHa2tqMGDGCAQMG8MMPP9CrVy+cnJwYOnSoyiLjQghRGH7+\n+WeGDh1K48aNmThxIqdOnSIyMhIvLy/mz5/P6NGjGTduHB999FFxh/q3GBoacvToURwcHBg5ciQ+\nPj4kJSVhZ2fHsWPHVB6XEkII8eGpUKECiYmJZGVloa6urlL34sUL5ZrHb2JiYkJgYGCedYaGhmho\naPDkyRM++eQTlbr09HSysrJQKBR4enqyYMGCAr3fXyUkJGBkZFSgttu2bcPb2/tvnV8IIYQoTayt\nrZk1axYrV65k8uTJ7N279603eRMSEgBU8qlCoaB79+4EBgbSr18/Dhw4wJgxY3jx4oWyzYIFC3By\ncqJ+/frKm8N/vUkcEBBARkaGckPfnJwcnj59yrFjx7C1tS1QfyR3i4J44zSJ27dvc/jwYe7evcvo\n0aPZt28fWVlZ9OjRAx8fH1JTU4sqTiHEe+zZs2ds2LCBFi1a8PvvvzN06FDmz59P/fr1OX/+PL6+\nvoSHh/Pw4UPq16/P6NGjuXPnTnGH/bfo6+tz+PBhZf+WLFlCy5Yt6d69e6mdnS2EEKJwmJmZUaZM\nGcLCwnLVzZgxg1mzZv2r82tqatKmTRvlDKi/2rVrF927d+f69evExcXxxRdf0KpVK9zc3EhMTKR1\n69Y8evQo33PHxsby+++/06pVqwLFMnjwYA4fPqzy8vX1/addE8XAzalZobQRQoj31YABAwCYOHEi\nlStXZurUqW99Mig8PJyKFStSs2ZNlXIbGxuuX7/OjRs3iI2NpWXLlir1hw8f5ttvv6VVq1a0bt1a\n+f7BwcFkZWURGBjIsmXL+PHHH5WvoUOHsm3btgL3R3J36VTU+TrfAWYfHx8GDhyIr68vo0aNYvbs\n2ZQrV44JEyYQGBjI8+fPcXBw4Lvvviu0YIQQHyY1NTXOnz9PvXr1GDZsGDVq1CAyMpJt27Zx7tw5\natWqhbe3N1OmTCEmJoby5ctjbm5O7969VdaeKun09PQ4ePAgf/75J87OzqxatYpatWrx2Wef8fLl\ny+IOTwghRDHR0tLC3d2dOXPmEBYWRmZmJklJSXh7exMREcHIkSPJyckhJSWFR48eqbyys7ML9B4e\nHh7s2bOHrVu3kpycTEZGBkePHmX16tWMHz+eli1bcuXKFS5cuMCFCxfYtGkTBgYGnD9/nsqVKyvP\n89cL5Fu3buHh4UGvXr2oVatWgeIwMjKiVq1aKq/q1av/vQ9MFCvzplVwtjXJt97Z1kTWXxZCCEBd\nXZ0VK1YQHR3NN998k2ebrKwswsLCWLVqFZMmTcpVr6enR6dOnZg2bZpyFvJfRUdHK3P3hQsXgFc3\njx0cHDh16hRpaWnY2tpSvnx5ypcvT4UKFejfvz8XLlwo8Aa9krtLp6LO1/kOMG/ZsoXvv/+enTt3\n8tNPPxEUFKRcW83Q0JApU6awZ88e/vOf/xRaMEKID5O+vj7ff/899+7do1WrVnz++eeYmppy8eJF\nNm3axNWrV9HX18fCwoJhw4bRoUMHfv31VywtLenfvz8dO3bkwIEDBb7ILk46Ojrs27ePly9f0q9f\nP9avX4+hoSH9+vUjIyOjuMMT70h2dnap+PcphCg+zs7OeHp64u3tTbt27bC2tubatWv4+flRt25d\nFAoFu3btolOnTsqXlZUVcXFxKBQKbt68iZmZWa7Xhg0bAGjUqBG+vr6cOXMGGxsb2rZty7fffsvi\nxYvp1atXrnhycnJQKBS5ytu3b4+ZmRktW7Zk4sSJdOrUiUWLFr3zz0eULAO7NsjzonWQnQkDuzYo\nhoiEEKJk+N/cWa1aNebNm8eGDRuIiIjIlbPbtWvHN998w4wZM3BycsrzPI6Ojvz6668qG/PmlaP/\nt3zPnj3Y2dnlWn6rZs2aNG/enO3bt/+rvoqSryjztSInn3n6tra2ODk5YWdnx9WrV5kzZw6RkZEq\ni4mXVnFxcVhbWxMaGkq1atWKOxwhCtXSpUu5fv06T548IS0tjWrVqmFsbMyRI0dwd3dn1KhRyrZu\nbm4kJyfj5+eHi4sLaWlpaGtrK+tHjhxJnTp1sLGxeeOxUVFRLFu2DIVCQatWrZgyZQoAX3/9NVFR\nUZQpU4YJEyZgbm6ucjzAxo0bc/UhJyeHsLAwNm7cyJEjR+jduzdubm40btwYf39/1qxZQ3p6OhMm\nTGDgwIEcPnyYZcuWkZqaypQpUxg8eDBaWlqF/tkWpvT0dJydnUlOTmbnzp0MGjSIcuXKsW3btlxf\nAETpk5OTw62434lJeERMQjy3n8UDUM+wEiZGlTAxqkyDajXy/WIohBAfEvluXnpFXHv43w2CFIzu\nbUrbJjJzWQghPgSSu0uXosjX+W7yt3TpUubPn8/69ev5+OOPWb58+XsxuCzE+2769OkA7N27l3v3\n7uHu7s79+/e5efMmISEhykHihIQE/vjjDypUqKA8dtmyZbkecY2Li+OTTz5547GLFy9m3bp1VK1a\nFVdXV27evEl8fDx37twhICCAhIQEBgwYwKFDh1BXV+fBgwekpqaSlZVFbGxsrsdrFAqFcnZWfHw8\n33//PX379qV8+fKMHj2aM2fOcPHiRdasWcPs2bMZOnQoAQEB3Lt3Dy8vL2bPns348eNxc3Mr8KZD\nRU1TU5OdO3cyZMgQnJyc2L17N/369WPUqFF8++23qKm9cYl8UcLdivudWVcPo3j99/jf9HkxJZ6L\nKfHkxF5hIXaYVK9ZbDEKIYQQ/5Z50yqyHIYQQghRwhVFvs53BKN58+YEBQURHR3NTz/9hLW19TsN\nRAhR+P66i6yRkRHly5fn7t27APz000/Y2dmprKWY1wMNCoXircfu2bOHqlWrkpycTFJSEnp6ety5\ncwcLCwvg1ZpNBgYG3L59G4DAwEBsbGzo0aMHO3bseGMfKlWqhKenJ3fu3GHBggXs37+fGjVqEBAQ\nwPz584mKigKgVatWeHt74+npyU8//cTNmzepU6cO7u7u/PHHH//mY3xnNDQ0+OGHH/jkk0/o1asX\nfn5+xMTEMHHixLduAiFKtpiER/8/uJwHhZoaMQn5b5olhBBCCCGEEEKUFv9oitzBgwdJTk4u7FiE\nEO+Yg4MDhw4dAuD48ePY2Nio1E+fPh0XFxfl6+nTp8qBzjcdq66uzpUrV3B0dOSjjz6iUqVKNGzY\nkPDwcDIzM4mNjeX27dukpqaSnZ3NwYMH6dGjB/b29vz0008F2uBOXV2dbt268eOPP3LlyhWMjY2x\ntbXFxcUFMzMzfvnlF7p27cro0aNxdXXF0tKSyMhI1NTUaN68OYMHDyY6OrqwPspCo66uzpYtW2jc\nuDFOTk7s2LGDs2fP8uWXX8ogcykWkxD/1ja3nr29jRDi/TJ16lSaNGmisodJUFAQvXv3VmkXExND\nu3btWLp0qUp5QkIC1tbW3LlzR1nm4OCgsuZy06ZNMTEx4fHjx8Cr7+3W1taYmZnh5ubGn3/+mSuu\nJ0+eYG5uzsmTJ5VliYmJjB07lpYtW2JlZUVAQICyLiUlhblz59KuXTssLCxYvnw5WVlZ/+qzEUII\n8eGKioqib9++tGzZki5durBr1y4A7t69i6urK61atcLCwoKVK1fmukbKzs5m3LhxudYT/if578GD\nB7n2MmjcuDG2tra5jl2zZk2u/A2wc+dOevbsqVxfecKECfluphcSEkKfPn1Uyn7++WcGDhzIp59+\nSo8ePQgLC1PWeXp60qRJE5X4zM3NmTZtGqmpqcCrJ5BNTEyUP7u4uOT6bHx8fGjVqhUXL17MMy4h\n/ol8B5jT09Pzfc2ePZtHjx4pfxZCvFs+Pj4MGzYMFxcXXF1duX79Op6envTo0UNlQDgwMBCAJk2a\nsGnTJg4cOMDAgQMZN24cL1++xNramk2bNtG3b19u3rzJnDlzuH37NsnJybx8+ZL09HT8/PyoWrUq\nhoaGGBsbK2P4+uuvOX78OFu2bCEqKoo5c+Zw8+ZNXFxcePDgAc2bN2fRokVcv36drl27cubMGVq1\naoWLiwvffvstjRs3xsjIiPDwcJKTk/Hw8GDSpEnk5ORw4MCBv/V5VK9enXnz5vHbb78xefJktm7d\nSsOGDbl37x779+/Hy8uLoKAg2rdvj6amJuHh4TRt2hR7e3u6du1KSEhIiRq8VVNTY8OGDbRt2xYn\nJyf8/f05ePCgbJpUSmVnZyvXXH6TXxLiZeM/IT4giYmJnDp1im7durFz5858212/fp0hQ4bg6uqq\nXPYKXl18Ozs78+DBA5X2wcHBXL58mcuXL3Pp0iVatGiBm5sbH330ETExMXz11VesWrWKc+fOUaFC\nBb788stc7zlz5kwSExNV1oWfPXs2ZcuW5ezZs6xZswYvLy/ljdply5Zx48YN9u3bx8GDB4mOjmbl\nypX/9iMSpVDEtQcMmXeYIfMOE3HtYXGHI4QohRITExkzZgxDhw4lKiqKNWvWsHLlSiIiIpgzZw6N\nGjUiMjKSwMBADh06xI8//qg89v79+7i5uXHs2DGVc/7T/Pfxxx8rc+rly5cJCQnB2NiY2bNnqxx3\n5coVNm/enGs/lblz57J161Y8PT05f/48R48exdTUlIEDB3LlyhVlu4yMDL799ls8PDxUjk9KSmLU\nqFG0b9+eyMhI5syZg4eHB7du3QJePV3s6uqqEuOuXbu4evUq69evL9DnvXbtWnx9ffHz8+PTTz8t\n0DGidCuqXJ3vALOpqSnNmjXD1NQ01ys1NRUHBwdlGyHEu3Pnzh2OHz/O999/j5+fHzNmzGDmzJko\nFAqmTZuGn5+f8vX6DqqhoSFffPEFjo6O+Pv7Y2dnx+PHj9HV1UVDQ4MqVarg5eXF0qVL0dbWJigo\nKFdyvHjxokryVigU1KpVi8OHD9O5c2eWLl1Kw4YNcXR0pF+/fiQmJjJzLI9UigAAIABJREFU5kx6\n9epFv379uHfvHn/++Sf+/v6MHj2axMREatasSUBAAIsWLWLz5s1s3ryZVatWvXWZjPyUKVMGJycn\njh49ytmzZ4FXu9svX76cESNGcPLkSZKSkujQoQOXL19m+/btDBgwgEmTJtGiRQt27NhBRkbGP/yb\nKVwKhYJVq1ZhY2NDnz592LlzJz/88AOrVq0q7tCEEEIUgn379tGqVSucnZ3ZvXs3mZmZudpcvnyZ\nESNGMGnSJOVmuPBqcPl12ZtukG7dupWkpCQmTpwIwIEDB7CxscHU1BQtLS2mTJlCeHg4T58+VR7j\n7++Prq4ulStXVpYlJycTGhrK+PHj0dTUxNTUFEdHR/bt2we8mnE1adIkKlasiKGhIWPHjiUoKOhf\nf0aidPE/eovFvhd4+vwlT5+/ZLHvefyP3irusIQQpczDhw+xsrLCwcEBgEaNGtGmTRsuXbpE2bJl\nyczMJCsri5ycHNTU1NDR0QFeTYp0cnLCxMQEMzMzlXP+0/z3v+bMmYO9vb1y6Ud4lSNnzpyJs7Oz\nSk6Ojo5m7969+Pr6Ym5uTpkyZShbtiwjR45k6NChzJs3T9l23rx5nDp1imHDhqmc4/WM4nHjxqGh\noUHLli2xtrZW5t+8fPLJJ1hZWSmXo3wTLy8v9u3bh7+/PyYmJm9tL0q/oszV+Q4wr1ixAkNDQ1q2\nbMnmzZvZunWr8qWlpYWXlxdbt27F19f3nQQmhHilXLlyPHz4kICAAOLj4zExMWHPnj1A3msm/9Xr\nQeP4+Hg0NF7t6amjo8Ply5cxNzcHICsrCz09PQD++OMPXFxcCA8PR19fnyVLlvDnn38qz+Po6Mhv\nv/3Gxx9/rDz/s2fPaNu2LcOHD+fp06c8evSI4cOHY25uTlhYGP3792fy5MksWrSIJ0+ecO3aNTp0\n6KCMsUWLFrx8+VLlju4/UbduXZYtW8Yff/zBkCFDWLVqFV26dFHOmjb/P/buOyyqa2vg8G/oTYpB\nkQgIGgEFjYi9xBYsAUtQE4Ni14AlROxdscdoLNjitaKxFyI2Yo9KBDVG5IoVkQ4qRRAFZub7g8v5\nHKkmIhr3+zzzwOzT9hzKnrNmn7WaN2fo0KGsWbOGCRMmMHPmTNatW8cnn3zCsmXLyMzM/EfHfxNk\nMhkLFy7kyy+/5KuvvmL79u0sX76cn3/+uaK7JrwGNTU1ahublbqerYmZKOYoCB+QvXv30rNnT5yc\nnDAxMeHo0aMqy0NDQxk8eDBeXl588803KstsbW05deoU3bt3L3b/6enprFq1ipkzZ0rjdlRUFLVq\n1ZLWMTY2xsjIiPv370vLN2/ezKxZs1T2FR0djYaGhkpVeGtra2k7uVyOjo6OtEwmk5GamkpGRsZr\nnBHhfbYj+Ba/HI8s1P7L8UgRZBYE4bXY29urpIRKT0/n8uXL1KlThxkzZnDy5EkaNGhA27ZtcXZ2\nltJVaGpqcuTIEXx9faVr3QJ/d/x7WUhICH/++Sfff/+9SvuCBQvo3r17oQDt6dOncXZ2xsys8HVA\njx49uHnzpnQX0nfffUdAQAA1atRQWU+hUKiMr5A/xkZHRwP51/+vxgAiIiI4fvy4dH1fFKVSydy5\nc/nll1/45ZdfCh1X+Hd622N1sVe2rq6uHD58mKpVq7JgwQI0NTVp2rQpTZs2lXKaFjwXBKH8mJmZ\nsWbNGq5evUqfPn3o0qWLlCNq8eLFKikyCj61TEtLY//+/Zw/f5727dujo6PDsWPHgPwAc40aNRgw\nYAB+fn58+eWX9OjRgyVLlmBra0tAQACfffYZU6dOZerUqaxYsUK6lbddu3ZMnjyZw4cPM2vWLLKy\nsli/fj3Dhg1j7969mJmZMXLkSLS0tLh48SLNmjVj165d7Ny5k/r162NqasqZM2cKBdUOHz5MgwYN\n3sj50tHRoW/fvvz+++8cP36cJ0+e0Lp1a06dOsWyZcuYPn0627Ztw9vbm/bt27N27VouXLiAtbU1\nU6ZMITGxYguvyWQyZs+eTb9+/ejbty8BAQH4+fmxbdu2Cu2X8HrsTUoPMNuVIQgtCMK/w9WrV8nI\nyKBNmzYA9OnTRyUfYlxcHKNHj6ZevXocOnSoUAo6Q0NDtLS0SjzGL7/8QoMGDahfv77Ulp2dLc30\nKqCrq8vz58/Jy8tj4sSJTJ8+HSMjI5V1nj17VugCV0dHh+fPnwPQvn17/P39efLkCU+ePJE+CC1L\nTQXh/RcSnlDkBWuBX45HinQZgiD8LU+fPsXLywtHR0fatWuHt7c3HTp04OrVqxw+fJjLly9L+Zll\nMhkfffRRkfv5u+Pfy37++WcGDx6ssp+TJ09y//59hg0bVijQ+/jx42L7U7VqVSA/5/PLz1/l7OxM\nZmYmAQEB5ObmcuXKFU6ePKkyvm7fvp3GjRtL+aGnTp3KoEGD6N+/f7GvZcOGDVy9ehV9ff3XTk8p\nvJ8qYqzWKGlh5cqVWbp0KSdPnsTX15f27dvj6+v7RjsgCELJHj58SKVKlZg/fz6Qn5tx6NChODk5\nMWHCBJXbdQoYGxsTEBCAQqFg0qRJaGhoqAyMGzduLPVCVSaT0bVrV3777bdCKSy6du0q/S/4448/\nGD16NMHBwfzwww/MmjULLS0tateuXeoxypujoyMrV65kwYIF7Ny5k5kzZ/LkyRO+/fZbpk2bxi+/\n/IKHhwddu3Zlw4YNBAcHU6dOHXr16sXYsWMr9LahKVOmoKOjw4ABA9i4cSMDBgxAT08Pd3f3CuuT\nUHb2JtVQxlxDVswMZaVCgb1J8bfjCYLw77J7925SU1P57LPPAMjLyyM9PZ2IiAggPzC7fv166tat\ny5dffsncuXPx8/N7rWMcOHBAJWcz5AeFC4r8FMjOzkZPT4/Vq1djb2+v8j6i4GJZV1e3ULD4+fPn\n6OnpAflj1Lx58+jatSuVK1fGw8ODCxcuYGhoWGo/U1NTSUtLU2mr6A93hdezdn/pRZPX7v+L5vXM\n30JvBEH4t4iJicHLy4saNWqwbNkyIiMjuX//Pvv27UNTU5NatWoxfPhwduzYwddff13ivv7u+Fcg\nISGBsLAwlXSFjx49Yt68eWzevLlQekkAU1NTwsLCiuxPXFyctE5JDA0NWbduHfPnz8ff3x9nZ2fc\n3NxUxs1+/foxYcIEcnJyWLFiBcePH6dDhw5F9qmAubk569ev5+rVq3h7e+Pk5ESjRo1K7MvLxNj9\n/qmIsbrEAHOBDh060LhxYxYuXEjXrl3fmZylgvAhuHXrFrt27WLNmjVoampibW2NkZER6urqpabI\nUFNTY86cOXTv3p1GjRpJM6fKomDfs2bN4quvviIrK6vQMoBq1apJeSR///13NmzYgIaGBiNHjmTg\nwIGv8UrLT0Huq6FDh3L58mXWrl3LokWL6NSpE1u2bOHmzZt89913WFhYsHjxYqKjo/nss89o3rw5\n48ePp2XLliUO2OXF19cXbW1thg0bxtq1axk+fDg6Ojp88cUXb70vwuuxs6jBXDoTmZrIrbQkbqfm\nF/2zNTHDztgMe5Nq2FmIW9ME4UPw9OlTjh07xpYtW7CysgLyx9F58+axbds2mjRpQs2aNaULvaVL\nl9KnTx8aNWpEt27dynSMe/fu8ejRo0LjfK1atYiKipKeP3nyhPT0dGrWrMnUqVNJSUmRUnVkZmYy\nZswYRowYgYeHB7m5uSQkJGBunn/hERUVxSeffAJAcnIykyZNYsGCBQAcP34cGxsbtLW1S+3rtm3b\n8Pf3L9PrEgRBED4MERERDBs2jO7du0sflmppaaFUKsnNzUVTUxPIv74t+L4kf3f8GzZsGJCf7qJp\n06YYGxtL+7hw4QKpqalS3aPc3Fxyc3Np0qQJoaGhdOjQgQ0bNhAVFYWNjY1Kfw4ePIidnZ2UarI4\nOTk5aGhoSLO0Aby8vGjYsCGQPwms4FpcS0uLcePGER0djZeXF3v37i12gperqyv6+vq0bt0aT09P\nfH19OXjwIJUrVy71XIIYu4WyKVOAGfI/SZk/fz4XLlwgKChIytkqCEL5cnFx4d69e/Tq1Qs9PT2U\nSiUTJkzgxIkTLF68WCU/b9OmTRk1apTK9tra2sydO5fJkyfTpEmTQvu/dOkS33//PZaWlty7dw93\nd3fS0tJwdnbGwcGB3bt3M3nyZEaNGsWOHTs4f/48//3vf9mxY4eUA8rCwgJPT09cXV1xdXUlJSUF\nIyMjzp07p3LM6OhoRo0aVebbchQKBWvWrKFJkyY4Ozu/kXy1BXnlf/zxRwICApg8eTIKhQIfHx8+\n+ugj6U3ByJEj0dXVZcCAAVStWpUJEybQrVs31NXV/3EfXkdBypGRI0eybNkyBgwYwO7du2nXrt1b\n7YfwemQyGfaW1thbWgP5v8uAyLksCB+gwMBArK2tCxUg6tWrF97e3tSuXVul3cHBAV9fX2bOnImD\ng4NKDsniXLt2DQcHh0I5KN3c3OjXrx89e/bE0dGRpUuX0qZNG4yNjQvlgG7fvj0zZ86UgtQdOnRg\nyZIlzJ07l9u3bxMUFMT69euB/DuhcnJymDdvHomJifj7+9OnT58ynY9+/frh5uam0paYmPjOfCgt\nlM7L/VPmbw4tdR1BEISyePToEUOHDmXIkCEMHTpUarexscHOzo6FCxcybdo0kpOT2bRpE7179y51\nn/9k/IP8gn2vjtvdu3dXqYVw4MABtm3bxr59+4D8u2e/+uorvLy8mDVrlpTuYu/evWzbto0NGzaU\n2m+5XE7//v356aefaNmyJcePHycsLEy6q6moCWZ+fn588cUXrFixgnHjxpV6DF9fXy5dusTYsWPZ\nuHFjmSZSibH7/VMRY3WxAeaIiAjs7e1VgimxsbFcuXIFdXV1AgMD6d27NwYGBm+0Q4IgFObl5aVS\nTR7yL/yKc/78eZXnjRo14rfffgPg1KlTKstkMhktWrRgyZIlUtvYsWPJyMjAwMCAyZMns3fvXm7e\nvMnOnTupU6cOq1atwtPTEz8/v0Kfzu7evZtNmzZRvXp1+vfvz82bN6lTpw4HDx4kICCA1NTUMr9u\npVJJfHw8/fv3Jy0tDVdXV9zc3Pj888//8f8eY2NjRo8ezahRozh//jxr167lyJEj9OjRg6FDh3L6\n9GkOHjxIz549qVOnDgsWLGDixImMHTuW/v37F8rpVZ6GDRuGtrY2Y8eOZeHChXz99dcEBgaWWMhB\neLeIwLIgfLj27NlT6KIMoHnz5piYmJCXl1fo4m7QoEFcvHgRHx8f9u7dW6ig3qvi4+OLzOdob2/P\nnDlzmDJlCo8ePaJx48ZSyq3SzJkzR7rg1tPTY+LEiVJ+53HjxjFlyhRatGiBrq4uHh4eDBgwoEz7\nNTExwcTERKWtLLPRhHdH83rmeHSyLza3o0cne5EeQxCEMtu7dy+pqamsWrWKVatWSe0DBgxg1apV\nzJkzh9atW6Ovr0/v3r1LzDVc4J+Mf5A/rhbMGi7Jq2PytGnT2LNnj3RXrKamJk2aNGHHjh3Y2dkV\nuf3L+9DV1WX58uUsXLiQ+Ph4atWqxc8//yyN8a+uD/nj6pQpU5g8eTJdunTByMioxKCxpqYmP/30\nE19++SX+/v6MHj261Ncpxu73T0WM1TJlMffY29vbc+HCBSlJ+fXr1xkwYAAWFhbUqlWLyMhInj59\nypYtW6Tb5d4XsbGxdOjQgZMnT6pUxxaED9GlS5fYtWsXS5cuBfJvyxk6dCjdunVjz549NG7cGLlc\nzsSJE9m5cyePHj1i1KhReHp6Mnv2bGrWrKmyP4VCgZqaGllZWXh6erJs2TKsrKw4c+YMTZs2xcXF\npVAAvCzu3r3L4cOHCQoK4o8//qBly5a4ubnh5uaGtbX1mzgVpKSksGnTJtatW4ehoSEeHh5kZGSw\nceNG7Ozs6NChAxcvXuTKlSuMHDmSESNGFFvIoTzs2rULHx8fpk6dypw5czh+/HihT9YFQRAE4X0j\n3pu/n4qqTt+3sz19XAoHUQRBEIR/FzF2vx/e5lhd5gBz//79sba2lqbmKxQKZs6cSUxMDJs3b37j\nHStP4g9BEP7fpUuXGDNmDLVq1SIxMZHHjx9jamqKjo4OaWlpnDhxgh49emBqakpSUhLZ2dl06NCB\n27dvk5OTQ3h4OAYGBpiammJiYoJCoWDIkCHMnz+fZ8+eUbt2bdTU1IiMjGTcuHGsXLnybwWYX5aR\nkUFwcDBBQUEcOXKEqlWr4ubmRteuXWnWrNk/TmOhUCg4ceIEa9eu5cyZM/Tu3RsbGxsOHjxIcnIy\nvXr1Ii4ujiNHjtCvXz98fX0LzeQuLwcOHMDLy4sxY8awfPlyTp48Sd26dd/KsQVBEAShPIj35u+v\nkPCE/xUSkuHdsz7NHMXMZUEQhA+BGLvfH29rrC5zDub79++rVKZWU1Nj4MCBuLu7l0vHBEH452Ji\nYli8eDFJSUno6Oigo6PD+PHjOXr0KOfOnWPnzp0ANGvWjOjoaGQyGcbGxjx+/JiaNWvy6NEjunTp\nglKpJDY2Fk9PT7Kysrh27Rp//fUXhw4dwtPTUyVgvGvXLi5dusSZM2dYtmwZ6urqtGrViuXLl/PV\nV1+xYsUKBg4ciKOjIy1btsTZ2bnYYgTFMTQ0pFevXvTq1Qu5XE5YWBhBQUGMHDmS2NhYunTpgpub\nG506dVIpzFBWampqdOzYkY4dOxIXF8d//vMf/P39sbKywtPTk1u3bhEcHEz37t15/vw5jRo1wsXF\nhfHjx+Ps7Pzax3sdX375JVpaWgwaNIhvv/2Wjh07cubMmffuThJBEARBEN5/zeuZi3QYgiAIgvAO\ne1tjdYlJIdPS0qTvbW1tefLkicrypKSkvxW8EQSh/GVnZzNixAiGDBnCrl272LJlCyNHjmT27NnI\nZDLi4uJYt26dtH5GRgaNGzdm1KhR5ObmMnXqVOrWrUvfvn158eIFPXv2ZP369aSkpJCTkyNV9X2Z\nUqlk9erVUq5IPT091NTUmDt3LrNmzZJyRrm5ufHgwQNGjBhB5cqV+eyzz5g8eTJBQUGF/s+URl1d\nnWbNmjF37lyuXbvG1atXadGiBVu3bsXS0pJ27dqxZMkSbt269bfOY/Xq1Zk5cyYPHjxg4sSJhIaG\ncvLkSXr37o2enh6//vorDRs2xMDAgO7du9O+fXuOHj1aZAGGN8XV1ZXt27ezbt06vvrqKz7//HMe\nPnxYbscTBEEQBEEQBEEQBEEoTrEBZiMjI1xdXWncuDFfffUVaWlpzJo1i+fPnwP5hbwmTpxI165d\n31pn/40UCgUKhaKiuyH8C50+fZpmzZrx6af/Xxm0fv36BAQEADB06FAOHTrEw4cPkclk5ObmUq1a\nNapXr461tTWbNm1CQ0OD+Ph4TExM8PLywszMjJCQEKZNmyYFi9PS0vD09MTd3Z0OHTrg6OhIaGio\nNNO3Zs2a2NraquRJ7tWrF/7+/vz555/Ex8czffp0tLW1Wb58OdbW1tStW5dhw4axefNm7ty581rB\nWisrK7y9vTl8+DCJiYn4+vpy+/Zt2rdvj62tLb6+vpw6dYqcnJzXOp8aGhp0796do0ePcunSJUxM\nTNi3bx916tTB1taWy5cvo6enh4WFBePGjaN+/fps3br1tY9TVi4uLuzZs4eAgAA6d+5Mhw4dSEhI\nKJdjCYIgCCUbOnQoTk5OODk54eDggKOjo/R86NCh2Nvb8/PPPxfazt7enrt37wIwadIkle0KHgsW\nLADA09OTevXqqSxr1aoV8+bNU3kvuWnTJjp37oyTkxMtWrRg3LhxJCYmSsuL2o+Tk1OhlHcxMTE0\nbtyY7Oxsqe3VPhZ8MJ2SkvImT6fwnggJj2fA7GMMmH2MkHDxHkQQBKE4586dY8CAATRt2pSmTZsy\nZMgQbty4AeSPrYsWLSq0zaJFi5g8ebL0fPXq1bRr147GjRvj6enJnTt3pGV+fn4qY3vDhg2lsb99\n+/acOXOm0P7d3d05cODAG36lwrvmbY7VxabIuHTpEikpKdy9e5d79+5x9+5d7t+/j4ZG/iZr1qyh\nc+fOfPfdd+XawX8bpVLJrdhoIlMTiUxN4k5aEgC1jc2wNzHD3qQadhY1Sqz6KQhlERsbi5WVlfR8\nxIgRPH36lJSUFBo1aoSjoyNz5sxhzpw5UoX7ggvER48eYWRkxIsXL9i1a5dUNd7c3JyxY8fy/Plz\nqlSpgo2NDcbGxgQEBKBQKJg0aRJmZmYq1X+///57lcryr+ZfNjQ0xMXFBRcXFwDy8vIIDw/n/Pnz\nHDt2jOnTp5OTk0PLli2lR8OGDcuUVkNfX5+uXbvStWtXlEol165dIygoiMmTJ3P79m1cXFxwc3Oj\nS5cuVKlSpczntmbNmixcuJDZs2dz4MAB1q5dS2JiIh07duTRo0ckJibSpk0b1q5dy9SpU/Hx8WH4\n8OEYGhqW+Rhl0aZNGwIDA+nRowcdO3bExcWFM2fOYGpq+kaPIwiCIJTsP//5j/T9d999h62tLaNG\njQIgLi6ODh06sGrVKtq0aVNkFXnIrwzfv39/JkyYUOxxJk2aRN++faXnN2/eZPDgwdSqVYs+ffqw\nb98+du7cyerVq6lVqxYZGRksXLiQ4cOH8+uvvxa7n1edOHGC2bNnk5mZWWIfnz9/zrRp05g5cyar\nV68u4QwJ/zavFg2avzkUj072fNNRFPgTBEF42e7du1mxYgXz5s2jVatWyOVytm/fzoABA9i1a5c0\ncask+/fvJzAwkICAAMzNzfn555/59ttvOXXqFJD/fmDJkiV07NixyO2L2n9Zjiu83972WF1iiowq\nVarQvHlz+vXrx6xZs9i6dasUYD516hRTp0597dypH7pbsdFMu36M7XHXufIsiQwtyNCCK8+S2B53\nnWnXj3ErNrqiuyn8C5ibmxMbGys9X716NQEBARgZGSGXywFo1KgRLVq0YPny5RgaGhIaGkpycjLN\nmjVj48aNVK9eneHDh/Pnn3+yZ88eHj58yMyZMxk+fDixsbHSrCrIz1s8Z84cfvvtN86ePSu137hx\nAycnpzL3W0NDAycnJ0aPHs3OnTuJiYkhLCyM3r17ExUVhbe3999KqyGTyXBycmL69OlcunSJmzdv\n0qVLFwIDA/nkk09o0aIF8+fPJzw8vMwzprW1tenTpw9nzpzh9OnTVK5cmUuXLvHpp5+ipqbG7du3\nqVWrFkeOHMHa2poJEyYQFxdX5nNRFi1atODw4cMEBwdja2tLp06dVNIbCYIgCBWrYEzp3r0748eP\nf6N3ttSpU4fGjRtLs6Bv3LhBgwYNqFWrFpD/Ie7EiRNxdHRUmYlckl9//ZWFCxcyatSoUsdDHR0d\nXF1duXnz5j97IcJ7paiK9AC/HI9kR/DfS0kmCILwb5Sdnc2iRYuYN28ebdq0QV1dXaqn07dvX+7d\nuwdQ6niblpaGt7c3FhYWqKur4+npSXx8PImJiSgUCiIjI7G3ty92+/JM3yi8mypirC4xwFwSmUyG\nQqEgPj7+TfbnXy8yNRGZWvGnXaamRmRqYrHLBaGsOnTowIkTJ7C3t+fIkSMAREdHk5iYyIkTJzhw\n4ADt27fn1q1bnDt3jtjYWCZOnMjixYs5fPgwX3/9Nenp6dKMp6VLl5KQkMCjR49o3bo15ubm/Pnn\nn6SmpjJz5kyUSiXa2toolUpGjx5N3759GTt2LJUqVfrHr8XKyopvvvmmUFoNLS0tli1bhrW1NQ4O\nDgwfPpwtW7Zw9+7dUgfRatWqMWjQIPbt20dycjKzZs0iMTGRbt26YW1tzciRIzl69KiUFqg0derU\nYdmyZcTExODp6UlMTAy6uroYGRkRFxdH1apVuXLlCo6OjgwaNIiIiIh/fF4KNG7cmODgYC5evIip\nqSmurq6FZp0JgiAIFWvMmDEoFAqWL19e7DqvcwGoVCoJCQnhjz/+oFmzZgB07NiRw4cPM2bMGA4c\nOEB0dDRGRkbMnz8fXV3dMu23VatWBAcH07Jly1L7mJmZSWBgIO3atStzv4X3W0h4QpEXrAV+OR4p\n0mUIgiD8z9WrV5HL5bRu3brQMl9fXzp16oRSqWT79u00btxY5bF9+3Zp3cGDB9OjRw/p+alTpzAx\nMaFatWo8ePCAFy9esGjRIpo3b86XX35ZKCXGmDFjCu0/MrL4/+XC+62ixuq/HWAGePz4Me3bt39T\nffkgRKYmlbrOrbTS1xGE0ujp6eHr64u+vj5+fn588803TJ06lYEDB6rcDvPo0SOmTJlCZmYmdnZ2\nmJqaIpPJ0NfXJz4+Hrlczvz58wkNDWX37t3o6ekxfPhwEhMTCQgIICIigszMTE6fPs2LFy/Q09Pj\n+vXrbN++nSVLlpQ5r1NOTk6ZL6wL0mrMnj2bEydO8OTJEwICAqhXrx5Hjx6lXbt2mJub4+7uzpIl\nS7h06VKJM8a0tbXp2LEjK1as4P79+xw9epQaNWqwYMECqlatSvfu3Vm/fn2ZPlDT09Nj0KBBXLp0\nicDAQKpVq0ZSUhJVq1YlOzsbdXV17t27R9u2bXF1deXMmTNv5BPlBg0acOLECcLDw1FTU6N79+5l\nnq0mCIIglD8dHR0WLVpEQEAAV65cKbS8qAvMV291Xbx4MY0bN6ZBgwY4ODiwcuVKpk+fzueffw5A\n8+bN2bVrF3p6eixfvpxOnTrh4uLC0aNHi9xPwePlVFaVK1dGrZjJEC/3sVGjRjRu3JiQkBDc3d3L\nfB5SU1OJiopSecTExJR5e6Fird3/1xtZRxAE4UOQmpqKoaFhseMq5E/e7NevH2FhYSqP4lJZhYaG\nMmvWLKZNmwbA06dPadq0KcOGDeP8+fOMHDmS77//XiVH87Jlywrtv6QZz0W9DjF2vz8qaqwuNgdz\nWRgbG7Nly5Y31Zd/PYVCkZ9zuZSsIrdTk1AoFCX+ExKEsqhSpQpt2rThwYMHrF+/HgMDAxYvXky/\nfv1ISEggOTmZzp078+DBA27cuMG9e/dwdHQkOTmZgIAA/P39MTWY9ovgAAAgAElEQVQ1lT4tTUtL\nQ19fHxsbG86cOYO2tjaQnzdZR0eHyMhIsrOzGTJkCHl5efj6+qoUGSxJv379CA4Opl69etSrV4/6\n9etL35eWu1hDQ4OGDRvSsGFDRo8eDcDDhw85f/48Fy5cICAggLt37+Ls7CzlcW7RogUmJiaF9iWT\nyahbty5169ZlwoQJPH78mGPHjhEUFMTEiROxsbGha9euuLm50bBhwxL/Ths2bMi6dev44Ycf2L59\nO2vWrMHAwACA3NxckpKS6N+/P1WrVmXChAm4u7tLaYj+DkdHR06fPk2HDh0wNzend+/e7N+/X6Qy\nEgRBeEc4ODjg5eXFxIkTOXjwoMqyggvMknIwjx8/nr59+5KZmYmfn5/0geWrx5g3bx4A8fHxBAYG\nMm7cOGrUqEHdunVV9vO6Xu1jbm4u+/btw9PTk6NHj1KtWrVS97Ft2zb8/f1f+9iCIAiC8L4xNTUl\nPT0duVyOurq6yrKnT59KdxeVdcLRwYMH8fPzY8aMGbi6ugLw6aefsmnTJmmdzz//nGbNmnH69Glq\n1679Rl6HGLuFsvhHEUxNTU2aNm36pvoiCEI56dixI8HBwQCEh4fj5OQkDWKurq7SzKZDhw7RrVs3\naTulUsmmTZvw9PRk4MCBbNmyhTlz5iCTyfjoo48ACAgIIDs7mxYtWqCrq8uQIUPYsGEDs2fPZty4\ncSqV7Uuye/du7t69i5+fH3Z2dly+fJkxY8bw8ccfY21tTbdu3Zg6dSq7du3iv//9L3l5eSXuz8rK\nCg8PD1atWsW1a9eIj49n6tSpaGpq8tNPP1GjRg0prcbWrVu5d+9ekQP7Rx99RN++fdmxYwdJSUks\nXbqUrKwsPD09qV69OkOHDuXgwYMlpqQwMjJixIgRXL9+nV9++QVra2uUSiVqamoolUpSU1OZOnUq\ntra2rFq1imfPnpXpnBXFzs6Os2fPkpyczMOHD+nbt2+p50oQBEF4e7y8vKhcubJKHYMCZb3ANDAw\nYP78+airq/P9999L7V27dpXSYgF8/PHHeHt7Y29vz61bbz7fnqamJn369EFbW5tr166VaZt+/fpx\n7NgxlcfmzZvfeN+E8uHlXvrEgbKsIwiC8CFwcnJCU1NTpUZRgSlTpkizkMti1apVLFy4kDVr1qik\ny7h48SI7duxQWffFixfSZLA3QYzd75eKGqtLDDD/+eefrFu3jvPnzwPw22+/0bVrVxo0aEC3bt0K\nzbwQSqampkZtY7NS17M1MROzl4U34uUg8uHDhwkLC6NRo0bScplMhrm5OUqlksTERK5evVpo+eDB\ngwkICGDz5s2sWLECa2trIH9G/qJFiwgJCWHlypUAUiC44HtjY2NSUlJK7GNaWhopKSkolUpMTU1p\n164dPj4+/Oc//yE0NJT09HR+++03Bg4ciKamJrt376Z79+4YGhri5OTEgAEDWLJkCcHBwSQmJhZ7\ncW5oaEjHjh1V0mps3boVR0dHDh8+TJs2bTA3N6dnz54sXbq0yLQampqatGnThsWLF3Pz5k1+//13\n6tWrx6pVqzA3N6dz5874+/vz4MGDIvsgk8lo0aKFFND++uuv0dbWRi6Xo6mpSVpaGitXrsTS0pKZ\nM2eWeu6KU6tWLc6dO0dWVhbXr19nyJAhZQ70C4IgCOVLTU2NRYsWcfjwYZX2102XpKGhwaJFiwgL\nC5MuLDt37szy5csJCwtDoVCQlZVFUFAQDx8+pHnz5v+470qlUqWfCoWCwMBAsrOzcXBwKNM+TExM\nsLGxUXlYWlr+474Jb0fzeuZ4dCr+tmqPTvY0r2f+FnskCILw7tLW1sbX15cZM2Zw9uxZ8vLyyMzM\nxN/fn5CQEIYOHVqm8X/fvn1s3bqVHTt2FJrkqaGhwQ8//MDly5eRy+UcOnSI69ev06VLlzf2OsTY\n/X6pqLG62Huxg4KCmDRpEra2tqxZs4ahQ4eyYcMG+vfvj4ODA3fu3GHu3Lnk5ubSu3fvN96xfyt7\nEzOuPCs5x7JdGYLQgvA6LC0tyc7OJiAggLFjx/Lw4UNANQC9YMECnJycVLZ79ULyZTNmzEBbW5tV\nq1ZJ+Zz379/PuXPn0NTUpEqVKiQkJBAXF4e2tjbGxsZF7mfVqlUsXrwYmUxG7dq1sbW1Vfn68uPl\nHI9ZWVlEREQQHh7O9evXCQoKIjw8HJlMJqXXKPjq4OCAnp6eynE1NDRwdnbG2dmZ7777DqVSycOH\nD7lw4QIXLlxg69atUlqNVq1a0bJlS5o3b66SVuOTTz7Bx8cHHx8fMjIyCA4OJigoCD8/P8zMzHBz\nc8PNzY1mzZoVuiXK1NSUsWPHMmbMGE6fPs3atWuJjY1FXV2dFy9e8Msvv7B06VL69evH2LFj+eST\nT8ryo5bUqFGDc+fO0a5dO86ePcvIkSNZvXq19LMSBEEQ3p5X//fa2Ngwfvx45s6dq7LO6/6PtrGx\nYcSIESxZsoT27dszcuRIDAwMmDNnDrGxsUB+jv4NGzaUKX1Faf2WyWQEBASwc+dOqb82NjasWLFC\nXGh+QL7paAdQqIBQ38729HGxq4guCYIgvLM8PDwwNDTE39+f8ePHI5PJaNCgAQEBAXzyySdlGv9/\n/vlnsrKyCtU82LdvH02aNGHGjBlMnTqV5ORkbGxsWLduHVWrVi3PlyW84ypirJYpi4keffHFFwwc\nOJCvvvqKy5cv069fP6ZPn66Sr+3w4cOsXr260AyMd11sbCwdOnTg5MmTWFhYvNVjR8Y8YNr1Y8iK\nK56iUDC3fmfsLa3far+Ef6fQ0FB27drFkiVL2LZtG7/++iu7d+/m999/58iRI4SGhnL06FGysrJo\n3bo1gYGB1KpVi1atWnH+/Hn8/f2pUqUKX3/9tcp+IyIi6NWrl8ps5wEDBtC2bVt8fHyIiIjg+fPn\nVKpUiaSkJKKiotDQ0MDa2hpra2tsbGxUvtaoUYMXL15w584dbt++rfL1zp07GBgYFAo829ra8skn\nn0h5qyA/IJ6QkCAFnQu+3r59GwsLi0KB55o1a5Z4t0B6ejp//PGHFHQOCwvDyspKyuPcsmVLatas\nWegNgVwuJywsjKCgIIKCgoiNjaVLly64ubnRqVOnYoPtCQkJbNiwgbVr16KmpkZmZia6uro8ffqU\nzz//nIkTJ752WqLExETatm1LRkYG33zzDT/++KMIMguCIAjvlIp8by78fSHhCf8rEiTDu2d9mjmK\nmcuCIAgfCjF2vx/e5lhdbIDZycmJQ4cOYWFhgVKpxNHRkX379qlUmoyJiaF79+5cvXq13DpYHiry\nD0GpVHIrNprI1ERupSVxOzV/NrOtiRl2xmbYm1TDzqKGCAAJ/ypKpZInT57w4MEDoqKiivyqp6cn\nBZxfDj5bW1ujpaVFTExMoQB0VFQUVatWLXLms42NjVTcLjc3l9u3bxcKPD9+/BgHB4dCgeeC/NKv\nysvL46+//pICzufPn0cul9OyZUtplnNBnq2XxcTEcPjwYYKCgjh37hzOzs5SoUBbW9tCx5HL5Rw9\nepQ1a9Zw7tw5DA0Nefr0Kerq6tjZ2TF16lRcXV3LnEonJSWFdu3akZycjLe3N7Nnz37Nn6AgCIIg\nlB9xkSoIgiAI7xcxdguvKjbA/OWXX+Li4sKIESP49ddfmTx5MoMHD2bs2LHSOqtWreL3339n586d\nb63Db8K79IdQkBdV5FwWPmRKpZLk5GSVgPPL30dHR2NsbFxo9rOlpSVaWlpkZ2cTFRWlEoCOjY3F\n0tISW1vbQsFnS0tL1NXVSUtL48aNGyqB5/DwcAwMDFQCzvXr18fe3r5QoQSlUkl0dLQUcL5w4QL3\n79/H2dlZmuH8alqNZ8+ecfLkSWl2s76+vpRKo1WrVlJQvEB0dDTr169n7dq1aGhokJaWhr6+PkZG\nRkybNo2+ffuWqYDDkydPaN++PdHR0UyePJkJEya8mR+eIAiCIPxD79J7c0EQBEEQSifGbuFVxQaY\nz58/z8iRI1FTUyMvL49FixYxffp0HBwcqFOnDnfu3CEsLIzNmzfj7Oz8tvv9j4g/BEF4vygUChIT\nEwvNei74PjY2FlNTU5Xgc/Xq1dHR0SEnJ4e0tDTu3bsnBaAfP35MzZo1i0y7YWZmRkxMjMpM5/Dw\ncO7fv0+tWrUKzXa2tLRUuePg1bQaoaGh1KhRQ5rh3LJlS2xsbJDJZCiVSq5duyYFm2/fvo2Liwtu\nbm506dKFKlWqSPvNyckhMDCQFStW8Oeff6JQKNDS0kJNTY1x48YxYsSIYlNvvNy39u3bc+fOHRYs\nWMDIkSPL7WcmCIIgCGUl3psLgiAIwvtFjN3Cq4oNMAPEx8dz/fp16tati5WVFXfv3mXr1q0kJiZi\nZWWFh4cHNWvWfJv9fSPEH0LFELO1hQIXL17k/PnzmJmZqTyqVKlSKL1EWcjlcuLi4opNwZGYmEi1\natWk4PPHH3+Mrq4uCoWCrKwskpOTpXzP2dnZUlHBlwPPlpaWpKSkcP36dZXgc3Z2dqHZzo6Ojhga\nGgL56TleTqtx4cIFFAqFSh7ngrQaiYmJHD16lKCgIE6cOIGDgwNubm507doVR0dHKZB969Yt1qxZ\nw8aNG4H84LNMJmPQoEFMmjQJKyurYs9VQT7nGzdusGLFCoYMGfI3foKCIAjCu+zcuXNs2LCByMj8\nwi6Ojo6MGTMGR0dHAJKSkvD39+fcuXNkZmZSrVo1PDw8pForly5dwsfHhz/++KPI/R85coSVK1eS\nmJhI9erV+f777/n888//dn/Fe/P3U0h4PGv3XwfAy/3TcqlILwiC8KEaP348R48e5dSpU1LBvkuX\nLjFgwACVOkQAOjo6hISElLr8ZXPnzkVTU5OJEyf+rf6Jsfv98DbH6hIDzP9W4g/h7Xg533RkahJ3\n0vLzTdc2NsPeROSb/pCFhISwb98+kpKSpEdycjKPHj3C0NCwUOC5atWqhdrMzMzQ0dEp0/Fyc3OJ\njY0tNv9zSkoK1atXx8bGBnNzcwwMDJDJZLx48YInT54QFxfH7du30dDQKBR4NjU1JTs7m3v37kmB\n54iICKpWrVoo8Fy7dm3U1dWLTatRMMu5efPm6OrqcvbsWWl2s1wul1JptGvXDh0dHbKzs9mzZw8/\n/vgjd+/eJScnB3V1dTp27MjcuXP59NNPizwfz549w8XFhatXr7JhwwY8PDze5I9XEARBqEC7d+9m\nxYoVzJs3j1atWiGXy9m+fTv+/v7s2rWLSpUq4e7uTs+ePRk8eDDGxsZcv36d77//Hnd3d0aNGlVi\ngDkqKgp3d3c2bdpEgwYNCAkJYfjw4fz++++l3klTHPHe/P2zI/hWocr0Hp3spar1giAIwt+Xnp5O\nx44d+eyzz7C0tOS7774DSv8AuLTlAKmpqSxatIiDBw8yePDgv506UYzd7763PVZrlMteBQG4FRvN\ntOvHkBXMWP5fatkrz5K48iwJZcw15tIZe0vrCuujUDGaN29O8+bNC7UrFAoeP35cKPCclJTEvXv3\nCrVra2sXGXguKiBdMHu5KC9evODhw4fFzoBOS0vD0tKS6tWrY2RkxLNnz7h8+TKnT58mMTGRBw8e\nYGRkhK2tLfXr18fd3Z1KlSqRm5vLo0eP2LVrF9OmTSMuLg57e3sp4NyjRw9mzJiBjo6OlFZj8eLF\nhIWFYW1tLc1w9vHx4fnz5xw+fJgFCxbw9ddf065dO9zc3HB1daV///789ddfLF26lF27dnHs2DGO\nHTuGo6MjixYtwsXFReWDHD09PU6ePImLiwuDBw9GR0cHd3f3cvt5C4IgCG9HdnY2ixYtYunSpbRp\n0wYAdXV1Bg0aRGpqKvfu3ePs2bM0atQIX19fabv69eszb948jh8/XuoxbGxsuHjxIrq6uuTl5ZGS\nkoKBgcHfugNJeD8VdcEKSG0iyCwIgvDPHDx4kMaNG+Ph4cHo0aPx9vZ+Y+Ns3759cXZ2pmPHjnyA\n800/GBUxVosAs1BuIlMT/z+4XASZmhqRqYkiwCxI1NTUqFKlClWqVJFu4y2OUqkkPT1dJehc8Lh2\n7VqhNqVSWexM6IKgtIWFBc7OzpiYmKgEZJ89e0Z0dHSh4oPx8fGkpKSgVCoxMDDg+fPnUp7nrKws\nHj9+LKXnqF27Nu3atUNfXx+FQsH169c5dOgQ4eHhqKmpUb9+ferXr0+/fv2YP38+eXl5XLlyhUOH\nDjFp0iQUCgWtWrXC3d2dGTNmEB8fz7Fjx5g4cSI2NjZ07dqV0aNHs2LFCgICAvjhhx+4ceMGX3zx\nBVWrVsXPz4+BAweioZH/b19HR4eTJ0/SqVMnvvnmGw4cOMAXX3xRrj9fQRAEoXxdvXoVuVxO69at\nCy0rCCjPmzevyNthi/vwtyi6urrExMTQqVMnlEols2fPRl9f/591XngvhIQnFHnBWuCX45FYmxuK\ndBmCIAj/wN69e/H19cXJyQkTExOOHTtG165d38i+t2zZQpUqVZg8efIb2Z/w7qmosVoEmIVyE5ma\nVOo6t9JKX0cQiiKTyTA2NsbY2Bg7u9I/fcvMzJRmQ7/8iIyM5Ny5cyptz549k4LRRQWlO3bsKLWb\nmppKAehXZz8rlUrS0tJISUkBICEhATU1NXJyckhNTSUtLQ0LCwtsbGyoVKkS0dHRhIeHk5CQwP37\n97GysqJevXoMHz4cMzMzsrKyiIyMZOPGjURFRdGoUSO8vLwwNjYmJiYGT09P0tLScHV1Zfny5RgZ\nGfHjjz8SHBzM8OHD8fHxwcfHhylTpmBgYICWlhbBwcF07tyZHj16cOTIkX+UQ1MQBEGoWKmpqRga\nGpZY7yI1NZXKlSv/42N9/PHHhIeHExYWhre3N1ZWVjRr1qxMfUxLS1NpS0xM/Mf9Ed6Otfv/KtM6\nIsAsCILw91y9epWMjAzpTqQ+ffqwfft2KcCcnp5O48aNVbZZtmwZLVu2LNPylwvJl5UYu98vFTVW\nvzcB5v/+97/MmDGDe/fuUaNGDWbPnl1sflGh4ikUivycy1olr3c7NQmFQiEK/wnlzsDAAAMDgzIV\nJn3+/DnJycmFAtIPHz4kLCxMpT0tLY3KlSsXSs/RtGlTunXrhpmZGXp6ejx//pyMjAxiY2OlIPT9\n+/elIoTGxsZoamoil8t5+vQpcrmc7OxsoqKiiIuL49mzZyQlJfH06VPq1auHu7s7enp6xMTE8Pvv\nv3Pt2jVsbGxo164dL168YMmSJfz111+0bNmS+fPnExcXx+bNm1m4cCE//PADPXr0YOXKlZibmxMc\nHEynTp344osvOHHiBJ999tlb+IkIgiAIb5qpqSnp6enI5XLU1dVVlj19+hRdXV2qVKkiffD5MoVC\nwdOnTzEyMirTsQr236xZMzp16sSJEyfKFGDetm0b/v7+ZTqGIAiCIHxodu/eTWpqqnRNlpeXR3p6\nOhEREQAYGRmVmGO5tOV/hxi7hbIoU4A5MzMTf39/evfujY2NDePGjePYsWPUrVuX5cuXU7169XLt\n5IsXL/Dy8mLEiBH07t2bgwcP4u3tzYkTJ9DT0yvXYwuC8OHR0dHBysoKKyurUtctyD/5cr7ogkdE\nRIRKe0pKCpUqVZKC0Q4ODrRt21aabZabm0t2djapqanSLObw8HA0NDSkSsAKhYI///yTe/fuoaen\nh1wu58mTJ+jr66Ojo0NiYiKZmZlERUWhp6dHeno6e/fu5e7du1haWuLg4MDly5fZt28f+/fvp379\n+mzevJnjx4/TpUsXPv/8c86cOUOLFi3K+zQLgiAIb5iTkxOampqcPXuW9u3bqyybMmUK+vr6tGrV\nit9++41u3bqpLD9z5gzjxo3j/PnzJR7j7NmzbN68mU2bNkltOTk5ZQ5M9+vXDzc3N5W2xMREBg4c\nWKbthYrl5f4p8zeHlrqOIAiC8PqePn3KsWPH2LJli3QtqlQqmTdvHtu2bePLL7+skH6Jsfv9UlFj\ndZkCzH5+fkRERNC7d28OHTrEyZMnWbx4McePH2fOnDmsXbv2jXfsZX/88Qfq6ur06dMHgJ49e7J5\n82bOnj1Lly5dyvXYwt+jpqZGbWMzrjwrOQWGrYmZmL0svNc0NDQwNzfH3Lz020sUCgVPnjwpVMAw\nKSmJhISEQkUMNTU1sbKyonLlyujp6aGpqYlCoZDyOz958oS0tDTU1dV5+vQpV65cQU1NDW1tbSB/\nJnZubq5UfOnOnTukpaVJbZUrVyY8PBwnJyeqVKnCkiVLkMlktG3blvPnz9OkSZPyPn2CIAjCG6St\nrY2vry8zZsxAXV2dli1b8vz5czZv3kxISAg7d+6kUqVKdO/enZ9++onBgwdjYGBAaGgoM2fOZOjQ\nodLkDaVSKdUwKGBgYICDgwM3btwgMDCQrl278vvvv3Pu3DlGjx5dpj6amJhgYmKi0iYKBL4/mtcz\nx6OTfbG5HT062Yv0GIIgCH9TYGAg1tbWODk5qbT36tULb29v2rZt+8aO9ToF/sTY/X6pqLG6TAHm\ns2fPsmnTJmrVqsVPP/1EmzZtcHV1pW7duri7u7/xTr0qKiqKWrVqqbTZ2Nhw//79cj+28PfZm5Qe\nYLYzNntLvRGEiqempoapqSmmpqY4ODiUuK5SqSQjI6NQzuii8kgnJiaSl5eHiYkJWlpayOVyXrx4\ngbq6upQvS6lUIpPJiI+PR6lUoqmpSUxMDBoaGiiVSh4/fkz//v3R1NTEzMyMVq1aERISgrOz81s6\nO4IgCMKb4OHhgaGhIf7+/owfPx6ZTEaDBg0ICAjgk08+AWDXrl389NNPfPHFF2RnZ1O9enVGjhwp\nTeaQyWSkp6dL+R8LeHt74+Pjw5o1a1iwYAF+fn7Y2NiwevVqbGxs3vprFSpGQeX5Vy9c+3a2p4/L\nm69KLwiC8KHYs2dPoZnCkF+I18TEhOjoaJVi9EUpbfnL65V1XeH9UxFjdZkCzHl5eejp6ZGTk8PF\nixelapPZ2dlv5VOLZ8+eSbeHF9DV1eX58+elbiuSkVcce5NqKGOuIStmhrJSocDepNpb7pUgvB9k\nMhlGRkYYGRlha2tb6vpZWVlFBp+TkpJ48OABMTExJCQkkJ6eTm5uLnK5HKVSSW5uLvD/n2Dn5uYS\nGxsLQKNGjdi4cSOenp7IZDLU1NTEmxBBEIT3gJubW5EXqAVq1KjBsmXLil3epEkTIiOLrz7eqFEj\n9u3b94/6KLzfvuloh7W54f8KCcnw7lmfZo5i5rIgCMI/ERgYWGS7mpoaZ8+eBWD48OHFbt+0aVNC\nQkLKdKwFCxa8fgeF98rbHqvLFGBu2LAhCxcuRF9fn9zcXDp06EB4eDhz5859K7dQFxTIell2djb6\n+vqlbiuSkVccO4sazKUzkamJ3EpL4nZq/mxmWxMz7IzNsDephp1FjQrupSD8O+jr62NjY1OmGWQv\nXryQ8kbHxcURERHB1atXuXHjBnFxcWRlZaFQKAAYPHgwgwcPLrSPgoBzwUNdXR11dfVC7a8+L2ub\n2O7NbKepqUnTpk3R0Cg83N+5c4cff/yR7Oxsnj17Rps2bRg9ejQJCQksXLiQJ0+e8OLFCxwcHJgy\nZQqampq0bNmSCxcuFPl7FRoayoQJEzhz5szr/fIKgiAI76Xm9cxFOgxBEARBeIe9zbG6TAHmuXPn\nMnv2bO7evcvChQupXLkyP//8M/r6+kyfPr28+0jNmjXZtm2bSltUVFSh4iRFEcnIK45MJsPe0hp7\nS2sAKWAlci4LQsXS1tbGwsICCwsLnJ2dpf+ljx8/Zv369axatQobGxu+++47Vq5cyZ9//klWVhZK\npVKa6axUKpHL5cjlcmm/ampq6OnpYWxsjIWFBVZWVlhbW1OtWjWqVKkipQcxNjZGJpOhUChUHkql\nssTnZW2ryO3y8vLeqX7KZDI2bNhAzZo1VX4HMjIy8PX1ZdWqVVhZWaFQKPDx8WH79u3s27ePWbNm\nUb9+fQDmzZvHypUr8fX1LXYGe0JCAps2bSIvL688fmUFQRAEQRAEQRCEd1iZAsxmZmasXr1apW3i\nxIlv7VbpZs2akZOTw7Zt2/j6668JDAzkyZMntGrVqtRtRTLyd4cILAvCuykiIoLly5ezZ88eunfv\nzq+//ioVlnB1daVVq1b06dOHGjVqsG7dOq5du4adnR3Pnz/n3r17ZGRkAPl/4zk5OcTHx/Pw4UNC\nQkJQKpVoaGigqamJmpoacrmc3NxcTExMMDc3x9LSko8//phq1aphbm6u8rVatWqF0iMJb8bJkydp\n3ry5VJ1aTU2NRYsWERERQbVq1aTgMsD48eNLLALy4sULZs2ahZ+fHz179iz3vguCIAiCIAiCIAjv\nljIFmJVKJWfOnOHGjRvk5eUVutD09fUtl84V0NLSYv369cycOZOlS5dibW3NmjVr0NHRKdfjCoIg\n/FspFAqOHDnCsmXLiIiIwNvbm8jISMzMVAtv6urqsn//fpo2bcqOHTs4efIkt2/fZv369WzZsoUm\nTZowcOBAKleuzOHDh7lw4QJ37txBLpcjk8kwMTHho48+QkdHh+zsbB4/fsyLFy/IyMggIyODGzdu\noKuri4GBAXp6emhpaZGbm0tWVhZpaWno6uoWCjybm5sXaqtcubLID/0aUlJSsLCwUGnT09MjOTkZ\nS0tLlXYtLa0S9+Xn58eQIUMK/e4IglBx7O3t0dHRkQr4FBT6mzRpErVr1+bSpUv4+Pjwxx9/FLt9\nUFCQVBQwJycHHx8fYmNj2bhxI1WqVGH16tXs2bOHzMxM7O3tmTFjBrVr136bL1OoYCHh8azdfx0A\nL/dPRboMQRA+SLdu3WLt2rWEhYWRlZWFkZERbdq0YcyYMezcuZN169YB+bXN5HI52traAFhYWHDo\n0CHat2/P48ePVerd2Nvb4+vrS6NGjYiNjeXzzz+XJt4olUrMzMwYNmwYvXr1AmD//v1MnTpVipHJ\nZDL09fXp0qULEyZMQENDg4yMDObNm8f58+dRKBS0bt2aadtoLRsAACAASURBVNOmYWho+LZPmfAW\nvc2xukwB5gULFrBt2zbs7e3LlPe4PNjZ2bFz584KObYgCMK/RUZGBps2bWLlypWYmJjg4+PDV199\nVWIQsUaNGgQEBODh4UFoaCi2trYsXryYuXPnsn//ftatW0dkZCSDBg1i37591KxZk6SkJE6fPk1Q\nUBCXLl3i5s2byGQylEolNWrUoE6dOtjZ2aGlpUViYiKRkZFSMUIdHR00NTXR1NTk6dOnqKmpoVAo\nyMnJISUlhYiICORyOc+ePSM1NZXExESePXuGmZlZkcHoV2dFlxYw/RB8/PHHREREqLTFxMRQtWpV\nEhISVNpTU1O5du0a7dq1K7Sf5ORkrly5wsOHDwFIS0tj7NixLFmypPw6LwhCmezdu1cKEOfl5bFk\nyRKGDRvG6dOnX2s/z58/Z+TIkWRmZrJ9+3YMDQ3Zv38/gYGBBAQEYG5uzs8//8y3337LyZMnxYd9\nH4gdwbdUKtPP3xyKRyd7qWq9IAjCh+DatWsMHjyYoUOH4ufnR6VKlYiNjWXlypUMHjyY/fv34+Xl\nBcD27ds5fvw4W7duLbSfFStW0KZNG+n5li1bGD58uMqYffHiRSnIHB4eTt++falbty5169YFwMHB\ngb1790rrJyUlMWjQIHR0dPD19WX+/PlkZ2cTHByMUqlk/PjxzJkzh8WLF5fLuREq3tseq8sUYD5w\n4ADz58+nR48e5dIJQRAEoXzdvXuXlStXEhAQgIuLC1u3bqV58+ZlDgS4uLjw/fff07NnT86dO4eO\njg7a2tp88803fPPNN0RGRrJ+/XqaNm1Kw4YN+fbbb+nZsyd9+vQBQC6XExkZyalTpzh+/DhXrlwh\nODgYTU1N5HI5NWvW5IsvvqB169aYm5ujVCqJiooiMjKSiIgI7t69S2hoKHp6eujp6SGTycjOziYz\nM5MqVapgb2+Pubk5xsbG6Onpoa6uzuPHj4mJiSE5OZmEhAQSExNJTk6mUqVKRc6CfvWrkZGRyvn5\nN+WRb9u2LevWrcPDwwNLS0tyc3NZtGgRLVq0IDY2luvXr1O/fn2USiX+/v7o6uoWGWCuWrUqx44d\nk563atVKBJcF4R2koaGBu7s7mzZtIj09vczbPXv2DC8vLzQ0NNi8ebN0YZuWloa3t7d0J4SnpyfL\nly8nKSmJatWqlctrEN4dr16wFihoE0FmQRA+FLNnz6Z///6MGDFCarOwsJBqmGRkZEgzhF+uZ1Oa\n3r17s2DBAmJjYzEyMiq0vF69etSuXZvIyEgpwPzqvs3MzGjTpg23b98G8q9lRowYIU0a7d27N/Pn\nz3/9Fy28FypirC5TgFlNTU3KxykIgiC8H5RKJadOnWL58uWEhIQwdOhQ/vrrr0IpEMpqwoQJhIWF\nMXr0aNavX6+yzN7eniVLljBv3jz27dvHsmXLGDVqlPSJvrW1NQ4ODjg4ODB69GgAnj59yuXLlzlz\n5gwnT55k37597NixAw0NDXJzc7G1taVVq1YMGjQIZ2dnatWqRXx8PHfv3uXOnTvcvXuX27dvc/Pm\nTa5cuYKBgQGGhoZoaWkh/z/27jyupvx/4PjrVioqSpK9RIREFLKXMbaIbMmEaSK7ZB2MfRljUNkq\n68iSxjrMjHXsS8oyMiZibKGy3Eolbff3R9/Oz52Ky5SKz/Px6KF77ud8zueEPve8z+e835mZvHz5\nkvj4eIyNjalZsyYODg6YmJhgaGiIjo6OFNx++vQpDx48IDQ0lJiYGCkYnZ6ejqFRBXTL66NRVpdM\nXS3K6JfDtGp16pnUxLpmHVo0boKxsTEaGipNp8WGrq4u33//PTNmzCArK4vk5GQcHBxwdXWlTZs2\nzJs3j1evXpGSkoK1tTVeXl5AdlDpzTzL7u7udOvWrahOQxCEt3jzQjMhIYGgoCDq1KmDvr6+Svu/\nfPmSb775htevX7Njxw6lGibu7u5Kbf/44w8MDAxEcPkzcD7iSZ4XrDm2HYrEtHJZkS5DEIRP3uPH\nj/n7779ZuXJlrvc0NDQYP368yn29OWcnJyezceNGKlSoQO3atXn69GmuNufPn+fJkyc0b948z/6y\nsrK4ffs2R48eZeDAgQD88MMPSm3++OMP6tWrp/IYhZKjqOZqla6InZyc2LBhAzNnzkRdXb1AByAI\ngiAUrJSUFLZu3Yqfnx8KhYJx48YRHBxMmTJl/lO/MpmMjRs30rx5c9auXcvQoUNztdHW1mbgwIEM\nHDiQGzduEBgYiI2NDba2tnh6euLo6CgFY/X09LC3t8fe3p45c+agUCh4+PAhFy5c4OTJk5w8eZL1\n69ezbds2ILuYXIMGDWjdujU2NjYMHTqUOnXqoK6uTmZmJtHR0VLgOScIHRUVxZ07d3j16hWxsbFc\nv34dNTU1UlNTkcvlPHv2jIoVK2JqaoqpqSlt27aVvk/OTGflnfOkJ78iVZ5A6osEUuSJXP7zCudO\nnOTVi3hKvUwlXh5P+fLl37oaOudPPT29//R3UJAaNGjATz/9lGt79erVCQwMzHOf69evv7XPM2fO\nFMjYBEH471xcXKQnLjQ1NWnUqBErVqxQeX9vb2/MzMy4fv06ERERNGnSJM92Fy9eZPbs2cybN0+l\nfuVyOfHx8UrbYmJiVB6XULT8d/+pUhsRYBYE4VMXFxcHoFSHZOnSpVJq1/T0dObMmYOTk9M7+xo/\nfrx0jaSurk79+vVZs2aNlK8ZkFJovH79mrS0NJydnZVu7EZGRmJrawtkB6MNDQ3p2rUrgwcPznW8\nDRs2cPjwYXbs2KHSuYq5u2QpqrlapQBzTEwMf/zxBwcPHqRq1apKKxhkMpnIjSwIglAMPHz4kNWr\nV7N+/XpatGjB8uXL6dChQ4Hmw9TT02PPnj20adMGKyurfO+aA9SvXx8fHx8WLVrEzz//zJIlSxg1\nahTffPMNHh4e1KhRQ6m9TCajRo0a1KhRg379+gHZhaWuXbtGaGgoJ0+e5Ny5c/j7+6Ovr09aWhqp\nqak0atQIOzs7mjZtStOmTbG3t1e6GZqVlUV0dLRS4Dnn+xcvXvD69WsSEhK4d+8ejx8/5vTp0yQn\nJ3P/UTTPnz5Dq5wuZYwN0TE2pExFQwzr1ZJeuzdqS6/GLXn69KnS6ucnT55w+/ZtTp8+Lb1+8uQJ\nampq7yxYWLlyZSpUqCBu6AqC8J/s2LFDysH8ITp06MCMGTNYtmwZ48ePZ8+ePZQvX16pzd69e5k7\ndy4zZ85U+WmGLVu25LnaSxAEQRBKEkNDQyC7eHblytmBugkTJjBhwgQAevfurXJKDB8fH6UczHk5\ndeqUlKrq4cOHjB8/nkWLFjFjxgwg+4nSXbt2vbWPzMxMFi5cyKFDh9i0aRM1a9ZUaXxi7hZUoVKA\n2dzcPN+q0KKQhyAIQtFRKBScP38eX19fjhw5wqBBgzh37tx/Ciq8S926dVm7di19+/YlLCxM6a59\nXkqXLs2gQYMYNGgQ169fJyAgAGtra+zs7PD09KRLly75ppjQ1NTExsYGGxsbRo0aBcDz58+5ePEi\nFy5c4MyZM1y8eJFbt26xa9cuUlJSePXqFdbW1tja2mJjY0PTpk2pU6cONWrUwMHBQan/rKysXGk3\ncgLP8XI5GjraaJUvi0ZpbTJfp/PyUSwJ9x6RnvSKV8/lHHk+h0oVjaVVz6amppiYmNCsWTNMTU2p\nUaOGtPJAoVDw8uXLXIHonCKHb26Lj4/HyMjonauiK1euLH3QzEt0dDTe3t7UqlVLynH9b/fv32f0\n6NHs378fgBUrVnDgwAEqVqwIZKfF6NatG8OHD2f37t2sXLmS/fv3S/nbxo8fz4ABA2jWrNlb/x0I\nglCy5OTQHzduHKGhoUyaNIl169ZJn/1XrVpFUFAQa9aseevNxn/76quvcHR0VNoWExPDkCFDCmzs\nQuEZ7tyIhZsuvrONIAjCp6569eqYm5uzc+dOKQXgm1QNLn/osXv27Mn27dtV3uf169eMGTOGuLg4\nfv75Zykorgoxd5csRTVXqxRgzus/iyAIglB00tLSCAkJwdfXF7lcLuVFzikiUdicnJwIDw+nf//+\nHDlyROnJlrextLRkxYoVLF68mJCQEBYuXMjIkSOlVc05BaPextDQkC5dutClSxcgO0gcFRVFaGgo\nFy5c4OzZs4SHh3P//n0OHDhAQkICKSkp0grnnK86deqgpqZGtWrVqFatGu3bt5eOkZWVhccva4lL\nSiDpcdz/vp6S9DiOlKcvSHr8FA0tTcrXrE6zBo0xMDBAU1OTZ8+ecf/+fYKDg7l37x7R0dFUqFBB\nKQCd89WkSROlAPSb0tPTiY2NzRWMvnHjBseOHVParqWlRZUqVQgJCaFhw4ZK/chksrfeCN67dy9B\nQUHI5XKlfdzd3enfvz+Q/W+tW7du0qry1NRUFi5cyIIFC1Q6hiAIxZNCoSA2NlbpAlhXVxddXV2l\ndurq6ixdupSePXuyatUqRo8eza5du9i8eTPBwcEqr37KYWBggIGBgdI2VecQoejZNayMayeLfHM7\nunayEOkxBEH4bMyfP59vvvkGNTU1XFxcMDQ0JDo6mqCgIG7dupXryZ//4s35+unTpxw4cCDf9FV5\nmTlzJnK5nK1bt0oLRVQl5u6Spajm6nwDzMuWLWPEiBGULl2apUuXvvXi0dvbu8AHJgiCIOQWFxeH\nv78//v7+1K9fn5kzZ9K1a9ciSacwe/ZsHB0dmTJlCsuWLXuvfcuUKcOQIUMYMmQIf/75J4GBgVhZ\nWdG6dWs8PT3p3LmzyuekpqZG3bp1qVu3LoMGDQKy81BfvnyZCxcuEBoayrlz57hy5QqxsbEcPnyY\n58+fk5ycjLW1NU2bNpVWOpubm0s5S2UyGaUN9SltqI9RwzpKx1QoFKTKE1Hcj6ObsQV37txRSsGh\npaVF7dq1adGiBcbGxujo6KCurk5aWhoXLlxQCkAbGhrmGYA2NTXF0tISGxubfM9doVAQHx9PXFxc\nnqvW37VyQl9fny1bttCxY8d895PL5WRmZqKlpYVMJqNnz55cvnyZEydOSEH5wlyhIQjC+3vXTR+Z\nTEZCQkKux3FHjBjBuHHjcrWvVq0ac+bMYfLkyTRt2pTAwECSk5NxdnZW6nPnzp2YmZkVzEkIxVZO\n5fl/X7gO7GyBS8eCr0ovCIJQXDVq1Ihdu3bh7++Ps7MziYmJ6Orq0rx5c3bs2EGDBg2ktv91UUar\nVq2kfrS1tenQoQPTpk1Tqe/Y2Fj27duHlpYWrVu3lraXL1+eY8eOffCYhOKrKOZqmSKfq0I3NzdW\nrVpF2bJlcXNze2snQUFBhTK4whIdHU2HDh04duyYSqvlBEEQitqVK1fw9fVl37599O3bl7Fjx2Jp\naVnUw+LFixfY2toyb948XF1d/1NfycnJBAcHExAQQGxsLB4eHnzzzTdUqVKlQMb66NEjQkNDpZXO\nly5donz58hgZGUkr+V6+fEmTJk1o2rQpj8ooiK9hgG4VI2T/Czr/m42OMVPaKhfuUCgUxMXF5Uq7\nkVN0UENDA3Nzc2rXro2ZmRlGRkZoaWmRlZUlrYC+d+8e9+7d4+HDh0oBaBMTE6UAdI0aNd6ZImPC\nhAmYmZnlmyIDoHXr1lKBvjdTZDx58gRjY2NGjRpFy5Yt2bNnD//88w99+vRh6NChhISEMHfuXFxc\nXESKDEEQPpj4bF4ynY948r9CQjJG9LaihaVYuSwIgvC5EHN3yfAx5+p8VzC/GTQuaQFkQRCET0FG\nRgb79u3D19eXu3fvMmrUKJYuXSoVlCgOypcvz+7du/niiy+wtLTEysrqg/vS0dHhm2++4ZtvvuHK\nlSsEBgZiaWlJu3bt8PT0pGPHjv9ppXbVqlVxdnaWVtxlZGRw/fp1KeAcGhpKQkICz58/JywsjNgE\nOQ8fR5Oekop+rRoYmJtgULsGBrVNpKBzXf3c+adlMhnGxsYYGxtLKw1yKBQKnj17phR4Pn/+vBSA\nlslk1K5dm9q1a2NnZ0etWrXQ19dHXV2d+Ph4Hjx4QHh4ODt37pQC0AYGBrlWPjs5OSlVlc4RFRXF\njz/+yKtXr0hJSaFdu3b07NkzzxQZOSu+IyIi8PHxYc2aNVSvXh1DQ0P09fXR19enS5cuZGVl8fz5\nc+rXr6/0aP13332Hvr4+EyZMIDQ0FC8vL6VV1uXLl8fX1/eD/z4FQRCEomXXsLJIhyEIgiAIxdjH\nnKtVysEM2Uvq//nnHzIzM4Hsi+S0tDT++usvxo4dW2gDFARB+NzI5XLWrVvHqlWrqFq1KuPGjaNX\nr17FNs9Vo0aN8PX1xdnZmbCwsFz5uT6EtbU1a9asYcmSJWzfvp0ZM2YwfPhwhg4diru7+3sVpciP\nhoYGjRs3pnHjxnh6egLZBe3CwsIIDQ3l6PHj3Lt3Dw1tLRSZWSTef4T81j2SY5+TkfIKfbPq6Nm1\nJdX+Hk2bNqVWrVpSeo38yGQyjIyMMDIyomXLlkrvKRQKnj9/rrTi+ciRI9L3mZmZUvC5efPmDBw4\nEDMzM8qWLUtSUpK08jk8PJwGDRrkCjCnpKTg7e3NqlWrqFGjBllZWYwbN46zZ8/mGqdCoUAmkzFz\n5kxiYmL4+eef2bx5M3v27OHu3bt4e3szbNgwtmzZwq1bt6hVqxYzZ86UUqUEBwcTFRWltKq5ZcuW\nLF269IP+rgRBEARBEARBEITiS6UA89atW1m4cKEUXJZ21tB4r6TigiAIQv7+/vtv/Pz8CA4OxtHR\nkZ07d741/25x4urqSlhYGAMHDmT//v0FlhNaV1eXoUOHMnToUC5dukRAQAD169fHwcEBT09Pvvji\ni3cGdd+Hvr4+HTt2pGPHjkyfPp3Ih/c4EXGJUxfOce3KVeJu3SXjZTJVqlXDxKgqic/lbNiwgSlT\nppCQkCCl18j5UiXonEMmk1GhQgUqVKhAixYtcr3/4sULpbQbx44dw9/fn9u3b/P69Wtq164tpd64\ndesWBgYG6OvrS/vPnTuXtLQ0+vTpQ7t27ViyZAmLFy8mLi6OrKwsXFxc0NDQ4NGjR1hYWKCvr49C\noaBPnz6EhIRQv359Ro4cSVJSEs+fPycxMZGaNWvy119/0alTJynH3M8//8zq1avR09Njz549GBgY\nUL9+fTIzM5k8eTIPHjxAQ0ODGTNmYGFhUTB/cYLwGTl16hTr168nMjI7p56lpSXjx4+X0ibFxsay\ncuVKTp06RVJSEpUqVcLV1ZWBAwcCEBoayuDBg3Ol1zE3N2fatGk0btwYAAsLCw4cOJBnfveYmBjm\nzp3LpUuXKFWqFJ07d2by5MloamoW5qkLxcj5iMf4774GZFeiFyuZBUEQPszNmzfx9/cnLCyM5ORk\nypUrR7t27Rg/fjz6+vq4ublx9epVNDSyw3daWlrY29szY8YMqVifhYUF2traUi5mmUxG48aNmTp1\nKubm5gCEh4ezYMEC7t27R7Vq1Zg+fXqe1xzCp+Njz9X55mB+k4ODA7169WL48OHY29vz888/k5yc\nzKRJkxg/fjxt27Yt1EEWNJErRhCE4iIrK4uDBw/i6+vLn3/+iaenJ8OHDy+QFbofW3p6Ol988QXt\n2rVj7ty5hXacxMREtm3bRkBAAImJiQwdOpSvv/4aY+Pc6SoKUlZWFgBpaWlcvXpVSqtx4cIF4uPj\nady4MVWqVEFdXZ0XL14QERGhFHTOKSRYq1at/1TgIy9yuVxa+ZwThDY0NGT58uVSm8DAQGJjY5HL\n5UpFGVevXs3mzZs5deoUmpqaZGRkMHHiRBITE3n27BnlypXj7t27lCpVinr16jF06FDWr19Phw4d\n+Oeff5gwYYLUV1xcHF26dGHr1q3cuHGDO3fuEBoaSr9+/fj+++/R0dHB1NSU1NRUYmNjOXXqVIH+\nHAThUxcSEoKfnx8LFiygdevWZGZmsnXrVlauXMmOHTvQ09PD2dmZ3r174+7ujr6+PteuXcPLywtn\nZ2dGjx5NaGgo48aN48KFC1K/qamp/Pjjjxw5coQTJ04gk8neGmB2c3Ojbt26TJ48mcTEREaNGoWd\nnR1eXl4fdF7is3nJsv3wzVxFg1w7WUgFhQRBEATVXL16FXd3dzw8PHBzc0NPT4/o6GhWrFhBVFQU\nu3fvxs3Njc6dO0s3il++fMmoUaOoW7cu06dPB3LfFM7IyGDp0qX8/vvvHD9+nLi4OLp3786CBQvo\n2LEjv/76K7Nnz+bs2bMffHNYzN3FW1HM1SqtYI6Li6Nnz57SxeWff/5J586d+fbbb/n+++9LXIBZ\nEAShqCUlJbFp0yZWrFiBjo4O48aNY9++fWhraxf10D5YqVKlCAkJwcbGBhsbG3r06FEoxylbtizD\nhw/H09OTsLAwAgICsLCwoGPHjnh6emJvb1+gq5pz5PSpra1NixYtlO74x8bGSgUEQ0NDCQsLo2LF\ninTs2BEjIyOSkpLYtm0bkyZNIjExUWmVs42NDWZmZv8p6GxgYICtrS22trb5tqlSpQo3btxQ+tlE\nR0eza9cuqlatKn241NDQwMfHh2+//RZ3d3esra3p2bMnv/76K927d0dHR4eYmBilvjMyMvjtt99I\nSEhAJpMxbNgwNDQ0yMrKYvTo0dSoUYMKFSowefJkvvjiCyC7EnZSUpJS3mZBEPL36tUrFi9ezLJl\ny2jXrh0A6urqfP3118jlcu7cucPJkyexsbHB29tb2s/KyooFCxZw6NChfPvW1tamf//+bNmyhYSE\nBKWnH/4tLS0NHR0dRowYgaamJhUqVKB79+4cOXKk4E5WKLbyumCF/69SL4LMgiAIqpszZw6DBg1i\n5MiR0rZq1aqxYMECVqxYQWJiYq599PT0+PLLLzl48GC+/WpoaODs7MzGjRuJj49n3759tGrVio4d\nOwLQrVs3zMzMCv6EhGKhqOZqla7A9fX1pX/Ypqam3Lx5E8i+WI2KiiqUgQmCIHyKcvLXmpiYcOLE\nCdatW8elS5cYPHhwiQ4u5zA2Nmbnzp14eHhIc0VhkclkNGvWjPXr13Pv3j3at2+Pt7c3devWZcmS\nJTx9+rRQj/8mY2NjevTowYIFCzh69CgvXrxg7969dO7cmYyMDM6ePcvRo0epVq0aLi4u2NrakpWV\nRXBwMO3bt6d8+fJ06NCBKVOmEBISwp07d1DhAaP30r59eyIiIkhJSQGyV5x///33xMfHS4/cHT16\nFDc3N/r160dERAQKhYLffvuNjh07oqmpSZcuXTh58iQGBgbcuHFD6vunn37i+PHjuLm5ceLECVxc\nXJDJZLx48YLIyEjS09MpW7Ysx48fB7JXa7x48UIaiyAI73b58mUyMzNp06ZNrve8vb3p1KkTZ86c\n4csvv8z1vp2dHbNnz86378TEROlm3duCywCampr4+/srFZz9448/qFevnuonI5RI5yOe5HnBmmPb\noUjORzz5iCMSBEEouR4/fszff/9N3759c72noaHB+PHjKVu2bK73nj17xqFDh7C3t1fa/ua1Q0JC\nAkFBQdSpUwcDAwP++usvKlasyOjRo2nevDkuLi6kp6eL1FafoKKcq1VawWxvb8+sWbOYP38+LVq0\nYP78+bRu3ZrDhw9TpUqVQhmYIAjCp0KhUHDixAl8fX05c+YM7u7uXL58GRMTk6IeWqFo3rw58+fP\nx9nZmQsXLqCnp1foxyxXrhwjR45kxIgRhIaGEhAQgLm5OZ07d8bT05P27dsXeFqKt1FXV6dBgwY0\naNCAb775Bsh+nC08PFxKqxEaGopCoaBFixY0aNAAPT09aaXzhAkTSEpKyrXSuWbNmh98Hrq6ugwb\nNgxfX1/c3NxITk7GwcGBv//+m1u3btG7d2+pbdeuXQkODmbJkiVER0cjk8nYuXMnOjo6qKurExIS\ngqe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SUhIAc+fOldq5ubnRvXt36XVgYCAJCQmMHDmSIUOGEB4ejq+vL8uWLeP8+fNF9rMQ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\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xZ84cvHv3Du3bt691OTRFBiGEEEIIIaTZ43A42LJlC06dOoXs7Gw2XV5eHiNHjsS8efN4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\n38IAAAAASUVORK5CYII=\n",
+ "text": [
+ ""
+ ]
+ }
+ ],
+ "prompt_number": 43
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Without `flotilla`, `plot_pca` is quite a bit of code:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "import sys\n",
+ "from collections import defaultdict\n",
+ "from itertools import cycle\n",
+ "import math\n",
+ "\n",
+ "from sklearn import decomposition\n",
+ "from sklearn.preprocessing import StandardScaler\n",
+ "import pandas as pd\n",
+ "from matplotlib.gridspec import GridSpec, GridSpecFromSubplotSpec\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import seaborn as sns\n",
+ "\n",
+ "from flotilla.visualize.color import dark2\n",
+ "from flotilla.visualize.generic import violinplot\n",
+ "\n",
+ "\n",
+ "class DataFrameReducerBase(object):\n",
+ " \"\"\"\n",
+ "\n",
+ " Just like scikit-learn's reducers, but with prettied up DataFrames.\n",
+ "\n",
+ " \"\"\"\n",
+ "\n",
+ " def __init__(self, df, n_components=None, **decomposer_kwargs):\n",
+ "\n",
+ " # This magically initializes the reducer like DataFramePCA or DataFrameNMF\n",
+ " if df.shape[1] <= 3:\n",
+ " raise ValueError(\n",
+ " \"Too few features (n={}) to reduce\".format(df.shape[1]))\n",
+ " super(DataFrameReducerBase, self).__init__(n_components=n_components,\n",
+ " **decomposer_kwargs)\n",
+ " self.reduced_space = self.fit_transform(df)\n",
+ "\n",
+ " def relabel_pcs(self, x):\n",
+ " return \"pc_\" + str(int(x) + 1)\n",
+ "\n",
+ " def fit(self, X):\n",
+ " try:\n",
+ " assert type(X) == pd.DataFrame\n",
+ " except AssertionError:\n",
+ " sys.stdout.write(\"Try again as a pandas DataFrame\")\n",
+ " raise ValueError('Input X was not a pandas DataFrame, '\n",
+ " 'was of type {} instead'.format(str(type(X))))\n",
+ "\n",
+ " self.X = X\n",
+ " super(DataFrameReducerBase, self).fit(X)\n",
+ " self.components_ = pd.DataFrame(self.components_,\n",
+ " columns=self.X.columns).rename_axis(\n",
+ " self.relabel_pcs, 0)\n",
+ " try:\n",
+ " self.explained_variance_ = pd.Series(\n",
+ " self.explained_variance_).rename_axis(self.relabel_pcs, 0)\n",
+ " self.explained_variance_ratio_ = pd.Series(\n",
+ " self.explained_variance_ratio_).rename_axis(self.relabel_pcs,\n",
+ " 0)\n",
+ " except AttributeError:\n",
+ " pass\n",
+ "\n",
+ " return self\n",
+ "\n",
+ " def transform(self, X):\n",
+ " component_space = super(DataFrameReducerBase, self).transform(X)\n",
+ " if type(self.X) == pd.DataFrame:\n",
+ " component_space = pd.DataFrame(component_space,\n",
+ " index=X.index).rename_axis(\n",
+ " self.relabel_pcs, 1)\n",
+ " return component_space\n",
+ "\n",
+ " def fit_transform(self, X):\n",
+ " try:\n",
+ " assert type(X) == pd.DataFrame\n",
+ " except:\n",
+ " sys.stdout.write(\"Try again as a pandas DataFrame\")\n",
+ " raise ValueError('Input X was not a pandas DataFrame, '\n",
+ " 'was of type {} instead'.format(str(type(X))))\n",
+ " self.fit(X)\n",
+ " return self.transform(X)\n",
+ "\n",
+ "\n",
+ "class DataFramePCA(DataFrameReducerBase, decomposition.PCA):\n",
+ " pass\n",
+ "\n",
+ "\n",
+ "class DataFrameNMF(DataFrameReducerBase, decomposition.NMF):\n",
+ " def fit(self, X):\n",
+ " \"\"\"\n",
+ " duplicated fit code for DataFrameNMF because sklearn's NMF cheats for\n",
+ " efficiency and calls fit_transform. MRO resolves the closest\n",
+ " (in this package)\n",
+ " _single_fit_transform first and so there's a recursion error:\n",
+ "\n",
+ " def fit(self, X, y=None, **params):\n",
+ " self._single_fit_transform(X, **params)\n",
+ " return self\n",
+ " \"\"\"\n",
+ "\n",
+ " try:\n",
+ " assert type(X) == pd.DataFrame\n",
+ " except:\n",
+ " sys.stdout.write(\"Try again as a pandas DataFrame\")\n",
+ " raise ValueError('Input X was not a pandas DataFrame, '\n",
+ " 'was of type {} instead'.format(str(type(X))))\n",
+ "\n",
+ " self.X = X\n",
+ " # notice this is fit_transform, not fit\n",
+ " super(decomposition.NMF, self).fit_transform(X)\n",
+ " self.components_ = pd.DataFrame(self.components_,\n",
+ " columns=self.X.columns).rename_axis(\n",
+ " self.relabel_pcs, 0)\n",
+ " return self\n",
+ "\n",
+ "\n",
+ "class DataFrameICA(DataFrameReducerBase, decomposition.FastICA):\n",
+ " pass\n",
+ "\n",
+ "class DecompositionViz(object):\n",
+ " \"\"\"\n",
+ " Plots the reduced space from a decomposed dataset. Does not perform any\n",
+ " reductions of its own\n",
+ " \"\"\"\n",
+ "\n",
+ " def __init__(self, reduced_space, components_,\n",
+ " explained_variance_ratio_,\n",
+ " feature_renamer=None, groupby=None,\n",
+ " singles=None, pooled=None, outliers=None,\n",
+ " featurewise=False,\n",
+ " order=None, violinplot_kws=None,\n",
+ " data_type='expression', label_to_color=None,\n",
+ " label_to_marker=None,\n",
+ " scale_by_variance=True, x_pc='pc_1',\n",
+ " y_pc='pc_2', n_vectors=20, distance='L1',\n",
+ " n_top_pc_features=50, max_char_width=30):\n",
+ " \"\"\"Plot the results of a decomposition visualization\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " reduced_space : pandas.DataFrame\n",
+ " A (n_samples, n_dimensions) DataFrame of the post-dimensionality\n",
+ " reduction data\n",
+ " components_ : pandas.DataFrame\n",
+ " A (n_features, n_dimensions) DataFrame of how much each feature\n",
+ " contributes to the components (trailing underscore to be\n",
+ " consistent with scikit-learn)\n",
+ " explained_variance_ratio_ : pandas.Series\n",
+ " A (n_dimensions,) Series of how much variance each component\n",
+ " explains. (trailing underscore to be consistent with scikit-learn)\n",
+ " feature_renamer : function, optional\n",
+ " A function which takes the name of the feature and renames it,\n",
+ " e.g. from an ENSEMBL ID to a HUGO known gene symbol. If not\n",
+ " provided, the original name is used.\n",
+ " groupby : mapping function | dict, optional\n",
+ " A mapping of the samples to a label, e.g. sample IDs to\n",
+ " phenotype, for the violinplots. If None, all samples are treated\n",
+ " the same and are colored the same.\n",
+ " singles : pandas.DataFrame, optional\n",
+ " For violinplots only. If provided and 'plot_violins' is True,\n",
+ " will plot the raw (not reduced) measurement values as violin plots.\n",
+ " pooled : pandas.DataFrame, optional\n",
+ " For violinplots only. If provided, pooled samples are plotted as\n",
+ " black dots within their label.\n",
+ " outliers : pandas.DataFrame, optional\n",
+ " For violinplots only. If provided, outlier samples are plotted as\n",
+ " a grey shadow within their label.\n",
+ " featurewise : bool, optional\n",
+ " If True, then the \"samples\" are features, e.g. genes instead of\n",
+ " samples, and the \"features\" are the samples, e.g. the cells\n",
+ " instead of the gene ids. Essentially, the transpose of the\n",
+ " original matrix. If True, then violins aren't plotted. (default\n",
+ " False)\n",
+ " order : list-like\n",
+ " The order of the labels for the violinplots, e.g. if the data is\n",
+ " from a differentiation timecourse, then this would be the labels\n",
+ " of the phenotypes, in the differentiation order.\n",
+ " violinplot_kws : dict\n",
+ " Any additional parameters to violinplot\n",
+ " data_type : 'expression' | 'splicing', optional\n",
+ " For violinplots only. The kind of data that was originally used\n",
+ " for the reduction. (default 'expression')\n",
+ " label_to_color : dict, optional\n",
+ " A mapping of the label, e.g. the phenotype, to the desired\n",
+ " plotting color (default None, auto-assigned with the groupby)\n",
+ " label_to_marker : dict, optional\n",
+ " A mapping of the label, e.g. the phenotype, to the desired\n",
+ " plotting symbol (default None, auto-assigned with the groupby)\n",
+ " scale_by_variance : bool, optional\n",
+ " If True, scale the x- and y-axes by their explained_variance_ratio_\n",
+ " (default True)\n",
+ " {x,y}_pc : str, optional\n",
+ " Principal component to plot on the x- and y-axis. (default \"pc_1\"\n",
+ " and \"pc_2\")\n",
+ " n_vectors : int, optional\n",
+ " Number of vectors to plot of the principal components. (default 20)\n",
+ " distance : 'L1' | 'L2', optional\n",
+ " The distance metric to use to plot the vector lengths. L1 is\n",
+ " \"Cityblock\", i.e. the sum of the x and y coordinates, and L2 is\n",
+ " the traditional Euclidean distance. (default \"L1\")\n",
+ " n_top_pc_features : int, optional\n",
+ " THe number of top features from the principal components to plot.\n",
+ " (default 50)\n",
+ " max_char_width : int, optional\n",
+ " Maximum character width of a feature name. Useful for crazy long\n",
+ " feature IDs like MISO IDs\n",
+ " \"\"\"\n",
+ " self.reduced_space = reduced_space\n",
+ " self.components_ = components_\n",
+ " self.explained_variance_ratio_ = explained_variance_ratio_\n",
+ "\n",
+ " self.singles = singles\n",
+ " self.pooled = pooled\n",
+ " self.outliers = outliers\n",
+ "\n",
+ " self.groupby = groupby\n",
+ " self.order = order\n",
+ " self.violinplot_kws = violinplot_kws if violinplot_kws is not None \\\n",
+ " else {}\n",
+ " self.data_type = data_type\n",
+ " self.label_to_color = label_to_color\n",
+ " self.label_to_marker = label_to_marker\n",
+ " self.n_vectors = n_vectors\n",
+ " self.x_pc = x_pc\n",
+ " self.y_pc = y_pc\n",
+ " self.pcs = (self.x_pc, self.y_pc)\n",
+ " self.distance = distance\n",
+ " self.n_top_pc_features = n_top_pc_features\n",
+ " self.featurewise = featurewise\n",
+ " self.feature_renamer = feature_renamer\n",
+ " self.max_char_width = max_char_width\n",
+ "\n",
+ " if self.label_to_color is None:\n",
+ " colors = cycle(dark2)\n",
+ "\n",
+ " def color_factory():\n",
+ " return colors.next()\n",
+ "\n",
+ " self.label_to_color = defaultdict(color_factory)\n",
+ "\n",
+ " if self.label_to_marker is None:\n",
+ " markers = cycle(['o', '^', 's', 'v', '*', 'D', 'h'])\n",
+ "\n",
+ " def marker_factory():\n",
+ " return markers.next()\n",
+ "\n",
+ " self.label_to_marker = defaultdict(marker_factory)\n",
+ "\n",
+ " if self.groupby is None:\n",
+ " self.groupby = dict.fromkeys(self.reduced_space.index, 'all')\n",
+ " self.grouped = self.reduced_space.groupby(self.groupby, axis=0)\n",
+ " if order is not None:\n",
+ " self.color_ordered = [self.label_to_color[x] for x in self.order]\n",
+ " else:\n",
+ " self.color_ordered = [self.label_to_color[x] for x in\n",
+ " self.grouped.groups]\n",
+ "\n",
+ " self.loadings = self.components_.ix[[self.x_pc, self.y_pc]]\n",
+ "\n",
+ " # Get the explained variance\n",
+ " if explained_variance_ratio_ is not None:\n",
+ " self.vars = explained_variance_ratio_[[self.x_pc, self.y_pc]]\n",
+ " else:\n",
+ " self.vars = pd.Series([1., 1.], index=[self.x_pc, self.y_pc])\n",
+ "\n",
+ " if scale_by_variance:\n",
+ " self.loadings = self.loadings.multiply(self.vars, axis=0)\n",
+ "\n",
+ " # sort features by magnitude/contribution to transformation\n",
+ " reduced_space = self.reduced_space[[self.x_pc, self.y_pc]]\n",
+ " farthest_sample = reduced_space.apply(np.linalg.norm, axis=0).max()\n",
+ " whole_space = self.loadings.apply(np.linalg.norm).max()\n",
+ " scale = .25 * farthest_sample / whole_space\n",
+ " self.loadings *= scale\n",
+ "\n",
+ " ord = 2 if self.distance == 'L2' else 1\n",
+ " self.magnitudes = self.loadings.apply(np.linalg.norm, ord=ord)\n",
+ " self.magnitudes.sort(ascending=False)\n",
+ "\n",
+ " self.top_features = set([])\n",
+ " self.pc_loadings_labels = {}\n",
+ " self.pc_loadings = {}\n",
+ " for pc in self.pcs:\n",
+ " x = self.components_.ix[pc].copy()\n",
+ " x.sort(ascending=True)\n",
+ " half_features = int(self.n_top_pc_features / 2)\n",
+ " if len(x) > self.n_top_pc_features:\n",
+ " a = x[:half_features]\n",
+ " b = x[-half_features:]\n",
+ " labels = np.r_[a.index, b.index]\n",
+ " self.pc_loadings[pc] = np.r_[a, b]\n",
+ " else:\n",
+ " labels = x.index\n",
+ " self.pc_loadings[pc] = x\n",
+ "\n",
+ " self.pc_loadings_labels[pc] = labels\n",
+ " self.top_features.update(labels)\n",
+ "\n",
+ " def __call__(self, ax=None, title='', plot_violins=True,\n",
+ " show_point_labels=False,\n",
+ " show_vectors=True,\n",
+ " show_vector_labels=True,\n",
+ " markersize=10, legend=True):\n",
+ " gs_x = 14\n",
+ " gs_y = 12\n",
+ "\n",
+ " if ax is None:\n",
+ " self.fig_reduced, ax = plt.subplots(1, 1, figsize=(20, 10))\n",
+ " gs = GridSpec(gs_x, gs_y)\n",
+ "\n",
+ " else:\n",
+ " gs = GridSpecFromSubplotSpec(gs_x, gs_y, ax.get_subplotspec())\n",
+ " self.fig_reduced = plt.gcf()\n",
+ "\n",
+ " ax_components = plt.subplot(gs[:, :5])\n",
+ " ax_loading1 = plt.subplot(gs[:, 6:8])\n",
+ " ax_loading2 = plt.subplot(gs[:, 10:14])\n",
+ "\n",
+ " self.plot_samples(show_point_labels=show_point_labels,\n",
+ " title=title, show_vectors=show_vectors,\n",
+ " show_vector_labels=show_vector_labels,\n",
+ " markersize=markersize, legend=legend,\n",
+ " ax=ax_components)\n",
+ " self.plot_loadings(pc=self.x_pc, ax=ax_loading1)\n",
+ " self.plot_loadings(pc=self.y_pc, ax=ax_loading2)\n",
+ " sns.despine()\n",
+ " self.fig_reduced.tight_layout()\n",
+ "\n",
+ " if plot_violins and not self.featurewise and self.singles is not None:\n",
+ " self.plot_violins()\n",
+ " return self\n",
+ "\n",
+ " def shorten(self, x):\n",
+ " if len(x) > self.max_char_width:\n",
+ " return '{}...'.format(x[:self.max_char_width])\n",
+ " else:\n",
+ " return x\n",
+ "\n",
+ " def plot_samples(self, show_point_labels=True,\n",
+ " title='DataFramePCA', show_vectors=True,\n",
+ " show_vector_labels=True, markersize=10,\n",
+ " three_d=False, legend=True, ax=None):\n",
+ "\n",
+ " \"\"\"\n",
+ " Given a pandas dataframe, performs DataFramePCA and plots the results in a\n",
+ " convenient single function.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " groupby : groupby\n",
+ " How to group the samples by color/label\n",
+ " label_to_color : dict\n",
+ " Group labels to a matplotlib color E.g. if you've already chosen\n",
+ " specific colors to indicate a particular group. Otherwise will\n",
+ " auto-assign colors\n",
+ " label_to_marker : dict\n",
+ " Group labels to matplotlib marker\n",
+ " title : str\n",
+ " title of the plot\n",
+ " show_vectors : bool\n",
+ " Whether or not to draw the vectors indicating the supporting\n",
+ " principal components\n",
+ " show_vector_labels : bool\n",
+ " whether or not to draw the names of the vectors\n",
+ " show_point_labels : bool\n",
+ " Whether or not to label the scatter points\n",
+ " markersize : int\n",
+ " size of the scatter markers on the plot\n",
+ " text_group : list of str\n",
+ " Group names that you want labeled with text\n",
+ " three_d : bool\n",
+ " if you want hte plot in 3d (need to set up the axes beforehand)\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " For each vector in data:\n",
+ " x, y, marker, distance\n",
+ " \"\"\"\n",
+ " if ax is None:\n",
+ " ax = plt.gca()\n",
+ "\n",
+ " # Plot the samples\n",
+ " for name, df in self.grouped:\n",
+ " color = self.label_to_color[name]\n",
+ " marker = self.label_to_marker[name]\n",
+ " x = df[self.x_pc]\n",
+ " y = df[self.y_pc]\n",
+ " ax.plot(x, y, color=color, marker=marker, linestyle='None',\n",
+ " label=name, markersize=markersize, alpha=0.75,\n",
+ " markeredgewidth=.1)\n",
+ " try:\n",
+ " if not self.pooled.empty:\n",
+ " pooled_ids = x.index.intersection(self.pooled.index)\n",
+ " pooled_x, pooled_y = x[pooled_ids], y[pooled_ids]\n",
+ " ax.plot(pooled_x, pooled_y, 'o', color=color, marker=marker,\n",
+ " markeredgecolor='k', markeredgewidth=2,\n",
+ " label='{} pooled'.format(name),\n",
+ " markersize=markersize, alpha=0.75)\n",
+ " except AttributeError:\n",
+ " pass\n",
+ " try:\n",
+ " if not self.outliers.empty:\n",
+ " outlier_ids = x.index.intersection(self.outliers.index)\n",
+ " outlier_x, outlier_y = x[outlier_ids], y[outlier_ids]\n",
+ " ax.plot(outlier_x, outlier_y, 'o', color=color,\n",
+ " marker=marker,\n",
+ " markeredgecolor='lightgrey', markeredgewidth=5,\n",
+ " label='{} outlier'.format(name),\n",
+ " markersize=markersize, alpha=0.75)\n",
+ " except AttributeError:\n",
+ " pass\n",
+ " if show_point_labels:\n",
+ " for args in zip(x, y, df.index):\n",
+ " ax.text(*args)\n",
+ "\n",
+ " # Plot vectors, if asked\n",
+ " if show_vectors:\n",
+ " for vector_label in self.magnitudes[:self.n_vectors].index:\n",
+ " x, y = self.loadings[vector_label]\n",
+ " ax.plot([0, x], [0, y], color='k', linewidth=1)\n",
+ " if show_vector_labels:\n",
+ " x_offset = math.copysign(5, x)\n",
+ " y_offset = math.copysign(5, y)\n",
+ " horizontalalignment = 'left' if x > 0 else 'right'\n",
+ " if self.feature_renamer is not None:\n",
+ " renamed = self.feature_renamer(vector_label)\n",
+ " else:\n",
+ " renamed = vector_label\n",
+ " ax.annotate(renamed, (x, y),\n",
+ " textcoords='offset points',\n",
+ " xytext=(x_offset, y_offset),\n",
+ " horizontalalignment=horizontalalignment)\n",
+ "\n",
+ " # Label x and y axes\n",
+ " ax.set_xlabel(\n",
+ " 'Principal Component {} (Explains {:.2f}% Of Variance)'.format(\n",
+ " str(self.x_pc), 100 * self.vars[self.x_pc]))\n",
+ " ax.set_ylabel(\n",
+ " 'Principal Component {} (Explains {:.2f}% Of Variance)'.format(\n",
+ " str(self.y_pc), 100 * self.vars[self.y_pc]))\n",
+ " ax.set_title(title)\n",
+ "\n",
+ " if legend:\n",
+ " ax.legend()\n",
+ " sns.despine()\n",
+ "\n",
+ " def plot_loadings(self, pc='pc_1', n_features=50, ax=None):\n",
+ " loadings = self.pc_loadings[pc]\n",
+ " labels = self.pc_loadings_labels[pc]\n",
+ "\n",
+ " if ax is None:\n",
+ " ax = plt.gca()\n",
+ "\n",
+ " ax.plot(loadings, np.arange(loadings.shape[0]), 'o')\n",
+ "\n",
+ " ax.set_yticks(np.arange(max(loadings.shape[0], n_features)))\n",
+ " ax.set_title(\"Component \" + pc)\n",
+ "\n",
+ " x_offset = max(loadings) * .05\n",
+ " ax.set_xlim(left=loadings.min() - x_offset,\n",
+ " right=loadings.max() + x_offset)\n",
+ "\n",
+ " if self.feature_renamer is not None:\n",
+ " labels = map(self.feature_renamer, labels)\n",
+ " else:\n",
+ " labels = labels\n",
+ "\n",
+ " labels = map(self.shorten, labels)\n",
+ " # ax.set_yticklabels(map(shorten, labels))\n",
+ " ax.set_yticklabels(labels)\n",
+ " for lab in ax.get_xticklabels():\n",
+ " lab.set_rotation(90)\n",
+ " sns.despine(ax=ax)\n",
+ "\n",
+ " def plot_explained_variance(self, title=\"PCA explained variance\"):\n",
+ " \"\"\"If the reducer is a form of PCA, then plot the explained variance\n",
+ " ratio by the components.\n",
+ " \"\"\"\n",
+ " # Plot the explained variance ratio\n",
+ " assert self.explained_variance_ratio_ is not None\n",
+ " import matplotlib.pyplot as plt\n",
+ " import seaborn as sns\n",
+ "\n",
+ " fig, ax = plt.subplots()\n",
+ " ax.plot(self.explained_variance_ratio_, 'o-')\n",
+ "\n",
+ " xticks = np.arange(len(self.explained_variance_ratio_))\n",
+ " ax.set_xticks(xticks)\n",
+ " ax.set_xticklabels(xticks + 1)\n",
+ " ax.set_xlabel('Principal component')\n",
+ " ax.set_ylabel('Fraction explained variance')\n",
+ " ax.set_title(title)\n",
+ " sns.despine()\n",
+ "\n",
+ " def plot_violins(self):\n",
+ " \"\"\"Make violinplots of each feature\n",
+ "\n",
+ " Must be called after plot_samples because it depends on the existence\n",
+ " of the \"self.magnitudes\" attribute.\n",
+ " \"\"\"\n",
+ " ncols = 4\n",
+ " nrows = 1\n",
+ " vector_labels = list(set(self.magnitudes[:self.n_vectors].index.union(\n",
+ " pd.Index(self.top_features))))\n",
+ " while ncols * nrows < len(vector_labels):\n",
+ " nrows += 1\n",
+ " self.fig_violins, axes = plt.subplots(nrows=nrows, ncols=ncols,\n",
+ " figsize=(4 * ncols, 4 * nrows))\n",
+ "\n",
+ " if self.feature_renamer is not None:\n",
+ " renamed_vectors = map(self.feature_renamer, vector_labels)\n",
+ " else:\n",
+ " renamed_vectors = vector_labels\n",
+ " labels = [(y, x) for (y, x) in sorted(zip(renamed_vectors,\n",
+ " vector_labels))]\n",
+ "\n",
+ " for (renamed, feature_id), ax in zip(labels, axes.flat):\n",
+ " singles = self.singles[feature_id] if self.singles is not None \\\n",
+ " else None\n",
+ " pooled = self.pooled[feature_id] if self.pooled is not None else \\\n",
+ " None\n",
+ " outliers = self.outliers[feature_id] if self.outliers is not None \\\n",
+ " else None\n",
+ " title = '{}\\n{}'.format(feature_id, renamed)\n",
+ " violinplot(singles, pooled_data=pooled, outliers=outliers,\n",
+ " groupby=self.groupby, color_ordered=self.color_ordered,\n",
+ " order=self.order, title=title,\n",
+ " ax=ax, data_type=self.data_type,\n",
+ " **self.violinplot_kws)\n",
+ "\n",
+ " # Clear any unused axes\n",
+ " for ax in axes.flat:\n",
+ " # Check if the plotting space is empty\n",
+ " if len(ax.collections) == 0 or len(ax.lines) == 0:\n",
+ " ax.axis('off')\n",
+ " self.fig_violins.tight_layout()\n",
+ "\n",
+ "# Notice we're using the original data, nothing from \"study\"\n",
+ "lps_response_genes = expression_feature_data.index[expression_feature_data.gene_category == 'LPS Response']\n",
+ "subset = expression_filtered.ix[singles_ids, lps_response_genes].dropna(how='all', axis=1)\n",
+ "subset_standardized = pd.DataFrame(StandardScaler().fit_transform(subset),\n",
+ " index=subset.index, columns=subset.columns)\n",
+ "\n",
+ "\n",
+ "pca = DataFramePCA(subset_standardized)\n",
+ "visualizer = DecompositionViz(pca.reduced_space, pca.components_, pca.explained_variance_ratio_)\n",
+ "visualizer()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 44,
+ "text": [
+ "<__main__.DecompositionViz at 0x1137f8450>"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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s1aRJE7MxJk+ejLu7O3Xr1qV27dp8/vnnjBo1iubNm5vaREZGmvp7eHgwevRo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4s6NDX/529UCCGEEEIIIYQQ4lnRK8H8/vvvM378eAwNDenTpw9ffPEFI0aM4OrVq3Tu3Pmp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\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27MDdu3exYMECAPlz706ZMgUDBw7EyZMnsXXrVsTFxeHnn38utY6RkZEQBAF6enpFrjc2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\nAABJRU5ErkJggg==\n",
+ "text": [
+ ""
+ ]
+ }
+ ],
+ "prompt_number": 44
+ },
+ {
+ "cell_type": "heading",
+ "level": 3,
+ "metadata": {},
+ "source": [
+ "Figure 4b"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "lps_response_genes = study.expression.feature_subsets['gene_category: LPS Response']\n",
+ "lps_response = study.expression.singles.ix[:, lps_response_genes].dropna(how='all', axis=1)\n",
+ "lps_response.head()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "html": [
+ ""
+ ]
+ }
+ ],
+ "prompt_number": 48
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Not quite sure where the elbow is here. looks like there's a big drop off after $k=1$, but that could just be an illusion. Since they didn't specify which version of the elbow method they used, I'm not going to investigate this further, and just see if we can see what they see with the $k=5$ clusters that they found was optimal.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "kmeans = KMeans(n_clusters=5)\n",
+ "lps_response_corr_clusters = kmeans.fit_predict(lps_response_corr.values)\n",
+ "lps_response_corr_clusters"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 49,
+ "text": [
+ "array([1, 1, 2, 4, 0, 1, 3, 4, 2, 4, 0, 3, 2, 2, 3, 3, 0, 1, 0, 3, 0, 1, 2,\n",
+ " 0, 0, 3, 3, 2, 4, 4, 0, 4, 4, 0, 4, 0, 3, 4, 2, 1, 4, 4, 4, 3, 1, 4,\n",
+ " 0, 0, 4, 1, 3, 3, 4, 0, 0, 2, 0, 0, 0, 4, 1, 3, 4, 3, 3, 4, 2, 2, 4,\n",
+ " 3, 0, 4, 0, 3, 4, 2, 2, 4, 2, 3, 3, 3, 1, 1, 4, 1, 2, 2, 2, 1, 1, 3,\n",
+ " 1, 1, 4, 3, 3, 3, 3, 1, 1, 4, 0, 2, 0, 0, 2, 3, 4, 4, 2, 0, 0, 3, 2,\n",
+ " 4, 0, 2, 4, 3, 4, 2, 2, 4, 1, 4, 3, 0, 1, 2, 3, 3, 4, 4, 0, 0, 4, 3,\n",
+ " 2, 1, 0, 4, 2, 0, 4, 2, 4, 0, 1, 0, 0, 3, 3, 3, 3, 1, 4, 3, 0, 2, 2,\n",
+ " 3, 4, 1, 3, 4, 2, 2, 2, 3, 3, 3, 4, 0, 1, 3, 1, 0, 2, 1, 1, 0, 2, 4,\n",
+ " 0, 1, 3, 2, 1, 3, 0, 1, 1, 2, 4, 3, 1, 0, 0, 0, 3, 3, 2, 1, 3, 1, 1,\n",
+ " 4, 4, 3, 2, 3, 3, 1, 4, 3, 4, 3, 0, 1, 3, 3, 3, 3, 3, 1, 4, 1, 0, 3,\n",
+ " 3, 2, 4, 3, 2, 0, 0, 3, 1, 1, 4, 3, 2, 4, 4, 3, 1, 1, 1, 0, 4, 1, 1,\n",
+ " 0, 0, 4, 0, 0, 0, 3, 4, 3, 4, 3, 3, 3, 3, 0, 3, 4, 1, 2, 4, 1, 2, 2,\n",
+ " 0, 0, 0, 4, 0, 2, 4, 0, 2, 4, 0, 4, 0, 3, 1, 3, 2, 3, 0, 3, 3, 3, 2,\n",
+ " 1, 2, 2, 2, 2, 2, 4, 3, 2, 4, 3, 2, 2, 3, 1, 4, 1, 0, 0, 0, 0, 1, 4,\n",
+ " 1, 4, 4, 3, 1, 1, 1, 1, 1, 2, 1, 2, 0, 4, 3, 4, 0, 3, 3, 3, 0, 3, 2,\n",
+ " 2, 3, 0, 0, 2, 4, 4, 0, 1, 1, 3, 4, 2, 0, 3, 3, 0, 1, 0, 0, 4, 3, 2,\n",
+ " 3, 3, 0, 2, 3, 3, 1, 1, 1, 3, 4, 2, 2, 2, 2, 3, 3, 2, 0, 1, 1, 1, 3,\n",
+ " 3, 3, 4, 4, 0, 4, 3, 3, 1, 0, 0, 3, 3, 0, 3, 3, 0, 1, 4, 4, 4, 3, 4,\n",
+ " 3, 1, 3, 1, 2, 2, 1, 4, 0, 1, 0, 3, 2, 0, 1, 1, 2, 4, 1, 0, 3, 0, 3,\n",
+ " 3, 1, 3, 1, 4, 3, 1, 1, 2, 4, 1, 4, 2, 3, 0, 1, 4, 3, 2, 3, 3, 1, 1,\n",
+ " 2, 3, 1, 2, 1, 0, 0, 4, 3, 3, 1, 3, 4, 0, 3, 0, 4, 0, 4, 1, 4, 0, 1,\n",
+ " 3, 0, 1, 0, 4, 3, 2, 2, 3, 3, 1, 0, 2, 4, 1, 1, 4, 0, 2, 2, 3, 2, 4,\n",
+ " 1, 0, 3, 4, 2, 1, 1, 3, 3, 0, 0, 0, 3, 3, 1, 0, 3, 2, 3, 3, 0, 2, 0,\n",
+ " 1, 3, 0, 3, 4, 4, 1, 2, 4, 2, 3, 3, 3, 2, 3, 2, 4, 4, 1, 1, 4, 3, 2,\n",
+ " 2, 4, 0, 1, 2, 0, 3, 0, 4, 0, 2, 1, 1, 3, 0, 2, 1, 3, 3, 1, 2, 0, 0,\n",
+ " 1, 2, 0, 0, 4, 3, 4, 0, 1, 4, 1, 3, 0, 2, 2, 2, 0, 3, 1, 3, 1, 4, 3,\n",
+ " 4, 2, 2, 3, 2, 0, 0, 4, 2, 1, 1, 1, 3, 3, 1, 2, 2, 0, 1, 2, 3, 0, 4,\n",
+ " 1, 3, 2, 2, 2, 1, 2, 1, 4], dtype=int32)"
+ ]
+ }
+ ],
+ "prompt_number": 49
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now let's create a dataframe with these genes in their cluster orders."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "gene_to_cluster = dict(zip(lps_response_corr.columns, lps_response_corr_clusters))\n",
+ "\n",
+ "dfs = []\n",
+ "for name, df in lps_response_corr.groupby(gene_to_cluster):\n",
+ " dfs.append(df)\n",
+ "lps_response_corr_ordered_by_clusters = pd.concat(dfs)\n",
+ "\n",
+ "# Make symmetric, since we created this dataframe by smashing rows on top of each other, we need to reorder the columns\n",
+ "lps_response_corr_ordered_by_clusters = lps_response_corr_ordered_by_clusters.ix[:, lps_response_corr_ordered_by_clusters.index]\n",
+ "lps_response_corr_ordered_by_clusters.head()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "html": [
+ ""
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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/+nlyvdc/3Faz+zj96a9T9PCD9D/+Y8ILb0URBcqZ5NyUQM3KDciSyKXRBJV6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Xzs5z9fIqFrIaO07+kjMX3EWioPP0qVlu6K6mzmvn4ePTfMV5ArmmGa2ylU+/NIHbJnPV8iqe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4LqK6WQLBA7tJ/u54+y6qkHyI32ERuK40qN8fLvP+Cale0U0hkO3fc46//wXWbP9pOZSzF1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\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2I5itjL/8J4quvxkAtWoJYjLM2C9/QNHWLTBvHcbZ3Yjz1vC/uHvPYLmuMm372qFz7j7d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\nwiqLTKdVvr+1kw8vq6K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42lSYT7bWpm6G1c0rU1U8Fpn1USvy4EFybVs5PVdgY1hmoiTh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/6j43+ysyo57bhOvcRUqIfrG2Sk/Dzm5CBLDRuw+6PI+XlclWXSjiih5gYEvcKo6cN7fA+/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\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K/UfwhkIIskwwP85bqytsWrkIRTBRjDwvFaIsKvbydLmRzb4ctZd2QW0P6ad+zUN0cGd5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Fddg3H4MDYhQ2BgJzQuZvgbN5MZHsLd1kD8yAEMC9cznSowGssSyeRZUOFG1AoEMzp2sUBIsNBR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\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heprdCSdeiwEQeLYvzcZaJ1aDwC+OTLI+/C4EF+DSkuQEBWMpw8G4iZDdSDRXYmnAiskg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q8tA5ECV0VyEvh6xUui20TcW5q9aMFBnLQ86zfQi6xmG9Eo/FQKnDgPX4cxwsv47Nc++Rbj9L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cNdyFPXUu4xcXIwzoroUUTdBxEVubeNLy+4Dmu4G02386v15TB4HqMkomo6NdoA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\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bRAe6zZtLqgl0IugqB6Ua5ne9gWB3IReUo1ucJL1VWIO9CJEp0mcOEt74FbwmkIP9iOk4\nutWNdvZjxKb/mkMebie44x38V95K9txhlKoGkkc+wHzezRxPe2i1hNFPfkBsybX88egot80v4YP+\nEBt2P0j/pd9DkQRkUWC2NoLmKmZaVfBaJLpn0sz2yMjhUYRUlD16BS0FNsZiKg2Js6SO7ya16cvY\nhSxCNkn02QewVVXxcsmlXFtr4ZmuBDc2eBFj04iZGCFPLSZJwD56AnSNRNkCJhM5aEfbxEf0VK3H\naZIoTAyCoRPzVGNLBZGSMxhCzgsglzV9+srm/6K2j7z9ma7/ade19333s97CP6QCj574m9f/bmdV\nNFnQU3GkQ69jpJPEO8/iWHYl8nQvYsNylIo5dDzzXTwrVkNhFUyNIGYS0LSW9CsPYy4uRY9HMMIB\n9JlJZFlBdnhIhyZJqQbT5wJI/mIMUcZQMwh2F0Z5E7rFzeQHe3BWFFJUvQAxkoSxbgL7D1JYWM7k\neBDv+nIMUcYlaejRIHoshJxNo5vsqKEg9vqFFGQmeWXcxBWJPrTAOOKaG9H3vogWGEOpmI1Jz6BX\nzsM0fpZByjF3daOuNPDrYaa276Do6gIMXSN2eB+uvDIyvWdyHV9dQ3IH0POqGA6kaRZymZx6IopS\nVos+2UePUEjNqT0gm7hooQ9TOgyCiHP8FNnhHoS5lYijnSjZJKmpESR/EaHOIcS+MfxLFyObLWQH\nO0mPjWA1WxDyqtBPfUjw2BkK2zaTLpiTa/IaOjHZRanJYDohs7rShWmqC6YmMbJZjEWbiWV0JuNp\narwWeoJxVnz4IPGrvkf11BGy1UuQ5q7DX1RBtu8MYdscCk2gjvaRaTGYiKWRk0EWeWQ0q51wRsNt\nllmYJzOjAbKJScmL1yIjWg9DAAAgAElEQVRBUiWrQyCZxWmS8Vz9JRarHvJnulB7T6IvvwYpMoEt\n2YXN7kHwF+Frf5fBuk3URM8RL2rCZ5U5GdQZi6VpKC9GGTtDumIhg90zNBbYER0eRqIppv1ltOhR\neqMiTaWunMiJDnFqDFaUl6FpBr0zaeYN7GGgaAMlTjPe+irOhVOskYZyWbmRCcaiJtZWWdH3voBc\n04To9qPbPIiAxyoz9PLrVH2zAc2ZTzCpMhJJITo8rC+3IY31kTq2m7Z1d1LkUEhkdbIdHzPRVstY\nNM1MKktrkYPi1AQZVScs2PBl0xiygru2lNPxNHaTyL6hEF6LQmOBna5gnHWt5zGT1ijRsoz0BAkk\nVJZmzvJytporFpYh+KyIoRGSeXmEUhp1XjMzKY1oWsM0ewG9wSTViy8iZfESC2fQTVb6UzKaDnmO\nCsYmZpjwW0lkNYLJLNOJLGt8bjqSSdKuEs4MRrCXlqMEwwQSWeYX2bC0bSSp6nzzzS4eX+9mUrcR\ny+hIgsBEPEsyq/Hh2Ulu3txCWtNR7R6GQml+tq2TS1pLsCkSgZQVl7eKve1TzPLZSWkGmgFPHhri\n3za2MZ0yiGRUnP4aBDVNqaCjyXnEkhqFi85n/1CYq9QMrYV2dAMKslMokpWBaJZYRkMwWfCsOR9B\nMTGR0ih2elFqWjBSMRT7LqTYFJojn8zH72NduA7DmUdENbCX5iOd6UPTDcylFejREB6LRNBipvvj\nEeY/tIqsriO6fFRtbMayYG3uvLGa8VokhDlLMQwdQTaR9ZZjr6nBVlEOc5Yz0/trKjctQnY4oH4F\nUj0UWOzIjUvRLG4cC5aCKKE5C3A0tiCkojgrCrAV+3CaJeqL7ETSOgXF1QTqN9J4vJP808No7mLk\nxqVUb+nL4Ulb1sNoO0Ymhbz8cuateoe85pzjX6grpXJ1Bcmsjuj04ihxcFGxk7fGomw2DHxN1Zjq\n5pId7kGbmWSqfZIKi8R4CpylC7HXnES3eTHXzUUsrMK3KIBg6Bi1i8hY3IijvThXbgJZQLDaMSQF\nQVEAMEsissWMbDEzuexWygPtGHMWkadruTUL5zCSEimVQalqYPLdd0mHojgXLgVyEVj2Ih+BpIbN\n48j5Aqy5t3XigvMpCgcInu5C8hbgX7qYQF4j7rFeopIDi9+NEZpEq13MxGAaYf75jMVUahMzxOQC\nHB+/ibj+VvQzBzFCkwBYBQGlvhHdZAVBZCSc60SWuYuQg4M4TFaIBcBkobXYSp/lSmZlBsj4axB0\nlfGYis9djdORj9jfwfs9QW6y95M+sQfBbCG87g7M5/5EvPUKitJjkF+Ob+OFjHvmkG/vBLsH27It\nZJwFTARj6F4Pckk1dllgLJTi5fZJREHAu+VanI4UH0yKrPelELIqGcmMyTCQj/6V3WIbdd5iNFcR\nM7YSUuNxXNEh9oU91FY2MfG7R6iYsx/DV0bKU4HZ60RecjEjp5Oopjw2VFsIZgWmNB+NwS7asyXs\nHQhy72w3TA6gqEm6AirjsTSL6lcyFcgyO3aWeHELJkMFA+TIeC53fLw/Jyg+Y7Eayfzviq669rI1\nn/UW/p/W3+2spt7+HeHT7eRfeTPZvjOoUyPEt3wD88v/TnRwAv/CFox0CumCO5C6D2CoGQw1mwvU\nz8TJHn4PwWQhNdiPfd5iku1HGd58L3V92zCa1iEPHiN+aBf2ZRsxXAVEnOU41Aic/hApv5Rs7+nc\nqMHUCFJFA+q5w0zs2E3hdx/AOPBajvgimzDVtqBbnCTe/wumwmKUhiVo430MPPUXqr9/P3rPMfRk\nHNlfhBoYJ7TkOuxv/JIza+5msdZLuqQZKRVBTMyQ2f8moY4eCm67i5mXnsDVMhfBaiewexcmpw33\nqo25PEJPAbGdr5KJxPFtvAgtGkIqqSNTOAcxk0Db9kfigyO4Fy0hO9xN4sJvEr//TgxNp+K22xh7\n6XmKr70RdI1o1TKSqvFJ9mTe4ReILr0e+dmf4N5wMZq7BCkeIL7nLZKXfou8qVMckGexyJVGP/gW\nv5DWcPVz32L3lx5hQbGLRFb7xLg0UbM25/ZtuxgpMsY5pfL/D16PBxATIY6KlSSyGk/s7+ehSxsB\n2DMUYXWFi5mUhkUW8Z/ZyuDszZRbNJKCCfPOJzCyGQTFxDcza/hR7xN4vvRjxHN76ChdQ1PoGKfc\nraRUnfk9bzHRegUdUwnWlpjg0F8RnR6mZ23AJ6YZzSiUnP4r8YWXk9YMZFHAsfP3TK76AoWHnqNr\n3rVYZZEyu8iZQJYWa4xJ0cOpyXgueUAI8NywTNdkjJ/UBsn0n0Wau5aTWT+z/WaeOzXJDef+hPnS\nuxhIm9neG+AL5rNMVK3io8EQ11j7QVJIHd6BpW0jmt1PwlFEOK1RRAQpOsUhylmc7SRRMo/umTTD\nkTSbhU7Ukibk4CAvhfMJp1RuGXuV8ZWfxyKLjEazlLoUgkmNI6MRNtZ6CaU0tvcGuGOOma2jAq1F\nDvpCKSbjGTbVeNnaFaTUZaY3mGBRqZtEVqPaY+GjwTAN+XZ8FpmikQPsssylzmelYypBuduCwyQS\nTKrk2xQUEXb2h7hKPf6JWcedCfJYVxZFFLmqMZ9wWmPlV56h58+fo30qhVURMUkCR8einJ2Ics/0\nS4iX38veoSgru14mvupWDo5EaSqw843Xz/Dsdc30RjRMkkBXIMG6Shd/OjHB+hofmg4HhnMCfE2l\nm56ZNHP9MvoHT6Gt/ydM6TAh0Yn72OsIzWuQJrtZ8kKaH1w7jy3eKEz2EalZiWaAUxEwtj3O4NLb\nUESBUlOW1/qSVHms1PstPHNqglvnFeXyis9tRS6pIXPuCNurr+AC5zQERhAdHrL5dQgn3kesmUfW\nX40yM4Tef5Kp+gvIO/E6U3MvpUBMIIVGQZIgHiJxcAfxS79F/kjO3S7VL+GcUMwcbYihX/+Mkosu\nQCmfhTY9ClXzMHqPIjk96HlVCFom1zF0l8BHzxE5eRL3goWkllyNadujiOtvJfif38e3ZAlHfvwE\ni3/1bV5V5nOFdpJM/VrMPXvB7kUb7swJ0Y6PyMzbksvT7DvAwOOPUf7dfyPrLELc9VQOH9u6Fib6\n0cIBjFQcacEmxFSUcfcsCgf3oieiGMk4kzt3UfKFu8Ew+Er1ZbmkgGgIvWYRYu9hAtu2krflMrRo\nCGPupk9Mn9r7TxA41sHIF37JotDhHAwmk8qlD8xfh+bIxzDZ6b3rRmpuvgqA0OFDCKJI3mXXY6hZ\nAsUL8I8dzfkRnF4mFl5LaWaM2Jt/wtG2muxID+bZreh2H+qpj3Jv8rIZ1LF+pPkbAdBcRUjhXNZ1\nfqQX4iG04ga0D55GqWwgNWcN8u6n0WYmEZ1eJH8RR0vW4TLLOEwi8m+/Rd66dSTmXcS7PTNsrPES\nz+poukGpkkY4u4czZWuZTmRZUGRH1Q08RhxDsTIYh6Sq0yDPkHr3ScwXf4k9AYk18gi/GnCwqspH\na0HO1Z/W4eREgjKXifLMKGIyzIS/iWBSo2cmQWO+nTK7yMdjSSbiGeYWOqjY/SiPFl3JVU2FpFWD\n6pG9GMWz0B15SD0fM16+nGAqd9+dmYzRkO+gxCFj3v57zM3LOGKaQ6s1ijDcTmfxcmYxhW7zkpGt\n7BuKUuW1YJVFirNTfBR10BVIcGNLAUnVwEUKQzajTHXnTF5FAlHJgTs6hCHKHM/4mD9zCKOwll4x\nH0nInReXF6bp0HyUOhXe6w7SF0zwLdNRnjIv4588w3wkzQFgTW3eP0rH/B/Vc11Pfqbrf9pV5ar6\nrLfwD6nlhWv/5vW/a7Dqds3Bs+J8JFlGdnqQnB5soori9eOY04DQvAYCQ4TKWnGoEbYpzVR0bud1\ncysNoVMoZbW5w2LVNTDcgamsBntZHbLNTq/qxOPzY/F72ac0kJeXx1RCw2y1YXL7OfW1eym94SYG\nvM14nFY0TxmZg++Sd/HVuZnPvBL0xrUYZ/fl6FGxUA47OG89QnSKbMN6/MuWkXCVInUewFCzpNqu\nQB44jsPjQqmdR0fCjPib7+Nes4nX+tPUTx/FVNWAeP5tKJFx+p56CU91AUNvvE/ZTbcgOxwINfMR\nzWaOCpX4F68n2rQWl5TFKJ6NGBjEcBaQfP6X2FZfgq1uDhTVIupZIv468tIDuOfNI91yAZ58G9OV\ny3Bkw0hdBzgqleP5y08oqK1E9vhRHB7MtQ1gsqEd38EPJmtZf9FFRDM6Z7Ju6rwWTBYbJrNMy5w6\nSmr9dIhFnG8do3TkIEJsmsna9RTFehkuX85wUoAnf0Z20Ubqkz1kP/gLxlg3sabzqRo7QJUU4eIF\ndUz95C48mSEaaorR7X4iaZ3S5ACiLKN7Snjp7Axz9/2OxPAY1qu+jjA9yJxFyyidPx8xHePpaBkP\n7ujmhvnF5JkMim0CsbJWCiePU50aQhRBSMWJNmzElxhF6DtKl6mMwuHDKAPHMXfsxlY5G73rKCc8\nc6ktcJAfHSDtLuPpUxNYZJHq3g+IFtRT6bFQ2r0dwZVHfXkx64oEOqRS7AffJLXgQiqzo6hvPcrC\nNetQ9BRGQQ1us8hCW4zs0R04IsOUNy+kV8jDZzMjNK1GO7YNMR1DOLGdsaJWCtPjDNiqmWNNgaHR\nrzpoTJyjsKQck2Ag9B2l56GHONG4gS80Ohn/y1MUb7oE18gRXh5XsCkyLXIQvz8PiyzmRG6dD+nc\nR1TXN+PRwlT0fohS0cz2viBlLgsryhy8fGqCi+vzePHUBCoCm+u8eC0yDjJkD7xFbVkeiqeAGo+Z\nM9NJbCaJQruCRRaYSmqkVYOqinJ0yYQggCMVoD9rpW86zuGRCD9/s4O7b1hMnc9GmUPkg/4wbSVO\nmhllZZkdY6IPsbIZu8WMIzqM2ZNHjUvm/cEEX1xSjsNI0R5SKX/hR3xc2Eax08LJiSivnxynJs/O\nRdH9JPNnYZJFOqbiNBpjSHklyEOnEDNx/jwk0VZfRdjsx2SzM7+pkhV9f2W/sxVTYTVxVUc34LWz\n0yxoaWDnWJZl1lzMVd1LP6Zm+QokxcTb5wJU++3UTR1Cj0cRfcUIFc3YXR7sEgy56hDefAxx4UY4\ntx+q5yF8/DqJ2mXEXnkCbcEGxPJGvGe3YQyfxYiHGCpqQ3vxN7guvgVz+4f0//EpbD4zQjqOXNWM\nLRXEVVeF3rqZ4JMPYLrkTtKvPYK5ZQWJsvmYYhMI2TQxbzXWoSNoY3045i3GaNlAICtiPfsRZp8f\n++x6xKJqSle3MvTcX8gs20KlOgGdOSGnDnchNq5Aik4h6CoRVzkW0UDKJrFecjvimZ0oggZ1i1B8\n+QRfeBzzhmvo8bXgr6lnUvRgObYVR3Q4hyUtrEEoqsF63jUgigiTfVxw0yoEk5n2vDbGUiJJbyXy\nwfcQEwEetJ7HaqOH/tIVSIqCw+1kevWNVLrNvD7toK6hGXm6Hz0aRBvrRe8+htlhI2/lcia3voWz\ndRGOBcuRshHOPfIntGu/xqHRKDUVFQwULUR47zmKa8u4/5wJaf56Xpu2s8wawtBUjOJ6tDN7eNa5\nhklbKTWNLYzh4tbXernJN0r21B6UugWIsoLuLkGZ7MSYu4mjlFBy5HmiHR1Yq2dhqFk+KL0Al1mh\n0q3w8w/7WHPddRwTyxBEgYFQioWOFJLFTlLVif7b3XzYdhvN+TamEllqXRJX/vk4p2Y0qvLcPLSn\nj5tnKUjxIPKs+fy6Q8WqSHgKSqj12fnhO2eZX+FnLKZSYtH51Z4hrrP10m6ZBZ4ijo/HWKyMIzjy\n8L94H29Y5zGvyJHDL+sG2qwlbCwWCaoy92/vYtniBYg2F6bOPTyanI3XaubNjgkQBMZiaR7+oIdb\nm90c9bRyyxtjrKvPx+ly84PjGpvn+LnznSEuneVkMCEynchyaiJGa5EDxWQmqUs05tt44vAI6/NV\nHj4Z5d3OIMua6jBJEjOazEcDITz+Ar63e4IVVT7CrjI0swubLPLgR/30TseJyg6yukFzuocuzcWS\ncg/e2mYmEiqzTQkKS8oodZpQlL/7IvcfXjOZaVwm1/+afwDjf+FfhaP6b35/f59gdexdhLxSNFcx\nSdmO/exOhIIKkrtfIz4yhWt2DfLSS1APv5uLc8qkEK125OIqsv0dRNtP4910GekzHyP5iwkfPoj7\nzp8int5BoH4j/uQ4M888jKOyFGXtdcz8+QG8azagN64jrkvYJAO5ay9GYS263U9SyM33VRnTpLc/\njXXeCpBNqMPdKFUNpNsPIhdWkO3vQLDaUSrrEXzFEA+hR2dA11HH+uhe8k80JbswRJlswSyk6CTd\n3/kas+7+Mmr9GtLP3Y/1qq8zmDFTE+kgfWov8cFhlNt/inPiDKOeeorDnagDHUhOD2pgHLlpOSlv\nFakn/hXjtvtwn3kHOb8URJkexxwqlTjGiR2MNl9C+dBeKKz+r1zWENmSZpTxDrSZSYy6xag7n8Hc\ntAS1vJW4LuE8ux11YojQqXa885oZbruJl0+Pc/vCUtxqiITJw9HxOGvUs+yS61FEkWWmcYyRLvTG\ntQhaBik6AcCfJ9zU59lZYgxw1lKH78nv4r/zhwxmrWiGwYnxGFdZ+jDMDtpNVdR6zJhCQ6TcZQzf\nfT3FD/2FsVgWSRB4vWOCr1tO01G+jsaJXMTSf0fB1NpUBENHV3JdBkHNdVDLR/YjeArRnPlI4XGi\n+fWcmkyy1BEl7SjkwEiM5WVOJDXF5M/voeju75NyFJJSDdqnEqwwTyCExglXLiOQ1Kiww8/3jXLX\n0nIkMZflGcvqFDsUAgmVAiLEFA/uYBfaYAdizTyEbJpeWy1VBJgx5+PRwogDJ1GnRlDmLCLqn8Vg\nJMsch8ZIRqEqeJLY3vewzG5GqF3IqFJILKMzZ3wfO2wLSGQ1LnFP02uuwiQJmGUBw4DC6VPoNi+d\nYjEnJ6LYFAm3WWaZN8Og5uTEeJS1VR72D0eJZVQunOVjz2CEZWVO2qeTDEfSWGSRlgI7Z6cTVHut\nlDkVRmJZTozHWFTipCuQZIMrzJBShKpDbeQMA54mim0i7/dH2VTtJmvAqx3TuMwy59d6kY9vRbQ5\nOebNEYRm+y0cGo2hiALL/DpPdSZZWZnDLR6fiFPtsZCXnuRA3MXifImhlES5Ocu0ZgageKaDJ4MF\nLCh20WzkZufeHkzjNMtktRzWtWpwN5TMpoMCRiNparxWqtVRBk0llIlRhGyaTt2P0yxSRCTXEbSU\nUhzuJFvUgLHtcXbMupb1VW7avreNY/+6kK2jAs0FDsr2PcHBlptRJIHFgQMMPPU0BQvrUeMp0jd8\nH4ciEvnVN0l96ReU6wFeGpG5zj6AYXZw+PNfp+WOzUhb7gQtizLdC4ZOvLiF2EP3UnjNreiRINqs\nZTx7NswVRx/FeuN3kSLjjD78b7j/9bdYj7+JMXcTwd98D4vHiXrjD3BngugH3ySw7BYsz/4Ixy3f\nxZDNGNv/gLlxcQ40cO4IwsprkfoO01e8DOWBuyjasBpW3YCgZUgJJiyHXkWsX4phdecId4FxEoND\nGLpO6PofUbz9Iaxt56H6KgCQxjuZ2f4m3vMuznUWq1rQbV44uzc3H/74vyBIIo7qilxGdjZLqv0g\n9970Rx6Z3I2YyMV7vTNt4bxTfwJRYte3nmVV+wHkD5/kxIMvUrq8Dl/zLOR1N6AffjtHk5q9InfO\njLZjJOOkGtYj73mO1PLrsWVCHI6YWTS0jZn9e/Gt20hozwfYivwom+9Anuwi03+W0Xd2UPn1b4Mo\nkj74HuNrvkjl9HF2X38vS++7JedD2LUbR1k+jvVXoNt99OPH/ODdFH73ATj6Tq6B0rGH7llbqD78\nNOm1n8OemERz5CNmk/SlTRTaZKYSGuWOHLVw2llFMqtTbJeJZHTypk7lCFuzF3DSXEeDR8pFegki\nYcHGqx1TrK/2EUhmaWOIMWct4bSO3yYhAg6ThKlzN28rc7mgSM8BVwptpJ6+D+fqzUTLFvLw/iGW\nV/lQRIEKt5lSOUlX0kJ9JjevLYVHmcxvwf7GLzHPauE3aitfL57CkEwMOmdhkkTGY1lahncQa96M\nQ08Q+fO/47rl2/RlLFTs+wPyiisxJJmw7GFbT5ArZzkZSYkEEiqFjtyYxu/2D/LTpgzpwnqSqo4s\nCljVOJOahQIhB1uIOUsJfv/z+O57AtfEaTJdx5louwG/VWI8rpJWDcyygCRAeWaUPrmE8ViG05NR\nnt/bz+9vWgBAfeHfdnn/v6q3Bl79TNf/tOu/Bev/tlpdfN7fvP73xwDe/wOCrGAsvRJ5ZhAxGUYd\n62eq+WIkEQwDHH/9JbYFqxBMFgxdRy1tQTi9g96ajXgsEl4hTX9Kpspm0B0VqE91g6xw1ChjniPJ\ngOqkwqbnEIyHX0Sum0+fvZaq9CBIJnSznSnBTZ6cJfvXh0lcdA9uMUt/UqR816M86LuMbwRzbnl1\n9c2YI6P0CPlkdYM55jhh2UMso+GzytgSU3SqHupcAjOqiM+Ig6EjdB9Eb1iDmAwzY/JjVUR6ZtJ4\nLTLPHB/l9kWleIU0cnAQAH2iH2POchBEBjNmyi0aytgZTtqbaOh4jW2lW6jzWdENqE90gqFzzDSb\nFmcGMRmG8R72u9toK7Igh3JoWrl9J8G6tXhObyXQdCE+MY3+4TNIa25ASEU5mfHS7AGpcy96bRti\n31HU+jVI0Qki1gLc4T4Oa8UsMgXok4qozgxiKDYYPEW2eRNPn5zgc/V2upMmap0CYVXEfehFnvee\nx+pKDxWxHnSLk7NGXo7f7kgx8/j97Nj4bS6e42c6oVJoV1BOvsto3XmUJgcQ1CzaWA9T9RcgieBP\n5rIWMwe2Ip93G0I6RsRawOHRGGv6Xmds0fWYJJFQSmMslqbKY8FrkZBFgXe6g1yROMBU/QUcHInw\n6vERfrAp9/rIKgs5ZO+PvkDxhhUElt+KZkBZoo92uQJNN6j1mrFNnuNjynnv3BTfnyvBeA/brPMZ\ni6W51daL4crP/XBLJlSTgy++cpprF5azWTvNKe9CmkZ3ozVtQA4NE3OVY5ZFlDM7yPR3YJm3kh5X\nE6VOBRGDuGrwx6OjfN3WTmD2BtKqQUYzqI2c4a1MFReUyrw/qnFepQMpHgBdQ4pNESpo/oR+9kbA\nyZJSF690THJnow15JkcTMxQrw4KXUsK8OiJQYDexypETcK9ECrjCOI1Wu4SRlEiBTcYcHf8kUFwO\nDnJEmcXCzDmMVBwjncqNqMxelLvhQ2OolQsRkmFiZh+uyADPjju4vlrCkC1c9fw5fnBBPW91THDV\n3GJMksDewRCXH/ot1lu/T1dYxTCg8p1fYm2cT1/tRuri3ehWN2OmQsqCp5nOb0EHphIqDaN70Gav\n4OCUxuJ8idif7+ft5V/jqsa8XB5qdoBzSiVT8QwpVcemSCy1R+jBj/+5H+K54W6k8CjTha08enCY\nf62cZrqwlR9u6+YXW2ZzdjrF/Kl9/Dw0i++UTuTQkW2XocwMMfiLHyKaFEq/+SPGH/kpoa/8isZ0\nH4KaIl3WylgsS6lZI/3yr7DUNZJccCn2zl0I3qIcirM/Bz4RVl5LT0Km5vjzyPmluSzK2kUYR99D\naN1IwuLDeuAFpvcewOJ34b3gStT8OqTQCLrZgTCWo4qNv/4Kzn9+GPOuJ5HnrUV35DNy3zfwN1Vj\nvuqbyEPHCe3cinfLtaSKmxF3PYVSMZv4xzt5d+GXqPZYmedIQscejLmbkAeOoBXNYei+eyn/0a9z\nBrrQKIbZDpN96JEgNKxE0FUMkw1DsSKoaaTQCGrPiVxzYdkVJF/+NbZL7uCrBat5KHYK4ehWTlVd\nQOVr9+HbcjWGZMIIjpE6ewTtsm/hGDqM4S7CEEQELUPMXYktE0LQVIR0lBFrJcVn30aqaMCQTOgm\nO2OChwK7jGAYpHUQX/k5gxu/Sd2ZV9BXXk/8se/h+MJPEeMB1F3Po6y6iqcHRW6R2tFrFiFPnEN3\n5BF6+TFcbcsRy+tz3euCBkzbHkVedhnjj/yUgvVroO0S5NAQhCcJVizDdTiH8daOvAurb8yduZ4y\nhu65laof/hxBy6B6K5Cik5zMeJknjvFqwE3bU9+m70u/YmWeQdrkxD52Kid6p3oZLFpMNKPTFDmJ\nWt6KvuNP9C2+jTmhE2T7OzDNXoBucRJzlZPRDCYSKiICVkWgvGcHWstGjJ1PMrnsVkqTAxgme+4B\nZvtTKJvvwJBMTP78HiSLiYLNF+XGuRQ1B9zJJMjufhHJX5zrvIdGUCeGiLVegjMxwb6YE7dFpsEt\nIIVHCToq8I8dZfCJx6j4yjdQ+04jtqwl9vJvsd34z+ycu47zd/4BdJV0+0H2N17PSl+W35xJcmtr\nMe5wH51yOfWJTj7UK2kpsOMb3M9WsYnzyy3M6Ap5sUH0/lOEmi/EmxwnYC3Ce+hFtBXX/3eADlaL\n5VOQMv/zGo+NfKbrf9r11/43Pust/EPqjua/nR7xd8VqOh7l8PlbWPLQd5jZvR1nQwOji67HJAoU\npUaIv/M0giiieL2E1t6B98CzKE3LIBpAK2lEGm1HHe1j6qN9FF1yGUJBBUQDqOODCPM3kXn7MWxt\nGxj8w2NY/S7yrv08+ngfgsWG6PRhJMLgLmDmtSfxrDmfiddfwb9kIdOLb8C/54+o530Bk6EyGIfa\n2DmMRBhtZgo9HkEuriK6byfOy25HjAfQbd6cKDy7C8FXjG7zoh3aytCim6ge2o1etwR1+5OYF22E\nZITsYCeRJdfhHzqAnl/D9B//g+TkDKWXXkj48EEGd57GWeam4qK1uQ6u2Yo6Pkh84eVM3XMTpb9+\nFmXf8+ixEILJkjtYCioI+uvRH/02Ezf9lNnHn0MurIDiOo5//ss0fu78XLdDzSCYLET37cTW0ML0\nvMsoDp7JdXhqFyN17edFcR7htEqRw8zmWg9iKsyIZqczkKQp30a+oiKc3sFA7SYqrBpycIDfDDuJ\npVS+ndfPPb3F3PNneawAACAASURBVH9+HdapTkhGeD1by6W+MO9GfJzvTzIm57G1c5p/anTxSk+C\na83dJKuWYI5NsD2YM+dc5prinUQRG8styIF+ftVv5xuzDWLOUuzxCZ4fUXCYJC5KHkIsqv5kpk2e\n6kEb6eRVxwqWlbk5OhZly+Q21h4opbDAzl1ranGYJOYV2lDO7GC3ezFPHRzi/s1ziGY09gzMcFVj\nPhbR4Ke7BvnC4nIGw2m2d03x3dWVyO07uWewgq+vqqLIoTAey+I2SzjjY9y9J8bmxkKqPFbmuODJ\nMyE+XxBAd+RhyCYA7to2TnvfDO9+dSkHR2Os9GV5eUBHlkSudE3mZgCTw+g2L3Kgn03vZHjm5vm4\nzRKDkQzVcowVj5zm9btXYJIEfvB+F19cVkm9V0E6tY2dnuXM8lsZiWR44dgI62bnkdUNHCaZ3d3T\nLKjwsLnOx2Akk4vQEgSKlAxB3Uwko+E1S0QyOmenE3ROx7hgVj7jsTTp/xJ7OZqUwHhc5ehYFIss\nsqDIgc8q8/jRUe4qjaB5y+hKmDBJAqcnY1zsi9AjF1PpVHji+DhVHhuthXbe7w1yXVMBmmHwTvcM\nC4qdABwejVDqtPDAzi4+v6KalgI7jx8c4iezwnS5GhkKpyl2mAkms3is8ifd1H1DIa6r9yB172eq\nMvf5HB6NMa/IzldeOc1fLinFsLoJZkU6phNUuC2UW3UMycS+kRjN+TYGwhkGw0nOq/EyHMlyYjyC\nwyxzoW2cdGE9lpHjufvbVQQH30BPRImt/hzZh+/Bf/f9cPQdUj0dONdcRKakBTk8ihgPYJjthD21\nmN/6FfKFX0YwdMTEDCOmYhwmEcfHLyDVL+aeQzoPtsSZzG/BRxJpZphrdqb5t4saqM0MoTvykaIT\nZDsOkuzpwnnp58jsfxOlrA513gWYR0+jBUZ5INbAxrp8WhwpaN8NjasZ1uyMRDI0vv0zpq/5PrVj\nBzidt5i6Dx/G0rwUo6QeaWaIQNF8fKNHSFYswtq7D3V6HLm6mXeW3UTjtS2UbFyFlF+KXr8aKTZF\n9sj7SCuvBkNHDg7mzpKZSSRvAaI7D0NSUPNq+JqjhYcD+5Aikwi6yrS/AX+oG83uR8wmMQbPYMxa\nzLtNmzj/6XvobLqC2efeItV2BZIAlmAvYiqKIVsYd8+ieOo40399kdhtP6VMzn1W2XOHMdQsA203\nU9O7jXMP/4GGBx4ke3Q70weOUnjhFpLtx3CuvYS3V93Olo/+gG5xkvFVo0TGOKN6aTJFyO5+EXQd\nc9sm0ke2I53/BTTJjByfRprs5hV1Nhf3voBS2ZCb+/wvyITmKkKc6sVIp1DH+jDVt6FODDCx9W3s\n334Ed6iHbMdB0itu4MBwlPL7b2f2PV8FfylCaJxkzfKcYbZ9N0JdG1lXMUp4JEem81UCoB9+m2hH\nB86mZmJn23HOXZCjBAJKxWzCpQuRRAHzR09/8j3IzSsQ1CyTnll49jyJtOpaMPRcIsrMIEJonK78\nNmal+iA+g5FOEa5bjX3vM8iFFWQ6j5Hc8jXs+/+C6PR8Mm+st27ht4dHubsmg2Gyo+59hamPT1L0\n7Z+T2fooB5fcySp3MkeAW3k9SseHCL5i+n55P+WXbeb0Q8/R8s+3k5h/CZoBw7dfSdOvf40hm3Nx\nkX3HYdYSpOk+1LK5yNN9HBUraZ34CCrnIqajAEjlLZ+eovkf1Iej73+m63/alVATn/UW/iG1peKy\nv3n974rVvukopVKcUzELBb+/h/hXH6RGimCYbLkuYyJEX/5Cys1ZBlMKVlmgZKadx6cLuWh2HhlN\npzJ0JmcyMHSyzqIciq+0BXnkFL2+VirNaQzZTGdExzCgQZwm9f7TWM6/FWG8i72OBayIHEavyXWG\npKETPDRTxd3VKcZtlRSICTImJ6ZjbyJWNiGoWdS8asbTEqX9uxDyyjBMNlR3CXJ4FN1sR7d6Uaa7\nOaSX0qb3ozkLOByzslTvQ5saRm/eQEdIY9bu32CZv4aRgvkUnnwDpXwWhmTKIUw9xblDOZNEj86Q\natqIbfIcZ801zB7+ELGoml3pIpaX2Ij+/l/xXnMH+mB7LprLW5ZDwMoWDkzprJSG0a1u0s4iZHQE\nLUtYV/CPHUUrqEW1+hiNZVF1g2qHwHgqx6pXRBiN5Tpdzoe/xuiXHmC+T2QwKVETPA52D93mSmoz\nQ3Qr5dT0buNU2QYcZpGq9jcB6JxzEYU2mYmESoXLRPp33yE6NMHw3b+hxGmmemg33WWrEAQYj2ZY\n0vUqptkLcgkOgRH0RBSt7TKGI1kqTr/GyNzL0Q2oGt5LtmEtH/aHKXaamZs8i6FmGS9cQJ6icmRa\nxaZIZDWDGq8Zz9BBQuWLcUeHOJjJo80S4lDKwyJXmlNxG4okUOFScpmbmkG1VSUmWHJ87NOvIzat\n4kjKTWu+mZmH/pn453+GVRYpinTnHlScBWQ0g+FIlnA6m5v5lXKCbiKWYbkzxrQpH5+cQ+Zmd73I\n0JovUW3OYMhmTgc1dMPALIs0KGH0Mx/RU38xFlmg3JghYPJjlUXsM70c1oqxyCJNSogzWQ+aDq36\nAMRnGCtaxNlAkpFIiuXlHnpnkpxnnGOP0kBK1Slxman1mDk0GmNOnpX84Dm67XUAVDoklKlujOlh\nOkpWYpVF8m0yWzsDtBQ5sSsi47EMNkUikdVYmJdLVih2KOQrKh0R2NUfRBIF5hW6qHCbKQ+fpccx\nhyoC6Kd3fxK7JiTDtKcduMxSjoSkJdDMDuTIOPuiDkpdZmIZjcbBHZwoXc/8wMeMVq7k5TMTrK/x\n4zZLqDpUGdOc1X2ICGiGQVrVmWeLMyZ4KIt2k+08gjJnEb8ZdnJXRZxxRw15chYEgcGEmBPS2ine\nNc1jY6lCUDfjO/oKYtMqdKub/zg0zYa6PBZO7kGPR4kvvJxgSqPILmOd6KDLVkfJ2//B5EXfonj7\nQwxvuJtqMZJ7fV1YwXjlSvw2mVhGxz91mtThHWgXf52hL10Dv3g2dyZFTzH+4jOYPQ6GLv8ehXYF\n7cGv4//uI/TdcTXWXz2HRRbJS41zRvNzdjrO1eZeDIePHlM5lcdforP5Khr1YTRPGXKgn31aGbGM\nyibrOAFPHelf3o1878P4ZBVNMmOaGSD43G+ZueU+Kg8/i1LTjFrSxBv9KcpcZmxKLs9W0w0kUSCY\nVJmbPJt79V9QSZelmql4DvkrvfoL4pd+i+wv7yZ614MookBS1fFZJIpHPyZWvZxdA2FKnRZatX5I\nhPhK0208PLWHU0kHVkWkzpJiIGMlpemICMyyppAnu+jytFCrTRC0leDr/hChoIL0wfcInjxH5M4H\nqI+1o81MIvzXg2BP6XJ8FhlvbAhDMpFwFOGcOMNMfmMOjz1zhFPehZS++GMAPDd+jU7NxyxrirTJ\nSV8oQ53XzOBXr6Puvl8iBAaZLFuK+72HkTZ/kbcH06x492dk4yny7/4x+v7XMM1ZSKejnlBKZYEz\nhW71Iux/if7m3I9itZJAUDOIiRkA2k1VlDhyxsjSXb8ju+WrBJMqsayeQ6bqE4yaiynp28Vg5Wru\nevkUr19ThZgMo9v9uQi8glmkjRx+uCl4mLP+NqIZFadJpsAu4zYSiP1H2etYQInTzHgsA8BSR5Qx\nOY8iIvSpDmoIIkUnc8IQwDBQe0/SMecSLLLIB31BbmstQjAMesNZzk3HuYR21KpFuazsjIM6S4qg\nYCea1qkUw4hDpznobcNvU6gWI5xI2FkQO4FaNpf+lEy1kiChuHI5wooVDJ2BmEZdso+XQnlcMttP\nRtP5eCRGictMhctEXyhN3mPfYvqOX1LhMuFsf5+h2g1UZMcJO3KkJb/T9j8WMJ9GDUZ7P9P1P+2S\nhc92BvgfVSWOir95/e+K1ZnffYfE5Azpr/wHZVKc6f/8Mb7Fi1AaFhN64ynsdbPITo6i5BVi6DqC\nrCD5ixDKGhBmRkgc/hBrU1su31RRco7TeAQjk0K54PO5m7rrGIll1+PQE2Te+i2Th8+SP382hqZj\nrqxDC4wjKApSfilGMp6LFoqGkLwF9PzuMWbdew/9v/tP9KxKxfVX5cYRUgkyCy9F+8v9WK+8+//j\n7j2j5KquPu/fDZVzVVfnnFsttXJCAQlJICQhEIggsgFjgmywsU16DOZxAJxwAGwcCAZskzFIIBBI\nQijnLLXUOeeqrlx103woL94vfr3WzNjDrNkfz1rdZ9+qVefsu/c/IHYeIdNyDC0eQ0tlcC9aiaHr\naPl1CM270JouwtS6i+6X/0zurElEWzvx3vVDEi8/juPK9Qjthwh99imZaIKCm+8kkdeAvesAWF1f\n6ANaJ81FK5lM7w++ifW/fofjnSex1k8GVUGw2tEmLuPM9Zcx4Vu3oo+PIlgdmEprUfLqkUc70LrP\nMPLZZ3gbqrBOng9kjQe0/tbs805dhn70UzJ9nWTWfJeBmEqNNcGo4OLkcIJFzjAZbwmioSGoacTm\nHYg5xSin96KMDPK7sutZ+/5j7L7ucS6q9iO//mOsV32bwR9/k+Lb7qTf34hVFhhLahRsfgrZF0Q4\n/3rkoXMYVhfN5JH3xmN4rl6f1Xt971eYl97I/rXX431tA1V2FSnSD6JM0luK5egHoCpIxbVorlyM\nI5vpnryW3A0/xdB1HCtuQu84hlA9k0HJj/Tc/fjv/gHdaRMd4RTnh3ejx6PIZQ3oNk9Wl1ZJIo91\nofa2INTMzI5XgyXszuQz1zqSZQm37s1inM/sQZl1OaZdf+NnwjxuP/Zbxlt7KbnsYuS6GQjJCDvl\nekIphbkfPYnv6js4cttd5E4uJf+7j4MgwunPUbvOEusewNB1bEEf9gvX0W4qpjLRSmL7u1guXU+P\nkr1spufIjP3mIbYuf5CZRVlt1xnOJGGTj/veP83PL2kgpuh4LRKQVVxYXihyICzx/slBbp9dwtaO\nEDcUK1z8Zj9PXTGJX37Wxs2zSrOFva5T4rYQsMDJUQWbSeTN4/1MKnAzvdDFWFKlwSPQnRS/wJH5\nrRKSKLDx7CjjaZVbJgVojxkMxjIMxjNcVmYmjI3BhIrHIhFN62ztGGVNfS4ZTWdH1zjTCtzUp1p4\nI5LtSB8diHJTUy6tYYUKr5mPWkNMynPyy+3t3DWvnOF4BosssqsrxAWVAXoiaS7M0xgWPIynNZpH\n4rx9pJfvXFDDBGGIs0IexwajhJIKNQEHi029dDqrKGYcw+IEQycl2XhiWzsLqwIMxTOsqg3gFBRW\nPH+Mv908nXBKI2CTiP34bgq++ShRWy6RH96J/aFn8Uc7GH/3RbrXPMykTBtqsAopMoBy6BPkmSsQ\n+poRTCbCZXOxf/oc8nlrEHrP8NHq73LBb7+G6PQi2hzojYuRokMIaor2J39A8YrFmMobCJfMwjd4\njL6X/4RokvHe9xRs+DXSRbchZBIcXncDU95+h6En76Pgyqs5dP8TTFp/BeL51zH05H1fSEdF65ei\nv/A9vPMuQMgtxRjqIrxzC8mhMO4Hn8YwDOypMULPP4nr6z9FTo5llUnaDzFYdQF+m4x5rB3t9B4M\nXUfOK8k6+7m8aKMDyHklDG54Hz2j4msoQ1dU4v2j5F97K30vPUdw8SKk6il0//IJir/7AxBEDJMV\nTn+OPnk5bM9OzgxBhOFu9FiY5mf/TN36m2HCQvTd7yDNXEn3jx+geO1liFYHoZrF6L+7H8f6n2Dr\n2Et7cDpd4ykWRg9mzWIcbvpzp2L84h4Kb70bQUmix6PosTC7C5cyzzaK0X2anjfewlWai3v6bDId\nZ7JwjEwKc+NcRt54Ae/dP2bX/KU0bNuCe9OvMS9cS+cT36No5TLM5fUYZgeCrnLGXkvdyH703CoQ\nRD4asTAlz0Fc1Sl1m5HDvYhKgnZrORWJNmI5tYRTGnn27G+25x/NgXynjDU2iJiO8nE8l0XlHja3\nhZmS78wa5xgGu3pjX9j1KvkNpF77KU+X38gD1Un+Mhrg2mCIral8agM28qUUxyMyVR/+BNeytRgm\nC6q/nP5H7qBw1XLuGpjAD5fXkhs+x4CnhqSqk1B06h0KQjqGfmonmVmXMxhXKZXjSOMDaM4cMFmR\nek/wmbmR+QENKTJIJi9rL+zUE+wdFThP7GbMX4u3+RPEYCmxYB1WVA4OZ6jx2/CmsjbX7w3bWJ2f\nhZT0q1b+68MzPLa8jhJ1iH5zHgXqCN1igGI5i+9tS5kpdZsZjCsAVOS4/p01zf90PLrj+1/q/v/u\nuLR++Zedwn8kpuXM+afr/7JYjb3639hnX4garIYTWxhqWEFBvJ1MsAZz2x4Ei514wSTsbbvQk3GU\ntpMoq+7FuvtvnG28HI9FojDdn+1U5dWBIBJSRdzm7Li0zGPGPHyO9K4NdJx/N/WxU6g5lSRkJ5ta\nQ1xeZiJpchFOadhkAc2AuKJzdCDKGkcfmiPAL89o3D2rmLii49EiSOE+EnkNWJQ4YsteBH8B0WA9\nGc0g0H+IscLpDCdUqhw6crg7+yz9p4hve5f0FQ9k5Tt2vMbeuisZjGeoz3FgkQWqk+0kg7XY2nYh\nmK0gmRjb+Bqe2QuQcgoxJDMt9kqqRw5xxjeNerWTk1JpVlB6+CyaK4igZkg587Acfh9j0lLEVIQe\nMYBZEsmPtfHSoIeZRR4aGCLkKMJzfAM9dRcTV3RqvGbk0TZS/krMmSia1c2n7eNc7BzihX43N2oH\niE28mM86x3lpTye/WjOR3N0v0Tz1OhrObUQub0QNVjGYFtjaHmJddRbj+GZbClEUaMpzEUlnu50F\nThNvnhpiTUMQmyzSMpZmsjzMoLUQ84v/xYvT7mZ6kYf5QQEhHWPDkIVL0ofQK6YSNXnxDR7DMNn4\nJFVAntOMzSRik0WODMSYXuAimtEA+KhlhKDdzJwSD80jCaYXOHnpSD9FbisWWaQ2x0GN18yzB/q4\neUoBziPvYaSTjM24CrMksK83yunhGOsnOtg+kr1M5hQ5GU6o2EwihgF3vnmcr82vYGkg/Q/XGYlP\n28fxWGQ+ah5iQWWAqfkOzJJAJKPz+JZWfrqilpiifzHWrxTCjJoDbO8cZ3G5l4Si0x/L8NTWFh5d\nXp/1FXeLtEQMzo7GscoidTl2yseOoebW8H63yqrhT2D2mi/G293jKRRN52BXmCunFDIt34H96AbU\naasZjCsUSXEOjJuY4TMY0KycHIozo9DFUFylIXmWK7dp3DSnLAtncBls68vQELRTNHCA0aKZeLVx\nhEwy62ikpjAkMylvKUnVwLXnr8jBIvb5ZjKbbj5OFbAkz0CzednXG+PcaJypBR58NomUahC0y7h2\nvMRnVVdwfvu77Kpew0LrEIgyb4+4uDwwjpiOg65iyFbG/LX84vMOvrWgHNv7P8N6wTrGnUV4Wraj\n1c7j2KjKFEsYoa8ZvWQiYu8p/i5ORBQE5pW4CYTOoTv8aI4A0vHNKB2nkZfdxI4xE+e1vMOlbY1c\nP6eM+hwHdpNEdfP7bMlfykViC4O5U8gdOIQRj9D35pvkLpiFPOUClEOfwNJbETMJTkdF6gJWhuIq\n/o0/Q1dUMlc+hEs2kOKjKFtexTpzGZ3uOkyiQEG8HeJhYiUzcPUeQiloRBrvRT22HblpITFvBbIo\n0PONdVTedA39E1bht0mknnuIAxc/wPnHX8LSMJ1MxxnME+cx7KsBsmYlgqGjDnYSm3gxts9ewIhH\n+GTSV/BZTczu/5T++hUUde2gOf88GhJnSBRMwpwYRd/3PuF5N2KVBKw7XgEgPu96bJueJj0yhnH9\nI3g6dzNaMgd/tIMDWgETdzyNafU30E1WRE1hICVQcPYj1L52UBUSgyP4V63LSvnFRvhGcD5P93xI\natsb2OcsJ31yD4aSQfLlMrprN4E5szDmXUNCNRiIq1R4zYiaghTuQcgk0XzFqJtfpG/RnWxrH+Pa\nzteyUlSZFM3P/hnfM69jM4kknvw63toSLCtuQ3XkYIoOIGSyfvLDLz9L7qVXonScRpy7BmXTH5E8\nAUyltWjFk2hJWanVeom4y3DsfQ25bkbWXVDO2tACxF76EQNXfA+PRaRg6DBjhdPxRdoxZCuCmmLc\nU0Fc0ckXYrzbrXN5TpQxRzGB/kOohY1ZmcQF1yKf/RzBk0ssWEd7OE1dwIry1x/zeuOt3FySYcBS\ngPn5hzE5bIRbe0l/5xlqQkd5K1NFlc+OzyZl8apNFzGU1HCaRByiRkbIdvZjGZ3SY2/yN99S1lVZ\n0Ha8Qc/GrcgOG+p3n0V76Aaq//tJ9M4TyLklKMFq5L6TKGUzSGkG4htPkLj8fhKKTuG+VxAWrAND\n5532JJeXWxDSMQxRRlSSHFWDTHKr9CoWkt+6lvpHv4chW8gc3kLvvNs4M5JgWZEJebQDw2wj+elr\njJ5s59hNT7JCOYZoc5AonoYsZCW6RpMqZSNHGCucDoAogEMyeO7wIF5rlsx10/SS/70q5n8zhuID\nX+r+/+44NX7sy07hPxKLCi/8p+v/Wg2g5ySau+AL1xfR6kAIFNH3x99gdttx11YiLbwa9dNXskLZ\nkTGU3laMJbdiGTzDyOt/wtM0iWR7G9aiQoxkHP2SezHteQNt7lVY+0/Q/quf4a0pwXPZzfQ98zMK\n7/42AIbJnnWtGh3IklN8uaQr5pBUdQTAO3QC3Zp9U8vs2YhgsWEk45irm4jt346aSuOePht96kqE\nfe9mu7k1U0kf2kLogjvx78yC2/NsAsK+d+l5532KLjqfnpnXU961nbHqRVnlgW2/RnS4MdIpIhfe\njWfr77E0LcAQZZI730PyBDDiESxzVmCEB1HaTzJ2/u0EiSL1nkSwuWhzN+C3SniHT6HbPAijXeAM\noOZUIMZGMGwehEwCcbgNRInwJ+/huexm9O4zRCdejO3jZ1EjEWS7Dbm4ChoX8dfWNJfV5+DsPkCi\ndAb7+2LMy5V4/lSEcq+Npd4YUmSANl8TadWgVhim35zHru5xFpV7MYkCfTGFeinEScVLtc9Cyz9I\nZWZJoC+qUOI2MZzQqJXGGDEHCca7EJQ0u/QS7CaJkUSGxfkiO4YNFniSvNUnUZ/jAODoQJSrG/z0\nxHWSqg5AqdtMb1RhNKHgscrUObNF64BiJmCTUHWDc2NpJjuTDAse8tq2oVfN5GjExGA8g8ciM9ub\nxjA7vjAOyHWYeeVAD1dOKSTfZWZL2xgei0y5z5btCqRHEJQkcU+WiBVOZ/d8/fgA3y0bR8mtYUyV\nyQ01k86fgJwcQx5p54x7EhlNJ2jPykGZJQFLJsq7XSoLyzwc6ItxQbmHgbjCqeEEFxZKnImbcJpF\nCh0yYiaOFOohk19PWtV589Qw5T47CUVjfokbBxl2DirUBezs642Q1nTml3g4MhinwGmh3GtmOKEi\nCGCVRM6MJGgI2sloBiZRIJTKdkJ9VgnLthfonX0jmmFQ0bqZ8caLcZpFmkdTVHgtWI0MrbEsNrUh\n6GBCpgP13CFO1K2hIcfKrp4oogCKZrCo0My2vgwzCp1sODtKXY6DQpeZwkQnffYy/FaJtGbgPPgO\nkWlrcJkExAN/513PQiIphevDnxCds473z45Q5bPTEU4yp8RDVaYbITzAcMkc9vVGWZ6vo+/bQHju\ndeR07ESrnouQjnIgYmHm8E4oaaTPlEeBnEJIRaHjKG0VF1A9cogbDjh44Yp6RjIio0mVhu6t7Aku\nYFaeGXmkjdMPPUTp0unITifSxV+DAxsY270L370/+/9UBU68hHzhLTTfcSNqUmXyL36chQBFQ1kD\nkdX3Ymz+A3JBOZIvFyW3lrhgJfzIbRRfdQWi2cree55g5sZ3GP7lwxTc8FW6nvsNeQtmIS5ch5iK\n0PrQvVQ+8TSp93+Pc95FZEqmYXz0O8xTFoOhM/buy3jXrWfouZ8Q/PqjtNz7Neoe+i44vKjNB4jP\nvhr7zlcQZ63KkgI/fp5YZy+GrtO68SgzX3+JE1/7GhMfXg8ljUixYVIHtyL6cokcO4a9IICluol4\n44Wkn70f710/RPn7r2h5eyfF50/EUVmJFMin7U8v0/5JOxduzbL/1dajtE64jNJPf4kcLOLei37A\nrzY/yrkX3mTbhhZu+s06bJPPY6R8HsGxZozw0BfnfuIPD+OemlWZkIuqMUwWBCVNKDgB26anOfrb\njTRcM4+Wd/eSN60c5d5fUnjodZSBbs6+sZP8GZVYA27Mbgex1d8hcHIjL656hNv2v5hlwj/zFEVr\nLsFIxumevJagXWbo2zdQesVKRE8AdB3JF6TD34Tr5UfwXXMXDLWz2TKZqQXZF9kqr4WUquOOdiMm\nxzEkM2qgHCEd49MRU/blonEW4ZJZHB2Ms8AVQ3fmIHcfoc0/BUU36IumKfNYKbOk6Vet5JtVBF1l\n1LCRG21Dt3oYNQdIqwYfnBvhK5PzkE5sRvLlogbK2T2WHeFqhkG130Y4peGxSBT17GKsfB5eJZSF\nyvTvRR3oIjLrakwiOE5/knWIE5NsHdBZau2nzVZJiVOEHVkCYHvRPEqOvM7g9KsJ2CRkdN5uDjG3\nxMOmllGmFrgpcVtoGUuyuyvEdyri6FYXI9Z8WsZSzKOdvyeKuEQ7TlfR3Ow5M3gMJbc2+3m1HSI6\n8WIE4JP2ME15TqJpjTKPGbtJ5MWjAywuD3BmJMbejhD3zC8HoMjn+F+vYP4N8WbbX77U/f/dsfnM\nri87hf9IPLfi6X+6/i+L1YHxOIGzn6LXzcc03MLg6y8hW834rrkLIdSbtRYtrEZQkmjOICe1AIVO\nE+69f+NxYx5f2fI4hasuRo+GMNfP5LStlpyXH8Y7aw79H35M4UM/R2zZi9LfgWh3ETt1HNlmofmN\nXdS9tZHEr7+Nd9IElOFBHEuvxBjqQunvQLvgFv50uJ/bRzcgLvkKxufZTpHoDaI5g2iHNyNPnEf0\n/ZdxXnEHdB5DdLjRc8oR42OoOZXIvccZLphG4Mxm9GQcPTwEooSlcQ5KfgMb2qJc5hpCzanIvo2m\noxgHP0RqGU77+gAAIABJREFUmIMhW6HrOKLDTeLANlh7P2YtjXR2J12vvErhj/6AvvEZxJV3I6bG\nSb7zLPbVt6Of/Jz+Dz+m9Pa7MZJR1LLpRAwzvnNbs84vnjwyezZirm5C8OSiBqswBBE53INudmAc\n2Yww8xJO3XIdFX9+l5axNFPTZzhhbwCg3qGwsVvBbpJoDNrZeG6UWyoM4vZcWkMZ+mNpLvLFiDgK\niKQ1zJLIscEYs4pc2E0ibPotppnL0Z1Bntg3yvo5JbiSQyCI9Et+9vdGmP/h4+RcdSuGbELQVA4J\npUy2xxF0lZc6BG5o8KBt+j1/KFzLuol5ePUoGYuHaEbHf+gtNhdcxNmRGDdMLmBvb5SagA3HM/fR\nc8uT5NhNlJqSCM27+NA1h67xJB6LiVnFbmqiZ1DaT5I414zppkexnvscpW4hLaE0E0azHskfShOZ\nXuCkczxNU64d+chG5NwSwrkTUXWDg/0xlqWP8iaNbGke5uEl1fTHMky3Rfho1IbdJDLfm0Ye7+OE\nrY7aI39BnHclUvdRTvpnIAgwGMuwWO5GUJJ8LtUxKdfOZ53jrHb0k86fwLtnRphT4qHIBqbeYyiF\nExFjI+iuXLb1JLGbJN482sf6eeVE0hoeq4TbLHJsKMFfD/Zw17wKWkMJKrw2rCaRGq+Z48MpREFg\nstBLi6kEl1kinNLQMajr+IRk0wqODMQpclsoM0ZpNfyMJRW6xlNcXm4hY8peEieGkzTl2hn4x1iu\nyKygb/8rpyZfRyil4DRL+GwyW9rGmJzvYjylUuyxklJ0agNWnCNnMcx2Is4i1OceRLj9cfxduxkq\nnsPpkQRpVWdJgcQ9m3v5zqJKImmNptRZzrnqcZslckPN6A4/ZzU/ZR4T5vQ4YscRRJeXVNEUJDXF\nlt40SwokMiYHlvgw5zQvNeIYz7Ua3FGSQExFOWypp3kkxlV5cXancphZYOfQYIIZvZ/yZ9Nsbi6M\nZbtmho4Q6mXw7dfIu2wt/YWzCR56g32Vq7OwEWcQQVdRP/oT0sVf49ydNzDhse+h233oNk9WKURT\nkULdxIunYWv+LMtMH2kjnDsRz9g5BCWJkYqj52ahBepwL3KwiJOOBuqFEdLuQiyxQQRNQTu9m7Fp\nVxAQkojRQSKeClyJQdT9H2SJqhPmMuav5dRwglkn/0Js0W1ohoHv4FuIExeiHfiQ9Pk3Y1PjCOkY\ngqagdxwj03YS88o7+GtrmmtLDbaMWVmSOoKRX0PPTx9h5Bu/pqkli1EPTb+CnI6dGPk1WWUMwPj8\nr6iLbsZ6egt62WTS9gCWTBR5tCPLNeg4zT3LHuOxJy8hsGwl7UXzKBdCCEoSIZPkpLmC6s+fRVx9\nLweXXcjkjz8m/fwj3Gy+nNcLD2GpncJo/lQCA4fRfCVo+zeS7u8lvfZB3CaQmz8ntO0jxNt+hLd7\nX7aLOusSpKEWtNxqEEWk+GiWINtzjFjJDMyf/gHLhFlorjz0k5/T+/4mChbPQTrvcji3Fym/Int+\nSmak8T6OZPzUBqy0jKUBaA0luMI3ynGxhE1nhznWHWZBbZCPTgzw1hIzb0XzqfJlMZZTlRbOOWup\niZ7hnKseqyRy44sHuPeiOi7xjjHiKsdPko3dCpdaO8kUTuLIcJrptgiCpvDXfhulHitFbgtD8QxF\nLgtJNev+VuQy0RtV6Ium6QgnubDKz56eCA1BB5Utm0hMXoUjMUSnEGBb+xg31tqyHI1UiONxOxM9\n2UbAsbBAntNEvpRi64DO4pFt6NMu4chQkmmOBNtDFordFirNCVrSduIZjTMjcSQBrvYO0e6soWL4\nILgCGENd7PbN4Tyhk3dj+ayqdPHMoSHuCXTzq9ESqgMOpuQ7eWTTWb4yu5Q5+RZOhzUmWGL04mFH\n1zh2k8gaSzuGrnPPSQ8AT1/R9L9Yvvx74uOeDV/q/v/umBdY9GWn8B8Jh835T9f/JUI32LsfHG7E\nrsPo/mL8M2dkCUI2D7QdRqiajjHYihoaxkgcpnH6coSWU2hz1nC7ZiEgLEQuKEd3ejFEmVphmMTd\nP0EKtdH9teUUJ8ayGqUzlmNYHFj6O7DPXcGM81eRfO/nBG+4C4a7MZfXQzyMOtwLS28lo+isrgti\n6q4lokvYUnH0TAoREHQVc3UTmsmOY8pMusUApQVVCIkwhsmKoCQRk2FQMwzEFLyj/chFVaRaT2Ob\nMJUOTwPF6SilHhtRfy324x9CPIJUPRWhtBYjOoL+D3kcYfur2JauQ9v3NnJ+KViseKqKEIwsQ1XZ\n/irMXo3jonX8uRNuysmn8LLVWZkhUaYlJlJjGkOvns0YNrw7/4y5ciKCxZbFDe19g+iMtQQSIVrl\nAipmXsIYNib84QV6kioeq0Rq11ZsixvJaAbbhwz8NhM59iyZ4ZqJuRjJUcbTOlZZZEmFh2PDFiap\nEZxmMx0pmVyHBYskYOk6yOHpX6FJGuXouMTFdbm4UiN8HnGwSOykwKGRY3eRc/VtHJfLaPAICEqS\nKWqULaNWlirNFLknsq0vQ/n5d1AwEMMjZLughwcSWfvUhktRRhO4LTJnRpLkOcxUZbppvuPnTLMm\nSJisHA+ZKalfxkWSygcdBqvyFHS7wMFUDfbGevJny5h2/Y3B6VdxuH2cWYVOtsaa6I2kKPdKHBuM\nU+6zsqVjnKXRMOmpK2kdSjI900xdTiOqfR45vQnumldOwCYRV2QMk5U8hxm7ScKwmknv2Ik0u57E\ngpswgKPWJqqtEvligoxqJuqr5+0zI8wuMuPtP8y0gsn84kScO/IMrnH1oAL6zo841ngFk4ZbMawu\njo2qlPusSILAA4srCXbupCV/DkUuE4KucV6xi/OKGxhPa5R6vEgCONUIR4d13FYJVQPNUUDPYJrz\n3THMjjzCKQ3q5mI/tRl/8fkE7TKpt16k6qKbqHSamGWOQzyG2QGc2EL5xJUMJ1RKu7Pkt509KeY5\nvdhMIg6zhYxmUOo2M6/UxwRhiKNSEKskUuORiTz7ANrXHsdmEmkfTiHvO0vjdb1ohQ34TTpT8hz8\n5cQgizs2c9vsq8h3mJBFAdVWTLmsIp37nN4336Rw7VpKGy7AnBhFHu+jp2w+hd27aA2nqXVLjCQU\nxp9/Aum2HxGW/NQqvaS3vsHaFd8kaRKxd+xlHAWbScIw2WjrTzCPdly2GtSBLr4yrRpjsB+hoJqx\nvzyLZ8oUenY04592FnPJHNr/9nem/voahNEMUnv2Jef0K1uo1zVKl05HHe4l0/YxpkvWI7UfIHPm\nIOG+IZxVBxGqm9B3vkFaVXCHh0mdPYx56Y1obZsx2k4ysO8o3Z+fo+n2pTQs8tDy6INU3nQNna+9\nTSoUJzCxnGDtdNKfv0P7ht1Urp6PnlfK9m/9ienrF+OduhTfua3MK6xFlU1ZWFNsGN3lpdXwUzQy\niO3I+wx/8ikWn5NE/xjuigJkh5WBJ+/n2ssuJfbmHpZccgNdv3sRgPhAmHKvhUxPG6b8EgJnP2V8\n7+dYfHuwVDeh9LTQt3UfxUAqNIy1sJbksw8wcOMPKT65h0RXN/27T/HYk5fw6P3vc197P5UPFKC5\ngui2fMZ/9wgTlq3k7MZdVK24k6lfX4Hp+EdEwjHevc5CZFM3ejSE9dJpKD2tyLqOJkoIoojn4FtI\ndTMRfPmkwzHyY72MffR3Nv5wE+u6rkctm57FzIeHaP/T86jf+yNl5TOxH/8YsbqJVNEUYhkN5+gA\n5XfeTWTbRlxDLWh1czG6T6Af34EaHiOtqNhXfBdn+y7cubPQMZhT7EY7vYPaaTVMrFUJTW/EMAzm\nlfrI7HueVRfdSWs4jcMkopmyRf3IO68Su/oxquNH+eiu2RiCAM3HGRKL8R55g3nzbqRTacT1x0eY\nccVtCPEkna4a5pVCRjMoV/pw+4pxfvwMyoqvYz/xEZ2VS6jQBigqLKHabyNw/H2WTr8UW7gLvXER\n5o+eJRmJUL78Bs4rzUMKt3FCKGXy0TdxzL4ZQc8Ss9xWK36rhHB8B3nFi5CMAvb2JwjYTchjnXis\nE5BFgQPjJqb7VQy3hfG0SpnHitF9lHKrCz1Qhu7MQbR6+OjAELO97aStuUjhHq6eWIowPEBjrosl\nsf1o8lyW1OfiscqEVBGrbKA5c7ClNeaWeBiKZxj1T8UsCazk/w6b0//XdElrH1vyZafwH4neJ/b+\n0/V/TSezezFkE+qJnUjxaFbYv3oh0R/eicXrxDs6gHTe5QiJvcjljegdR1H62xHr5pM/do7+nXvw\nR0P0bd1H0dK56Mk47QvX03D2ILNmrkbo7qXtlXdxFu2g8Jp1mPJKUHJrkEc7kC/5OkKoCzWU9WyW\nCysQFqxDCvdiOAooHTmCVjkD57ENyBPnots8jL31R1wTJpIa7GbsRDu+hjJKKqehHtsOshnJU0jq\n8HYGlqzHs+0Z8q6fQ2LBTbjOfMLY6U5Kl6zFIonoezfQdN5a+lIaobfeJX/+NFK7NyBd+k30D3+L\nubwB4fjHDO3eR/rDTym6dCX6+Cja+CieK25H/Yf+YLK9DVfwIPH6C7jMY8BYCFHTEJIRtPFR6opd\nGK0nUZqW4zQMpJkr0U1WRp5+lNxbclFiYayygDrci/PVF4jn+gisvQtpvI9X2n3cPzcf3eEmYJMY\nT2vMK3Hx4pEBch1mLikWEc8eYZ9vJn6bTtXxN9hauooit5Uhw8l4XGckkWGeZZBtvblU+ZvYdLQf\n/+QCxqMpusaTBMp95DsNFHslppE26nJy0ZNORqIKXSYrsujAbXWTUMZRus6yRyriW/PL6IpksMoi\nZ+MSZslgjjPKs/069TkO/DYTtQEHNdYEY6ILI5k9ZFFVupIKaU0jEO7EMNtZ7UpgpGUOJ5womsFY\nUkGWrLimLKMvmmEgmqIlJBNNqwzF0qRUnQVlPkyigFUWCc++Bq+uMdkeJ507BS2icHwkg6YbHOgd\np+HcDipnXYYueJgabyPirECXRUwzLqRrPImOlbGEQr4zOzIrtA5jlUsIpbJQglppjP7gFHrHU3xj\nRh77BhIUeBopU4cQ5qyhMTFKNFhPUjU43juKrhuUeGyUea0EyqfRN5zGY5HY1T2O3SQxtcDJwf4Y\nQ/E019fYaU7ZaAxmWfeHB+K4LHZmF0m0Ri0IKRWnSWJU8ODtbcVfvYShuErZittI2wMMJ1Ssdh8Z\nzSCe1HE3rGBHRxiLJFKQiiPueYvP0tOZNm8Nff1xLJJIKKVQ65XpCCeZaAnhcOYxmlSojHcgf+1x\nRpMaRbKIwyxSff96uk35GDqUte9jq9GA3SQhyCaq/RbeODVMicfKngRMKzBR6csjZ0ot3WULcaoG\n9oFmYvs/Q72kAaXzDNaCuWiiiUtq/Ti0xYjhVs5oheSbwTZ9MdZIG2FvFXbg19ta+cNVTYQMg0tq\nBYzeDsKoSMtuQTN0Wh59lLoH7sN/bVavb9rjBQj+AgLhFjzXXs6G1hAr2rciz1lN5PVnCEwoRF19\n3xemJ/qZowwoZopNZqQVd+KLDmKYbHQKPorHR5HyKyAdZ7y1F2N5gKFnX2PCq2+Rn/cpwYWjmGqn\no/jLsQY80LiQivVFpI/vRl/5dfSjH2BafhsN8y+l42c/Ivfxe6ldvQnXrd9H2/sOzF5DXJdwmK2c\nSliZFA/TWb6Ymtg50g4nejRMYPZ03rj+Ka4+s5nQ80/iXrQSOXASKViMtaQEvfMk7ooCPLMXIDpc\nfNwXZaFJRpy5Em33O/TvPkXt+lsQ88oZff998udO5OhTr1P/7gcYJz4isGotg5qOoWRwTZ7OXx9+\njzu+/XXua+/n5787wNMPy4jthxmsXETeujswepqpuGo5Z2MCdcVVKD0t5N76LRjtwtE0DTm/nMGM\nji+T4tRjj+MsCpCJxMmJJ2muXEnTzmdwFgXZe+UtVCyfyBVPXY10dicES9D6Wtj6lSdY9uHvyLhN\n6H9/Cn3FnRi6ysnhJPU5VtCzv0fHpV8l+cnLJFbNRK6cjyM0jGPCLPR4hK7xJLb8WSiqQbUlgW71\nQMN8LCPn0NwFWGUBAYGOcJJqRcHSvoc+aQKSKFAuDFPhtyFdcROy18x+vZGChEa+RUOrmEGpaEKY\nfzUus8RwQqP4mq+jm6wY3mICyLx7ZoRrizLorYdxJ3cwtOQuTvdE2RWuZ71FokvIZ3Q4SZnHjFTe\nyKH+OHP9HoZ0O7lLb8aUiqJLJmqVQQzZRLHdjLb0q3R2R6k1dXHSVkO9OEro2V9iW7GGfKeJsdff\nZmhpFXNccTIl0+hpDaPoZs6NJgA7M9zj7OqIsgu457wLsKISUkV6xxSa+g6R751M/Mh+5l55ISl7\ngODh9wk3rWLZ8CmU0BAjmoVr/V28OyaS5/AgCLC7J8qcIhcPf3iWR5bVsLUjDEBT3j/vlP2fjpHk\nyJedwr81fnTzbV92Cv9H41/CADIjPYRNPgYTKnWWOAmzF/PGX2G64Dq0w5sxUnEsE89D6WrOasjp\nGubKRgyLE32oE23iMkydB9ByKjBOfIZUN4tBayF5yR52JgPMix1C6T6HadpSRhzFeD5/EfOk+bw2\nFuBq3whD3hqGEypeq0SOTUYQYN0rR3j9smIif3mKvrWPsLs7zG15IYzQAIK/AMNsz2oKzrmUHWEL\nC0Z3Irq8IEro/hKMc/uQAgUM5U/D/eFTWM6/EgbbET05ZM4eQjhvLWIqgpiO8tqIj2VVflw7XoJF\nNxJVBTxH30MqbchCCbqP8IlQx6JCM1KkH81bjHDkQ8TySUTcZRwaiLPIPsKTZyS+O9GE7szhmUND\nfMN5jr6y+RSm+4m7i3F27kWPR0DXs7kCejyCFCgkFJyA/bMXEM+/DiEdQx7r5J10BZd5R9mrFWIS\nRaZp7RySKmjyGnzQlWZKvpNiI4SYCKH6yzAkE2Imjti6n032GdTn2KkYPUK0eDq6Ac5jG9CGexGs\nDoQF6zANNqMN97DFex4lHittoSSzCp2Y/vID+tc8RF80zSv7u5lfHeDGWhsxyZmVlPFbePfMCFML\n3JR5zDgiPUScRXzSHubyYBxDtiCN95EonMyWjnFWmTsYzW2iNZTCbZGp6dzCrpz5TC/IdmPnRg/S\nVTSXAmd2VKYZBqW7/oS47LZsp9KblXN5+Wg/d0/2Ifcc4/eRclbXBUmqOqXCOJ+OmFjmHCHlr0R/\n7XGsl96BoKQR1BQxTxlmSeTIYJy6gA1JyEqBZTSdep+JzpiGQxbJFWIIZ/fQV72UjnCKuZ4U20MW\nLJLIeVIPYX8NkbRG2dgxjjkamWBLMIAbETg+FKc3kuLG2qwETK/moPDYOxhzruBMSOGzjjHWTcxD\nFgWc6TEOxe1M86ikzS4+aQszFM8wo9CDbhj4bDKnhuOsFM/RmzuV8XRWc/aCSj+9kTQVXisl3Tvp\nLZ1PceQsna4aStQhdGcOI4qMzyoxllSzJCuLiF8JsT1kwWc10RpKMDHXSbUtg252sLcvTkLRKPVk\niXilm3/B3rl3szBoYBzYSEfTFYgIxBWNer+FHT0xZhQ4cCSGUN35dEUyuM0SdlMWV1rMOEOil/xk\nN2elIo70R5hT4sFllnDJBsbHv0dadB2b+nSKXFaarJHsBEcQ2TeYofz57/Lqkgf4TrCLz6xNTMt3\n8OapYepynDTl2emPKVSaYmjbXyMzOkrfjmMULZyC+ZoHMQ02E97wKr4VV9Pjrado6DBqyRTQFPYu\nW8WsrZvRP/wt0kVfxdR3Iss2D+SjTrgAYe/b2WJo1qWENJnOtauY9uNvkpiwDGd7FjOmFTUihfto\n//kT+B9/kYxuYHvzcdRkGu+q6+h/4Rnyb/8Wut3H8K8fpeCGr2al6yQzQrifcMksZFGg4yuX4yzw\nUP7Vr6IVT0LqO0V0x8fYrvk2YiKE0XqQka1b6dt1lqY/v4Q00JxVMJlWj7l2KkYmhRYaQh0dwHzR\nLST//jtaLvw23HUlk79/L1r9QlJ/exJp3cNEfvEt/DOmcqRhLdZvrSNnYikF625Ey61C6jvNmcBM\nqk+/C7pGpq+TgeX3UZlsw5Bk1pes5Nfbn0BPxjFXTfqHBmsCva8VrekixGT27NFajnC2bhV+q0xu\nZgi6jmcnc1YXwy8/i7MoiORwYq5uojl3DrXCMIKW7RTqHcfprLmYiv7dqIPddE+5iryNP8VaP5ld\nX/8ZC/78OIaSQXS4SZ85iLhwHcLRj2HiIvT9G+mectUX8n2HjGJEQaA2YKEvphBL66Q1DVEQqHjt\nUfyLlyF5AkS2bcS58gZ2poLMTx4l03GG3fVXMa/ASmvUQBLBb5WJZTRUHVrGEizJM2hX7BgGVA/s\nIVWzAJOeoe/760l/5xmqlW767GWEUxoVXjP6X3+EY8pcusoWUqoMEHcXY/n8ZeSm8xmwFpFjkzB1\nZk0XBq2FFIROo3Sc4mzDpeTYZHLHW+hxVpFv0Tg8qjElaGFvfxZe5LZKSIJA61iStKazwjvOYTWX\nsWQW9rOoyMK+IZXzxG6UvDqkyAAJZz7OodMY4SFaCs9D0Q3cZoneaJqg3Uzxvj8jXHAz5r7jKB2n\n6ZuylpLe3ai9rYTn3cip4QSTch10hNNMsY4zZAoSNMbZ2CewoszGot8cZMcNucCXr7N6bOzAl7r/\nvzteOvz6l53CfyR+vuQn/3T9XxarymA7p75+FxN+8yza4c0Isglx0iJGLLkEW7fR/87b5C69ADkn\nHz2nnMTGFxDXPYz2yg/g2v/CPZBlqw387SVyb7kXIdyP4Q4iKGk0V24WW+QJ0PvyS/gayrBU1CO6\n/V8wLl39R1GHe1E6TmObuTRLfPAEaPNPwf/6f+O78DLOeiahG1BtyyDGRxHScdT2E8g1U0lsfQv7\ngtUYpmxRY5gdiEOtqKVTMUw29A2/oX3+HTREj5PpOJPFm/mCjJ84Rc5Vt3JEKsdvkyhwyOjv/RI1\nkcS+/AaEVJSuZ55CjScpvXI1gs2BWN6EbvMgj3aQOXcEYd5ViKlIFtPWfZpU8xEsFfX0T1iF+/Uf\nYr/xYdj/HsKUZYwJDsL3XUftgw+h5lQi9Z7A8BYgRIYwnH46LKVUDB8k8tkm7Dc8iNx1mCOuKXzj\n1UNcNreU6oCDS+29qL5i7vl0kIsn5DEp10HpyBHeU6u58PifsM5dRbujijdPDLB+djGvnxpmQamP\nqvAxDLODH52z8XBNkjv3wW9n6py0VPGjj5v5y2ILT/e4uLssiZhJoltdbBr3cnooyt2zitnfFwNg\nvrmf/XoR0xwJdLsP09A5/rvZyhWTCmgMH0YwW8kUTcbcezSruyhKvKhNxG014beZWGQf4b59Ksc6\nQzy+ZiKnhmNcNSGIbbSFlwY9WCWRq3NCoCq8Fc1ndpEbu0nk4U1n+cWqOtrHM/zm83aevqgYtAzN\naQfHB6LkOi0oms5FYguar4T9cQc9kTRT813s7x3neF+EH9VlCyMxPkabr4nrn9uLIArsvMrBWdcE\nKk6/z+malWxuGeGeJiefDgosKvdgGCCnwtzz6SA/XVGLAXzcGuLCKh8LfriVvQ+eB4bO8udP8N7t\nMzEbKqbeY7waKyOtapT77Dz4l8Pcd1kjg7E0l9QFeWJLK1NLvcwv8/HOiQGuairIWscmVWIZHUUz\nmOgTuPb1M1w9o4S3DvWwZmoRpweiTCnysLTSiwCousGhgTh5DgvFbhPjaQ2bLLK3N8oKcyeqp5AD\nMRt+mwmzJFBoF7+49B794DTFfjuPLK3m5HCCaQVOXCaBx7a089h5fmKymx9uaeP2OaV85U/7uWt5\nHavrApweSTJT7KXFUsZPtrTyk5V1ZDQD/R8nTEbT2dQyyoxCD5PdCkmTCysqzeM6xS4TK3+zm23f\nmMG4JuM0S0QyGpIgkNEM8gcP8dtQMddPyuOut0/y4LLa7P9UDUIpBU03WJwHA7qd/NMfIOeXZ9UE\nIgNZwwdRRjvyKZG515EzchLVU8iA6CX/9AcwYWFWqgzYMSKwYHQnQlENiDKqr5SOiJKVNUqOIYd6\neG44l1urZQQ1+9LTbS3lxYO9PFKfRrd5OJh0M9UvIp7ZjlBUl7UwjYbRExGiTauyGsmhbjaPu6nw\n2ajWB+EfBgToGr8ZLuDuSg3VU4i5/yTnXPXURE6BIKL6S5HHur5QBejBQ/HpjUi+IL2Fs+lduxJv\nmYfqnzwNbYfQJyzKMrotDjCy+Ebd5kEK92XtkKNjWdev0DDm+Wv4YNoa5jbvw6PHGHn6UeK3/4SK\n/t0YhfUYUrajqieifGPhAzz0wGKCj/4WKTbCpiGZFd5/6IxG+jFka9ZJr3s7WmiID/KXsypPQQp1\no8ejRPdsY/Dyh6ge2EPHH/5IxV13IcgmlK6ziE2LUfdtRJp/JW/WLOLqrb8BUUKw2FE6zyBMWw5q\nBqH1AKLDheEvzl5mkSFa/JOpTrSRzK1jw7kxrnQPYpgsJP2VtIczTEw2Z79rQydRMClrqFA+Ayky\nQGrra/Quu5dKIQyGjuYMsqsvQcnP76Ty/v/K7qGrvBMvpNxrZ6rSAsA5Zy1Bm8xoUqNy7Ajhoumk\nf3Ufrm/9gtRzD+Fdtx4pESLTchTBbEWYsoytg1Dlt1F6ZmP2zrPY6MqZQln4JG8mSrk8kP0s9X3v\nYyquYiP1LDn6R4aW3UORGEVQM188hzjUilbUSOaD37Nt2tdYYekmfWovki+IUDWdT8JOxtMqq2r8\nmFNZ23EpPpq9E2UzmiuPrpQp+12Nj2ahGUoa5dRuxk+cYuhwKwVzG3Bffx8Zq4+xH9xF8W13Mhho\nJKCGME58RkvdJTTET3HOPYEKOcappJ1JSgfnbJUU/MPa1eOw/c9XLv/G2DGw5Uvd/98dNa76LzuF\n/0jkOQr/6br0/e9///v/f3+kpeL4AwZ3nQ2yatn5yGoMres0Yzm1uAZPYbHoSDkFCAXVGBYHVp+H\nEWsB7kQfnwvllPudGCYb9nnLUZy5CDYnuiMAgHFqB2LNDLA4cM5fRnLSMmxWmdT+TzDnFyO6g9mL\nIG9Q0AtVAAAgAElEQVQa/vJqmk1l5CT7iVeeh8si4iwrQ/MWsaM/zcxCJ2ImhuouAEFAzCmm21yI\nd9IctKNboCJbnMrjfej+EsTEGEOCG5/Pgd8ikArWoJRMwmER6H79bQrufhDNW0SeEOOzfoV6h5rN\ntfMYscZlmEwy/kmN+KZPIdqwjPciAUyuAJgsOJKjiPnlKNvfYK9vJsFgEJPFjLm4mvjeLQRqajHJ\nOvScAaDD30hJooPAzGnongLCshezv4BxkxeTJ4igawxoVvxeN7YcH92WIlzeAMEj7zB76RJEQUAS\nBWrUPt4b95NUdQaiaQYTCk1VpeS67fT8/BccmHc9s7Q2XHkl/Hx7B/dXJ4lYfNiCxQitB5g7awZC\n6wFWTilDPfYZObWT2NefZEZDNadG4v+DvfeMtqwq871/K+y1c9775JzzqTqVM1VFUUAhUWkRFBBE\nVLqRFu32tojZBhXBRhRRwYASLTIFVKAKqupUjqdOqDo5h33CzmGF98Pm8n7xevveoa9jON5njPll\nfVhzjr32mPOZc/6f/4+60iLM0UlGnZW0eEUMSSFvx0NUVhWTsPiRXQHKB9+FQDFiKopudVKU40cz\nwO9xIahpBC0DJgt4C9Bzqyjxu5BFiZXKFGlfOZcWSWxsLsFqkmgI2hmNZJCcfko8VpYF5Wx1fddR\nGlpacZ7dQThQzfISL94DvyXfIaD5iqmTZjEuHCUnMUFDcQ6n52EuqdKcHiS2ezuVpUGOxR20vvUg\n35ksZCqS4vrmHMYtBTjnB5GDJZicFr5+WQ02PYHb6WTh1afJWX8pq4pc9MYknGYZVSdL7IlNUVNW\nzFxSRxAE/DaFQCaEEfST43IgmxSeOTrCzcsKYP+z6I2bKPLY6A7FmY6n6ZmO8sAijYbKMuaSGmvK\nvVxU4uK9oQUKXBb8NoX85Cg/PDrPunIv7w7MskyaxJZbiCwKLCry8Mv3+7m4PicLLJAXkASBybRI\nC2NITj+uyDCuVAiT1UG538HrUwpzugWHIjEVS+O3mpAlkdmkyngkxch8kq9vqebCXAKPxYQiifhm\nOhmTfTRe2MFxcyWVATtui0x5gZvHd53n04u85NkkJiU/DpOIrMi02uIYz/4Q96LVhFWRSEanLd9J\nmd3gjaE0B0cWaD39B/TKpfiIkXE4WG6ZI6G4scensPW1Q14lI5E0OQv9nDVyWJHqYljJodpn57Wu\nKbqmorgsWSlI8LffIK+tDdGTi25xIUVnyOTWIo52gjOAmImzfc5DiyPDvKOIgDqHZDbz1rwLs9WG\np/8g5TaVcNlKhHefRqhfw4WIQO3sceRMDMPmZcycz/ogCGqK02kf9tcew7dkLS2FXpTOfSwUL8UA\nPEdfINF2FWmzG2P/i8iVrejTo5hK6jF17SOa34xFlik99TxCXjliOs6Ur57Uc48yV7+B+tQARs8R\n1KEu/MVlzG9/EkvdIkZMeQiv/oLBstUEpDRuLcrw4z/FUVrItK+GxrYgdpeE2HIRsbeeYahyPdZ3\nfoXJ4SDTdRj1/AnEutUwdAa9oBZmhrJ2VCuvBEOncm0hDgXE6DRmpwVrZQsLrmLsyVl0m49pdwXW\nwRO0FUl87z/3sG1rEaLTTY0Sg8l+Urm1ZN54ApPdznEtSFHPLlDT1CxehnHiLb7QV8y6FW1YJjqw\n1q9Ae/cZ7Hl+pBUfoddUgKesjgkceAJ+GDxN7aYqhuquwCulmXnuSWyX3EDylccxFxSTOrOf6X37\nsboUZJudc/d/B/eV/4Ry7FUsDjt1uW707kNI3hz2zMgEbCacnXvQ56cRA0U8eDJOdW0943HQrB68\nLgs+Euh2P/OyGwsZypnF6ZHQZ0Yx6tdni9JkBU2HXFOKVPsb7FHqaJs9wrSjGL9JQxHBnJomXtKG\nIzpEd95KAlIK2e0jUrWeWU2mPmDFq0eI730FZdE6tNwaHnxviE3KOGJeNYHpcxgOH8ZwJ5nBbsYK\nl1JdW4Xd6aJ92qDIDoO6G5fNwoyjBLsaJd54Mb2zCWrdIkJlG1NPP4GruZVyt0K9z8yFqEBAziDF\nZ1nwViHbPaSsPpTu95DyKpDO7SMzMULsRDsji64hUdyKvvtlym+5EcXrRdZTzDsK8W+6gl7Dx77B\neWoKAow98p8cr7mImuH3+UM4l5WuJDkz59BzK/FpYX7THePMVIxlRZ6/clrzfxbRzAJW2foP01J6\ngqQe/4drAUvun/1+fzFZNeZGkQoqaa4qxfn+b8Ew0CNz2DvfRQDk3GIyg90YbZejjJ/DcOfjCg+i\n166lwpzCsDiRIpNEbHnYp7tRD72KULEYMR5iqmgFVocbIZMg5chlIa3jSs0itmxmxlGM7fQbaGN9\nOKtaEfQMHpcDaaoXxSRltbSHX0M7d4DA4g1YXnsIvfcEFpcDQU2hufNxKiLS/DCUtSKFJ2D4HEYs\njGB3MusswyyL9BpecqMDCIqV3qiIr78dWdSQjRQUNyCc2UWnuZSmVC9qx37MTSsxKQpCJsWEvRS7\nIpEwOVmu9+MM5tMdSlKYmaDLWoNWtQxBEMjPTCOmomiuHObeeR13bRVIMpLDhVGxBJfTSVTxYnJ4\nMMwOzLKIFJshKtpwnN8HTj+5kT7GbKXYB4/jcdnoM7wEnWaStgCFTjNnJiOYvvdVYpuvZWOFj5XF\nbtZJw4jJMDgD+MVxFsqWEf/uvfS1XcqW6iABbR7x5cfQmtcz7ixDN8CSW8KC7MaeCZMOVnKZL0xX\nwkqJ20paN/DOXiDsKsY7c44SdRK5dinz25/kP+dKWVzkQd/+BNa2tXSlnfhPv4ZS3kJcNRiIi2Qe\n+iq+Rc2o54+jDZ7jkK2R+vQgaZufMcMBBoS+fidly5qZkTxE0jrVPjP2+BRHQqAKJtzndiLnFDHo\nqOKYWESZx8JIJE2gcRlJZz6N8ixdQi6pnBps53bzrn0RTTl2fDYF/ZlH8X/0VhJ5jVT6rMg9B9lh\nqufezdXk+X04O3Yw+ORvyGuuoqKsnMDkSc4olfjbn2bu7HkCDVUMGl5GwiliaQ1ZFEnpBl5Jw/7O\nz+nJWUx3KM6K8d28kChheZGH8UiKWMZgRVUAj9WEZbKbw6ZKEARWFrmwmkzUF7rp01ycmojSlmfn\n7FQMp1nmf7zUweIyLy+dmaC8pIiqgB27KZtcFr79ON3FK2n6wDLG7zbTmusix67gJ0qf5kIzwKMI\nWVnMS48irLianUNxImmdY6MLlPusvNwxycoSL6FEhpmESonbwn2vd3Ld4kJSmkE4pZLjUEiqBgEp\nhWHzMx1swMAgaFcIWGXKPRbqiz2UMUuf5uKt3hD5LgsHB+dwuLwULl/L6TkDSRJQJJGi8UP8etRG\nKJHmk615WIwEMXcx7wwnWFroJqG40QzoiEh4DzzPi3IDzTl2bC4Xbw8lWdJUT2ueg1yLgS7KvHRq\njLvXluKxmiitL0Pt2I8+OYCYW0bm+E6EijZSgQqiT30P2/JNFBUUYhnrYMBcgG6y4YqPU+534I2N\nkShdiml2iD4xSPSJ/0IJ9VBQW4Vh96N5CuHIq3jsCsJkdj6w7n+J8fZOhlZfjePnX+H8MzuxX3sD\n7w3Os6g8yILsYTap4Rk9idG0EdliYVr2YT6zkx5PE3Xj7xM+cRit9zSW0kpsgsbem7/PFTe0slC+\nmr77voanIgdhYRLzR+7klwMiG4IGZotI0OdG0DIYVhfiyBnGLvoM1elBRLsHQUuTLGrBXt2Iw+lE\nGj6N2LAGo359FkVqtSIaGurJPWhzU8wcPIq7dRGhp39K4sq7UV96HHNRKYLJTNRbhj8+xoy9EIss\nkjbAOtmNd8PFXLy5GLlxNWIqxoizhtAPv0FgzRrMeUWM/u5JFi+pRHR6kAP56OePwvKraC7yk1AN\n3FNdmCYvYG5dx2DDFXilDDHDhC86hG73Yzq7h9mmy3HbTbhlHdVXhjR0HGZHiQ2OICVCqFd8EU/b\ncoyRbihuQIkO4VqxGaPjfcSqJYSffgh92+eJmX0k1CwIQypfROw3j+JuaqCsqpbRSJpF+hA2QUXU\nUhizE4R81TxzdpJlk+8xnLOYNzLF5DevYC5t8M5ABE2HhZRKpd1gqnojPbNxmqtKeeTQOEtqyhhO\nyvjql/BS9ywXvPXIooDk8BOzBuiYiVPrt3JkLErJaDtvV1zDjODkyEScXKeZ0qN/YrBkBV1CDrri\nwPTedmz1rcz5qsh1O0gik9ZhPGUimtFoH4+zxJVCnh1k17ydaFrD5w/yXM8CK+UhhmovY1w1kzfX\nhTuvmL1jGWRXkN65FL6XH6Td20bOwefRm9dzwlpDeVMT0bZtlC50ctmve7nnji1oJYsY8zeg5JTi\nWuhnUHNSZlV54dwsimIi/4qPkVANCi68yzcu+GmtqyQn1MUurRSry8PaQjuLc+3IJuVvlIb+9+JE\n6CjhTPgfprkV99/19/xbxf8qWf3LmtX9zxFvvQLXxOmsz6rNhVq7DmP3UxiJGJbWNaR6TjLw4lvU\n3P8t9MkBTtz/E/THX6Dg518id8tGkv09WK+6k9jzj9L+wJvUHX0f1zPf4uxl/8YqbxrtwHZQ05hW\nXEHmyJukNt2OZsDcV2+h5LuPERVtuEM96HYf4Wf/C9fHPo+gJskc2YGw9U6mv3sX/uYqkE2ETnQS\nn5qj7BPXIUgSQvUyDNmMuvcZ0qHZLBXL5ya97W4c3XsYK9+A782HsK6+giFnNSUzJ1ELmxF0FWn0\nLJmBTkbf2st8X4iidXUE/ul21PPHmdnfTv5NtxF65Y94brwb4/xhhJqVaMd2kFx7E8qbjyJvuQXj\n6OtkJodRLr8DKTyJ2n8WsW4lww/cR+ln7iB56n2SoQXMXiemq+8h+uS3MH/6W8x97y4Kb/0s6lgf\noy+/juv+x5lLapTqMxgmM/tCJtYUOzGPnkabGQNRRCiszeqAbXMfIgjj999G2Re/koUkxENI0Rk6\nlHIamEBMRlDH+5D8+WC2I2hp3siUc/HwayhVrWihMcRgCYcoZvHZZzC1XYzqLeGXJyf4rHiKsaqL\nyTn4m+y11prrMU12o45e4GjxFu575RxvfSyPCXM+VpNIWjMIRgeyfypJgYleotXrcZzfh5FO8msW\n82lOIPnz0EIT7PWtYWPqNNi97L78M2x68UfsVRp55vgoOU4z9/u6EXNKGHHVUHBhJ3rjRv75jX5+\nepGHOXMQ595fYWy5AyXUS3Lvi1jqlnxoa9aj+6lL96ONdCMVZG8EBkwFCN/5DGX/+lWm/vAL/Bdt\nQs4t5b/GfdzQlEsso1OSGEAb6sRouYTdoynynWbK3AqO8DBpbym7B7LOBP7hds4Hl9ExFSWe0bm0\nyscP9vaTUnXuWlPGq91TfLI1H0mAPQPz1AUclHsUjo3H6J6JUu2305Rj47mOKa6uC/L7U+NsqgjQ\nOxdnKpYireosKXDTlm/nI48f5vc3L8Esizx1YozXjo7wyMcXUeU1c3gsylpfhlU/Ps32e9ZiEgVe\n6ppmOpLi31blcSKksevCDO29IdZUB7iuMY9vvd3NL6+s4OWBJNcGY/y0T+KlIyN8/5omcuwmfn5w\niDyPhRf3D/LKF1bywLv93LOujAPDC+TYzbzROUlrkZvlH0g09g8tUBdw8P7QHBZZpNBlocBpRvtg\nypmOpXn66Ah1+U4ur8lBEkEQwCqLjIRT9M4mcFtk8h1mPBaZWEbL6mhdZqwmkVv+eAqPzcSvLi/g\nltdG+c2lQcLP/RT3qoswSppB17Lo2P3PILZk6VOGKCPoKvr8NOPb/0TeXf8DQVcR5scRzDbS508S\nX3MTnvETqNOjGJkM8c4zpD5xH75zO5BzSznvqKGifzd602akCwdJXziNqagKsaASdB0Mg1lfDf7p\nsxiJCHosghYaR994K6bwODO//iGBTVvQmrcQSkFOzztkhnqQLr0DeXYQzVOIfvBPjCy5gdKBPRhV\ny+Hsu4w3XEGexUA4/XaWjKdYSJw7jvn6LyN17SPRcQTJ7iB0opPcbZdnSXmrP4oUnSb93p/oXf95\nGuZOfKiP/58aV9HhQXL7idZuxHp0O5HTx/F87E7CjkKk576HvW0t53JX0TB9CCMZh9JmDNmMmFwg\nE6jkHmsdD/76U9ja1iKYFASzjVRHO9Laj6Lu+QPznb3k3Pav2VqCNdchTZ5Hy69H2/M7zE2rMMx2\nDmkFrJDGiL79LNaaJoxMGrmgHC1QjhyeQJ0c5EL5FjTDoJ4phGSEeG49iqEi9x9Gj0UwalYiRadh\nYYrkmYOIVjvmplUgiKQKWzCPnERz5iItjIFsJp3fCIaOPDuAmIqR6e9AT8ZRmtagjfcyXrOVgr7d\nqM2XoEx2QyJMumwZcngCo/cYemQeYeU1zOpmckMdGLKZA1oRqyLHOOxewuLTTzO66lbKo1mLs1TX\nMaT1H0fIJNE73kPyBpktW4Nb1pEWxoi6irGdeg2xrJmF7b/mgdJb+G5eL5LbT+rcYeRVVzOl5HB4\nNMy2AoO0xZvVn7//G8RV1yCkE6gHX0KpW8o5dwv1EwfRy9vQD7+KUlbHWKCVr7zWyZPX1WXrEhwB\nhEyCBVcp1h2PMr3p84xH07SN7YGGDUxoFqJpnXBKZen8UZLV67CGLmAIIp/aHefJ6+oQ1BQJOWuN\n5xw9zoCvhbLZ02SGenjCfhG3t+ZwZCIJwLoK/18xpfk/j0dPPfJ37f//j/9e3NV69599/hfdAOKt\nV5DUDCacDRQWtGIbOopw+CVMjavQRnrQHQGUikbKb7CAJJFq3krNtYeZtprI+fpjaK88jLm4HMPq\nxlbbwOZT99KfMrCVFGcxeqoZI5VA3vgJdF1HsNhxzvQAkMrzoyoORE3P0kysbqxBH8NSgGIhRPey\nW8lN6eTf+a8k976ICATXr/6wQImGDUxqFvLDFyCnCMmfj7B4K0LPAczTXWSmR5nP1wjanWS6j1K4\nsgjUNHO6ibcuRFhfuoRiVxDf4CCusnyk27+LHhtH8ubgbagkVdiC92N20s48eiu2Uq9OEensxLNk\nBrFxOcbgSYzWzViGzjAnuTgUF9ja4EZtf4WSL9+PkFjAdOntKBcOk+nrYCBmUFJWhtj+PLmXbUU3\n2xEdHoru/Qbq3idxbLiRpJSHCZ21hWDqayc90ImgWNBCE7xlXUYik2Q2UEDSnI+oG5jv/yVMneCl\nrhBeq4mW3CryZZExtZACfZDzFZciiVA1doDOH/4XFz/xHJ1ffRbPL6+n0FuErqY5fiHMyrolhBzF\nnBqJsqHMhzjnwaGImIqrIacc5kcIBRqIuGoRYxl++k+t3PhmF39YOUGiZCl9cymOxQJsDaT4j/YI\n8XQB1zpjrC1bhHHibT5TNEKsaCuWjnfQ5qZYr+6Bolq6lHKa2/eR2f9bll+0gpSaT6nHSuLNP2It\nayWjGxjpJGHdxA+21TKV1nlvYJ7rVl7JXFqj/9a7WPbzB9Cj86TzG5EiU9QyyaC9gqJaP8bgKdIN\nm3GlNY597hHiioXa9RuInTyC64oaNpb7kUSBaEZn3F6Od+hl/viBj+euaJASl0Lm6FskN95ORtPx\nkECwuSk79TzloshTzk3MJlW+tbmMQ+MJrLKAIot4I4Mw0YfZtpQqp4Fw9CVWlTczbHKzosCOmI5x\nTX0Qf2KCaxqyu0yTKHBHnZWDs9krRP2P3+XxG79C3vABCJZyW1sxV9fnUmpOEdcVqnxWpKkutt+z\nlnhGp3r6CNfVrySjw3vjcQI2EzctKuCG1nzOTsU4PRnhIy0FGLKZj9TYUPe9ieTcxDc+0kC+Q6Eo\n3MN31hURkRxsqw0S+94XuPlfHiHY/x7rK9ezq38On0Pho4Ew07KHQN97NBWuplwMY67wUWzRsjZM\n/aeRCqoIeaowiQI/uqIWZ+g8ujyPuv9F9tXfyCZfkgJziua6rBYxrRnIooBJNGE6+zaGdzVn5mW+\nva2eyWgazebi6kUQfu7H2MrKEHz5oGb1o6aZCxz74e9o+dP1SJGss4hucZIZbce/uD6bqGppcPrp\ne+DbWINechZvIVm8BCmvHtNEJ9Gdu8lLTWeLNBUr5h/9M/rXHiGJjN1fSPsnv8aq79yEqbAaZkcZ\ne+5ZCm+8idSF0xipJKayepIjw7jGz2LEFxAlkXTfWUy6hm96lPj0FJJFQQJUbzHy7BCnf/IsRX+8\nEW16FLEKjEya3CN/QK5bTv/Tz1J0+UbmTp3lyE/2skmRSaVVzKVViI3rcF2ax+j9d+AoDODNP4pa\nvYbzL+6jcd21aHNTaKEJUhMTONdeglTWyNz2p3Deeh+29DyJZdeS3PMus7YCZmIq5tMXUHLzqeo5\nQc/rByi//lKUgkpen7Wzzamjb/8hD/76U3zl07/le+F/x/L2Y1lKVSJG2uLFXFKDMjqO1nkQIxFj\nXnTii4Xh9E4iFwYQbC7k5vUsM0cgoZMMLbDwxlvkf/HrEJ/jeMzGEpsXoSqHPIvMQkqDZDq79qgJ\n9Hd/TyqVJDo4inO8n+TcHObrvoh44TRaLEL80NuYL7mFN87P8pHwEP2uBnILchlYSFOiGjhjE+h2\nPwlfBVJBC5phoEz3QMMGTo1G8TVuwZyYJ51bixSfZTyaoXSql0Tr5YxHVUqNFH4hQbx9B7aVl7Kn\nb4YVtnlyChSMRIxcu0yXWkVeQMaZW4tusoJiR/YGIViKhwTyaDepkiXMRDLkLf4ItpHjOOoauGdp\nGSNqCQUWHWGgi68fTfCd0nYuhEqR5HF269VsLVJ4t/I61pid7B6HSyx2Olwt2GSR3sLVBCQZerpQ\n136C/ok41y0uRJ4dwDCZ+beDUT63qpQcUUCpWUS+FGdKMBE7eYT7xqr58XoP70WzOtN3bYvZEJ2k\nSy5FEuGTyxOcndU4N53k4wUL9BhBcvIWY9YMej0tlOQ3crNkRp7tQzf+/EnZ/9fRktP49x7CXzW+\n/frP/t5D+JvE/ypZ/Ysnq7svTLPOnUBIRRCis0xtfwbfqtVok0MoNYvRo/NI/jwQJVKly/j1yXHu\nLI5jmGxMKTmkvvUZSm+7Ddw5EAkhyCai+9/C2pT1a83kN2Ia7yCy9zVsLcvJ9HVgqmgkfvow1ms+\nT2L7Y1g+/m9IFw4yUbwa/74nMBXXEK3dSCxjEDz7KnosjJGMIbr8IErIZY1keo6hTo9iKqxkdtHV\n5EwcB3N292eYzDw37eb6nAh/nHRybfdTmNddg2F2YnQdQFt2NYMLaQQByrvfQM4vQ50eRR3pRVl7\nTbZCGTBEGXlumEznIeSWDZw1cmkSJgn9/lGCV38c3eZB8xRliR8fvJcdP0OpagF3Dpozl1nBTiAy\ngDH+we655yTmhhVkhroRFm1hXrDjFlII6TijuCnsfB2xagnCwgR7pDryHWaqL7xBou0qhsJpJEFg\nPpmFAtT6LRwejbKOPrbHi2jJdaIZBtOxDEsL7EzFVIrnzjLsbSLfmEdzBJCOZZ0OUu1v0rv2s9TY\nNYaSJkrPv0lX+VbqbKksOvfoW8jLtzFjyUMSwJsOIc0Nc8La+GFVuaob1FkT7JuRslXz8ZP0//xx\nlG/9mvmkRjStUu6x0DkTZ02+hf3jSVYWOrEMHwPJxDPhAlYWu8mxyaQ1g+mEiqZDjTrMsKWEkonD\nvCk1sbTAgd0kYpvqZspTzYudU1xXn8NcUqNGH2f6Nz8h+Km7GDIXMRFNU+G1kNYM3jwf4vaCMOeV\nUk5PRrnWN8eMswzrC9/HWtPEROMVqLqB3yrjHD+F5sylU/OhSAJVYwfQKpczkJQ5Oxkl32lmpXqe\n52LFNAQdmGWBoFXGuvdJlKY16HYfY6KPWEZneCHJQkrlymov5+fTlLsVJFHg1Z5Z/uutbr53XQvV\nfgs/ax/mzhXFdExnE0tREGgwxtifDLIy18RQXCSjGwT/eD+uz3yDEyENpyJTt3CKPVIdq4uc7Oyf\npyFop2LuNKmSJRwciVDptWKWBRwmEZOeRpfNTEQzjEfTOM0y9foYfwq5KfdYMX1wdX92KspVZRaE\njj3orZfSNZeh5v2fkd52N7Ef3UPgSw+yvTdKRjPYWObh/eEFrqmw0xMVKHcrnJjI+j3WGJMIapJu\nUynlHgWlr51M+QowdJTxDoxMij7fIkqsGjOqCUmEXxwe4autZtqjTpYP7UBYtIVxzYZmGGg6nJiI\ncOXEm/Q0fZSG8fc5l7+W8p0/xlK7KHvjUFDNyE/+E8Vpw3vvjxHe/S1nGj5G0/GnsolS42oGvn8/\n5Xd+lp4fPULh+kXMX/UViua7SHcdwVAzpKenGN32FYrffghTTgGR1TfhlA2Sv/8u1qq67LxTWg+p\nWPaksqSBs+TTKM+h27zIg8fQcypRXXkMLqSpGjuA4M0jk1NNOAOecztInDtBdGQax70PY9ESzPz4\nq7i//DDm6CTG+cMYqSR6PJLl3PtLQFfR7X5CP/smvhUrSK74GNZECM4fwmi+GOP9Z1GqWph56Y84\nvvAAcw/eTeTOH+BUJLy7f4bk9mdBJIsuY/xbd1F8860YriAAmrsQeX4Y1VdG4unvoyZTdD93mMX/\nfDlyUSV620cQ0zHSJjsJ1cA3cphU51GUFZdxV/E2vjhxGv3LN3Lk7se4fnQ76Bqm0npElw8jEWHo\nqafIu2glpuJqjNxKBC2d1YKKMvqhlxGtdhaOHsKzegN6w0Vw7HXk0nrmA3W4F/rRvCWcvPpKlv7i\nR8wH6og/8M8ceqKdq06/xqQpyOTN19D4uWsxVTRxyNLAcvMsA3IepUaILi3rmdozE+NqoZP+3OUA\nnA/FsZkk8pwKFf27obSZpKuA6bhKUjVwmyXyZzt4eMzPTS15/PHsJJIocEe9nbjswJaeZ1B1UmLV\nMCQTe4aiXLxwkLGqixlaSGGRRap9ZibjKoUOE/NJjVx1hu0TCk05DiyywEQ0TXOODRM6KiL9C2nq\nhBnE2WG0/Dp2jMOyAicj4TSLxTE6pSL65xJsndpFZ/U26nxmpOg0x+IO2twqxsl3OFu5jcWZC8Ta\nIEAAACAASURBVOynnGq/hXPTcQI2BUEASRDonI5yRbkdKTxB2FnM8YkYG2b3o7VsRYpOcyRmpzFo\n48BwmK1yP532BgqdMs92TDMTTbGtLodzU1EW5buoHtyN3riRzgWDmXgGsySy0hnj+ZFs8eInFhf9\ndbKZ/8vYMfzK37X/v3bMJmf/3kP4m8Qnqm/5s8//oma1dPYM+lAHe81NFA0ewLX5KiIVq7H7fKRP\nv4cULMQoqEXMJJi1BFkXOoCQirNbKyXPoVC4pAl9doJMz3HkgkoMxYbUsgFZBG12AkkwSB3bzeTW\nL3JGyKd0yWoir/0OyawgG0lMviCS1Uam4yAejxN10eWIipm47MRnRJgINqFULsKsxsgMdqI0r8VQ\nrMhON8ayqzA69uHye+EDbrnmLUI9/AYVPXswV9RTV1ZMrHIVksOPlAyjj/RgnH4Xz8RZ/EUlyIpM\nZqATU34p2vw02uA5xFQYKRVGjEwjCCDmlmFYnOQa2cp/Zd1VzNiKkN75FSYtgeDwQKCYOcEOB9+A\nZBg5txTt1G6cbgdH9QIKvXYSObUo8RnIpBAq21BtfqQ/PYjQshGpcx/mIy9jWrwJzREk7iklltZp\nmm6ns3gjx8cjrLFM05O04rWYcCoi/qkzFIwcoSN3FcsO/JTBwmU09O1gylvFbEKj7PBTDFVdQlm4\nmxNaLuG0TpdSQrk2iVjRgmD3cmgixSKPzuupIlbmZ0EGQibBfvdSyo0QpvYXUPe9zEue1Uj+Ymr9\nFlqS5zH78/FYJCSThXynQpk+zZCrhsy6K8l3mMgRohTYRFyhHgr63kPMLSFx76dRLvsoJ1Ie+gwP\nzU9+hZJlrQhmGzsHo3gtSvY01+UnMHqEVMVqqq1pesIgCgJmbw5D4TRXmIexZ8KYPUHSZjeuFRt5\nf95MqzGCb+9T2FvWcXQ8xkddk4w7q5hLaqzteY5E3Ua8oW7EpZeRLGzCHx9DcXqwJELodh/nM24q\n9z/OhUArQ+YCihwSnpMvU1/sJxjMYdeclYsrPOT37sbrcSFbHZxzNdCZtjORMfPU0RFuCM4SzM2n\nxRxmKG0hoxskVQOLLJLR4Z6LykmoBjaTyIpiNwlVp9mlETBDX1jH4Q1QLS+QUFy4zCI+q8wfTE0U\neRxUOkGSZXRvIbXJft6YlNjmj+OyWWhP+lENiKSyljtTsQyKJPJC9xzLjCF6NTeLgmYCcoaUPYhh\niDSde54ztiocisyG5GlEXeWAtZkip4mu2RSly9YzFFExVmylfTzOlXkqIcNCnd9CmcfCDw6MUuGz\nYwDlHjOFc+dQvSW8NWNhhWmCnpQdw1eM/pv7sXvtPJ8sQ/AXU+EwSG9/GFdtC/ZEiLV5JsRkmCJz\nmt8mKlhsjdKTtNCQHsAXG+HlcRNnbJVsLXdh9J8kMHkWc9sm9Pwa9J4jvG5exLI6H6arPodl7DRa\n02YyhkDQKWersNNxTNd9gQVnIaVL64mcOoYvOUrq/Cksiy+ir3wzudU1TN9zG8I9P2Y80Ehh704O\nXPsZKh5+Ar1zfxbHXNJMpv11pLatdGgBjC9cT/76JWS8JQx//2s4t30CZfAYlj2/x4gvkGy9DOtU\nF4nfP4TJLGBbsYW9tVdTe+Z5xIIqXEU5GMd2YEz0IXmCiBYbUtUiFnKbSf3xR1jqWtEtbpzV1YhW\nG5LdjRSeQEgnEENDyAXlACjrrsE0cgpF0Ri975uM//QJvGUeHKsuRvDkcOErd1HywK8QJi4wGmzF\nEx1h4Fv/js2cwhTI43TFJZTZkgT/7bso6QXUsX6komqk6BSZN57AMnYWuaASQTEjmUxU/8fXeTiv\nhU8+9S1abXHE8laIzELjBvS+E/QUbqC3cRNlM2cQRJHpgiXYeg+SfP81pOQ8aCpSzRKsTUsRba4s\nfdDmRJRE4s8+irWxDXXvM5Te9ml0Vy688Rhmt4PGRx9l8uFvkLtiFfmXbSZdfxGhXzzI8Je+T8li\nL8P+Rg6FdFblmAj07KK86y06aq9hLqHSaE9S4YCS0Xb86ixCoBDd7mMyJVHEAsHEGPbJTuaLllIX\ncCAIsKbYTdlvv4Z95Sau/X0HnyxO4ZY1Rgwn3sggFTad0/YGzs/GWV/sJP/82wgn36HD00QlMwxn\nrJjtLhqDNqbiGU6MRwglVEqe+QYzjZsIjrTjDeYiL4yjTQ4RLWih3GvBvvtx8upb2TNrJaMZbCi2\ns1cvpu34k2hVyxHMdvJPvoh+4ThdTR+jJXQYdfQCfa5qzLJIc8BKkDBel5OumThVfhuybEK1uHEu\n9FOQl0dq1zO8KDXQYo2R75CxzA9TmRzgjLOFemkWS3iMuopSCtw2ZhMZLu57kWTJIryKweO9Bl6L\nwprxncRya7HanVT7rVT7rVgU098gtfnvx1C0H0mQ/mGaRbZgM9n+4Vqlq+bPfr+/eLI6GIpSFO4h\nk1fPyJdvJm9VE5lwHGtpKbG+PmSrGXNxObI/D6OogU7NR8WeR1i4/EukNJ3SicOkOg6jrL0arftI\ndlLXNSS3n3jZChIZHW/XOwhFtWjuAoQTb2Jk0ujhEPOdWVsQzz0/Qt/+Q5TNN5Hc8RS2tnXstbSw\nzhlFnBtBLWgk9JP/YKF3lLLrtiK5/dlFyKSQ7jmBvOa6rO2WKCJY7SBKGC2XgK7x3Pkoq39zL+V3\n38uMv57Mw/+Kt6kaefOnuJC0UPbez1FWbUPrOszCyZM4yosx1y8lenAn0g3/gfjqwyhVLaiTQxib\nbsU000v60JuYl29Fc+aSVpxYQxdIH9tJfPNnGbvjo9TedQtGKolc2cq4sxJJFMid7US3OEnueRZL\n00oESSJesRrpzZ8ib/wE8kw/LyTLuarCznBCpMguEtMEjo5FuShf5vS8QEvPS/zEtI5ij5XN5V7c\nWphZ0Yk/HcIQRLozTqpP/IFDtR9jSX4WZakdeZ09FdeyrsSFyVARUhHUXb9HdPv5jftibitOMmkt\nIq3pxDMGM/E0q+0LCOPnIacU7fxxTMXVHJarqPRacGvZgqxz01GuLZHQrB66Q0kePzjIow1zWYuy\n+x4jmtb5ymudPHJVA1NxlbRmUOFRsA4cwnDnMusoQRAEjo5F2OKaZ3/Cz5mpCJ9sycOCSkyXSOsG\nA/MpommNlYUOpuIqVlnk5ESUTYVmOsPQKIWQ4nNozhyEoTN0FqxFEgTe6Z3hzkVBTsxkKHIqhNM6\nFS6JDCLK4T9hKqlF9RSwY0JkTbELV3ySESlAx3SclKrRlu/EAF7smORf2gLIs4P863GRu9aUUUb2\n9CbXJpNQDaJpnfFoliNeGenkhVgRIwsJvtCWw4Iq0h1Ksizfxh86phlfSJLvtlAXsPOr9iGuaS3A\nIouMR1KkVI1PVZt56GSEL6b2sLP0KjKazrYiiR1jOhdCMZYVelhlmSHhKWE0kiFokwmnNEpifWAY\nvBjOYWfXFNe05GcLtVwWNon9PDWXh9ss05bvBGAhpeGzyvxobz8XVQewmSQ2zR8g2byVpGqwq3+O\nj9LBQMEqykMnedOo4ZJcjR+ciFKX42Bn9zTXtRag6QYX2ybZm8qj3GOhUIoh9LQzU7uFmYRKrSmC\nFAthyApH1Vza3CovDalc65vjh+cV7lmex+ZHj/LujTnsigXZUOIknNbxRYeYtJUgibD2q2+x69uX\nYJYFbH96APtFV5M6/BaiN4fUcD+2bbdy8KpPsvzbtyH784ge249j2ycR0gky3UdRatpInT1AcnSM\nyWNdBFoqcX766+xfvYUNzzyIIJsIv/s6tvoWDtzzCKt/fDdieTMTTzxMYEUbO29/jK2nXmf8oftJ\nR2KUf+nfs/r3aC9q32mMldcx8c3PU/SJG8kMn0e86CZG7/scJTfdiF61As7tpf/Jpzm2vYvrTzzP\n3PO/xH/lDSSP7iIyOEbgn25n9OcPU3jHP2e1thZntqCooo3xh+4nZ8MqYhfO4/ronaiHX2d8TzuF\nV23DVFCGoevMvPIcnuUriZ49ibN1CVJxXfY9CzMIJhPq1Cgnv/8kjS+/gXLiVaTCGqK7XiAyNEn+\nLZ/DCM+gh2eJnDpGaj6aNfvX0h9qz438atTDr2MkYvQ89y6KXaHmni9wV9ud3DfbgfbgXVj8Lhzl\nxYh2F/OrbiQ4fpz44V3oGRVby3JEpxctUM65lIPC576Jo7wENbyAUlD64emvPHySeMlSMpqB/dCz\nnHzgtyx94scYZjvpfS9w/sV91N5yJcKGmxi691YcJbl4GmuR88sQgyV0mStwKCIzcZVmZxoxPkfC\nU8LQQoY6dRDVV4Y8dR7d5kV1Z4tly9wKCdUgnNYYXkixPkdg65PneOG2pbzcPcOnClNodj+ff2OQ\nX7QsMJizhBybjGW2j2FLCYVihBcGdTaUeXnz/AzVfjuaYbA+doJ0zXoWHv4Sv199Dx9tzGU8mqZ9\neJ47x55jYMMXcJslQgmV2q6X0eamMDeu4LitiTZhhJCrApci8oeOadr7ZnmsdorRotX8+ugId68u\noWc2Sb5DoXj+HE/M5HK7e4g+3yLCKY2jYwvcWmfn7TGNi8tcjMdUitQpDLOdvrQNQYCq2IUPYSaa\nbpCbmeZ3wxI3jLzI0ZZPsTjPhgF84vcnuXdzNUG7giiA73f38aW8T/KljVWUe8y0j0S4KF+mN55V\nGzbk/X0JUo+ceOjv2v9fO3Z3/GP5xv7PePmmP/zZ5/9bn1VECSEVIeGrYCKqMhxOcpE4iOopICS6\nyZ3tpMdeQ3WyH0OxoXUfZrTpSvLf/yXdy26lymtGM8AWGUO3efldV4TLqgPc/PQJdlztRbd5eX82\nu+MaDadYVezmR3v7eHSJxiGhFLMkMR5NsbTAgVdS2T+RZm1QQN35FENr7yDHJuPQohiinNXE2f1M\naBbyCLMge7LXdboAZA3fK1wSKiIGYM7ESJnsxDI6hsGHZKJqphFGu5iuvIhgfARhdgRBsRAuWIxr\n8iyaK48DYRv1AStJzaAgPoh6eh99bTdiGJDnkD80iUeUOKbU8qcz43x5fRlnpuL4bCbGwikKXGZq\nB3bSVXoxZlmg1Jn9HcJpHatJRHr1YU6v/CyNe39C7KovYxjgk1UeODjBsmIPl9CTtTcx2RDTUd5P\n57NWHkX1FjP5/S9RdPvn0Jw5HIk7WSGMoA118n7+ZlYWOlHmh7PG3a485s0B3OlZHu1McV1DLuG0\nRmOsM5vQJ8poCDponD0KosQhWzNDC0mutw7wcrqCrd1Pc6DlZjYqY4w6K/FaJA6NRlnb/wqmkhr6\n/YtwKSJOs8RTJye4vUxDioWybPVQDxg6dxwSeHydBTGxwGlrHYVOBfepV0A2obdeiqCr7B5J4lBk\n5pIZLvdG+Px7Mb52cRU5VomrnjzOQ9c0Ux/votdZT4klw+EZg0V5No6Pxyh2mymN9aH2nuJs9ZUs\nYhi1+yiSNwetehWGbGYmaZDbu5tY/cVYBQ0hscCvLqhsqfSj6gY10S6MVBwAw1vIr4YUbs8JZRfL\n/S/zSs2NLMl34f7t1/B9/A7S+19hd8unKXFbaIp3Mp/bgis6im7zou35HaET5wje+wOEkzs4U3Yp\ni/RBJn/3cwJfuB9pfgxDsaJb3ZyJ2bCaROqi5zAkBRJhwnt3YF+0DHWklyNtn2ZtppPo/rcQRBHL\n1Z9nynCQ0/0WWmgCgNk1t5CjZjct05KX3OQYhmzmlQmZK0otGLKZiCpgf+cxpEtuR3v7l4h2F/pC\nCPPqK4jtfB7JoqBsvIFeIUiFOYmYWECMTBEravtwUxU1eXBFR9G6DyNVt9EjFQJQadfRdz1JOhRC\nEEWUwlKEldfA8TdJLrsWy5E/cbrichaZ57M3I6EBkoWLkLQU8sBRIgd24Vi2nv7CNZSaYojJCAl3\nEZbwGIgS4swARiZNuGIt8R/eTc6llyJULkE7+iam+hVMeGrJSU0gxefIBKuQwuNZT+S5kSz6WNcR\ntAzbYwVc5Z3DGO9Fa9yMPDf0of+qMdrNTPVmcqdOgiihzU1hqBmm6y4lNz6EmIphxBeym2FXkPg7\nz2JtWopQWIvefwpBVjCql0Pn+xiZDFLVIgZ/8G0Kt23BVFLDYGARBTYR4fBLTO95F+1ffkzeiecR\nnV7U1kuJpLTsnPDmTxHt2UVfbl6H7ghmqX4zI5z93k9o/PkTSAtj6BYnCV8FswmN4vlzpAuamUvp\n+I6/iFy9GCGdyM7xg11INUtQz7Vjql/OhKMC/5Fn6Gv5GFWWJPLUeZDNXLUzw0tbzBCbQ52ZYKjh\nSvrmElxsHkNz5ZC2eDHpaZ7pDnOTfYCJ3Da+7WvkhgtHKXabKe7fw3D5RopYQLd5s4b0rz+J7Yrb\nOJ320uxMI5xvR69dmwWsuAuZ/ABkYR87RbygFZOWyi5aZ3cxXrMVt1lkIaVTmBzmnYiPtSUuzMde\n5kzZpTQ70xgn3/l/5QPhQQRd5flZH1fX+NARsHygIY4d2YulqgGxopV0++sMrb2DmoUzpC+c5pWi\nqxiPJFnxgbtHbcBOlSVJ+LcP4LztGwzFsvO9JAgoc4P8atjCLY0etLeeILbl87xxPsRNgVn6bdlk\n2d+zC0Gx8I65la3KMC9EC7gmX8VQbHBuL2JJA8ZIN/GmrYgCWKKTTMoB8sMXMGQTujMXzexgPqkh\nCDAazqDIAjWdL6Ovvp7puMpYJEsQfPLwMD/YmMtQxophQOn5N4m2XIHz3NsY6ST60quYSmT9l73h\nfl4IedB1g+l4mm01QdxmCU98HEM2Ixg6A4aXikQf6RO7SU1NY29dzpHcdRwfD/M59yAdnsXUy3MM\nkC2kmo6n6ZqJcemO7wNQ8LW/r8YyNh/7u/b/145vnvj233sIf5N4cON//tnnfzFZHZ2LETQWkCLT\naCPdIEokWi7HPj+AIVvQHQHk6V7SeXVE0zrTcZVqpgnb89neOc3NJSq6I4Aum5lPanj2/YruJZ8i\nz2FC1QxmkxrVPjOm7n2EK9bi0OOMZMwUfWB6LMZmSec3IM+PcizlZVHHsyg1i3lsOpctlX7KxTCG\nxQmGwWRGJk/P4t2mJS85mWmM7nZG67eR58jy1zOIRNM6pydjLM6z82LnNHcURtAcQXSzk5gm4Jk9\nj+ovYzQpUiTFUC0elK530ZMxxJIGBpQiSgf2MF97Me5TryAoFjItl9I+GqE+YMMnq0gLY2jOXMT4\nHB2an+Lt34Wbv4l1x6MIJhOmpZegD3cRqr+UYHyEYaWA4+MRtlZ6ee7cNJ8s0Xm81+CzFTBjzuHF\nzinKPDYuMzq57aSLX1xWQER2sX84zOWpk3xvppwvx97g7vRGrmzORzcMuqejfLFe5tf9Ap8JTBLP\nb8YAhsMZ7Kas3rE8OcBvJt1UerMaQJMosloYJPL/cPde0XaVZdv/b7bV+1p77b127z29V5KQhBII\nvQvS7KLoZ8HXAuprQdRXwYKAAopSpIRekhBIISG97WTvnd1736vXWb6D5fDIv+M/PnUwxnuNMU/m\nyfPM9sz7ue/rvq6CRr71dhff2FDDKx2T3Da3AOPdJ/it+0IurCugMX6GbMk8Er//Drmb7+NzL5zm\n6bpehLolnMz56QsnWVnm5sYnjvCjK1qp9phxmUSU8Q72annJKVkUeOLoMF9eVU7HdJoylwlXYhSA\ndiNAwCpz61+O8+qcQZ53rMZlUVhZ6qR7NsN8cxjVHeKrr3dyfkMBa9/7OQ/W3QHAdfOKqfKYmE6q\nFJgNBhIQTqu0Hv8T92jruWVxGa+3j3PPAjvPD0JON9hY7aMo3sOwvSq/2TBrdMYgndNRJIFih8Jz\nZyY4r9JHOqfz4XD+PQvYTFxZaUaaHeKQUM5zx0f45oZqXOkpMvYCwmmNUKybN5NFbA4JiKkIvVIR\nB4Yi1PisOM0yr7dP8NXaNNmCOg6PJFgSsvFi+zRFTjM1XitBu4wyO8ixXID55jA7Zm1s9CZ5ZcKM\nWZa4SDuNWrGIP52NsLjEzZzZIwCc8i6iZWQ3g1XrKTn5EpGjR8jd8UN29Mxws3OAcGgBX3z5DD+4\nuBFNNyiREuwcz2/qnjjQzx+vaaIvYWCXRUKJXjRnIWHBjjc7ze1vT/D4nBn0slYisoddfWGuSB0k\n1ryZlJp3+jkzmWSNI0puzwtYFq4jUzIX4d3HkRdsRLc4EboOIpQ0oDkLEbQse6cEfFaFVkYREzP0\n++ZSlh1hfzrAKrWdNmcrDeYE0lgn7d6F+KwSR0bjZFSNrYVZXhk3Mf+xL9P2qV9wYWQfNK1ByKXz\nXdjv/xXh8q+gnH4HIVTDrKuK6ZRKhduEHBlFTEXosdWgfv0mqh/6E91xgaqDTxA57w5iGT3vhKRr\nGKkE+73LKXKYqDClMCQTsce/j/eyW9An+vNi64FiJgItvNszy9UVIr2qg2pmSGx7BOuNX+f/vN3P\nA1vqyWoGjv4P6Q8uIvbZa6m7ai29az9H7dltfDuxiB8sdyKMtNNZuJwah4E81Uu2qJH3+iKsKHVi\nPfwSSlkduaImjH3PkR3ux77ucmLbn8e18UoSRS3Yej4g29PG/uYbWO3X8ookvor82p7Jc8nlR+/B\nVuRHu/JrOKKDiPEpNFcRUnyS9LHdmJdeAKJI9sRu0oOD2OcupKP6AppG9pLtO4t+0ecQ3/w1Snl9\nnnfffgRl8QWMPPRjeu98gKdrF/M/L32R7KZPY1UTTBvWfMBcM494oB6rmkDsO4pe0sz+iIUVY++C\nrhObv5Xwt26n/Mbr6anaQOnOB3m1+TZKnBaWeXNI0XF2ZYvxWhTmiaNo544izDufuOzCFelFPb0P\nde3NmKMj5Pa8wF+rb6DEZWF95iR6KoEYKCVZ2MS29mkurfehGWCSBKyRIfS+k8RbL2LvYJSL3bP5\nn6GhM/77B5m8835qvWYmkypFex9DXr6VX3QYfLLrCV5f+GkubfBjPfsu8Yb1OCL9xN0VPH16gjtr\n8gL+ffgpcsiY1BTy0EnU0rnsm9CIpFX2987wffsRru6q44VraxhWrRTu/FW+GukNIrr9nCjZwImx\nKLeWJNHcxQymRIrf+y2TGz5LyAgjpWbRLW6EbILcke1kz/9Ensvc8QG7g+v5oG+Grc2F2BWJcjtM\nZgQKz+1gunETkYxGzeAeJt95A/PnH8CZniJsDuCfOInmKoL+kwxWrac8O8Jz43aKnGZWljjI6HBo\nJM7iXf/Dt/zX87HFpSxJtfFCtoarXBPc+F7elezZW5f+KzHMv4ypxPhHOv6/G6cjJz7qKfxHsK54\n8z88/0+D1XQqReYvPyByxT2EsuNkdv4ZS/NS1MbzkE5vR5sew1BzmJuWoFvdCNkU2mA7kXlbcWdn\nyLzxGLY1W/NOKWY7+sAZtAVbEN77I3LrKgRNRcilQJSJv/8K5opapMZlZN7/KyO7j1N57/1I0TF6\nf/0QJRecx9lHXqLxzstg4x0o073EX/8Ttqa5iNXzyOx+AcucFSBKAEyXLCEwfZZwQTPucDf6WC9C\nqAbG+0g3bUB7+ge8suDT3OQZQ49MoTasIfKre/Ddcjcxe4hYVqfw0F+QvEEkbwF6LEyq7RDKNV9H\nGTuLoGVRxwegaTVi7zG0+lVkX/oFpsu/QPqvP8d6xWfzLiFqjmzbfsKrb8Wz+/cA+ea0yDRCeQvG\nQBtG3VKkyBix7c/j3HwtQjZBonQhtslOsqf2Ii+7NJ8JmurFUMx5W1c1wwxWJEHAYRIxj5zmiZkg\nK8s85HQDRRQIORQsRhZDMvGdHT3ct7GGm58+wVM3zmM6qRLJ6DSM7CF2cA+Oqz9LwhrAEe7Ny1r5\nK+nJ2pBFgepYB23WOirdJmZSGqEzr6EvuhSl7xBYXQy76ylUVIRcklHDxeGRKDU+Gy2mKB9EbSwp\ndmAKD6J6SmmfyVDvsyAIcHoi7+09HMtR3bOdobrNlIoxNJuPjKpzbibDvOF3EUSJv5oWs7zMzYmx\nOFtKJXSTnZ6oRp04g+oqYiKh8q032/na+XXUHnoScf0t+Ux730n6azZT8v5vMa26jF65mLF4luXO\nBDFLgAd29/G9JTYwDHK7/8rU0bMUff1+2pNmqjwmbH+jJvSZSqlUxzhjBPFYpHxQe+JFECXeDp7P\nbCrHukovpke+TvDGT7JXLaF7NsnHHf10+eZxcCjKddUmkrKD8aRKlSnN871ZFha7qEt2cVSq4oPB\nWQodZq6osnL3O4M8eJ6XF0Ykrqh1cXJaxWuVGY5mWOmM52W4wiohh0JfOINmGEwksmyq9jCdVDFJ\nAv5oD78asPOZZhvSaDuTJUuYTKrEMiolTjMfDkcpdZmZE7SR0wyOjycodpr5cCjCtS0F7BuMYZby\nWfFma5KenANJEDgyGuWSOh8D0Rw1DoNHT81wVVOQa353kHfvqMtvFtVpYpYAkymVjGpQZM/TIrJa\n3urVJApoBlhlgY7pDCZZIGCViWY1arRxDElhXA6Q1vLNLU7ZYDCuI4vkbWPTGqmcwbYzY2xtLkTT\nDRoOPY7o8KCvvuHvPuWW4eO0f+8HVD/8DOx9BnHJFiJP3I9sMeNcuYGzwWU0zx4Dqwt9YgDJX8SQ\nr5VCOUv44XvxrlqD5PajB2sY+sm3Kb7vV4iJmb9bhwIY6QQz5StwkUY48Q6iw4MQKMGYGWV82wv4\nVywlNzYIQPf5d+P8yafxNVaSu/HbeDt3MrNrO2OHuwg+8jw+KYeQiXNgy7UsfW8n0V/fg/kzP8Z6\n/FXEqnlojgK0tx9DWXc9ufeeQS4qz4/nLULQskTeeRFrRQVdf36NwsX1OFtakRZsYvCH91B67y9A\nV0k8+yBaLodsMZONJnDW1yCtvhp0nfATDxAfnqTsxuvpr1xPhT71t3UsQ+rILqxLNzPqayFw5Dn0\neBgjkyZ8bgCTy4bztm+j73wc0eUncvQI8Vv/m9Dex/jSFb/kV8cfQZAkpsuW44sP5NeYiUGyPW2Y\nLv4k4sBJRLuL/bd+ldbbziM9HcF9109Qxs5iKGZir/4J+eP3klYNJpMqDTNH0YK1zP7hykMbgwAA\nIABJREFUfgKXXMXUay9g9jhJX/dNCkaPMvXKcwSuuY3M4R3IpTV0VW2ifngPRvkcEi89jOv8y8kF\n69EVC6aeA3lLXXeQXKAW/bWHyF18F08cH/37tyNYHOiRKWZr1uKN9qLb/flr8JQi6Cqx39/H7PX3\n8dypUe6pTZEqqEf54BnEeefndb61HIkD2xm58MtUOCSkxDTiZA96oBL95C7k4ipmSpfmA0NHAdrx\nnbxecgmXZY9BcT1ZTxlTKZVCs8Gvj07weQ4x1LQl/21kZpBn+smUL2ImpVHYtwetdjny4HG0kla0\nPc+SPO82Dg7HaAnaKUn0QiLMGfdcGmy5fIJISqDu+CPy5ttRdzyBeNFnUMY7SBU2YTm3h/HyVfRH\n0iwMWlDGO3g2HKRjIs6NC4opP/gn/hy8hFuL43+nP4j9J4nWrkV46nvErsu7f5X7HP++iOb/Aa/1\nv/iRjv/vxo7OfR/1FP4j+MWmn/3D8/+0werEWALv4nW4zBJiJoFS2YJa0orxzu9QKhoRzFZQc+yw\nL6Iy6OP9iI2yhlYQRMz9R5CWbgFBZNZdjS09jV6xgJGkgbNuATGTB/GD5xErWumxVuOfu5TnkqUU\nBIIoLasIlnswpof4r8FSFt5wM+HiOTSvaUIsa0QaPI0+1kd0w504XE5UbzlyWQOCycqMvwmLImE+\n+TbnilfhMouY4xMY/jJ0ZyGCzUVWcWCrqOXIrEBT28ucbriCm588yic+dT2CluPIrEDDqeeInDyF\nY8la9FiYdON6LOV1CGoGw+bm6XEHgbo5zGgmhqylSLKCq6KGYcNFMOhixlmJ4vAiqmm6ilcxGM1Q\nUV2JUD2fs1IpQbeVcWsJlpI65JkBRt11+GpqUQPVGCYLU6oJpxZDrJiDMHCKYUclWXuAjNmDLMvI\nkWFsuRjW/qMYgQqE4XZ+eEJlTU2ACreJotQwe6ZE6pLnOJJ2s6UhgC09w8bWCnSDfMawawftpedR\nUl9Dbt82LEWlGP2nIViF5gjilVReORfGXlCKzyLjzIU5HTYoL/IjxydIFc9FsLqwWUxIiam8dqFo\nYu9AmIsrbOj7X+RHvS6WVnix2WyIx9+goLQCKTlDV9JEy+huRh0VeCwy58wVNEx8iKSl0Y/vxBSq\nQpXM2CsaEW0OJLub8XiW5aVOXu1NUOK28nbXNK3lRYwlcpRlhtja6GfvuEpj/BzhsoXMCg5cLice\nMUtb0UqKhBi7pwQ8VgXPm7/Cnptl8bJlfP6tQS5pKUKsX4LTKyPlEgQm27ljT5ora6xo/ip0QWRc\nt3N6Ik7Tmw+w2zOfpqZGjPI5lL75c8K1q4hkVJqKFIyCSsrlBFXFRRzNeJkrjFFUWIRz6CiSqwC3\nReH0jdey4aYt+LUIhslGgc9LldeO26xQONNGTV0DAT3CvimYG3KjGlBol+mcSZP5P3cQml9BQbgH\nu5HiaNLOWmeUWp8NOTrKgGqnIt2HNtRJfesCHFMdaFMjSCWNxLI6X9vWRmupmwa/nSKHgiwKHBtL\nsNYexp+ZpCV8ipk//5bqCy6lSh2lMDeFmEngmzyLKVTNfHGcsOzCZZbgpZ9RtHQjWc3gioXFnEvk\nG7/sfQeRA6UIosxgNIMsiVTGz8GLD+GRk3SYKyk9/hxysJTizAg+u5nUw9+mrKWOlLeSjGxHBw4M\nRVlgjiCg4zr4HH4r6O89Tbp2BWUOEZ/DSqFdoaLnXeLtZ7AvWYdxZi/ZUHN+Excfx9NYiZyJoPZ3\nQMs6LLkZrEvWk+s8yklHI9UeM9mDb6GuugH90BsotYsw9xzAMWchhpojN3iOaO0anFNtmMig9Z5C\nCVWgx6MIigIOH7ZwP5qnlD9M+VgkT5H6cDuR5dfjlyKYqlsRZYnZNbdRYTNwmpMIl30By/uPI8gK\nto3XEpxThdXlBtnE6VtuomRZNZ7GWia3b0dbdQmzDz+AZ8kyhn9+H46qcsSyRmSTjDrWD4DoLSRz\ndBfmK76AWr0YrzGIc/N1dJWswvLyL5DMCj0NGykwaZhkjVh7B4rdiveiqzFSCY7YmhDMDjLzN1AQ\n70QJVbBt2sG8oIWIs5SkI4TUvJJZcwEF2QleFxupX7CEmaqVFM5tRRFziL5CJFGAlvOQZ3vxGjHS\ny6/j8mvn8fn5n+SCH9/HkcksZYP7oaCcvocexHfXD+lMKjw5aKKhtpb6S1ciLrkEZ0kBYniYNnsT\nfpvCz9NNbMicZMZRSrklR3bPC0h1CxEG2zDXtCALWeQrvoRdjcNoF5ZQiPSJfShbP88Byjg1EaOy\noYU94xrNzdWMuRvoj+crJ+ZzBxBK6pl1V2N673HECz+NKAi0FNgZy8pc98oEyxe0sCPm5rkTo5xf\nH0SeHWTYUUU0q+NJjaEsvQCnzULOEAgrPuyKiDUyyI8HfLTU1zFjKWSHuZmVRSYEQ8eQzUjZOLlA\nNZI7wH6jnPpUN9OBZuypCf6otbCjfZJsUS0lhUHM+/7C/b1ObFYLKVWndf5Cfr63n7t/tZ/5C2so\nLinjlweGaZuIU1TdxF2vdLB+2QJ64gbB+lbiusREIsfpiTgtIS9SNsGMKUAKmX0DEVpH99I+51ou\n/81hPv7xq3l/IIboKcInpEgGavnmW53cND+EdfAIo/5WFheaaSry8Nv9A8xbvY6gw4zi9JPWDLri\nIiGvg46EieLMIM7EGI7pbqSy5v9AaPP/Hy7ZRbGt9H/NsXdo/0d6P/9TuLDmgn94/p+bAux5hqPl\nm5lfYMY02kasaA5ZzcB96jWMTJrc8muYTKqUdu9Ebz0ftBz6u09yr7SZHy6S89nWroNQOR9xuh/U\nLJmaVZhm+0l7ykmpBq6Dz7Kn4hIm4hmuqnMiT/ehj/WijvZinrMqzzGLTyKmIhwz1TPHmQVDZ8xw\nkFJ1agb3kDi+H1NhCHPjoryft6sIKZUv3+QCtcide0DX0eP58q2eSmCqbORdoZ41/a8xOO9qiuxy\n3jdZlBFyadL2AuR3/8DwW+9TedvHobASzV2CcPwtBJOF0bqNhLQZMHSk2EReemWkE61hNe8NZ9gQ\n/gB1tBdl6cUMmooZT+RYmjlLtqcNcdlWtPefRt38aY6PJVmZPsWTyWpu9Yzk75muki2oQzryCkLd\nUqTwMGqwjl3jMK/IjqbnS1bpn9xFcN1qtFU3oBv5jEOpNoXRdQihfnn+nts96GYnaUchyt6/5L20\n511A+DffwvO5HwLwcneMqx0jfPqwzHe7H0X71P1IooD34DNICzYhJmaY8dWzdyDCJQVppi1B/O3b\nSXccR02kkT9+L9GMTmFqCN3qpj1lpcSp4DrzDpK3gHbXHJ4/NconlpQiiwKqbjAUzVK17b+RFAXH\n1Z8l/eYfsK2/CnSdAXs1nSvPY+P2x9GGOjDmXwTAS11RQg4ziw78BuvaK1B9lSjjHQx5GumdTbM6\nczr/fH1lZFzFWKe7yJ09iFK/iHhBAxYji5BLMS04KUiNIKYiRHe8iO3au0EQ6UqZaIye4txPf4Y9\n5Gfo9vtZ4IU/nIlyXUsQ9XffYPaW76NqELTLuIQsOcmM+dTb/Eady+cqM2gdh5icfyX+vX9gdOVt\nJHI6DZYUd7w+xM+3NnN4JMb5RQJSeCT/jZ3eh7zkYqK2Qpx6EkMQQRARMnEELYegq8zaSzg8EqPK\na6XILtMXydIqTjJlK8Yr5v7G8StmLCPhNotYD7/EvtLNrLVMMGKrYCSWpcxlZs9AmDlBJ36bxHQy\nz3mrPvlXDtVeTpXHjNMsYe98H9Hu5OQ3vsepb/6e+SEnDZYUhmxhJCOhiAK7+sJcM/EG3fOup3H6\nEOGKFTjVKF0ZG4YBOgYORWQ8kaPSY0bVDIJEEXIZwtZCPMlRzuqBv/HZDawd7/O8OIdr7YP0uFsI\nvvYAHed/CUUSqPGasY2f5ZBYySJbnCnFTyA7iRSfQo9M8WimkUvrAxSqUxhmRz4rdPHnSGsGrvHT\nvJYp5+LcSeI1qxmM5Sh65j6OXvwNNrhjZHb+Ga74KpaZnnz1R7Ex+9SD+C++AiOVwChtJuMswnTk\nZaLHDuH4xH+T1gzMO35H2yOv0fr8KyiTXai9p4kcPoj/wq3oBdUYFif6vr+iNK9g2luH8/3fw+ZP\nIqXCTP36u3iaavJNmANn8+9rIoZod/KiYxVXVijoH7yAtHAzhsWJIZvRXv8NiYu+iFsNY5idyBPn\n0Ea6kLxBjEyaruKVVDkElOGTjAfn4zZLWEZPo3pLkWeHyBS3opx9j+TxDwBQCgoRz7uJtGjGtPNR\nxlbeRqFdoTecpTecYsmr/43/gksZLl1JkZJFGTtLonQh8jsPI4eq6K7cwC/39HLX6ioao3luZ6q/\nH/cFVzPlb2Lis9fScPenkNx+ckVNCIaOFB0ltfslZi76MpMJlXq/GePZH/HyvE9yTaM3r/1pdua9\n7qf66AksolKY5WTaxVzTLGI6Si5Yz7lwlpbUOXSznQFzKcV2GXm6h1lXFb6ZTrThTlLzL8WqJjhz\n+00ULq7Fv24DmXkXk9MMsno+Uy8d2sZo8yV8OBzl8goTGcWOJAiYJzowJJmEt5qzUynmd7zEuTlX\nU+MxI2kZDk6orJSGEHIpou++gv3yT/KXQYmbQkkY72Wqeg2+06/ze2kpN7QGufGp47z48QV5PV81\nw/M9aa53jeTNIgqq+SDuZCaVI6cbrK1w43rrQeQLPwGGzpBqJeRQMA+fJNffzoGKi1l88GFM53+M\nTs1H53SC1eVuplMq1aYkJ2JmmvY8hHnzrQiZGL/ut3LHghAz3/8shd96kANjec5va9DGeEJlMJKm\nNWhHEiEQ62PGWcnJiSTrLWP8dsjBHQtC/HzfACG3hUvrA1hlActsH91KCamcTpNHYlYV8QoZpPgk\n49ZSJpM5XGaJ4sNP85fAhXysxsSons+oVvg/2sxqZ+T0Rzr+vxszmf+d0lXLg2v/4fl/3mA11k2f\nGMT1xH/hWbqcybmX4bfJSMdeR6iaD4JIj+Cnuv89JH8x2a4TyAUljFSsJrDrYUzVrQhWO6kjuzBV\nNiI6PAyXrqR08hgDgfmUp/rIndrLyPY9FF+wDj0WZlvtjayt8OCxSGQ1A9v+p1HK60ke3MnMlq/g\nt0pEMjpFo4dQS+eiTPWQDbUgpiPQvg+5sIJs+yGkghLEoipG7VWExvJdc/GKZdi79tAdWk7hth9j\nufnbHB1PUvX0d8hFk5R86ot845TCjxvCTBct4OX2Sa5rfxzrpptAV3lx2s2lfc9jWrAera8NbXYC\npaSGvsefpOzqy/IuNRd9DnNsjLdnbFzoiYKWZcpZScHgAbYZTayv9OCe7iTsr8eZmUHsP0GqaQPW\nyBBRRwmuaD+xV57EuXQNgi+E5itH7DuKOtqH5C8iffYo5q2f5Z1Rg1qfldrcIGI2xWlLHZVv/wzh\num+g6QausZPk+s7ycvAC2kaj3JPbyXOhrTQXOJg/+j7v+1ZR47VQbNHpiELIoTCTVhmLZbEpEk6z\nlNcYlQ2ktp0YdcuJSw5cnbs4HFjBQCTNtuPDfHxZBQtDDtxqmG1DYFMkan1Wjo5EWVjsotKlkNYM\nplIaKVUnllFxmGRqvWbaJlPMc2t0JBXKnArds1msikitOcmLAxrXeKYwJJkRWwXj8bx+rMciEU5r\nvNo+zsfmhUipBoeGI0wms1xYF+CltnEaCvKLos+qsNKVRDv0Ot0LbuLwSCTPW7OMMe+X/eyfexjb\n4nWccs+n8dRztLVci1URqVcHOSvls8l2RWBnb5jWQgfRvzXgzfUYpEQLKdXALAnsHYyyqecFpFVX\n0ZG2ErDmu18Lp9vyZgTRMVRPCd98u4s7lpbjs0qYJIHJvykY7BsI4zDLbKh0c3AkTkbVucA0SLKo\nBRmdvphGrTrMCT3EfAY5pJdQaFcwAIcioj70FcJ33o9JEihxKhwbS7CwMM99bQ46CFhlesMZFEkg\npxksHXiL3HA32uVfJZ7ViWU13GaJqZRKozBFzB6ibTJFgT2fdfVZJCZTKhZJZCKRY8H0h2j1qxjP\nCJgkAeM3X2PPlm/iNsuc17sN1t7EUEJH1Q3uefUM913USJM7z4d9eyDFkcEw33ScJNl2nD803cHn\n5zhJKU4cU508NeXjolofvq73eFqYxw2FMbK+KpTIMFOWIvzqLIfjVha7c6gWD+beAxipBOPV6zgw\nFGFuoZPqiYMIViexojlYtBSGbEaZ7sm7F4VHmPbW4T62jfdCm1h1+GHCnYMUfv7bzJr8RDI6pQce\n563qa7ioxoN4/A2G6jb/XWvT/Kfv4PjEfzOR0sh99046P/NLNltGOWetpobpfIOVYuHVQZWFT36d\n8s99iUigEYca5UzSQqswzvOTDi6v92EabeP5eDGXl8sMqVYSd12H8bOnqfOZEbUcL3fHWFvh5sRY\nApsiscyTQeg+zHT9+STv+wTytx8hdG4H73pWMq/Ijj8ziTDaiV7WihQZIxtqRopP0Z51Uu5SGI6p\naIZBS6YbzVuKPN3HVKAF/+ABpt7Y9veye6aomcFY9u96o4dvvJ3Fz/4JcbqfM9/9ES3f/w7oGp8+\nbueuNVU0WFKERSf7BqNs5Qzt/iX4bRK+3CzTshdJgNOTSVYXCHzBs5j7H/sYlpomlPKGfINRMIWY\njjHwq5/hLA/inLsQI52greVa5mt9fPjxLzD3zbfpms3QcOhxepffQY1N5cFjMywv8zInaMWmJdk5\nqnF+IMek5GUklqXOZyae1fHbZM7NZChxKozFVQQhb7JR0fE6ybMncSxZi2h3ormL+e7RNPfNBTEV\noc3eBEBLsoN2eyON0VN53d3q5YzFc8Rzel6v1THGh0IFS+UxppyVeIUMgpomZfZizUaIyS4cQg55\ndoDwy3/Eduf3GYhm2dM3y7IyD/UOgwQmXD17OVOwjAKbjMci0RvOUmSXUSQBRQB5ugfNFUKKT6K5\ni0HLIZzaQWTOJUwkVAr/+l18W64jG2pBmTjHB3oZPqvCjp4pFobczC+y8cFgjIDNxByXyqhqQRLA\nLIv4pts5Z6uldzZFyGmm3GXCtu8p3infSmvQTpkxy4Tsx2XOK6ZIdQsRYlMctbXSPZv8u750Vfd2\n9gfPQzMMlpc4+dY7XfxkcZ6aJ5c0/cuBzL+CgVjPRzr+vxsuyftRT+E/Ao/tH1/XP3WwyrlLqJ7o\nYFoUUUprCE0cQwvWoA51w0gv2ekZqhcsRwjVkOs8Aquvh/AQnpd/gmnuMhJH96J4PAiiiDo2gGV+\nZd51KBHFGhLJ7t1BenScgT29WINeArd/haWimwKzwVhKozTWRe+CaylxKuRKFhE6+RpyqBqTr5z0\nib3IpXPRpkcI+5uIq3Y8hz7A2RwlNzVOzzNv0PiteyjOtZMd6ESpbMIx1YlgtbOnb5Yb/T5yBiw8\n8xzKFddjOHxkAzV8xpEjZy3HN3WOW+pD5Lpl9K4jyKFKLi/zoo/Z0Ue6kUrrGX97OxZ/L+X3/Rx5\nph8jm0ZJTDL7p19w4Q2fRshkUPva8M3zoxfWscXhxRAEBDWNMxdGSkyT7jyG/W+uW1ZvGeO2crSB\ncdybC0BXEeOTqHWrMBrWoh94Ae3Kr6HvfYqqedczFM0g//K7VNz7U+odJoYu+SpvnRjDIolsqZ+D\ntWgul2fDTCTMRPe00y6s59J6P1HvZvrOTrLWn6M9aqdZmECYTWIPNlDsUMhpBu/0zLK0xEVfWGVu\nw2qEtl2E6zbjqFpAnWIhmdP40rpaPhwOs8GX5vB1NzP03T+wqNjNTCrHBbU+plIqP9k7wD1LvEwk\nJJbYYvTZ/FhlATkTZSiqcXQ0y4YqH7/+cIi1VX4K7DLanuew1V7LcbGCecIoQYuAYSh0z6Yocdrp\nmM5w9/JSrv/zCX5+eQtXNvr5cCTBwaEorUUuLkwe5s/MZVO1hzR27A2LGI1nKHKYiaRzvKkG+fEd\nQfQPDjAWWsIv3uzg0U0XMjCeYmO1B2ZgPJ5lPJ5lKpllaYmLeFbn1ESMq5oKyG17gBcbbiXkMFPi\nslDltSIv3cKRqInT42Guaw0yEs/hH+tDL57LlKmQoule6godSCI8eWyErxYOc9rcilkWWVjsQhDg\nyGiCwUia5gIHUV8zI9Ec08kcumHQr/ppDMi8Mxlks3qMU/IiNB3K0wPkvvI/HO4JE7SbKLNonJtO\nstiRYmO1l2NjCYodCivFQXqsNRwZjSLVLYSFF3FkLEGhw8RMKseh4SglLjPNUoQJoYAVlil0i58z\ncZkKZpHsBZgkgWJ1knjdWuzJKWKqhzpLEuG2L3PsSJgr54QQV1zJh2Nplvk0erMWnrlpHq93zSIK\ndmref4gtC87jwmUNSOF6Zl96Ffs8GWnoFJbKxWAYXNtSwEA0i7d8Dnt2TXFeZS3FnbsZKVtJwd4n\nEEOVzNgW85kDo1zQrHNJ3XKEnb9npCDLZfppjqWXMPDEE5Tc8yNEAXq+eCsla+ejLDkP7cgO5Llr\ncJlE9HiY87pfQNp0C9nD/4UUHSOgDeIOtSD4i5hf5MAQBCSnh8CrD3B4zV3Ujx4hDMRyBuG0Rt2m\nNcgFdpKvb0O48MvknCGU+AThx35A5XXfJbioASM6hcOXpPPzd9D0yDOoYjlbvQKmwSPkRvoIlVch\nhQepEATGGsvoz6qkcgri0z9CX/Y5/OkJJNHBnB0/Y3J0GovfRaC4jgGgODVIuusk60JReh94kYJ7\nv583FlFMJA+/h315CrWwgTqHGenIK8RKNxLParQaKfSDr2I0LMY/epThPz9F6MorSOpAx2HivgZ8\nFhkjm2bkf75H0/Wr0A69jiZKOEr86BYnTA7y44sW5M1Ixs/hS0TxeZYx8NsnGLizlcZIJ7nyhfgO\nvIDUsITVoydgXOT+xz7G1+98int/uAWfrrF1RQu5V55kZtNdlN9+B9rsBIJsQtc1Kl76Pmy9mQVf\nuoK4apDTDKTzbsBIg9ixl7ub56PbTIjxcZKv/Z6KLV9DHD5Bob8E7/5XSFz0RWbSGg6TyHA0TZM8\nS0/OTTKn0T6V4PbGlVg1jXjdWkbiKomMxqKyNBD++z9wJJqhpnwuickUuVALM5qCnspLu+3un+Km\n4jT6wAAV9Q3ElSrcsohhWJFnBtDffBD5/EsZdrQSz6qIQgmO/WcQrsrisUg0Bx00GWOI47PYQy2I\nTh994RQOk51ETGcsns+ElrsUpNPb0WsW81JvirmFIRp6D/KO3Iq5cD3LFZGhaJra5lY6HY0c7Zjl\nqvoaKjIC4/EcLUEnGU3n1ESSeFZlZZkTaeAUL0wWs6bChyIJuLpP4F/ayLYzUVaUlvD06QlumZ1g\n/Vo3igAJLYD/g6eZWHwdJUXlxD2V2A2d3pkUXkt+UxvP6Ig2J2lVp2c2SYPfxr0bazgxk7+ORf9y\nGPOvYSg58BHP4N+Lo2OvftRT+I/g/8vB6p9yVqPpLIOGG3nJJmyKiGHzoh98lY4/vcnQ7jNYvVbs\nWz6OIZvRe04gVM1n+IdfI3bbDzHtfgZzSd5pxbLofJJtR9Em+jjhnU+1Ok7CU4GtupXhPz7O3Mcf\nx75gBWI6isNbgKDlsO58BOZsAFHGNXIMczaGWr+KSdmPTRaYee0F7MvWoZ3dj6XnII7OPSSGp7CW\nFiPkMrhri0ktvhKpcz/pvnOkuzuQRJ3Z93ZQcf4l2HoO8rZUT/mZHSgLN4KkwKFX8fg8+TK8twKp\n/X1iZ86QHR/FUlZJ2FuLLTEOlfM4phdRV+7AtnANRu9xBE8hRjyMECjFumAVIKC5ihCj47yaDGFY\nnASOvYhQXJ/fFR9+jVjtGmxlNeg2HwPWCnzde7B5A5hjvYgNyxFTEXKFDfTffTOBpQuhrBlFz3DQ\n3kq110wDk/gWzEHMpTBEmZhgYTqZY2ONn8LIOWYVH87kGN6CIkpam1kzvJ2h4Fzap5JcSRtT3jqq\nZ08i6Cq5sx9y0FxH+bFnsXrcFAULKTBpiJKM1WLmQ72YVnmGEykXVckePAUhJpI5tiYPMuJvoe5j\n17Gk1MtUSuP+Hee4rLUQ/66HCS1ax5v9caq8NgrUaVwuNxaTwuNts1wz9hqVC1cQzWpsrPZSceQv\nuI0EsRNHaJKnKSaM4S1Gme5jRPRzYChMgd3CYmOAXTNm/C4zG6V+HuuBpaUuFhbZGY5lqZEiVNXU\nYdbSmMKDxN75Kw0NFVS6FAr9PsyyxAq9GyETx5mZIlNYS7NXolGJwe6nETJJqvQpnp+0c1FdAJMk\nUiHHWJDrZWfEQd3iFTx5dILPNimIVgfl3Tv5xViAjdVeVqqdSIJAID7AbNVqTown8j+2aYnN1V4e\nPjDIV1ZXYBx+k+L5KyjLDOExi/QmRMpcZvrCadZXudnRE8YwQDUMHCaZR/f3sbLah8us4B0+gaWi\nmRK7yIzsYSKpUuYyY1NEXKQo9LgxTDZsioQsirjMIpLNxY6+KNGMRl/Ohs1iZSSeJeQw4bcqfOvl\nNm5cUopXyuEPd5M9vIOjvoX4bQqCxYEjOUFCtCFb7MiiiAB0RjRK29+C0iZ0k42+cAqX3YHHIqGY\nrfRHMsykdfojKTZpZ5HrFkAqiuEq5M0ZO/MLcoRal8CbTyAt2sjsH+7nTMUqGv1WcooNt93CRCJH\nYWUdgcEDCA0rmAo00xSwckUgzIzk4d3eWRYumENJbgy1dB7Cz76Imkzha65BkhUsl9+KaaQNubQe\ndA3RasOQTQgzIxjJGPrwOXzrNqLHwwjeIrRDb8CyK3Cd3Y7Rtgexej7JkwdpqisGXSV2+hTqvjcp\nU2aYOXScQpeGHp3Gevxd5JHTqM3rsM9bRuDQM6jRKNLiC8m89jAlH7sV3VVI+qkfkNz9GpZQiP6n\nnqWl0oLae4pc72kcjc2U5iaY+t1PcZQFmdtcS+SvD1O/YgUmxcDstjF9vB215xTZaAKbEKP9z7so\nue4afOs3EXvrGaybbkAbaAc1i1xSCxgI7XuZ3L6Dko2XUn3yedSRHrJjI5jLqslGo0hfAAAgAElE\nQVT1tRNpP4fVqjMQWkK0uBX3Cz/CVdPARNECvOGzRHtG0K6/B8vEOYRcEmPkHP0LrsVpEik6+BTa\n+ACRo0eo82p4liyjavYsyeP70U7vRb3w08idH9D/1HOQCOO9+Do2X9aK2ePg7ot+yJa7roa5G0lp\nYOk5hDY5jNy6htShd3Fc90V6pELEXS8hLNpIlTqKIZsI9O7F0FQioXnYh49hTPZjCpUTUHLok0OI\nkohSUs3EA9+mdsV8jsct6AaEAj4e+XCQW6oFakMF/PZkmFU1fkxDp/HbZYIeJw1KjKijBMkdZDKl\nUWg38WzbOIIgcGQizU/f7aI66MRrkQnYTaRNLrLBGhRRwC4Z3PNWN3OL3RjOAJN1qziV9ZBRdYZj\nGRr8NmqX1JJ2FaPqBnU+C3/oSDOnvpbRpE7EHGCBT8IX7WNW9vKj7Z3cMD+ERRIQ3EE+nFXYOPwG\nJ83VpL3lfPyB91m/qJQX28bZ2ljAPqECt0Xh+FiUAmdeM/XISJRLA0nCgp2njw5zbjLB4jIPg1KQ\ntvEYBwfCVPpslFVW8MZAml3tE1zTWkDXbJbqFRuwiAYvdszgt5twl5Tj6j9EonolhmEQVTw8sn+A\nTQ0FTCSyzKZVcv5Kjo5GuXNugNueO03AbWOlO02hkkWyfbQ6q/vH9xLNRv/XHLu7jjAamfxfd1ze\ncOk/fH7/lAbwfvcUy869yM+UdWxpDNJqDJN4+2mOr7ubFd4sUmSMXMdh5DlrENQsEV8d1l2PYW5Z\nnu8YVVQELceJiMQ8W4Ih3JRYQZ7qYcKdd1IqsEm4TCLy7EA+OOtvZ2rBVQSOPMdLBZtZWuKiPDPE\njKOc2a/eTMfdv6G5wE6FPgVAvxigKn4uP4/WVRjD59DmXsDrXbM0FTiofP/XaJfejSwKpHI6Flkk\nq+lMpTSqpo/ns77jg+iLLkU+tw/BU8j7uWICNhNFdpmzUylWWacxRBmj+wiROZfgjQ/mJXfUNKN6\n3tEklB0n4Qhhj4+SfudPmLd+lhf7c1xr7cMwO2g3VdEYOcFRWysL5AmeGLLwsTlB9NceQt58O/r+\nfGe5qbKR8eB8gpEuNGcBKbMXx+BhtuVqWFfpwaaIvNU1y1bHOL32Gg6PRNly4lH0q75OfyRLvddE\nXzRHtTnNiajCnNPPMLH8Zg4NR7nCNsSP+jycXxtgoVsla3Jijo+z7+IbWPXrr+dpDZVNqGMDeR2+\nNTcxEs+hGQaGAXXpXrZFC7i4+1mm134Cn1XGMnISBJE+VwMhh8JANEu5y4Qy04+QTREL1LN3MMa6\no7/Dtmwz4cK5qLpBfySLRRExDMioOsVOE6HJ4+iOAIZkok31Uuk2cWoixaJjjzN23qcoFWNMi24C\nHduZbdyENz2BMHaOyYpVBM7tZKrufGbTGo3xMySK59F98+U4Hn6eMhsk//gDzMEA5kUb0bylcHIH\nHbUXcXYywVWuCe49a+Zb0geIVjuvetZS47PRbE2ya1Jm3eh2BEVBrF6AMXQWo34lYazIooBt/9P0\nzbuGQ8NRri/TERMz6ON9hJs288ihIT6/vCxvVaj0o4cnGahYS3l2hIS7nA8Go1T7rNROHQW7l0OU\n0VpgxTLTwx/HnCwv9RDNqEwlcywocuDb9wTh1bcSHDmEYHEw6m0iq+mcmkiwodKNKT3LpOBmMpmj\n1RjGMNlB15gwF1E0fhQjlSDXtO7vWowLPTp7Jw1WlDrpms1Q5zFxaDTJ4mI7Txwf4456EwOak6Oj\nMbaOvIa49BIMxcaMphDITqI5C4nl8h3+DpPI2akUC/wSgpbDOPY2Y3Muo/DQX9DPuwVTeJA+uYji\n3b/DMm81I75Wnj41xt2NEmFrIf6pM0z4m5hIqCRzGk0BK87ho5zzzMEiiRSrk7w5baXKa6XJGONg\nNsDu3hk21RbQFLDQNpligTRG+IXHcN7+nbzdaP1C1OEuckNdpC/5Mo4Pn+Vs0xUU/vGbFF51I3oq\ngeAKcFSqYr41hnbkLeS553FMDbJAGkNzhfJcw84P0BtWk932IJaLbkd1Bjk3kyGnGczpewuxag66\nsxB0FXXHHzGt2IKYTYGawdB1BFkBXWPmrReRb/8+09+8HWdZIR1XfodlozuhaTXZNx/DdNGdGCd3\nkVp8BY6OXejZNHJBSV5fV85rMMe8NZjfe5zdddew7P1f4Fy9mfEXn6XggovQI9OITi96bBZh/ibk\n6Bi6xYnefxrZX0Q4tAC7kebIjMFyrZtcYUOe5/zCD7FdezfG4dcR7C5oWkNKcaLqBqmffpHsXT+n\n2JZfow/mgsw/8jim2rmkTnyAIInYlm3Oc/GdHoSq+UyYiwh27YTyORgWJ9nXfoNy2RcR0jG2jYhs\nrfMizw5gSCbuKlrPQ+Pv/V3RoyzSjmb3ox56A3H9LQipCG9NyGzR2xgrWU5W06kItxELzSP2ky9Q\nePEWxGA52bb9CKKItGQLYnyK0Sd+y/idDzDXNMuIXECo820m3noL6Uu/wCoLec1tt4L64gOYtnwa\nIZOAvhOIxTW8MOvnsiob4pn3OFCwhpXSEEOOGkbjee63zyoxk9LYNxjh8mIdw+JEOPE2xrwLeKk7\nzlXFGtqRtzDVL2TmtafxbriYv2Qb2FjtJRjpQg1Uo0sKiUe+Se6W72H86ivMnB1A+OnTFDlkzK/8\nFC2dxbnmIvRUguHSlVhlAbeYQxlvp9vVQvXMcXIVixHUDEImjphN8EbYTWvQzrs9MzQEHCyf3keu\ndTPmqXNEPDW4unYjKAp9jz5KxdfuRXMXIyZmMBQzSZMHe3wUYfQcetVCpLEORoMLcL38ExxrLyFd\nPBdz9z6eytRzQ4WAYOi8PWVmU7kNQxARswmk2SGyoWbEAy+wp2Qz65Vhxtx1BOQcAOa/VRA/KgzH\n+j/S8f/deKln20c9hf8I/p8yq8VCFGO4g3krVlO081cwZwPynDWUWTT0D19m4rWXsX3sHpLPP8Tk\n/Mso6N2D2LgcfbiTQXslBXIO7f2/EBw7icnpxO4NIkVGELMJHPERfFYZ86ntTD756zxHU82SOXsI\nt5HgROWFbDQNkbYV4JzqRAmUYb/wKqo9FvYNRmhKnmOfUY4sisieQuxuJ2I6Rm6gA5OkUXlyG0E5\nRaq7C0dZKbrVg/nE68hmE7Jiwmm1QM8R0g3rkN1+lKluDG8xQniUco8Vn8dD21SKlcoo4/YKbO27\nkALFSMfeJrZvB7mTe+n59aPYzu0juOESRDWNJTqM5irEVFKJFJugprIKcbyLROlCimPdGBY7McWD\nbfujlK/YhKtzF31zr8KfmWB2+2tYr/oCurMA19hJDIuD7Lt/wVwzjx5TCUu6XkaumsdANMuK2Q8Z\nLlxIVjMod1vwNS8iJyiYZZFPPX8aUZGw2R3My3Qy27iJopkzVLe/ybOW5dy+MET3bJrBlACCgG52\nUHzb/+XuPYPtKK+07avjzvmEfXLOR0c5ISGBkEBkDAKDcR6DPRhjzNjjgHG28QvGNh6cCDbGJmOC\nSEICIQHKOUsn57TPPmfn1Lu73x/bn78/Lr/fO+Opqc+rav3p2lVPh11dq9dzr+v+BCF3LVLdAgR/\nJVS1c/+wB5/dSlt+mB/si7KxuYgBw82Kcy/wZOlV/PqDQa6LbudA0UoqhAQuu423RzKsyPcgJWcx\nPGX0CUW8MzDH8go3R7zzqbdkmBK9jMZzeG0yVllk32iUdTVO4prJAyc1dkyZdNaW41QlvKlJyj02\nnsg1YphQXezFkYvwHrV0pM5yWq7mF90Cq2p9TDprCrazygSmaufhs2ku+5cbODyj05gd5v3qi6lf\nuIw3Z6x86dUeWpeuwGeVWVDqIGML0Fzi4qS9AbOinaRmMBxJY3O6uO+dHq7deAHPRYoIC05qqqsZ\nylqQRIHXe8JYGhbxrTfP8q8rqpHtTi5/epCPXLycB/aM8TXnSUYctdz11FGuWLsIh03F4fby+rjJ\nbCZPk9/GU0cnKKlpJmML4LPKjCc0TqWsfMgywPaInXUzOylpmsfPPxhi1bp1vDMQwVZay7awynA0\ng9+mEnSqdIfTDKREPBaJJn0c3VNOVHYzotkYjWXxldcgBcrZN5FhXvosFUYY3VlErZziR3tDXN0S\nYCKZp12N0Z0quFm9MZhmIpGjxmujvL6eD2YVKj54jL7iBVgcbsYSeR7dP8KqGi82RaTUoWCZPM05\nSrm/38bKGi9eKUvaXc77M4XO8VjZQopUE9dsL52tLahaEnt8HN1XyfbRDOerE1QIcSZxczTrocSu\nUmZGEIw8waIiys0oD5zWKXZaWFjuRtNN8iYMRTM0iRGUVddg7PgTmaEhLC3zEbQsZiaF2LgYJTJG\n0CkxvuQ6LNv/iFrdiB4ep9zvKIDqEzOIVjvOrY9irWnkuObn5KxGdUsHAmD1OCE0zDm5graxnUhl\njbjsCrm9b7DVOo9Gt4jYuIgpuQhHqJvZrZvJXfBRLNkoSCq2ikq0t/5I8Se+gG3+CiYMG47aDiyy\nhNXthPAY0fZLODKZpHL8EHMLr8GqyBh2L9p7f2b0mecoWdyF7HRT55awllciiBIWlxU9Goa1H2Pg\nB/cwtfs4ZauXoA2cZKhsOX6nQr7vOJaZfmRFotwGgqEzIgao0iZRfV6kXJz8vIvp+cbXyGy8idLZ\nM8w+9D2CX/gmziOvIafnEFQbgV1/wtK6mMFHHsURDHBq7R2UKxnEsgbM8lbEbBx73x4kTxHHpBrK\nJg4iLrmM7pRC/J7Pcd4VF6JvfYzB3z+B+5pPcPkd1/OF0gu48rYrse15Fqm8kZy/FnHoGIydZf+t\nd3Ph+jr0pvNw5uZw9b6P3rgS62w/4uwwoqoiKgp6aAxl6UbyB7cg1HbhWriUnNWLb/oklkOvIS6/\nBm3llXj3P0O6Yh7BfX8kXNaFfeAgRscFiJhk3n+F94LruMI5hTB2FrNhCS6nk97bPkNdrZVgcyeu\nbJjhrEokm2etbZqxn32fmSWXUazm0XY+R+XxbSh6AkEqyCgkM0ds8XXMK7FjmKC9+EvkheuwjJ9g\ncMEmKrvfxLXkPEo2bEDxleI88AJjKz9FEVFMPY9Ztwjv9CkcuQjG6Q8w4hHUXS+iBitJeKqx5JPk\n7X6k9By1J17GV15Ja1UQ52NfR736NqQ9zyG5vJzKefBWNyLa3TiVJLScx2BaJJALYdq8WOLjGKd3\nFWxxd/+Zc7XrcT9+N6ZhoHg9JF79A9Pb36PzuhtRD7yMUN1Bfc9bKIKGfngrsiKhB6rJilaU4nLs\ndic2q8q0JpMyROK6iM9u+W8pbv6/xnh6mJyZ/afJWD6KXbX902Wnf/7ffH5/t7Oa3fEkU2+9jfVr\nD+HVowg9+xFdXlK1y7FPncGw+zAVC1IyXGCszoxjNq9AioyzOVnG5XM7Eevnox3YgnTBzZxLW2ib\n3gd5DT0axjjvBizjJ9FdBV6hfmQb+dAY8eEpfHf+BGW6m94ffpfSJa2oPi/qqqshNESoZhW9sxnq\n/vA15Lt+ju/QnwtbfJ1rEPIZDGcxvRkrroe+RPCL38aweRBTcxg2L2IyDJJSsGA9bxODKYG6ni30\n/vaPNH3hFqjpIr93Mw8HruCTC8oAGI7l6EidQxs6i9wwv4CX2vknlLbl5I7tRK5oID/WV7B2PfA6\nr1ddzWVDL6EsuACA9PbnGd94Fw1juwlve53Ax+4A00B3lYIoMZKCukQPkaJW7O8+irjuE4ipORg6\nTrj5IvzHX0Wfmy5wHUURIx7h7LwbaLdnMKwedo8lWBVUkWKTxFxVbO2f4wbbINnqxZgmWKbP8XKs\nmIvqvEwm8wDUOQXeH89wgWOWk2Yp8ayOxyqT0nTcFplYNs8idw4pNoVW3Mh0ptBd1b79aXLffJT6\n488jLbgI89xeTtRupNMncGgmz6Kzfyb0wT5Sd/yMOksOaW4U0+Jg3FLGU8cmWFblxaXKLLBGeSts\nY0OlBal7F6InwBFLK4lcnnklBdemnG6ye6SAZKolzNwTP6PomptAEBn3tVM+e5IedzuqKFAhJXns\nXIZbgxG2ZsrY4I4g6DlC7noePzzOndl3EZdchtB3kL7adbzdH6bWayeW0ei49xZOff0RNqV2Y86/\nBCGX5FjCRv3mH+G94mb0kbPEF1yFp/8D9MaVTGUFKmIFO2DD4eepYYEPt/nREHGMH2PU30l5eoQZ\nZzWB9CRSIkS/pwPdNHEqEqGUhlUWOToZ59paC68O52gKOGi1Z4mKBXcauyJilQXcqSkGhSJKHDLO\n2V6Ih5kILgGgRMrQm1ZpSfdiKpYCCcNXzVRKRzNMimwS9hNvMtp0MZIgYJgm1emCoYeQiXNUrqf2\nlR+Sufk7+G0SUmKGB09lqPHZ6SxxUuFSiGZ13KqIMzpEzl+HZGicixpUvXIvris/xri9hopoN/mi\net4YSrOx/3nCqz9N92yalGawvtaNkM8i9e9HUFRCFUsJp3SalBiIIvl3n0KpbOS7sy2sqvPjsynM\nP/IHlCUXw9Qg2kg3iBKh1Z/mg6EIm+pUHj6TZHmFl67ul5ldcgMBkgxrNsqdCkpsgpi9FMME1+4/\noTZ2FSxKZSun9ABdmW6OW5vpSp5iyN9FBVG0bY8zsu4OGggj6DmMviOMtl1OxfGXSCzdRCZvUnz6\nddILriSczlNxcjOCJGFqORJLN+Gd7SFV3Iy6+xnOPvwc7Q/9GgQRs/cAic5LcXe/i1G/mIGv3kb9\nD36KODuCoNowVBvhp35DOhyj/Gv/66+UimElSKWSRYxPoXsqMHY+yZbaa7m83GQg76RBG8Mc70Hr\nvJicbmA/8SapeZdyNpxhkdcgpzhQj7wKnevIvvIQ7y75HBvLRcK//i6lN3yCxHuvYb32DkzZQvrJ\ne7HfcCfG/ldJnD2Nd9OtCLFpIhWLUV+6D2HTVzFf+F9kwlGykQTOimLGrvwarXOH0Ss6mf7p1xn+\n1I9Zao8zJASolpNkXv4V1itvBVmFfA5x7DQ/ma3jy005hLzGhLsRx7M/IHXjPfhtEsrsELdXXMJP\nX7gdtb6TfGgMaf46jma9LM6cJh+eJLR1K6WfuZPQEw/h/dID6IaJmgqDrCKc243ReRH9t99M0x23\nMVy1igqrgTwzgDE1SK7/JMqltyKmo4xbyijPjGEqNvL7XiWy+pOEUnlmUjmKHSr3vdPD71rGeV5e\nxJUtAQYiOZqPP4tw/k1I0QLBI+qqYs9onNVVLpzRIQbVSuqjp3gtV8vFtS7UiVPorhKOZjzMtyeZ\nFgsfc77QaXZRR0vAyms9YeaXuvHZJGRRwCaLhan+/m301m2gUU0wdPcdVF19MeLyq3joZJIvtEho\nO5/D0rGMfM1idFEh/dg9xG78FqU7f4ulZSH56oXMmRbiX/sER/71F2xyjJL3VXI6bWeeMULklSdw\nffQrRAQH58IZRqJpPjT0PAe7Ps78nQ+SCUdx3/YjhhI6DbkRuuUqWrID5AO1DCQFdAPqvCpqZIRX\nZhzMD7qoywyS8DeS0gw8Fon3h2N0lToomTtHNthO71xBs9pV7vmv1pv/pTg5d/h/dP1/dFRb6v+n\nT+G/Jdz2v92B/7udVSw2PJ3tKC4/YjaBUdnJWTFI8f6nkBxOiEyhlbUhDZ9gsmwJyoHNKKJJunYZ\n7ePvFxaQJfS5EIosUGQVmAx0YO3ZjdrQhdB/mPxYL3r3QWTRRC6rQehci2PRKhKiHc0eoHxBM4o/\ngOjyEa9chFRUhV00qAkfxXLVrXgmj2M2LkfSkgjRaUhGEHMJPKffwX31JxH0HEI+95cLMpFjU4Sc\nNYh7X0XqWsuzp0KsLJXxttYi1C/iQMpFZUMTS0stnI5AcPtDFM1fhTDRTbjtEiwn30ZyOtEnBhBV\nC2Y2jVzbgWixYk4P0td+DS1FNsy6hVjD/eSO7GDvkluocFmYcVZRdP5GxGyCGVs5roljCBPdmCX1\nWEaOIgYbsdotCJO9SKZGfqwPR2kF5lg3O27/LY3/sonE/vexrr+JI3NQe+hpaFpK9dEXOOVso8QK\n07qKYUJVahhx7AwRXx275lQu80b42bEEsiRS6bawYzhOSjOoP/AkUtsqyl0KhybiiIJAUtNZUu7E\nVKyEJB+iKDAczdGa7sG7Zh37oirRYAcpyYmvqo5SMUlOdbF/NMY8ewrHZR/hbBTq0oO8ni6n0Wky\nkFYpcqhAYUrfr+g0WjKYig2zuA5TtXFwJo9N+Utn0OZj5lu3Mu9Dm1AkgV0hE++qjYxLRUwrAXQT\n8JaRN0zuevkUa9sqCdhVNEcRxQ6VsOBCs/vxi1nOt0xzunw1wZkTvKIsoMlvI5LJs3HsDTrmdVK6\n/kLa3SAkZpH1NHl/LcOxHJnOtQjuYmzl9UQ1AedsP7niBhyKxJTkxbrvz8w2nM+je4e51huiR/fi\nKa3EF+5mzFFHaS5E3BHE8AR5/Mg4i8oLbNIKbZqAESOleig3I7wfMmkJOPCKOWbyMsV2mcD0cRRL\nwebQowrETQXN5ke1WDgSgc7cAIJpkFRc2A9thmSMfONKxhKFj5FqfRrR7mHcVcdcWmcsnqXBZ0Gy\nuRDyWYbu/x7By65lrnUN4VSeUotORnFxXqWber+NEiHFRFbCBAxTIKl4kEWBjCFQbJexdp1HwhJg\nLK7heOsRdgWWYgD+HS9Q4pfJFjXitsj8YHsfXVUBpJJaEs5yToVS1Hgt2E69jWS1YS68FNHu4vzm\ncqo8VqqMMIrXhzE1zHj9OrxCGpZehfWNB5HaV1MspjkTF1hf68GsmYdn/AiIMhOGnWjGwHv4JSwz\nA9i8frTmVUhGnoSnGkUwKdXnyAfq0EyR3ltupf5jH0cN92Mm5qB+IdO6jUBiGD08wVPRYpaKkyiV\nrZyYThHyNuBWJUp73kas7UCyWND6TvJ8vgHrj+5if8cGitoWU3vhMgRDR5wZQus9jkM10VrXIgDK\nJdcjbnsUua6TfFE9UngI27KLcC9fzfGsl6oDf2SvdwnzU6dJvP4E24PrKH7uhxw//zY2CD0IsRDO\no29gRqYJvbMdbdklWF6+H3n1JpKmQoM+iZiYwdz7MmOvbcXfXM3ZtmsIulSsL/+Uqeu/jX/qJN2/\newmvEkLKxrDUNGF6Sxl77DcE/v1B9N1/RiqrpyfvpqytE1GUOPP9B/D98BGKuro48s3/oOvDFyNq\nafqUcorXXUFdZghBy/DMkM7iEhtSJsIZTyclQ7vYI9ZRqyRpaWzEOnSIkd8/SmVTkOS5U/iEGFO+\nZmx7nuWS65dy16aHuPC8ItTzNzH7+AP01a+iLnKOZ6+4m7ZnX8Q2eYa+P25mcs01KKKAqdpRjryO\nPjuJapHwXXUTkc1/YqZ1DUlDxDdzDrNmPqrDhl5Uj5iJYrdaEfoOIeYSyCWV5P78Kyo9UB3rpSQb\nwihrpPbYZjqlGaTqDnrnslTOnsE4sZNo+wZs4ydIeapod5vEdQmrzY5737OIZfXUD+9ixNuKxyqD\nIFCuTYGsYlicOA+8gD45SNnIfqYqFrE46OQnO/v5cKMVd3Icm5bAc2orck07xdkpjMHjeG/6PLOV\ni5nMyayv9yGl5iAeRiytQ9Bz6FYP1uwM2/VKHM89Qnjvfoq7mrAqEg45xkBxF64/3Y+ncx7TopcS\nKYeybCNiOoLl6BuM+VpoKXIgNC6h2ZxE8Xg4u+RmqiKnybuCWA5uprjYR76oHnb+iSO2Jrp2/xIl\nGSJWvZSGN3+C0LUWR2oaRc/y5rhOe++b5Cs6qJSTRBwVDMdytPa9SdFcL1JN5z+kmPnPRkpLoIqW\nf5o8FjnCaHr4ny4b3M1/8/n93c5qNJnGfnobkq+E4Ud+Tfk1VyEF6yCbRA+NYaSTBecPCj7V+pm9\njG/ZztnP/ISLjTOkDu4gF0tiGkaBp7loBTMtG5B//008n/wq5pGtJE6fwBrwoMVTqD4vmY234504\nwuE7v8XCXz2A1n2Ikz9/Cm99AEESqfn0pxmvOo/KyFl0V8lf3UHGn3wcRzCAraaG+LluPJ++G46/\njbHkarJP/RAtmSbz0e9ie+q7+DZex/Ajv8b4ykMU22VOX30ZS397P+e+/z2KHnwGT24Ww+5D0DUm\nfvBF/K01qBU1iDYHqTPHmT0zSKCznkw4SvGmTxB96wWsVVWc+tWLBP/0Cjz4JdKf/yn10/sLWi53\nALHjfNA1tJ3PITjcyEsvQ4qOkz70LtYVlyEYeVJ7t5Cdi2NoefwbrmDosceovOE6UicO4TrvIvS5\nEIgitK/5K7vOIgsUxQuMwj7Di1MRscgigckjGMk4ryrzMUyTtmInecPk7tdO8+L8KQ4F11DrtRR+\nHyt4xecdRVjHjxMu7iSdN5hKaGiGweKAhKCl+GBWYWWlCzGfZSwjUu6QSeVNumczlDoUhqNZ7IrE\n2ZkkvaEEm7rKChPkTgWLliSnOHjsyATr64uocMm8MxDhqsxBuisvoOHYM0wv+0gBDyTCYEwjp5t0\naINE/E38++vnuHNtPWdCSS6s9fLgriHuWl3DaLygh9o/GqEvlGQimuHhy6t5eSjHNTVqgdtrcSDk\n0oT8Lbx2bobza330z6bZkD7CQf8yFgYkpPg0ujuIPNNP3l/DyQhYZZFmaZZBAjx9bJxbl1Vy344B\nvrmunrxh4tWjTN5/N6HbfoYiCbQ4dTj8JkpVEydtLTx3bJwr2kspcSiMRLMEXSrZvyCQgmqeXx4N\n41BlNjYG2NYXJpbNMzSTYlG1l4Vlbmo8KqdCKQAWF8l/5ahaRRN5bpg92RIUSWCJGmZHwoumGzQF\n7EwnCx9nrQEbr/fMMq/USZtXYtdEhl++189XLmqiwWflqROTrK31c3As+td7UuezMZXIUeez8tiB\nUeqLHFzfXowgwLlwhtfPTvOl86qR9Cyv9CdZWu5CEgV6Z9Pc8pP36L6zhKGiBaiiwInpJK+fmuK8\nej+XNwfI5g2mUnl0w6RDCoMgMmMpwSYLHJxIsqZE4KeH5/jiyirUidOMur0KqJ0AACAASURBVJvR\njP/XwerZM7N8pDKPqdoICy6cqsjOoRiSAB6rzJKRbcgVjZi5NHqwhZzqwjrbT/c3vkbFr59j4os3\n0XDvL5j7wwPoWh7fnT9BnTiFNngauaSi4KY0bzX5gZMIC9YjpqPkj+1AXHYlYi5J/JXf477wcgRR\nxMxlyE8OI5fXkTq4AzZ9FXuoG1OxFnZxHAH6vv1V9IxG0xduIXFwN55LNpEt70Q+tgXBaseoaEfU\n0oQe/zkAJR+5Fd1dCqbBuc9/hqbf/Rlz2yP0Lv0EHfGT5EuaCrr+A29ippMgSiSGxij60M2Yokxy\nx8uMvXeUlru/wdjjj1B6+WUAmB0XEn34u/hv+hympKLtegnBYiN84CiBpQuYPXSc4CdvQ3cEmPr5\nt4n0TdB+97+RbViFkphm+lc/JHj9Tcy+9QpaMkPJp+5AP7MXln8IeXaIxNvPkwlHsd9+H8oHTwEQ\nP3US97/cg3DibQ589T9Y8fSvMKweok8/iHvxclKnjqIG/FgXXoApKZizE6RP7OUrn3icz9/Yjq+5\nguIv3w8HNqNNDGLZ8HGmlWJKtBDTSjHByDlOWRpo3PVbrAvXEHrxSYqu/xSG3VfYSTvzHkJlC8M/\n+T5V3/gxv+rO87GuIKNxjY7IEfSKTqTZYfTxXlKnjiJZVQRJJLzxLioyIxjOYu4/OMuaugDLBl9n\ndtF1hTkCbwViIsSMs5riWD/391r59KJyAhOHMQI13LJthkfW+zFVO0LPXjIdG7CHuhF0jfxYL1Jt\nB6ZsJWQrx68YZJBxnN3OXNOFWGUB1cgxlZOoGN/Hu5Yullc4EQDb8EH6/QuosWQLMyBujZDpwGeV\nSGoG3vQUWVcQJZ9GmTxDOLiQcPovTnY2md0jMSo9Vg6Px/hIRY78wS2c7PoIXV6T/TMm50mj5Epb\n2DUS54LYfkZq1lB28GlOtF/PIn2Aw1Ids2mNep+N6pMvwZIr2DWt89qpKX58QZBfn4xxe3mEo3I9\n08ksa2s8yMkZDHuBoQsgV3b8pwvNf0Q8dOzB/9H1/9ExMDP2P30K/y3xwEX3/c3jf7ezOp3I4o6P\ncdg1nwZnHGHRpRhn92KmE+Qnh5nZfxTXtbfCzAhm3SIklwcxNEDF6ouxRoaRSyqRVQnn8nWIZg65\nrhNHOoQYm0QpKsNoXI5F1kicPYfn/A0IFit2I4mRjBO8+eMY7lKmyhZQW5Km6NqP4aopw2hdw+mw\nhvO1X2Ovb0bIF7pzkb27cQQDZKZDuJcsR7JaEXxBzCNvYVuwCmv7EhyjR7G1LeBNvZFOdZJM/VK8\n+SglpXmExqW4lBi25oWkZSe9EY2gFsJdW4G4+nribz2P9aIbkfUUgfWXIsoSqkPFTMYQZQF1yQZK\nlzRjK6/HboTxNM1nS9RDw8xxlI6VGBYn8twokQVX48iG6fO0E9CjyEVBTLub8Ucfwn/dp7DVNaJ6\n3Yh2F+7mBqSyBiQzh+QvhdI6ZjY/z2TXpeimSblLYTyex9+/C8nmwHAVkdVNNp+boWvqAGJtB80e\nmZhgI2BTqHFJ/G7PCJXzFrGs3IkjM4tl6izG8BmM2oVI2TimKBH/9XcoXnURVlWhzlFgAP5w1ySf\nsnWjpGaQUnNMK0VMJguGEFVWjbSjlGYXpEyJSo+FqypMigd388PTIlkEmk+9iFU2CFbV47PJvDcU\nZXG5m4dHrNT77cgNC1BEASc5RC3Nvukc583tQ6tbikVPM6tLLAg6qfVaOTGdYmNTgGTeoNguc3Ym\nxSbvDI7icordVtqn92OrbMFjEZGSs+jDZwqat/5dJL01VLlVolmdaq+FqOymJ6JRLSUwj71D7uwB\nhPbzKbHL9MxmcHm8lBx/hbXtlcREJxc0+PGHTqJabRhWN+MLLmZLd4grK6E3bSVQWcWYpYJdIxG+\n2JBF9RTRM5tmYdBBiR6hJyHiUCWimsixiRhragO4LRL9kTQui0Kx28LN9TIv9iUodVpwqQUHqJGE\nTkdALbjDhXoYd9RikUWK7Qrjup1qjwVVktAN+O2eIT7TbiekyaQ0g9YiOzNZE5cqM7/SQzpv0KqP\nMa++mpmUTpFDxW2RWODM4HE6cFpkjk8laS52IokiTx+dYJ0/Q8DrZVW5jaGEgS7I2BSJZm0YT2qK\nObWYQKWbRR1t2BQRKBSwFzcXMxhJY5Fljk8lqPFYMUwBm9uLJTqGTdCRZZkStx1LfBxvURDDBHdu\njgnRy3RSo94lIs8NYfGUkJIdGLIVVRJ4o2cWURC41DOH5PRjr6gnv/81BNUKk73Is8OIdjd2KYrD\nJmK35ZlovJDy+gre+9wvaN7QgJnPYcxNITSvQArWEHn5cdSrv4Ay3Y0+1oPUuhwpGSZR1Exy28so\nZhJj6TUwfILY4QMobhdyUZBeWx0l+TAoVkLeRhwzPRCZoPLOb6D3H0e78otYExPIkTHiu7ejnHcl\nw4YbX+g06qWfwhLuZuB3f8R7+fUoU93kx/vx1leiTw0RLHKj9R5DTEeQBBO5qByxaTHGgksQ+w6g\n1rZyzt5EcXYCq1Nm4o23CF6wErF9FeGyBcivPYg1WEq6/SIyiguHnsCITONasBi5dRlMFM5Vqm5F\nnO6h4uaPMbv1VZx1dQiTfUi5KOKC9VhWXIorYMcIjWAkosj+YtKBBizJKewbbkTSs8TefgXHhdfA\nZC9S20oSpW144ydxNDaReud5bLWFrcvpXQexuG2QjmLMjCK0rCC2azurz6shcO/v+fbGf+PSr38O\nMT7F3MEj2JdegCsbJvvOU1g7VzP5wD3UFYOZTWHE5jj3p+1UbFyD7qsk9MBX0WensDV34rzi44jp\nKF21FbjGj6AGKlBVBUFLYzh8pHZuxrn8ApSSCgRZIVbaitPhQJnuZlWFHYfHh91fhMXh4oOIBafD\njmb10j+X5VDMwvUdxUiigIqOlAgxv6MVj55Ajk2BvxwlVfhPnJCqcNd3IA0cosfTiUuVsIgmW/qj\ntNsz2CIjmP4qxGwC56mtGPEIQvU8SmdOIFhdzLmqqYj3Ypz+gAqPiqlYGc0pGCYIgL1vNxYzy9Gc\nHzlQgVfUKI724XI6UfQsoZzMfGeaeQEVY/9rmOs+TUY3yQkyxXYFy+l3URSJMTxUVlbi0SIIla2U\njx9Eq19OmZikXkmRU514VL2wM6hK1AYcyKqVWq8dX2yQoJKlsqICgJGswmg8T1BMgiAguov/0XXN\n/1VMZkfxWN3/NDkamUAUhH+63FC3/m8+v79brFr3PEPs4G78KzdgLa1E6NmLYLGSm1/Q/smqhJQM\nkR3uRxU1RAws9S3Y0jPkR/uQyuoRMApb5HkNwV9O6p0XsDR10Vu8CP+5txFVK8LGzyApCkJyjr6H\nfo2nuZb82f0oVgWlqAqbTYVsCsHQwVNM9dRh9PAEssuJUd6GfmInqeExiq66AUuwAlPPI1msCIYB\nle0Iho6g5wi/8RKKRaTFJyJ0rsWdmSHy5C8IXfFvOF1u0u+/hqOsFFWLUyznMc7sRi4KIggCUmSU\n9MGdWGobwV+OGChDFEyMxVdint2D7C+GoiqiT9yP46JNSJPnaFET6DMTJNrWE8orWHc/T0/xQkrD\nZxEr21AtFiactTiHDxM5doKpN97A6cgjz78QojMIiooxM4YcrGE4MJ+Q4KKq2ELaX4dJofNnkQSs\nRUFmbOXoholuwoXuBLLbR764kb47Pk3XJasJCy48e5+i/fwLWFVkogwdxPRVoh3aSmawD1tpCabd\nS1T145w+gZSYwe71Io6eJB+oIZ43aex9B6mymVPWJhqdJkV2BX3/6yArOIrLSEs2iqwCWV3A9s4j\nDD7/Outu/QyCIFBRVYHhKcP21q9wChmEYCNVR5/D1bKE3tkUi2f2ckII8sFYClO2sVYcJNdzBEVP\nI0bGkYMNRDI6dUqCyiIvnv4PcHm9WDNz3Ld3hsubvVRp03RYU+RHzhEp68Sfj2IMnWCy/XIcssDY\ngz+i5ZpNSKJAfX6cvK8Kp0WmTk2TcgRRZoeY2XUA19KV6IqdEoeCgxyy3UZyy5P4mtuxnNmOWdoA\nslrwTLd5iGsGL/fEuc45hjl2jqMEKXdZ8ReV4NQi6IqdR/aPsqZcpVrNYHO6KTq3jZWLuihPDBBR\n/ZjARXVehqM5WvrfJl7aTqPfyh8OjxN0WVElEc0UeW8oQqctzZziA1Pg+HSCs6EEdT4bAPXWDKua\nyrAP7CPjr6XEoaAZJsmciccqUeuxUGRXGMy7KNVClCf6MbwV5A3wKiAceRNbcTm1AReSJDGb1rCq\nEk17fs/Z0qV4HRbCf6F4GIDT5abbKMJtERmIZmgOONg+EGGeX2I8ZVLlsRDPGdT7rFS5LZTaZRyq\nxFMnpigtqySrOJnJibgtEn05BybQMHOYP6erGYlm6Cp14o0Nki9qwERgW1+YGp+N/kiG9WUi39w6\nyIZFLfgljYxoYeBHP6Low59C8Fcw9tiv8S5agjbcTfLcGeau+RpFr96PUtdGRbPEqf94Fs+/fh2x\n9wDHvvFj8tffimvwAHJ8EtHhZuh3j6MPn0GITWAvLcNiRJn84DDO9dcwdO93KL/+wwj1C0lVLSKo\n6pj9h0nv3Yq3LEj3D39A2eWXYkwPc+h7f6DxshX03/9jMv3nOPCzd0nu3kp64yb8x99ALq8n/sE2\nqm65HSGfZfiXP6OoqwFh3oVEt20mffY4tss+Sf7w24R372Jm53uMr74R75af41i+Hgwdn1VGtqoY\n4TGSYyFys3PI0UG8xQGG/vQcWjROYP58lBPvkB/ppv+lHTjcImpdG5ZgOWY6geJwIOQz5Ef7cF5y\nI/lAPVJyBrWug6i7GnXv8xgLL4OBo6j1HSAriGfeL8wLBMowTuzEcd7FGEOnEQwNMTXL7nwZjalz\nyMEaMr1nCW24DXdtG/66UgQMyGtYmhcW3iHt83EuW4Pat4+1S/2cq1pFaWwA16Ll5I+/h5jPIi+5\nFLlnN8TDnH74VcrWn4cRmaHmlluYe+tFlMXrkUaO4OxciHbuMFJyBtHQ0N97jtiRQ+QXrGNat+Ez\n4ghaGrVlIYJiwQg2IqPhVkUELYMgCOg9h9Er2kjLTnIGBGwKA5EMdUqSqngPVTV18MyPyLWtRrXa\nkOJTuGUTKTmDKamIWoZhRwO53/2A9IL1BI0IkZJ2XM98D3fXcvQ3foPcthr3ya3EjxzA4Xeh7X2d\nsS3v4l93MYK/AunIFoTYFFOeRqZEHyViHFQb4two07ZyphIaLfoogreUsKcBuyIiCAJ2I0XSVYEk\ny+hbH+PZTDXn+zXExAzZs4exVDXgddjwzvaSsRdhj42h9R4hW7eUqayA0+1FQUewWJGnezGdAUQt\ng83uwOw7hMPrY0yz0mWJ4jBS+Ge7ydUtR45Nknn9UforllHulKmwGYWZFkNH9Ab/4QXo/02cmD2K\nbur/NPnKwfcZnw3/0+UnFn/4bz6//yO66jx3qmCjevI9pNZlTNprKDn3FoKsIDrc4AqQDhTE1TuG\nomyyD6OVdTCYNGlM9pLvO4ZS3YyZy2B6g2T89aiZOcTUHAlvHa7BPeSnx0gu3cS+sTgXlZrIc6OM\neFqpnNjHy2YblzX5eeLYFLf4RjENgwO2DhbbYrw0qXKd3E28ehkzaZ2KXY8ilVYjBevQR7sLgGnV\nWkCqNK8gZS/GERulVyylxqMi6hrseQFWXMtIWkQRBUoPPIW48lqkyBjauYOIDhdGNIyRTqJUNiIG\n68gPnCxQD87uJXpwP5Mf+R51XhVLLs60Ycfyh3uQrRYc19+OYORJ/Pk3uK+4mdyx91CqmqCo6q/3\nWMwm0UOjzLRswGuVOB3K0DX5HnrHRSihXiL+JmJZnWhWpyt5ivvGSvhyi8GOpJ8mv42y7rcKgvye\nN3jRfT4Ly1zEMjpdfhFB1xjVLFTYgD0vkFl5IzYtzuGozGJ7Av3QFt6uuZrFZU5m0nkavBayj38H\ne0s7jzvWUuu10VXqYDyuUfaHr7Pn6ntYUekpYJBcRZij55hr3YBmgPXJ7xC78VvkDUjnDToiR/jm\nQAlXdwZZag6TLW1F3PkEqVUfxZmdRczG2ZUppjlgw2OREDExELDM9KDbfMizQzweq+LmVg+7JnOs\nqHBxfDpFkV2hWogiJcNsjpfQVuzA+tMv4PvWbzgwnmBNkU7ksXs5ec03Od+VQHeVIGbjJGR3ATZv\nlTkxneTiYg05Os4ZeyujsQznV7vZ3B1mWYUbv1XCnk/Qm7Hy7LFx7jivmmNTSRp8NoJKDnluhERR\nM8enUpQ4FRrGdqM3rWT/dJ65jMYlNQ5iuoQvOcaWiJuUpnOd3M1k2VJ6Z9MMRtLUem04VZmT03Fu\nLo0xaa/hd4fGuKGr7K/uXwfGYny4Go6lHOimyVJG0F3FSPEQA/b6QodUnOVo1otmGASdKnNpHY9V\nYsfALDd3lTKZ0OibKwzS7Uj6efrQKPde2sybvbNMxDJ8clE5YzENVRaodqs4osMc1IpZPFnYRj0j\nllO3/UFCl3yJCjmNFJ/i2Rkfm+qt7JmBVZYpdqRL6Cyx41VF7vtgmIubi6l0qQVHrGNPM7r0oxTZ\nJGwHX0JsXUHipUewf+zrKOMn0d1B9KPv0Df/RhodeZTpbvo9HRTZJKZSeU5PJ7kiuY/fGvP5rHwC\nKVBOtryToWiOUFLDZZFo9SnE8wLq8z/imc5b+PjUK6jzVqOVtiBqafT3n+X0/JspffQraF/4KfGc\nQYvLJP30fTg+9DmmJD+nQikaf3UHdV+4k2zlApIP382fl9+Bxyqzcd8vsbd1IbSsYPOUilOVuaDC\nwraRDCu23YeraxHasmsxgbmMTrFdZjqZp9guc2giyfKAiTRyjNGy5RhmQdYwEMnRpc5xIO1miVdH\nOLeb/rp11LiUgpHGQgdSYobXU0EuqbIy8KVP0nTXnbyrdFDpttCYH2PGWY1PNpAH9pM8sBPnqksw\n8xr5msXsnypIQZYHTAzFhiFIqKffIdZ8IaNxjeFoBoD1Q68gd65i2FrNjsE5PumbZMTbjv2Je/Bd\n/xnMsR5mWjZQMn6AfM1ipLlhBtRqatQ0Qs9eTpWvYV78BFgcmIoFc6yn8I7sOczUouvRvvsZKn78\ne5Shgxj+Ko5nvdS89H18V30M3V2CaXFh7nqOE63XMpPKsa7CwpQmU6rk+aJ7AQ9GD7NjIs/ScifD\nsRx7RiL8S5sT4ewHCJUtJDw12IwM+luP0rf6s9R5VNQjrzLz7rtkPncf1dlRYu4azobTTMSzLCpz\nMZvWsSkisWxB390TLkhtFpW5aZneyw77Qi6wTXNGqqTZBaGcRCxnABDJaLgsMm5VoiI9RMJbxyOH\nxvnYgjIkoeDoljdMHLKAMt1NtqSF8YRGNm8ym9ZYqU6yI11CqVOlylUguGzrjxDN5BmLprm8tYQt\n3SFuXVJBNFtw/at2qxgv/QTbsovZYdbRXmxn/1icEofKvtEItzWJnMn7qPGoTCQ0nIpIiZjCVGz0\nxQuDlgnNYC6to8oCdR4VE3j25DQLyzwMRlJcWa3y+ohGtcdGZ89m3q+6lHVmN1pZB6ZsAdNA2P8y\n20rXs77GSX9Mp8KlcGA8gShAs99GcGAn+wMrODuT5OJ6P2fDac4vU/nFoRAAX1nb+F8qNv+r0RM9\n9T+6/j86gmrV//lH/z8Ml+1v83j/brGam5tk4Ku30XjXl+h/8EFqP/lRxhvXE7BJWGf7SW9/Hktd\nK4LVTl/1BdSdeRV9+XWop9+ht/J8KlwKjvFjJHdvwXrppxGycYRYCL2slbjsxnXyTcTaLrQDb2Lp\nWIFhcaB3H0JqW4nuDmJsexR18Xry5w4ita1AyKUR9Bw7aGDV6FbSSz7E1v4Iq6o8BKM95IvqEJOz\niLkE2pn9hY7hjV9G0HOYqgNBSyNk4kjJMMetzbQPv8OxinXM9+igZTCPb0ewWAsYlKpmdriX0Vli\nRxUFjN/fg8Xrwr7ofJIHdmJoeSJ9YwQvWoMcCGLULSRt8WFPTALQbQRonTuM6S4GQUTMJjHmpgr3\n5a2fMrzhLlrMCYbV8sKk9r3/Ss3nPo9h8/wlfUT/49/xffbbDGUtNMROYcpWBhwN1E3t53WxgzdP\nT9FR4abCZeUq1zS9tjru297H3esbC0Mwp7bxlnM560deQy6u4FjRCsbiGS5zh7lzn84Dl9SQE1VG\nYhp/PDTKd84Pcs/OCb6yto4XTk/TXuxkRYnMxseOs/XGCrLucmyD+/iXo25WNwb4lHOAN4U27IrI\nigoXm7vDXJc7hNG6BiXUy6+mApQ5LVze4Cl8XWtpEEQANF8VvXNZcnmTvrkU1waifGFPjg/2j/LZ\nD7XT5HdwQZmMEurl/OcTWO0KW24oB0nh8+/OcueaeopsEiu/uoVHvryG6WQOj0VmXZWdsTT8Zs8w\nHeVu3BaZao+NlkABm3LrCydxWhX+/cIGdg1HaArYWWEMkPdVFl7uCYEvv3ySeDzLE59awqlQio3e\nGL8ZlNnXF+bRa5p4/FSEa9uKGY7mmOfO8/DpOCsrfRTZZfaMRrmiyc8NTxzhpY+0Esqr3P7iSb53\nWRttI++iz02ztfoq5pU4KLOLfH5zNwGHyuqGAJfUOHjyzBzhZI4vdlh5fVLikgYfggBpzSjgsrrD\nbKq38tDxKOvqA2zpDrGpM8hYrKCJrbWbyIMH0YMtTIpeHIqITRZJ5w2cgsYz52LczHFEbzGDnjaq\ntUmenrDxEf8MIV8TKc1ge3/Bc7qlyIldkajzqngifTw/6+dDjW5mNZFDEwkuqHGz9t6d/OaW5SzW\neuh2ttIUOsDZwFI+96dDvPjZ5eQNE79icCaiU+1WGY7lqHCp+PreI9OyFuvcICOWShyKyLW/2c/O\nG/2YkSleFDq5osnPmZkMXdYYYnKWN9NlrK9x8npflI4SJw1ihPejNibjWTpLXXTow6QCjchbf4Og\nWpHL6hAsVrB7QZLQB08huv2Y5a0IWpqoqwrfxBFMxYY+3ovQvIKwGkD+w7dwL16O2XUxmmRh+2CU\ni2tdyOfex8gkeUJawkcGnkJZewPi7AiJmuWF+yEMkPdXs2UCLtOOo4cnkdpWIkQnIZ/D1HXMYBOI\nEhg6uxMuGnxWysb3IdhcBa1lYoY+bxf1qT5MiwMhOkm6egm2qTMIeg4MHSOdJNN0PuqxN5hovoTS\nfX9EbexC91bwyrzLaVpbTed995LvPQIrNyHPDmKM9yGWVKNPDiCX1hQYsPmC3lufm0YKlGGmk2y5\n/Cts6NmNmAwz89j9nNr0HdZMvwst5xV03eO9iE4vz62/kw899jn2tN/Eam+Ww0k7S2b3ozeuQDz7\nHmYuQ3bhlVhPvIVYUs0pSwNtShQxWfhvzb70B2Zv/i51Yox9V36YVX+4D30uhJGModS0os9OYrSs\n5oueRTx08JcYdQv/yvUWl1yGmI1jjvcgeQLo0TCSrwTdWYxpcYBpEBI8RLM6jQPbMOamkZZezvZZ\nKxclDiB6iwlvfhr/pk9DMlJoRsTn6H7secoffgHf1HFMxYaQz/JMrJzznrmbik3XIflK0IJt9CUE\nHIqIb/N9WFvns6dkLatsBQ12ZvvTWC66uTC1v+IyTNUORh4hPgMOL0I6RubEHhLr/xWPbKCMFdYy\nHH50ZzHT37+dvTf9kGvKDaT4NObsBEYmCW2r4dwe9IWXI7z3JGrDPDANRnydlB56Frm0Cn0uhLbs\nWuzjx8h1H0FccTVCz37i7RezcyjKVZ4Z0oFGbMMHEUSRXP+pQtOpcw0Rexn+2W5M1YaQTSKYBplD\n20lPhTjx+Acs/+YNWOatIu8pp/euf6X+xsuQFl2MODOIkYwjFVeSH+vl3ZJ1XFgKAzkrDdoY47Yq\nKmeOASA2r/pH1DL/6fhnK1ZPzf1zXc//E9fU3vA3j//dYjV/qLDFO1e3msmkRqswg5DPsCNdwpoi\nnVnRBUBJ6AQYOnl/DaKWJuOpZCyh0aBP0SuWUulS6I/k6EydIV/cSFy0MxjJ0TeX4oIaD05VIpbT\n8R15GaW6BWNuCsqb6RZKaZ7YXXCW8VUizQ6jBds4Op2mzKlif+IevDfdXsBV2X2kVC92LcZw3sFw\nNMOa5BG6S1cQyxYcfsrmzhAuaqdvLsMiV4bDcStLjUFSpW1kfvsNPJ/6OhHBgT81zr/vz3Lv+hpM\nUSZrgKN7J2ZFKwdSLortKiUOGUd0GFOxkd36ONmrvkwmb+Lf+TDZ9Z/FfqJQiJ80imk58HuOLPg4\ni7pfRlx0CcgqYjLMaaGcFo9Id8yg9H9z955RcpTX3u+vqjrnNNMz05Nz0ChnFFBAIBDRZBtzjAHj\nBBwbYxw4jhhwwBkw4EPOOcgkgVDOKIykGWmCJoeenunu6dyV3g99rj/5+t71Lvuy7rvX6i/P6u7n\nqequqv3s/Q82A4HICfpcLbhMIn2xHC6zgYBVojOSYTojs7bGQ17V6Z7O4DQbsEgi1VqY3Sk3i0MO\n0nIhIemMF3bxKwI6Qj6NGO7lVa2FS1J7eNuxjHmlTso7N9NVt5G26YPg8NNlrmXnYJSr2opwDh9C\nCbUDICUnSf3tSZQrv49DkJESYTSjFTGfQjPZkT98gt7V36R1bCeCr5Qt+XJaimyUKhF0swMxOUmP\nIUTIaWRrf5yzKlwIgKLp7Bqa4awKF1v6olye3cdA3QasBoEP+qb5YkmCQUsl5afeRfKXEg8tIJHX\niGYVAo98h7Lrvky3q5X6yKcoFXPpSYp0RZJc4plCMznQrG4MoyfQfBX8pVfnso9+ifNbD2A9tQ2l\neTWRjEpHOIWs6ZwvH0NpXIlx4CCY7QiawpCnlbK+j8m2nYOmg+3khwUoi8HIu/bFLA058eYmGZUC\ndEXSOM0StV4L3gMvEV10Jf5smKyzhN7rLqH56dcIZ3UcRhFJLFReDo4mKXdZaDDOIA4cRW1eBapc\nSOrj47yrN3KuL03WWYJZTtGXNVFj19FFA5Gszr6RGZoDDhpseYRcZRHlLgAAIABJREFUkm7NR5nD\ngO3oO0ihRlRHAEHJoTmLMUz3I+QzyCUtSNFBJp9+kMANdxQMNVK96FMjiN4gqZI2jGi81RNnVZUb\nf26SGWsxkYxC9bFXGZ93BeUzp7luh8pfffsLslC+Cmasxbi6tjBau5ZQbgTVXYaQTxU2aWcKcjFq\nNIyhpJK+wAKcZhGfnkIzO4lkVJxmiWMTaWRNI+QyE3zjPizX3U04oxIa3Yc83Etk8bU4TCInIxlO\nhpNcPauYWLbwG67pf5OpJZ8nYFSQ4qN0imUEbQYsr92P+crvoO94HsOcNbwStnFZcheDjRuplBIY\nImdQiupQtz2PuuEWbOMnUDxljOOi2CrBjueQ5qxF6/iEdPcp7NfewaRmJadqVMdOoEumAkZSt1Mk\npNCPfMjbV/2CC7t3MJo3UjGwnfGa1dhf/DmuTZ8n8vwjpG74BaEjrxDZtRf7t3+LPVWwCjX5/Uyu\nuolQopcDVNDgs+BKjdGpBajb9keeqvkCN5fG2a2Ws7hIAl1DzCUZFX14LBKOSEE+TDq9q5B8tixD\n6zvM8CtvwA/+UiCpHXsHfe5GpOggWV8tUxm10ElaeD7q3jcxN85Fs/tANBQ2zLtfRfKXIvlL0M12\nztz/M2r/8w56nS3URg6B08/YY3+g+LzzEOoXoRtMSLFRBDmDrmlkKhcivPkb8rEkf6z/Et/39ZBt\nWUtG0TGKIGvgG9yDUr2QyG/uxPKt3+LKhFH2voW0/DKSZh8OOUba5OEuZyvnduyhKWCnQZwu/Let\nbt4Y0ri0VCFrL0J6988cWfClguHBmRMICzYixcc5/ZP/ouH2b5A7uR9z4zySTWtwxAcQEhE6nO34\nrAYUTUfWdIqsBqzbHkdaeRXjqoXA1oextC9D9VYw8uu78f7XwzjCnegWJ7pkRN72Elvavsg5p5/n\n1KIv0eS3MH3/bQRvvoNpWxnebBiAqKUY+99+y3uzbuASzxSKrxrto8c5OucLzAnaEHe/xPGGi2g9\n+izGBRtIvPFXHJ+7hS2LL2b+11bRe+WPmXfsWUz1s5kMLcJ76FXE1rNQj32Cnk2RXnMjzuPvYigK\nobrLmPzLvQQvvxa1tAVt35uIiy9EMbswj59E9VWyJwKtRbYCqWv3C2SWXoX5k8eZOnCE4tt/hm6w\nYIgNMWCtpjI/inzw/YKldM7LgvHtvGNfQjQr84XcXrR5FzCVg5JkX+GaOH0Q5m/kcBTmFlv5/ge9\nADxw8WerBpDIzHym8/+r48D07s96Cf+WWBs67x+O/9NkVT3+Ebqq8gptXBbSoXMnUnkjZGZQwiMF\n3GhxOULjEjS7D6FjCwdLz2bhyEcway2GiVMgGdGSMUSnj3xpG+x4DkOwgtTB7WSn4njPWkm8fROq\nrhNI9DPx1z9Q8rkriVctQ33sB7hu+jEIIqpoZDqjUCwkSTz7G7wbr0Bxl3E0bWd+8ihaagatdQ1x\nRcR9+A0MNbNQ7X6kTBQhM4OuaaCpqNEwymg/woW3Ih3eTGLWRiIZBZdJwrv3WbKD/Vjrm9GSMcyz\nltNpbaTBnEQ3WBA7tyMFygoan7EuokWtxHMqfdEsS0IOMrKGa8+z7K6/lBUTW8nMvRBH5DQHqGCx\n0oMSHkKsmoWYTaBODqPGp9hcupGL0vvRElEMTQtRTh1EnLOuUMVwFiPkU+jDp5j422b8C9rRUjNY\n5q1G85YjyGl25YLIms7CUjtmg8j2gRmaAzbKEz2o7hIEJU/q9Yc5ueHbLNXOkCpp48RkhsVKD6nS\nduyjR4kGZ2OXdLTNf+b08puZFdkPwEeWuZwdMvNsV5zWIgfee26k9qe/YkfSxVklJgRVRuw7SLx+\nFd3TBY1HdceLhYdb+3oMg4eZDC2iaGgvgtWJ4qtEfu8xjNUtSGX1jD/xIP47f4f21u8wnPtlUPLs\niVuYX2pHEgSEDx9hT+s1WAwiS/QB9JkI+2/9OeJTb7BY6UGXcxyxt2MxiBQ9ezeBi65EcxYzYSnD\nv+9ZdtZdyvJyJ9sG4tR4rUSvuZAlD/+CdPl8zB3vcyC4EoAl+gCqtxx158sYFl/AadVHozCJoCkk\n3nycsT0naLrr22i+CnYkXax0pRAzcbTwAOnWc0jmNUri3aSLm0jmNeI5lcmUzBJPjo/CErU+K4dG\nZriyOIFmdiLmEii+anpjMoIAx8NJbEaJtdVudgzOsMaf54zmIpnTOBVJcnmthZPJggauURSZ48gw\nLbnxCjme7koA8MUmOyOymXJtimHRj0kSScoqtfoUutHMn05kOKc+QFviOF/Ya+K+TS0UWyXSis54\nqsAadpsl4jmV/liWRWUO9g4naPRbcZgk3OoMJ9MWWtwChql+NqeC1PtsmCSB7qk0Rkn8+zWQVXVe\n7BjnkpYgqq6TymtYjSJTaZmQy0Q4JfPwrn6+vqKG+s33k7jqh5SlBzguhGgzxkjZirFlp9EO/g1x\n8SYOzpiZf/IlHvOex1cMHTypz+HqWcWYslF2ThuJZxUWlDkpy44UmPiJGFrVbOjcSU/deSiajv/R\nO5Fuf4CRGZmg3YDHImEQwDjeSdjbhF+Jckp2Un/oGUy1bQgmC+l9H2Be93kUT4iptELXVIa10gD5\nnqOFym1VC7rZjuIpRxcleO8hPmm+liqPFem//oPoDx5jbrEV/f2HObXoS7R4JJS3fo91yXl8KFey\nJgixx+5Bu+kX7Bma4SLHBPlj25GKQkildaBrHPrqHSx49Pfk9r+PtOFG2Pc6w2+/j3j3o0TSCvOT\nR8kc3s6pVbcylswxN2gnqE4XNpXRCV4S57D+o19hK/VjXXUpx799J6EVrWSnZjB7HHiuvbVgDnJy\nF1LjwoLN9L5XyS++DHMuzuYRnYvsYyhFdZxJCVS7jORfuBfHqk1Mvvo0gWtu5pSxihK7AdfMAHFX\nFdpjP8DylV9gUnO83pfiggYf1u4dUFTFcT2IrOrUb74f58VfYsJaTrEyhZBPIURHGQ8tJZZVadLH\nOK4Hac/3kTu4hfdbruP99mV8/epW/G1VBFavRktECe/Yj5LNEfrZQwx+58tU/OZJ2P4sj3s2sOmd\nn1H61TuYsFUyEM8SScucVeHCfXoratMK9ocViuxGrAYRWdOpIoouGRBUhajJj2vfC3S3X47PYiDY\nv4OPbPNZVeXivZ4oQYeJJr8Vd/wMo7YqYv8D/wiGj/A3tZ5zfWm2RG3U+2ykZJVZ4b2I7gCd1kZq\nPCa2nIkhCgLnSz0MB+ZwZDxJS8BObeQQuZqlDCXy1BiSaFYvxoGDfKvTw7LaQmcjpUl0TWWQVZ2W\ngJX3e6NcldlNbNYF7BiMc1EwT9oexJYYRZoZJxGaz7GJNIuLDYipaSaMRQUr7UycHXErq42j5INN\nmIePMFU8G29igOOUcnoqzSWJnYWqLtCRstHe/x4DTRdQZc5xMmkgkpZZfuxJBlfcTK0lz6exgjlI\nuRpBl4ykLT5s2UI13RQo/5clNP87sX1sy2c6/786FntWfNZL+LeExWr5h+P/vLJ6+D300gZyrjJ0\nwIiGITqIZnWjH9uKaHciFlUWbrD+EhKHD+A5+zzU+BRC41KEXKLQ/rY4EfIZECV+1Wfjzqo4h431\nzMt1kT99mFRfH67Lb0E5+B6s/zL7R5IsDjk4PJ6iyl1g/loMAraJTh4L+7mspQhv91am689maEZm\nlk9CF0SMgwXilRRqRFDzDHlaCfVvQ6uZB5qGmE+hG62MST7KY12FnXFxI+LBN1GnxhDOvYW+uEy1\n24Ss6fx+9yDfm2NGjPSjVC0groj4ej4pVImaFhJ9/Qnc19xGzBzAHzmJZraj2f2FRMbmJWe0Yx/r\n4EOlmka/FatRJJ5TqT3zMersczGNnUCZGEBvX4+YmibrLCEla/hOvgdNy+DEdrSFFyNoCsaxE2gW\nJ3KgrpAUzwyQdFfhGj2Mrml0e+egarC1f4qu0QQXtAU5J3OYZMMqMopOcbyHrfkyzJLIWDLHvBIn\np6bSLCh1UJTsRz19iJ6WSwg5DWwfnGFVpYtoVuX1zjBXtgUJqtNoVjcR2cD+kRn+/Ekvr9+wgLxa\nsNssHv+UPt9cVF3HY5HonsrS4LdwdDxFrdfK8XASt8VATtGI5xQWlrnIq/rfE6Se6TTn1HrYPjDD\n2SEzh6dU5haZEf+nQtedNmEzCgTtBeH3qKWY549PcNWsIH3RLP3RDH5bQcd1eYWTkYRMXf/H/GS6\niR+urkRQZQyxIcYdtQzEs8wrsZORNcJpBdv/2L46TCLjSYVKt5F9I0kcJon+WIbVVZ6CPJKQY1w2\nYTUIXPfsEX54XjPLzGGeHXewpNyNWRKI51Rmje8i3riGlKwRNKk8cSLGdbMLm4rJtEIVUTSzgz8c\nLnhub2rwE5jqRDeayQUaADBHugk7a8kqGiUOI08dneCipgBvdE1yQ7ufuCJyfDJNjcdCZaqPbksN\nqgYtiQ5ylQsw9R9AT80w07gGm6Rj6N4F/hAZXy2WjvfRlTxdNefSPZXCbJBoL7azazDG0go348k8\n80rsnJzMklNVyl1mgkdeY1flRlYrXZzxz6U2cYpc2SyeODLOTRWZv1czxSf/C+/FX2TYUUfQJmEc\n70T1lKGZC2LpmsVJ1luNdaITOdhEV1SmTZpC0DVGzaV4LRLmbBRpvIDxDRx9AxZcQOb5X3JwzX+y\n/MjjXBtfxcb2Ug4NRPnl+Y3YO7dwS28FD9cMFNrPZ7owlVVhKKsh9vFmHLPmYiipRBBF5NBshEwc\nofcgeuNS4k/cj2veQoQ56wqGIaIB+dMt9Cz4AnUeM4ZUBHHgKJH6s3GZJPJP/xTn8nUMly6hInqc\nAU8b3Pc1in76CMa9L/9dyk9qXc7pu+6g6c7b0VIJ+p94htrbbkOdHMEQqkcePIWeTWFsWYLcuY/c\nyuvghXtwLFv/9+Meb7+Y4h2P0rPkBlqzvaR3vIXBV8TAGx9i9jgJXXwBw6+9hbXYy8iOLuY98GOS\nu95n6ngf3sZK+v52CPd/v0alEEf79H3kFddi6XgfRJHprR/iv/YWxGyCU/f8AovfTdmm80DJY6xt\n59HJItY/911MTjvpcJSa+x8k/Lu7mTkzjtlrp2TpLESnF3PLIvK9HWT7exnc9F2aLSkEuVD1NIZP\no0yOYAhWMeRuxvbU3bgXLiGybRux08M0fPMmtFSCkTc3U3XrHUTffhbXvEWo0TDm5gXIodloW58i\nNzqCa90lyMWNcGgzktvPZM1K/KSQYqPIpw9hmLUCtWsfsYMHUG66j+J8oaIpyGm0oS4+LVvDosRh\nsHtR/NWoRhsiOuM/voXKr3yzgLcND3K0+CzaBz9AMFnQmlYwlDMSeP1enKs2gqYiGIzIZbNAUzmT\nMVDx8R8wn30VyuEtGFuWkCxuQXjpXmb6xyi55HOkj+zmo0Vf5fwaB1J0kO67v0f1Qy9hjA4xYSmj\n6PjbZHtOYm2dj968Aik2guoJoXzw3xjPvhr6PkXXVGhYAt37CgRXqZLQaz+H63+Cfc/zdLV9jlZ7\nHuXDJzAv2sAJUw2tjKN4KzEOHCRTuRCjmisQEGM52qOHwGCi2zuHIpsB+65nEJ0epLJ6NEcAaWac\n3436ua1oFHVyBNrXMvPU/dhCpcjnfR3ztifQ134JU/8BYqEFeCJdDDob0IFy4ugWJ3HVgMsk8lrX\nFO0lhQ5se+lnawowkOj5TOf/V8dlj3zjs17CvyUOffu9fzj+zyurQx2EnbWciWVZZJ1BULKo3Z9i\nqJtdYMnWLUCM9KOrKqLDg+IuQ8pEyRc1oGo6BgEM0/2kPNU4J06ApqA6itCcxXTG1EKVZvAwJ73z\n+MvuAX5XP06+5xgjK79CXfIUumigx1ZLXtWZTsvML7Xz6KFRvtFiRlByDBuK2TkYZ9O+P2GfPR9a\nVqBZ3BjD3eSCzWi6jjkxjphPgqoWMGHRYdSpMd52reBi7Th6Pos8a0PhoTRyEi2dQHL70XwVpJ1l\njCRkyt66D9e6S1BdJYjZOKrdz6DqJOQ08kl/nIVlTkQBDo4maX/5RyRuvp/68AGGS5fgNotMZ1VK\n7EZ467ekNt6GP3wM1VWC4irh5GSWOeFdpJrXYtnzAoa6OQXvekFEyCYK2NepAg6wSSu0WF/tTnBZ\nk5eYDIqqcyaWo95nISVrSAIE7UZMU72I2UTBh3r/2/S2X4H2rasx/f4F8qqO8e7rafjW7QwUL8Bt\nlnClxhBjo2BxIp/cgyFUx2BoGSlZQ9V0Wm1Zcm8/zMi538IgCpQ7CvAIXriH9xd9jbYiB83ZHrbk\ny6nzWamWC5afhul+tuVKWBndg1hSUyCZzV/ProyfJadextS0gAF3CyExQeTBn6F97Vd8Op7kvGIF\nMZ9ixBwiklZo8xuZyhWIXPbGZvRVn0fMp4nqZuK5gttKbsfrjK67jayq0WgvVA6GpQCJvEZb/gyp\nra9hClVhbFyAfHIPW2ouY3bQTpGt0AY0KRlG80YCNgOWjveZad2AU0tjiPSR2vU3rOuv4agapMxZ\nYNhPZxRabVno2sWZunOotulkBROxrEooO8SguZyRmQLRpclvwT/TR4dYQUv3ZobaLiKv6owmciz9\nHwiHW5RJYsKpzNCRtDAYzxBO5VlR6UVDp95rpns6R1uulx57PboOo4kcFS4LWVUj5DBi/egviGuv\nZwYLuq7jPvASUstShszlJPIalS4j4ymFnukMs4N23GaJ4RmZrKIRchWqTGdiOXxWA6OJPF6rgUqX\nicSfvsvE539Ko0skiwFbYpS0swyzCKejearcJqz5OCfTFopsBYOJRr+NhnQPckkLptEOFF8lhulB\nhr2t/HJrH1fNC7E8fxIl1I6UnGTSWsZESqbFBZOygcD+51BWXYcO2AYP8sBEKf/pOMV292JWujOc\nVjw8sneA+8+rZzipUDOxHy2TQhnpZXTrPkb2DpJ56g3WuhMM3X831h89itssMZ6SOTmZZqMjzKGb\nbqPp1c04hg6Ckmdmx4e4zruS7nt+St09v0VKTjK9+UXsleUcnvtFFo59gmixF/SOi6oY/v0v8Nz9\nEIn7vsnuR/ZwWcdmdiQcLNzxR6znf4lO1Ufymgtpf/d9zPkEykdPYaxsLOhv9u0md+ow5gXryQWb\nMZ3ezvPrbuOajrc4rhXh/OUtVN90E1PvvYX7qz9HkAsEt/5f30PgF49jlRPoBzeT6evGftGXC9ev\n3YMg5+iyNVJ/5kP0WesY/ck3qbztTpTuwxy593HmvvoqUvceBKsduWYJM3++C7PXibV1Hicr19Hc\n8RLa6i8ibn0cY3ULajRMZu6F2E5+iNa8ij1rNrJ8y5tkX/8TplAVhvZVoOYZ/uMvKf2vP6G+9xck\ntx/B7oLms+jJ2aizKUiJCU5LIXQd6jpeRnL7kfwlyKVtaJKR4+EMZU4TwfQgWW81JjkFchYkA4Kc\nZfKR+ym+4Xa+ETqX3zx3I9YFa5gqmUfyp1/B95NHyP75TrSb70P97e1kv/EA1gfvILjpIrItazkT\nz5OVNVwWif/riZdXdUySQMhpJJJWqEx0k/z4NXo23MFsywyp1x9GveaHHA+nCbnMFNkKeqUNfhsZ\nRaPea8YQH+Oc5wZ56voFBKwGDJnpAi6/axePMJ8Gnx2nWcJvM1JjztOdNlF39AW21VzC2SPvsaNi\nI16LkWhW5o1jYzxQ0oU2v6Drq4sG0s/cx9SJPuLffaSg5qGMc1oIMpNTsBkl/FYDLrPI0IzM8XCS\nTQ0++uN5VF2nNX6ME67ZlNgNxHMa1UxxKOOi0WfhyESK1UoXB6xttO9+ENP6L/LupIlzaj2IcpZD\nUyq7BqLc3iyRdpTgiJxGnx7jdOly6uwaUmKCRwfNLCxzYzNKCAJUbf0Dj1Vey/VzSpjOqiTzGr/6\nuJsfbiiIvDcV/2PizP9XcWRq/2c6/786+mb6Pusl/Fvispqr/+G44Z99SPbX4k9HcQcDJJ/4Fe51\nF6LbnajDp9FlGe3IR4jNiyE6jhxsKliXuizkFA2roGIcPYEWn8KhKejZJDj9TD/1O/yf/xqtdj/S\n1BiCwUhr7gy3r6pDSGkIZiuhbQ+hnn01I7++m6Ebf83KoMSI0Yx1uo9bayBlLcMxeYpyq8iVDV6E\n1FyU0X4sJdUobg1toh/j5CBoGtHmcwgkJ1HtfrTO3YjVbcTbF3ChOsPBmYU0vH0vprZzGMdNaHIE\nqWUpsreSrCZgeONXNK24GM65nPTud7Au3wSpKKLBQrnbjXGym/U+J+LgcbqLFnF2mYm0302RNolg\nsQNg1bJUjhxEq55PXpHxpMf+fn5VTSeZV8j3HMOmqehmK4KmYIj0kQ/NQcomaDAnUUNtZJMamsOL\nof8gFzYtpzcuYzYIVOeH6cj7COTCHJq2Ueu10hvL0ZbPoBTVI8VG0FZcTTycZfGdt/LeVIaFZQ58\n119Nat/HhNfOxm2WkN0hTBO9CJrKiYdeo/LZN3ECJXYjkpLl02kTi5eeQ/XhFxBWXIU01Yfsq8Xc\nPJuLGrwFwgNOxqdzBB0m0BUMQ0d4MlmFwySjJWLkTr+O5ewrUF0ljEwkEJddyvsTAm2iQNzgwX/7\nfWQFiYDNSLdsw2fzUkqKCCa64wXmbkNjM/vqLuaszk846FuMxSATcpoQwuOY1n+RvukM6+yTnEpV\nYDMGKN3/DK6V17NvqoJFG68nbC5BFGBmYS3rXCYOj6d4ZN8QP6kYJVy+lKrpI8jHetHNFk5NZViq\n9oGuYV96Du/E3NiMMm2dr2JsWsgZJcSLEwrnz9pIjRzj6LSV5gC8dHycz7WVU7r7cUrOuRlZK1Sf\nVbsfHwZ6mi6k2CwxkshT6jAjiQLvdE9hkUT8NhPrbZPsH3FyeWsxH/ZOk8gXjj0ta8SzCkrfUbpD\nIao8Flabx/k0W/739zSH6nh3OM+5FSL9WRFt0ZUUDe3FXlHJick0ZoNA52SK1mI7xRYB40QnWUMt\nFS4j3mwYvecAYs252I0i4VQep8nA0Yk0c1ub8VgykFEYyDtpFTTSskbuse/j/I+fE8+pDOdsnImm\n6JhIsqKyQHBRihvYOZxEFGpYbrYhB5tIJDQ2tBSzLH4AweVDl4ykXOUUhzuJ2+qREmOY7OVMLLqW\ng71RLkzuobv2HG73ToFczRy3nYhqo8ou0jU2g2GqjwpXKbkT+5E33Y4NqL5pNsmxXzOvwsnE/T8g\n0jnOHIOCcawT1dHE+qF3SPT3suDR3zP2y1sxzmrAWN3M6df2seCi6zHaLQUpqv5O4r0j+C64inlH\nnkLJZREvvJXEX36Irmp/v5bLbrqVZaqGbrJS77NibZ2PmIlTGQhyyiQhqzqWbALz4nORO/ej6RCr\nWoZbVVEdAbTXfsnUyCTXfvIn1J5DtMh51BXzEaxO0pMxvMc/Kiiq2JxU33QTk4pGGjs+UcKx5GxG\njEEqTFFyh7Zgal5IM6ehKIQ2M0Z+JoXcsRNjdQvznn8ajn5AfnwQNBWjrwKL34397EvQjWbaTDOo\nosTgN6+m4fZvontK0UfPYProUcT62eiawsqn7kVV8tjPvoQRdyOh+Gk0R4DSc9dx5JKLWPTQvRz4\n6vcoeeFtymd6aUpFyW7die4vpW7ppYindpLq78XW7iFROgfHdA8Zfz2lDiMlsVPoJiuRn9xC6LJL\nEcqbIDwAwRoCq1eDpvCb527k29c+xu/eL8XtqyLvtDF22zXUfvk63h6KM/DANr627HniVhNa7QIs\nnR+T8C+l1ltoL/bHctR6zHhzkxzKuKixaai6RDrYgn3jdcwRJkHWcK27BG1mgEjaTchlxqqk6I9l\nWBeQOak5GEnIVKt5VjQX47NISHK6YEkerEaZvYGb5QyPdKa4bnYJJybTGEUz05k8zZWNzA7aYQQG\n4xlKHWayisbNy6owJBMoqSlyNj8nJjPMa2gicqyH1iILPdEcWXc5dUoGnEYOR2R+v72PR5aJ6KYa\nlpe76I3lqPMUvk8fTxE3KTTbcuwKq5TWBgkICnZRRRIE0pULCaQUjOf8B+k3H2bDNd/jmWMTfGFW\nALdZ5AtzShnTdIoFjS5TDQ3pU4wnc9SbVYZMZXSN9dPgtzOdkbEZJRzHern2goLUo8MoMp7M8+Bl\nbQzG5f+tpOVfHeFM+LNewr80tvcc+KyX8G+J/7tk9Z/qrGrxMH13fYOeeRupXLUBvWMbupxnpHUT\nntQoyY7DCNER9HwWgy+Iy+VGN5iZ+P4NSGsuxZyLo4aHkJwetHiE/dY2lGf+iq+mCIOSBk1Bt3vB\naMWjxMBo4f1Nd1C7cQ651jUEgjYI1uHRkuQkG7bTO9BDzVjGThTYtTY3U6ITp55CT8WRAmVoAx0F\n7daSWkQ0Mi/+GWv7IjSHH73vCAZ/KbbkGIPmEN6/fAdXdRnx6sU4TCLm2BCpne9hal6AOTGGZDIx\nUboQXMVYlSS9f/gjviWLweZGO7gZg6uwCx30z6Eu0UXOVYby6cdYS4KMv/A09hUb4Y0HMM5aXnBp\nGe6iu2IlnlPbOOyeS+XgDtyVjYjHt7Pnrr9StqgCefAUzDsP+bUHMFY3I6WmQRAwOTyIJisGQcc8\n3Y/gKcFiEBlSHSw2RRgwBJldbCOtaPgtErZMhFMUM3TzDYRWz8cRKMVkd1LrtzOa1gl4bBgb5hGS\nw6Se/g32lnbim59DWn89xpFDBMr96N4Qxt0v8KZcRZnLjLd/P5LbTyLQgMloxBQfQXT6GP3VD3At\nWsYR2c+G9EE8x94jcWAnkpykZuEqZgfMGJUUosWK3HUQs9NBU0UphtgIH0UkVrtSYHWyezTDUDzH\nkpATn1XC1b+HI5QzxxyjSI1SpCeQu/bjmbuKzCsPU+sXCVTWEZMh+eivcM5bQPnhVwvatPXzCex/\nDmnh+UxpFqxGiakf3ELVnBqyzlLK5DAR3Y6qw5XeMI/FyllhjSB3H+ZIw8VopU20TuzlpHcu4st/\nRExPUbdsDZ4nf4iluAh1vJ/ArMX4rCZeOjFBXUmAhsRJTuRaGqE4AAAgAElEQVS9lLstdEwkmd1Y\nyYhm59BYknKXGYuexyEqzOgGAlYDmg7FdgNjSYUKtwWzoeA+FpJSGJzFnIykqPJYOTAap8hmpk6M\nEhdsBMtK8Xq9aBqcztv47dYevrKkolAVtVQgIFAhJenLGKg1ZXg9HiApa6yudOLPRzDZPZwIp2gJ\n70c0W5k0+kjJOkViBtHlx+r2MZKUWepVODApM5NTaPUKpHy1WKIDZG0BDA4P4bSCc9kGRhIysayC\n12rAYzHSO51mRbmdrQMztI/vYsBaiUkSURDwZ8Ypyk+yJ26i21BCqxAh6SjDGTvD31JB5pXYiQhO\nOiNpimxG/DYTnkCAKc2MZHNxOlvQYv3z7gFW1HgxWowEikuYUUTcLQswdW6F6rkwPYqnxIIpOYG9\nsYmy89aQL6pDMFlxW4wIE72ceeVDLFfegmPVBRiSE1C/mOJyCYPNhmfxEkRPMWLTEpweiXTLOqxi\nHkNxiB6plMraUo63X0bz0jlIZjtdqpeq9ech9ezDZdKJf/wO0sorMMlJiovzmBoWMHbfnZjzk4xv\n2487O8h9o8WssYQ5aKylyi1hPet81GAjgieIMBNGXHIxTPYXoAMmkdzQGcyti5EHuvj1oItNnin0\n6rnMvPM0xXVVjLsb8bhtKEPdiL5SNLsf2VOB2LMXS0kJUkkNgq6gVc9HmOxHOusyps1FuOpaCs5b\n2ST5ve8QW/klKlYuQ/NXQv9RBpsvoMhlIle5kI5plZLUEEwOkOvYhbe8gkygHlEUkXIzhC6/FM0V\nJHTeagRXgN1xC5UBN1LDfBjv4w/hYpY2hDAKeeLtm0j/+jbE6UHMta3oJhu9qhvsXoIeDbGqDX20\nm+GqVThNIlrvYcTSOkyBIOdc2Mbt5/6M9fNEbNXVeGe3IY/00jZnNrPr8ohmE4ar70LKp1BDbTie\n+RHOyU7SNYs5MpbAbzPjNgmE1EneGJUYimdp9wjkbQG6MmYCNgNTz/yJU20XstqbBbOD57tiXNxc\nxLRmxm6S6I1msT9/P+uuupJYVsWVGkXpPYbSvgHT4KeIsTEWaMMIxz5mr6kBj9XEXLcKJitpgx2H\ny0F7qQf/qQ8pPfgmxUvXgdmBsvVZrG43gX0vIK26CtcFV3MyksNrMeDNjKMd2IxYXIlgsbOwwovP\noBBQp0kaPYwm8nRG0rSZ4mglDVTo00jhXl4cNnL22Idkn32IzN4PaV06j7TRRVCZJmnx46htIv/W\ng8xfeTYxVaLkfzSmrR8+jFHP4Q2WwkAH475myjx2vMkh6qsrmG+cotaUoeTI6ziv+w6OyS6Ewx/w\n0GSAy/MHkbxBbDYrLrOExWT8N6U3/+/iTKIHQRD+j3mtqFzKvLL2/+NeQWvZP/z9/p/tVo+/z3Dd\nOqpmTjHy3w8R/M795N95EEvzAgS7i8T2d7G1zKa7/nw8FomSZB+q3c+Y7qLok4exzFmB6igq2KKK\nEtmjOzHVzoLiKoREBEEUUb0V9Goeavs+pPeRJ2n87nfQPGWk3vor6jU/xKml2TqusTa2m1z7uVji\nw0TtIVxCnrxkJn7/rViLPHjPvwrVEUCKjZA7uR9jVQvZptWYZ0YLrRlBKJBbPBUMZSWCdgPm9BQJ\nsw+TJGA9tQ3BV1oAm48eR7O6UZ3FqJsfJNY9SPHGTQj+ELrRjGb3o5mdSIkJujUfogCqBsUv/IjY\n539aYMvaPAhqnlxoNicns1S+8hO8V9yImE0wUzIbK3KBpNT/KYLZSmL7u1gqq5Hmb0CzuDAMH2My\ntIhA3w5Ep4f86cOYGuchl7bxes8MVzpGkINNjOckuqczdEWSLAl5sBhFWsL70CpmYYiNkiubxUdn\n4mwok3jqVIqV1V5qjGkM04MogVpGZDMOk4Q/cpJUSRvWU9sgWIPqLuPRYxHObwgA4Hruxwhf+lmB\nNRvrKZyfIx8xPO9KfBaJ4YRMk1NHlwq6l96ZM3ySKabRbyV45DX0fBZpwXlk7UVMphXK9j+DtOC8\nApFM15CigwxZKlF1CtCET19nqO0iTk6mcJgkajwWQp2bURZegnn8JLFAM4fHUxwbn+EbhiMYikLs\nNTQwnVGYG7RTkhtDTE1xyNREucuE7dV70a/6PoIgYM1MMW30cnA0ybn+TMFet2ML6uQI4trricgF\n56h5ASOZZ+/F1jYXQ7CSUd8siowKWdGMLTHKsKGYjKJTb05z38E4ty2vxCgKSGoOQc4wLTqRBIFf\nbTvDvbNyjHuaKIl3o9l9bItZWV7uZPdwgqDDxGQqT5PfRpGYYeekTpPfxsdnopzf4COeU6kKH2Kn\neRZLyuyIqSl0qxsxHQVAzMaR/bWIh94mPuci3HqapGjDM3YY3eripKESiyRSJcYZxk05caYMXsyS\nwO7hBEtCBSiLQRR4rmOCi5qKGJopKFJYDAJln76EoXUpSU8Nn46nWHbyeaQVlxdUBtwhOiMFfOtC\nt0zO7GbfSBKA2cU2DKKADrgyYaRkhFRJG9MZFVXXKfnoT+QvuA3X6GH6fbOpzI+inj7IxOyLCWx9\nmPHVX6FCCSOlo+yXapk/+AEv2ldwfoMPAXjjVITrg3FejHi5bOxtDMFK+h9/EmdlkLF9p6l7+g2s\n3Ts48r37qX3xbdzT3bwxU8SFse1omRRafArR6cHUOJ/Uznc49shHLHnhYbSBE0R37cC/7jy0fJbc\nnPMx6QrCsQ8Qauai7N9ccMoCooePElh7DpMfvo9/6WLk5Vejv3I/psv+s6DecP/tFH3n1+iiAWPf\nXibeeAX/7fdhHD1OtmMPgiQhrr8BfftzyJEJrHOWkzm6G3NdK0MvvUbJ0lko6SyOJWvI93dx4sHX\nmPP4Y6iuEka//2XiZyI0Pf8WptPbC2RWi53c6cPIiTSOTdcjpqPsuu7bzP1oCx0bz0UySfibirD4\n3ZRe80Uib71ELpqkaMlsTHPXkD++i9iK/6AoNQjjfZx57HEqr7gIdWq8gNu0uzDOX496+iBi81LE\nSD+5zkOY6mcz9urLuOtCGGxWTCsuKTxk4hMIZhuaxUnSXYU9NcHQfT+g4tqrEQPl6DMR9GwaZWIQ\nc+titPgUyuQIwvLLEU/tJLLlfQI3fpehX9yF2eMkn0hjctrwLZiNqX0FX6++mDtvX47F7yZ43S0M\nPfgAgR89TP9Nl9P0mz+g9x4iMXsTZkkgp+q4shFS1gDOwf30BOZT5TQSzqiUypOI0WHkvuMINheJ\nY5+yY+0dzC91UtLxJszfyN8Gc8wpcRDNqBTZDYTGD7LP1s6SdAdK+WwEXQNNQbF4MM6M8fqEGZ/V\nyNn2aRjrYZdnCSvkTnabWlniynAy50BWdYySwFgix+Itv8b9uZsRE2E0RwCt9zBKeBh149fZPZRg\nrS/LacVDvVMnqRuZSCk0MFlQUFhzHdJYJ1Ol8/HmJhFT08inDqIrMruarmRVeCsjTRspcRiRckmQ\ns7w9JrJpaitiy3JkVymDM3mCr/0C+dq7ySg6ZfIEYjaBbraT91Rgig2BpnBUKyWZVyhzmgvqHnIU\nbf/b9C/4fMGBzlSAE4VcJpwmCYAil+1fknT+78aWkb99pvP/q+OPW5/5rJfwb4k3v/DcPxz/5wSr\nkU62ZwKsKBIYls1U6FHGf/8TghvPJbprB+65c0kvu4bs77+NJiuU3HInY3++j+B37if26M9wVJQg\neosxtC0n8daTmDwODBd+E337c3TPuxaHUcT+xA/xrl6HaLXT8aNfMuuPf0D2VSEdeou++vNoTJ0G\nQJ0aJdWynp1DCTbaxlF6j2KonU3+yFZmTvfhrKkgF5nGYLcUVAZWrSdSfzaBnk/I950gH42hqxpK\nNod44z24e7ajVcwi8vA9FF9wMRNvvY5vTjPTq2+mWJmiW/VQ7TbBO39ASSaxnft5eg2l1I3tRa1b\ngiqZkfa8hDB7LamX/4Rr5Tko4RFmjh7+u5yW6ilHOrWTN03zuCiYR1By7M8HWDy1F72iDd3sKLTp\nwwMI5S2k/vYkzpUbmX7vNUxOO6lLv0tRsp/8vndJrv8qLpOIsXc3+xxzaS+2kVd1PJEuYpuf589N\nN3Lb4NNkLv8exZET5EKzUTUd7cV76V7/LeblulCnxtHa1qCLBtj5An2zr6D22MvkRwYQJBHpiruw\njB4DQJdzYPdAKkZqz4ccXnUrK+ljzNdG6VQHn1DHqUiKm6pktJ5D6HM2ENfNPH98gqDDzNpqD84d\nTwDwiHcjlzQXU57sBV0n5m/kk/4Ymya3kF1yBWNJmSKbAW/4OAgiysQAYmUr2U9exrbsfKbefBbP\nNd/gsX6pQFR68ifYvnAXsmRm5oFv8ckFP2BFlYeyM9sYrVlNsVVCQ0Da9TxaNs3YR7uouOueQlK8\n+yUEq52euvOoO/4qU3v3E7vxfhqkGFGTH+e2vyK6/AXTB0eAY9MadpNE0fM/wnv2uWgVs1DsAUYS\nMjajSF7VUHXQdQhte4jIuq+haDrlxhy6IIIgMp43MJbM825XmIGpNA9d1oooZ9GMFkzjXaieMo7O\nGBEFgWReYUmplSOTOeJZBaMk0DGRQNV05pa6CNhMRNJ5ypxm9g3HOb/Bj9Mo8ExHGL/NxPnVNmY0\nI1aDwO/3DLGuPoDTZGAsmWM6I7OwzElldpBBSyWV2UH6zZU4TCI3vXiMVU1F3Gb4FLF2HqPmUgZi\nOfpjGXomk3xvVRUdk1mqPWacBh3jVB9vxPxcahtGN5j50UkTi6u8LCgtEPdWulJsmbZS5bFyaHSG\na4uijDtq8VtEPjgTJ5lTOKfOxzdeO87Tl9YinN6NYDBx6QE3P7ugBYdJ4ulPR7h2Xhl90xnWhszo\nkpEnOyIsKXfTak3zVI9MhdvK2pm9DNWs4ZnDo3w78nIBE3r6OPb1VzDpqiXQ8wkA+/1LmbPnIbKb\nvoVnupt8x06mDhyh5JLPIQ90Yly4Ad1oQ7N5UQ0WjMc/QJkYItXXh/Cln2GXdLquu4z2B37JtLcB\n94l30eaej2m0g1zHLqSiEFoihmHWWUw7qzE+9zNsn78ToWML+uwNiJkY0ad+i3vuXPpfepvanz9A\nxFSEX8jAyW2E33uP4Ne+jxQfRfVWsC1mZWVQwjB8jC7vfOo6XkZPzyAFKwt6q4kYw+9swf7zx/Er\n0YKck82LdOYggicImop86mDBEW/0NL0VK6kVYoUqau8B1KYVHJ5Sadv2ByLHevH++C9MZ1VCFo1d\n43kWlTnovOwCKla34P/qj1C3v4Do8jO9Zzd89ZcMz+SZdeRp9FwWwWrHGKpDjYY503YJDekeNLuP\nTsVLk1MnqhkRH/0+tlI/luYF6JXtDP78TqpuuAG1djFRzUgg0Q+iASYHQJE5VbYSVddpPPIcxspG\nzgQXE3IaMRx9D6GyDV00cFrz0yhMFoTrRREpMQmJCGrFHKSxTsaK5zGcyLNIHyR/ZCvPl3+OWEam\nyG7mrEo3oSOvkFpyFWlZw2ORGEnKSIJAlRom5SgFYCav8W73FFe1FdE1lcVlNiCJ8GHvFDc3GBF0\nraDh6ozSI5VSY9cZzxUStFB+jKy7nD/tG+Y/F5cg9exBrVkIooSgyjx0fIav16ooHdtJLL2GeE6l\n7OM/YVp5GR8lvKz1pNAtTvqyBdJvw+HniC7/Im6zxMMHR7i9dJKZktk8fGCEa+eUUja0m7uHy/n+\nmhpMusKv945xZ5vEoZwXi0HkTDTD3BIHXZE0pU4zqgbVHhOubIRu1UMqr2IzSvisEg6TyJlYnucP\nj/CTswKIvQeIN65BEgrnxCQJSILAwdEEy8qdbB+cYZNlGNXu54URI6VOMy8dHuFbqwsWu83Bzxaz\n+krfP06C/v8a9e7P1mTh3xVz/Yv/4fg/TVbv+eg0d4UmGAjMpXT34yBKmBvnIg/3YiyvI9d1CGNF\nI4LZwk7zLEZmsnwuvo3UgksZScrU7/vvQiut9SyEkS7UhmVonzyDaHOi57Ow6vMYp/rI7X0Xy4K1\noOTIdR1CTcSwrP4cysm99LZfQV3Hy0wsuArbU3fju+w/2K2W01ZUYNebxk6ihQeJ79uBo7kV0elB\ntNjRsikkbzFYXQhKrmAyYHYg5pIcy7qYbZxib9rDWVo3ajTMcM0aQp2bEZqX8+qoxJKQi8rwocJN\nxWgm8clbOJasAYcfpf8Exsom0BRyJ/eTWXMj01mV2plONJsX+dAHSEsvZhg3lSN7UKOTzMy/FNf+\nFzHWFG6y3bZ6ahwCxrETKJMjaNEwgs1VsLcrrkB1lSBFh5DLZmEMdyMX1WOIDaEZbQi6xkm9mAZf\nQejePHKMqaJZvHIyzOKQh/5Ymks8U/SYq6hlmueGJFqLHGQVjQMjMS5sKiararSlOul2tVKXH0L1\nVsKBt9CSMfRMijMrbqHS/b+4e88guapzbfvaqXOaTpNzHuU0CiCJIILIwWCCMTbG2Bhw4BwcXwcc\nCSYYYxswNsbYBkwSOUtCSEI5SyPNjCbn6Z6ezmGn70f78P1xueo9r0/xfe9Ttaur1p+1e/fMXs96\n1nPfl8LR6SwNPiv7J9Isr3QVLYV2vQqnfw4pPobqrcQ2foRosIOheAGPVcJtFYlmdJqtKe74IMpZ\nbWHOjmyi9/d/5cQ3HwNANUwWl7sZTxboDAp0pyXqvBacYwcxZRsbkmFWVRf7Hi2SwMa+Gc5qDOC3\nSxyeytBpneGYGUbVTWq8Fnwn3uN41VpePz7F7QvdvD5qcn5yO2Y6gRQop7diFVVuhW3DSSRRwKGI\ndMoTbMuXAjA37MAui+jP3YX1gpuYwMPesSQtASetE9uZrFtNsqCjGibtEx9hVM9lBC+SIHBwsigI\ne3YITqnxUiUmGdTc1BeGGLXXopsmDkUkmBpiv16GVRap9Vr4YDBOS8BBvcPgyCx89a/7+M11iylz\nKWwemP3YhWAspSKJAs2Hn2dw4RU0CLNg6EwqIYI7niK9+nokAdKqiVMR2DQQ56wGH/G8QVYzqHLJ\niIU004ad4USeSEblzHovopZnRpPZPZZkQamLkENGUjN0p4sL7Ug8R7nbSnc0w6VlBfQD7yOsuJQj\ncZF5I+8z3XYugZ1/hTXXcjhSYM9YnGvmlTKeUqlzSxyYzrMoIHFoxiBZ0JgbchBI9KHbSxDVLIbd\nC0c3Q8capMQkQj5FumoxFlNDFWQsapo7No5z79riSU2FOll8MUkKg5SQ10yOR1JcWFm01Wl+734s\nV30H8+1HMHUd28I1mJKFw//xLeb/4gfk9m9BPucL6DYPJ669mNbPX4S87DzM7p0ILcs5+Pkb8daW\n4L3rSXyHXkVoXgaCiNm3j+m2c0ncfjWt3/8BiXAHrtl+xEKWmdeeJjs1S+h7DyPm4gj5dNHMX80V\nN6KuIGI2juavAVEmJTqwvnY/yvqbGNHsVOsRpHSUib/9AV9zDbnzvobLyLDz3ItZ9cxvMWfGEexO\n+n71K6rWr0FZeg5CYgqteiHS7AgjD92Ft64c5fofoux6kdzJLtynXUT+6A4s89cgGBqG1Un3d75J\n2W+exSELCDtfBK2AVBLGaFpO5NffJ/DVnyFm46hbX8Cy4nwKWzeQO+9rOLb9BUFWGH39XepuvqX4\nv1vWjpSaRszG6XG30dC/kdiHm/BfdRMbllzJRcffp+tLn2fsx3/ijIn3mN21A09HG5a6NvTqBWSf\nf5B8LIn/9LNRxwcw81mkc7+EMnqI6Q3PELjmy+z9/K3Mu2k9tkVrMAs5MnXLsRgF5Eg/qVAr41+7\nmrorLwRg7M33uefB7Tw8/DqGo4ThO2/HHiohfMPXPxZ5DtnrqCRO3OLHLgtkVIPAbC+GzYNpc6Nb\nXcizo2TcFThjfYiZWdSydrrTEq0OFfQCs5KXbcMJzmks4ZmjU9T57KyscjOb0/Ef2IDUtJCIu45A\nIcqIUELJi7/Act0PiOWKJCpFFAg5ZFIFHY8C8uwIR8xS2n0SOWS6oznm+0zEXAIxMkCyphNJFLCq\naYTjWzlUcRrzLTGSjlI88X6GbTV8NBznyrIsgpZjxtuI18wwolopdylYBnajl7dj7n6VvU0XsbTE\nZEq3URE5iFoxlyMxE79dwiaLlEaPooaaeHukwMq378Zz4w+RZwbR3SEErcDujJsVWg/7rW3MLRH4\ncLyAbpgEHRb8dgmvVcL62v3kL7idibSGXRZQRIGy/DgRR/FYt8zr/D/LYv4PY/PYO5/o/P/uePrA\nK5/0LfyPxKPnPfxPx/9lsjr72HcBeKD2s3xnTS3Ga79m4NUPaXj072T/dCfWcBBBsYAooc1MI1mt\nKKdfjXFoExOLrigaTrt9yOUNmLkU2brl2Lo2kju+FzlUiaVpAYXufZinfRbBNJm573biJ0cpXzkX\nx7wlCLKCIFswMgkkbwAtMoGw8CzEwQNozacwc/9/Ev3CXfgf+yaGqlF5w82YmTimqtJVtpI5ySNo\n0QnkQBnqyEn06VGk0prisZvDjVnVgdm3r5gcRsaY+eB9nJUhbB2dHAyuYEFkB/2Vp1ArxkEUSf7t\nAXzrLkIP1iNN9SJYHSTL5mGRBCx9OzD81Yw+8GMy3/g13kfvwLztPsqGtyN4w6RCreR/921sAS9K\nqBQzm0auamSi9Rwq+j/AKOSQSsLkD27FcspFiIUsaHkA1LGBoqfj7BTq6EksC9agnTyE2L6KhKOU\n0aRKmyXJlOgjltfpjqRZ3+RHxETY+SKZrsN8VrqY56oOMLDkWrYOxrj65FOMnfk16swIUmoaM5vE\n9JVhWt1IiQlM2Uoq2ILNLJA2FT4YjHOxfRh9ZgJ97llFKk7fQUZffh379x8hr5lUzx4DQaTQe5D0\nyqtxH3gFoW0VaXsQZzZCn+mj3logIxWrwiWFKBlHCNtHzyAsWY8cGyEa7ED/B5JSN0z6Zgu0SzMI\nEz3kj+0ifnKU0DnrkbyBInnIHabgCHxM86ksjKN7yopH9+3zSS68iL8cmuCm6ZcZ27QTe6gE9eZ7\nqZ49xi37rVSV2Pn61N/Z0/llFm97GPu8FVBW9LjUDmxEi00ztec4NZ+5Fj0e5UTbxdR6LdiMPIKh\nIY13sc8xF900WezMIA4f4Uh4BZVuC/vGUyyvdDGR1pjJqizxCxyaFajzWcmoBvdv6ecLy2sI2GWi\nWe3jzw5hiruPmVzSUcbO0VmWVXp55dgkV84rLyp+w3kGCDCRKhDJFIjlVE6p8ZHI6Sy0zNBthpBE\naLAVin3hso2YoRDN6LRnu3l0KkhHyIXfodAmx9mRLC4iPrvMK8cmscgiV84t442eCC6LjCIJXBp9\nn3fLz+HscoEt0wJniP0MeNtxWyS2DM6yqNzN/vEk5zd62T6WwSaLJPM668wTHPcuoMqjkMzrhOQC\n746qvHJ4nHVtYS4cegnWfYGDkxnymsFkusClZQVM2QJHtyBVtZAKt2PX0tz85jCPnObhwRMm1T47\nq6q93PL8YR66bC5bBme5OrONxP7duG/4AYZsZfaB/yB8zU2oJ/ZQGB0kcuEdVDhEpNR0kWevFjGb\n/+UUIhga8Rcew3fBtQhajsiGpwlcfj2Fox9hbVmI7imyzUd++SPKTltRHItNQ+08xMgAJ+55kLqL\n12JZcT7JV57Ec97V6M4AJ267iY577mbPDV9l4fdupP8vz9F0y5cx6hbyzpyzWP2Ti3EtPx2tvpPp\ne26n9CvfxezdjamqRD7YAkDwG79ASkfRXSG6b7qGlsf+hjLdizpwjP6/vUjjzx/kUN7HPI+GPHmC\n3MFtZCen8V7yObQj2zDiUZKDYwiiiLXETXo8SmBFZ3FzqutIgXKm3ttI+Nv3M/ajW6n4wa9g18tI\n7Sswx3rQxgfIT0zgvPiLmN07kUKVHLvzFyTHUizd8Hf0D57GOn81ujPAxK9/SmjVUtTTPoc1Hyfz\n3ENYP/sDck/8iB2nf4Nanx37L2+h5pZvoHbtRF54Bj3fu4Omex5GTEUwZ6cQnW6OuufSJkQQ8yny\ne95l9L0dlHZ2kJ2OUbL6NAp9R8lOxVDTOcKfu5Vbq8/nN30voo90I1W3Mfb4rwkunYtSP4eT5Sto\nSvcSD7bhHSyCCBKGQqpQRFlHMirLKlyMJFTaCv08Nu7lxjqdvKcC20wfhjOAbvchmCZJ1WTPWJKO\nkJPwR09ycN7VLEsf4h2xnTOqbMjTJ0GUOGapo1VJ0m94cCnFFrlX4kHOaSxhNKlSq6SLlWQ1h+YM\n8kp3lIuHNyCt/jRSYhx94CjJg3tx1FRza2Y1t69tpOnoC4wt/BSVUpr3JwXONrqKbQipA/SElmGY\n4FREKowZMHRMqxM5OkAkOAerLOLs/RAClYw5agE4OpVmyWs/x/OVnxN/+Nu4aytQ1l7JS1N2FFHg\nvBorpmJHM0ExCgxkBKIZlWa/nSv/uIcfXdjBkh2/5cMlX2KdP8uOlJulFU6U2HDxdNEV4tn+ovjz\nusWfrM/qwwd/9YnO/++OhpLaT/oW/kfivJpL/un4vxRYTdUvp2TeMkr9XsrG9yAoCr7WWhQ9jewp\nlvQtc1YhWm0Ia65hv28+FQ4R6hZityjIqUmksgZ0fw2iXkAeOsTEKxvwXfFlhLJ6tGAjisuJmEsw\npLuoLHNgMdPYa2oQlpxH4aNX2d10CZX1jWzMhGgqdWE4A2ihRixT3TiXriYoq9itGo6qcsRQNbq/\nBnOqnzKrDqKM1rQSMZ+C8iZENYNc08ZI5Qqy/jrSkhNh89+Jd15OPtiINz+GUtWE0b4Wn8OCuft1\nfHM6Ebu3k9n4It5zP41W3k5CdCEGqsFiRxYFpMQkL6fK8JQEMLe9QcPCFuwBD8OeRhwVDVhmBrDo\nGWJbNuO+9W6ssonQfgpG9XzG0jrew28jeYNk9m/Duu5aRDWHFmxgQC7H7Q9hVs9lZ9JJjV1DDlXS\n62hkyt/CmGqjxCYxnCgQLvEyldERBYEKt5WAGkW3uLCKGpY5nZRVVVE1uJ1NljaWVvrw9n1ESewk\nRvtqpOws06XzkTf9GYuvhMc7P8+i266jO2cjZDGw6sayNOYAACAASURBVBkSpgXHa49gPf1KpL7d\nmN4yohWLCLvy2GvacMkgpWd4cLQE6hbSPHsIMVjFQb2Unpksrj//BN7fgNuhYrdZOJBxUs0MH0Qk\nrA0LsNkdSFoOqyQwlJXIqCZ+MU/QJjKBi1xJDd3hxdQxTmrZpxD3vcVQwxlINic2VPQ9b1FSXsYJ\nsYJwsh91xWXkS1uxvXY/c1afRa5uCRVNpThWX4BL0lEDdayoLUESJfZ45nFebi/S8gswho4hlDUg\nJSaItZ+Dq6YJp5Qk19/N5NobscsioenDSMlJJuxVPD2qsLzKQ5M6wnEzhBqoY/9EElmUWHrsGayV\njURUhRqvFVshQVayY5EEjkeydFb70A2TJnOSN8c0Xj82RZnXRr0Z5aEDKZbX+RlN5Kn12vHaFdqD\nNkwEynJjvDMt43conO5N05WSiWVVXFaZyunD9EhltARsvDecJeh14xrazZi1nL5YFm9pJQUd7IrI\nfFeepKWEsLPIKK/2WFgT38mUuxZBEKgvsVPhsZJVDVqkWeobGpBSEeKSB39ZJamCgfOZn5JsW43D\nIlHrs/HkwUkCDguNfjsuq4TX7WIkJ6H/Y0ssyhZkUWImr/LZ+WGsNpkJOUB75gSVfjdetxvF6SZh\nWpGq2nmy32RpSCGBjctavRh2H6VuO6tKVI4n4IOeCF9uVGmsqsASG8Y454soB9/APLSRyL4u+ldf\nR6jnAwbe3Enl5VchYtKdtVKaHSX1/guIGAgCxari4Q+Z/OgQ6fU3MPmj2ylbdxpGMsbo6+/iW3sW\nYi6BMdzF7MVfx1+IYCRm6H3kScwLP4dTKGCM9+C67GY+THlobakmX9rOQE7GeOt5ps79AnM+/xnE\nYCW+gER+0QX0pyWW33wJhUXrEQ9tJPX+i/jmz0GsbEZWJNT+Y/jOu4r4jm04pSSioSJa7YSXzmF7\nIUzeFcafGCB42mmcsDXRocSg+yMwTfRVn8ZVWYFgGAz96Ul8S5fiWHclzpYOLM3zsPtdCIoF7dRr\nMY98gFwSwvmpWxDyKXxLlzH75P04z/wUYiqCaHOAIKKsuZxZSwDLwH6k0jq8ZTYqvv9L5KH9mAvP\nJfH8I5x44Pc03fkTBh//A2NLz6dcncRaXoUwfBRrTSOuqmZq80O4y0sQZRmpvBHT6iJ9znWMFCwE\nfF7y4SYiv/sFtX4TyhoRTAMlWIYr5EQ58xqSH77HE1/5A4vO7sB53XeJv/sKsS0buereW/neUAUb\nzr6B9deuwua1kT/zJhSbjZzsRHUGEQWBw0aIjCHhkAWOTmcoc1nQDJOQQ6H0xNtoTSvpzHVBdBRZ\nMnkjESDodWOTBH67Z4yWoJOxZAGrLFJeUcqD++LMm9NBvc+Gc/IoanlH0a9UFOhNy1S4FcZTKgGf\nj4GkTivTTOOk5PDr/DVdiaY4KRgmO4ZmWT2/ESGfwrS62Wlto2b2GJMfHWTRVddik0WsDfMpGCZ5\n0YpmwAuTVi5VD7DLtxSfTSavm5S7FPKyg+m776Bn/nmUOWW2TZvU+axMOqrQ7CW83z/LigAURCtV\nyzqRhw6grvs8faWLKdOjNFeVgygRVHRMyYIgCshdHzBorWKRX+Sj8SzHppJcOq+c7vBiEASEe75B\naP3luLUEY2KAjdMSTqeLVMHArki0hFz/9sTmfycS+gxBR+D/mks39U/0ef5PRbO37Z+O/8tk1SIY\niPvfQK5qxy6omFVzENMzaG1rkdIRpGA5ZjyC4CtFHDqMpaIJ85l7sMxZjqhmEJw+9N79FGoWIA8d\nItt+Bq415yEc30qyahHJgolD0BDGuvE5FFIV83F2LEJxuWGil/TKq+meydJiSRMOlDD4za8QWrmU\n3oKbkDoNhgFTA0g1bQiGRmbX+8TeewPHxTcRs5di15LoO15GrGjAtDiRJROttJXdkznmdL3EVKgD\n7Y1nKQ1ZmHTX42uaCyVlHIlL9M3mCB/bhM0qQGUbikXEmBxCRkXa/SpKaTXm/ncQ4xPgK6V15iCe\n5CiFi75C4nc/xdnQQCLUQuDDPzL55ttMn3IVNXU+lEKRODTjqcOZGsP17mNYm+YihmsRl67HcJQg\nx8cA8PZuKbom2N385cgMDXX12AJl6AbUSElKrTryzhepVdKom55hg9jCua5pdicsNFoy6DYv2gfP\nsrdsNZUeKyUNraiKk4ymU1dbgRGbpN/bhvujZ3HpSSJLryL5+7tZefunyTafSk30EBFHFa5clHAo\nyEbvYlodKrmtr2DxB3DODqLHppHzCYwjWxDLG3AHyil1KjhHDyH4wuyOK1R5bdQvbMe97lL2Otqx\nlpQCAi+PGFhlEa9NQTPApYC57y2CdU24HHb+fmKW+c4cjl3PY9QtJOyQeYcGFghF8MO44aCybxNC\nZBBWX8umiEyT30bvV27EesGV6L/9Ju5P3YIFDefEUbSq+WRtJUypCt5CDIeWYqhgocZrJ+atJWgx\nYawHYmOILi+jSogZHASaOlA6luO2KpRMH6XQvZ/MvPV0RbJcWGfDYrGyOSIXzfMPvYynaT5VHgv9\ngTk47A5MINy3Ga1qLlsG44wk8qyr8/L04QnW1Plwx07S0dREUoelFW4cRpYpyc3ZjSUEnBb+un+U\n5qCTxsIQDl8IYecG5i5cwGBaoEbOEBVczAk58VhldhYCvH5sklPrSugY3shBsYqkp4qmIy9S2rGY\n/eNpan02BmZzjOYkKtwW+mJ5JFEgmJ8iUT6fjpCDKm2KkF1kz7RGW9CJXy5gSgqGK8TPNw9wfnYP\nMV8DQWYpaZ6PUxHZNhynymOn3G2letdTfKQ00bDjSU4EF2BTJBrFWeTdL1MqZ+ho78CWmiThayCU\n7OPhUR8r7LOMmB4MEwJ2mT1jadqCLnwf/pH7J8KsDahMGnbqpvfyjd0mDptCW7mbhdogo9ZyXF2b\nsWkp0h1nkapZQvXiNszHf87IB4cIP/Y8lld+idp2KuE9zzKz8W2mD/YycPF/UqlOotUsQkxFcPlt\naM0rqF3YSH7OWcihKqKvv0TwlFPY/eVv4v7qnfjtMpLVSq6+k1BYwpWdRq+aj23F2RgfPktl/1aM\nZRcj79lAwOsgNL8Z956XiL32PPmVFxH746/x2XPIdXOx9W7HbmY5dt+jhH/8KIWaBUg2F+rWF7DN\nW0mydA4lbh3JF0QIVNJrqcZVXk+tkiEwvBuhsgVjehjvsfcxho4hB8pBklEyEbLl85C1HCUL5mDW\nL0bKJ/lLxE+4tAJnuJJk+VxsW/+CHKqgt/k8wpkhxNgoQj5D4sABnMtPZ+iBXzB7zo2UWAyE6QFU\nfy0TwQ5sJWFsLieimiG/620YOYb1klsJlRoU2k+D49upO2U1+r53iW3ZhK28HD06zklfO4HtRba9\nIEqMP/U47kXLcLlcpA0Rf89mFIuMNnCU5Ike0p0XsTMmUTO4jSc86xjPywTOuJAzT/UgWi0oHh+W\n867HOLQF9+pzWVchsP7qldzW+VXOfeIJ7PERxHwKp91G+nf/i9yidbitEk5FJJgaoMFWQHD6cMgi\nOd3EUdXMUFJD91WiBuuxqSkCgRD7J9IYiCwud1M+vJ2mUi+zOAgoOsuaKottADVzOJj3UnbgBcRA\nObz0INXN9diGD/HgCYGpAlwWTHJCKKPVHEc7sRfn3FOZH91FyUwvSxYtIia6cCeGYGqAssZWrCV+\nWH89FSfeJKgUEF1+nBYF79QRss5SLLJEbdhHhVMmg4Vk3qA3lkMURcrOvhgDgRJ1hvqAC1EAb3qU\n+MM/ZMW6U9kwCqsqXUR0K5b9b+D0lyC7g6StJczkDepdAlnBSlI1ORHNUTq6jweH3KysD2KTJRZU\neVFEEUUSmBNyULqgDRc5TFHG7nRR67USGtzOmK0CRRKo93+ybQAz+Wlsku3/muvd3i0Mz47/X3ed\nUXvGP/39/mUbQC6bhU1/Innq9Xj2PI/UsoRCoBFx0xOYhRyiJ4C64grs093E/c14Z3pIvPE0mau+\nj2malJ7ciOguQY+OI9YvQDuwkakV11Hxj6PxnVTTGd9LbOOb+K75apH85PRjdu8gs+ACbJKAMriH\n5JY3cV76ZaTkFGYhy9eOenloUQEjOYORSSLWzkWYGSF/Yj/5qQiOa7+JlJwk+tff4j/9LMz6xUBR\nMW0qDh48rnNb/gMOtF5KiV2moX8j6lA3ytori3jT8ZO8Zl/G+gYPlpEDqOVz6PvqZ2n++X2YgoiU\njqIO95A8tI/Bdw8x5+bLkBafjWl1Mo2bkJhFjg6gBeqQYiOkN72I8OnvIL58H5aGuYjeAIav2Mdj\nHN/B3rpzWerKMiH6imbTMweZLl9Myf4NZJdeit0sYH70QvGZr70WVbLy18NTXD8viClKTKU1Qht/\ny9hpN5NRDX63fYBfnRaArq28EzydcwJZTKuLrOzkrs39lPlsXN4exiIJ+Ae20V26gqf2jhBNF7j3\n/FYc6UkePWlyZUcYuyKi6iaiAPG8QWV2sIi/9VYgRwfQx3p53racq7wT5CvmIieniFoC+I0km6ZE\nMqrOugYfFi3LtGbhwGSa5ZVubLLAi10RllYUqSatxijD1ipKnQpZzcBl5hD0AmI6yuZsmEimwKX1\ndl44meFTtSI9eRcjiRxem8yigMSuKY2fvnWclz+/GCk5SdIeRhIFCrqJXRYwn7+b2AX/yWRK5WQs\nQ7PfSYldoieaZZ1lBNNiR939FpYl64h6GhiMFwg7FY5NpzlHGeSQrYW5jGM4Azx9Ms+qah91Qoxu\nzcd4qmjsf++Hg3xvuR9TsdObMGlyagh6gQ3DBssq3JQ7RN7sS5DXDSZTebIFnTX1fgIOhb6ZLFVe\nG5IgMDibJacZ+O0Kq81eYqXz2Twwy8XBNIKWI+ppYMdokvagE4ciEtaiJGxBUgWDVMHAaxUZT6ks\ncGU5kS9CD3wKHI4U6J/N8inHENr4ADePtvLDs5vJqAZ1HoWcbvJO3yw90ymuX1SBRRLojeVoDdgp\n6GbRakud5rIN47x0YZCUu5KZnE6NOoEpiGyMuzk9UEAwDR7t1VlQ6mFZuYO8AY7MNKYkczBlpy1o\nw7r3ZUSPH9HtR4+OcU+ijdMbAyz15JEj/eieMnRPGRldwNO7Bb3lFLpmdUqdCn8/OsmX5/nYPK5R\n4bEyJ93FNrGZ5f4iCEI/uhW5eTGmJGNanIiRAcbLlhLa9xy5lVcxklApfe5O3J/7HuKJrZiaysPa\nQm5RtyE3zGc20II33o+YT/PUbDmXtQex93yI6PbTZW/BbRUJ73kWQVYQ3T5yXfuIX/CfVMSOkdu3\nme5VN+G3y4S2/J79Cz9LpyWCqdgRUxFmAm34R3aRrOnEM3UMzVNG5JGfETpnPWbLKsRMjOenHFzh\nmURQsxSqi3/P/0XF2512MifkIP3A7ci3/RKfkUSOjaAGG1AifWR3vsVs9zClN34dNVCPZeI4m9RK\n1nrTSKlI0dP12DZmFl5CVjOI33g58354O6ndW3Bc+XVyLz2M/fwbMI5tI9N1GPdF1zPz9CP4r/4y\nTPRx/P7fojzwNOEXfo73rEvQXSH0A+8jdl5QLAgM7SezZzPW5nnQsZbob3+IzefmyDl30DnwOoLd\nieQNEHvvVfznXYHurSDzymMkhyYRRBFbwIPzCz9m/Ps3UXn5JWhj/aCpZCYjJK75EZWHXmJs/qVU\nGVGmlBB+u4RyYgvq4HGsHZ1g6GjBeowdLyOecgViOkr6zafgqu+R1wz8mTFyG59GlJUi4lkQ0Swu\n5HyiCGExQ7SOfYjRtJz+vIVatwIfPIVS1YheNY+Cxc3+iQyKJLDMHELz17BxTOX0gZcZXnx1URCW\nPIbmq2JWKSGQ6OOoVEOr20Qe3IsRauDJQZGllV5EQSCvGYiCQL3PwqaBWc4ffY1NdRdzxsR7CAvO\npPDGY1jOuQHNGWTotquo/8wViO2rSDpKKRgm4enDqKVtZJ++h2w0jv+WnxRpcD//Dyru+Bl0bUVs\nXITurSD9j2qyoBcYy4mMpwoMxXN8Kpxh1FJOhTGDONlDofcQ0ppPI411gSuA4fCRe+tPKFd8C7GQ\nRuzbwxb3ElZWuUkVDNyKQDRnUJoeYMhWw56xJJdbTvK7WBVn1AeIZoo+q6sbAv/O3PN/O/ZFdnyi\n8/+7Qxb/pU3+/29jvn/pPx3/1wSr/n3oI93EF1yE881fIa3/EkgK3QmDOdkeDIu9SEkaH4C5p6Ft\n/AtyuIpXfWtY3+RHUjOw62X0eBRLXTvZ9jOQBAHb6IEiOm/fm5iZBGOdn8GhiPjNNELPDgRR4uSj\nf6Dxhz8D0yDz7rMIkoh93gr0phVkBQt2VLZNFAg5LbSJMx8LA0zJgtl/gMNVZ7Iwvhe9rBV6diLI\nCoN1p1Ox8WH2Pvgapzx5D0gK+cr5SLs3QPupRVX/eVcXVbV2L7qnjMKLD2BrXYhQ3c7s849hC3iJ\n944SWL4E0elhdv6FBGZOoHbtJNXbi3H9j/Hu34BS04o2OVj0NjQ00uXzsM/0ofceQHL7oLQOQdeY\n8DQR7n0fs3kFuZceppBM47vsBgCG7vsZ1d/6CcbxHUitncSclbhlk7wpAmBDY9dkgeaAjRJJ40jM\npMlvxZ6PISWn0Ye62FdzNkulcT4qlLHSPsO2TAmnikOQjtEbXobXKlEi5BFzCZL2MI4PnyS/9nNY\nRXiuK8oV7QG2jqQ4PX+I95R5nOHP8cywyEWtAWw7n0NumM9RSz2xrEqZ20LQLpNVDSbTGmUuhXB+\nAiSFLtVLLKvidyiEHDJ+skwbdpQ/fBf5S7+g57Lzcf35ZQ5PJYu9Uv3P07P0etqcKhO6je5olrDT\ngkUSaJ4uEr3as930uFrwWCQm0yozWZXOChe2Ex/wsjyfS1wTZErbGYqrtIhRzN7dzM49n4l00Sgb\nQeRgXGLB8LscrD4Lp0VCEYWiElbUizY9Wo73JwUGZrPcVJnkhViA1TXeovra0Dj53a8TfuBvPLxj\nmDvSbyDYnEXxYeMp3PjcYS6YV05H2MVMVuXUkACGVlxAS4oJcU80zUVlGlNyALsscPwf37NOm0Dz\nVqAiMpPVKbPqRFSZEMmP0aUD3nb+uHuEb5xaSyynY5OK/nvh/AQ7Mj5W2iLkfDX8bvcoty0rJ5Iz\neas3yoIyDyOJHG6rzOnyMG/mKjmr2oYqWrAUkqQkF57eLbwozafcZWX5+PsIsoWX7J3MCbloKLEg\nmCY7x9LopklPNMPnOrz8ctcU32pVGbLVoBtFEZ0ogN8mEYj1oIUa2TtdYDnDjHqaSOYN2rPdaJ4y\nNoyJLK/0cDyS4czUbsyqDhKOUnwzPRhWF7q3gt/uGeNrVXF0d5ik1Y/70Gv8WVnOM7uGee0LS9Be\nvBdb2xKmG0+jxAKWkQOMBBfAvbcxc+sDtGz9HfK5X8Tc/SpKw7ziO85RQtReRjhyFDObxEgnGfrb\nM1RfdQUjzWdTbUQRM7Mc+vo3mfv7x4laAgR7NzP52itEv3gP7cObkEJVJEvn4BrciRGsA0Gk95u3\nElrUjGyzkugfp/TMNUUMtdWO5A0gunwkmtZg/+AJpEAZ2shJNjRdw1XOQdSydtj3JnK4EkQJvbQZ\nIZ9GVDPMeBsR/vR9XF/8Kebbj2BZdAamJKMd24EgimR6TuC69IsIo8cxNRWhqhX9xG5Ep4cdX7+X\n9qtXUHLRdWAaH/fASysuJmPzoxrg2vgox/7wBnNuvgxh9dUMZEUa9Uk0XxXqc3fjXHUuptUJmoo2\n2IWRiGLt6CSz8x2sZ3+O3WkniTPXceYzdyIGqzDsXtQPX0Ba/yWk2REmHLXM5HTmzOwp9sgrDsTp\nPox0ErNxKSM/+yZWn5vgf/4SaXYEY+gY2fnn4Zo+galYOUI5cye2YTQsAV0j/tR9+K6+FcMdxnj3\nccSzbiRrSriSo4iZGF9pupLfHP0TW61zqfYWKVSyKGAdPcRseC5OM4fYVewLTh/chWfdZcRCHbi1\nBO9MCJxV48DY9GcKp9+APR9DUPMwcBAxXMPEnx9Fu/U+HtkxxE87cmj9R9hWs57V0W0fI1JH8FK+\n9XEsLYvQY1PMzj0fx2v3waV34Jg4iikpkIwwUbkCzTCpjh4kVr4I50dPI6y8HFNSsEx0FUW59gaa\nYwdRaxaT1AQAPKJKV9ykw5riYMbJfL+IsfFJpFWX8Ydejc/ML8U+08dfJz2srvURcsgom/+EYHOy\nq+58VsZ2sCuwgk5LBEHNEfU1ERjewf3RWm5Lv0t05WcJHXud/KILmUhp9MWynDb6FqLbx6GyNYyn\n8qzrf5HuhdcwN9PFa4U61tR4cOgZxIF96A2dH9vrKaX1/45c5r8d2yc3f6Lz/7tj38TBT/oW/kfi\n1gVf+6fj/zpZPfI+ZkkFMXct3sIM/YaHgxOpf1hbyGRUg1KXhY58P8/FQ1xertKHn6xqcHgyRY3X\nRk4zqPba2Ngf5Zq5pThFnV2TBQIOBY9FZN9EilOqPTx/bJrr5peiJMYR1AzDthpCDpmRpIpdFkmr\nBtUehaxq8FpPlKDDwrp6H3vH09R4rfjtEq+eiLKs0suGrkk+Pa+MMhI8diJPz0QKt03mB6tCPHwo\nzoWtYbxWkTd7Z1ha4SWnGcwJKGwfyxDJqLQEHPTHsrQEnDSUWHi1e4bLQmmm7RXkNIMqI0raEcYm\nmiCIiLk4708KxeNur4UPhxIsr3TjNHN8OKmz1pvmwS6Nm5ZWYhUhb0A8rzOZUsmoOn6HQqlDJqeb\nTKc1RpM5tp6M8rNOByNSkHs29/HTc5rZPDDLqmovWc3AIgqUJ09iKjY+ygVZWuHkrg8GuGN1LS90\nRQg7LZwZ28pU6zmUahFem7LSGnQyHM/RErATchT7m8ZSKt2R4nHr0wdG+capteQ0k6xmUJ8b4KhU\nQ7sYYUAsVmJLdz7F38Lnc0FzAPemx1Dq5zBcuZIKm4E8fZJ4sA2XliAuefBqs/yhu8DcsBu/XeFk\nLMNUKo8oCjT7nSiSQLPfxsBsgXnODK+PCeR1g0c29vLijcuYzevUDG/jWOlK9o8nOLO+hFhep8Kl\n4D70Gq97VxN0WKjxWplKq4iCQHuwiJ09GcuxyC8yqcq81DXFJW1hwk6ZLYMJAGp9NrqjGURBKCrj\nCxmyog3VAN+J90i0reNENMuiMieD8UKxgmkTMEWJnpk8XmsRMTiT1fhgYIYr55TyQtcUN5fHUUtb\nEbMxpPgE43/6HaH/uJuhnELV9scRXT6kjlVM2otig6CsIqWmMfr2Y+Zz5Ad7UUpKUOrnMPXqS4TO\nWY/adxT9wq9jnx1iQKnAa5UQBXAoIuKOF9CnR7GuWI9h86JufgYpVIkeHSfe3U/gy9/H2P36x8x6\n0ekh03WI2ZOjVHz7bji2hcKiCzl22fks/sU3obSeXqmcJn2cYUsFVUaUYTGA84/fpeSm72MeeJev\nTHTwkPk6uWgc+xd+gmWmn6knHqL0+q+QeudZ7B2LSM5dj/vIm4gWG7Et75G//ieUJ0/S72igRk6T\nfuZBnFfcipSOEvU1MZMrOi20DrzHput/xuk738CwumHrM0xt2kr57XdiWF3IQ/vZal9Aa9BOIDtB\nxFZG4PCrjHdcQOi9XyOf8wWOpS2UuYpWSf5rb8Xo248cqmSb3MbK5F62OBexqvt5xM4LGTK9OB//\nNv6v3gW6iv7241g7zyHub8alzmLYisIr6cRWhHANs75GsqqB+6W7cJ35KZLBFlzJUYaVMmqSPUSe\newLvrXehTPdiTA2Sn3MW0sY/IneejzDejdq6BuvYEXRXkKg1RHjyQNFTtGkZeXcZBd0s9iZOnSgm\noYEGpCPv0ld7Gs3J40SCcyjJT2M4SkibRZN1p6gjZOOI+SSFHa+DpiIFyoju2EXg63dREC3YUpMw\neAjqFqJu+TvWpWehlVQjHN1Ecu56PNkpjCNbkErCCHYngmIl9uZzqNf9iODANszSRjKvP4Fz6Rr2\nlyxjkTiGYBpMuRsojR5F95RxKOdhgTyNtv89lI6VzAZasL/1MHJVIx+F17JyYiP6kovIawbuSDc7\nqabSbSW0+RHMQg7bvJUYpc1FilrdfBKuShwfPokyZxXdP/o+nvufJqxOk3eVYuvaiNZxBrMFA/+R\n15Fq2jFnxsk2r8aWKlaiySbA0Lllzud4aGYH0uhRtn/xB6x84Y8M3XsngbkNZC7/DsGe9xHdJWD3\nkAm1sOeU01h13y2Ic1Yz+7eHcFaVM/T2dnKxLPPuu5vRR39F+PRTEWSFkZffYPIbv6HwqQtY9atv\noE0MseHLf+SqHX+icPIwk0s+TZnNRDjwJgB7Ks9k8bG/kx8fxdbYjrDgTBBlhGwcQcux78bbWPzH\n3zH267uouPLTdN3zEIWUysJffJtC7yEsTfMRbE6O/fAnVK1dgHv91RgT/RjxKNMfbsddU4ogiqRG\npwl96TsM3vmfOMoChC+8FEGSim42fYcwVl2JHB/H6NpOYv8eXI31RHYdpPT880BTic67kGDP++hz\nzkTc/zr5k8dwLD2NeHUnykv3sHPlLawJqKSffQj3qjOK/uT7N2Gdu4r8ke1g6MRPv4nw5AG63MWN\n4ZzyT9a6qn/X8Cc6/787vt79rU/6Fv5H4r/ls5rJ5rB0b0FrWc3h6RzzSwRG8xKJvI5NFrHLIj6b\nhDM+xEm5vMh8FmaKx1f+eoYShY8Tzf9aYD1vPoB20X8wnlLZN5bAKovMLXVR7bZQ0A2sGx9HPPPz\nRAoiyYJOoziL6goXEzRJQP7H0S6AIzWBoGaKhv9A3FOLb/Yk29UKOsNycUdnmhzI+wBYrPfT62zC\nY5F45USEL1i60JtX8u5wjjPqvExnNMqlDLrNQ1Y1cMgCg0mViWSBVZGtCFWtDFmrsEoiQUVjRpMJ\nz/aAJKG7Qrw6YnBxIEmvVE6dR0GeGUDQCqihJsYzBlXaFPd1GXx9eQXfeKOXb5/eSJlSYCinUL3v\naZT25ejuEO9PSZzlmSXirMKliMTzBtGsViQDNCXnNAAAIABJREFU1XhJFXQOT6VZHyrQpXppG3yP\nvoazaBrbjpFLo42cxLZoDeOBeah3fYXy//Ur5JlBTIsDoZDBVGykvTU4EyMkN/wB7zmfYpfUQLXH\nSlckw1pPCik5yfSLf8VRFkC54lt0xwrMUQfQfZUAyJMn0EKNSIlJUu89h/Xq7zCVMwlt+T1KdQsD\nTzxJzbWfRnS4maheRUXsWLHKsu89lOpmCi1rsHRvob98JWlVZy7jHDTKqXAX2wBqp/Yy+tSTlN9+\nZ9HwPtiEmE/Sk7XRIkwjJSbI7d+CbdlZvF2o5izPLIWPXiW6v4uS7z5MRjUITh8uiuxKqhC0AsI/\n7JKkyR60qvmkTAXntr+gTo0xdu7t1Pe8hTZykpNrvoJpwhx1gCcmfXyuKkfy5Sfwnn0Zv118HWec\n2E3j8VcR21bwQdJDiU3Bb5eozo+g+evoi6sE7dLHxtz5sg6GEgUahaJCN++pQDEK6G//nuj+LpK3\nPkCj00DMziJmZjGtTjRfFWnNxBft5oStgRZjHHP0BEJVO5tTPlZX2BDySTIWX9He5uAbyIEy3jWb\nWWcZQdALHHd10Mw0u3M+MqrB6bYJhHya2Fsv4L7qa+g7XkapbMQsby6amU+chNJ6zNET7A6dwqIj\nz6CfcQMbB+KsrfXiTIyQ9lTRFcmy2KshpqaZdNZRHv1/n/OM6MapiNgSY5wUQjQoqWLbiN3HYLxA\ng62ANDuK7g7x6mjR6iue16m35Hi2r8DV5VlMSeGGdyLce2E7szmdBiXF4bSD+bYEnQ8eZfetTeju\ncLHy5gohZmIYNjfr/3KSN5eM09d0Ls3ZPgyHD8NRwo5JlRqvldd7Inwxv52BjgupVzJwZDMnmtbj\ntUqUaxF6DD8ui0heM3FbRUKJPobsdUymVUqdCpVikqy1hG+/2c1Da7xsjDmo9RXBACGHzJbBOOv2\nPUriwjtwKgKjSY2cZmBXRLqmU1xq7cdwh9G7PkKPTfFzx/lct6SSWFZjsc+gJ2PBwKTVofJRBJr8\ndnpnsnQeforNbdewsspNVjPpjmbZOxbnthaRR/tgbZ2fdEFnfthBqqATjByl29VGfyzL2c4pTii1\nOBWRSEaj0qMwk9WZzalMpgqUu63sH0/w+fkhpHQUdJVfHNa4cn45ugEtYpT3Yg6CDgsORWIoniXs\ntNISsLJ1KEFnpRuHIvJGzwyXCF30l3YSzxXXh1ZjlL9OuFhW6WXHyCyXtAYZSaqMJnIMxXM0+h2s\niWyBjrVgGhgWJ4/un2RVdQntQRuHpjIsc6bZnXbSGd/LLu8SmkpsTGY0knmN5pd+yp7136az0k0i\nr1MwTBqMacYtpZSJGbZGij6gK/0aX/Wv4Ic/Px//LT+hOy3RkTuJ1n8Ecd5pDFKC1yrh1WZBsjBU\nsFJrRon84V64+R78igGGTndKoMypFMVYqQG0QAPCzheRG+eTLGnEHenGjE2gta5mMKVTLyYQTIPI\n43cz9pmfUe1RUA0oSxSBKuLMMK8bzZxTbaPvq5+l9TvfwjQM0h+9i2vtRWR3voV17RVsTvtZVuHC\nPXEYIzXL+HPPUnrH3XDoPWg/lfhT9+FduhzaT0Xo38fxqrW0T+0k1Xgqjv2voI31kzvva6Tvvg3Z\nacO/fDn6yiuRdr6A6HSjzj27iHZVIgixUbbb5hH8yeep/+2zWMaPFp0yYqPoZS0IhWzR01a2QNeH\nHLjzN5T+9RW2tXWyomsnFfv+TvLoEY5d9D06K10UdBPX8B4ApLbV/6Z05r8XsZHYJzr/vzsKif9v\nYGz/3VHaEf6n4/8aCnDwHUSnhyOOdgDa7FlGdSdZrdgHWOpUsA3vxSzk6A0vo3F8B3t8S+lUu1HD\nLSSwMZ5SyWtFIU1LiQUxF6dg9TKe0qg59Dz98y6n1mshmtEoywzC9DBjtafisYjIosDGgTiKKOC1\nyXTKE+zSylhROI5a1o5gGnwUgYVlTuyFOFujEnU+G7oBtWKcTdHiC3Y4ni0y1y0j7BRqafbbSRV0\n/HaZVMHgyFSaUlexGtMTzRHPa5zrneWkXHxheyzFpFw3IasZ2OVicpzM62wfSVDlsZLM65S6LMxl\nnJNKJTZJLPZTCXxs2L6iysPusSTnVogcz1hp9UDCUPAIBYQj7yNWt1EINNI/W0CWoMZjQZ4dZXva\nyypnnDGllMqR7QxVrqQmP4KQjFCoW4acnGLjjI2+WIZzmwL0z+ZwKBJL5UmMoWMYC89jQ/cMc8Iu\njk6lWF5Z3OHaFZFAfhoxMwu5JFrFnGL/VY0L9fl7sV5wE0/0qFw/L8iGnllOee4HuL/9a946GcNl\nkTm7VEeKF8lkmVALtsQYhiuIoOXJKm6kDffyY8/l3LSihlpijEt+Sq0mM6pITjOomdpLf2gJummi\nG2CX/x/u3jNIjvJs2z46TJ7ZSZtzDlrlHFFGQoBEzmCwAUcsGzC2Hxvb2MZgPzY2GHgAA8YGAxIi\nSyCEhHIOK60255xnZyfH7v5+DC+/XK763scuqt6rav7cVbt910zP9NV9nedxChzp86GTBKx6mQ3+\n4yn4tyByLGSnxGkkN9zL8Xg28+v/Tteir5FjlXnyWB8/naVDUOL06POxf26gkJJR/JqeQ71T2I06\nyp6/n8iDz1Bs12McqqfRXEW3N8LCPBvuuAet5Rii1YFSvpj+qESfL8pKeRDCUyRH+hBmrgGdEdQk\nLRET04QxRgw5PPpZJ09cUcVH7ZNszk6i6Yx0x42UClMkDm0ncum3ONAzxWVtr6dMgYC+tJbOghWU\nGOIERDP2jtQo8v8wNYt/+QTxvX/n0Jx7WN31Dv5ldyCJAo9+1sVja/KRAqMoadmIwQmQdIieXmJF\nC1Df+R36y+6m+0ffoXDzWsTVd5D84EkMq24kdmAbk43dZN/3U8I7X8I8cyFt+avIT9Oh++jPSJu+\niRjy0Cu4ybLIjIeT5BlV3mz1s+nQE5i+/htM422pnHBTFJoPM1y5gWxdHE02IMYCRN97FkNROeKM\nVQhDLSilC9GNtrAzXsyMTAu5RhW/qsOu+GkKG6mxCzxTN8E3fbtpn3sbVU3voIYDqXQsJQld51Br\nLmHquZ8TuOs3FA8dZ7s6jVKXGYdRJsMsY+86wofSDC4PHCPR04x8+bfQZAPJd/+AoXI2WvlC+n/x\nPSYf+B9musQUUP/Aa+hnLMfjqiLt5JsIso66siuYcexZ9Ktv5nTUQe2+P6JzOJBX3sR7QyJX5aok\nPn0FefNWJG8fgn+ck+YZLFY6abNWU66OwkgngslC+NQ+DJu/hXZmF1oygTx9WerzTcSInfoE45xL\nGM2cjevMdrRlN9HjT1B0+lXES25G/exvJLxeotf8kP7brmL662+hG2tDi4Xx7v2QsTNtWP/0BlkG\nDY7vQFNVpDnr0QQRsb+BsV3v4+8Zpmd/L6H3dnF5//tMnqvnwnW/YM3QJwx99CmdH7dQdfVM3D95\nBsNIE4nWMyTGhlKSJ1nHgbSFrDKNoRrtiINNjBYuI9vXjpKWiXZ+L3J2IYmBTmLLbsHafQzNkU2z\nlE+NOoSmtxAypWOOTjIuOclo2oVQsRBhuI3xomW4xBhi8yGiMzZg7juDFvKjli0gvus5pGsfQj/a\nijLUwQvM5V71NMmBTgwLNxA68B6iXsa09IrUA5GOegRzGv073qf0e/cTPbsfee3tSGMdBAoXYvjs\nRWJr7mbqV98k74FHCL33Av7uYfLvujuVXuafRM4qINHXhmhzoqkKQtk8Gu69l7Iti4kHQthvug8p\nOEHd1h8zfest6EpnEO+4gBaPMr70K4Qfuo2ye+5MpZctX45oshC6cApz7WwiLfUYr78fBBF1319B\n1iOsuROOvIkWDaWicwc6SQx2AiAazQS7+4l6/JhzXNhv3kr045eJTQXQfe3X6CUBUUkwGhMYuWUz\nsx66E2HeJoRYkOjuV5AMBozz16LFwiQLZiOc303zn/9G+dXL0K++meSZ3cjzN6Kc24NgtKSwk7KO\neFcjCX8YX88wOT/5I7rxDqK5M0moGvr9L6feo54W5HV3ILSdQPGOcXHa9czRTZCs24sWj6bkKHoz\nyYuHGNt/hPy7v0nSVYjkH0U1pq43X7YMYGrY96Ue/99dK/9+7Ze9hf9IXfjh3n+6/i+b1bhvgu6Y\nnh5vlPw0Ix+1jaXYl+MhTDqJablpRJMKX52TwwM7W3l6TTpPXIxwZXUWF0b87GkeI55UKHJbONww\nyo5vLGQ8nGTb+SHuXVTAVFThkd0t/PbKaRzq9bK0wJHCyCBwetCH1ZBC8hhkEVXVKLCbCCcUPOE4\n2TYDRlmkZSLEia5J5hU5ef1IN19dVca20/08uK6Cdk9KCP+XPe0YTDLv37uQa146w1PXz6LNEyI/\nzciBbg9Ok47pWTbebxhhSYmLQX+US4pcHOqdJC/NyMdNo1w/O5dBf4w+b5hbZuWgAaGESu9UFKMs\n8sS+du5YUoSqalS4LUyE41j1MtvqBrl7cSobvXk8iCgITM9MITwkEQ73TuE265mWYebCSJD364dx\nWfR0jQf57qoyatLNLL7/fRqfvpqnTw6wqtRNQtFY4kvp+toVRyqa1CCy//MnYKeHgvx6ZxP7Nmjs\n19WyKlxHU8YivJEExY7UmFwvCQTjCk6jjKppaMCgP06Rw4AkCKTLCfyanqmoQqEFWn0qVXYRVdJR\nNxLik9ZxfqzsJ7ziK5gljS6/Qv7Hv0e74ceYkiEmMaEXBd5pHmdlsQtZhHBCYzKSQNU08tJSgQai\nAAKpxrndE8VmkPjD/g5+sr4Sh1HC3bYPT+VaeqZilDmNjIeTKJpG2ZHnOTHnLoodRgrjQ/TpU4a1\nfDmCarCR1GAqqtA4HsZlSqVD/R+pR4ZFT5UuwAvtCVYXuzHpBIqCnUy5K+mZilOQpkMQBAYDccqd\nBkZCSUQBrDoRq15iZ/sk83NtWHUi42GF4/1e7qg0sW9EY16OlfSJRtpt1ZRPXqD/ry8S+N5TVFoU\nRh97AF2aGffyFZzMW88iZ4Jh1Urm8b/hPd+Ao6aMkSPncNUUo3e78Da046gs4OyfPmLBwc8AaPVE\nkUSBAlsKNyWd/QA5uzhldEnLRjj5Dr4zp75gacr3/R7d67/CkJnO4N4TZMypxJCdzdC+Y+RfuYGJ\neTeQM9nIRyu+xsbT20EQmbDk45SSjMclsrsP0pSznFp/PcGC+QjbH+P40u8wPdOCY/cfaVn+HSrd\nBsZ+fBd6m4X0+dPx1DWR/qOnaL55CzXfupGu194j9Ou/IwoCPVNhrsyD8I6n6b/yh1QZI2zrTnKr\ntZc6Uy1ZL/6AtOIcLGuuRbHnQOMBtFkbUPe+jLTyZiTfCB3mUsri/fQZCyn2t6JY0xmV08nuPcJ4\nyQq2N46ysSKdIpuOqbiKoqa+a+PhJJIgYDdIZBDgQiB1DhbZ9YyEkuglgSK7nu6pOJoGKhqhuIIv\nmmRBrhVJFOieitHni3JpsY2LE3FsBgmrTmR/zxSzsm0Upuno8cWpdhmYiqduTDQt9VuhEwWCn69J\nIpwe9LOy2MFkJIlOTN3ceqMKcUWl3GkgqaZMbZNRBadRSiVoeROkm2QujoXISzN+YdApc+qJJDUy\nA13E6/ajrb+XxOu/5tVpX+Pu2dkMh5Kkm2WiSQ2jLCAAw8EkBlng7FCAaZkWiqwS+uFGeu015IkB\npLFO3o6XUZNhIZ7USDNIJFQNSYSKQAvxzou8l7WRa7PjdAluBv0xMix6Kq0aDVPQPhnieqkFNauC\nvR4D6+1+WskinFAw6kRePzfId5cV4Tz1JmPzbyRLjtMalJBFgWyLjC+mUNB7iNHSVWR1HfgiPWk0\nnPycwwxuA4QUAVUDSYDuqTglDj3BhEpc0ciyyJxfu46Fn3yIGAtyX+YlPHXsD7Q+9RKVz78GooTU\neoR4Rz2iMxNp5iq6fvYDCjetQFp5M4133UHW/DIOP3WQ+TfPJGf5XGITkxhzshCMFsaPnCT9p8/Q\ndsfVVN52GbGREQYOXmDaIw+THB9Ezsgjlj8bz2P3kb5sMf5FN+H2d3H2nq3Mf+ZxfHveSRnVPCNI\n6bn49ryDbctdDDz5G/Ie/BWxPa8Q6Bsl8+Z7mNr5D2y3fB8hGaf9wfvQWQyU3Hs3osUGwMj213DN\nn0P3tp2U3X0bgbrT9Hxynpo71jN2ppH8rf8FmorgHSJRthQOvsrQJwfIXj4XfXENozs/wF6ehyjr\nMCzehCbrSdR9hq6ggu7nXqB061YwpZHsbuBN2yXc6NnD2V++QuGqSrK/81+oDYeQy2bR/NOH0VkM\nFD/xV6TWI4yXrQIgx/Hl0gDe+OXHX+rx/901f2vhl72F/0hV2Gv/6fq/blanxhBDHlrFPCp0fmT/\nCP32avIGjqHmTUPQVMSojya5EEkQqA42kcipZTIhYtOLyAJMxVW0z59I5p3fwad5m7g0V0KaGiSe\nVUXg84QiS2SCsDmDwUCCwjRdCsRstCMFxhASERRHXsr41H2ByOwrU+7ilmNQsxzV4kYMeQgbXRhF\njbapJOVOA5KawJcUUQGXGkBIRAmYMvHFFPI1L6/2QrpZz7wcG13eKAszJKS2o5BbmRrTBMdpEvPJ\nsepSIyJRpjVioMxhIKFqGEQQ1CR+RWIymsSqkzgx4CPdrEcUBBamC2g6E9rnrs+0oTqSo/1ocy+H\nk+8iVS9C1Vt4o1djaYGDuJJq5qrcRo72+7nSHUSIBhhzVuE48AL68pmES5eiV2LIgxfBYKHFWE40\nqZL5/AOcuenXTMu0YJREgnGVKnWQ3X4Xa9u3fWFWagnpkASBAX+UuTlWnJ2HaM5eQjyp0TMVpthh\nJqGqFKQZyCDASa+OHJueuKJRZlE5PJJgtW6QpKuQxIdPY1q2mUfbTVw3I4fig88Qm/QRvflhrHqR\nkWCSfn+US8yToCaZtBVj0olYhi+SdBUixD9PMRIlhGQMeaILzWAh4i5HFlL0Bmm4hXb3XAIxhZld\nH9FZfSVJVUMQwG6QCMRUKkxRmkJ6AjGFhRkSXlWHcdujfLTgWyzIs5NQNfI//j2Ga7/HqQmNcEJh\n0cE/8UzZnWyelkXN0BEUn4emp15jxsNbactbgcuUoixUd36M4h3jVO0tLDN5uKBk8aeDHVw/N58N\nY/uQi2vR9CYatCx2tYzxQOIA0pz1nI3aGQvFv2hqOr1htnRvR1z3VSaTMi4pgU/VEVc0ggkVmz7V\nQAXlNMIJlRZPhOX5VsJJDeOeZ+lfcS+SIDARTjDXoRIQzdjUMIMJAxpQoHnpxYnTKOEcb6LLVkXe\nyb9TN/0m5jS8iW7OGqLOYhQNGsfDLBIGeLzDRInLzJVVboz+Idq0DL79Zh1b11eyqchE3aTKrCwz\n4YRK03gEh0nGJIu0e8IoGqwrsvJBh48cq4ElvtOMFC0n3Sig6znNa5Eybs2NMqjPIVsXpycq440k\nybfpCSVVSvRRRlQz+RMXaLBOxxtJEIgrrB/8CHHWWkKmdPS7nmR83X10eiMsH93PPvdKluTbiCsa\n7slWuiwVmGSB4WACo06kWpyEvotoJXP5cFjEqpe5pCiNo/0Bqt0mcqdaaDFXUjV5Dl/BQna1TzIt\nw0qaQWI8HMdl0n3ReN5QaUPZ9SznF93Loe5JNlVlUqv0cTCWzUrDCPjGwJaeSkDSmxCScZK2TC6M\nhlE1jXkZeg4PRVmab6PDG6MqDYKajqP9fjaledgXymC90I4aCRFtOIF29Q/Y2zXFZppSUaHeFKFi\nascLdGz5CQu0Ph7vNPPVeXmkx1MymERmJV7NQMZUBy36EnKsMiadiK5xH6IzC03S0aQvBqA20k6z\nqZKqkWMkBjuR529ECnsZef0l3N//XcqU5O9H05lQGw+jLbqGQELj9Ysj3DM3l+aJKFaDSIkuzMWg\nkTmJDhJdF2HhFnQjzfhz52COT9Eas1DVsxdqVqAabEgNnxKqO4HtspvxOcoYCSUZD8VZnCEixoKE\nTOkkVHAG+9H6mxFtDlpdc6kQJ6mPOZgTb0MTZQIZ1Vhb96P4PHTXXsVUNEnXZJhrfQcRa1cQs2Rg\n6joGFienxWJybXryxuqIFy9AN9lLs5DNZDjBkrGDfHfpA3znllq036e0cdO8dSR6mtEVVqJmlpE4\nvIPPpt3OtAwzeSYQIj7G/vwImfc/hiYbaPWlbiaUtx4nfvVD2GKTJPe9ynuVtzMt04pVLxFJqriM\nKbd2x2SE6rcfwblkGU2lG1NmS1eCQdVG0WQ9va6ZRJIa6SYJ8W8/4/SlDzE/15b6/Un2EnCWoWjg\nmOok4i7nxGCQpfk2BAEEVSGkCIx99ybKf/sUXkMG9tPbiS65CdO594l2NGFZdz2ebS8yefuvUNTP\nGwJzHKHlCGOV68kZP88BsZLljhhC9zmSg51IWYXscSxnY7QOLacCgOe7RVY+9W2cz2yncTzMygIL\nQjKG/6VfEbz9l+QaVX5xcJA7F+QztuUyjNt2MqPlHaTZawHQZRb/rxuZ/00FIv4v9fj/7hKi4pe9\nhf9IWZ3/nMf7rw1W/Re/SNFwXH4zqtnJznEjZS4zb18cxmqU2aq/gJxZQKethkILaKJMQgNJEIgm\nU7npeklAFKDTG2Na126Yvop9owIb9P3sjOazMTOJYk1HTMbQJD2eqEoGAdDUVLa6ZiXLoH0xzlQs\nbj7qi3FFZowpYyaO6BioClPmHCw6kR5fPOU4HzrLfw9ns6rUjSQIJFSVcEJlQW6KOadX43QEBGoi\nbXjSpxGMKzz2WSdbV5RSniYwEhXINij4VR3ukToUey5ixIfmGUS0uxlNn4FVL2L2dEAywVR6NVZR\nQZV0HB8IcEngLIIjk0TrGc7XXEdC0VjkUiAZ52u7R3hi8zRsR/6WQqz0nEMtmo12Zhfjc68nUwhC\nwwGYuY6InDL5FKbpMJ3agTB7HbtHRDbZvQi+UVR3ET1COgCWFx4i4+4HSRx9NyXGt2dyf53E1xYV\ncrx/imkZVip3/ILzWx5mRf0r1C+8G50okpemw26QONofwG6Qmav2Ety3g8NL72NjtA7Bmc2UuxLb\n+Q84U7CeeT27iS64BlMylDIHqEkOhFwszzMjtx4mUbMKQVXwxvli7NeYvwpFBadJIh8fx31GVmgd\n1JuqqbGp1HlT583HrWNk2AxcNy2TNCGOT9XRPBFhiTPOGGl4Ywp5Vh32wbO0O2fRPB5iQa6N7HAv\np5LZDPhjqJrGdRlBBCXOB750ylxmShx6Tg4GWdH/Mc8ZlvPtUoUmLZPaeDexrGpETUFMRJBHW2m0\nTSfbosMZ6CVoL8ISHmNUTier8zMEUUKNhpicfjnpwT4+DbhYH7tAW9ZiKgcP05SznBrZy4Q+g6zJ\nZjzv/wPrNx8jGFdx9R1HzSxDmhqk2TaDmlATf/HkcFdZKuJUmuhGs2cjRnx4P9qG76afEUloJLfe\nSM3WuxCLZ+BPK8K0/0WkFTcgj3fS/cyfybtsDXJOMUrZIk6Mpi7KQslsxGiARGYFMU3EOt6KoMSJ\nNZ5ATHMj1q5A8g3RaKmh4MPfYpm1kNiMDcQUjYaxMMvFPiJZNYiCgL7lAFo8yvPqLDaWp2N/7WfY\naqcjLL2O+Dt/RH/tAzRPKVRf3I4091KE4TZaMxcjCFCuT2lOZ6u9aJLMkLkI18dPoL/8G/hEK8cH\n/BTajeglkWyLjK1lL4HqddjCo4ieXrx7P+TE+oeYnWUhO9IPgshL/Ua+4v0EfcVsYheP0jTvLmZY\no4R1aQwGkqnGbd/zqJEQuo13Q8tR2ovXUzV2AqV8CUL9HrTpazk1rlDw/P3k33IrE8XLyJxoJDnS\nw3u25VydHWfqH0+SvOvXjIeTTI+2k3Tkci5gZI5bQh7vZFckl405MKBYcBgkQr+9D/esKkZXf5N8\nZQL14gG0ZTehG20FQUhJKHR+xHiIHl0uBUYFMeRhVJ9FdnSQBi2L6cIoqsVNUDSjaODydbI3nEWO\nzUCFK5V+JgkQVVLymXQpRkgwYu89jmC0MOCaTnbrJ2gz1iF7+2iSCymx6+n1JYgmVWYHzqO6i0ie\n/JDYmrtRNTBLGv0PfIWSe+9Gy62iS3BTqo5/cSMpKHGUvmaoWc65gJHpGSaeONrHylI3i3IthJMa\n+3umuEpo5rx9HpVuA5aRRkLZteiVGM1+aBoLcl3wCEL1UjS9mSfPT3Hfwjw8URXX0VdSkg2diYm3\n/orz7h9zLmBkgTiIqrciegc4pp/GUqGXRkMZNbIXTW9m/yisU5rA4mTSWYFd8TMl2nAFeoi7y4gm\nVaTtv8FYUkmkowXTTQ/SFpJIN8k84qzlzyP7U5Iaey6q2cloTCCnfS9Sei6RU3voWnUfVcYIiDLK\n4W1Idjejn3xK1mUbmJx+OUZJIG2kHjUwheDMRjNYSJz+GHHNV5j4w0NM3vs7yo48j27FtWiygXHJ\nie2932K4/gGkzpN05yyhWB1DjAbwfvAqjmvvRvAOoSkK4XNHsK7cTCKrCqnlEGr5IlpCOmonzxBr\nPMXFF/cw++238b3wCPa589CSCVSfB/3SK0m6ijnYF2DV8KdoC68iqgqcWbSSla88jOIdJ9DYQNqc\n+aAqSM7MVDLi9GVEMquoHwuzON6CYs/l1X6J27Xz9JesJprUGAnGWDawB6lyPoo1A3myj23edK5q\nexV9+UxC549jW7WZA1oJS9t2IM9ahRCYSP22fcmaVd/I/1sygLPK8S97C/+RWpO38Z+u/8tQgCnR\nhi63nNCM1cj2LJ696Of2ggQTmpnN1ems0A0zlrcQq38Ah5wkeeAN5JgPKc2F3H6MLkM+BYkRJIMZ\nw0QHLTELTYYiKmUfSaMTlwEqLArahX3IgTFEg5HuhIn9PV6m57q4b3cfFXmZFNhkdF0nEOZfwU+O\n+8nPcLCo9S0OW2YzFVUYVYzk6hOYYlNIPXW4HDaispntw3ru9n5M9sxFWPUSaQaZClfKEGEZuoBS\nf5Bhdw36d/+MuzAHi93FSIwUwkinY+vPd16RAAAgAElEQVS7DdxYIjGqmhjVZ9IfN5DRfQyUBIJO\nh3GwgURWBbISJ370fYz+Yd6N5FDT9C51hlIahUyqx+t427WWpQVp/HZ/J7u7glxebGRPf4zrcqIQ\nC5LMm45y9B1GS5Zjd1ixCEm0hoOp+WFBLYa2w2TJUeoiVgrUSVrM5SwT+oikV9BGJp3XXIt8wy1U\nxrph7S00h/TkFheBLR3Fmc+nnVPcbu3iQsKJy6Qje9UmBvwxRvLmMiPTTPZnT6NvO4ZcOpMCp5Xh\nUIIcEyTmbiLHqqdbn4tX58JuENGl5zAUk7jloyAmu5GkZCDj6N/pLFmDomlogojNnUFvWCDT00xT\nwk7rVBIhpxJFg/rRAG2eMHHZgiAIjOsy0Eki9RMps0dShRvtI9SFLSwT++nETV7fUQaNeThsNiYi\nChpwuNdHeVk5bp3Cx51TzMiy4pXsNIwGuaZQ4PykyvTJOv4WKmHTiacJ1KwgJ9LPoGIht3YOcRUK\nx+rITDPSLBdwcTSETpYZi0l4jFmon49u9wxrSKKEYLThj6s4cwuRguO0569kMqKQ4+/krSE9s2dM\nJ7t9L73Fqynr3M1AxkxEYFhy4a9dRUbDTgx55SjuInSebhRbJpOChQl9BovybJhGm5ASEWIFcxGT\nMeLHP8R47VackVE8go2Km26CjCI0sxNDMoxO0ginV9CuualYOgdK5qA5cmgNykSTKjntBxBLZxOw\n5XNsMESl7CdRt5dg7QZM2flow53gH6Mpc3FqNL5gNbLdjaQmkXb/D/7iReidWcQUjbqRECWmBKHS\nJSx2JLCe2EZ4y/2IxTPZ1xckt2k/w1UrSTfLxD56HXNWKobZkl2IKAgosgmHUaJLScNt1mGRQY77\n0fJqaJqMs8IZI3uqlQzVT5tiZ8hcSGHnpxyRKyiRAviX3ETNsecYzJuPy2YGSYcim+hz1pA3Uoe4\ncDOywchEUofr+Ku4auZhqP8Yz/wb0TcfwZCdT2fmfIoPP0dHzVWYdjyOt66e+OLLqfacwXrFHQiS\nyIBqA3sWht7z1BtKaQ/LzF69Gr8qE4ipZEcHmdrxEqVz53DEI5GflUG3X6Fs4CgfBFzYjXry11+O\nTovhkBKcv/c7uEqd+MuWYA0OgaQj7cw7COULEHvr8TuKSNPBkUmZUqeRyT//nPDc9WSKEYTBZrp0\nORSGunh7Kp31pQ4yG3ci9NZj0anokiFMvWcxdp5CyK/B1H0C1edBDU5hajmInJmPpCb466iDS9Vm\nBFsGvqRAOKHgs+amUF1zNrGz088cz2lEUcOx5jKU9rMEy5cRTqq4EpNoXeeRjCYu6kpxltai8w1g\nePMP2NMtVFTV4DbrMEsaxuAIrWEdhuxSvJEkcQXS/V28NWqmJjstxSX1nSN0/iSyGoGhNpaVpxPf\n/RLm7jP41nyd+NvPoreZMW28nfCOPzNRvpS8cD94h2lwzcNulEmPjjBlzKQ7qiNPGad44ARdhZfg\nkFUmNBPmA3/lpHkapcY47/YrzHQI+GtWYUnPQutvQKpZgicObpPElq3XcV/2alZM1+M/uo/Iyb2c\nzFmEmFuFc7QBeeYqumMGbG//N5FZ62l3TCOj6wjODVej5VTyVmeEOTk2YrtfQZ67jt7f/YLA6ltw\nEmbkuT+QuXY1kRf/gHvdZbRYKplQDJybvojal15F130SX8kyssO9HFx/G/HW01hy3dSVbcR98i2I\nhTAuv5J2YylNnhhFDh3iZD+u7hOppjQcJP/+nyK0n+CjqhuYVlGCzmwmOW8z9UEjCCmZV0FhPh1h\nPRowe/MccOXS8cenybpkIcNzb8A23Mj4tE2YB+uRMgoYwEEooWDKKEB/YTfTPedh4RacwX5cg3VE\n0stwjzYQu3CE2NmDCCuuY3qaipRXzllDBRldx9Fn5dItZZE/eJpXmE3FqTeJ93ZgnLf239/Z/P+o\nRDyJIIn/z7yOeg4xFZv6f+41N+P/grPaOOz/ImNc9vTw7KibTIuBWdlWmsZCFNpNZFt1BBMKXZMR\n1qYnQNazde8wTy238v2jITZOy0InCph1EjMyTbxcN0yGxcDb5wZ4a3M2h/0WVA1yrAYC8STH+rwM\nTEb4jekkx0o3440kmJ+bRo7gRzXaeavFi1knssUdYPuohYSqkWaQubxAx4d9cXSSiCgIzMyysKNx\nlGA0SWWmFas+pX/NsRlYka1jNCFzsGcKgyyyucLJe22TrC918vM9HcwtcnBrVRpPnZtg62wHHs2E\nomqcHPSztsRB2kg9O+PFzMqy8kb9MA8Veum115Bl0fGbA908vCyLaQ/t5/hvNzIaTnKyf4p+bwSX\nVY8kClSnW2kcCyCJAjdMy+STTi9LCuwUGBVC6Dk2EKDfF+FryinaSzdSbtOYTMo8dbSXBy8p5pd7\nO/nd2jyikok2T5S5Wh+KI49HT0xw//IiDIKKoCSQfIPs8DiIJVUuKXJglEV+uruN5yuGeF8/h0vL\nnDSOR6hwGbH7ujmj5FDtNnKoz8+GIgtCIsLPj05w29x89JJAhlmiP5DgWN8UOklgcb4Dp1GiYzJK\n03iQO2tsfDaU4FCnh+tm5jKbfn5wTsCsl3hwRRHjYYWzw37SzXr2tY2zdXkRroSXTW/2sOvOGSRE\nPaeGgqgapJt1FNv12MZbGHFUccmPd/PoNxfTMOSn2G3m44YRbllQgEGW+OOnbVy3sIDbZmZx5fOn\nePG2ufxufyf3rShJjf5cSYZVKza9yNrHD7J963KKDDGEeBjF4ua99imuKRDwyQ58MYUsS8p4d9VT\nxzj4g+U88lk3cwpS758vmmR9mZNXzg1x7/w8rPU7oXYVL7WEWVfqJnffkyjROOZLb+ZUIpP5DgUh\nHiZsycL362/hqCwgEYqgqSrONZtIlC5G8o+g1B8g0tlO2sYbUCcGSPS04OvoxXXfb+DU+wCI05Yx\nqs/CfegviGkpwLYwdyPKoTfRFVWjhgOo3jF0hZX0/OUvWPMycN31A5Szu4l0d5GMxlC++mtcasoI\nZbrsKyTsecjxIPGdz6LLKUaumENwzzbMV3yNXjGd/LNvICy9jslnHibj9m8xYSvG3baPQPU6TPue\nZ2rVvaS37yPeUY+65QGCTz6I3mbGccWtxE7v4fDMO1k1/CnCrLVokh4pOA6aSnDXq9iWX4qvYCEA\ntsgY9Najlcwlue9V5PVfQTG7EBNR5Kl+Bp99grSfPEPaaAOhox9hWbweTW9BNdrwmrJxNOxCNKeh\neMdILL4eXTKCGAuiSTLyZF+KXXriHYS5G/D97b9xLF1Jfe4qqtwGzEMXCB7+iJ4ND1BtjiFNDRHJ\nrCKcUHG27eN81gpmG30kj70LgG7RFTDWjeIZQVlyA4KQuq+UEmHGk3rMOhHjR08S9wWI+0O4b7wb\nIeJPGap0KmLIk/oDTU1RGLTUbFaM+Ei6Chn45f0U3nYrgslCoqeZPcVXMzvbSpZeIfbWHzBWTofa\nVSiH3kQ/ayUeRzkOItRddz3F66fjmDsXLZlA8Y7RufI+qrs/Qcotp9VUTrkpjhj2krywH9XnIbJp\nK47Bs4znzCU92IcYCxE+/hH6TfcSkNPo98eZkejhv7stPFiZRLVmILQdQ61ZSUdAIBBPMl9KhWUE\n5DS8UYVCOYQUHCe09y3U63+Ere8UyZE+RmZdQ17fEYIVl2ASFKS2o6glc0ka0ujwxqgZPEio7gSm\n8mokdzZvKNO4KXEG0eZgMHs+mgYOY4oqo1divN8d5hqXl6S7FDEeoikof6Exdxol0qIT0HUORJHe\n4tUU6mOIHScRTBaCx/diW3kF/s8+4Edff50Mg8SWNcXM/MnXEc1pKf7sihu/CAyQpoYgGsCXN4/o\nkw+gTzPjXLOJjswFFBuT+F/6FYlQlME7fsP0c6+ArEdyZrA/fVXqya+qoBTMQh7voOeZp8hZvZhw\nXz/yXb8k8dJPcW65g8l3XiFtxkwEgwk5t5RPEkWsc4aRguNMvPsP0r71mxQFJS2X+Ms/w75iPcdt\n86jNMOEYbyKaMx39VD9qTz37XSuozTCTHe5FtbhT51oixieTZtY0vYrkzkEwGJnYtxfL959Av/9l\nAivuZG+Xl00VLszxKWTvQGpS2nyS5Ko70QDzeBtdj/+Kgi2XImfkoZYvQpMNaIKIfqSZeHYN+uFG\nAMI5M4grGo2XrgdgxfGj//uO839RF3a3fqnH/3fXxr23fdlb+I/U8O9P/9P1f9msRqJRpKif7riR\nprEQCVXjWqeHe48m+cbSYma6ZRRRx2QkSe7EBZKj/ezPXMOq/l0I8y+nNWIg63MIs6LBV9+8wD3L\nS1hX4kCO+VGOvEVk5V1YhATakW1MLbmVkc/Zo9PSTbx2cZR1pW5MskiBt4GkI5/9npSrX3Hk0hU3\n80nHBL0TYa6blYPNIGPRieQZVT7qCfNx0yj/s0Bl3FVFLKkxHIyntExDJxHMduItp+mffxslg0cJ\nVa5E0zTODIcYC8a4psrJaw0TrC11cXEsxC+3XeDjB1ZgV4NfXHSj9nzebp7ApJO4JlGH4h3jk9zL\nuHRgFz2zrqdxLMiVebBrSGBjuZONz5zg51tqKXeZGA8lmSUOo5qdIIhIQ01ctM9mujjOpDmXdE/q\niy8FxtAMFoRYiBOhNBabpxATYXp+/yi2X75EKKGS9uYv6b/6J9TqpvCbMhkJJkmoKoGYwiKXwv5R\nWJxv49HPuvj1MieqycneHj/NYwE2VGTgev5BImNe8i+7BGH9PbzT4uEm+wgnxBKq3SZGQklyrTLe\nqEKuPkGdF14+2ce3lhXzbsMIDwy9jn/LQ/hiKiUOPfK5D3jHuowbrIMMu2rJTIyzc8yA+vmpllA1\nrsnT2DOuo8JtonzyAm/HyyhxmDDrJMouvsVrjnVUuC0M+qNccf4FLGuuTbm4zU7q405ybTrcQgQx\nMErUVYrR28MFNYc0g0SZv5GprJmkte3n9/5K7p9h4pDXwBqxm0R2Dd5kCrtm8XYxaCoC4JljvWys\nycQbSVCTYUUSoTzSTdJZQF9Ux3g4jlknYTdION56lOjNDyMJYNjxGIeX3seaYjvJN39D+JofYt//\nAuG1X2cwkEjpnbUw0XeeQnfzfyGdfg85txRlYghP9Xp8MYXyUAeRYzsxzVqGkleLPN6J/8AurItW\noZQuRFDiCNEAXlM2giDgnmgi1nQSeeHlDMvpSIKQks0oSaZ0TkZCSSrrt6EvrSWRU0tPRKSk81PG\n9+wh66prac1cTLlVRd3/dzzLv0qmGEbVWxDUJJooo332CsKaO1NpNSEPEWcxJt8AcUcBhrFWOh59\nhNgv/kqFy8CZoRCLnAnao2YqDEH8r/0B59V3MvX+37HNW4xQvgDVZMeryCQVjezYMKhJNJ0JjyGD\nSEIlwyyj/xy9NZo5m9NDAVYef5rxzT8k97On6b7k21R07U5p1Rs+Qcwu4YkeC99YkAc7fkvLqq3M\ndMuppt+awfb2FOP54liAG088hePWrRz0WVjlP8V2cRbz/ue7lP38URrIIZxQsOplaqRJPpk0o2oa\nm2Lnubs5kyVlbr5aojEsp9PmiXCJM4YUGEMZ6eY7/WXcMCePDxtG+O2GUn62r4dsh5HFBQ6KX/sp\nWbfeAyEv3c89z9iD/8O8jg+Qc0tQ04vp1FxUBltoNFdRLU7yYrfAmlIXpZqHV/slbis3IrSf4E1p\nLremDfDVU3peuLwQqf9CChlksNBtraDEcx7VmY+mM6Ace5fhxXdg+1wvmdt9EMFk4UlPAd/LGiGQ\nN5e4ovFp5yTZNgOX+E6hli3gpZYwt8/M4pNOL1eU2pB8Q/hsBVwYDVHlNuM2ihzsC7Ag14ovpjAc\njLMoeB4lr/YLzfn7PVGucfu4+2CELTNziCkq3kiCG2ozebd5nFtnZqWYntZ0FFGHYawVxZHHQMJA\nfvMuhKrFdOGi2Jj8/KKU0mS3T0apdBkZjyQ50e/jhtoMYkmV4wOBVEhHjo/B5/7EB5t/xuUV6WRZ\nUv/7iR4L352XwR9Pj3H/LAvHvXpybQYKzr2BuGgzvQkLI8E41ekmem+6krl/fgwtFkZz5rF9xMTi\nAjsOg4QvlpriZO97msimrXijCra//heO+QvQFVXzsieL22vsdH33Dsr+9DJC2zE8lWtxEcH38qPY\nqirp+MdOpj3yMKHcWXR6Y1gfu5f0x1/BfPFjhis34L33Wi4e7OPSh9ahxJP8qvobPJnTCjXLkUIe\nHm8zUJFhZVO5k7FwEodBQi8JKG88iuna7yKPtfOjVgffXVaEQRY5MxRgdraV+tEQNoPEzEwzp4aC\nzMtJ6QBN597Hd+YkmqLSf+Mj1Jx4gZ4V36Bq8hzhMwfQXfcDzo/HmAgn2Bi/AKqKlldNm+KiUp7i\nfMTGjAwjynt/wDhnJSNZc0n78L+ZvPxBdnd4uL1vG3J+GR0l6xm7YgMr3noGZagDAP2yG/5tDc3/\nTQ1fHPlSj//vLqn0y97Bf6YyLdn/dP1fNqueQBibEmRcs2D8xy8Yve5h9NLnyRl6EWdsPHVxO/8p\nn+ZtYl33O4g2J++kreCaAoHXupLcXqgStWZhiHpTuhxrAWlSKhhgkSNGxODEIKhMRDUy1Sk0g5UD\nQ3Euaf4HU6vuTSGOAu0p/IfZwWdeMzFFZVPsPL+ZKCHfaWJLVTrmg38lsvIubKFhko48IgmVyahC\ngVFBkVOjf19MwS1EUA6+Tt3MW1kUbeLteBnXGbtpsE5n2vARErVriSRUHGMNPDuWwT0zXCnzj6eH\nZHopUv8Fht58ndxbbkeLRWnNXkp1rIukuxghEea9AbjWNsK9JwUeu6wSV2iABi2LaaYw44KdTzo9\n3BY5xgvyIr5eClOmLCw6EfnMe4hmG6IjA2/mdKxCgqlnf4rzqz9EM9oQQ5OcCKXiK99p8XBtmZkJ\nxcAdr57jkxUhlLKFdIZl0k2ptCDV4iK441mOrvo+6/MNIAhokp4Xz4/wTXsvjY45VFsShCUzRlEj\ngYheiaHJqSxoe3wSj+wkklR5+fQA315SiMMo0eeP0zAa5MOLI7y0BHzp1Sga3LO9nm03VqPqjDSN\nR6lNNzAQTBJJqnR7I1xh6EM1Owna8hAEgYO9PjbmgOQf5ekBG982NuOtWM3+nikah/3cPjePkvGz\nCLKOnckyHt1xkTe+tZiRYJxwQmUsGOOGzAB/6Td9foLruTIPTk4ZKHcZee3CMHfOyaXPF2dOpJFQ\n/lwsI43cc0rikUsryEqM0yOkUyQH0GQjYtSPYs1gIqrxxsURLq/K5NPOCVaXuDHIAoP+GGadhNuk\nY2+Xh+umZTIeTlJ8+Dl6VnyD+tEA12UESTry0fWd44WpAqx6Gate4q1zA/z00ioy3vg57s03o0yO\n4Klci1NM8Fz9JFdVZ6aSZYJjjMluNE0jZ6yOcOF8DPEAmqRHDHuJ2rLp9MYoSNOTFh6lTXVzqHeS\nu2Zl4Y2l3M+CALndByG3Ek3S0Su4SdOLKWPbSCM7grlcWelC5+lGNdhoilm5MBLgVuqJT1tLQtUw\nxbzsH5c52efl2uk5JFSVmZEWPkqUUGg3Um2OsaM7zuoSJ9/ccZFnrp1BVt9RenKXUOprRNOZ2BXK\nYkORhXpPkll2hdawjm3nh7h6eg7lLgPW8VZe87i5qSqNT/oiXJYR57DPxMJcK/qYj46YmXJDmJaI\nCW8kwYIzf+HH+iv4+boyHONNaILIpLuasVCS7fVDfGVeHvligOS+15DX3fHFk+NRfRbZsWFGDDk4\nPn6C+OYHkXc8jmXJevrSZ5PfuY/63FXM6NuDMm8zUnACsfcCajxKS9E6quwiH3UH2VRoQGjcj6g3\n0lmwguKG91Kxn537UGsuwasZcCW8tMRtNIwG2Nz0MpLFhuTOQSyZQdJdiiYIyBf3IKbnk8wo4+32\nANdUuwknNeyethTPcqgVskpSNygRP8nxQcZqNpGJnynJjjM6htp8jM7qKyk59Qq6BZehWNwoOjOh\n537MyKlWzJlp5P/qWYSLe9nnXsnKi3/DOGtZ6unymZ345l2L29uO5h2hv2AZhYF2Etk1SP4RRv78\na7K3bEHMLEJJy+KMT8d8a4SXOpJcW5OBWSeSfO1XyBYjpnmrOShXs6xnJ8qym5HP7+J45kqWTh5D\nTM9H05uIOIsxezqYtJeRRhQx4uN8zMFcYYCksxCxcT+RpnP0bnyAmpHjRJtOYVh3O2rjYTpqrkK9\n/yaq//hnusQMSoUpAMTgBOGsGvTJCJx6H3HGqlTSoS0XU8RDxOTGF0sluum3PYptw40otgyGtTSy\nzm5Dl1tM/6t/J+dnTzP51I8YPNzC6MVxPhzw89ShxwnO3oz5+BvoCivx5MxFevUX2OYsQJt5KZJv\nEEFJ4el877wIX3mEfn+c2qFDdP3lFYr/+DektqMo3jF0RdUksmtQ9/0VyZmJVFCN5hvD+9nHpM2a\nA6KEWLuCxKHt6EunQ3YZoY/+hvnyu4h8/Dfkm/4L/VQ/KHGUllNI1QvBMwiqiuDKIdl5gcTi6zH4\nh9A6zxKfcyWmnpMkepoZWHAbhYIPofMMFM0gYc9D33aIjuzFpG97hLQFSwmcPcHoqRZKn9+OfriR\ncHZtik/taSY52otQOpfY3lcRjWbk3BKoWpLqC55/FPfyFYhmG8g6tHgUyZVN5NQeziz8Bou7P0Sq\nWQyaytFYFiUv/gCAwsf++u/pZv4vKxQJfqnH/3fXiy0vfNlb+I/U1jn3/9P1f9ms7m0fJ8dqIMMs\nYTNIJFWNZ04O8L1FubR4Ewz4Y1xmGuairpgzgz7SjDquzkny2PkIP5pvZ9egRrpZT8NYgNUlLkq7\nPmW4cgPHB3x0ToS4Z34eZ4eD7G4ewxdJUOQ2s3VpIa83jPJ1+SKB6nU8sreTuYUO9reO85d1Lupj\nDirdBgwxH4+emuIHK4q+4J96wgq+WAJFhZlZZjomY0yE4+R+jkkaD8UptBsp1EW484NefndFDRl6\nhafOjvNR3RBP3zibCkOQroSV7Pcfx7rpVoKOEvS7nkRfPY9kxTJODkdYkGvluTODpBlkJkJxVpW6\nOdwzCcCGigymMUKbmEOlNkpdIp38ND0D/jjzEu2ckMqwG3RIYorTOiPDiCYIvN/q4arhXbTMuIHh\nQAxfLMn6UiejoSQ6UeC95lE212RSNxzgLwe7+NP1M6mWfXSqDkyySK4ywT17J3l5UZJEbwvitGWI\nER/jripcio8R0rBt+zWn1tzPGnsA+huJNJxBf+MP0cSU891tkjCPNBLvuIAa8BJc9022N42xsTyd\noq59KJ5hlh0v4fj3ZyJPDaFY09E6z7Lbvox5OTYyO/Zx2LmEZw518Y/8BjwLbiKmpJzu5l1PYKic\nQ3K0D8XnQd3yALoDrwDw/fBSnjAeSWWtz9/EC+0J7vZ9ijRvI8JAEyNFy9nTNcnRDg+barPY4vIR\ncRYTjKs4jBKBmILNIKF9+BTja75FfrCTVkMJdoNEZmKcTs1FqeRH6D7H2czlLAjVc2brI8x++228\nmoFgXKV44ChCeh6q0U674qA63MYj7Va+vbggBfIePQemNBSLmwHsABT7mkk68xl94mH67/4d4YTK\n8u4P0OWXoTnzOB5xUeVOpWKFEyrON3+Ba+M1jL79Ohl3fg/x/+PuvYPkKs/8389JnXOenhw0UZpR\nDiAJIQkJZAHGEQwmOK3D4nW+Dj/vOu2usdd5veuIMSaYZIMNRggRBUI5S6PR5Bx6pnPuE+4f7Z+r\nbpWv695bu+u6fqref7qq65yqt+v0c573+/188wkqwTbEgdeQfBEqwTaUxRGMfIrZ6AYipVnOqEFW\nlS6h59KITh+VcAfy5GmMQg7BE0INtlJAwbl4+U/HylpsCqN1LQweQWzq/RM031CszEgBHCaR6UwF\nmyIyHC+w0zILmUUEix09l0avX444eR7R7gJRpnT+EEp9O0a0gzEh8Kdj72ZtDkMQeTHl5Or4QaRo\nG7/LhNiz+CJi50bEUpZSqAPz+DEMZwDNGUZKz1I6+FvS132cxYJKp5TgdNHNamOCQrAdaf+PUNbv\n4bwRZkXqNCcdffT6ROSlMUqhDiS9gvH6o+iJBXLX/QOpkobDJBKMD6AvTiFGmjEkE0KlCIU034tF\nua03gltSUeYHuOzopMWU50hCYePS6xjtV3D/QI7bW6Qq3WR+kFcsvfTt+wa2930VqZRFik+gL0yw\n2HEN8YLGRKrAbnmUg0Ib647/lMSujxIeO4je2MtIxcF4ssjVCy/yG9cWrmvz4Rg/QrJ+PY9eWOCa\nVj/NuWFGbK00mgqkxOrEy6kIvDaV5Yqzv8SyciuTnm48T34d+5470JwhxAsvcbrmKtr9FhyxAZ7M\nhNjd6qWgGjhlA8HQmfw/3kvT576MIZk4lHOzMawwV5IIn3gEcd2bGK/YqXn+O6T2fBK3WcQau4zm\nqaUg2ympOpPpCs0eE57ZUxhmB0+kglzd5OH4TIYfvzbKV/Z0MZYssMedYFippa0wyoS9hcjh+1G3\nvwfht99k+pqP0bZ4Et3fWI3AvuJGVE8dxgv38nDkekZiOdbUe1jKl7mjUUcsZzmQD/PAsQmu742y\nodaFWRYoVHTqp98g1bKZN6YyXFHnxFFJ8pUjKT498yD2695N0dPAQl6lcfE02fq1TKUrHJ9JcUv5\nKHrftZhmqmEVuUgP5kqOwbyJduGPpjGtwoy5hmPTaa5Pv/Yn/vFHt36Wz8TO4XroS7g3bEGo6+Cr\n5wX+sbtCwd/G04Nx3hopk3vqp0gWE9O7P8FLo3He1wJiLs4RoZHugBUrFTAMPvbcOD9cVUKLzyG5\n/bwstrPx1C8Q997N3Jc+SPETP0DTwWESKag6bXOHETxh0DUeSoR4VzABuSTlobOo29/DsZksC7ky\nvWEnHaVR/uG4wLe8pxFW7uS1uMLmgAHnXuQXpk3cpR5BaFuHlI1Vf0OFFE+mg+xtsiFlY5RffRzL\nqq0Me3ppFFN85ViGbW0Brq5Ujwghd+oAACAASURBVPH/N0MYQcRQzIxammhJ96POjDDbvZewTULK\nzHOh4qFHTpB86Puc3PM5dppnEHSV7057ef+aKLaRQwBIy/+6mtX5Swt/1ev/V9eg5/xf+xb+W2pz\nZPuf/fwv0wBGT6L6mxBLWaYEL3X6Ek/MKlzX5mMuV0FEwPFHYP6r42muDuk8PFLhLV0B5nMqDVKG\nJ6dgW6ObJ/pjvL++wKKjgXhBqzrbFwYYtLbQbDfQJDNybpGM2YcnOcyEtanqpnUESKnin6gC7uw0\nmivC0fkyV+qDlKMrGMto6Aacnq0mYtW6LKzXRsiEe7jtwdPs7YvS5LGy3VfkxbgFp1linT3H+59f\n5O4tLVyMZXlrV4ChRIlTs2lW1biocVSTSnxiiYGsRK1TYS6rMpstUecyY5IErLKIVa4y/n5yfJo7\nV0fxCiWeGiuyKuKkefJVnrevpc5t4eRMml2tPvxqglHdRVtuiIuWVkQE4oUKIUc1cvXkXI56l4X/\nfGOcD21qpN4h8q8HJ/nH2mkyTZuQRQFLaoq0oxZ3chh9bpSD3k2E7WaOz6Ro8ljJlDWev7TAN3bW\nM1+RCZsNjs6XOTOfZnuznwaXwnPDCcyyxDW1CifiBs0eC/uH4zhMEg6TzPKQnaBY4JnJCm/KHOJc\n3Q56LWkwdF5M2Gj1WWnOVdmkiwWV1vIkmqsGRIm0rrBvKM4t4Qz/dlnmoxvrOTCapMZhJlWqMkvX\n1DgwSVWkl26AAbwwmiRsrwY5hB0KXouE6fBjZNe9jVNzOaJOMy6TyGy2Qu/IH/hqYTW3ra4l90d2\nZUnTaHCZSZQ0mt0mzi7kWesq8fi4zjK/nUa3idcn0yiSyIqQHUGA+WyFOpcJf/8+WLYBqZBAm7rM\nyejVAKzLnWUhuq4a92uuMJSTkUToj+W4QR7ikKmbY9NJPtLn5ZPPT/Gdzc6qc9oZRjj/Ahf/7Sf0\n3vNlvjUd4BMNaUrHD6BsfTvi0jhqXS9KbIhKsA15aQxKOYxCDm1pFrm1j8kffY+a//U9Lr33Fmp+\n+jhFzeCVsSSbG9z4rDL29BSF5x9Estowb7yOgr+NxL/8PYGNq9EzCUSrneS2D6D+20cJv2kPgqxQ\n7D+Jqa6F4fseo+37P69KUJLTLD12L8IH/hVvbpo3Cj42ZU5UJ6WJs8wE+ogmL6F665DmBzkgdLDT\nGKB07g2kaz+AoBaRkjNo3jpi3/siwV27KPftQXv4nym87XO4D97H3MbbacgMMmBtw2eVcO37Ppa1\nO8jVrGA0WaZLXGTJGsHx9LcY+PVB+n787+TcDdjOP4fkDaLOTyJ5g+iFHD/TerlzZQQ5NYshKQjD\nx5lq3UFt/zMUVt+IIgo8O1QNaECrYMhm9ON/4MmaN3FT4mWONlzLpsRhUu1Xc2Y+R8tPP0Xtp77K\nouInlBlB9TXxqwtx3nHh5zi27mXW10OqpNHikpAyC+h2H8pcP8UTL/Fk+7u52T7OiLeXqEPBdOll\nBG8E1d+EISko8wPodh9iLk4m2Eksr1X10qUsYimD5qpBP/IUyQ0346skOJmzsY5JCq89xdENH+KK\ny48zte426q06ysx51Ng0ktsPokSyfj2e5DCVQBuCVmaxIhNJDUI+ycDXv0UhUWT5Q49xKq6z4tB/\nMLbto3QYs7zx1vfQfesVODq7EZ1eKr3XYpk+XZ3s6ipCKccFexddxhyjcpTaV/4T89qdlM+8glGp\ncGz1e7jClccw2YhjJZgYBEHgJ7Nu3iecRoy2Ug4uqyaWjTyPOjWMuPdulPkBDKU6ODAUK2lriBdG\nk7zVHUNfGKdw/ji2Xbegj5zBWHktB+cqbBeGWfr9IziWtSFvuB7GTnM2spU+OcZ5PUiXC4yDDyP3\nbkXQVEqhDgQBJtJlGk8/Rm7TLZzdejX1+w/QVJ5i+MtfoH73FejFPOa2XgYbt9M+ewjRHSAZ7CZZ\n0vhGcAU/OPp9Jpuvpm7gWSR/DYfu+iySSWLd4w+gn3sZQTEhNXQR/819+K57CyPf+x4N3/gZ8vQ5\nMq8+S/mdX8D1+v1MrbuNxXyF8A/+AYvfxdStX2O1Nkr5zCuUr/kg1pNPYfTtAklBOP0siy+9ROCD\nX0BaGGI6spbQ8UdInz2L945Potu8FJEZipcwfeZddH/hk+jBFgStjDE9wMQDDxHdcQWlubnq5PuK\nvUz88Ds4G0J4d15PrHYdJdVAMwzqjQS62YFQzkP/a8jRFmJP/ArfXZ+mtP8+Uns+SXjkZbIdV+Oc\nOoGWWGChYzfO334da/cq/inexVfWmNEvvo62NEdlz91IAthmzmDIFvKvPsnkrk9Q9+y/sXR9dbLa\nHHD+F7Y0/+9r7sL8X/X6/9V1X/bev/Yt/LfUZzd87s9+Lv+lL+l2HzHVRM7wELBIlKQw/XPj7F3m\nwy6Lf5zQqHhN8Pvzc2zf7MRhMjOdqeCzSIyWHARsJQqqgdssY0wNYF3eRK5cxpyNo7lCCCoMpEES\nyvhtXnylOJrViyQKCJUqw1QWIVvS2D+c5Ob2IIJaoidooz/bTQMiuXIFr1UiVVKpt1R1j5RKLORU\n7tzUiNeqVNmn2Sna/cuo0xY5lnPxsatcDMfzPHx0goBNoTNg45YWhYLJhO3i82Q6d1KRrJjlCrJY\nbcw13UAWBcxSNYWpoEK2rHNpNsNSXiMjKRQqOaCaRmSWRR4/O8vHr2zAKmhMVNwErBJCugqc//or\nY9yyMopVFrFV0uQrAkuFCh0RJ5phIOWWyBRVSgOnmA+to6wZNLnrsGslhEKa0+Et+CQBp1lkddTF\nTLrE7kCJ100S8uIIKUsrrsfvYdVtX6TFayFIhjJuesNOxlNFhDP7URp3M5Op0OK1oUgCdU4TAUVl\noWxhd6uDhcJuKpkyYmyEAd9qoMjgUp6aU08jXf8JXCaJU8Uoq6fOoC3Nke/ey7VtPjTBwTVtIBfi\nNLhtdDl15NQchmxB0xUyP/8q7pvvRnMEUcaO0eDuRTcMZrMlVs4fRJ2fRM0kmEyXcZgkcmXtj7G9\nAnpygTuvrKvyDK0SBlAnFREuH8TWvQtTfolj02XWehxsbbQxkS7hT49gkUPYFImai09zrulaVpqT\nGIITdeIypoZuDMnE1LJdNMoio8kiWqgNm1KdekiZeXz2Bv75hWE+trUZ9cwQlvbl7GwNkDBkbltb\nR8Jixp8c4vCCypZQPT2f/Qgv08qBC5f58MxL6BUVc2IKtXENxisPoK6/HkErozsCSJUCBGpR3AGM\nQpr6d9+OEBsiO5NlsaAxly0BcGQ6zbYmD1abF9ObP4qhWNFyS0xnKyy7/T2c+uSXWPkf30YoF5jP\nVejefQ2lvj3YFgawWe3owRaa35FBzMaYszUStrgwuewkyhr+fIJGbw1GLM9YskiTM4QkCsz7upjP\nVVhhtlNnsfDYQgtvubqZl2bK6IbAjsQoYnKByHv+HmNxmueGE1wjiuQrOj63n0xZ50C5jkOXZ/j8\nlTWI227mcMHF2lKK8/MqXeVznHI7WPbGeVqvX8eEKUpU0NF6d3N4Nk/f0HNY3vxhSmY3fQt5jN9/\nH2Pn7ZTNbk4ENrPGJmOsvBZbPkbJHmRVjZMp4IWJOE1eM5u33kpTrIQY3YKSFyh2bUdXDSIOM8VP\n/ADVaWJ+qYjP20C8IvL2nhB2x3YuOzo5MhJnS6OHwZRKxB4mllVpvnSC+DV3sxmomN1IhWqDNGpd\nRSatcVPsVZ5zbGBzfScAwyUPE2Mp9oZKVEwRZspmRKsfWROwbryFWK7CtOqg3qWgV9xYN99IQDEB\nUFINlJnzlOtWItt9UCkx7mjFoRukPK1MJ8uICEiiRtATRb90lI6vfJXsC4/z3B+jpOVgLQ6TSPaJ\nX7Lq73djW7cDzR0FQeDpwTg3tnRxcLbMNusCWrCNWlFhohTlmy8O8/3r7+bARIaa9e+hw2nQVJah\nkkK49Boj4S2EyjkWwiu5KyRyIb6NjmO/YHxTI2XNwFixE1NTLxVBwFDMnNFrGEvm6Qw48OsGqqZj\nLE4x1ng1TbrOhLmObGsUd1nCawF1YgLf7hspNG2AAz9FWXcdzQ4Ti3qUoA7y9EmEjjVM2RoJWaoO\neJMkcN+xKb7W2ISzFGftx6/DZNU5ng8jTiRxnB9k/MV++OVHEEsqot1JeegMtssncQVr+cjN3ei5\nDD6LhDo3geT2MzYY5+0PfQpDsWBsejvi2ecY/+49hNZ2UmlcS8M7b0IaOoyaWsKx9w6Gihqe9dej\nagZNHjOXDw7Tcm0PRVUHUaS0ECNX1pEmLpPvvR6bIGJuXUPml7/Gb3EimCzEcio1K3fg0jUMScYQ\nRKzlDG0+J86v/mOViWv3IRQz6IkYdTfuAV1j6pXnWPb+d6EOnyE3G0eQRDylIpmSjo6BYYDm9COe\n2Qe6Br07ych2vFu2YcgmYqcGqd82gd7Yh/X4bxHql7H4wgEKrbsY+cmLbH74zUR0C9rJ54gdOs5S\n/xQr1u1CDTQz7ukhev53TB44wdLmCm3ty6kVM3/sKP66zerfWn14xYf+2rfwP1p/EV2lGgKu3Cxp\nyYXPImMAXWEnJklkMl2hYhi0J88gyCai4RBYnLT6rNQnL5Kxhjg0kWJF2MGhyRQ7W7yM2xoJHf4V\ncutqjicEgh4Xr44lqXNb+O2FOXrDTixvPMKTQhe6IRD1u3m0f4mnLixwfCrF3etreHK4CvhWJJGS\nDk9eWuRNpjFcFPEHa1getHJ+IUdjtAZNlOnzCgylNGYyJdrrwrwymSUcCPDZ3/fzIfUgkZ619NZ5\nKKo6oiAScDuIFzVcVpkZ3cl3Do5xZZOXwPQxrKF6mh0CLrNEQYPRZBGzLFGo6Lx1RZj3PnCKjzUl\nGdQ8NLotDAlBNliTtNTVsFjQ8Cs6Pz05z7aoiWt+PcOb+6JsbfYQMBnY33iYcut6OqUER5bg5nYn\ngdICl/UAb1seZDjYh1UWaSuPcyRlRlZMuPQcBau/igJTbGg6rClcQPM3suXCA0gtfbhffxD5HZ+h\nqBoEl/qZt9ZS0Q1qjCS2e79E6caPYpFF/FaJjtRZRoUAPeURtDMvYul/ldHQKi4t5WnyWCg/9H1q\nN2+nhMxG0wLF3uuwHHoYl8NCxe7H4fYiKxK2gVfJPPJTnGE3OU89w1mB8H9+Em/XMiqXjiEGa7lY\ndlGrZJkIrWKxqHNJ97OpdJGsPcpgPEfO10Ko/0UuXfVRQKDeZWJZ6hzm8y+Qii4n1NSCb/4cS7Za\nXhiNs+Xy4wx+/d/wfuDzHBhNUeP3stldZKjioKhWiRHCb75P29XX0Ti4DznSSL/qJujzoqg55lu2\n4hg5jFHbxUJZotZUIa8raIoVy2/uIffofYjX3YH58X/lhqtX41ETXAyuBwQ6/Bac6UmmNBtui8yZ\nvI31UTuCVmEh1Mcyn4Xb2k3oI6exXP129NlhBF8UvX0TcjbGQ5MSvZYMRnwOPTGPnlpE79hMJdDC\nhODF8o530Z48Q/TiPhrWbGEtU1iNcnUy5wwhqiW0/T/Hl5uBxj4qb74Ty6FHoOcqHL//DpZVVzGs\nughkRqlMjyJGmpF9IfTJS9hDNYzqbqIRN+bXfk165Y0EK0s8lIlyo30WzVPHpaRKw0s/oNYlUWlY\njSAIrJPn0M8fJNC1mo6B3yO1rkSfHyffcgVmo0xNTS2x5k00L51GqGljUXDQ7LGwo/8BxLY15E1u\n5rIqdeMHsTR0EzBpLCo+lq9uQd54A4fny9Qf+B5692Zas5eRujdyMu+gzqJT47IyW7sGi9VWTfM6\n+wTz/k5UUcY2+DqKScZ49NukfvETdrx1J5fyJvwP/CPxnm24XO4qu9QsYM/MEFCXcDtdKIvDJGQf\nnjcewNbcgyU2iO6pwStVaKsJMJ+r0Oox45o6wbwpTLi+HpvFjKsUJ4aDaHkW3+xZ2hw6kieCPz2G\nEV7GdKZMc2GU/pKdnXVm5PgY4swlMu4GdAPqp9/gshhhNFFg89wLWEePI6lFXlU66T38Y5QNe1B+\n810qW24hVtBxq0kEw+C5eRGvzYRZEogef5jFcA9dhUE0VwSFMkPubtxrtjGeKtN2/gkqV97CZLpC\nY9RFcf3bUAwVAYNXMi52R0WUxVHEH30F+brbmS2K5CsGzUuneVO7j4zspKyBLAmcWyzTfeYhjM7N\nEGhgKmcgeGs5OZulY/4IzrpWFmpWErTJhI88wH6hjf2zGt1BO/M40QG/zUSXOsFIxcG2xCGMtvVY\nbTaEcAs/OT5Dk9dGOwskRAcRs0alYTWLRQ3HwmXUwZPox/bji/gpPvBtpje+C+G3P8S3+kqk7CK2\nchLT2EnWrF6FWVB5LuGkc0UPSz/+Ku0tAcJ3/h2JtXtpv+vdhA78gIbWFjRvHUbLGhShglHIUrj1\nM/jlEh+P7iB8z3+yaK1lx903ccqzCqfdhiIKEGxiYuUeapUCWX8rpmgrciVLqf840rI1BJb60X0N\nZFSRqB6n+M73EevdwfKQDWnwDfI73of39JMc6rqZepcJx/xFDMWKy2/CGD6J0NSHdP8/Y5HLyN2b\nyNlrsKSmMEx2xJd+Ccs2oB59hqmaNbiHXkWLTSNe9S6EYD3jW99JPtJFMODF7dFw3fV5DF8dr3Zd\nwfor3LiGXkdqWs74t76O96bbSf3qW3jqIqSar8AkGFzo3k104ADfj9exLn0WqW4Zzp5evMkRgh//\nX4iCwAZ1GLG5D5tT5vSNn0b69mdxOSp4vU5IxZh+62dY7dXZV6mnbfIgLE4gNfz5ZKL/qSoXykgm\n6W9mTUjDJNX439wKW6N/dv/+ogxAPbWPj41EubY7TNhu4keHxvinXcs4PJWm3W/j3HyWp8/O8Ksb\nG1gwHITEPONlK78fWOCDa2t58tIiZllkJJ7Ha1W4vUVioOJkPlumL2zn+eE4fpuJgM3EvssLvH9t\nLf2LBTZGzMwU4NxCjoqmY1MkWnxWjk6lWRF2YFVEVK2KMQlIJc4mBTTDYDpdpC/i5NXxBLpuYJYl\nWn1WxhIFvFaFa0pn2GfqY5nfSrOS54HhMre1mnhtSaLVa+XV8SQNbgsV3WAiVeC2JhEkmVcXJfIV\njYFYlt6IixqnmWxZxW1WGE7kuabFw89PzfLu3ghD8VIVei/leGlB5NRMijtXR3EoIiN/jAJMFDWi\no6+Q7biapwaWuLndyU/PJ1gRcvKbs7N8YGMjR6aT5Csaf9cqsPfxab78pi6eOj/Hp7c28djFGLfP\nP4UUrEWqayfjbcVWjJMx+3Bd3E+yaxdffn6I7zZNVo9xJo7ymnk5W4whNEeQC5qf8P1fgA99A5Mk\nVCUNiTEenHPw9pGHMa/YBGY7qr8JKbdE7L7vMnXr16rH5VQd4hPmOhrGX0VLLCB1bWTR0UD/tu3E\nfvlb+iIOah0K1thlSqEOhhLViWC+ohGwKYgCFFWDFreCIQgIhoFYSIAgIgwd5WdaLx9wjTEeWkPk\nhX9HCjeg1LVyWF6GTZFYvnCY+d/9ltTffZP6/d8GIHvDp/GaQJkfoNJ/BMlfg758B1J6DtUdJVkB\nq1wF4xsma1W/KfqoEdL8fhr2xg4w2vNmQjYZ7WdfID02i/dr9+KePMozdLLHk0JILzAbWkVFN4ha\ndFKaTP9igZURO1Y1hyGbkRcGGXcuo04pIcUnSO97FOWOf8JUTCCUC2iuCKbZCzyYivLmTj8mtcDI\nP9xJyx3vRG7sIvP842Qm5gl/4qvkH//3Kqh/6CxD2z7KbKbE5gYXl5dKhO0yiwWVrsFnYPV1TJcV\nEgWNPnEWITWPFu1GP/QE+5tuYk/hBILdheEKoY+fh2UbqmizbIzFQA/+6WOkX9mH7dbPwPGnEVZf\ni3FyH2PL30yzkudi3kK3Q2W8ZKZx4Bl+YdrErStCmCo5uPgKCx27CRz+FXomyfyOv6dh7iiCxU7h\n2AGUujZEm5OJxq1Mp8ucX8hwU1cQiySQqxhkyhpFVafWacIXv4zmjiAnptAzcY6617Au9jrqil1o\nuoFt7gKZcA8mSUDpf5lT/g00ecz4Y+erBsdynpIjzC0PnOZta+p4Z3eAJy/HefMyD/OFKnpuVaSa\n/FPnkBFLGY4nJTr8VkYSJZxmiWXFUZ7Jhbm6qdpMm2WBuvj5ahZ87TrOzOXYUOvAVowzolXlQqPJ\nEg0uE6IAFZ2qtAWwSALjqTIrLSkOpu10+G3YFYF0uWqGSxU12v1mjs1kubLGwljO+L/oFyN2hUOT\naa5xpzmjBql1KTx+cYFWr41Gj5WheJ7lITufe7qfr+3pQhAgVdRYYc8zVHbQrk1zXA2z1rTEb2J2\n3maf4qjchtcqU1Krz8sNtU4eOj/P5gYv8UKFimZwdRjU5+9D2v0+5lUTQ/ECbrOC1yoxkigiVj22\nbLUnSTtqcV46QLHnGqYzFUySQB0plmQvBydSvMWfQrd5mVDt1J14mMG+dzKTLjGWLLC92fcnV/5s\ntkKiUEEUqpjDepeCW1J5YjDDMr+dnmM/Z/iK93NsOsWNHQHc6XFOaRH8Npkzc1na/Daa3Sae6F/k\n1nAaxOqhoeaOkv7JP6Le9TWCuQnOfeQf6H7wCYxXHmBw1bvIlFRWhu3IxWQ1TCA7gT52DrFlFUdL\nPu5vXsP3lw6hH34KLbWEuXMtiZefw7GsDVNLD1pioYoXHPg9cqQBQRQZ+88fYvriT/DbZDTdQNFK\nKLGqI/5lvZGVYTszH3w7XV/4NHqgibQ1hFPLopvsyIkJxEKK/JH99G+5mxXnf01m4DLuuz4HF15G\nbOxB9bcgX3oFwRuBfBK1YRWXM9WUMqNcYOnpx/G++2NoJ/Yhrd6FdvZlZp59gfSn/oNOrwKCyO8G\nE1zf7kM+sw8x3ETp2H4kbxClqQvd7mfOXINO1Qvy+kSS2wqHkOraSQY6yVV0Pv7kBR56Ryf6gXtR\nmnsYCG2kK3MOJIXZB37O9BuDWO79Lcuz5/nufNXd/Yktrf8VPef/57rwwtBf9fr/1dW4+s+75v//\nXv93CVZ/cbJa9NRxzTI/U+kyfpuJvd1Bvvz8EDd0hzk3n6HRY+VN3WHsNhtv+/lx3r0qyItTBXa0\n+Dg1l6PVZ+PsfIb2gIOj4wl2trgwZAtFVae1MklrfS2HJlOsiTrx2c3YFYkOp05FMnN6LsfKiAOH\nScFnUzg9l2FDnRuXqZodPZ4qoogiOUNiPFXEbVZ4aWgRu1lmIVfmjpURdENgNlPi9ZE4iWKFvhUr\neGEkzvKQg/4UtHhtfP7ABKIs4jTLpIsq7QEbC7kKNzVIHEmZKQgWUkWVjXVOQg4LA0s52nw2mjxm\npjNlGj0W7j89yyuXYvhcVsqazmy2DCYbq4MKBV1kqaBSMap/QmWt+m6gBluwKSI2k8J4VuOKejd2\nRUKQRSZTRSYTBd7UEcJid7JvYJEPp56mb+sONAO6gzaszT0YI6cZqNmEwyQxU1F4YyqNr7mLqXSZ\nhXyZrWGB82UPUVLEzSFeTFiwunwYwPiyLSQKKu2lMQZVJ1OqnajLzHTNKsruWtyyQVJ2c2hJpGnn\nXjJlnZaFY4xYGonhYixZpH74ZYztd1EwuZnPaay9aT2d5Qm8ThsjBTM2X4hfnZ1nd6CM2e6sJvxU\ndGRRoMlURErPIkgKwh91hefTMmlvM8lChcaWNkJzp5CbehiJbmRQ89DitSAKAjO2OqKlMdKNa0i2\nbMLatwWXSSRZMbDIEtO163EFgkyWzVTMLgbiJTIlnabEWQ6oTeg2L4HcFKfyNvKClc2uHJIs4s/P\noNidzPfsJn/FXmJ5lWAkSrucYcFSg7McR/HV8NxwgoDTRuDiHyiFO/hN/wIbTYt8+XiO1Z1tBM4/\nzYi7E7+WgqVJZmrXcmCySO3T30MYPgbFDOHlG3DKgCgTuOoqjNlhJE8AdXYE16q1iNkljMwikifA\nkX+8n96PvI9Wu85oBvoXs6w3LxESC4hqETE1i2PsOKFl3chLY2Re3YciVtBi04TXXIV84WXEphUU\nX3qE4vg4tpoww//8JXxbt3Gu6CDqNHP5ez8j8PZboa6bU3GDaH0dJcmKJzlC2RHCoQi4FQMpu8iC\nvZ6AXcFy6hmO1u6k3mVCiY0i2hzYzr/A/PMvEd9+B/Kx/Zh230nyiXtZ7LoKURAoaQarbTmEA/dy\nyrWcNdmzPJ+0s3bqAM+ZVpD7u1uxmzKYVm6hdukiotVOzFID930RdcvNOGfPsH/JSrs5h+yvJTh/\nmkpNNyfi0FCYALuPqzoitHqrMcdui8JYqkKLx4zVJKHqkCiq1GeGGBZDWBUJEZhIlWh0m7EZRXSr\nl+FEkRVKnMt5E3+ImdB8DeiGgIFBumQQkYuksbKQr7AicYL9KRfZskHIrpAsaTSqc5xMyawI25gq\nW7ApEqPJInaTjNssMpYq4bHK2BWJgE3hDyMpWrxWVN3ALAk0xc8yqwRZOXmA044VrEqfRAw00BGw\n0yMskFdclFSDDr+VG3pC5Co6FkmgxVLEOPMCYYtG+tlHeNHZi9sX4MrMSc65eumdeB7nxRepCXlo\n8tsxoZLWTaz3aoQ9TtoXjlIMLSNWtwaHxcxzwwl2NznI61Vt/kpHkbJkY40+gXrhdU5a2mh0ihjO\nABdieSIOE86pkxyrBAjaTUSFDFIuTkzx423tJlkRkUWBKxtcHJvJ0Bmw4T/zJIGWTmpe/hH2FVei\nGhCxy4xkdLYk36BWziF0XkFw5hR9Yow5Wx1JyUXEoRBNXKJ19jiB2lokrUx31E9KcnMoLtHklCgq\nDhbu/xnR625AcwQ59Ilv4i/3s3D0LMv27sVmteJcvIzefwhrfBTR5efw+79IwJejsaOD3dtrKJ9+\nlU++/YfsumklotWOff12BFFAXZiiNDZITWszoz/4AZ7lnaBp2KMBLK0rUc7uQ/JUn0Nn9rwTIT6E\nffsNhMwGtvQgiQ03o/36mzhWb0XKLSGUc8Tu/Q7WTbtJvvw8LRvWIVvMmHweBJcfCQ090EJaV+DV\nR8ieOYalvhnBZKH03c+SNFbaVwAAIABJREFUOnEUMxnmDl/A7RXQM0l+tvvT1EcEgqvaqa0Ng1Q1\nEvYwhzB6EkNTIZ9GXPcmCq8/i+xyc/bTX6J9Wy92GRyvPcBqv8DYfQ+QufY98MNPU1Pr5rWsm2sm\nnmL25aPEDx+lfX0XhmRCc0WwkqY4O4Nvz9uwjBxjzbr1rKt1och/UXX4314T5+dQK9rfzBJaCxTE\n7N/c8ph9f3b//mKzKo0fZ1YOsHz4DzhaqzilsNfJpViOoqYzniywpsaJKzfL9lVtuPQ840UZr1XB\nZqqakqbSZdZGnSyWVDaIM2StISZTJc7lzHR5FdKqQIfPjEWRCM8e53A5hN8q02EtYrbYCOeryKOe\noA0/OcwWKyXN4I3JJNf4C6QEG06TTCxfJl1WUQ2DtVEPBlDrMnHHv7/B265sYlODF5MsIksSrV4z\nDpPMqbkM7+yrYVXESbs+h8cf4sHTM+xs9ZHDzHSmxIqwjXq3mXhRQ9NBNwy6g1Yy5eo06NJinptr\niqTMbna3+ugWY7i8AUaTRVrmj3HfuMKdXQ5U0URRNahXClitVuzpKc5mTLjMEhZZYjBeoNeSoSw7\n6AnZ6Y/laPbZCDsUfnFkit5tu/BaJJIlDZ9VRhQlztna6Std5qlZGYdJpsZpJmRXCNllvv3SCO9q\nlTiWs9NWE2Qyb+A0yTjNEi2nH0FrXInTLDGsOpEEgYjDREUDWRRYyJXxeLwIgsBUukT909+kvqWO\nKf8Kok6FsVQRh0mmti5Cf8HKqbksm5xZDLOdXyWCyDY3r08kyJYNbujwU5SsLORVFnJVlFWmrBFy\nO3lxQWRZdoDD5RD1YppZ1Uy+ovHEmVnqfXb2LVlZduh+TH1XEcur5Cs6dkWkyaZjcns4kbUScZoI\nCnm++PI07UEnUyWZsWQR2eJAFiGkqBwYS7O10c2jMwo9IQc1DoXzRSeOPzYvZocLpf8gh4NbyQlm\nOtLnGMFHT9CKZe4i9806iTjNJM1BxlIlypqO06SgRdqplzLoip1IOESD30FYzEOoEY9ZQrd5ufSV\nb9Bx3RacviCBTTuQ4uNIq65BNbuwCDoLJfCkxpBcXnSrC9liQWxZiaSYSB07jHD9h/AW+nHV1zAm\nhhhNFgg7TOhWD/OGHc/4UTInj3Bh43sxBIn4PZ/HFvZhaetGqWslY4tQ3vdrTBQwd6/D0trJ9P33\nEr1mC0bXVmqcZuTsAtpkP4Od26lTFzgwp9MS9hMvavhkFYvTi2n8ODOWOmw1zaiGQFPsBHpqCVtb\nH3nVwJWdge7NaEMn8b31LhwuD8VjL2BavwuGjhJYdzUNmUGSJj9TZRO18X4aV27ghbSb6UyRdcs7\nqEgWem55C/mjL2Jet5NMoB3TbD92uw3z6m0oagEWxpmx1lJbU4NNL6B56lAWLhOTAwSnjvOHUi2K\nKFHWDepLU1jdfppyI8ixIfbF7WwZfxpLax+zopdMSatycPf/gPamGqySwawSotFcIlYWqXEohE8+\nRu/GzUStIMsyzQ6RdEXAZHMQKUySEN3k3HWsDNtp0uZRTj2LPxLhuYSDq71FzItDuBXwTx7D39TJ\nM5cXEUWJ9UwiuYKcnc/TNvQsHcv7sJlkbIrIXE7FE4oym9OoRDqwmySeSzlp91sxDJCtDpySRp1d\npGSIzGRVWgujaI4AplPVSbuUW2Ju336u6nQybG0k3NiGxyJTCrdjs4jorjByagbNHaXJISJgYFkY\nwHD6UUoZZnQ74YlD+BuW4Z49jV9NYjzyPewtbSTNPnyVBEbXVjw2M5b4GJisJHUz7blLJOvW0m5T\nORkr43nwHuxNjRzIuKnx2GmMnyUYDDFTMBiOFzDLEpH25cipaVLd1xBMDZH71uc417kDh0kmJOVR\ng23MfONzHFt/B0aombBdISAUePhSkmHdTVd3FynBjmKx8dv+RVaG7eiIeBw2TKUks48+TmLnOwhI\nZRyx40Q//20C61aiHt/HoKebmsIUYqAOvXktJXuI2K8fpPl9t3NcaiY49CpKpJ6du1v51G33svdT\nt7H0u19jiUbBMDA3d5Kq6cWVuoSw8y7kXIy5J5/CsfU6pPgkCX8HNkUkqA6glyqMdm/H//i/YK2t\nxVlJYGnpAFcI3epB0MpYhByxho0ErSXmwn2oj/8H4799geDO7RiuCCczZgbjBaxP3o9kMePo7CH5\n9EOM3fY1WsUZUpfHUWwW3Bu3IFhsrPv83bhWrkW0WEjXr+NwTCNnKPi9XpgbqgZbJBZQvAFMwRBG\nMUdk+5VU2q5AGj/F04EddKoz+DZtwhaqR9x4LcaR36G2b8C3fCNBfZrgO+7ioqUV39iRKjJvdojQ\n3pswecLIxQT7ki5GEkU6gn9+YvY/VZVMCbvT/Dezpm1jZCrpv7lV72j6s/v3F5vVhZ/dQ3D9ZoRw\nE0XBzEuTOerdFl4eXqLRa6XFW23kpNP7cAdDnL3jLppuu50GIU0kO0bCFGB9UMZl5Fhd66Vf8yOJ\nsDp7lg6/BePsiwTae7Gc34/dooDFwTwONAOKgpnjMxla7AZ5yY778ksYvlpyKASmj9Hc2k5asFHR\nDEaTBepcFjbVudhgzxLweniif5Eap5lP7mik0WcnaFcQgIjDhGfyKOZLB6k7+TQPi51sOXc/UmM3\nccFBoqSysdaFW89yOW3Qade4nDY4PpOhL2KnZ+EI5kqGxPe+hH/NWrKSHd3iZvPkPsxzAzxQamGz\naY5gKELWVc9exzyG1Y314P3ceUTiXT1uxLPPk6hdg/z1D9LQ7MObm6ZZTPGHXIjNnhLuSoINS0cJ\nh/xUnvkRfbv20jf5POZwI9L9X8bZ0ox+5HfUKgVKTetZPnEApb6Lugu/o/zCo9C3jet7wix+90tE\nr70JTVQI2hXuPzHNTc0mZLcXwRGgrBk0us3UKBVciWE8bheqoDCZLtF+7D6S9atZ4VOw+d0IWpmi\nswbXxFGGhSDJokqLS0ax2lkx9DSllvXIpSx9bgPHH36IddV2fFaFcG6M0mM/wLxqGx9+7CzbO4I0\neSzov/gisfYtNGVHqMuPg7eGwbxEjcOM32mmohnsavVi71qJY6Gf6PwZaqUsdm+QgmjGVMlQf+Ix\n/JEgwsxlulb0YpUF2sQErS6RQHoMh1lhomKlI2BjKl1hl3oer92CcPBhZsO9rPAIFA2J2sRFaOzF\n6nRxYiZDa0srtRYD6Y3HMApZWlasxiIJ1GiLRDwOOsVFPOf3YW/uQX/lIX62FGbdS98lTAKtaTVL\nqsLLkzlUQabrtndRMHsIx/s5ELeyrDGKWMry4pLCT47OoCgSZn8tbrGEoKngDmEoVspHnsHW0YPs\nr8G8bgfG8AlerkTY647zxLjKrvJZjlX8LAW7WNboI1DbxMBSkf7uHcSXXYm7tgWzLHJ4CeY6r0Jv\nWolf0TCSCzh2vR3F5cGQTfxhskLB4qdBn0RrW09SdFDjtFDQdPwWiXNZBZ9V5jJBDk0mUWSZTrfA\nJbGGZLib+05Ms6deZsDWit9uQUzOIlqsICvIm9/MQl7HsvIqHu+PcTRj4chYAq9doXtZI8/MGPht\nCudnM9SHvEylStitVi7Ub8Jx/1fQ117D6Kc/RXzPHfgcdv7ljQWucqYpept4ZSqL1+XE9tLPKPTu\n4b4T06xat546l5l/2jeA02ZCt/lpSA/wmt5A3BZlc4OTe8ac7DH68VpkzmdlFElCa9/IiayVJ0fy\nNHlt+Aae572vFAkGfbR1tnNsUedivMRctkxFkIg6FPaPJBgsWvjOS0O8Z1WIdzxwFnsgRLh7Dcrx\n30FTHz5FRb1wiNftvTTU12FCpTfi5JWJDLI3wqGJFFfUu/jlooeHTs2xo83H4/0x4vkKIaeVR8/N\nckWDm/3DcTbUujk8lebSYh6PzYKsKDw6kCDsMDOZLlEX9LF/LIO9tRdNVDC5/Li3XsMnzll4y/Iw\nzwzGWe6TOTpXRPbXg8WJbLEyUTTxxOUErSEPZtFAjE+iRTpJV8AvlZkTPaRsNSyZAyx2bsPqC2OR\nRWy5eXSbl1gJbKF6Zkoy7/v5MTL+KJ0BB2+57yy3r28gs2onSqSFR8/McnWLD3NykkVHPWG7wtf2\nD3LLqijO0hKCrmKdOYce6cC54wacVjOz2TLhUBgkE8pVN1QNoolBXo6beGkqz42dQdYzydMxC5Pp\nEsu8FnrsJX54JsHqGicn5ws0KQUi27cguUPYJ45htYHQsRHVFUFamiA48hrZvusxCTpifoknpwx2\nvvcmdG8tTrsNu1hh5BcPErjqKvZ+6jY+0nMne27bhByIYKy9gXKgGfOBH6Nc/xHky69BoAFHjQ85\nFwPDoPD4j3HLebTkEt7162nMjpLY+Xd4tDRPKKsx//CrOMUl5HKG0tF9nF59B8ssRfKv/A5PcY7i\n3ruRr3s7RbMXy7n9VKLdrBcm8fZ2s/DSq4w/cQDTP/2IrvI48ZdfIHTHR6gMnUO59i4mXW3YjzzG\na4HNNKkLyKMnqZ89QcRIsvjgj7Bddyvlk69g2fY2yseeI374MLbGJubad1FSDWwuNy6ni3KgGev8\nACeIUlANgj1raPY7cJ35PXLvVZSPPou5cyN6tAP1sW8imU3IoXrOvu8DhG69i2jAS6PbjMWk/Pd0\nof8PS5DB4jb/zazMMR15xvE3t2rag39+//6SZrUcnyF57z24P/gVxFwcQSuzL1WNnewL23FRRDh3\nAKmhi4tyA136TNWhaHERU6tO1v/NZ02VNBoqc0wqESIOhYGlIt3mLCOai1ajyr6bkQLUCGkMxYY8\nfY5k7RpieZVWMcmSyY/23U8QueNDXDA102nKoNoDAAwlSizzmSmpOs7EMKeppydgRkrNkLbXMJku\nI4kCHdYSIyULPouEJArkKjohi8BC0aA/lqc3bMcnq4zlBVqEZBXtoRYpWbxY+1+kMj1M/ur3YXvh\nxyhrd6FPXkJoWU3RGamaeCSNw3MlNo7/gey6t+EZf6OKFpkb5Kh7DevlObKeZlwLFxlzdVAn5ZBj\nw8wGVxLJjgBwSW7EbRaZSJdYO/k8ck0T/zru46aeqj6lP5bl6iYPJknAACRBQBBAufACbzri4ec3\n95Gt6PTHctxonaQSakfMJ1g0h5jJVJhIFbiu1cOzw0n2NFffdJW5fub9PZyYzXJVo4tcRcckCtiF\nCkcXVGpdJhoqc/QTqiY/KSK5io50z4eJfunfKQomHONH0GqXIxRSjIkhWrMDjLk6ePjMLJ/aWENF\nkEmVdLIVjSa7ALrGwbkKPUEbT16K8YFwnEeSIerdVgYWs7y728NAGhqevgfrrZ/jcwfG+fAVjXz3\n1VG+sKOVUKLahESdZhqsGr++nKGo6rynSafy8q85tvb9XGldYsIUZeD/5O48o+OsrrZ9PWV61xRp\nNOqyZMuSLfeKMR0TCDV0CCQkpJGQNyE9vCkQIEBIqAESeugQYzo2GOOKu+Uiq1i9jzTS9PqU78ew\n8isr37feL+9irey1zp/zY85Zc+aZ5z573/u+p9KcZQujmV3cfiBNmcvMDfp+OqrPYN9ojGsrVRRX\nEONYO4Km0O2Yw57hOEGHCZMkMsdnoX0yzeKgDdOxTXzqXQ3A0jIzhnA372WC7B2cYVVNCWcUjiCY\nbbxbqMUsi5ySPsizhTksDbmYtftJRHeA6QUX4lVmGBPcZBSNejGKlAiTL2tCzCUQlDw5mx/raBvp\n8las48eYKGnCK2R4YyDPhRWgG6z0pUUqHAYSeQ27UWQ6o+IwiriG9iCYrIy/+BTdV97GilID+b//\nEWNVAxPzLyDYsxmq5rE34yRoNyIKAv4tj6Kd8x0yis5gLE+rNIHiqWI0peC3ytiGD9Djnk+VucBV\nr3XzzBXzMaUmeWlILPLBJz6kr+mLNKRPoFlcaFYPUnISIToGVje7qWSxT0aVTEg7XkTPZxlYcjU+\ni8zRcJHT/m5nmJtTGxFkA/0Lr8AkCZRYZOx9O8nVr0bVdTIFDZMs8mHvDM0BO3lVJ2ArfsbyXQ9j\naV1FrO4kJlIKsyx5chseQrLZ6V99I/UdbyE2rUI7tg0AXSnwsvcsrqwWENRCcU4yoJsdRSOGbIyc\nyYVRzSFoCvFn7iR+1a/ZMxLnkvx+DpWuYX6JiGH0KDvkObjMMrM7NhR1di1OdEFE7diD2HIyk3+9\nF8/8Jk4svY7q9/+Awevj6IJrme8REA5vRBnt5+3Z13BytQtF1XFuuBvx8p9hHT5AuHQBJe3vI9bM\nZ8YWwjt5lIi/Bfvmx5heeyOlgzv+ITQ/9Pe3AShfs5DcdAzT9b9GREc89C5a6zoEtYA81YtQyKC6\nQ6Tf+iuWS77HiGKhMj9K2zduYuEfb0M32dCMdnSTjRHFgkEUKBvaiVa9gLa4geZdjwKQOudmlId/\nROll15Eom4dBFDAN7EUQRfIVC4g/8nMix/qoe+h55Ol+3oz5+KIniuoMMpiRqNj5VwozM5gv/h5C\nNgH9bQhGM0JJEKGQYeqNF5n58m2ENv2Jgyu/g0ESKKjF6pZ7YBd6LotW3Yo40o5eWuRERixlCI/9\nlPRX76D88HoGX32T+h/eQqH/OOEt2/GvWkJk7yHKLr8GraQS+g6hRsbQ81lMLStJ796Iee4yUo1r\nsU+fILnxZbKRGK4FC5BbTkLt2E3iaBuSwYBsM2O85IccveJiFjx4D4VAA4nHfon3i5dz7NbfMveu\nOykc24lgdSK5vGg1C5Amusns/xjJ4QZRQkvMUEikcZ52PtPvvorrypuZfOxOvDffCbvXI9e3kt72\nJnKJn+hJ12OSBOK3f4uy009masnllHz0CFo+i7GqsWhAcXQHhjnL0A0mwk89gMXrwnX2l1BtXnSj\nhamHf4Mt6MX2heuIPPcArpvuwjh2jOTWt7GvPptc1yHkJevQjm4F2cjUJ1vxrV6BOO8UdKOF2NO/\nZ+zyXzNXGyb7yeuIsgFT8zIEo5l85SKUv9+DqXEB2txTSTzxa0xfu72IBayW/wnG/LdFOpX5XNf/\nd8cN7/9nqgG8eMnT/3T+XzdYtW1kqmY1vt5t7HItpXs6xTOf9HLtmlpmMgUm4zl+c2Y9k2kFEQim\n+hiw1DASz7Ok/SUe95yD32rEYzEgCgJrqpz85cAoFU4zD310gk1rEmwwLkTVod5j5eVDI4RKLEzG\nc/y6VeT5MSuRdJ7ecIrvrqmhxqzQn5VpSHSgOst4e8LAulke7t81hMdqJGAzUmIx0B/NcEqNhy39\nM3SHk9R4rZhkiaUhJ12RNGdXWchgIJnXkCWBd7sjDM9kSGYVwokcX1oY4oyBDaRXX4NraA+ZmuX0\nxfIYJQGbLOI3quwJK0givHs8zM2rq5EEEAW4d9sAv54PW9I+1oxsZKr1QjojabojaYam0yyscBF0\nmLAaJDxmmfLCBFFrkCPhFCsrHGwdiGM1SORUjTXlZsTkFC8MywRsRrb2RPjOyqqirV1zCcKBdxDM\nVoRgPdmSOl46Gubq0b9jalnJaEkLgePvIjQs41DWxTy/mYm0ikkWKDn8FsqSC4uWt2YnciKMONnL\nQzNVXDO/jPbJDK2f3I/17Kv5+5SDS4w9qJExaD6FjydgJJHl4jk+LIKKkImBKCIOHAZ/JScMlRye\nSHKxN4aYS7FfrmehHOagEuDJPYPc84VGzLFhhFwK1V2O2LOXn4/WcEfVCO3+5eQUjZ1DM3zH2kV2\n9loyj/0cx+xGjrRcwZN7BklmC/zlkmak5CRdips5kb0c8SymPZzksrJMUYB80184OP9qGkosGCWB\nQ+MpTLLIskIXz8cruNo5TL63KHytrbmaEzM5mvN9RL2NCIB74jCqo5QXhmWurJX41sYJHvlCNfLM\nECSmwOFjL5UsTR1Gd5WiWT0cSZqpdRvJ3v9Dyq66AV0ycMxQw+yut5FcXiZq1lCW7GX8yYcw2IqO\naiVX3Ih64hCG8homXn8BNZunZP5sJna1UdJUTcdLO6h45W28e19Cz2cJr7yOgE3GMD1A5sMXsa1a\nh+KugO7d6Lksck0zusGEkM8w/cZzTB3uwXf/S7jUONred8iPDGBffTaF0X4MlQ3kg81EchCcPMTY\ni89Set75pA7twhQMMfLBJ4TOXoscqqcw1I3k8aMuPp/pjIoogEkScPZuJ9OwBlM+gRzuRrc4IRYm\nsfMjkiOT+FYuZWrZVZR91kzoDLeTL2uCLc8ih+rRQnN54oRCncfK8pAdi55H3/EKgtGMOHc168MW\nzt77CObaRqQ5yxFyKTS7D3Gyl7GyJZTlxtAHjyGW1xdF3UURIRPn+WQ17WNxbj2tDlO4k2hJw2eK\nECIGAeT2zUWAM3SUHw9WcNdqN1L4BDe1e7jvvNkYpnp4YsTOFS0BLIfegtmr0E124roRiywwkixQ\nKycZUBzcv62P28+eRftUhsFYlosrBXJvP45txZnkKxeRefo32K66BW3X30msugazLBS/r6leCoFG\nUkLRTlTVYDyZI5lXOat/PcJJl3M8IdIiTvL3SRtn1LqLKgbTXWi2EqImH05JpS+pcyyc5LyJD1Aj\n48jrvl58JoeOgabxpnUZF/hSTFvL6Y3mqHObcBdmkOPjoORQ3SFeGJJYVelGFiEkpRAHDzNauYrp\njEKV08iB8RS3vXmMD769nIm0Sl7ViedUAOymopVvvvULPL5/FEkUuHFxOS8eDXP+zvtJXPVrQlIK\neWaYbnsjRrF4AXniwChXzitD02EyXTSYOTNkYEY3EZg4xB5TE0G78R/volB+jD25Ej7uiXD+3FJU\nTS9qDRtE+qNFDemg3cTCoJ09IwnO8WXRTXak6UEK3QeR564gt209osWGfva3MI8dRRnrRWhYRuyl\nBzHYLNjOuZbk289gbZpPbN9unDf+Fu3jZ4medD2+7o/Q81kQJY7c8Sh/ebubB7behVi/EH3oOLqS\nJ9/fwfQ5P8D7wZ8wL1zLxGsvEP7Knfj/+hM8c2owzlkMgVoym55HzeYx19Qj2hwUBruQXF4O3vsS\nS156hujz9+OcN5/DjRcy360jZuMkX38U53lX814ywLyADecrtyPbzEyfewuh/GcWxqPdiIFq4u++\nyOiFPyeRV1jQuR5D42KEfAokA7nQfPjwCUY3baP6J7+h/45byfziL5Q8/mPKrvsWO7VKSiwGHCaR\nimgHUf9cXNEehEycY44WmjPdTL/9IoYbbkd56r9xLFwKc09GmuhmLLAQ899+RTYSp+ziS1FnrYTd\n6/9xhqbTvvz/CWP+/2LHy22f6/r/7jjWvOPz3sL/StzY8u1/Ov9/NQXQ7D6E4XZiOz7CcfUP+dXO\naX5+ai2qppNRdLx6AnSN98dFzrWOo7rKeKorx+XNfnpm8rS4QUpMEHNUYnn/IVJn30RG0cgoGjUW\njaxY1GXVddDQKbcbsJOHvW8iubwIgap/eFBn3FUY9/yd0ZbzCVpFDk3mWBCwkCjoOAwCkWwxw5TI\nqQSEJBzdwu7QmSwLyAiFDIrZjSExTuGTVzix+huEHAYm0wqJnEq6oLLCLyJ2bkeLRYoWckvOQUxN\nFzMtg8cQZANq42q0T55HXrIOKRX5h+tQ2NuE2yiyfTjJojIbkxkF5xM/Q/r23ZQkB4uZmugomB0I\nShY0FcVfz5GkmVKbzHRWpTnfx7BjFi6TSPtUpugn3rCIN+J+zp3lIZrXeK09zPWtZYwlFfxWiaFE\ngc29EW6ceY+DLVcwL2BFeutPTJx+E86Xfsszrd/kmt0PUHLlN0HX0Ox+1O2vkjz5K3iSQ6jOMu7d\nPYFRFrGbZC5vDjCSLJAtaNR5TNjJc3Bao/XIi7xfczHzSu30R7OsKZXouek66h57hbu3D/LVxSHK\n0gMwOcRH1kWcYRrlZ0cM3L7agzwzTNjXjFkScES6KXTuwzB7CbrRSurdZzh0yvcp+eWXMfzpJaYz\nBf6w+QR/za1Hv+a/ifz4yzxz/m2EPBbOqPOybXCGS5r85FUdl5bk2a4M11fmueitKZ65shXb/vUI\nC8+mL2dkc+801y8o48RMjiZ1mD5jFR/2RmgJOFg+sYW20Gm0WlPQvbvI3Trtq0xnVaLZIpdxPFWg\n1mVkx1CCnf3T/OCkahRN5/yHP+Xd767EoqSQRo6CpvJQrJavLyrHNNFBoXQ2BybSTGcU5gVsbOmf\nYSye5Uf+QXRVZapmNYqqU9qzmY6KtXhMn3EstQgRk5+tAzEuCmSKFokzAzA5gBaaS1vahs8qk/yM\nu5tXdU5MZ1hb7WQoXiBT0Ci1yaR++mV6f/BnzrKMs1OtKHJvZZHxVIE3joe5uSbNmK2W909E+HKD\nie6slVnHXmd84aW4TCJDiQJzI/vY61hI0G6k7MgGhprP582OMAuCTqbShc+qK3Ye3jlATtH47fTL\n5C/9ObFcUQv3zo97uWttgL68mTptkilzGYIAGzqm+EpjEVB3aV5q3UXzhrbxBO2jce5rCFMY7uEp\n91lcO7+UwXiBGpeR6YxCStEwiMVuc6HvAIIosdW1jOf3DfPQhU0YZobIuyvJq0WrZbtBZMdQnNWV\nTvaMJlnniiLEwyiheWwbL7C03E44pRDJFFjk1lCMdiZSBSpzwxQObiZ58lcIpxT6ohnOHPsAbdVl\nbOiMYDfKND9xC11f/wNnFI7QG1hGtZxAsZTQF82zbzTGVaE8o4ZS/FYZSc0h5FNERFfR1EMWmMmq\nSILAdFahaXwXgi/Eaa9E2HyZl0FLDQAVWoS4JQCA1SDybvc0PquRJp8FWRRwRbr464SH5RUu5toV\nxMREMatW08SgbwHlpiKgnCzIlFhk5EPvoEbGkU66lAMxmQUBC4VXf8/O5d/GZZaxGiTm9H2AaLYB\nUGg+nRteOcJv1s3haDjJulkeRCXH5uEsZxuH0Ew22oVyGo++xtCCy6jufIfH5eVctPEuSm+8BdVZ\nhrbprxiWn8eoKYiq6SQLGgdG49R5rPhthuLFtmSGpLuWjKITSPSiGe28PWnmi+5p0pteZuzcW6hj\nmrglwJ6RBKfUuBgGhFzYAAAgAElEQVRJFOiPZlkesjOTVTkxnaHeY8EkC4wnCxhEkQZrnsk//QLf\nmtXoKy/lN5v7+JW0nUJ4lPbnPqH6tbd5u3OKr1hPUBjsItXbi9Fpw7r2IpSSao5GYUHiUDFj2Fv8\n/9dXXIKg5BDaNvK9k3/KPanj/HHHIL+Yk0e1eBB0DXSNfrxU2kWeOTLF12wnGHruWUpXL+ajhss5\nvftljPNOAtkAqspuKskqGi1+K/FffIX1l97BzQvc9OWM1GmTiNFR8l0HMbauZcxRT4A4A4qDuqn9\nDAQWU77nbxhnL6ZQOhupczvH732YuXfdScbfiGVwHwC6vYQhcxUdU2nml9oJ5MZRD2xEjUWIrvsv\ncmqxmhbQouiHNyM3LESXzaScFZi2PYdcWkWbbwUt3W8iGIxkFl2AffoE6uBx9FwWqXEx2lAHN/bV\n8vi5VaReuJfwJT9n1nQbqq8WAKOv4t+DZv6H8Z9mt6r9ZyWK/xGOkn/Obf6XYDW78QmM9fM4bG5k\nOlNgZYUDMZ/maExkvnEGMZ9k0llHYPIIhWAzQibGG6Mip2z8PZ0X/zdVLhOqriMAw/E8DV4zvtQw\n/Ybyf5Ti0wWNEoPGUBqqmSH/4bOYV3yBZyI+rp7j4mCkCCRzikaVy4LFIFBuK3YVytFh9uRKmO21\nEM+pTKQKNPst9MzkmJ/tYibQgqMQ5aOwhKrD4jd+y/DVt+OxyCTzKnNKTMgdn/DD/hD3nFpKW9zA\nAkuCGaOXWK5o5dkzUxTAXzerBKdYYCgjYpFFAErTg+hGG12Km0ReIZZVOLn7VTZUXsjFyR0k559H\nuqDREclgN0osMUzybtTFuiDokpGkYOaZQ2NcvyCIe7KdsLeJtvEUFU4zBU3DaZIosxn42fvd/PL0\nerYPxiixGFjlKLrfFLx1GCY6GXA0FM9L0dnUM0XQYeaNQyPcdV4TnrfvRTTIyOd+m5Rgxtm7nUfT\nDXx9XgnHYzpNbgkhl0BQFboUN5II1S4jkbRCMNWHLhmZslUUsxXeDPS38Z5jBSZJZG3fG4jLz0fs\nPwSAHmxASk6iJaIIBgOF0X4ONF6ISZJon0zyhYYSnKkxhuRAUWZJmuDHewvceUY1b/cmcJkNjCVy\nLA05qTPnETKxzwTz/Qi5ZDFTMtRGum4VPddeyOBtz3BajYuMouMU8uiyifxLd2Jddy2qzYuUnGRf\noch/cZpk5qQ6UB0BHuvR+WZVlgd6jdywqBxd1xlPKYQcBky5GKgKYdGN1ypzNJxhgSmKlJggH2pF\n2PUqkwsupmcmi9UgseHYOCV2I6uqPJTaDBybTNPst2KURFzv/gE1m+fQKd+n0mWiamgHgsnMeHAp\nPimHlJxEl4zoBhNi30HURBTJ46fQdwzDsi+gWT0I+TTCWBffOO7nznMa+f6Gdh7/UguvH5/i5Go3\nldlBOqVKgnaZvKbj3l3MwirTk1haV6HWLkHq24doc1IY7iHbcxyjP1C0T81nQRRRm0/HMHmiaI06\n0cF0SSOeeB8xVy0WWeSlY2GuHH+LyVXXkfrhVWz95oN8rTwOsTDDweVYDSIlo/uLL9T5JxP1NpJV\ndEoHd9D/l79Sfd01dFSsZY40gy7KiIViBnwqo+KzSKBrGCY6+TBfwSlD75Ab6qPz9P/CapCYrQ4h\nxCfR3UGYHCDbsIafvtfNqY0+WsscZBSNpvFd9IVWU6uOI2QTFEpnI2gK+tYXiuXX2ctQj+/i9+py\nfqx+wnsVX+S80gLq/vcJL7uKYM9mRF8F4+7ZlEU70aweUrZSHJMdhD2zKY0cIxpowa4mEQcOMVN7\nEt7wYbKhBYwmC1RaIaVJOFJjqIc+Qlq8DnGyFzU4h+6slcbBzUV/9k9eQS6vRVdVtGS06OqV7CnK\nbql50BRyZg9GXUFMhBkzllKeHUFt3wkrv8TUvbdQev5FAEzWriEQO0Eh0IjctQ01Ms6OqnM42RYl\n/MR9+M88m5G/r8f880fwJfoR01FUVzmCmi8C9uET9M27hDpjGt1oQ57qhUwczVNBD16MkoDvrXuw\nnXoxqqcCMTEBkRHU2iUIah55Zpjs3k3I676OLogIuoZmtCHmigLwuigzmDVQo4VRXeWMpRRyP72W\nYzc/wgW+FGImxrB7DmValNF7fkHF936K6gzyx32T3Lgk9A95LOuR99DnnYH63mMYV55btP6UzQhq\nnslnHsD7zVuRxjrI162AD5/A1LQUzeJCN1gQlBwjxiDKb79GxUVfREtGmfr0ANafPIh972tkln0J\nkywyGM+TV4uJksm0gveFX+G++mboO4Su5BHNNtKHd2NdsIrs7LUY8wnE9AwZdxU/sjVxf+wA2tYX\nkZes4/DXv0n5qkZKL7uu6J6mFBgILMYii5gkAaukI8dGSb//HNZ116L1H0b2h8ge3Ipx9QVE/vYQ\nvvMvQ5ANKJMjZDvaSI1H4Ft3Uzp1BD2bQjCa6Xv4Qcp//zRScqqopmK0oB/ciDR7KSPmSlyv34Hz\nlHPJHtxKajSM57IbUd0VSB1bQTbQVbqCeruOFB8n9d5z2M+6nBO3/Yrqi9chLTqLLrWERmkaZed6\nDEvXEVv/JJOHupl12z3osonjBRfNhX7677uT0mVzkf0hJhZfTsXEPqbefp2Sr/yIuNmHe7qbFyJe\nAK5d9PmC1ehY7HNd/98db8Vf+7y38L8S186+4Z/O/8sGK80V4KgQZCpdwCiJTKYVgjaJ549OYnd5\nMbv9yIKAbDAQF23Yp3v4NGFh5UmL8fm8vHdimolUnraJIp9wScDEkbSV3pkMY8k8c5hgf0zGajKS\nVTRKSBOZfTqvjwhcPMfH8WmFD7omSSoqPquRWSVm7tvazxnVNqRUhA7dx3P7hslqIAgi73WESXzm\nSpOw+LnuuYPMrS1nOJ7DbpRoOWkF7QmRVo/I9pEUTW6JvL+eEruZkYxIpdPIhGpi13CclZ4863tS\nRNIF1tZ4eLV9Ak00Ek7lKbMbGUnkkR0lXPx8B43lTj7smmRltQdf0yKaPDIf5EN0RNJ0T2fY3DnJ\nla2l7IsaWH94jLPnhkjpMrIoMJ1RmOu3opudWFDojquYJJGxZJ5EXqXGovJmV5S5ZU5mMgVm+6zs\nmgLbU7+jf+4ZRAwl1DgkwmmNqXQet9nAXL+NZ7b2cdNSD+q807BYjXRL5XgsEmOWEPNL7QiSTCqv\nEyuA+9gHaEPHKTVkwFuFVU1jbXuH/e5FnMiZKbMbMUgiJUffY2vwDDwWA7c8u58bLj+TvpwJt89P\nl7kW1+6XeMd5EgTqyXkqcQbKOJ6QyCga5zR4MOl5QCcrmJlI5alwyFSU+pjMauQUjVNCJvLIDMay\nfDqRpaI0gD0+DJpCr1RKVJUoIUOP6mbO3KLJRJ3HjK7r5JEwp8J01Z6K12FDl42kjS7aJpLM8VnJ\nKBq+mR6mvHOYX2rHsP9t+j1NuCwG3u+OcJojSgQ7Y3mZBGZcJoneaI55tgwxkw+TCHumRWb8TThM\nEkcmknitRi5vsGAyFxv4gnYDsiRSl+jicNZB1aKVSPEx9hlrWe7Ok/lkPfrMONbhNoRZS9ENZt4Y\ngcb2DSjjA8j+ckSLjUTbASz1TQgjHfQ45yC8+RcWXXAxg7E87x4d57pZIgGvl4yi47aaSOoGhhN5\nVE1AeO9vWKuriXWcwOzzMlPWQvzJ+zAaFGJth7E1NCKddBmirlA4cYgN/rNolqZRu/Yx4GrAq0bJ\nW71EZTcdUxlqtEmG8mbqhnbSU7qIuSGBRXNq2BR3U2/T6MzbGUnkqDFkUEZOIDtdmMInsDkc9N51\nO7XXX4MWn2bQ3Uh/1oDX7SQmORBFgU9HEkiSxJ/3jFD36r1UnHMRTrOIsOQcgkISl8uJlE3wfq4S\n44M/g4tvwt6znbqmFhYFbbzZOcXykAPdX8NMVsV97AOoaGJCt2IXFCR/JXr1/KKV5fQwTQuWMBVo\nYVl4G6PeZnKV8ymVsoTdDVj7djPhqsdaUkpGtiGKAmmLD+/IXlRvNSfSEj6bEbXtY/TahfTqXkQB\nXCaRzhkFsyxi0zN0BZZgtTsxZSKoriCBiYOIzhKQjWTmnYUUakQJzcXoL6c9IRMoK2ffRA6LxYqt\naxtGQUWXDcVmO7MT6cC7dDVfgl9I4myaix6fotDXzsfG2cy25osXHk8ISdQJVNZhGGpDKsQwLDiN\no3c9wceL11H95v1Yl66li1K8BpUpdz1sf4Og38pLU24W5LrIh+YhagoIAm6riUhepKw6RMLbwIwi\n0Z2zYSuvY/toBtffbsMSLGVqx25Sy87DFetHs/vRRYmoKjOelzCbTIwlCwSsImI2gcMA/iWtqM4y\n7G4PBXuATEHHPd6Ga/FS7uixEypx0hxw4DMLIIi0T2WpkNMkHBWYGhcjTw8Q883B2LkNPVCLrXUZ\n8kyxISylG7B6fexUK6jOj9BjqsTq9GCUBErrA/TWnIqvNMDAsy9Tce46ZIuVE6qL4Ng+HB2f4Ond\nhXx8G6VOAwanA31yCKmiEXW4C9HuIjvQhx4PY/W4UTv3IGQTCIFaVnlGsDYvgsgIpGYILGnC5LZT\n6G9HDlSgpeLIwVm4Oz/CqmcR8imUI1sJ727DZikQ2bmLRNtBrOWlyP4KbA2zUadGUadG6Wo8l9Lc\nOJEDR/CfczG6xU3YXs3+rBPrh68TmFuJ5q0GNDqyVvwVlWgWN67e7RxrvYLMPT/Hf9lXMC0+Fa1z\nD6ngXIatlfhI4nbaUd75M9ObN+GY24w62oNn8SIMs4uXEavHTxQrwp73MVfVISpp3LMqEWpbEYeP\nUfDVYDMbseXHyEeTjJ/2Laon9qBWzOfEnx6jbOlszLKA5iylsdRFk8+K0fD5NlgNH5kgHc3+x4wO\n6Sh5tfAfN5aWLv+n5/cvM6vd4QSVDpkXjk1ySo2HSFpBFAR6ZtJkCiqiILC6qljaevnIOJe1lOEy\niXzcH2PPwAz1fhszmQLHRuIYZZFvrKxhMJZB03Wa/HZGEzk2doQ5d24pI/Esyypcn1magl9I8c6w\nSk7VWBYq6nqeIg6wqVDFR12T+J0mrmkN8slAlAVlDqpdRh7fP8ramhJ++PoRvnFKPZIAJlniwY9P\nEHRb+P7aOm595zg/PqORoMPIbz7o5Dtr6qhwGBlO5BmMZVFUjSe29bGuNcjikAuf1Uh/NENLwEam\noNM2HufSCo23JwzUeoqE8VhWoWMqxZJyF/3RNE1+O70zGUptRobjWc6oc3PGvdt58dsrikLfwwlM\nkkjnVJIvzfWzoTNCyGkmaDcxkcqx4cg4R4eivHdNPbtmjIwlcywLOXmxbYyV1R6SeZVzxe4iMb/l\nLORsFESZVfft5+OfnMzDu4cJOs1cOMdHNKui6Tp3b+nlW6tq2DE4Q4PXRtBuQpagO5JhZUVRfuyl\nURNXu0bZotfS5LMSGN3L9QfsPP6lFrb0x/BZjVS6jDiNEuOpAmUfPYRp+Tn02eqZySgsjOxmum4N\nVoPItsE4Z9nC3LRbo8Jj4dqF5WQUjRue2o/TZWJRtYfVtSWsrHAwmizyf2tNeXTJwI7xPJVOM52R\nNKfXuhCVHL/fNc4PVlfxdNs4X1tQhqDmefLoDOfP9qPqRQvBjqn0Z78XB+mCRvm+F/mw+gKCdhPz\nzXFyNj+beqPMZAt8uSzBaxE3AOvqPbRPZVhqS6GZncjRISLOumLWe2E5yXwxa7/YmqRf95BVNTb3\nRri8pRRvZpxu/BwNJ9k3MMNv9I/IjoxiW7CUE7VnUv3xAxjWfQ3N5OD4VRcx71c/KBopzFnOtK0C\ntxpD2fQMg5t2U/+Nr9L7l6cJrmzBvvZ8eu65i+prLiO6aweOm36P+vrdDJ7+fWYXBlC6D2KoqCfy\nzms4ZjeiKwUkbxlqZBzR6SVx+AB77/uQs7Y8Q/y9lzj+0k7c1S7qv3wxcsMiIq8+ifurP0E3WBFy\nSYZuu4Wqa65ioOZUqow5xMQEo9Zqyo6/S++sddgf+SH+NatANvK8fQ0XfPogsZ4RKn71J6ToKP1/\nvIvqH93KxBMPoGsawcuvJrr5HUweB2PbD1P5h2eQo8Mw0Yc6E2bsg49w/vLP2A+/jbboi6BriNkY\n+oEP6Hn27zTeeTdq517k2haUnjYEiw193hkIxz5GrJlPxlVR5KGSxRDuYsw7j9xvv07VNVfTFlhN\npdOAd7qzCO4GOkie8jVcWhLN5ECODqO0fUx85dUMxQssUHqJlMzGN3GIwnAPXU0XUGKWP2taU3CZ\nJJyxPnLb1jN02vdoyA+gCyJKSQ1iIYO+/11Yej66bIJtLyC1noaUnCzSbqwedIMFaWYIJAP7DQ3M\n730XafYyNKsH5cOnATA1L2fAt4CQWUPMxFB2vYFcWoUYqELx1aHKZgyxESKWMgITh4rWq53bwF8J\nkREEdyn5tk+QS6vQNRV1YpCpfUdx/uwhMopGYOIQhWAz6Bry6DEEg4l8sBlDuBvdaKFTCFK392nk\nZefCwBH0hmWIuVRx/6KMoCloO15FOOly+jIyAauM88RW1FkrMIS7SH/6PoOnf59ZxiRSbBwycXK1\nKyi8cDuFVAbH9b9ASkywpxCg2mXGr8eIyW5cR95GLqsBXUM32SAVBU1FdweZslXgSw0zY6/Evvkx\nupd9lSqngXheo8QsFek6qT5IRFDCI8iBENg8jDnq8R/egNi0CjE9w7ZLv8OCLR9h3v43Op9cT8Ml\nJzPd3kvZhZegh+YQff5+BEnEtXItVM+n/1e3UPfjn6P46mD/O6Dk6X/lLcqWz8V82S2M/ua7hC76\nIlJVE/m2rein38APrHN46MCjzNSfzPEzz2D1y4+Q2PgK5mt+QTirEwofRPNU0C/4qEv3MHD/3Thr\ng7gv/SZa78Fi81WgnomHf0fw0isYef5vVHztW6R2vIshUI46E8ay9AyUsX7Gm89j+KIvsPBHlyOt\nuAAMZtIv34fjnCvJH/wY05zFqK5yxPQM/X+6Gy2v4KwN4rv8a6jOUhLP/wHn5d9Fl2Q4vp1s11Fs\ny08jd2QXcnktibb92G74LVJ8HCk2ipZKQKAaMRMjG1qAqe9TtptaWOEX0T9djyCKiPNPRWvfAUvP\nRzi8keiOrRi/eSfmrc/AqdcDYLZ8vg1W21889Lmu/++OLx+/6fPewv9K9P52+z+d/5dgdWQmxfGp\nNI1eC0Fliuven+ScliBBh4mCqhF0mBiMZVk3s5WHtMXc1GRCzKe44xj8bIGFPxzN8c2lIUYSCiGH\nTPtUhjneokZpLKcy264yXjAiiwJ7RuK0BOxU2otc1IU+Ax8MpNjSPcWXl1RQbjcwFC/w2K5+vr26\nhlq3iRW/3MTj312NwyTRbIjSo5cQchiYyap4zBLPtI1zXWsZg7ECPTNpmnw2NHRmhfcyWLaMyuwg\n+9QgC/wmjkUKWAwiT+8dQtF07jrZx5/b03wj9TFTSy5HEAQOTyQ5SztOYbALceXFpCUrHZEsdW4T\nJlnENtPLo0NWvlmdZ1PczVy/jTJDnnv3TnH+3FIe3NaHURL53boGbnz1CI9c0sJMVqUu0Ylq9RQz\nQPkUfYYKqswF5MGDTFetxCCCtRDn7Ge6+OC6Rp7tznFFSwB563P0LrgC4+1f47VLfsc1rUHKZ9pR\n7cXSt961G23JBTy4Z4Sb51n5eFKm2W+lLDOE4qkipQpMphVqbTrCkQ9JtpyDbf964osuomR0P0p5\nM9Oaibyqk1d1SiwSDi3N4+0JRqYz/OLUWnKqjv7Urby67Luc21gExyGHgemMSnXPRg6Un0p/NEO9\nx0qTz8zm/hiDsQw+q5Ev+eIISp5OUy0N4jT7M04kQWAqnef0MgGOb0OfdwZoKs93JekYS7CytoQF\nZXZCYgJdNpOXLTy6d4SvLy7H3vkx7cGTaDLEuHj9COuXJyjMPhlt/b2cO76GzefK/GWmgq80GOgt\n2Kna8jCSP8Tm8rNpDth4Yu8w1y4KUSUlEDMx7mgXmRd08vbRcW5f14jHCJogYRpvR3WVMag6qLRo\nbOhNcV5DCeaRQ4T98/B2bEJrOhlBUxhRLNgMIkZJwN75MYLJzOhLL1B286+KZUKjBXlmmNyRHZha\nVlIY7ELPZ+n4y+s0fGkNosODvOxcxHS02KFtdtCRsRB8+de4vvIzhFwKZfdbyMEahNBsYo5KXLE+\n8jvexLzkdLLl89k7mmR5uQ3t3YeRz7geMRVBbd9JeMt2Aj+9j64E1G15EEPFLPqeewVrwEP5Dd+h\n0zKLepvGeF6m7Ejx5Y9YpI1on/3GMgYHpm3PIS1eh1DIUDjwIYLRXOyI94QYNQUJxU8wYK8nnCoQ\nsBmo6HwPPZdlaN5FuEwSogC2XS8iefzotYuQwifQSirZlnSy1jSOavcjnNiD1rQWKT6O1lfMTCNK\nSIkw8cBcbJkpdKMVsf9AscTZ/gZS00rGTUHKp4/S42ymVh0n765EUrJM3vsjyq+9AaWkiozJg2Pk\nADPBhVg/eQo0lek1N/D0gRFuWRYgK5qw5IslRHHwMJPVq/En+9maDVDpNFPV/iY7Ks76zHnJhF/M\nkH75PmSbGdOs+VC3iGHBQ07VcTz6I/zf/x1oCm8Na5xS4+aBnYP81+oqHIN7eLXQwCX5/YgOD2OB\nhfgNCnHdiCs/Dce3I4dmoRtMdMjVNJjT0LGDXYG1LA9ayDxzG45la4pcSH81YnqGP4z4+F5hG1LT\nis9oAAUSohVXfADVVc4D+yc5d3aAEovEdEZlTvwISqABMRVh3FKJ36QjKDneGSqwutKJwyRh6NxK\nX3AlBU3nk/5pbgzGEJQcB42NOE0SNVqYITlAVX6UVyZsLA25qHAaMA0fQjM7SLprMUoioq4iaArq\nO4/w4byvcPb4RsR5p6CZ7BjCXUQC8/GkRtAFsei6ZzOg6eAxS0iFNFJiAjGboOCfhRQfY1PSx5nO\nKHFHJWZZRNN1DJ++imAyI4Ua0WNhCgMdrK+8mMsNnWD30v/Hu7D4PeQTaXIzCWb97JcovjqOXnVp\nURVBKaCM9iE63CjzzsJwfAujr7xMSUstxrO/iqDk0GxepBOfctOib/LQ6CbGH72b4KVXMLP5Xaxf\n/x36G/diPukC8v4GhC3PokTGCe/roOam76N4KshueBRjsILcyCCWi77N8G23UEhlqb3jAXpVJ3WG\nJOP3/ILArQ/BlmcpTE1gCJQXn8UVFzGpGCnNTyBoCokNT+FYtob83NOLz3333wGYWXE1JQdeR1i0\njt5bbqT2mkvRVlxC/7cuQ7njOax3fYPqH/+KRwfNfKNeYEL24d3+JMaFp6KU1CD37QFgrHw5JZse\nxHjylxj6w28JXXIhzF6J8tHfMK6+gNwnr5KPJrFcdyuGiU46zLMIvXUXAO4b7/j3oJn/YfR+Ovi5\nrv/vjkczj3zeW/hfibtPveufzv9LGoC1YzNiaR05VcfucNE2keKi5gDvdISpKbHSM53BLIvMyg9T\nOacVd3IY1VNJBpmcbONEJI3LYqJtIkE4rVBqNxHPacxkFdomEljNVgZiWUptBt4+Hua8OhtxtSiS\nbzUZODyexG01YJQl7EaJSpeR9sk001mFvAbXrq3jyESC5oAdq93BwfEUIYeR5w+PE8koVLksJHIa\nbx6fYDpTYF2NlWcPT1Lf2ITPKrMnamJ+qRVZKxAww6djadpGYoQ8Fj4aKGouNs9rYf9UAbtRIppV\nqKypQ85GGXXU4ZEVIjmBKrNCpCAi2r1UOM0kZSd+m5FwqsBkTuS0Og/PHRxldZ2X8+YGcJkkqrwO\nsqqOKAgYPKVMaFbCqpkZycl4Ms/ecJa5JTIzkou3OqeI6Sb2DUU5d0E1HVNpliknEO0u/FoM12nn\n0FAV4r3uCK0eeDNiZyhvojbRA6HZhNw2dk+qtARsRDIKaaMbb/QEaYuX19snmOV3YosO8PS4jSXi\nOBOueuz+cuSe3exXfLRmO5ky+umKZKgZ2kHEVUdtiZXZ4T2YJ3swnXQ+VaU+Srs+ZGvez3yfmWhe\nwy+maVc8fNgxSYXHwq6hGO8eHefgQBQdKNi8VJSXMxDLYbK58FhkEODhbX2saiin01hJZXoAgIzs\nYGW1B0XXmS+GkeITbI67qLdDnc/J0ck01ooG0gUNr81EwOuiomMjh5zNGJpP4gstQey+ID67mYxo\nRtMhVruUXPlcBEQMkoBBljBKEs537sdcUUvcWsrykJPRVJ7lFU4MukJMEZBdpXQkBD7pn6G13E3P\ndAZRFFGdQcoix9BCTcjDRyj4G7B/+GcsDQsxx0dRx3oRQo3YvDYSFQsZLFhw2u1o+94lsu8wBi3J\nxLbd5CMRQqctYWLXYTwrVyOJOjH/XIyFFIN4qHEbsdU3MCp6sR35gEJ4FGnZuegGM8Z9G5BcPtJH\nDjD02ga8p51JbbqH6AsPgKJgNOooHXtQZiYpWXcRUiaK6igj/vIzuJetwHfqadhXnYEgwITgwmfU\n6IwpZO67Hc8FV6Af/ohns7OoeOUOjPFBTNVNCIlJsjvfw1ReSfrwPgy+UkRnCdp4L8a9b5LrPoIw\n72TqhRkcZhOiy4/SuQdny0p0Xccsi0yXNmNzOMjb/Izffzue1vnIniDyxifIHdyOceW5aGYXdO4g\n03oe/TkTmG0U7AHMH/+VkaqVWDc9ihyqZ+O0lebWBcT/di/+MjeJLe8QaGqhX/DjSw2St/qwpQcQ\n6haRswWQ3vwDYtNqDAYjyuEtWFpXk7SV4bWZKBdTmAb2ow8dRyqk0IOzsaoppMQkobIyBNmAzSwT\nKAtSG+9A2/AY1rIAxoZWJjduxH72ZSg71+N1WXD27MJ56rlFnmF0jJKKOjwdm1jbWIopMY7m8FNd\n5sfgCYBswpGfYVIqwde3jQ8LIRpMKSJlC7EoKTwOG1J6BsHhwesvxdzxMbLThZ5NocWmENEZf+1F\nWi/4Eg6HpQgQ295jOjAXt55ByCVJmLycVOnEK6QxmCyU7H4evX4pYt8BBJMV/Y1HsPrcCFqBulAZ\nhaf+G2tDMzyQkSEAACAASURBVFrpLCRZJijEaQn5kEfbUafH6TZX0eI1IqWmcCkxVFc586b2UmJU\naUvbcO98EWH+6ciyAcN0P2M4cRaiEJ2gyW+GqpZiNvrTN6B6PnmjA1MhRcEVQnzwhxyoXk2ZzYBD\njSNHR8j7G4iafFi0DIgys7SiprVssiDufBmjzYqQTfHa+bcy+5c/JbHhSQbf/5Tqy67Bduwj1PF+\nStaejtGk42yag/fkU0hWLkZ58U5CXzwLKuYy+KffY/PZQdeZfPrP2BtmceDu16lc24Rc28LUk39g\nYt6ZdFz9dS64YQX2qnJ+euX9nHV+C5KoIc9ahFzZ+I+LglDdgjDaydiOI8jpcUxLTkeKDiOYbZjq\n55Ld9gau5jmMbDlMcPEssk/cy+Rb6yk/fSXC8HH0Qh7LgpP49Pv3Uja/DIOkY3W7QTahd35KdmQI\nS1MrhnQEX7ACaegYWnQK69QJDMFqBIOZ1MFPsV9zC4KuY5npxHZ4M/HecYzpUdbMr0azeTg4rTGr\n1EnMOxsjKvrAEaLbNvOhexHzpAhjoWVULF+M0n0AOVCJWDsfrXM3os1JvOsENquGHmqixGpgpH4N\nqTkn4bOZ/jewzf9zjHdPoSraf8z45ccP0N4/+B83bj71q//0/P51g1U6hXTsIzoq1lJqlXmzc4pL\nmwPcs7WfG5dXUiZlychFAFRVGEft3ENn43m0ZDoZ9czl/RMRloRczPEY6JgpcGAsjsskY5BE0gWV\ny2xDhP3zcJkkBmJ5sopGjcvIW13TtJY52DMcxSRLBB0mql1mKh0yP3n/BF+YW8qp4c3sqzyTH73U\nxsIGHxe3BjGIIg6ThNMk0TmVxmc10mpNsXHSQCSdx26UCDnNLPCbmMrq+A9vYEfFWaxxFgn/I456\nEjmNapcBRdO54ZUjfOfkOhxGGY9FpsQsYZU/Y/7rGmJ6hgMpKw0lZpzpCXoFLxZZJDS+j8nQUnYM\nxmgO2LEbRKYyCtOZApu7p7hhaQXbB2OcXO3GahDpj+ZYpPbRaZmF3SgykSzgscjU5AZ5Mezii40l\nqDok88Wmr0+HE5wSMvFSZ5wat4UmnwWXkKMzKRHLKvxpywmeu7IVOTXFrpiZ5eU2CjrkVZ3fbe7l\nC3NLWRK00T2dA2BRsg2lYj6CrtGRMtDoMSIUMtz6yRjXLqngtcNj3NqsIuZSKK5yrnxzhJaQi1l+\nO+tmlRBOKTRYsoypVtonU5wWMiGoeXJGBy8fDXNuow9BgMf3DHN5axBdB69F5s6PezmlwcdYIsc5\nDV6G4zl+vv4of7qsla5ImkavlelMgRa/lcPhNCtCdozpCIO6i/bJFCap2OjmMsss9Bl4qSNKpcvM\nWtM4T405uXp+KSt/+3+4e88guc5qbfvasXPu6emJmhw0yqMcLMmyJUc5EZwAY4MBEz6OwWBzwMAB\njg/ZxgcwNtjYBmwccRJOypaVZWmkkUajmdHk0DPTPT2du3d4fzSvf/FS3/fCKepjVT1/dnXVfrp2\nV+/1rLXu697BF66dx4fmlqAceJb+eVej6VDnUxlPFrhvRy93bahn72CMSxsC3Pb0Ca5cVE5jwMHS\nMgeDswVK7EVB3//m0Nr73uVtZT5NARvVmX4OGxVUuS34bUWmsLLnScwNH0U3TNT0NMN4sD1yN4G1\n68id70KtbqSw/Fo6ImkODM3w+UbQj71J/NRp/B+/C2HwJHosgrDo4iKvNBEBQUDMxDFsHiK/vA/r\nnT/FN3mabbkqNtV6kNJRzONvAzC56FrEX9yFf+niojvNpbejbX+CmTO9lNz+VcRUFGNqGKN5LQgi\nUnwEpkconO9EWn8D6BpRxUege3uReSlJHL/nv2j+48uYz30fS20L421XUHr8Bd4Ib+Yy93Sx8pvP\nMvGn5/AtaEVedTVmz2FEuwttrB/RUxRZCLWLYKgTyRPA1Arg8EEqhh5qIGnx48xFEXMJEi8/ju2W\nbxL98Z345jUxebCD4U/9iLbtP6Vn85dJF3SaA1aC02fQ/NWIyUm00wcQV1yFoTpQpnrIn9iDoFpR\n6+ejecrRD7zEM2VbuSG7H7FmPqO2KsJn34CWNZiylfNpkbBDRnnlJxSuvJN3hhJcVONGTE1jHNnG\n7KqbCIwde7/jENBixFU/ed3kyeOjfKUigmH3orvLyD1/P7P9Y5Td8FFyJ95BCoRBVpEaFhFz1+LN\nRoojJ4PvMV6xEs0wKet+A8kXIl7RjiIJqAeeRWpq54xUSaNXxXj5fqyLLyDf10l27c3YBJ2MKWE/\n8SrxQ/vxrl5P8tgBLEE/8kUfxTj4Mm9Wb2XFq99j5pbvUTewC6PlAnpTIs3RY+jlczEsLqRTbxVx\nYS4/+e5jCKs/gClbGLv305Tf+wBiLsnRtJOlctEIYOR7d2HxOvEtaEVZdimHcn5CDpVYRmMqneci\nb5JhOUTp7l+R33IH8l+EtF4yoOeRZiPkDr9J9pLPYd/3OwRZQVywsSieUqwIuRRSapp8WRvGy/cj\nXfaZ4myuKGMqFji7H3PBZuSh4wiKhWz5AiZSBapTfQw76ym1S4ipafbHrayK7EYOVXHO1UIdUaTZ\ncTodrdR4VFQjz8T3vkjJmqWIVjuIEiy/irgu4zn6PHK4mrHwUoJH/oix7iZEU0fpOwByEanV519E\nfeIMA55WqpO9kJxGq16M+e5zfPHS/+T+VCfGKz9DWf8hpqxh7IrI7H2fo/zmWxguWUxl+jzm1Aj9\nv3mUqqsvRW5eStLfgGOmv4ihcvnQJ0dAFIm0Xobn1R9h2XoHaHn0Ay+hLLkIw+rinajC8nIn1nN7\neSjdyC0Lw6j5BFHBQSA/zaN9JgvDLpYZ/eijPYy+/BqVN95EvG4t3pGjmJ4wjHa/X5E/ZZYyGM+y\nZfTPjL6+A2vAjbe1AdEb4nnPehb99DPYQl4qv/o9ppQAwcI0XZqHFjGKOD2AYLGzlzpWje/gj7bV\n3GjtAUCau+Hvyzb/zhg4MvxPvf8/Or4z/r1/9hb+R+LXV/zyr17/m8nqPa+d5vYVVUz9ZVZVlQXe\nGYhxdUsJHRMpGgM2Htjbz90b67jhsSM8eP0iqlwKB0eSBO0q1R6VV7qn2FjjY2d/jJvrVXIWD52T\nGZbrffwqEqTGa2NTSMew+dBMkEQBUcvx9NlZAG5osCEmJjivVnMqkqTeb+eVMxNcNy9MIqezWBzl\npZiPeaVORmZzlLssJHI6C/wiI9miD/WbPdPoJswvdZIuGESSOeaGnPhtMh957Ai/+Wg7dYmznLE1\nkchrSIKAyyLxTMcYd66p5sBIkg3OGX47pBLLFKjz27mizkVvwqQ500My1MoD7w5yzbwwE8k8C0sd\nnItmaA7Y2D+coNpjfb81vrs/SnPQyVgyR0vQQYPPwkNHRrijPYyGyNGxFAXDxK6IdE2l8FkVPFaZ\n5Qd/iX71XeT1IrT90MgMt85185/7I9xbNc7DszV8KjTFhL+VvG6S1Q3KnQp/6prmgjleyo0ozw6L\nuK0K80MOjozO0hVJ8ukzj+D8+L18Z88wtyytJJLKs9Srk1NddE/nWKDG0F0h3hlJsyH9Hj3hldSp\naZ7r15gfdpEtGCx0ZtgfUyl1qtS4FXIGDM7mKbHLJPMGQZuERRZRps9zWihHFgVqvSo5zSD7lxGD\n8uwIvx+zc2mDH4+koUsW8k/+B+pH7iWW1TkwHMf3F4buVc1BnEKBmF5MIseTBeZakpwtuGjwWeiL\n5dFNE79VorQwiTB8mtHa9QCcj2VpL3Owa2CWLb4kSMWhfyGfAlHGlBQe7xeIZQrcuqSc33WMc/OC\nIg/SN3seMT3D75Nz+HCtTOH1X/NC8y0cGYjxfcchjjdfQ7qgs/itHzPzwX+ndPevECSJ6PrbSRcM\n6pNnQcthJGZ4QVnMB5yjGKoNoZDDsHlIOMrwDOxHm9OOPHISw1fJ0H/9O46yAP5bvgTnj2MkYnxx\nZgkPXFyB2H8MwWIj3/0eUkkFcukcsmXzEHc/gdqwgH5vG2VOBVHLMZmXCJ3ZhlxWR3L3y/S+dICW\nW6+gZ9nHaDj8OEZqFvXCmxDGz6E3rOJUtEhrMEfOMlS7kdmczvzYUYxMCmPuRkYzUB05ymTFMvwd\nryBVNiGYBtmjO4ieOofzrgewvvsUna3X0fruL7EuvoBDllZePT3Bt+YbDD/4A1xf/yXu9AQztlLM\nh+9B/tR9KC/+APvyTWB1oZXUY0gKPbEc1dt+iOOyj3FCK8FtkTC+/jH8P/odytPfxXHNpwF4dlhk\nZZWHMqeC0rmd7c5ldIzPsq7GX3z51vuIpDQOj8S5Lr6bozWXsMIc4KRax1s9U/xbk0m3UErHRAKf\nVWFZuZMnO8bpGIrz9YsamEgVcKkyjx8Z4pMrqrHIAomcQWviJN8dKBqUfHV1OT8/FuHy5hLCDoWB\neJ79QzOcHU/gssrc0l7BWDJPpdvCmck027sn2dwSIuRQaZWiPD4gEnZZubjSQkfMxGeT6ZpKszls\nIqamGbJWM5bMU+22cHwixdoqFy90TVHQTT66sBQpHUWK9DISWoxbFdFNeO70JLe0ednWn+bSOjeC\nodERNYhmigiyRF7nkvwJfp5s4DPie9wdaeZLF9QS/wtLtUkbot9SxB5dZhmi19lMXfQ4kfASeqJZ\nloWt9CeKn621GxQkC6+ei1LjtVHnteARiq5SNy4IE+zZxUjNeqbSGm6LRKVbQT75Jtm2i8n84m48\nn/kuL/UmuE48w2B4OXNmz2LEJjCr5zOAjxptnGfGbVS4rVR7LCiiQEjKIsWGeXzKz5pqLz6LxL6h\nWWZzGovL3MxLn0Eb6yfXfhXa49/GtfVjaJ3vUlh7I47hY+iecozOve+3+qWjLzP83J8IfPc32FMT\npF/5NeMHT1P/rfswZQsDQgC/VcJppDElBSkRQUxNk+/rRFt7I4qRJ2Uq2BQR6fCfyLdfhfjag2Qm\nJnF/+PP0mV7qjEkOpL20lzkoGCa2Yy+RWXIVtnwcKTlJ7MXf4vxkEaYfyeiEbBLCvqcRLDbE2vmY\nFhdG516M5Azy0ktgqJPxl14iuHQeojfE2J/fpPyKS5l44y2CK5cgbLoVKTEBhs6so4yCYeKTDcTk\nFH1CgP5Ylgv9RQHY+MM/QbKqOMIBZKcTy/ItnP/h95C+9RvKDv0O0zDYNucaLj7yEKnxaUI3fxoS\n0xRqVyCf3gGlNZiqg6MZNy5VpiV/HgBpzsL/+wzmHxBHXj79T73/Pzqqm/+609P/3yP0f/hefzNZ\nzWSzWEY6KAydQ2xZiWlx8MqwQVPQwWQqT4PfRujQH5DLajgVXE6T34pkFIoculySgiuMnJpiRvHh\nJcOYZqXULiHHBumRyqje8xATGz9D1ch+BEVlJLwUmyximiaB2Dm0YC1Pn51l3RxvsYr2szsJffpu\nBEMjai/n7HSW5UGB8xmZSpeCYmqIySmyrjCO8U7GfK3Ec0U8zmzewGORmM5o1LgVxGycjOop2gZa\n3Xxn3wQ3Lq5g70CMQ+ejPFQ/VKw8HfszwsJN6DYv6ngXgp5n6sXfE/jAxzEtDpKuCs5FcywIyIin\ntrPDu5oVu+9n6MqvMjfegR6LYM6/iO4E9EynWVvtwZcY4KkJFzeUZdDeexvB7kYOhsEVLGJXLH/h\njHW8jZnLcKD2SoJ2lal0nkVhR5Fv5ypFjg5S6D+NNjHI0IVf4OEDg/xgqYTuKWc8J1GuTyEMdXJH\nbwUPbG1h84P7ef725XgOP8P3zdV8vS7Oq7lqrrAOk+8+VsRQZeK8p4VYpEaRkpP8eCTIF+cW3Xze\nLZQzP2QjltXJ6SZ1fW+xJ3gBS3f+FNfazRx2LKAlUGyxJ/IGZWKS7x2a4WtryojpMn4yvDKYp8Jt\nZXFAYjAtUm03GMmKCMDBkVnWVXvJ6QYjs3lcFol5U4cQXX5eTJXjVGVi2QKKKLA1fYj+2k2EHDIZ\nzWAyrVHjUemayrJ48l2+P9PIXfaTiHPmobtKuWfHMPddNIcv/rmPf7+wHpdFwrL3SSLLb+R8LMuS\nMgcHR5JsVEYQDI0uexM7zk9zZVMJvbEMHouCQxWpZxrt4CukN36CqYxGY7Kbp+NhtjT4mc3plKsF\npMQE/WolqiQQFpKYspWYoeA3EsXZy5YLyL94P/Ervvy+8lnqeIPh+k1UahGkdAx9cpj4wb2kbvo2\npXYJdawTzV/NnwY1tjb6SD7yddI3f5vSY8+i1M1HdwSYthaZnLoBwaPPYGTT7Gn8IAvDDvynX8fM\npDBXfRBpdhxMA8E06JfDnJ5McUlsD/nFV2I9uxtjzgLkmVESpW1EM8WkwjM7AILIebmcvG7Smumm\nUNpMFhmLYDCaNkjmDeZOHkSvW07+xftRr/4Cpmwhawh8ZVs3D15eXzQPiI6R7z9DZvMdvDeeIpop\ncE04j+EIoIx1ortKSdhLOTiSYP0cD9bRDrocLTRHjyGoVozULB8/6ecX17ZhSU8jmAZ/nlTZcPAX\nWK77InHTQiyn47fKzH7jVkLtLcgfuhu1Zx94Qmi9HcwcOoCzqpTvuq/l2/4zGLEI4qprEAc7ECSJ\nl5lLe7mLkncfR1m0gd+OOtm65ycEPngrx8U51HhVRhIFxhI51hx5iL3tn+LE2Cxfmitz+vN30Prw\n44ipabq+8mV8//0M/OxOSj7/TaKSh5KRw+ixCEcqNlHttmBXRDzT3Wj+ar6yfYSfLDE4+42v8+7n\n/puPuwc5bGtjwZFHsS7egJlNgt1LPNCEq+vt992e0jufRy2fg5FNoyy5iMEffpvw6kUMrf80NTYD\nfdsveXv+x/FZFdZoXWRP7EN0uMitvwWbluL1EY3LvHFif3wI3wc/QcpXx1iyQMP4AaZq1hDITSKm\nomihRs4nTSpcClnN5MkTY3wm9hpmJsWTNTdyK+/RUb6B+Y40cdmLLzGAfuYAgsONOW8TWdGC8cS3\nsd/0laLBS7IbbWKAExUXssQcxIgMUOjrRF13LUIhC0Ay2EROMzg6lqTSYy3OybY6kIZPYmRSUD2f\n1EuPFP9zp48wWbUS23P3kb7uHgJaDOPYG4hLthB79Pv41m1gsuUSAhaY/uGdBNdfgKAoiHY3ottP\nIdSEufcpjMQM0mWfwVCsqP2HMeLTCLICosT5h3+No6IE/50/Qh0+Tnz7KwiSiJbN4f7YPRS2PYR0\n1b/xRUcbP5s5gvbmo1jalpOpWYH1zA6MfJbxl1+l4qabyXUeInq6j8DiuQxu28ucB34PO3+Lmc8i\nbL6drliBardKzwevIPyHlzk6mmBjjYf+j11D4wfWcXL1HSwZ3YkUrkXQ82hj/cjldXTam3EoIuWH\nfodgdyMs3oLQ9Q7Z+VuY/eEXGdzdxeJXtzH675+kcM9D8PVbqLn+al4tuYirvdNMu+tw73uCxNqP\n4R8+xGConUpjmohSQknX6ww2bKaq82WUqkZ0Vwjj9D6EeevRtj+JYLGibf406oFnmdy9l/DtdwIg\nV7T+4zKa/4tIxv61OKu3vv3Zf/YW/kfimQ8+/lev/21TgKGT5E/sQa1rA1eAuLeeSFrjmY4xLm8p\npcqtEJw4jh6fxpi7sTiE7Qoy7KwntP9xxlZ+lCotgmBoCIXs+ypNuX4Bx6li4dQBJmrWIYkQmj7D\nhL+VYHYcwxEg/+L95K/5Ck7yyNP95E/tQ5AVlJblxY1rWSaC8wnmJ5lSSwiNF9tzUmICU3XQkXWz\nQI0xYyvFY6YxRZkUKu7ZAbK7nkXZ+gWM7Y8hbvo4YjpGR97HotRJxkKLCVoFpM7tCOWNGIOn0RZd\njiVyltyBbagt7RjpBPrEIPLqa+jDT/WhJ8HQkQJh+houwff4v1P4xH2Ez+9Gj0WQw9WY/koG1fJi\nhSp+nF8navlEaBpECXNqmPj+XSgOG5JVRbzmywi7nkAurSbf04F0ye0cmtSJ5zQu1U8xXLaCgmFS\n6pA5PJpk7dh28u1XYUlOMPP7B8jPpgksW4RcUY+Zz9I/ZyMBW3GEYTipYd77cap/9BjKRLHSl31v\nT7HVlpgg463GMd5JYuefsH7oywjHX8dcuBkpOUnBPwcpOcV5w120o7X7EPIp8tseJr/1yyQLBiGb\nRM4Ay74/IM9dWUTwmCakZxgItVMT7Sg6tgTKinaGkTM8nwiz9LEv89QV32JFtY+uqSSfqjWKbcGe\nwwiqla7KYmX0fyf8bkln5udfI3jD7ZxT5xCwyfiSQ4jZBOn923hjwSe4skrGPP4WmaXXFA0sOl+j\np/4S6jqeJbfmRmwdRQtKdfQk+fL5CH/hfQ64m4sJWmIIw+7jbMZCixQj6yzF/ON9ODZczcTvH6H0\npk+i9Z7gaMNWAnYF72//HXdLI2OrP86J8SSGabKhxotDFpjK6FhkEfeR5xAWXFjkUlqcCHqeuOQu\nWgVbpGKCoI9hWF3IsWF6Xa30RNOs3vcg8WvupiLezbS/GV/3dsbqL2Tn+RgbanyU2iXSmsmTHeNc\n3lRCtZJhGgejiQIvnhrjqrYwC90FBgs2qtUcR2ICK4RhdFcJcdmLf7ho0VoY6EIOVRCpXElJsp/T\nYiWNfgtiIUtPUmRbd4QvtJewdyxP0K6gSmLRitMp88KZKa5sCpDIG4RPvsSZxsvpmkpR77NjVyS6\np1O4LDIXlJgkJCcnJlIAHBqa4cqWUuK5Ak5VJuSQSeQMdvdHuXleELGQYfuYTp3fxstnInxm+A8M\nX/RF6npeJz7/CvYMzLC1JEO3WUJL7Bj/MRTmAwvKaHKLCLkEYjbx/gHMtHmYeeQ7+DddijYxyOO+\nLWxtDuJ56+fsmPcx1la5sJ/8MzNzL8GmiMhvP0J+0yexZaYRR06jN6zikY4p7iiZ4JlUFYvCLkYT\nOc5OpbArEjeVpRGTU5iGgeGvIvLL+wh95h5M1Q4ndyA0rUAYP0e++ziXDa/gdx9rZ2f/DDeqZzns\nWsxYIsdW5wS6O8SA5iLkkLGaeSbzRVa0TRYQBIFvv9XDA8sFXkuUkC7oXNHox9ZfdDIzK1owRZkx\nwUvIISO99xqJeZfiMtJ0pRRkUaDOLaEMHmMwuIg9AzM0Buw0+KyIj9+L97pPIMRGed5oZWm5izd6\nikYIa6o97B2YYWOtj7L8BGeMICV2mVCsKKSp9igcH0+zrMzOdNYgPNvDw2MePlUSYbpkHpNpjVKH\nzEiiwLnpFEG7ytrMCaLVq8hpBjZFxBs9VxQt2X30y2Fq8sPsSgdZ707Sqfk4MZ6gMWAvsoy3P4K6\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/dpYSn419szYODsW5YMNqzqUknj+foTOmU3CX85P94+REhbMpCXVOG28OpElKTqqlJINC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\nxo8jFtMYVjdSbp5KrBuxnEWa7KUyeAbB6kBecTnVY6/xzqoP0bX/QdT21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+p9f4rc5n+M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\n++67j2QyyebNm1m0aBEXXngh48aN47e//S2JRIK6ujruvvtuLJa+fynQsdy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JWqd+rVZL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\n",
+ "text": [
+ ""
+ ]
+ }
+ ],
+ "prompt_number": 54
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This looks pretty similar, maybe just rearranged cluster order. Let's check what their data looks like when you plot this."
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 4,
+ "metadata": {},
+ "source": [
+ "Their PC scores and clusters for the genes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "gene_pc_clusters = pd.read_excel('nature12172-s1/Supplementary_Table5.xls', index_col=0)\n",
+ "gene_pc_clusters.head()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "html": [
+ ""
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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iYAo0NTXSrVRzZZ2XpC7hP/ssiiqBYeCpbkY5/jxq83KKu//IVM16lijzOJNj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\n/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Afjhq/iX78RLu5nbvvtBLVZdLsPQ7WiaBlsyTFMyUlEwWCw4XLcisGhshXUKll2rvsg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2hkXmB+1c7xomai9nKpVHkAuxtzVL1iAJkBIthNIqorOIfsODyySz3Jkgq9jJagaWurm4D/8d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5MZ/JpC71yG7dVOZjNF5uVl17Lw9BlK9euZyppE8tMUXnqI7oYyHMEKYrkSFS4rvz89Rapo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\n77uFC65WmoZeZ6ZlO7Gpk5juMIz3UGjbhiKAmEuQsQUxTItQuo+Uv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GMnUDfV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\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4kSdb0Jhf5ubAUIR4RuGuxZVMpAr0TKUo5DV+t6eH5pIFmKQirERzBRkZylLpNqPkNWST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\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ux3AG6NV8RFwyTslkz2iWpREnATXOpOAjVdJp9YiI2SiCmmfUWo3XKuFJj3JeiBB2yJTF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LJYCTgtWHMzZSbr/Es5HtOpeONXiPPW4nvtV0Tqail6qojOnqVTDxK2CRyN6fxtqERLdSVW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\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pvPF2Dt/5ADNeeSZL1wkPIqYipF2laLvfQ568hP7nfo/BZSXcPYSjqgjZaMB28a1oko6e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ljMyM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\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SqkEko1EmxXizR+eqWiff2dDFaDTN69fUoO97D3n6crRz+5ErmhHUNK/GSviya5Aztgaq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uJ2hT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\nBZ0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eqXkNlsw8iqqg9x7F192Mq60FW3M7hXW3snDfD3Gu2YCgatiuugnNoWFkkniLC5Tr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l1dHQieEGKsFUbPId/+T6iVdQh1S5GtEqLTi7Z2J9r6G8hbEm4ylGOdXLHV4ReyGNF25opQ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\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3IBQyaDVNvBrXaZs4BL4KvMYSIgbG0jzJLfcTN1TKs6NYuofp2AbMJ7+Nr6ERyxkEScZq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\nRK5CYII=\n",
+ "text": [
+ ""
+ ]
+ }
+ ],
+ "prompt_number": 56
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Sure enough, if I use their annotations, I get exactly that. Though there were two genes in their file that I didn't have in the `lps_response_corr` data:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "gene_pc_clusters.index.difference(lps_response_corr.index)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "TypeError",
+ "evalue": "can't compare datetime.datetime to unicode",
+ "output_type": "pyerr",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgene_pc_clusters\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdifference\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlps_response_corr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;32m/usr/local/lib/python2.7/site-packages/pandas/core/index.pyc\u001b[0m in \u001b[0;36mdifference\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m 1325\u001b[0m \u001b[0mresult_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1326\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1327\u001b[0;31m \u001b[0mtheDiff\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1328\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtheDiff\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresult_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1329\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mTypeError\u001b[0m: can't compare datetime.datetime to unicode"
+ ]
+ }
+ ],
+ "prompt_number": 57
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Oh joy, another `datetime` error, just like we had with `expression2`... Looking back at the original Excel file, there is one gene that Excel mangled to be a date:\n",
+ "\n",
+ "\n",
+ "\n",
+ "Please, can we start using just plain ole `.csv`s for supplementary data! Excel does NOT preserve strings if they start with numbers, and instead thinks they are dates."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "import collections\n",
+ "collections.Counter(gene_pc_clusters.index.map(type))"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 58,
+ "text": [
+ "Counter({: 631, : 1})"
+ ]
+ }
+ ],
+ "prompt_number": 58
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Yep, it's just that one that got mangled.... oh well."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "gene_pc_clusters_genes = set(filter(lambda x: isinstance(x, unicode), gene_pc_clusters.index))\n",
+ "gene_pc_clusters_genes.difference(lps_response_corr.index)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 59,
+ "text": [
+ "{u'RPS6KA2'}"
+ ]
+ }
+ ],
+ "prompt_number": 59
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "So, \"`RPS6KA2`\" is the only gene that was in their list of genes and not in mine."
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Supplementary figures"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we get to have even more fun by plotting the Supplementary figures! :D\n",
+ "\n",
+ "Ironically, the supplementary figures are usually way easier to access (like not behind a paywall), and yet they're usually the documents that really have the crucial information about the experiments."
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 3,
+ "metadata": {},
+ "source": [
+ "Supplementary Figure 1"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "singles_mean = study.expression.singles.mean()\n",
+ "singles_mean.name = 'Single cell average'\n",
+ "\n",
+ "# Need to convert \"average_singles\" to a DataFrame instead of a single-row Series\n",
+ "singles_mean = pd.DataFrame(singles_mean)\n",
+ "singles_mean.head()\n",
+ " "
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "html": [
+ ""
+ ]
+ }
+ ],
+ "prompt_number": 62
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Notice that this is mostly red, while in the figure from the paper, it was both blue and red. This is because the colormap started at 0.2 (not negative), and was centered with white at about 0.6. I see that they're trying to emphasize how much more correlated the pooled samples are to each other, but I think a simple sequential map would have been more effective."
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 3,
+ "metadata": {},
+ "source": [
+ "Supplementary Figures 2 and 3"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Supplementary [Figure 2](https://raw.githubusercontent.com/olgabot/olgabot.github.io-source/master/content/images/shalek2013_sfig2.png) and [Figure 3](https://raw.githubusercontent.com/olgabot/olgabot.github.io-source/master/content/images/shalek2013_sfig3.png) are from FISH and raw sequence data, and are out of the scope of this computational reproduction."
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 3,
+ "metadata": {},
+ "source": [
+ "Supplementary Figure 4"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[Supplementary Figure 4](https://raw.githubusercontent.com/olgabot/olgabot.github.io-source/master/content/images/shalek2013_sfig4.png) was from published data, however the citation in the Supplementary Information (#23) was a [machine-learning book](http://link.springer.com/book/10.1007%2F978-3-642-51175-2), and #23 in the main text citations was a [review of probabilistic graphical models](http://www.sciencemag.org/content/303/5659/799.full), neither of which have the mouse embryonic stem cells or mouse embryonic fibroblasts used in the figure.\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 3,
+ "metadata": {},
+ "source": [
+ "Supplementary Figure 5"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For this figure, we can only plot 5d, since it's derived directly from a table in their dataset.\n",
+ "\n",
+ "Warning: these data are going to require some serious cleaning. Yay data janitorial duties!\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 4,
+ "metadata": {},
+ "source": [
+ "Supplementary Figure 5d"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "barcoded = pd.read_excel('nature12172-s1/Supplementary_Table7.xlsx')\n",
+ "barcoded.head()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "html": [
+ ""
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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iU9khY1mJEIBJUeBw+q5iEwrc2+X7IGXX8aWUuOOmySEtHWHH/O5hfKVoSOW/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\nY5uKkDEl6tT0H/cyAEWRIEnGs+gSVXadZrpElSFBMqdSBYxGEX9F1ES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qQMaiiiD6+kch\nT+fYGJ+IZ2VooCIBleUqxiJRW0wvUSWsveAsAEYC53Vtjdg7nUPF+geM+Ay85ldJdd69xudsYwye\nP4yv6dUFnXWYue7L6zbWa1DESS/1IK/1pM7wAPZ39CbUT8UxCvs7etF9YgjNS6qxrq0xoUzidaF5\nSTUa6sqx/9AJDA5F0Ly0GrVVIXQe7ceZ0UksqghicCiCsYk4rO0cZaUKYnHNrLMqEqDpyeuisxH7\nliUgEDBidW11CABwanAMoWAAX/zI/wMA7N0XRm1VCA115bb6tzhecX+y3kdSYRxbODw1YFx22WW4\n+eab8cEPftC2/Lvf/S6+973v4Re/+EXGBTl27Bi2bNmC8847D1//+tdRUlIy6zb/9m//ljQ/x0K5\nCVj5PQFYtsvnTLKULMFPqvWsr6VSWaZi2dJqT+vmg0gaN1viKWeCT+dywe/fJcotxtbiZb22003q\nm2rbjqMDCfkxAPfYbE34WShELz/rMQbV9JNVe4mt8x1/Ge/n10KPr+l83+a7rpJpIvvZtputPir2\n4Zak05rk0i0BaKGpLFOT3gesyaK9JvBkHFtYPOXA+PSnP42tW7fi4MGDWLVqFXRdx6FDh/DLX/4S\nX//61zMuxMsvv4xPfOIT2LhxI2677TbP211//fVYv369bZloxV5o/J4ALNvl6wwPoKt70JZkyS3B\nT6r1ANheS2VkPIqXXuv3bSLQkfEoduzucE88FZAhS1JCgk8hnQRUtDAwthYn67Utkml6Tepr21Yk\nW5uOLckaL4DE2LxnX7jgGi+A6SRzjptFLBZPKz56ia3zHX8Z7+ffQo6v6XzfZls3F/XKTBLZz7bd\nbPVRsY+4piXEU1034s1kNA5FRsE3XgBIeR+wHr+uG3V1r/cngHFsIfDUgPGXf/mXqKurw3/+53/i\n8ccfR2lpKZYvX46f/vSnWLFiRUYFGBgYwMc//nHcdNNN+PjHP57WtjU1NaipqbEtsyYUJSKi9DG2\nEhHlBuMrEVFmPCXxBIA//uM/xoMPPognnngCjz/+OO65556MGy8A4Cc/+QlOnz6Nf//3f8fatWvN\nf9no2bGQ+D0BWLbLt3o6meRsCX5Sred8LZXKMhVrVtT7NkFSZZmKLZva3BNPTS9wJvgU0klARUSF\ny3pti2SaXpP62rYVydamY0vbsrqEpHKCMzZvbG8pyITKzkSdABAIKGnFR68JBOcz/jLe03xK5/s2\n33WVTBPZz7bdbPVRsQ9Fll2TdAYCSsoEyIUm1X0gIVl0c63n+xPAOLYQJM2Bceutt2Lbtm2oqKjA\nrbfeCsnlatF1HZIk4b777st5Qb06fvw4LrvssgUxjtCN3xPY5CL5ozXJ0lzWcyaJAozEQZ1H+wEA\nV1xyvpmUzbqfr//gd5iKxnFBUy16pxN6AjAT0f3qhbcwNjEzj3WyBJ2qAsjTN6tQMIDIZAwBRYYa\nkBGZjCEW0xEISGheWm0mc3ImJLUmjXMea6pEUm7Lvb5OC8tCj63FJFWC3ky2FYk5RVLhVLFZJFq2\nJmuzxrO7dx60xdXmJdUYHI5geHQSVRVBDI9OYnAogtKg0ZH01OAYNE2HLEuIxXRbEjpFAkqDCtSA\n0VhToioYHp0EAFRVBPHuVQ0YHI6YSZEBmO8jEsw5E3WKe8Vc4qOXz3y+4y/jfX4ttPiazvdtvusq\nmSay95qYNFl9VOzDGSPFcmeSUVEXFImGRSwVcVgkHq6tCuFXL7yFicm4mVyzPKSaCTb7+kcBAA31\nFRiLRDExGcNUNI4SVUFtdQhjkShODY7ZEtSrCqBpQHx60TtqSgHArBsPj05iLBJFY30FaqtC6D4x\nhKqKIG7ffDEA2OKpOJaGunJsbG/xXL+fyzmgwpe0AeNLX/oSvvKVr6CiogJf+tKXIEkS3FaVJAnb\nt2/PeUG9Wmg3ASKi+cDYSkSUG4yvRETeJc2Bcc8997j+TORFOk/40pnWc7ZWa2BmOirRkium6LNO\nRWWdBtCqb2DMnHLqgqZaADNP4rqPD6G2OoTmJdV46uAbiMV0c+q9aNx4yldbbUylOjQaTTklalCV\nMBXVoQMoL1VwbsMiNC+pxq9eeAtT0TjKQypKVAWlwQAa6yvQ2lKHrvCAbbopa6u89bPLdQs0W7iJ\n/C/Tp5ZzidvW6asF8WRt/6ETth5kXZbtxZNDEd/F66KXhHga2Ly02px2df9LxxGZjKGqIoixSNSc\nZlU8NYzGNFsPOFUBotO/lpcqxv9DqtnjQpTDOrVhsin8nPe3dJ8SEhWyTHpt+ZmXqVVT9ZhwxkQn\nsa2zF9ds64v3ET0UACT0ChN1QhE7RZzsPNpv1iFFbH3y2TdQHlJRVRFEb/8oBociKA+pKA0GMDgU\nQSyuQdN0xOJGHVaCMdXqxGQcmm5MjyrLQM2iUgyPTpq9hEPBABrqKwAYMds8jmX1timoN7S3mOUW\nZbnhypWzTj/rRaF81yhznqZRBYDXX38dnZ2diMViCT0xrr322pwUbi7Yip1/6UzTJ9adihk1y5KA\n+7R0XqaeKoZppdJlHdllnWILyM00UpymauFibC0cmU496PV1a9x2TvtXbNauMO45zvvbaCSa1lR/\nRG4KJb6mWwcolDpDqnIme82tritiopOo2x49PmSbfSNZLgtrXdha/7Vym9q5UChyYrnXrqgHgDl/\nXwrlu0bZ4WkWkm984xv413/9V1RVVaG8vDzhdT81YFB+pTNNn1hX0zQzMGu67rqec3pQMaUSYAQ7\nMV/0QmM95ljcmGLLOW1qNseFcpoqIn/LdOpBr69b43axN14AQMfRfuzZF06YSlZINpU3UbFItw5Q\nKHWGVOVM9pr4GZiJBammN9V0HR1H+xHX7MtTrX+4523s2Re21X+d2zqndi4UbveLF1/rN+uvQHrf\nl0L5rlH2eGrA2LlzJ/7u7/4On/jEJ3JdHiIiIiIiIiKiBJ6mUY1EIviLv/iLXJeFikA60/SJdWVZ\nhiTNdIdzW885PaiYUsnchyQVxbRS6RKfmyQBAUVxnTY1WzhNFZH/ZTr1oNfXrXE7oCRO+1ds2pbV\nY2N7S8JUsrNN5U1ULNKtAxRKnSFVOZO95jattJje1O2fLEloW1afMHVoqvVXNi3GxvYWW/3XuW2h\nxl23cq9dUY/W5lrz92xPUU3FxVMOjC9+8Ys499xz8ZnPfGY+ypSRQhlHWOyYxJNJPKm4MLYWFibx\nZBJPKhyFFF+ZxNN9OZN4MoknzR9PDRh33nknfvSjH6GpqQlNTU0IBGZGnkiShPvuuy+nhUxHId0E\niIgKBWMrEVFuML4SEXnnKQfG2NgY1q9fb1smSRJ0XYe0EPvt06zcWkGTtU7PtdXV7cmX25Mxwdra\nLVp/AZhP+m64ciUA46nb4HAEvf2jKA+pWHfRElvLtXgSWFsVwuBwBL87fBKxuNFCHYtrmIza2wTF\nFZKqpVC0cIuniKJ1vLYqhO4TQxiLRM1WdHEs4hj3d/Sioa4cG9tb2PuCqEhlqydbsn16ffooYg4A\nW48259NA8YRQrCt6jjXUlWP/oRMAgBuuXGm+vmVTm5mwDpjpDdEVHjBjMQAMDkUwFY3b4mRv/yhO\nnBpFXDd6wi05y3gKeOzkqPkE0Rl/31FTavaoE2UbHI5geHQSzUuqMTgcQffxITQvrcbtmy8uithX\nDMdAxS0X39Fc9JJyK6ezZ4Xbe+3Y3YHB4YjZIyJZ3dXtZ7GttQeDeD9R1xX12aqKoNlzV9Rv93f0\novNoPwCYPdVEr7XSoPHn4JnRSbMXxeBQxFw+OBQxe7RJAOprStG8tBqv9gwiGtNwbsMic73a6hDW\nXbTE7DUsYjsAs57qvGeI15J91l7On5deMlQ8PE+jWijYip1/blMZOafdE8RUUUB6Uye5Tatq3Yd1\nejsrSSqOKf8kCagIqbbpuBRZQkCZyTfCKVQpmxhb82cu05rOdo16ne7auZ415lgV8pR+XgRVY9hJ\nocY+xm9/Y3zNzXfUra6Y6X7dyvnhO35ui41BNTEGX/OFvbb4mKzuKmKx9efJqL3u7DYNaSFQZMmc\nNdB5z6gsU/H9O6+yre/1/HmZ6pZxr7gkTeL5/PPPu/578cUXEQ6HEY+7z3VMC5vbVEZi6jkRtKz/\nNF1HV/egbTy0dZqqZO/R1T1om1a142i/uQ9jSqvExgux7lQ0XpCB30rXkfCHRFzTEZv+A2a2zzBd\nqaYSI6Lcme3ac05rKuJqqmvUNt31dLzUpgOmdTu39ZLRdfep8YqF+AOiEGMf4zf5XS6+o251RTHV\ncTbLuWN3R0JsjMXjtmPYsbsjIT7q+nQZHXVXTddtP8ecc6+icGNtXNNt58N6HCPjUbM3BjDz2djO\nX/dgwvlL9t1h3CtuSYeQ3HDDDSk3DIVC2Lx5Mz73uc9lvVBERERERERERFZJe2AcOnTI9d+LL76I\nZ555Bv/0T/+En/zkJ/jOd74zn+Uln3ObykhMPSemOnVOFdXaXGvmdhDbzDY1V2tzrW36urZl9eY+\nnNPbWUkSUKIqBTv1lCBJSJiOS5ElBAIz3Zw5hSpR4ZvLtKZu01En26eIl27TL7utl0whT+nnhXUI\nSaHFPsZv8rtcfEfd6oqZTnXsVs4tm9oSYmNAUWzHsGVTW0J8lKTpMjrqrrIk2X4WQ4OtCjXWKrJk\nOx/W46gsU215MMRnYzt/zbUJ58/LVLfW5VQcMsqB8d///d946KGH8MQTT2SzTBnhOEJ/YBLPGUzi\nScWAsTW/mMSTSTwzVQzHUKwYXw1M4skknm6fNZN4klNGDRhvvvkmNmzYgEOHDmWzTBnhTYCIKPsY\nW4mIcoPxlYjIu6RDSLyYmJhAKBTKVlmIiIiIiIiIiFxl1IDx3e9+F+9617uyVRYiIiIlfZAZAAAg\nAElEQVQiIiIiIldJZyG57777ILlkQdQ0DWfOnMFLL72Evr4+fP/7389pAcm7bIzzSmcc9Wzjzdz2\nkY39izHW69oaU451c8sPsXdfGMOjk1h30RIAsI3VFvbuC+PVnkHz9wuaagEAz3edhCwDNYtKMTw6\nCU3ToWlAZbmKElXB4NAE4roxPlCWgPgsg7OCqoSqiiBOn5lAeUjFBU216D4+BAB496oGbNnUNut4\nSSBx/DngPpYwVc4QgWMEiZLzmofCr9eR13Hb1vxB1tgollnz7Qgid5AzVlnfV+xD5LVorK/A8Ogk\nuo8PQdN01CwqxYY/WYaN7S349D8/jWMnR1FWqmD18nr09o9icChiTikYUGSUh1SUBgM4NThm/j41\nPd3p0Kh9WkMJwHSOY5SHVLz/8neauTjE/cDt2ETMdPLrOSbyg1zGwlR1QOv7W2PYbLl90uVl22R5\nHwB7vc36WnfvsHls4mdRR3XLMQTAlmdIxFYAttwW1hjZvLTazI8h6pxjkSjKQyo2/MkyPPnsGzg1\nOGbkYasOYXAoAgDoPz0BHUaeIVG/vbCpxtxe5IcbHp3EWCSK1cvqbcfh/Nn62Tnze2TyuTs/eypO\nSXNgJJtGVVVVLFq0CCtXrsSmTZtQX1+f0wKma6GOI9z68AFzvuOVTYux7eZL57yPqZhRASwJKEn3\nlez9Uu0jG/v/8B0/t823vXZFfcJ7A0CJKtvWU2Rp1nmzJQmYe0aY3JMkYM1y43oTn6Mor5h5QBxj\nZZmK7995lbnt1ocP4KXX+6Hr7vsBUp8PooUaW4XZYmw2YnAuuZXPGU+DqoISVcZoJGqLhc7YqMhG\nZnwRO5xxU8QYZ9z3c3wVrMfm/AwExkrKtmKKr7mMhanqgNb3F/UdwB6PslFGL9tay6nIEjRdd42T\nFSE1Id66cdbxioE4d9d8Ya/tuNzOKeD9nFk/e2ddmIpH0h4Yu3btms9yUAY6wwPmRQ0Yf5R2hgfS\nalUW+7AGWU3TXPeV7P3Ez2772LMvnPH+u3uHbTcusa71vcV+R8bjtvW8BH2/V6513TheyfK79bW4\nZcHIeBR79oXNlv+u8IC5vq4DHUf7IcuybT/JzgfRQjdbjM1GDM4lt/Lt2N2REE+nonFMRuPOzRNi\noxFPtaQxU9eBru7BmbivJV/Xb+KaDk2LJ8xaYi0/YyWRu1zGwj37wknrgNZY3NU9mFA/6uoeNNfL\npIxetnWWM1n9U9eRcDzJOOt4xaAzPIC7dx5M+Hxeeq3f898Fbj20rZ+ptS5MxSWjHBhERERERERE\nRPOBDRhFYHVLHVY2LTZ/X9m0OO3WbrEPWZIgSdPd1WTZdV/J3i/VPja2t2S8/43tLagsUxPWtb43\nYOzXuZ4iJ+ZzcXJJ+eIrkmQcb2tLHWRZtpVXkuzHWFmmmi3OYhuxviQBbcvq0dpc6+l8EC10s8XY\nbMTgXHIr35ZNbQlxskRVUFmmJsRC5+9imIWIHU6SBLQ2187EfUe88jNFllCiKq6fAWMlUWq5jIWp\n6oDW31ubaxPqR63NteZ6mZTRy7bOciqylDROusVbN846XjFY3VKH2zdfnHBca1bUe/67wMn52Vvr\nwlRckubAKFTFNI4wXUziySSeTOJJubKQY6vAJJ5M4in49RxTYSq2+MoknkziySSelEtswCAiolkx\nthIR5QbjKxGRd0mTeFr19va6LpckCaqqYvHixWZCQCIiIiIiIiKibPPUgHH55ZdD13U4O2tIkgRd\n16GqKq644grceeedKCsry0lBiYiIiIiIiGjh8tRt4q677sI555yDb37zm3j++efx/PPP49FHH8X5\n55+PW2+9Fbt27cLJkydx77335rq8RERERERERLQAeWrAeOCBB3D33Xejvb0dlZWVqKysxKWXXoq7\n774bP/jBD7BmzRp8+ctfxpNPPpnr8hIRERERERHRAuRpCMnIyAjKy8sTlgeDQZw+fRoAUFVVhYmJ\nieyWjtKSi6zPs800MtsMI+lkarZmYBYZlkUWafG6dZ9urLOMiOzDd+88aGZFBoArLjnfXN+asbk8\npJrZlpuXVqP7+BCGRycRCgbQUF+BwaEIxiJRTEXjiMaNrPZlpQrUgIxoTMPYRByqAvzByrPR+Xo/\nxiaMbPhigijrACwJQH1NKUqDAZSHjCmfmpdUo/vEEPr6R82ZTwBgQ3tLQjZt6+wkbp8NZxkhyq9k\nMwU5X08WJ52zBs02w5Bz+3TLaN1+x+4OAMCWTW1Jy+O2D2s2/P2HThhZ7KtC6D4xZP5sJeL8rv85\nDACoqgjixVdPmTOSiAz6Z6bjd3lINWNxbbWxr77+UQyNRiEBuKCpBm/1nQEAM3v+VDSO8pCKkbEo\n4jpQXaFi3ZqlGByOYHh0EjdcudIsu4ip4jPO9qwzfp+lhqhQpHMtJasXOeOqc+Y6t/2IupiopzrX\nSxXXxfZ9A2MAkDADnjX+ipjpJGZv2jD9vmJWp9qqEBrqytE3MGbGNjEzyFQ0job6Cqy7aIk5wwgA\nxGK6ObOeqIu+1XcGsbiGUDCAyGQMAUXGuQ2LUFURxKs9gxgZi6I0qJgzQF1xyfnY+8xRDI9OYu0F\nZyXUV611eDH7iPN4U52vVOeZ8XRh8zQLyd/+7d/ixIkT2L59O5YtWwYACIfDuP3221FfX4/77rsP\n999/P1566SX84Ac/yHmhU1momZy3PnwAh3veBmDMj7zt5ktztk+35WLZVMz4o70koKBElTEV1VKW\n6cN3/Bwj49GE5W4kCagIqZiKaub7WDm/yZVlqud9FwpJSjxOsVx8NgDM+bKd5yRb3w1aeBZqbJ0L\nazwU16skAWuW19viJQDXOLn14QN46fV+6LqxnSxJiGvGjirLVHz/zqsS3su6fbplBGbiQ8fRAfO9\nFFnCz/55Q0J5nMdhPc5iUFmmYtnS6pSfa7qfey7u0VQ8GF+9S+dacq4LwPw9FtdssU78DLjHWRED\nrazrpYrrAFy3B4yY6hZ/i5X1eFPF1VR/QzCekqchJF/72tewaNEirF+/HmvXrsWaNWtw9dVXo6am\nBtu2bcO+ffvws5/9DLfddltWCtXR0YH29vas7Gsh6AwPmBcyYATnVL0UMtmn2/I9+8I43PM2NF2H\nrht/YMdicYyMR6FNR2u3Mu3ZF06rgUHXgZHxKGLxuPk+1n9OxdZ4Abgfp1g+Mh6FphnBvis8gK7u\nQds50TQtK98NIkpOxEhx7Qm6DnR1D5rxEjCuSWec3LMvjK7wgLmtrsNWoR0Zj5pPDeca+51lFPHB\nWXmOazru3nkwoTzW49A0ragaLwDjM+54vd/83fm5pvu55+IeTbQQpXMtOdcV9SIAiMXjCbHOyhln\nu7oHk9Yz9+wL297LGde7ugfRkaTxApiJv4de7y/6xgtg+nh1PWVc1XTdVqe1rst4SoDHISRVVVV4\n5JFH0NPTgyNHjkBVVSxfvhznnnsuAGDdunXYv39/xlOp6rqOxx9/HPfccw9UVc1oX0RERERERERU\nPDy3OGiahjfeeANvvfUWjh49ildffRWTk5MAgNLS0owbLwBgx44d2LVrF2655ZaEKVspudUtdWYX\nNcDoTpXpmLBk+3RbvrG9BSubFkOWJEiS0T0sEFBQWaZClqSkZdrY3oLKMu8NVZJkdNcLKIr5PtZ/\nTunsu1C4HadYXlmmmtdha0sdWptrbedEluWsfDeIKDkRI8W1J0gS0Npca8ZLwLgmnXFyY3sLWlvq\nzG0lyejeLFSWqea467nGfmcZRXxoW1Zney9FlnD75osTymM9DlmWk8alQlVZpqJteb35u/NzTfdz\nz8U9mmghSudacq4r6kUAEFCUhFhn5Yyzrc21SeuZG9tbbO/ljOutzbVoW16fsv4myzIuWl6fUI5i\nJIZFpoqrsiTZ6rTWdRlPCfCYA6Ovrw8333wzjh07hqamJsTjcbz55pt4xzvegV27duGss87KSmH6\n+/tRX1+PgwcP4nOf+xyeffbZtPexkMcRMoknk3gyiSflykKOrXPBJJ5M4pnp+rRwML6mh0k8mcST\n8XRh89SA8alPfQqTk5O47777UF1dDQB4++238fd///eoqKjAAw88kNVCeW3AOH36NIaGhmzLTp48\nic2bN/MmQEQ0R4ytRES5wfhKRJQZTzkwfvvb3+IHP/iB2XgBAIsXL8YXv/hFXHfddTkr3Gwee+wx\nPPjgg3l7fyKiYsTYSkSUG4yvRESZ8dSAUVFRgYmJiYTlkUgkK7kv5ur666/H+vXrbctEKzYREc0N\nYysRUW4wvhIRZcZTA8af//mfY9u2bbjnnnuwYsUKAMCrr76Kbdu24bLLLstpAVOpqalBTU2NbRln\nLyEiygxjKxFRbjC+EhFlxlMDxq233orPfvaz2LBhA0pLSwEAExMTuOyyy/AP//APOSmYVGxpzYmI\niIiIiIhozjwPIfn2t7+NI0eOIBwOIxgMorm5GU1NTTkp1MUXX4zf/va3Odk3ERERERERERWepA0Y\nPT09CctKSkpw4YUXJqyTq4YMIiIiIiIiIiIgRQPGlVde6WkHkiThlVdeyVqBiIiIiIiIiIickjZg\nPPXUU/NZDiIiIiIiIiKipJI2YCxdunQ+y0FERERERERElJSnJJ7kL53hAQDA6pa6WZe7LduzLwwA\n2Njektb7dIYH0N07bL7e3FhlWz/Za87t+wbGAADr2hrN3xvqys1tunuHbdt39w5j/6ETaF5SjXVt\njebyvdPH0dpSh67psrZOv1ffwBgGhyOorQqh+8QQxiJRc7uJyRimonE01FfYyj84FMHg0ATiOqBI\ngCwby8tDKqIxDWMTceP3UgUTk8bPleUqSlQFAFAaDGBiMgYAGItEoQaMHQyPRhFQAE0DAgEJl198\nPgaHIwCA3v5RTEzGsOFPlqErPIDu40OorQ6hqiKI1pY6NDdWmZ+r9WfruRPnyfp5d4YHsL+jFw11\n5bOeZ6tk3y2ihSKdayDT68VrLHeL2Xv2hdEVHjBjnvP1VO8npLpXiN9FjF7X1mgrj4jj3SeGAADN\nS6rNmGuN7eL1sUgUjfUVqK0K4fmX+1AaDGD1snozFtZWhdBQV479h05gcCiC5qXVGB6dxOBQBFPR\nOBZVBM34WlsdAgBUVQTN8vf2j6I8pGJwKILTZyZQHlLRUF+BsUgU5SEV6y5agq7wAGqrQtiyqc08\nBnHvWd1SZ1sm/u8WaxkfiVJLFdtEHc/tOhKxTtQBnTHKWh8Sr+3Y3QFgpk7pVv+8e+dBAMAGy/Vs\nLYczNjrrpsnK4STee39HLwaHIxgenUTzkmo01JUDMGK0iDOiPuusO7vFIsCItaJuCMCMr2ORKCYm\nY3j3qoaE9ZuXVKPzaD/OjE7igqZaW3ze+8xRMw53nxjC4JARi5uXVqO3fxRnRicRjWlQAzIWVQRx\nanAMoWAAFzTVovP1fsTiGqoqgigNBlAeUtG8pBpbNrXZ7h2COBbncaYjnb99qPhJuq7r+S5ENh0/\nfhyXXXYZnn766aLsRbL14QM43PM2AGBl02Jsu/nSpMvdln34jp9jZNz4Y76yTMX377zK0/sAwEuv\n98P5bRGz3bp9iyQJKAkoKbdPRZKS77e4vrXpE+dOnKepmNGgUhJQUKLK5jm2rjubZN8tIqD4YyuQ\n3jWQ6fXiNZYfPT6UELOtcdzKS0y3xopk9woAONzzNiajcds+1q6ot5WnWEgSIEsS4lrijcUZawHG\nR8q+YoqvqWKbqAdKErBmeb3tOnLGtaBqj1HWOqTYvuPogOt1a61/vvhaf8Jr1v1UhFRMRTUzNrrV\nc0VZneXItlSxqFAEVSXh3uHkdv5TSedvH1oY5HwXgLzrDA+YFypgVDA7wwOuy/fsCycs27G7w3Zz\nGBmPmq3dqd6na/o93AK2ridvTNB1QNM0dHUPomMOAT/Vfhe6kfEoduzuwOGet6HpunkeYrF4wh8X\nyc6zVbLvFtFCkc41kOn14jWWdxztT4jZd+88mLQBYbaYbo0Vmqa53iu6ugfRFR5ALJ5YAX3xtf6i\na7wAjM8j2R8M1lgrMD4SuUsV27os9UhdN2KNuI727AsnxJZYLG7GqK7uQVvdT9eBl17rT3rd6jqg\n6XpC44V4zfrzyHgUsVg8aX1W1416sOj1lss6aKpYVChma7wAEs9/Kun87cO4vHCwAYOIiIiIiIiI\nfI8NGAVkdUud2b0XMLpLrW6pc12+sb0lYdmWTW2oLFPNZZVlquuYaef+WqffQwwXsZIkuC4Xr8my\njNbmWrQtr0+6XjKp9rvQVZap2LKpDSubFkOWJPM8BAKK7RyLdWcbG5/su0W0UKRzDWR6vXiN5W3L\n6hNi9u2bL064xq2vp4rp1lghy7LrvaK1uRatLXUIKErCftauqE/63oVMkgBFdr+xWGOtwPhI5C5V\nbGu11CMlyYg14jra2N6SEFsC00NANra3oLW51lb3kyRgzYr6pNetGIqxdkW962vWnyvLVAQCStL6\nrCQZ9eCN7S22Y8iFVLGoUATVxHuHk/P8p5LO3z6MywsHc2AUICbxNDCJp/08WT9vJvGkbFsIsRVg\nEk8m8WQST5p/xRZfmcSTSTyZxJNyiQ0YREQ0K8ZWIqLcYHwlIvKOQ0iIiIiIiIiIyPfYgEFERERE\nREREvscGDCIiIiIiIiLyPTZgEBEREREREZHvsQGDiIiIiIiIiHyPDRhERERERERE5HtswCAiIiIi\nIiIi32MDBhERERERERH5HhswiIiIiIiIiMj32IBBRERERERERL7HBgwiIiIiIiIi8j02YBARERER\nERGR77EBg4iIiIiIiIh8jw0YREREREREROR7bMAgIiIiIiIiIt9jAwYRERERERER+R4bMIiIiIiI\niIjI99iAQURERERERES+54sGjMOHD+P9738/1q5di2uuuQaHDh3Kd5GIiIiIiIiIyEfy3oAxOTmJ\nLVu24P3vfz9eeOEF3HDDDbjlllswPj6e76IRERERERERkU8E8l2AZ599Foqi4K//+q8BAH/1V3+F\nnTt34plnnsGVV16Z59KlpzM8AABY3VKXte3Ea1b7O3rRUFeO5sYq7N0XBgC0WrZtbqxCd+8wusID\nqK0KAYC5fnfvMJobqwDAtm3fwBga6spt+9jf0YvuE0OoqgiitaUOzY1VWN1SZytvZ3gA+zt6MTgc\nMcvQNf16a0sdNra3YM++MPYfOoGqiiA2tLegu3cYTz77hrGPZfUYHI5geHQSzUuq0Xm031zefWII\nff2jiEzGUFURRGkwgDOjk7igqRa1VSHbe/YNjKH7xBDGIlFMTMbQvLQatVUhdB7tx8RkDMOjk4jF\ndAQCkrmv8pAKABiLRAEA5SEVb/WdAQDUVodwZnTS9rlHY5r5GgD09Y8iGjdeq65QsagiaK57ZnTS\n3G9DfQUa6yswPL2sPKSiqiKI7uNDtnKWh1Q0L6k2z9fG9hbX8y8+e/H9EOfU+v2Z7bvoPIep1k21\nLZGfZfJdFdcWADNmit+t8bJvYAyDwxEAwPDoJAaHInj3qgY01JWjKzxgxlcRnxrrK8xlAGzbNi+p\nxrq2Rluc7u4dNt/DGlPF9gDMbUScFfFaEPcDALZ7gjXGWNexlgUw7hXdx4dQGgygsb4CtVUh8/if\nfPYNDA5FoAZklKgKAKA0GDBjuDWmrl5Wj/0vHUc0pkENGM9PFlUEUR5SMRaJYnAoYsTB6bgoPrMr\nLjkfAMzPsys8gN7+UTMGKxKw4vwa83gHhyLme667aAk2trcAgPm5ifvh6pY67NkXNvfrvEcCjHNE\nc7Fjdwc6j/ajsb4CG9pbzLqGiKGifrqurRF794XR2z9q1o1EHJ2KxrGoIojVy+rNeNMVHsCrPYOI\nxjSUh1SUBo0/Y04NjkHTdPzByrPNOpWIVSJ2irhxZnQSkel6YvOSagwOR8x4AwDrLlpixldrnLfW\n08Q269oaset/DmNwKILSYAATkzEz/om4LOL/4FDEVucTdUxrnVbEZ/GzuD8ARlwGjLqnUKIqePeq\nBvzqhbcAAKuX1+PVnkHz9chkDKHgzJ96It6K+vbEZAwAMBWNo0RVMBWNY2QsCk0HSlTJrDevveAs\nbGhvwf6OXjz/ch+GRycRCgbMsjfUldvui9bzC8B2L01Wf82kXpop1msLj6Trup7PAuzcuRO/+c1v\n8K1vfctc9jd/8zd45zvfiU9/+tNp7+/48eO47LLL8PTTT2Pp0qXZLGpKWx8+gMM9bwMAVjYtxrab\nL814O/HaVCxuLsvn2ZIkoCKkYipq/CFfosoYGY/mr0BFTpElBBTZdv4BoCSgYGXTYgDAS6/3Q9eN\nc7NmeT223XzprN9F6+slqmyeTy/f27l+z6nw5Su2zlUm39WtDx8wr61CJkmp7xkixkxG48lXKgKV\nZUZjtfV+JUmALEmIa+4fkCTNxFrGOcq1QouvqVzzhb0J11VlmYrRSLTgYyplRtRVAXv91fm3RTr1\n0kyxXluY8t4DY3x8HKFQyLYsFAphYmJi1m1Pnz6NoaEh27KTJ09mtXxedIYHzC8/ABzueRud4YFZ\nW/JSbSde0zTNNwFf140KoKpIgCSx8SLH4poOTYvDefo1XUdX96Dtu6HrRov3nn3hlN9F63dO03Xz\nfMqyPOv3dq7fcyo8fomtc5XJd7UzPICu7kHfxN1MzHYMcU1HXCvuxgsArvcqXQfiKT4gXTdiJOMc\nZVuhx9dUduzucG0UZH2RACOudrzeD1ga182/LaZ75aVTL80U67WFK+8NGGVlZQmNFZFIBOXl5Um2\nmPHYY4/hwQcfzFXRiIgWJMZWIqLcYHwlIspM3hswmpub8dhjj9mW9fT0YMOGDbNue/3112P9+vW2\nZSdPnsTmzZuzWcRZrW6pw8qmxbYuSF5a71JtZ31N0vw5hKSyTGWreg4lG0IiS5LrEBIxPv53h08l\n/S5av1eyJKGyTLV11Uv1vZ3r95wKj19i61xl8l1d3VKH1uZaDiEpInMdQiJiLeMcZVOhx9dUtmxq\nw/8eeINDSMiVJAFtswwhSademinWawtX3nNgTE1N4fLLL8cnP/lJXHvttdizZw/uv/9+PP300ygt\nLU17f/kcR8gknkziKTCJJxWbQhyjzSSeMPfBJJ5M4kn+VYjxNRUm8WQSTybxpFzKewMGABw5cgRf\n/epX8dprr+H888/HP/7jP6KtrW1O+yq2mwARkR8wthIR5QbjKxGRd3kfQgIA73znO/HDH/4w38Ug\nIiIiIiIiIp+S810AIiIiIiIiIqLZsAGDiIiIiIiIiHyPDRhERERERERE5HtswCAiIiIiIiIi32MD\nBhERERERERH5HhswiIiIiIiIiMj32IBBRERERERERL7HBgwiIiIiIiIi8j02YBARERERERGR77EB\ng4iIiIiIiIh8jw0YREREREREROR7bMAgIiIiIiIiIt9jAwYRERERERER+R4bMIiIiIiIiIjI99iA\nQURERERERES+xwYMIiIiIiIiIvI9NmAQERERERERke+xAYOIiIiIiIiIfI8NGERERERERETke2zA\nICIiIiIiIiLfYwMGEREREREREfkeGzCIiIiIiIiIyPfYgEFEREREREREvscGDCIiIiIiIiLyPTZg\nEBEREREREZHvsQGDiIiIiIiIiHyPDRhERERERERE5HtswCAiIiIiIiIi32MDBhERERERERH5nu8a\nMO666y7ce++9+S4GEREREREREfmIbxowTp8+jS996Ut47LHHIElSvotDRERERERERD7imwaM6667\nDqqq4oorroCu6/kuDhERERERERH5SGC+3igej2NsbCxhuSzLqKiowHe+8x3U19fjy1/+8nwViYiI\niIiIiIgKxLw1YBw8eBAf+9jHEpYvWbIETz/9NOrr6+erKERERERERERUYOatAePSSy/Fq6++mtV9\nnj59GkNDQ7Zlvb29AICTJ09m9b2IiArF2WefjUBg7uGdsZWIKFGmsRVgfCUicko3ts5bA0YuPPbY\nY3jwwQddX7vuuuvmuTRERP7w05/+FKtWrZrz9oytRESJMo2tAOMrEZFTurHVdw0Y6STwvP7667F+\n/Xrbsu7ubnzqU5/CI488gvPPPz/LpUvfsWPHsHnzZuzcuRPnnHMOy+LT8vipLH4rj5/K4rfy+Kks\n1vIEg8GM9sPYWrhlYXkKpyx+K4+fyuK38mQrtgL+j69++tz9Vh4/lcVv5fFTWfxWHj+VxW/lmWts\n9V0DhiRJnqdRrampQU1NjetrS5YswdKlS7NZtDmJRqMAjK4x+S6Pn8oC+Ks8fioL4K/y+KksgL/K\n46eyADPlURQlo/0wthZuWQCWp1DKAvirPH4qC+Cv8mQrtgL+j69++twBf5XHT2UB/FUeP5UF8Fd5\n/FQWwF/lmWts9V0Dxvbt2/NdBCIiIiIiIiLyGTnfBaD/3979x0Rd/3EAf56H5/FjYZIpqQn4W5nG\njwM0TjxTtKWbpqmbMJcrBB1l6kRXKBNTzyxzJpL+Ue3+QOfZNClW/PQPS/IHnErRSM38AU1XYgJ3\neNz7+wfzvlyoZN19Pm/i+dja4n0X7+fR3fNur30+nyMiIiIiIiKirnCAQURERERERETS0+bk5OSo\nHcLb9Ho94uLi4O/vr3YUAHLlkSkLIFcembIAcuWRKQsgVx6ZsgC+zdOTHmt3zgIwT3fJAsiVR6Ys\ngFx5fJ2lJz3WxyVTHpmyAHLlkSkLIFcembIAcuX5J1k04nG+9oOIiIiIiIiISAU8hYSIiIiIiIiI\npMcBBhERERERERFJjwMMIiIiIiIiIpIeBxhEREREREREJD0OMIiIiIiIiIhIehxgEBEREREREZH0\nOMAgIiIiIiIiIulxgEFERERERERE0vvPDjCKi4sxc+ZMREdHY+HChaitrVU7EgDAarUiISFB1Qx5\neXkwmUwwGAxITU1FXV2d4hl++OEHzJ8/H1FRUZgzZw5sNpviGe47ffo0XnnlFSRqra4AAA5VSURB\nVMTGxmL69Ok4ePCgalnuu3XrFiZOnIiKigpVczQ0NGDZsmWIiYlBUlISLBaLqnnKysowa9YsREdH\nY+bMmSgsLFQ8w7lz52A0Gt0/NzY2YsWKFYiNjYXJZILValU1T0NDA5YvX474+HgkJiZi8+bNaG1t\n9eqeMvarDN0KqN+v7NauydCv7NbO2K1ydisgR7+yWz3J2K8ydCsgV7/K0K2AXP3qlW4V/0E1NTXC\nYDCIM2fOCCGE2Ldvn0hOTlY5lRC//vqriImJEQkJCaplOHz4sEhOThZXr14VTqdT5OXlCZPJJFwu\nl2IZ7Ha7MBqNoqCgQDidTmG1WsXEiRNFU1OTYhnuu337tjAYDKKwsFAI0f7ciYuLE99++63iWTpK\nS0sTY8aMERUVFaplcLlcYu7cuWL79u3C6XSKuro6ERcXJ6qqqlTJ09zcLCIjI8XXX38thBDi1KlT\nYty4ceL69euK7O9yucShQ4c6vYYzMzPF2rVrhcPhEDabTcTFxYnq6mrV8qSkpIjc3FzhcDjEzZs3\nxYIFC8TOnTu9tq+M/SpDtwqhfr+yW/8etfuV3eqJ3dpOxm4VQo5+Zbd6krVf1e5WIeTqV7W7VQi5\n+tWb3fqfPALjwIEDWLBgAaKjowEAr776Knbu3AkhhGqZ2trasHbtWixatEjVHLdv30ZGRgYGDx4M\nrVaL1NRU3LhxA7/99ptiGU6ePAmtVotFixZBq9Vi3rx5CAkJwfHjxxXLcF99fT1MJhNeeuklAMDY\nsWMRHx+Ps2fPKp7lvoKCAgQEBGDgwIGqZQAAm82GmzdvYs2aNdBqtRg+fDgOHDiAsLAwVfJoNBoE\nBgbC6XRCCAGNRoPevXtDq9Uqsn9+fj4sFgsyMjLcr+GmpiaUlpYiMzMTOp0O48ePx+zZs3HkyBFV\n8rS2tiIwMBAZGRnQ6XR46qmnMHv2bFRVVXltX9n6VZZuBdTvV3Zr12ToV3arJ3ZrO9m6FZCnX9mt\nnmTsVxm6FZCrX9XuVkCufvVmt3bbAUZbWxvu3LnT6Z+7d+/ixx9/hL+/P5YsWYKEhASkpaUhICAA\nGo1GlTwAsG/fPowcORKTJ0/2WYa/k2Xp0qWYM2eO+75lZWV48sknFS2cy5cvY9iwYR5r4eHhuHTp\nkmIZ7hs9ejTMZrP758bGRpw+fRpjxoxRPAvQ/rf59NNPkZOTo8r+HdXU1GDEiBHYvn07EhMTMWPG\nDNhsNvTt21eVPHq9HmazGevXr0dkZCRSUlKwYcMGDBgwQJH958+fj6NHjyIyMtK9duXKFfj5+WHw\n4MHutbCwMEWeyw/Ko9PpkJ+fj5CQEPdaWVnZYz+fZepXmbq1qzxq9yu79dFk6Vd2qyd2Kz+7dpWF\n3epJtn6VpVsBufpV7W4F5OpXb3arn89S+lhlZSWWLl3aaf2ZZ56Bn58fCgoK8PHHH2PEiBHYvXs3\nMjIyUFhY6LOp18PyDBo0CLt27cKxY8dw+PBhnDt3zif7/90spaWl7p+///575OTkIDc31+eZOmpu\nboa/v7/Hmr+/P+x2u6I5/urPP/9Eeno6IiMjMXXqVMX3dzqdyMrKQnZ2NoKDgxXf/68aGxtRWVmJ\nhIQEVFRU4Pz583jttdcwePBgxMbGKp7n2rVrWLVqFTZv3owXX3wRJ06cwOrVqzFmzBiMHj3a5/v3\n79+/01pzczP0er3Hml6vV+S5/KA8HQkh8O677+KXX37Bjh07Hut3y9SvMnVrV3nU7ld268PJ1K/s\nVk/sVn527SoLu/Xh1O5XmboVkKtf1e5WQK5+9Wa3dtsBxqRJkx56caNZs2YhOTkZ48aNAwC8+eab\n+OSTT3D58mUMHz5c0TwOhwPz5s3D5s2bO5Wfrzzqb3PfkSNHsGnTJmzYsMF9CJpSAgICOr1IWlpa\nEBgYqGiOjq5evYr09HQMHToUH374oSoZ8vLyMHr0aCQmJrrX1DxkU6fTITg4GGlpaQCAqKgoJCcn\no7S0VJUP2SUlJRg7dixmz54NAEhKSsKUKVNw9OhRxd4I/srf3x8Oh8NjzW63IyAgQJU8HTOsXbsW\ndXV1sFgs6Nev32P99zL1q0zd+qg8HanVr+zWh5OpX9mtXWO38rPrg7BbPcnQrzJ1KyBXv8rYrYCc\n/fq43dptTyF5lPDwcI//MS6XC4A6L6jz58/j2rVrWLZsGQwGA9LT09HY2Ii4uDg0NDQongcA9uzZ\ng23btmHv3r0eh+QpJSIiApcvX/ZY8+UbdFdqamqwcOFCTJ48GXl5edDpdKrkKCoqwldffQWDwQCD\nwYD6+nq89dZb2L9/vyp5IiIi0NbW5n79AO2HeKpFr9d3KlytVgs/P/XmsEOHDsW9e/dQX1/vXlPz\nuQy0nyuckpKCO3fu4ODBgxg0aJBXf78s/SpjtwLq9iu79eFk6ld2a9fYrfzs+lfsVk+y9KtM3QrI\n1a8ydisgX7/+o2711pVFZVJaWipiY2OFzWYTra2twmw2i1mzZqkdSwghRGVlpYiPj1dtf6vVKuLi\n4sSlS5dUy+BwOITRaBQWi0W0traKQ4cOiUmTJomWlhbFs9y8eVMkJCSI/fv3K753V0wmk6pXcrbb\n7WLy5Mli165dwul0ijNnzoioqChhs9lUyVNfXy9iYmLE4cOHhcvlEpWVlSI6OlpcuHBB0RwnT570\neA1nZmaK1atXi5aWFveVnJX8G3XM43K5REpKikhPTxf37t3zyX6y9qva3SqE+v3Kbv371OxXduuD\nsVvl7FYh1O9XdqsnmfuVn13/T5ZuFUKufvVGt3bbU0geZerUqcjOzsa6devQ0NCAcePGYc+ePWrH\nAgD3VWjVsm/fPjQ1NeHll192r2k0GlitVkRERCiSQafTYf/+/di4cSM++OADhIWFYe/evZ3Ox1KC\n1WrFH3/8gT179ng8R5YsWYKVK1cqnkcmffr0gcViwaZNmzBp0iQEBQUhOzsb48ePVyXPwIEDkZ+f\nD7PZjC1btiA0NBRms9l9uK2SOr6Gc3NzsXHjRiQlJSEgIABZWVmK/43u56mqqsKpU6eg1+thMBjc\nt0dGRnrte9Bl7Ve1uxVQv1/Zrd0Du/Xh2K3ydSugfr+yWz2xXx9Opn6VqVsBufr133arRgiVv3eO\niIiIiIiIiKgL/8lrYBARERERERHRfwsHGEREREREREQkPQ4wiIiIiIiIiEh6HGAQERERERERkfQ4\nwCAiIiIiIiIi6XGAQURERERERETS4wCDiIiIiIiIiKTnp3YAIqVcu3YN06ZNQ1FREcLDw736u1NT\nU3Hq1KmH3r5t2zaEhoZiyZIlHut6vR7Dhg3DG2+8gaSkJADA1KlTcePGDeTn52PKlCke97916xaM\nRiNCQ0NRVlbm1cdARPRPsFuJiHyD/UrUGQcYRF7w0Ucfwel0AgC+/PJL5Ofn49ixY+7bg4KCUF1d\nDQAoLy+HTqcDADQ3N+Ozzz7DihUrUFRUhCFDhgAA/Pz8UFJS0ulNoLi4GACg0Wh8/ZCIiFTHbiUi\n8g32K3VXPIWEyAuCg4MREhKCkJAQBAUFoVevXu6fQ0JC0KdPH/d9O64PGTIE69atQ58+fVBeXu6+\nT3x8PMrLyyGE8NinuLgYzz33nGKPi4hITexWIiLfYL9Sd8UBBvVYd+/exaZNm2A0GhEVFYXly5ej\nvr7efXtjYyNWrlyJmJgYTJkyBZ9//jnGjh2LGzdueDWHVqsFAPTu3du9ZjQa0dTUhLNnz3rkqaqq\ngslk6vTmQEQkC3YrEZFvsF+JOMCgHiwzMxPV1dXYvXs3CgoK4HA4sGzZMrhcLgDAqlWrcP36dVgs\nFrz33nvIz8/3evnevXsXO3bsgNPpRGJiontdr9fj+eefR0lJiXutrKwMsbGxeOKJJ7yagYjIm9it\nRES+wX4l4jUwqIf66aef8N133+GLL77AyJEjAQDvv/8+TCYTjh8/jrCwMJw4cQKFhYUYPnw4ACA7\nOxuvv/76v947Pj7e/e8tLS0IDQ3F1q1b3ecQAu3nCU6fPh179+5FVlYWAOCbb77BjBkzcO/evX+d\ngYjIF9itRES+wX4lascBBvVIFy9ehF6vd78BAEDfvn0RHh6Ouro6OBwO+Pv7u98AAHjt/D2r1eq+\nEFJgYCD69ev3wPuZTCa88847+PnnnxEaGoqTJ09i69atKCoq8koOIiJvY7cSEfkG+5WoHQcY1CN1\nvDBRR21tbRBCoHfv3j47V2/IkCHuN4FHCQ4OhsFgQHFxMcLCwjBhwgT07dvXJ5mIiLyB3UpE5Bvs\nV6J2vAYG9UgRERGw2+2ora11r/3++++4cuUKwsPDMWLECNjtdly8eNF9+/nz5xXPOW3aNJSXl6Ok\npATJycmK709E9DjYrUREvsF+JWrHAQb1SOHh4XjhhRewfv16VFVVoba2FmvWrMHTTz+NpKQkPPvs\nszCZTHj77bdRU1ODs2fPIjc3FxqNRtHvsZ42bRouXLiAiooKTJ8+XbF9iYj+CXYrEZFvsF+J2nGA\nQT1KxwLfunUrRo0ahbS0NCxevBhBQUGwWCzuQ/S2bNmC/v37Y/HixVi9ejXmzp3rPkTv7+7xOLc9\nyIABAxAZGYlRo0ahf//+7t+h5BsREVFX2K1ERL7BfiXypBH8Ul6iTux2O06cOAGj0eg+56+6uhqp\nqamw2Wzo1YuzPyKix8VuJSLyDfYr9RTanJycHLVDEMmmV69eSElJwe3btzF06FBcvXoVZrMZEyZM\nwMyZM9WOR0TULbFbiYh8g/1KPQWPwCB6iOrqapjNZtTW1kKv1yM5ORlZWVkICAhQOxoRUbfFbiUi\n8g32K/UEHGAQERERERERkfR4MhQRERERERERSY8DDCIiIiIiIiKSHgcYRERERERERCQ9DjCIiIiI\niIiISHocYBARERERERGR9P4HPYPjNH6jG0gAAAAASUVORK5CYII=\n",
+ "text": [
+ ""
+ ]
+ }
+ ],
+ "prompt_number": 74
+ },
+ {
+ "cell_type": "heading",
+ "level": 3,
+ "metadata": {},
+ "source": [
+ "Supplementary Figures 6-20"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Supplementary Figures [6](https://raw.githubusercontent.com/olgabot/olgabot.github.io-source/master/content/images/shalek2013_sfig6.png), \n",
+ "[7](https://raw.githubusercontent.com/olgabot/olgabot.github.io-source/master/content/images/shalek2013_sfig7.png),\n",
+ "[8](https://raw.githubusercontent.com/olgabot/olgabot.github.io-source/master/content/images/shalek2013_sfig8.png), \n",
+ "[9](https://raw.githubusercontent.com/olgabot/olgabot.github.io-source/master/content/images/shalek2013_sfig9.png), \n",
+ "[10](https://raw.githubusercontent.com/olgabot/olgabot.github.io-source/master/content/images/shalek2013_sfig10.png), \n",
+ "[11](https://raw.githubusercontent.com/olgabot/olgabot.github.io-source/master/content/images/shalek2013_sfig11.png), \n",
+ "[12](https://raw.githubusercontent.com/olgabot/olgabot.github.io-source/master/content/images/shalek2013_sfig12.png), \n",
+ "[13](https://raw.githubusercontent.com/olgabot/olgabot.github.io-source/master/content/images/shalek2013_sfig13.png), \n",
+ "[14](https://raw.githubusercontent.com/olgabot/olgabot.github.io-source/master/content/images/shalek2013_sfig14.png), \n",
+ "[15](https://raw.githubusercontent.com/olgabot/olgabot.github.io-source/master/content/images/shalek2013_sfig15.png), \n",
+ "[16](https://raw.githubusercontent.com/olgabot/olgabot.github.io-source/master/content/images/shalek2013_sfig16.png), \n",
+ "[17](https://raw.githubusercontent.com/olgabot/olgabot.github.io-source/master/content/images/shalek2013_sfig17.png), \n",
+ "[18](https://raw.githubusercontent.com/olgabot/olgabot.github.io-source/master/content/images/shalek2013_sfig18.png), \n",
+ "[19](https://raw.githubusercontent.com/olgabot/olgabot.github.io-source/master/content/images/shalek2013_sfig19.png), and\n",
+ "[20](https://raw.githubusercontent.com/olgabot/olgabot.github.io-source/master/content/images/shalek2013_sfig20.png), \n",
+ "deal with splicing data from the molecular barcodes, RNA-FISH, flow-sorted cells, and single-cell RT-PCR and are out of the scope of this reproduction."
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Conclusions\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "While there may be minor, undocumented, differences between the methods presented in the manuscript and the figures, the application of [`flotilla`](https://github.com/YeoLab/flotilla) presents an opportunity to avoid these types of inconsistencies by strictly documenting every change to code and every transformation of the data. The biology the authors found is clearly real, as they did the knockout experiment of *Ifnr-/-* and saw that indeed the maturation process was affected, and *Stat2* and *Irf7* had much lower expression, as with the \"maturing\" cells in the data."
+ ]
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+}
\ No newline at end of file
diff --git a/examples/supplementaldata.ipynb b/examples/supplementaldata.ipynb
new file mode 100644
index 00000000..e90407d7
--- /dev/null
+++ b/examples/supplementaldata.ipynb
@@ -0,0 +1,488 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:002384ba46548fc44c25619e666c4a00c25a9edd0412d1360d3300fb3075e169"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Storing supplemental data on `Study` objects"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A recently added feature is the ability to store any arbitrary pandas dataframe on `study.supplemental`, and this will get re-loaded every time you `embark` on that datapackage. Let's start with the batch-corrected [BrainSpan](http://www.brainspan.org/) Allen Brain Institute's Brain Atlas data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "import flotilla\n",
+ "study = flotilla.embark(flotilla._brainspan)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "2015-03-16 15:56:43\tParsing datapackage to create a Study object\n",
+ "2015-03-16 15:56:48\tInitializing Study\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "2015-03-16 15:56:48\tInitializing Predictor configuration manager for Study\n",
+ "2015-03-16 15:56:48\tPredictor ExtraTreesClassifier is of type \n",
+ "2015-03-16 15:56:48\tAdded ExtraTreesClassifier to default predictors\n",
+ "2015-03-16 15:56:48\tPredictor ExtraTreesRegressor is of type \n",
+ "2015-03-16 15:56:48\tAdded ExtraTreesRegressor to default predictors\n",
+ "2015-03-16 15:56:48\tPredictor GradientBoostingClassifier is of type \n",
+ "2015-03-16 15:56:48\tAdded GradientBoostingClassifier to default predictors\n",
+ "2015-03-16 15:56:48\tPredictor GradientBoostingRegressor is of type \n",
+ "2015-03-16 15:56:48\tAdded GradientBoostingRegressor to default predictors\n",
+ "2015-03-16 15:56:48\tLoading metadata\n",
+ "2015-03-16 15:56:49\tLoading expression data\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "2015-03-16 15:56:49\tInitializing expression\n",
+ "2015-03-16 15:56:49\tDone initializing expression\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "2015-03-16 15:56:49\tSuccessfully initialized a Study object!\n"
+ ]
+ }
+ ],
+ "prompt_number": 6
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's take a look at how big this expression matrix is."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "study.expression.data.shape"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 7,
+ "text": [
+ "(519, 14321)"
+ ]
+ }
+ ],
+ "prompt_number": 7
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Yikes, 14,321 features is a lot! Let's subset on just the most variant genes. By default, this is the genes whose variance is two standard deviations away from the mean variance of all genes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "variant_ids = study.expression.feature_subsets['variant']\n",
+ "variant_expression = study.expression.data.ix[:, variant_ids]\n",
+ "variant_expression.shape"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 8,
+ "text": [
+ "(519, 564)"
+ ]
+ }
+ ],
+ "prompt_number": 8
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "564 features isn't so bad. Let's correlate all features to each other in this subset."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "%%time\n",
+ "variant_expression_corr = variant_expression.corr()\n",
+ "variant_expression_corr.head()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "CPU times: user 315 ms, sys: 3.16 ms, total: 318 ms\n",
+ "Wall time: 317 ms\n"
+ ]
+ }
+ ],
+ "prompt_number": 9
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "That didn't take *too* long, but I'm sure you can imagine it would take a really long time for ALL genes!\n",
+ "\n",
+ "Now let's assign this to the `study.supplemental` object with a name of our choice. To keep things simple, I'm gonna give it the same name."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "study.supplemental.variant_expression_corr = variant_expression_corr"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 10
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now let's save the object and re-`embark` to make sure it's there."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "study.save('brainspan2')\n",
+ "study2 = flotilla.embark('brainspan2')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Wrote datapackage to /Users/olga/flotilla_projects/brainspan2/datapackage.json\n",
+ "2015-03-16 15:57:43\tReading datapackage from /Users/olga/flotilla_projects/brainspan2/datapackage.json\n",
+ "2015-03-16 15:57:43\tParsing datapackage to create a Study object\n",
+ "2015-03-16 15:57:49\tInitializing Study\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "2015-03-16 15:57:49\tInitializing Predictor configuration manager for Study\n",
+ "2015-03-16 15:57:49\tPredictor ExtraTreesClassifier is of type \n",
+ "2015-03-16 15:57:49\tAdded ExtraTreesClassifier to default predictors\n",
+ "2015-03-16 15:57:49\tPredictor ExtraTreesRegressor is of type \n",
+ "2015-03-16 15:57:49\tAdded ExtraTreesRegressor to default predictors\n",
+ "2015-03-16 15:57:49\tPredictor GradientBoostingClassifier is of type \n",
+ "2015-03-16 15:57:49\tAdded GradientBoostingClassifier to default predictors\n",
+ "2015-03-16 15:57:49\tPredictor GradientBoostingRegressor is of type \n",
+ "2015-03-16 15:57:49\tAdded GradientBoostingRegressor to default predictors\n",
+ "2015-03-16 15:57:49\tLoading metadata\n",
+ "2015-03-16 15:57:49\tLoading expression data\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "2015-03-16 15:57:49\tInitializing expression\n",
+ "2015-03-16 15:57:49\tDone initializing expression\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "2015-03-16 15:57:50\tSuccessfully initialized a Study object!\n"
+ ]
+ }
+ ],
+ "prompt_number": 11
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's make sure our `variant_expression_corr` dataframe is really there."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "study2.supplemental.variant_expression_corr.head()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "html": [
+ "3.0
-
-#
-
-
-import sys
-import numpy as np
-import pandas as pd
-from sklearn.ensemble import ExtraTreesRegressor, GradientBoostingRegressor
-from scipy import stats
-from _barge_utils import timeout, TimeoutError
-from collections import defaultdict
-import networkx as nx
-import math
-import itertools
-
-def switchy_score(array):
- """Transform a 1D array of df scores to a vector of "switchy scores"
-
- Calculates std deviation and mean of sine- and cosine-transformed
- versions of the array. Better than sorting by just the mean which doesn't
- push the really lowly variant events to the ends.
-
- Parameters
- ----------
- array : numpy.array
- A 1-D numpy array or something that could be cast as such (like a list)
-
- Returns
- -------
- float
- The "switchy score" of the study_data which can then be compared to other
- splicing event study_data
-
- @author Michael T. Lovci
- """
- array = np.array(array)
- variance = 1 - np.std(np.sin(array[~np.isnan(array)] * np.pi))
- mean_value = -np.mean(np.cos(array[~np.isnan(array)] * np.pi))
- return variance * mean_value
-
-def get_switchy_score_order(x):
- """Apply switchy scores to a 2D array of df scores
-
- Parameters
- ----------
- x : numpy.array
- A 2-D numpy array in the shape [n_events, n_samples]
-
- Returns
- -------
- numpy.array
- A 1-D array of the ordered indices, in switchy score order
- """
- switchy_scores = np.apply_along_axis(switchy_score, axis=0, arr=x)
- return np.argsort(switchy_scores)
-
-
-def binify(df, bins):
- """Makes a histogram of each row the provided binsize
-
- Parameters
- ----------
- df : pandas.DataFrame
- The dataframe whose rows you'd like to binify.
- bins : numpy.array
- Bins you would like to use for this data. Must include the final bin
- value, e.g. (0, 0.5, 1) for the two bins (0, 0.5) and (0.5, 1)
-
- Returns
- -------
- binned : pandas.DataFrame
-
- Raises
- ------
-
-
- """
- ncol = bins.shape[0] - 1
- nrow = df.shape[0]
- binned = np.zeros((nrow, ncol))
-
- # TODO: make sure this works for numpy matrices
- for i, (name, row) in enumerate(df.iterrows()):
- binned[i, :] = np.histogram(row, bins=bins, normed=True)[0]
-
- columns = ['{}-{}'.format(i, j) for i, j in zip(bins, bins[1:])]
- binned = pd.DataFrame(binned, index=df.index, columns=columns)
- return binned
-
-
-def get_regressor(x,y, n_estimators=1500, pCut=0.05, n_tries=5, verbose=False):
-
- if verbose:
- sys.stderr.write('getting regressor\n')
- clfs = []
- oob_scores = []
-
- for i in range(n_tries):
- if verbose:
- sys.stderr.write('%d.' % i)
-
- clf = ExtraTreesRegressor(n_estimators=n_estimators, oob_score=True,
- bootstrap=True, max_features='sqrt',
- n_jobs=1, random_state=i).fit(x,y)
- clfs.append(clf)
- oob_scores.append(clf.oob_score_)
- clf = clfs[np.argmax(oob_scores)]
- clf.feature_importances = pd.Series(clf.feature_importances_, index=x.columns)
-
- return clf, oob_scores
-
-
-def get_boosting_regressor(x,y,verbose=False):
- if verbose:
- sys.stderr.write('getting boosting regressor\n')
-
- clf = GradientBoostingRegressor(n_estimators=50, subsample=0.6, max_features=100,
- verbose=0, learning_rate=0.1, random_state=0).fit(x,y)
-
- clf.feature_importances = pd.Series(clf.feature_importances_, index=x.columns)
- if verbose:
- sys.stderr.write('finished boosting regressor\n')
-
- return clf
-
-
-def get_unstarted_events(mongodb):
- """
- get events that have not been started yet.
- generator sets started to True before returning an event
- """
- go_on = True
- while go_on ==True:
-
- event = mongodb['list'].find_one({"started":False})
-
- if event is None:
- go_on=False
-
- else:
- event['started'] = True
- mongodb['list'].save(event)
- yield event
-
-@timeout(5) #because these sometimes hang
-def get_slope(x,y):
- return stats.linregress(x,y)[0]
-
-@timeout(5) #because these sometimes hang
-def do_r(s_1, s_2, method=stats.pearsonr, min_items=12):
-
- """
- do an R calculation, remove items with values missing in either
-
- input:
- x - predictor vector
- y - target vector
-
- optional:
- method - method to use (scipy.stats.pearsonr or scipy.stats.spearmanr)
-
- output:
- r, p (order as determined by the chosen method)
-
- return (nan, nan) if too few items overlap
-
- """
- s_1, s_2 = s_1.dropna().align(s_2.dropna(), join='inner')
- if len(s_1) <= min_items:
- return (np.nan, np.nan)
- return method(s_1, s_2)
-
-@timeout(10) #because these sometimes hang
-def get_robust_values(x,y):
- """
- get robust linear regression
-
- input:
- x - predictor vector
- y - target vector
-
- output:
- intercept, slope, t-statistic, p-value
-
- """
- import statsmodels.api as sm
- rlm_result = sm.RLM(y, sm.add_constant(x), missing='drop').fit()
- return rlm_result.params[0], rlm_result.params[1], rlm_result.tvalues[0], rlm_result.pvalues[0],
-
-@timeout(5)
-def get_dcor(x, y):
- """
- get dcor
-
- see: https://github.com/andrewdyates/dcor
-
- input:
- x - predictor vector
- y - target vector
-
- output:
- dc, dr, dvx, dvy
-
- """
- import dcor_cpy as dcor
- dc, dr, dvx, dvy = dcor.dcov_all(x,y)
- return dc, dr, dvx, dvy
-
-@timeout(100)
-def apply_calc_rs(X, y, method = stats.pearsonr):
- """
- apply R calculation method on each gene separately (for nan values)
-
- input:
- X - (cells X rpkms) pd.DataFrame
- y - (cells X psi) pd.Series
-
- output:
- two pd.Series of:
- R coef, p-value
- """
-
- out_R = pd.Series(index=X.columns, name=y.name)
- out_P = pd.Series(index=X.columns, name=y.name)
- for this_id, data in X.iteritems():
- x = pd.Series(data, name=this_id)
- try:
- r, p = do_r(x, y, method=method)
-
- except TimeoutError:
- sys.stderr.write("%s r timeout event:%s, gene:%s\n" % (method, y.name, x.name))
- r, p = np.nan, np.nan
- out_R.ix[this_id] = r
- out_P.ix[this_id] = p
- return out_R, out_P
-
-@timeout(220)
-def apply_calc_robust(X, y, verbose=False):
-
- """X and y are dataframes, returns slope, t-value and p-value of robust regression"""
-
- if verbose:
- sys.stderr.write("getting robust regression\n")
- out_I = pd.Series(index=X.columns, name=y.name) #intercept
- out_S = pd.Series(index=X.columns, name=y.name) #slope
- out_T = pd.Series(index=X.columns, name=y.name) #t-value
- out_P = pd.Series(index=X.columns, name=y.name) #p-value
-
- for this_id, data in X.iteritems():
- x = pd.Series(data, name=this_id)
- try:
- i, s, t, p = get_robust_values(x, y)
- except TimeoutError:
- sys.stderr.write("robust timeout event:%s, gene:%s\n" % (y.name, x.name))
- i, s, t, p = np.nan, np.nan, np.nan, np.nan
- out_I.ix[this_id] = i
- out_S.ix[this_id] = s
- out_T.ix[this_id] = t
- out_P.ix[this_id] = p
- return out_I, out_S, out_T, out_P
-
-@timeout(50)
-def apply_calc_slope(X, y, verbose=False):
- """X and y are dataframes, returns slope, t-value and p-value of robust regression"""
- if verbose:
- sys.stderr.write("getting slope\n")
-
- out_S = pd.Series(index=X.columns, name=y.name)
-
- for this_id, data in X.iteritems():
- x = pd.Series(data, name=this_id)
- try:
- s = get_slope(x, y)
- except TimeoutError:
- sys.stderr.write("linregress timeout event:%s, gene:%s\n" % (y.name, x.name))
- s = np.nan
- out_S.ix[this_id] = s
-
- return out_S
-
-@timeout(50)
-def apply_dcor(X, y, verbose=False):
-
- if verbose:
- sys.stderr.write("getting dcor\n")
-
- out_DC = pd.Series(index=X.columns, name=y.name)
- out_DR = pd.Series(index=X.columns, name=y.name)
- out_DVX = pd.Series(index=X.columns, name=y.name)
- out_DVY = pd.Series(index=X.columns, name=y.name)
-
- for this_id, data in X.iteritems():
- x = pd.Series(data, name=this_id)
- try:
- dc, dr, dvx, dvy = get_dcor(*map(np.array, [x,y]))
-
- except TimeoutError:
- sys.stderr.write("dcor timeout event:%s, gene:%s\n" % (y.name, x.name))
- dc, dr, dvx, dvy = [np.nan] * 4
- out_DC.ix[this_id] = dc
- out_DR.ix[this_id] = dr
- out_DVX.ix[this_id] = dvx
- out_DVY.ix[this_id] = dvy
- return out_DC, out_DR, out_DVX, out_DVY
-
-#
-
-def dropna_mean(x):
- return x.dropna().mean()
-
-
-import sklearn
-from sklearn import decomposition
-
-
-class Pretty_Reducer(object):
- """
-
- Just like sklearn's reducers, but with prettied up DataFrames.
-
- """
-
- def relabel_pcs(self, x):
- return "pc_" + str(int(x) + 1)
-
- def fit(self, X):
-
- try:
- assert type(X) == pd.DataFrame
- except:
- print "Try again as a pandas study_data frame"
- raise
-
- self.X = X
- super(Pretty_Reducer, self).fit(X)
- self.components_ = pd.DataFrame(self.components_, columns=self.X.columns).rename_axis(self.relabel_pcs, 0)
- try:
- self.explained_variance_ = pd.Series(self.explained_variance_).rename_axis(self.relabel_pcs, 0)
- self.explained_variance_ratio_ = pd.Series(self.explained_variance_ratio_).rename_axis(self.relabel_pcs, 0)
- except AttributeError:
- pass
- return self
-
- def transform(self, X):
- component_space = super(Pretty_Reducer, self).transform(X)
- if type(self.X) == pd.DataFrame:
- component_space = pd.DataFrame(component_space, index=self.X.index).rename_axis(self.relabel_pcs, 1)
- return component_space
-
- def fit_transform(self, X):
- try:
- assert type(X) == pd.DataFrame
- except:
- print "Try again as a pandas study_data frame"
- raise
- self.fit(X)
- return self.transform(X)
-
-class PCA(Pretty_Reducer, sklearn.decomposition.PCA):
- pass
-
-class NMF(Pretty_Reducer, sklearn.decomposition.NMF):
- here=True
- def fit(self, X):
-
- """
- duplicated fit code for NMF because sklearn's NMF cheats for efficiency and calls fit_transform.
- MRO resolves the closest (in this package) fit_transform first and so there's a recursion error:
- def fit(self, X, y=None, **params):
-
- self.fit_transform(X, **params)
- return self
- """
-
- try:
- assert type(X) == pd.DataFrame
- except:
- print "Try again as a pandas study_data frame"
- raise
-
- self.X = X
- super(sklearn.decomposition.NMF, self).fit_transform(X) #notice this is fit_transform, not fit
- self.components_ = pd.DataFrame(self.components_, columns=self.X.columns).rename_axis(self.relabel_pcs, 0)
-
-
-class Networker(object):
- weight_funs=['abs', 'sq', 'arctan', 'arctan_sq']
-
- def get_weight_fun(self, fun_name):
- _abs = lambda x: x
- _sq = lambda x: x ** 2
- _arctan = lambda x: np.arctan(x)
- _arctan_sq = lambda x: np.arctan(x) ** 2
- if fun_name == 'abs':
- wt = _abs
- elif fun_name == 'sq':
- wt = _sq
- elif fun_name == 'arctan':
- wt = _arctan
- elif fun_name == 'arctan_sq':
- wt = _arctan_sq
- else:
- raise ValueError
- return wt
-
- def __init__(self):
- self.adjacencies_ = defaultdict()
- self.graphs_ = defaultdict()
- self._default_node_color_mapper = lambda x: 'r'
- self._default_node_size_mapper = lambda x: 300
- self._last_adjacency_accessed = None
- self._last_graph_accessed = None
-
- def get_adjacency(self, data=None, name=None, use_pc_1=True, use_pc_2=True,
- use_pc_3=True, use_pc_4=True, n_pcs=5):
-
- if data is None and self._last_adjacency_accessed is None:
- raise AttributeError("this hasn't been called yet")
-
- if name is None:
- if self._last_adjacency_accessed is None:
- name = 'default'
- else:
- name = self._last_adjacency_accessed
- self._last_adjacency_accessed = name
- try:
- if name in self.adjacencies_:
- #print "returning a pre-built adjacency"
- return self.adjacencies_[name]
- else:
- raise ValueError("adjacency hasn't been built yet")
- except ValueError:
- #print 'reduced space', data.shape
- total_pcs = data.shape[1]
- use_cols = np.ones(total_pcs, dtype='bool')
- use_cols[n_pcs:] = False
- use_cols = use_cols * np.array([use_pc_1, use_pc_2, use_pc_3, use_pc_4] + [True,]*(total_pcs-4))
- selected_cols = data.loc[:,use_cols]
- cov = np.cov(selected_cols)
- nRow, nCol = selected_cols.shape
- adjacency = pd.DataFrame(np.tril(cov * - (np.identity(nRow) - 1)),
- index=selected_cols.index, columns=data.index)
- #convert to triangular matrix with 0's on diag
-
- self.adjacencies_[name] = adjacency
-
- return self.adjacencies_[name]
-
- def get_graph(self, adjacency=None, cov_cut=None, name=None,
- node_color_mapper=None,
- node_size_mapper=None,
- degree_cut = 2,
- wt_fun='abs'):
-
- if node_color_mapper is None:
- node_color_mapper = self._default_node_color_mapper
- if node_size_mapper is None:
- node_size_mapper = self._default_node_size_mapper
-
- if name is None:
- if self._last_graph_accessed is None:
- name = 'default'
- else:
- name = self._last_graph_accessed
- self._last_graph_accessed = name
- try:
- g,pos = self.graphs_[name]
- except:
- wt = self.get_weight_fun(wt_fun)
- g = nx.Graph()
- for node_label in adjacency.index:
-
- node_color = node_color_mapper(node_label)
- node_size = node_size_mapper(node_label)
- g.add_node(node_label, node_size=node_size, node_color=node_color)
- # g.add_nodes_from(adjacency.index) #to add without setting attributes...neater, but does same thing as above loop
- for cell1, others in adjacency.iterrows():
- for cell2, value in others.iteritems():
- if value > cov_cut:
- #cast to floats because write_gml doesn't like numpy dtypes
- g.add_edge(cell1, cell2, weight=float(wt(value)),inv_weight=float(1/wt(value)), alpha=0.05)
-
- g.remove_nodes_from([k for k, v in g.degree().iteritems() if v <= degree_cut])
-
- pos = nx.spring_layout(g)
- self.graphs_[name] = (g, pos)
-
- return g, pos
-
-class Predictor(object):
-
- from sklearn.ensemble import ExtraTreesClassifier, ExtraTreesRegressor
-
- extratrees_default_params = {'n_estimators':100,
- 'bootstrap':True,
- 'max_features':'auto',
- 'random_state':0,
- 'verbose':1,
- 'oob_score':True,
- 'n_jobs':2,
- 'verbose':True}
-
- extratreees_scoring_fun = lambda clf: clf.feature_importances_
- extratreees_scoring_cutoff_fun = lambda scores: np.mean(scores) + 2*np.std(scores) # 2 std's above mean
-
- from sklearn.ensemble import GradientBoostingClassifier
- boosting_classifier_params = {'n_estimators': 80, 'max_features':1000, 'learning_rate': 0.2, 'subsample': 0.6,}
- boosting_scoring_fun = lambda clf: clf.feature_importances_
- boosting_scoring_cutoff_fun = lambda scores: np.mean(scores) + 2*np.std(scores)
-
- default_classifier, default_classifier_name = ExtraTreesClassifier, "ExtraTreesClassifier"
- default_regressor, default_regressor_name = ExtraTreesRegressor, "ExtraTreesRegressor"
-
- default_classifier_scoring_fun = default_regressor_scoring_fun = extratreees_scoring_fun
- default_classifier_scoring_cutoff_fun = default_regressor_scoring_cutoff_fun = extratreees_scoring_cutoff_fun
- default_classifier_params = default_regressor_params = extratrees_default_params
-
- def __init__(self, data_df, metadata_df,
- name="Classifier",
- categorical_traits = None,
- continuous_traits = None,
- ):
- """
- train regressors_ or classifiers_ on data.
-
- name: titles for plots and things...
- sample_list: a list of sample ids for this comparer
- critical_variable: a response variable to test or a list of them
- data_df: pd.DataFrame containing arrays in question
- metadata_df: pd.DataFrame with metadata about data_df
- categorical_traits: which traits are catgorical? - if None, assumed to be all traits
- continuous_traits: which traits are continuous - i.e. build a regressor, not a classifier
- """
-
- self.has_been_fit_yet=False
- self.has_been_scored_yet=False
- self.name = name
- self.X = data_df
- self.important_features = {}
- self.traits = []
- self.categorical_traits = categorical_traits
- if categorical_traits is not None:
- self.traits.extend(categorical_traits)
-
- self.continuous_traits = continuous_traits
- if continuous_traits is not None:
- self.traits.extend(continuous_traits)
-
- print "Initializing predictors for %s" % " and ".join(self.traits)
-
-
- #print "Using traits: ", self.traits
-
- self.trait_data = metadata_df[self.traits] #traits from source, in case they're needed later
- self.X, self.trait_data = self.X.align(self.trait_data, axis=0, join='inner')
- self.y = pd.DataFrame(index=self.X.index, columns=self.traits) #traits encoded to do some work -- "target" variable
-
- self.classifiers_ = {}
- from sklearn.preprocessing import LabelEncoder
-
- for trait in self.traits:
- self.important_features[trait] = {}
-
- for trait in self.categorical_traits:
- try:
- assert len(metadata_df.groupby(trait).describe().index.levels[0]) == 2
- except AssertionError:
- print "WARNING: trait \"%s\" has >2 categories"
- self.classifiers_[trait] = {}
- traitset = metadata_df.groupby(trait).describe().index.levels[0]
- le = LabelEncoder().fit(traitset) #categorical encoder
- self.y[trait] = le.transform(self.trait_data[trait]) #categorical encoding
-
- self.continuous_traits = continuous_traits
- self.regressors_ = {}
- if self.continuous_traits is not None:
-
- for trait in self.continuous_traits:
- self.regressors_[trait] = {}
- self.y[trait] = self.trait_data[trait]
-
- def fit_classifiers(self,
- traits=None,
- classifier_name=default_classifier_name,
- classifier=default_classifier,
- classifier_params=default_classifier_params,
- ):
- """ fit classifiers_ to the data
- traits - list of trait(s) to fit a classifier upon,
- if None, fit all traits that were initialized.
- Classifiers on each trait will be stored in: self.classifiers_[trait]
-
- classifier_name - a name for this classifier to be stored in self.classifiers_[trait][classifier_name]
- classifier - sklearn classifier object such as ExtraTreesClassifier
- classifier_params - dictionary for paramters to classifier
- """
-
- if traits is None:
- traits = self.categorical_traits
- else:
- assert type(traits) == list or type(traits) == set
- traits = traits
-
- for trait in traits:
- clf = classifier(**classifier_params)
- print "Fitting a classifier for trait %s... please wait." %trait
- clf.fit(self.X, self.y[trait])
- self.classifiers_[trait][classifier_name] = clf
- print "Finished..."
- self.has_been_fit_yet=True
-
-
- def score_classifiers(self,
- traits=None,
- classifier_name=default_classifier_name,
- feature_scoring_fun=default_classifier_scoring_fun,
- score_cutoff_fun=default_classifier_scoring_cutoff_fun):
- """
- collect scores from classifiers_
- traits - list of trait(s) to score. Retrieved from self.classifiers_[trait]
- classifier_name - a name for this classifier to be retrieved from self.classifiers_[trait][classifier_name]
- feature_scoring_fun - fxn that yields higher values for better features
- score_cutoff_fun - fxn that that takes output of feature_scoring_fun and returns a cutoff
- """
-
- if traits is None:
- traits = self.categorical_traits
-
- for trait in traits:
-
- try:
- assert trait in self.classifiers_
- except:
- print "trait: %s" % trait, "is missing, continuing"
- continue
- try:
- assert classifier_name in self.classifiers_[trait]
- except:
- print "classifier: %s" % classifier_name, "is missing, continuing"
- continue
-
- print "Scoring classifier: %s for trait: %s... please wait." % (classifier_name, trait)
-
- clf = self.classifiers_[trait][classifier_name]
- clf.scores_ = pd.Series(feature_scoring_fun(clf), index=self.X.columns)
- clf.score_cutoff_ = score_cutoff_fun(clf.scores_)
- clf.good_features_ = clf.scores_ > clf.score_cutoff_
- self.important_features[trait][classifier_name] = clf.good_features_
- clf.n_good_features_ = np.sum(clf.good_features_)
- clf.subset_ = self.X.T[clf.good_features_].T
-
- print "Finished..."
- self.has_been_scored_yet=True
-
- def fit_regressors(self,
- traits=None,
- regressor_name=default_regressor_name,
- regressor=default_regressor,
- regressor_params=default_regressor_params,
- ):
- raise NotImplementedError("Untested, should be close to working.")
-
- if traits is None:
- traits = self.continuous_traits
-
- for trait in traits:
- clf = regressor(**regressor_params)
- print "Fitting a classifier for trait %s... please wait." %trait
- clf.fit(self.X, self.y[trait])
- self.regressors_[trait][regressor_name] = clf
- print "Finished..."
-
- def score_regressors(self,
- traits=None,
- regressor_name=default_regressor_name,
- feature_scoring_fun=default_regressor_scoring_fun,
- score_cutoff_fun=default_regressor_scoring_cutoff_fun):
- """
- collect scores from classifiers_
- feature_scoring_fun: fxn that yields higher values for better features
- score_cutoff_fun fxn that that takes output of feature_scoring_fun and returns a cutoff
- """
- raise NotImplementedError("Untested, should be close to working.")
- if traits is None:
- traits = self.continuous_traits
-
- for trait in traits:
-
- try:
- assert trait in self.regressors_
- except:
- print "trait: %s" % trait, "is missing, continuing"
- continue
- try:
- assert regressor_name in self.regressors_[trait]
- except:
- print "classifier: %s" % regressor_name, "is missing, continuing"
- continue
-
- print "Scoring classifier: %s for trait: %s... please wait." % (regressor_name, trait)
-
- clf = self.regressors_[trait][regressor_name]
- clf.scores_ = pd.Series(feature_scoring_fun(clf), index=self.X.columns)
- clf.score_cutoff_ = score_cutoff_fun(clf.scores_)
- self.important_features[trait][regressor_name] = clf.good_features_
- clf.good_features_ = clf.scores_ > clf.score_cutoff_
- clf.n_good_features_ = np.sum(clf.good_features_)
- clf.subset_ = self.X.T[clf.good_features_].T
- print "Finished..."
-
-
-def benjamini_hochberg(pValues, FDR=0.1):
- """ benjamini-hochberg correction for MHT
- pValues is a list of pValues
- FDR is the desired false-discovery rate
-
- from: http://udel.edu/~mcdonald/statmultcomp.html
- "One good technique for controlling the false discovery rate was briefly
- mentioned by Simes (1986) and developed in detail by Benjamini and Hochberg (1995).
- Put the individual P-values in order, from smallest to largest. The smallest
- P-value has a rank of i=1, the next has i=2, etc. Then compare each individual
- P-value to (i/m)Q, where m is the total number of tests and Q is the chosen false
- discovery rate. The largest P-value that has P<(i/m)Q is significant,
- and all P-values smaller than it are also significant."
-
- """
- ranks = np.argsort(np.argsort(pValues))
-
- nComps = len(pValues) + 0.0
- pSorter = np.argsort(pValues)
- pRank = np.argsort(np.argsort(pValues))+1
- BHcalc = (pRank / nComps) * FDR
- sigs = np.ndarray(shape=(nComps, ), dtype='bool')
- issig = True
- for (p, b, r) in itertools.izip(pValues[pSorter], BHcalc[pSorter], pSorter):
- if p > b:
- issig = False
- sigs[r] = issig
- return sigs
-
-
-class TwoWayGeneComparisonLocal(object):
-
- def __init__(self, sample1_name, sample2_name, df, pCut = 0.001,
- local_fraction = 0.1, bonferroni = True, FDR=None,
- dtype="RPKM"):
- """ Run a two-sample RPKM experiment.
- Give control sample first, it will go on the x-axis
- df is a pandas dataframe with features (genes) on columns and samples on rows
- sample1 and sample2 are the names of rows in df (sample IDs)
- pCut - P value cutoff
- local_fraction - by default the closest 10% of genes are used for local z-score calculation
- bonferroni - p-values are adjusted for MHT with bonferroni correction
- BH - benjamini-hochberg FDR filtering - check result, proceed with caution. sometimes breaks :(
- """
-
- sample1 = df.ix[sample1_name]
- sample2 = df.ix[sample2_name]
-
- sampleNames = (sample1.name, sample2.name)
- self.sampleNames = sampleNames
-
- sample1 = sample1.replace(0, np.nan).dropna()
- sample2 = sample2.replace(0, np.nan).dropna()
-
- sample1, sample2 = sample1.align(sample2, join='inner')
-
- self.sample1 = sample1
- self.sample2 = sample2
- labels = sample1.index
-
- self.nGenes = len(labels)
- if bonferroni:
- correction = self.nGenes
- else:
- correction = 1
-
- localCount = int(math.ceil(self.nGenes * local_fraction))
- self.pCut = pCut
- self.upGenes = set()
- self.dnGenes = set()
- self.expressedGenes = set([labels[i] for i, t in enumerate(np.any(np.c_[sample1, sample2] > 1, axis=1)) if t])
- self.log2Ratio = np.log2(sample2 / sample1)
- self.average_expression = (sample2 + sample1)/2.
- self.ranks = np.argsort(np.argsort(self.average_expression))
- self.pValues = pd.Series(index = labels)
- self.localMean = pd.Series(index = labels)
- self.localStd = pd.Series(index = labels)
- self.localZ = pd.Series(index = labels)
- self.dtype=dtype
-
- for g, r in itertools.izip(self.ranks.index, self.ranks):
- if r < localCount:
- start = 0
- stop = localCount
-
- elif r > self.nGenes - localCount:
- start = self.nGenes - localCount
- stop = self.nGenes
-
- else:
- start = r - int(math.floor(localCount/2.))
- stop = r + int(math.ceil(localCount/2.))
-
- localGenes = self.ranks[self.ranks.between(start, stop)].index
- self.localMean.ix[g] = np.mean(self.log2Ratio.ix[localGenes])
- self.localStd.ix[g] = np.std(self.log2Ratio.ix[localGenes])
- self.pValues.ix[g] = stats.norm.pdf(self.log2Ratio.ix[g],
- self.localMean.ix[g],
- self.localStd.ix[g]) * correction
- self.localZ.ix[g] = (self.log2Ratio.ix[g]- self.localMean.ix[g])/self.localStd.ix[g]
-
- data = pd.DataFrame(index = labels)
- data["rank"] = self.ranks
- data["log2Ratio"] = self.log2Ratio
- data["localMean"] = self.localMean
- data["localStd"] = self.localStd
- data["pValue"] = self.pValues
-
- if FDR == None:
- data["isSig"] = self.pValues < pCut
- else:
- data["isSig"] = benjamini_hochberg(self.pValues, FDR=FDR)
-
- data["meanExpression"] = self.average_expression
- data["localZ"] = self.localZ
- data[sampleNames[0]] = sample1
- data[sampleNames[1]] = sample2
-
- self.result_ = data
-
- for label, (pVal, logratio, isSig) in data.get(["pValue", "log2Ratio", "isSig"]).iterrows():
- if (pVal < pCut) and isSig:
- if logratio > 0:
- self.upGenes.add(label)
- elif logratio < 0:
- self.dnGenes.add(label)
- else:
- raise ValueError
-
- def gstats(self):
- print "I used a p-value cutoff of %e" %self.pCut
- print "There are", len(self.upGenes), "up-regulated genes in %s vs %s" %(self.sampleNames[1],
- self.sampleNames[0])
- print "There are", len(self.dnGenes), "down-regulated genes in %s vs %s" %(self.sampleNames[1],
- self.sampleNames[0])
- print "There are", len(self.expressedGenes), "expressed genes in both %s and %s" %self.sampleNames
-
-
diff --git a/flotilla/src/_schooner_data_model/_Data.py b/flotilla/src/_schooner_data_model/_Data.py
deleted file mode 100644
index 10fe854a..00000000
--- a/flotilla/src/_schooner_data_model/_Data.py
+++ /dev/null
@@ -1,285 +0,0 @@
-from scipy.spatial.distance import pdist, squareform
-from collections import defaultdict
-import sys
-from .._cargo_commonObjects import cargo
-
-class Data(object):
- """Generic study_data model for both splicing and expression study_data
-
- Attributes
- ----------
-
-
- Methods
- -------
-
- """
-
-
- def __init__(self, sample_descriptors, species=None):
- self._default_reducer_args = {'whiten':False, 'show_point_labels':False, 'show_vectors':False}
- self.samplewise_reduction = {}
- self.featurewise_reduction = {}
- self.clf_dict = {}
- self.localZ_dict = {}
- self.lists = {}
- self.pca_plotting_args = {}
- self._default_featurewise=False
- self._last_reducer_accessed = None
- self._last_predictor_accessed = None
- self._default_group_id = 'any_cell'
- self._default_list_id = 'variant'
- self.cargo = cargo
- self.sample_descriptors = sample_descriptors
- self.set_reducer_colors()
- self.set_reducer_markers()
- self.species=species
-
- def set_reducer_colors(self):
- try:
- self._default_reducer_args.update({'colors_dict':self.sample_descriptors.color})
- except:
- sys.stderr.write("color loading failed")
- self._default_reducer_args.update({'colors_dict':defaultdict(lambda : 'r')})
-
- def set_reducer_markers(self):
- try:
- self._default_reducer_args.update({'markers_dict':self.sample_descriptors.marker})
- except:
- sys.stderr.write("marker loading failed")
- self._default_reducer_args.update({'markers_dict': defaultdict(lambda : 'o')})
-
- def set_outliers(self):
- self.outliers = set(self.sample_descriptors['outlier'].ix[map(bool, self.sample_descriptors['outlier'])].index)
-
- def get_outliers(self):
- try:
- return self.outliers
- except AttributeError:
- self.set_outliers()
- return self.outliers
-
- def drop_outliers(self, df):
- assert 'outlier' in self.sample_descriptors.columns
- these_outliers = self.get_outliers().intersection(set(df.index))
- print "dropping ", these_outliers
- return df.drop(these_outliers)
-
-
- def calculate_distances(self, metric='euclidean'):
- """Creates a squareform distance matrix for clustering fun
-
- Needed for some clustering algorithms
-
- Parameters
- ----------
- metric : str
- One of any valid scipy.distance metric strings
- """
- raise NotImplementedError
- self.pdist = squareform(pdist(self.binned, metric=metric))
- return self
-
- def correlate(self, method='spearman', between='measurements'):
- """Find correlations between either splicing/expression measurements
- or cells
- """
- raise NotImplementedError
-
- def jsd(self):
- """Jensen-Shannon divergence showing most varying measurements within a
- celltype and between celltypes
-
- Returns
- -------
- fig : matplotlib.Figure
- A figure object for saving.
- """
- raise NotImplementedError
-
- def _echo(self, x):
- return x
-
- #_naming_fun converts input feature names to something else. by default, just echo.
- _naming_fun = _echo
-
- def get_naming_fun(self):
- return self._naming_fun
-
- def set_naming_fun(self, fun, test_name='foo'):
- self._naming_fun = fun
- try:
- fun(test_name)
- except:
- pass
- #print "might not be a good naming function, failed on %s" % test_name
-
-
- def plot_classifier(self, gene_list_name=None, sample_list_name=None, clf_var=None,
- predictor_args=None, plotting_args=None):
-
- """Principal component-like analysis of measurements
- Params
- -------
- obj_id - key of the object getting plotted
- group_id -
- categorical_trait - classifier feature
- list_name - subset of genes to use for building class
-
-
-
- Returns
- -------
- self
- """
- if predictor_args is None:
- predictor_args = {}
-
- if plotting_args is None:
- plotting_args = {}
-
- local_plotting_args = self.pca_plotting_args.copy()
- local_plotting_args.update(plotting_args)
-
- clf = self.get_predictor(gene_list_name=gene_list_name,
- sample_list_name=sample_list_name,
- clf_var=clf_var,
- **predictor_args)
- clf(plotting_args=local_plotting_args)
-
- return self
-
- def plot_dimensionality_reduction(self, x_pc=1, y_pc=2, obj_id=None, group_id=None,
- list_name=None, featurewise=None, **plotting_args):
-
- """Principal component-like analysis of measurements
-
- Returns
- -------
- self
- """
- local_plotting_args = self.pca_plotting_args.copy()
- local_plotting_args.update(plotting_args)
- pca = self.get_reduced(obj_id, list_name, group_id, featurewise=featurewise)
- pca(markers_size_dict=lambda x: 400,
- show_vectors=False,
- title_size=10,
- axis_label_size=10,
- x_pc = "pc_" + str(x_pc), #this only affects the plot, not the study_data.
- y_pc = "pc_" + str(y_pc), #this only affects the plot, not the study_data.
- **local_plotting_args
- )
- return self
-
- def get_reduced(self, obj_id=None, list_name=None, group_id=None, featurewise=None, **reducer_args):
- _used_default_group = False
- if group_id is None:
- group_id = self._default_group_id
- _used_default_group = True
-
- _used_default_list = False
- if list_name is None:
- list_name = self._default_list_id
- _used_default_list = True
-
- _used_default_featurewise = False
- if featurewise is None:
- featurewise = self._default_featurewise
- _used_default_featurewise = True
-
- if obj_id is None:
- if self._last_reducer_accessed is None or \
- (not _used_default_list or not _used_default_group or not _used_default_featurewise):
- #if last_reducer_accessed hasn't been set or if the user asks for specific params,
- #else return the last reducer gotten by this method
-
- obj_id = list_name + ":" + group_id + ":" + str(featurewise)
-
- else:
- obj_id = self._last_reducer_accessed
-
- self._last_reducer_accessed = obj_id
- if featurewise:
- rdc_dict = self.featurewise_reduction
- else:
- rdc_dict = self.samplewise_reduction
- try:
- return rdc_dict[obj_id]
- except:
- rdc_obj = self.make_reduced(list_name, group_id, featurewise=featurewise, **reducer_args)
- rdc_obj.obj_id = obj_id
- rdc_dict[obj_id] = rdc_obj
-
- return rdc_dict[obj_id]
-
- def get_classifier(self, gene_list_name=None, sample_list_name=None, clf_var=None,
- obj_id=None,
- **classifier_args):
- """
- list_name = list of features to use for this clf
- obj_id = name of this classifier
- clf_var = boolean or categorical pd.Series
- """
-
- _used_default_group = False
- if sample_list_name is None:
- sample_list_name = self._default_group_id
- _used_default_group = True
-
- _used_default_list = False
- if gene_list_name is None:
- gene_list_name = self._default_list_id
- _used_default_list = True
-
- if obj_id is None:
- if self._last_predictor_accessed is None or \
- (not _used_default_list or not _used_default_group):
- #if last_reducer_accessed hasn't been set or if the user asks for specific params,
- #else return the last reducer gotten by this method
-
- obj_id = gene_list_name + ":" + sample_list_name + ":" + clf_var
- else:
- obj_id = self._last_predictor_accessed
-
- self._last_predictor_accessed = obj_id
- #print "I am a %s" % type(self)
- #print "here are my clf_dict keys: %s" % " ".join(self.clf_dict.keys())
- try:
- return self.clf_dict[obj_id]
- except:
- clf = self.make_classifier(gene_list_name, sample_list_name, clf_var, **classifier_args)
- clf.obj_id = obj_id
- self.clf_dict[obj_id] = clf
-
- return self.clf_dict[obj_id]
-
- def get_min_samples(self):
- if hasattr(self, 'min_samples'):
- return self.min_samples
- else:
- return 12
- return self
-
- def set_min_samples(self, min_samples):
-
- self.min_samples = min_samples
- return self
-
- def twoway(self, sample1, sample2, **kwargs):
- from .._submaraine_viz import TwoWayScatterViz
- pCut = kwargs['pCut']
- this_name = "_".join([sample1, sample2, str(pCut)])
- if this_name in self.localZ_dict:
- vz = self.localZ_dict[this_name]
- else:
- df = self.df
- df.rename_axis(self.get_naming_fun(), 1)
- vz = TwoWayScatterViz(sample1, sample2, df, **kwargs)
- self.localZ_dict[this_name] = vz
-
- return vz
-
- def plot_twoway(self, sample1, sample2, **kwargs):
- vz = self.twoway(sample1, sample2, **kwargs)
- vz()
- return vz
\ No newline at end of file
diff --git a/flotilla/src/_schooner_data_model/_DownsampledSplicingData.py b/flotilla/src/_schooner_data_model/_DownsampledSplicingData.py
deleted file mode 100644
index ea85a6f5..00000000
--- a/flotilla/src/_schooner_data_model/_DownsampledSplicingData.py
+++ /dev/null
@@ -1,175 +0,0 @@
-import pandas as pd
-import numpy as np
-from sklearn.preprocessing import StandardScaler
-
-from _Data import Data
-from .._submaraine_viz import NMF_viz, PCA_viz, PredictorViz
-from .._frigate_compute import binify, dropna_mean
-from .._skiff_external_sources import link_to_list
-import brewer2mpl
-import seaborn as sns
-import matplotlib.pyplot as plt
-
-sns.set(style='ticks', context='talk')
-
-PURPLES = brewer2mpl.get_map('Purples', 'sequential', 9).mpl_colors
-
-import collections
-
-class DownsampledSplicingData(Data):
- binned_reducer = None
- raw_reducer = None
-
- n_components = 2
- _binsize=0.1
- _var_cut = 0.2
-
-
- def __init__(self, df, sample_descriptors):
- """Instantiate an object of downsampled splicing data
-
- Parameters
- ----------
- df : pandas.DataFrame
- A "tall" dataframe of all miso summary events, with the usual
- MISO summary columns, and these are required: 'splice_type',
- 'probability', 'iteration.' Where "probability" indicates the
- randomly sampling probability from the bam file used to generate
- these reads, and "iteration" indicates the integer iteration
- performed, e.g. if multiple resamplings were performed.
- sample_metadata: pandas.DataFrame
-
- Notes
- -----
- Warning: this data is usually HUGE (we're taking like 10GB raw .tsv
- files) so make sure you have the available memory for dealing with
- these.
-
- """
- super(DownsampledSplicingData, self).__init__(sample_descriptors)
-
- self.sample_descriptors, splicing = \
- self.sample_descriptors.align(df, join='inner', axis=0)
-
- self.df = df
-
- @property
- def shared_events(self):
- """
- Parameters
- ----------
-
-
- Returns
- -------
- event_count_df : pandas.DataFrame
- Splicing events on the rows, splice types and probability as
- column MultiIndex. Values are the number of iterations which
- share this splicing event at that probability and splice type.
- """
-
- if not hasattr(self, '_shared_events_df'):
- shared_events_dict = {}
-
- for (splice_type, probability), df in self.df.groupby(
- ['splice_type', 'probability']):
- # print splice_type, probability, df.shape, \
- # df.event_name.unique().shape[0],
- # n_iter = df.iteration.unique().shape[0]
- event_count = collections.Counter(df.event_name)
- # print sum(1 for k, v in event_count.iteritems() if v == n_iter)
- shared_events_dict[(splice_type, probability)] = pd.Series(
- event_count)
-
- self._shared_events_df = pd.DataFrame(shared_events_dict)
- self._shared_events_df.columns = pd.MultiIndex.from_tuples(
- self._shared_events_df.columns.tolist())
- else:
- return self._shared_events_df
-
- def shared_events_barplot(self, figure_dir='./'):
- """PLot a "histogram" via colored bars of the number of events shared by
- different iterations at a particular sampling probability
-
- Parameters
- ----------
- figure_dir : str
- Where to save the pdf figures created
- """
- figure_dir = figure_dir.rstrip('/')
- colors = PURPLES + ['#262626']
-
- # figure_dir = '/home/obotvinnik/Dropbox/figures2/singlecell/splicing/what_is_noise'
-
- for splice_type, df in self.shared_events.groupby(level=0, axis=1):
- print splice_type, df.dropna(how='all').shape
-
- fig, ax = plt.subplots(figsize=(16, 4))
-
- count_values = np.unique(df.values)
- count_values = count_values[np.isfinite(count_values)]
-
- height_so_far = np.zeros(df.shape[1])
- left = np.arange(df.shape[1])
-
- for count, color in zip(count_values, colors):
- height = df[df == count].count()
- ax.bar(left, height, bottom=height_so_far, color=color,
- label=str(int(count)))
- height_so_far += height
- ymax = max(height_so_far)
- ax.set_ylim(0, ymax)
-
- legend = ax.legend(loc='center left', bbox_to_anchor=(1, 0.5),
- title='Iterations sharing event')
- ax.set_title(splice_type)
- ax.set_xlabel('Percent downsampled')
- ax.set_ylabel('number of events')
- sns.despine()
- fig.tight_layout()
- fig.savefig('{}/downsampled_shared_events_{}.pdf'.format(figure_dir,
- splice_type),
- bbox_extra_artists=(legend,), bbox_inches='tight')
-
- def shared_events_percentage(self, min_iter_shared=5, figure_dir='./'):
- """Plot the percentage of all events detected at that iteration,
- shared by at least 'min_iter_shared'
-
- Parameters
- ----------
- min_iter_shared : int
- Minimum number of iterations sharing an event
- figure_dir : str
- Where to save the pdf figures created
- """
- figure_dir = figure_dir.rstrip('/')
- sns.set(style='whitegrid', context='talk')
-
- # figure_dir = '/home/obotvinnik/Dropbox/figures2/singlecell/splicing/what_is_noise'
-
- for splice_type, df in self.shared_events.groupby(level=0, axis=1):
- df = df.dropna()
-
- fig, ax = plt.subplots(figsize=(16, 4))
-
-
- left = np.arange(df.shape[1])
- num_greater_than = df[df >= min_iter_shared].count()
- percent_greater_than = num_greater_than / df.shape[0]
-
- ax.plot(left, percent_greater_than,
- label='Shared with at least {} iter'.format(min_iter_shared))
-
- ax.set_xticks(np.arange(0, 101, 10))
-
- legend = ax.legend(loc='center left', bbox_to_anchor=(1, 0.5),
- title='Iterations sharing event')
-
- ax.set_title(splice_type)
- ax.set_xlabel('Percent downsampled')
- ax.set_ylabel('Percent of events')
- sns.despine()
- fig.tight_layout()
- fig.savefig('{}/downsampled_shared_events_{}_min_iter_shared{}.pdf'
- .format(figure_dir, splice_type, min_iter_shared),
- bbox_extra_artists=(legend,), bbox_inches='tight')
\ No newline at end of file
diff --git a/flotilla/src/_schooner_data_model/_ExpressionData.py b/flotilla/src/_schooner_data_model/_ExpressionData.py
deleted file mode 100644
index c3b9f321..00000000
--- a/flotilla/src/_schooner_data_model/_ExpressionData.py
+++ /dev/null
@@ -1,160 +0,0 @@
-import pandas as pd
-import seaborn
-from sklearn.preprocessing import StandardScaler
-
-from _Data import Data
-from .._submaraine_viz import PCA_viz, PredictorViz
-from .._frigate_compute import dropna_mean, Predictor
-from .._skiff_external_sources import link_to_list
-
-
-seaborn.set_context('paper')
-
-
-class ExpressionData(Data):
-
-
- _var_cut=0.5
- _expr_cut=0.1
-
-
- def __init__(self, expression_df, sample_metadata,
- gene_descriptors= None,
- var_cut=_var_cut, expr_cut=_expr_cut,
- drop_outliers=True, load_cargo=False,
- **kwargs
- ):
-
- super(ExpressionData, self).__init__(sample_metadata, **kwargs)
- if drop_outliers:
- expression_df = self.drop_outliers(expression_df)
-
- a = self.sample_descriptors.align(expression_df, join='inner', axis=0)
- self.sample_descriptors, expression_df = a
-
- self.gene_descriptors = gene_descriptors
- self.df = expression_df
- self.expression_df = self.df
-
- self.sparse_df = expression_df[expression_df > expr_cut]
- rpkm_variant = pd.Index([i for i, j in (expression_df.var().dropna() > var_cut).iteritems() if j])
- self.lists['variant'] = pd.Series(rpkm_variant, index=rpkm_variant)
-
- naming_fun = self.get_naming_fun()
- self.lists.update({'all_genes':pd.Series(map(naming_fun, self.expression_df.columns),
- index = self.expression_df.columns)})
- self.set_reducer_colors()
- self.set_reducer_markers()
- if load_cargo:
- self.load_cargo()
-
-
- def make_reduced(self, list_name, group_id, featurewise=False,
- reducer=PCA_viz,
- standardize=True,
- **reducer_args):
- """make and cache a reduced dimensionality representation of data """
-
- min_samples=self.get_min_samples()
- input_reducer_args = reducer_args.copy()
- reducer_args = self._default_reducer_args.copy()
- reducer_args.update(input_reducer_args)
- reducer_args['title'] = list_name + " : " + group_id
- naming_fun = self.get_naming_fun()
-
- if list_name not in self.lists:
- this_list = link_to_list(list_name)
- self.lists[list_name] = pd.Series(map(naming_fun, this_list), index =this_list)
-
-
- gene_list = self.lists[list_name]
-
- if group_id.startswith("~"):
- #print 'not', group_id.lstrip("~")
- sample_ind = ~pd.Series(self.sample_descriptors[group_id.lstrip("~")], dtype='bool')
- else:
- sample_ind = pd.Series(self.sample_descriptors[group_id], dtype='bool')
-
- sample_ind = sample_ind[sample_ind].index
- subset = self.sparse_df.ix[sample_ind]
- subset = subset.T.ix[gene_list.index].T
- frequent = pd.Index([i for i, j in (subset.count() > min_samples).iteritems() if j])
- subset = subset[frequent]
- #fill na with mean for each event
- means = subset.apply(dropna_mean, axis=0)
- mf_subset = subset.fillna(means, ).fillna(0)
-
- #whiten, mean-center
- if standardize:
- data = StandardScaler().fit_transform(mf_subset)
- else:
- data = mf_subset
-
- ss = pd.DataFrame(data, index = mf_subset.index,
- columns = mf_subset.columns).rename_axis(naming_fun, 1)
-
- #compute pca
- if featurewise:
- ss = ss.T
- rdc_obj = reducer(ss, **reducer_args)
- rdc_obj.means = means.rename_axis(naming_fun) #always the mean of input features... i.e. featurewise doesn't change this.
-
-
- #add mean gene_expression
- return rdc_obj
-
- def make_classifier(self, gene_list_name, group_id, categorical_trait,
- standardize=True, predictor=PredictorViz,
- ):
- """
- make and cache a classifier on a categorical trait (associated with samples) subset of genes
- """
-
- min_samples=self.get_min_samples()
- naming_fun = self.get_naming_fun()
-
- if gene_list_name not in self.lists:
- this_list = link_to_list(gene_list_name)
- self.lists[gene_list_name] = pd.Series(map(naming_fun, this_list), index =this_list)
-
- gene_list = self.lists[gene_list_name]
-
- if group_id.startswith("~"):
- #print 'not', group_id.lstrip("~")
- sample_ind = ~pd.Series(self.sample_descriptors[group_id.lstrip("~")], dtype='bool')
- else:
- sample_ind = pd.Series(self.sample_descriptors[group_id], dtype='bool')
- sample_ind = sample_ind[sample_ind].index
- subset = self.sparse_df.ix[sample_ind, gene_list.index]
- frequent = pd.Index([i for i, j in (subset.count() > min_samples).iteritems() if j])
- subset = subset[frequent]
- #fill na with mean for each event
- means = subset.apply(dropna_mean, axis=0)
- mf_subset = subset.fillna(means, ).fillna(0)
-
- #whiten, mean-center
- if standardize:
- data = StandardScaler().fit_transform(mf_subset)
- else:
- data = mf_subset
-
- ss = pd.DataFrame(data, index = mf_subset.index,
- columns = mf_subset.columns).rename_axis(naming_fun, 1)
- clf = predictor(ss, self.sample_descriptors,
- categorical_traits=[categorical_trait],)
- clf.set_reducer_plotting_args(self._default_reducer_args)
- return clf
-
- def load_cargo(self, rename=True, **kwargs):
- try:
- species = self.species
- self.cargo = cargo.get_species_cargo(self.species)
- self.go = self.cargo.get_go(species)
- self.lists.update(self.cargo.gene_lists)
-
- if rename:
- self.set_naming_fun(lambda x: self.go.geneNames(x))
- except:
- raise
-
-
diff --git a/flotilla/src/_schooner_data_model/_MappingStatsData.py b/flotilla/src/_schooner_data_model/_MappingStatsData.py
deleted file mode 100644
index a98c0b88..00000000
--- a/flotilla/src/_schooner_data_model/_MappingStatsData.py
+++ /dev/null
@@ -1,33 +0,0 @@
-__author__ = 'olga'
-
-from _Data import Data
-
-class MappingStatsData(Data):
- """Constructor for mapping statistics data from STAR
-
- Attributes
- ----------
-
-
- Methods
- -------
-
- """
-
- def __init__(self, df, sample_descriptors):
- """Constructor for MappingStatsData
-
- Parameters
- ----------
- df, sample_descriptors
-
- Returns
- -------
-
-
- Raises
- ------
-
- """
- super(MappingStatsData).__init__()
- pass
\ No newline at end of file
diff --git a/flotilla/src/_schooner_data_model/_SpikeinData.py b/flotilla/src/_schooner_data_model/_SpikeinData.py
deleted file mode 100644
index c47c31bf..00000000
--- a/flotilla/src/_schooner_data_model/_SpikeinData.py
+++ /dev/null
@@ -1,104 +0,0 @@
-__author__ = 'olga'
-
-from _ExpressionData import ExpressionData
-
-class SpikeInData(ExpressionData):
- """Class for Spikein data and associated functions
- Attributes
- ----------
-
-
- Methods
- -------
-
- """
-
- def __init__(self, df, sample_metadata):
- """Constructor for
-
- Parameters
- ----------
- df, sample_metadata
-
- Returns
- -------
-
-
- Raises
- ------
-
- """
- pass
-
- def spikeins_violinplot(self):
- import matplotlib.pyplot as plt
- import seaborn as sns
- import numpy as np
-
- fig, axes = plt.subplots(nrows=5, figsize=(16, 20), sharex=True,
- sharey=True)
- ercc_concentrations = ercc_controls_analysis.mix1_molecules_per_ul.copy()
- ercc_concentrations.sort()
-
- for ax, (celltype, celltype_df) in zip(axes.flat,
- tpm.ix[spikeins].groupby(
- sample_id_to_celltype_,
- axis=1)):
- print celltype
- # fig, ax = plt.subplots(figsize=(16, 4))
- x_so_far = 0
- # ax.set_yscale('log')
- xticklabels = []
- for spikein_type, spikein_df in celltype_df.groupby(
- spikein_to_type):
- # print spikein_df.shape
- df = spikein_df.T + np.random.uniform(0, 0.01,
- size=spikein_df.T.shape)
- df = np.log2(df)
- if spikein_type == 'ERCC':
- df = df[ercc_concentrations.index]
- xticklabels.extend(df.columns.tolist())
- color = 'husl' if spikein_type == 'ERCC' else 'Greys_d'
- sns.violinplot(df, ax=ax,
- positions=np.arange(df.shape[1]) + x_so_far,
- linewidth=0, inner='none', color=color)
-
- x_so_far += df.shape[1]
-
- ax.set_title(celltype)
- ax.set_xticks(np.arange(x_so_far))
- ax.set_xticklabels(xticklabels, rotation=90, fontsize=8)
- ax.set_ylabel('$\\log_2$ TPM')
-
- xmin, xmax = -0.5, x_so_far - 0.5
-
- ax.hlines(0, xmin, xmax)
- ax.set_xlim(xmin, xmax)
- sns.despine()
- # fig.savefig('/projects/ps-yeolab/obotvinnik/mn_diff_singlecell/figures/spikeins.pdf')
- # ! cp /projects/ps-yeolab/obotvinnik/mn_diff_singlecell/figures/spikeins.pdf ~/Dropbox/figures2/singlecell/spikeins.pdf
-
- def samples_violinplot():
- fig, axes = plt.subplots(nrows=3, figsize=(16, 6))
-
- for ax, (spikein_type, df) in zip(axes, tpm.groupby(spikein_to_type,
- axis=0)):
- print spikein_type, df.shape
-
- if df.shape[0] > 1:
- sns.violinplot(np.log2(df + 1), ax=ax, linewidth=0.1)
- ax.set_xticks([])
- ax.set_xlabel('')
-
- else:
- x = np.arange(df.shape[1])
- ax.bar(np.arange(df.shape[1]), np.log2(df.ix[spikein_type]),
- color=green)
- ax.set_xticks(x + 0.4)
- ax.set_xticklabels(df.columns, rotation=60)
- sns.despine()
-
- ax.set_title(spikein_type)
- ax.set_xlim(0, tpm.shape[1])
- ax.set_ylabel('$\\log_2$ TPM')
- sns.despine()
\ No newline at end of file
diff --git a/flotilla/src/_schooner_data_model/_SpliceJunctionData.py b/flotilla/src/_schooner_data_model/_SpliceJunctionData.py
deleted file mode 100644
index ee0db622..00000000
--- a/flotilla/src/_schooner_data_model/_SpliceJunctionData.py
+++ /dev/null
@@ -1,33 +0,0 @@
-__author__ = 'olga'
-
-from _SplicingData import SplicingData
-
-class SpliceJunctionData(SplicingData):
- """Class to hold splice junction information from SJ.out.tab files from STAR
-
- Attributes
- ----------
-
-
- Methods
- -------
-
- """
-
- def __init__(self, df, sample_metadata):
- """Constructor for SpliceJunctionData
-
- Parameters
- ----------
- df, sample_metadata
-
- Returns
- -------
-
-
- Raises
- ------
-
- """
- super(SpliceJunctionData).__init__()
- pass
\ No newline at end of file
diff --git a/flotilla/src/_schooner_data_model/_SplicingData.py b/flotilla/src/_schooner_data_model/_SplicingData.py
deleted file mode 100644
index a111ad05..00000000
--- a/flotilla/src/_schooner_data_model/_SplicingData.py
+++ /dev/null
@@ -1,181 +0,0 @@
-import pandas as pd
-import numpy as np
-from sklearn.preprocessing import StandardScaler
-
-from _Data import Data
-from .._submaraine_viz import NMF_viz, PCA_viz, PredictorViz
-from .._frigate_compute import binify, dropna_mean
-from .._skiff_external_sources import link_to_list
-
-
-class SplicingData(Data):
- binned_reducer = None
- raw_reducer = None
-
- n_components = 2
- _binsize=0.1
- _var_cut = 0.2
-
-
- def __init__(self, splicing, sample_metadata,
- event_metadata, binsize=_binsize,
- var_cut = _var_cut,
- drop_outliers=True,
- load_cargo=False,
- **kwargs
- ):
- """Instantiate a object for study_data scores with binned and reduced study_data
-
- Parameters
- ----------
- df : pandas.DataFrame
- A [n_events, n_samples] dataframe of splicing events
- n_components : int
- Number of components to use in the reducer
- binsize : float
- Value between 0 and 1, the bin size for binning the study_data scores
- reducer : sklearn.decomposition object
- An scikit-learn class that reduces the dimensionality of study_data
- somehow. Must accept the parameter n_components, have the
- functions fit, transform, and have the attribute components_
-
- """
- super(SplicingData, self).__init__(sample_metadata, **kwargs)
- if drop_outliers:
- splicing = self.drop_outliers(splicing)
- self.sample_descriptors, splicing = self.sample_descriptors.align(splicing, join='inner', axis=0)
-
- self.splicing_df = splicing
- self.df = self.splicing_df
- self.binsize = binsize
- psi_variant = pd.Index([i for i,j in (splicing.var().dropna() > var_cut).iteritems() if j])
- self.set_naming_fun(self.namer)
- self.lists['variant'] = pd.Series(psi_variant, index=psi_variant)
- self.lists['all_genes'] = pd.Series(splicing.index, index=splicing.index)
- self.event_descriptors = event_metadata
- self.set_reducer_colors()
- self.set_reducer_markers()
-
- def namer(self, x):
- "this is for miso psi IDs..."
- shrt = ":".join(x.split("@")[1].split(":")[:2])
- try:
- dd = self.event_descriptors.set_index('event_name')
- return dd['gene_symbol'].ix[x] + " " + shrt
- except Exception as e:
- #print e
- return shrt
-
- def set_binsize(self, binsize):
- self.binsize = binsize
-
- def get_binned_data(self):
- try:
- assert hasattr(self, 'binned') #binned has been set
- assert self._binsize == self.binsize #binsize hasn't changed
- except:
- #only bin once, until binsize is updated
- bins = np.arange(0, 1+self.binsize, self.binsize)
- self.binned = binify(self.splicing_df, bins)
- self._binsize = self.binsize
- return self.binned
-
- def get_binned_reduced(self, reducer=NMF_viz):
- binned = self.get_binned_data()
- redc = reducer(binned)
- self.binned_reduced = redc.reduced_space
- return self.binned_reduced
-
- _last_reducer_accessed = None
-
- def make_reduced(self, list_name, group_id, reducer=PCA_viz,
- featurewise=False, reducer_args=None, standardize=True):
- """make and cache a reduced dimensionality representation of data """
-
- if reducer_args is None:
- reducer_args = self._default_reducer_args
-
- min_samples = self.get_min_samples()
- if list_name not in self.lists:
- self.lists[list_name] = link_to_list(list_name)
-
- event_list = self.lists[list_name]
- #some samples, somefeatures
-
- if group_id.startswith("~"):
- #print 'not', group_id.lstrip("~")
- sample_ind = ~pd.Series(self.sample_descriptors[group_id.lstrip("~")], dtype='bool')
- else:
- sample_ind = pd.Series(self.sample_descriptors[group_id], dtype='bool')
-
- subset = self.splicing_df.ix[sample_ind, event_list]
- frequent = pd.Index([i for i,j in (subset.count() > min_samples).iteritems() if j])
- subset = subset[frequent]
- #fill na with mean for each event
- means = subset.apply(dropna_mean, axis=0)
- mf_subset = subset.fillna(means,).fillna(0)
- #whiten, mean-center
- naming_fun=self.get_naming_fun()
- #whiten, mean-center
-
- if standardize:
- data = StandardScaler().fit_transform(mf_subset)
- else:
- data = mf_subset
-
- ss = pd.DataFrame(data, index = mf_subset.index,
- columns = mf_subset.columns).rename_axis(naming_fun, 1)
-
- if featurewise:
- ss = ss.T
-
- rdc_obj = reducer(ss, **reducer_args)
-
- rdc_obj.means = means.rename_axis(naming_fun) #always the mean of input features... i.e. featurewise doesn't change this.
-
- return rdc_obj
-
- def make_classifier(self, list_name, group_id, categorical_trait,
- standardize=True, classifier=PredictorViz,
- ):
- """
- make and cache a classifier on a categorical trait (associated with samples) subset of genes
- """
-
- min_samples=self.get_min_samples()
- if list_name not in self.lists:
- self.lists[list_name] = link_to_list(list_name)
-
- event_list = self.lists[list_name]
-
- if group_id.startswith("~"):
- #print 'not', group_id.lstrip("~")
- sample_ind = ~pd.Series(self.sample_descriptors[group_id.lstrip("~")], dtype='bool')
- else:
- sample_ind = pd.Series(self.sample_descriptors[group_id], dtype='bool')
- sample_ind = sample_ind[sample_ind].index
- subset = self.splicing_df.ix[sample_ind, event_list]
- frequent = pd.Index([i for i, j in (subset.count() > min_samples).iteritems() if j])
- subset = subset[frequent]
- #fill na with mean for each event
- means = subset.apply(dropna_mean, axis=0)
- mf_subset = subset.fillna(means, ).fillna(0)
-
- #whiten, mean-center
- if standardize:
- data = StandardScaler().fit_transform(mf_subset)
- else:
- data = mf_subset
- naming_fun = self.get_naming_fun()
- ss = pd.DataFrame(data, index = mf_subset.index,
- columns = mf_subset.columns).rename_axis(naming_fun, 1)
- clf = classifier(ss, self.sample_descriptors,
- categorical_traits=[categorical_trait],)
- clf.set_reducer_plotting_args(self._default_reducer_args)
- return clf
-
- def load_cargo(self):
- raise NotImplementedError
-
-
-
diff --git a/flotilla/src/_schooner_data_model/_Study.py b/flotilla/src/_schooner_data_model/_Study.py
deleted file mode 100644
index 422c3951..00000000
--- a/flotilla/src/_schooner_data_model/_Study.py
+++ /dev/null
@@ -1,467 +0,0 @@
-"""
-Data models for "studies" studies include attributes about the data and are
-heavier in terms of data load
-"""
-
-from .._submaraine_viz import NetworkerViz, PredictorViz, plt
-import sys
-from _ExpressionData import ExpressionData
-from _SplicingData import SplicingData
-
-class BaseStudy(object):
-
- def __init__(self):
- self.initialize_base()
-
- def initialize_base(self):
- self.minimal_study_parameters = set(['study_data_dir'])
-
- def _set(self, k, v):
- """set attributes, warn if re-setting"""
-
- try:
- assert not hasattr(self, k)
- except:
- write_me = "WARNING: over-writing parameter ", k + "\n" + \
- str(self.__getattribute__(k)) + \
- "\n new:" + str(v)
- sys.stderr.write(write_me)
- super(BaseStudy, self).__setattr__(k,v)
-
- def validate_params(self):
- """make sure that all necessary attributes are present"""
- for param in self.minimal_study_parameters:
- try:
- assert hasattr(self, param)
- except:
- raise AssertionError("missing minimal parameter %s" % param)
-
-
-class StudyContainer(BaseStudy):
- """
- store essential data associated with a study. Users specify how to build the necessary components from
- project-specific loaders (see barebones_project for example loaders)
- """
-
- def __init__(self, params_dict, data_loaders=None):
- super(StudyContainer, self).__init__()
- [self.minimal_study_parameters.add(i) for i in ['sample_metadata_filename', ]]
- self.initialize_container(params_dict, data_loaders)
- self.run_wrapped_loaders(data_loaders)
-
-
- def initialize_container(self, params_dict, data_loaders=None):
- [self._set(k,v) for (k,v) in params_dict.items() if not k.startswith("_")]
- self.data_loaders = data_loaders
- self.validate_params()
-
- def run_wrapped_loaders(self, data_loaders):
- """fill this container with loaded objects.
- loaders recieve kwards from data,
- data_loaders is an iterable of 2-length iterables item[0] is a dict of kwargs for item[1], which is a fxn
- """
- for data, loader in data_loaders:
- #formerly explicitly set things
- for (k,v) in loader(**data).items():
- try:
- self._set(k,v)
- except Exception as e:
- raise e
-
- def initialize_all_subclasses(self, load_cargo=False, drop_outliers=False):
- """
- run all initializers
- """
-
- #TODO: would be great if this worked, untested:
- #for subclass in self.subclasses:
- # initializer = getattr(self, 'intialize_', subclass, '_subclass')
- # initializer(self)
-
- self.initialize_sample_metadata_subclass(load_cargo=load_cargo, drop_outliers=drop_outliers)
- self.initialize_expression_subclass(load_expression_cargo=load_cargo, drop_outliers=drop_outliers)
- self.initialize_splicing_subclass(load_splicing_cargo=False, drop_outliers=drop_outliers)
-
-
- def initialize_sample_metadata_subclass(self, **kwargs):
- #TODO: this should be an actual schooner.*Data type, but now it's just set by a loader
- assert hasattr(self, 'sample_metadata')
-
-
- def initialize_expression_subclass(self, load_expression_cargo=True, drop_outliers=True):
-
- [self.minimal_study_parameters.add(i) for i in ['expression_data_filename']]
- self.validate_params()
- #TODO:don't over-write self.expression
- self.expression = ExpressionData(expression_df=self.expression,
- sample_metadata=self.sample_metadata,
- gene_descriptors=self.expression_metadata,
- load_cargo=load_expression_cargo, drop_outliers=drop_outliers,
- species=self.species)
- self.expression.networks = NetworkerViz(self.expression)
- self.default_list_ids.extend(self.expression.lists.keys())
-
- def initialize_splicing_subclass(self, load_splicing_cargo=False, drop_outliers=True):
-
- [self.minimal_study_parameters.add(i) for i in ['splicing_data_filename']]
- self.validate_params()
- #TODO:don't over-write self.splicing
- self.splicing = SplicingData(splicing=self.splicing, sample_metadata=self.sample_metadata,
- event_metadata=self.event_metadata,load_cargo=load_splicing_cargo,
- drop_outliers=drop_outliers, species=self.species)
-
- self.splicing.networks = NetworkerViz(self.splicing)
-
-
-class StudyLocalCalls(StudyContainer):
- """only very very quick things"""
- def get_mapping_stats(self):
- raise NotImplementedError
-
-
-class StudySystemCalls(StudyContainer):
-
- #For things done offline that update study_data
-
- def main(self):
- raise NotImplementedError
- #TODO: make this an entry-point, parse flotilla package to load from cmd line, do something
- #this is for the user... who will know little to nothing about queues and the way jobs are done on the backend
-
- usage = "run_flotilla_cmd cmd_name runner_name"
- # def runner_concept(self, flotilla_package_target = "barebones_package", tool_name):
- #
- # #a constructor for a new study, takes a long time and maybe runs in parallel. Probably on a cluster...
- # #same as `import flotilla_package_target as imported_package`
- #
- # imported_package = __import__(flotilla_package_target)
- # study = StudyContainer.__new__()
- #
- # #should use importlib though...
- #
- #
- # if this_is_not a parallel process
- # try:
- # make a new runner_name.lock file
- # except: #exists(runner_name.lock)
- # raise RuntimeError
- #
- # study.do_something()
-
-
- def detect_outliers(self):
- """Detects outlier cells from expression, mapping, and splicing
- study_data and labels the outliers as such for future analysis.
-
- Parameters
- ----------
- self
-
- Returns
- -------
-
-
- Raises
- ------
-
- """
- #TODO: Boyko/Patrick please implement
- raise NotImplementedError
-
-
- def compute_jsd(self):
- """Performs Jensen-Shannon Divergence on both splicing and expression study_data
-
- Jensen-Shannon divergence is a method of quantifying the amount of
- change in distribution of one measurement (e.g. a splicing event or a
- gene expression) from one celltype to another.
- """
-
- #TODO: Check if JSD has not already been calculated (cacheing or memoizing)
-
- self.expression.jsd()
- self.splicing.jsd()
-
- raise NotImplementedError
-
-
- def normalize_to_spikein(self):
- raise NotImplementedError
-
- def compute_expression_splicing_covariance(self):
- raise NotImplementedError
-
-
-class StudyGraphics(StudyContainer):
- """
- """
- def __init__(self, *args, **kwargs):
-
- super(StudyGraphics, self).__init__(*args, **kwargs)
- [self.minimal_study_parameters.add(i) for i in ['expression', 'splicing' ]]
-
- def jsd(self):
-
- raise NotImplementedError
-
- def plot_pca(self, data_type='expression', x_pc=1, y_pc=2, **kwargs):
-
- """Performs PCA on both expression and splicing study_data
- """
- if data_type == "expression":
- self.expression.plot_dimensionality_reduction(x_pc=x_pc, y_pc=y_pc,
- **kwargs)
- elif data_type == "splicing":
- self.splicing.plot_dimensionality_reduction(x_pc=x_pc, y_pc=y_pc,
- **kwargs)
-
- def plot_graph(self, data_type='expression', **kwargs):
-
- if data_type == "expression":
- self.expression.networks.draw_graph(**kwargs)
-
- elif data_type == "splicing":
- self.splicing.networks.draw_graph(**kwargs)
-
- def plot_classifier(self, data_type='expression', **kwargs):
-
- """
- """
- if data_type == "expression":
- self.expression.plot_classifier(**kwargs)
- elif data_type == "splicing":
- self.splicing.plot_classifier(**kwargs)
-
- def plot_regressor(self, data_type='expression', **kwargs):
-
- """
- """
- raise NotImplementedError
- if data_type == "expression":
- self.expression.plot_regressor(**kwargs)
- elif data_type == "splicing":
- self.splicing.plot_regressor(**kwargs)
-
-
-class InteractiveStudy(StudyGraphics):
- """
-
- Attributes
- ----------
-
-
- Methods
- -------
-
- """
- def __init__(self, *args, **kwargs):
- super(InteractiveStudy, self).__init__(*args, **kwargs)
- self._default_x_pc = 1
- self._default_y_pc = 2
- [self.minimal_study_parameters.add(param) for param in ['default_group_id', 'default_group_ids',
- 'default_list_id', 'default_list_ids',]]
- [self.minimal_study_parameters.add(i) for i in ['sample_metadata', ]]
- self.validate_params()
-
- def interactive_pca(self):
-
- from IPython.html.widgets import interact
-
- #not sure why nested fxns are required for this, but they are... i think...
- def do_interact(data_type='expression',
- group_id=self.default_group_id,
- list_name=self.default_list_id,
- featurewise=False,
- list_link='',
- x_pc=1, y_pc=2,
- show_point_labels=False,
-
- savefile='data/last.pca.pdf'):
-
- for k, v in locals().iteritems():
- if k == 'self':
- continue
- print k, ":", v
-
- if list_name != "custom" and list_link != "":
- raise ValueError("set list_name to \"custom\" to use list_link")
-
- if list_name == "custom" and list_link == "":
- raise ValueError("use a custom list name please")
-
- if list_name == 'custom':
- list_name = list_link
-
- self.plot_pca(group_id=group_id, data_type=data_type,
- featurewise=featurewise,
- x_pc=x_pc, y_pc=y_pc, show_point_labels=show_point_labels,
- list_name=list_name)
- if savefile != '':
- f = plt.gcf()
- f.savefig(savefile)
-
- interact(do_interact,
- data_type=('expression', 'splicing'),
- group_id=self.default_group_ids,
- list_name=self.default_list_ids + ["custom"],
- featurewise=False,
- x_pc=(1, 10), y_pc=(1, 10),
- show_point_labels=False, )
-
-
- def interactive_graph(self):
- from IPython.html.widgets import interact
-
- #not sure why nested fxns are required for this, but they are... i think...
- def do_interact(data_type='expression', group_id=self.default_group_id,
- list_name=self.default_list_id,weight_fun=NetworkerViz.weight_funs,
- featurewise=False,
- use_pc_1=True, use_pc_2=True, use_pc_3=True,
- use_pc_4=True,degree_cut=1,
- cov_std_cut=1.8, n_pcs=5,
- feature_of_interest="RBFOX2",
- draw_labels=False,
- savefile='data/last.graph.pdf',
- ):
-
- for k, v in locals().iteritems():
- if k == 'self':
- continue
- print k, ":", v
-
- if data_type == 'expression':
- assert (list_name in self.expression.lists.keys())
- if data_type == 'splicing':
- assert (list_name in self.expression.lists.keys())
-
- self.plot_graph(data_type=data_type,
- group_id=group_id,
- list_name=list_name,
-
- featurewise=featurewise, draw_labels=draw_labels,
- degree_cut=degree_cut, cov_std_cut=cov_std_cut,
- n_pcs=n_pcs,
- feature_of_interest=feature_of_interest,
- use_pc_1=use_pc_1, use_pc_2=use_pc_2, use_pc_3=use_pc_3,
- use_pc_4=use_pc_4,
- wt_fun=weight_fun)
- if savefile is not '':
- plt.gcf().savefig(savefile)
-
- all_lists = list(
- set(self.expression.lists.keys() + self.splicing.lists.keys()))
- interact(do_interact,
- data_type=('expression', 'splicing'),
- group_id=self.default_group_ids,
- list_name=all_lists,
- featurewise=False,
- cov_std_cut=(0.1, 3),
- degree_cut=(0, 10),
- n_pcs=(2, 100),
- draw_labels=False,
- feature_of_interest="RBFOX2",
- use_pc_1=True, use_pc_2=True, use_pc_3=True, use_pc_4=True,
- )
-
- def interactive_clf(self):
-
- from IPython.html.widgets import interact
-
- #not sure why nested fxns are required for this, but they are... i think...
- def do_interact(data_type='expression',
- group_id=self.default_group_id,
- list_name=self.default_list_id,
- categorical_variable='outlier',
- feature_score_std_cutoff=2,
- savefile='data/last.clf.pdf'):
-
- for k, v in locals().iteritems():
- if k == 'self':
- continue
- print k, ":", v
-
- if data_type == 'expression':
- data_obj = self.expression
- if data_type == 'splicing':
- data_obj = self.splicing
-
- assert (list_name in data_obj.lists.keys())
-
- prd = data_obj.get_classifier(list_name, group_id,
- categorical_variable)
- prd(categorical_variable,
- feature_score_std_cutoff=feature_score_std_cutoff)
- print "retrieve this classifier with:\nprd=study.%s.get_predictor('%s', '%s', '%s')\n\
-pca=prd('%s', feature_score_std_cutoff=%f)" \
- % (data_type, list_name, group_id, categorical_variable,
- categorical_variable, feature_score_std_cutoff)
- if savefile is not '':
- plt.gcf().savefig(savefile)
-
- all_lists = list(
- set(self.expression.lists.keys() + self.splicing.lists.keys()))
- interact(do_interact,
- data_type=('expression', 'splicing'),
- group_id=self.default_group_ids,
- list_name=all_lists,
- categorical_variable=[i for i in self.default_group_ids if
- not i.startswith("~")],
- feature_score_std_cutoff=(0.1, 20),
- draw_labels=False,
- )
-
- def interactive_localZ(self):
-
- from IPython.html.widgets import interact
-
- def do_interact(data_type='expression', sample1='', sample2='',
- pCut='0.01'):
-
- for k, v in locals().iteritems():
- if k == 'self':
- continue
- print k, ":", v
- pCut = float(pCut)
- assert pCut > 0
- if data_type == 'expression':
- data_obj = self.expression
- if data_type == 'splicing':
- data_obj = self.splicing
-
- try:
- assert sample1 in data_obj.df.index
- except:
- print "sample: %s, is not in %s DataFrame, try a different sample ID" % (
- sample1, data_type)
- return
- try:
- assert sample2 in data_obj.df.index
- except:
- print "sample: %s, is not in %s DataFrame, try a different sample ID" % (
- sample2, data_type)
- return
- self.localZ_result = data_obj.twoway(sample1, sample2,
- pCut=pCut).result_
- print "localZ finished, find the result in .localZ_result_"
-
- interact(do_interact,
- data_type=('expression', 'splicing'),
- sample1='replaceme',
- sample2='replaceme',
- pCut='0.01')
-
-class FlotillaStudy(InteractiveStudy, StudyLocalCalls, StudySystemCalls):
-
- """
- Load data, imbue interactiveness
- """
-
- def __init__(self, params_dict, data_loaders, load_cargo=True, drop_outliers=True, datatypes='all'):
- super(FlotillaStudy, self).__init__(params_dict, data_loaders=data_loaders)
-
- if datatypes == 'all':
- super(FlotillaStudy, self).initialize_all_subclasses(load_cargo=load_cargo, drop_outliers=drop_outliers)
- else:
- #TODO: import specific data types only
- raise NotImplementedError
- self.validate_params()
diff --git a/flotilla/src/_schooner_data_model/__init__.py b/flotilla/src/_schooner_data_model/__init__.py
deleted file mode 100644
index d9918cfd..00000000
--- a/flotilla/src/_schooner_data_model/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-__author__ = 'olga'
-
-
-from _SplicingData import SplicingData
-from _ExpressionData import ExpressionData
-from _Study import *
\ No newline at end of file
diff --git a/flotilla/src/_skiff_external_sources.py b/flotilla/src/_skiff_external_sources.py
deleted file mode 100644
index 0e0d06cd..00000000
--- a/flotilla/src/_skiff_external_sources.py
+++ /dev/null
@@ -1,323 +0,0 @@
-from __future__ import division
-
-""" interface with external data sources i.e. GO files, web"""
-
-__author__ = 'lovci, yan_song, '
-import gzip
-import pandas as pd
-import itertools
-from scipy.stats import hypergeom
-from collections import defaultdict
-import gzip
-import numpy as np
-
-import os, subprocess, sys
-
-
-def generateOntology(df):
- from collections import defaultdict
- import itertools
- ontology = defaultdict(lambda: {'genes': set(), 'name': set(), 'domain': set()})
- allGenesInOntologies = set(df.get('Ensembl Gene ID'))
- for GO, gene, domain, name in itertools.izip(df.get('GO Term Accession'), df.get('Ensembl Gene ID'), df.get('GO domain'), df.get('GO Term Name')):
- ontology[GO]['genes'].add(gene)
- ontology[GO]['name'].add(name)
- ontology[GO]['domain'].add(domain)
- ontology[GO]['nGenes'] = len(ontology[GO]['genes'])
- return ontology, allGenesInOntologies
-
-def GO_enrichment(geneList, ontology, expressedGenes = None, printIt=False, pCut = 1000000, xRef = {}):
-
- lenAllGenes, lenTheseGenes = len(expressedGenes), len(geneList)
- pValues = defaultdict()
- nCmps = 0
-
- for GOTerm, GOGenes in ontology.items():
- inBoth = GOGenes['genes'].intersection(geneList)
- expressedGOGenes = GOGenes['genes'].intersection(expressedGenes)
- if len(inBoth) <= 3 or len(expressedGOGenes) < 5:
- pValues[GOTerm] = 'notest'
- continue
- #survival function is more accurate on small p-values...
- pVal = hypergeom.sf(len(inBoth), lenAllGenes, len(expressedGOGenes), lenTheseGenes)
- if pVal < 0:
- pVal = 0
- symbols = []
- for ensg in inBoth:
- if ensg in xRef:
- symbols.append(xRef[ensg])
- else:
- symbols.append(ensg)
- pValues[GOTerm] = (pVal, len(inBoth), len(expressedGOGenes), len(GOGenes['genes']), inBoth, symbols)
-
- for k, v in pValues.items():
- try:
- pValues[k][0] = v * float(nCmps) #bonferroni correction
- except:
- pass
- import operator
- y = []
-
- sorted_x = sorted(pValues.iteritems(), key=operator.itemgetter(1))
-
- for k, v in sorted_x:
- if v == "notest":
- continue
- if not type(k) == str:
- continue
- try:
- if v[0] > pCut:
- continue
- if printIt:
- #[k, "|".join(ontology[k]['name']), v[0], v[1], v[2], v[3], ",".join(v[4]), ",".join(v[5])]
- print k, "|".join(ontology[k]['name']), "%.3e" %v[0], v[1], v[2], v[3], "|".join(v[3])
- pass
- y.append([k, "|".join(ontology[k]['name']), v[0], v[1], v[2], v[3], ",".join(v[4]), ",".join(v[5])])
-
- except:
- pass
-
- try:
- df = pd.DataFrame(y, columns=['GO Term ID', 'GO Term Description', 'Bonferroni-corrected Hypergeometric p-Value', 'N Genes in List and GO Category', 'N Expressed Genes in GO Category', 'N Genes in GO category', 'Ensembl Gene IDs in List', 'Gene symbols in List'])
- df.set_index('GO Term ID', inplace=True)
- except:
- df = pd.DataFrame(None, columns=['GO Term ID', 'GO Term Description', 'Bonferroni-corrected Hypergeometric p-Value', 'N Genes in List and GO Category', 'N Expressed Genes in GO Category', 'N Genes in GO category', 'Ensembl Gene IDs in List', 'Gene symbols in List'])
-
- return df
-
-
-class GO(object):
-
- """
- gene ontology tool
-
- >>> go = hg19GO()
- >>> go.geneXref['ENSG00000100320']
- 'RBFOX2'
- >>> df = go.enrichment(list, background)
-
- """
-
- def __init__(self, GOFile):
- with gzip.open(GOFile) as file_handle:
- GO_to_ENSG = pd.read_table(file_handle)
- geneXref = defaultdict()
- for k in np.array(GO_to_ENSG.get(["Ensembl Gene ID", "Associated Gene Name"])):
- ensg = k[0]
- gene = k[1]
- geneXref[ensg] = gene
-
- GO, allGenes = generateOntology(GO_to_ENSG)
- self.GO = GO
- self.allGenes = allGenes
- self.geneXref = geneXref
-
- def enrichment(self, geneList, background=None, **kwargs):
- if background == None:
- background = self.allGenes
- return GO_enrichment(geneList, self.GO, expressedGenes = background, xRef = self.geneXref)
-
- def geneNames(self, x):
- try:
- return self.geneXref[x]
- except:
- return x
-
- def link_to_geneNames(self, list_link):
- list = link_to_list(list_link)
- pd.DataFrame(map(self.geneNames, list), index=list)
-
-#TODO: move these to Cargo
-#from yan(gene symbols) -> mouse gene id
-neuro_genes_mouse = """ENSMUSG00000020932
-ENSMUSG00000030310
-ENSMUSG00000057182
-ENSMUSG00000021609
-ENSMUSG00000020838
-ENSMUSG00000027168
-ENSMUSG00000004872
-ENSMUSG00000041607
-ENSMUSG00000031144
-ENSMUSG00000005360
-ENSMUSG00000024406
-ENSMUSG00000041309
-ENSMUSG00000028736
-ENSMUSG00000018411
-ENSMUSG00000020886
-ENSMUSG00000039830
-ENSMUSG00000024935
-ENSMUSG00000062380
-ENSMUSG00000007946
-ENSMUSG00000033006
-ENSMUSG00000038331
-ENSMUSG00000005917
-ENSMUSG00000032126
-ENSMUSG00000031217
-ENSMUSG00000021848
-ENSMUSG00000059003
-ENSMUSG00000032446
-ENSMUSG00000035033
-ENSMUSG00000020052
-ENSMUSG00000048450
-ENSMUSG00000028280
-ENSMUSG00000020950
-ENSMUSG00000001566
-ENSMUSG00000057880
-ENSMUSG00000042453
-ENSMUSG00000042589
-ENSMUSG00000064329
-ENSMUSG00000023328
-ENSMUSG00000022705
-ENSMUSG00000079994
-ENSMUSG00000032259
-ENSMUSG00000000214
-ENSMUSG00000074637
-ENSMUSG00000047976
-ENSMUSG00000024304
-ENSMUSG00000037771
-ENSMUSG00000048251
-ENSMUSG00000000247
-ENSMUSG00000004151
-ENSMUSG00000029595
-ENSMUSG00000022952
-ENSMUSG00000031285
-ENSMUSG00000019230
-ENSMUSG00000027273
-ENSMUSG00000022055
-ENSMUSG00000070880
-ENSMUSG00000070691
-ENSMUSG00000029580
-ENSMUSG00000004891
-ENSMUSG00000031425
-ENSMUSG00000020262
-ENSMUSG00000025037
-ENSMUSG00000026959
-ENSMUSG00000073640
-ENSMUSG00000063316
-ENSMUSG00000027967
-ENSMUSG00000001018
-ENSMUSG00000042258
-ENSMUSG00000030067
-ENSMUSG00000035187
-ENSMUSG00000029563
-ENSMUSG00000070570
-ENSMUSG00000045994
-ENSMUSG00000032318
-ENSMUSG00000030516
-ENSMUSG00000030500
-ENSMUSG00000033208
-ENSMUSG00000033981
-ENSMUSG00000028936
-ENSMUSG00000020524
-ENSMUSG00000052915
-ENSMUSG00000023945
-ENSMUSG00000019874
-ENSMUSG00000062070
-ENSMUSG00000027951""".split("\n")
-
-neuro_genes_human = """ENSG00000131095
-ENSG00000157103
-ENSG00000153253
-ENSG00000142319
-ENSG00000108576
-ENSG00000007372
-ENSG00000135903
-ENSG00000197971
-ENSG00000102003
-ENSG00000079215
-ENSG00000204531
-ENSG00000206349
-ENSG00000206454
-ENSG00000148826
-ENSG00000009709
-ENSG00000186868
-ENSG00000132535
-ENSG00000205927
-ENSG00000106688
-ENSG00000198211
-ENSG00000165462
-ENSG00000100146
-ENSG00000119042
-ENSG00000115507
-ENSG00000149397
-ENSG00000090776
-ENSG00000165588
-ENSG00000136531
-ENSG00000183454
-ENSG00000163508
-ENSG00000136535
-ENSG00000139352
-ENSG00000163132
-ENSG00000146276
-ENSG00000176165
-ENSG00000130675
-ENSG00000183044
-ENSG00000189056
-ENSG00000104267
-ENSG00000111249
-ENSG00000184845
-ENSG00000144285
-ENSG00000087085
-ENSG00000151577
-ENSG00000182968
-ENSG00000149295
-ENSG00000180176
-ENSG00000181449
-ENSG00000111262
-ENSG00000170558
-ENSG00000101438
-ENSG00000127152
-ENSG00000106689
-ENSG00000006468
-ENSG00000089116
-ENSG00000159216
-ENSG00000077279
-ENSG00000143355
-ENSG00000132639
-ENSG00000104725
-ENSG00000128683
-ENSG00000020633
-ENSG00000075624
-ENSG00000132688
-ENSG00000123560
-ENSG00000197381
-ENSG00000189221
-ENSG00000176884
-ENSG00000131469
-ENSG00000178403
-ENSG00000143553
-ENSG00000016082
-ENSG00000114861
-ENSG00000163623
-ENSG00000128573
-ENSG00000104888
-ENSG00000109956
-ENSG00000159556
-ENSG00000104067
-ENSG00000091664
-ENSG00000160307
-ENSG00000120251
-ENSG00000116251
-ENSG00000155511
-ENSG00000188895
-ENSG00000078018
-ENSG00000187714
-ENSG00000115665
-ENSG00000164434
-ENSG00000102144
-ENSG00000160710""".split("\n")
-
-def link_to_list(link):
- try:
- assert link.startswith("http") or os.path.exists(os.path.abspath(link))
- except:
- raise ValueError("use a link that starts with http or a file path")
-
- if link.startswith("http"):
- sys.stderr.write("WARNING, downloading things from the internet, potential danger from untrusted sources")
- xx = subprocess.check_output(["curl", "-k", '--location-trusted', link]).split("\n")
- elif link.startswith("/"):
- assert os.path.exists(os.path.abspath(link))
- with open(os.path.abspath(link), 'r') as f:
- xx = map(str.strip, f.readlines())
- return xx
diff --git a/flotilla/src/_submaraine_viz.py b/flotilla/src/_submaraine_viz.py
deleted file mode 100644
index d758be28..00000000
--- a/flotilla/src/_submaraine_viz.py
+++ /dev/null
@@ -1,989 +0,0 @@
-__author__ = 'lovci, ppliu, obot, ....?'
-
-""""plotting tools"""
-
-import math
-from math import sqrt
-
-import numpy as np
-from numpy.linalg import norm
-import pandas as pd
-import seaborn
-# from ..neural_diff_project.project_params import _default_group_id
-
-seaborn.set_style({'axes.axisbelow': True,
- 'axes.edgecolor': '.15',
- 'axes.facecolor': 'white',
- 'axes.grid': False,
- 'axes.labelcolor': '.15',
- 'axes.linewidth': 1.25,
- 'font.family': 'Helvetica',
- 'grid.color': '.8',
- 'grid.linestyle': '-',
- 'image.cmap': 'Greys',
- 'legend.frameon': False,
- 'legend.numpoints': 1,
- 'legend.scatterpoints': 1,
- 'lines.solid_capstyle': 'round',
- 'text.color': '.15',
- 'xtick.color': '.15',
- 'xtick.direction': 'out',
- 'xtick.major.size': 0,
- 'xtick.minor.size': 0,
- 'ytick.color': '.15',
- 'ytick.direction': 'out',
- 'ytick.major.size': 0,
- 'ytick.minor.size': 0})
-
-seaborn.set_palette('deep')
-blue = seaborn.color_palette()[0]
-green =seaborn.color_palette()[1]
-red = seaborn.color_palette()[2]
-
-import matplotlib.pyplot as plt
-
-from ._frigate_compute import PCA, NMF, TwoWayGeneComparisonLocal, Networker, nx
-from matplotlib.gridspec import GridSpec, GridSpecFromSubplotSpec
-from ._barge_utils import dict_to_str
-
-def L1_distance(x,y):
- return abs(y) + abs(x)
-
-def L2_distance(x,y):
- return math.sqrt((y ** 2) + (x ** 2))
-
-
-class Reduction_viz(object):
-
- """
- Given a pandas dataframe, performs PCA and plots the results in a
- convenient single function.
-
- @param c_scale: Component scaling of the plot, e.g. for making the
- plotted vectors larger or smaller.
- @param x_pc: Integer, which principal component to use for the x-axis
- (usually 1)
- @param y_pc: Integer, which principal component to use for the y-axis
- (usually 2)
- @param distance:
- @param colors_dict: A dictionary of index (samples) to matplotlib colors
- @param markers_dict: A dictionary of index (samples) to matplotlib markers
- @param markers_size_dict: A dictionary of index (samples) to matplotlib marker sizes
- @param title: A string, the title of the plot
- @param show_vectors: Boolean, whether or not to show vectors
- @param show_point_labels: Boolean, whether or not to show the index,
- e.g. the sample name, on the plot
- @param column_ids_dict: A dictionary of column names to another
- value, e.g. if the columns are splicing events with a strange ID,
- this could be a dictionary that matches the ID to a gene name.
- @param index_ids_dict: A dictionary of index names to another
- value, e.g. if the indexes are samples with a strange ID, this could be a
- dictionary that matches the ID to a more readable sample name.
- @param show_vector_labels: Boolean. Can be helpful if the vector labels
- are gene names.
- @param scale_by_variance: Boolean. Scale vector components by explained variance
- @return: x, y, marker, distance of each vector in the study_data.
- """
-
- _default_plotting_args = {'ax':None, 'x_pc':'pc_1', 'y_pc':'pc_2',
- 'num_vectors':20, 'title':'Dimensionality Reduction',
- 'title_size':None, 'axis_label_size':None,
- 'colors_dict':None, 'markers_dict':None, 'markers_size_dict':None,
- 'default_marker_size':100, 'distance_metric':'L1',
- 'show_vectors':True, 'c_scale':None, 'vector_width':None, 'vector_colors_dict':None,
- 'show_vector_labels':True, 'vector_label_size':None,
- 'show_point_labels':True, 'point_label_size':None, 'scale_by_variance':True}
- _default_reduction_args = { 'n_components':None, 'whiten':False}
- _default_args = dict(_default_plotting_args.items() + _default_reduction_args.items())
-
- def __init__(self, df, **kwargs):
-
- self._validate_params(self._default_args, **kwargs)
-
- self.plotting_args = self._default_plotting_args.copy()
- self.plotting_args.update([(k,v) for (k,v) in kwargs.items() if k in self._default_plotting_args.keys()])
-
- self.reduction_args = self._default_reduction_args.copy()
- self.reduction_args.update([(k,v) for (k,v) in kwargs.items() if k in self._default_reduction_args.keys()])
-
- super(Reduction_viz, self).__init__(**self.reduction_args) #initialize PCA-like object
- assert type(df) == pd.DataFrame
- self.reduced_space = self.fit_transform(df)
-
- def __call__(self, ax=None, **kwargs):
- #self._validate_params(self._default_plotting_args, **kwargs)
- gs_x = 14
- gs_y = 12
-
- if ax is None:
-
- fig, ax = plt.subplots(1,1,figsize=(12,6))
- gs = GridSpec(gs_x,gs_y)
-
- else:
- gs = GridSpecFromSubplotSpec(gs_x,gs_y,ax.get_subplotspec())
-
- ax_components = plt.subplot(gs[:, :5])
- #ax_components.set_aspect('equal')
- ax_loading1 = plt.subplot(gs[:, 6:8])
- ax_loading2 = plt.subplot(gs[:, 10:14])
-
- passed_kwargs = kwargs
- local_kwargs = self.plotting_args.copy()
- local_kwargs.update(passed_kwargs)
- local_kwargs.update({'ax':ax_components})
- self.plot_samples(**local_kwargs)
- self.plot_loadings(pc=local_kwargs['x_pc'], ax=ax_loading1)
- self.plot_loadings(pc=local_kwargs['y_pc'], ax=ax_loading2)
- plt.tight_layout()
- return self
-
-
- def _validate_params(self, valid, **kwargs):
-
- for key in kwargs.keys():
- try:
- assert key in valid.keys()
- except:
- print self.__doc__
- raise ValueError("unrecognized parameter for pc plot: "\
- "%s. acceptable values are:\n%s" % (key, "\n".join(valid.keys())))
-
- def plot_samples(self, **kwargs):
- self._validate_params(self._default_plotting_args, **kwargs)
- default_params = self.plotting_args.copy() #fill missing parameters
- default_params.update(kwargs)
- kwargs = default_params
-
- #cheating!
- #move kwargs out of a dict, into local namespace, mostly because I don't want to refactor below
-
- for key in kwargs.keys():
- #
- # the following makes several errors appear in pycharm. they're not errors~~! laziness? :(
- #
- # imports variables from dictionaries and uses them as variable names in the code ... cheating because
- # TODO: needs to be refactored
- #
- exec(key + " = kwargs['" + key + "']")
- x_loading, y_loading = self.components_.ix[x_pc], self.components_.ix[y_pc]
-
- if ax is None:
- fig, ax = plt.subplots(1,1, figsize=(5,5))
- self.ax = ax
-
- reduced_space = self.reduced_space
- x_list = reduced_space[x_pc]
- y_list = reduced_space[y_pc]
-
- if not c_scale:
- c_scale = .75 * max([norm(point) for point in zip(x_list, y_list)]) / \
- max([norm(vector) for vector in zip(x_loading, y_loading)])
-
- figsize = tuple(plt.gcf().get_size_inches())
- size_scale = sqrt(figsize[0] * figsize[1]) / 1.5
- default_marker_size = size_scale*5 if not default_marker_size else default_marker_size
- vector_width = .5 if not vector_width else vector_width
- axis_label_size = size_scale *1.5 if not axis_label_size else axis_label_size
- title_size = size_scale*2 if not title_size else title_size
- vector_label_size = size_scale * 1.5 if not vector_label_size else vector_label_size
- point_label_size = size_scale * 1.5 if not point_label_size else point_label_size
-
- # get amount of variance explained
- try:
- #not all reduction methods have this attr, if it doesn't assume equal , not true.. but easy!
- var_1 = int(self.explained_variance_ratio_[x_pc] * 100)
- var_2 = int(self.explained_variance_ratio_[y_pc] * 100)
- except AttributeError:
- var_1, var_2 = 1., 1.
-
- # sort features by magnitude/contribution to transformation
-
-
- comp_magn = []
- magnitudes = []
- for (x, y, an_id) in zip(x_loading, y_loading, self.X.columns):
-
- x = x * c_scale
- y = y * c_scale
-
- # scale metric by explained variance
- if distance_metric == 'L1':
- if scale_by_variance:
- mg = L1_distance((x * var_1), (y * var_2))
-
- else:
- mg = L1_distance(x, y)
-
- elif distance_metric == 'L2':
- if scale_by_variance:
- mg = L2_distance((x * var_1), (y * var_2))
-
- else:
- mg = L2_distance(x, y)
-
- comp_magn.append((x, y, an_id, mg))
- magnitudes.append(mg)
-
- self.magnitudes = pd.Series(magnitudes, index=self.X.columns)
- self.magnitudes.sort(ascending=False)
-
- tiny=0
- for (x, y, an_id) in zip(x_list, y_list, self.X.index):
-
- try:
- color = colors_dict[an_id]
- except:
- color = 'black'
-
- try:
- marker = markers_dict[an_id]
- except:
- marker = '.'
-
- try:
- marker_size = markers_size_dict[an_id]
- except:
- marker_size = default_marker_size
-
-
- if show_point_labels:
- ax.text(x, y, an_id, color=color, size=point_label_size)
- if x>=-0.00001 and x<=0.00001 and y>=-0.00001 and y<=0.00001:
- print "error with %s " % an_id
- tiny+=1
- ax.scatter( x, y, marker=marker, color=color, s=marker_size, edgecolor='none')
-
- #print "there were %d errors of %d" % (tiny, len(x_list))
- vectors = sorted(comp_magn, key=lambda item: item[3], reverse=True)[:num_vectors]
- if show_vectors:
-
- for x, y, marker, distance in vectors:
-
- try:
- color = vector_colors_dict[marker]
- except:
- color = 'black'
- ax.plot( [0, x], [0, y], color=color, linewidth=vector_width)
-
- if show_vector_labels:
-
- ax.text(1.1*x, 1.1*y, marker, color=color, size=vector_label_size)
-
- ax.set_xlabel(
- 'Principal Component {} (Explains {}% Of Variance)'.format(str(x_pc),
- str(var_1)), size=axis_label_size)
- ax.set_ylabel(
- 'Principal Component {} (Explains {}% Of Variance)'.format(str(y_pc),
- str(var_2)), size=axis_label_size)
- ax.set_title(title, size=title_size)
-
- return comp_magn[:num_vectors], ax
-
- def plot_loadings(self, pc='pc_1', n_features=50, ax=None):
-
- x = self.components_.ix[pc].copy()
- x.sort(ascending=True)
- half_features = int(n_features/2)
- if len(x) > half_features:
-
- a = x[:half_features]
- b = x[-half_features:]
- dd = np.r_[a,b]
- labels = np.r_[a.index, b.index]
-
- else:
- dd = x
- labels=x.index
-
- if ax is None:
- ax = plt.gca()
-
- ax.plot(dd,np.arange(len(dd)), 'o', label='hi')
- ax.set_yticks(np.arange(max(len(dd), n_features)))
- shorten = lambda x: "id too long" if len(x) > 30 else x
- _ = ax.set_yticklabels(map(shorten, labels))#, rotation=90)
- ax.set_title("loadings on " + pc)
- x_offset = max(dd) * .05
- #xmin, xmax = ax.get_xlim()
- ax.set_xlim(left=min(dd)-x_offset, right=max(dd)+x_offset)
- [lab.set_rotation(90) for lab in ax.get_xticklabels()]
- seaborn.despine(ax=ax)
-
- def plot_explained_variance(self, title="PCA"):
- """If the reducer is a form of PCA, then plot the explained variance
- ratio by the components.
- """
- # Plot the explained variance ratio
- assert hasattr(self, 'explained_variance_ratio_')
- import matplotlib.pyplot as plt
- import seaborn as sns
- fig, ax = plt.subplots()
- ax.plot(self.explained_variance_ratio_, 'o-')
-
- ax.set_xticks(range(self.n_components))
- ax.set_xticklabels(map(str, np.arange(self.n_components)+1))
- ax.set_xlabel('Principal component')
- ax.set_ylabel('Fraction explained variance')
- ax.set_title(title)
- sns.despine()
- return fig
-
-class PCA_viz(Reduction_viz, PCA):
- _default_reduction_args = { 'n_components':None, 'whiten':False}
-
-class NMF_viz(Reduction_viz, NMF):
- pass
-
-def plot_pca(df, **kwargs):
- """ for backwards-compatibility """
- pcaObj = PCA_viz(df, **kwargs)
- return_me, ax = pcaObj.plot_samples()
- return return_me
-
-
-def lavalamp(psi, color=None, jitter=None, title='', ax=None):
- """Make a 'lavalamp' scatter plot of many spliciang events
-
- Useful for visualizing many splicing events at once.
-
- Parameters
- ----------
- TODO: (n_events, n_samples).transpose()
- df : array
- A (n_events, n_samples) matrix either as a numpy array or as a pandas
- DataFrame
-
- color : matplotlib color
- Color of the scatterplot. Defaults to a dark teal
-
- title : str
- Title of the plot. Default ''
-
- ax : matplotlib.Axes object
- The axes to plot on. If not provided, will be created
-
-
- Returns
- -------
- fig : matplotlib.Figure
- A figure object for saving.
- """
- from ._frigate_compute import get_switchy_score_order
- import matplotlib.pyplot as plt
-
-
- if ax is None:
- fig, ax = plt.subplots(figsize=(16,4))
- else:
- fig = plt.gcf()
- nrow, ncol = psi.shape
- x = np.vstack(np.arange(nrow) for _ in range(ncol))
-
- color = '#FFFFFF' if color is None else color
-
- try:
- # This is a pandas Dataframe
- y = psi.values
- except AttributeError:
- # This is a numpy array
- y = psi
-
- if jitter is None:
- jitter = np.zeros(len(color))
- else:
- assert np.all(np.abs(jitter) < 1)
- assert np.min(jitter) > -.0000000001
-
- order = get_switchy_score_order(y.T)
- print order.shape
- y = y[:, order]
- assert type(color) == pd.Series
- # Add one so the last value is actually included instead of cut off
- xmax = x.max() + 1
- x_jitter = np.apply_along_axis(lambda r: r+jitter, 0, x)
-
- for co, ji, xx, yy in zip(color, jitter, x_jitter, y.T):
- ax.scatter(xx, yy, color=co, alpha=0.5, edgecolor='#262626', linewidth=0.1)
- seaborn.despine()
- ax.set_ylabel('$\Psi$')
- ax.set_xlabel('{} splicing events'.format(nrow))
- ax.set_xticks([])
-
- ax.set_xlim(0, xmax)
- ax.set_ylim(0, 1)
- ax.set_title(title)
-
- # Return the figure for saving
- # return fig
-
-class NetworkerViz(Networker, Reduction_viz):
- #TODO: needs to be decontaminated, as it requires methods from data_object;
- #maybe this class should move to schooner.Data
- def __init__(self, data_obj):
- self.data_obj = data_obj
- Networker.__init__(self)
-
- def draw_graph(self,
- n_pcs=5,
- use_pc_1=True, use_pc_2=True, use_pc_3=True, use_pc_4=True,
- degree_cut=2, cov_std_cut = 1.8,
- wt_fun = 'abs',
- featurewise=False, #else feature_components
- rpkms_not_events=False, #else event features
- feature_of_interest='RBFOX2', draw_labels=True,
- reduction_name=None,
- list_name=None,
- group_id=None,
- graph_file='',
- compare=""):
-
- """
- list_name - name of genelist used in making pcas
- group_id - celltype code
- x_pc - x component for PCA
- y_pc - y component for PCA
- n_pcs - n components to use for cells' covariance calculation
- cov_std_cut - covariance cutoff for edges
- pc{1-4} use these pcs in cov calculation (default True)
- degree_cut - miniumum degree for a node to be included in graph display
- wt_fun - weight function (arctan (arctan cov), sq (sq cov), abs (abs cov), arctan_sq (sqared arctan of cov))
- gene_of_interest - map a gradient representing this gene's df onto nodes
- """
-
- node_color_mapper = self._default_node_color_mapper
- node_size_mapper = self._default_node_color_mapper
- settings = locals().copy()
- #not pertinent to the graph, these are what we want to be able to re-apply to the same graph if it exists
- pca_settings = dict()
- pca_settings['group_id'] = group_id
- pca_settings['featurewise'] = featurewise
- pca_settings['list_name'] = list_name
- pca_settings['obj_id'] = reduction_name
-
- adjacency_settings = dict((k, settings[k]) for k in ['use_pc_1', 'use_pc_2', 'use_pc_3', 'use_pc_4', 'n_pcs', ])
-
- f= plt.figure(figsize=(10,10))
-
- plt.axis((-0.2, 1.2, -0.2, 1.2))
- main_ax = plt.gca()
- ax_pev = plt.axes([0.1, .8, .2, .15])
- ax_cov = plt.axes([0.1, 0.1, .2, .15])
- ax_degree = plt.axes([0.9,.8,.2,.15])
-
- pca = self.data_obj.get_reduced(**pca_settings)
-
-
- if featurewise:
- node_color_mapper = lambda x: 'r' if x == feature_of_interest else 'k'
- node_size_mapper = lambda x: (pca.means.ix[x]**2) + 10
- else:
- node_color_mapper = lambda x: self.data_obj.sample_descriptors.color[x]
- node_size_mapper = lambda x: 75
-
- ax_pev.plot(pca.explained_variance_ratio_ * 100.)
- ax_pev.axvline(n_pcs, label='cutoff')
- ax_pev.legend()
- ax_pev.set_ylabel("% explained variance")
- ax_pev.set_xlabel("component")
- ax_pev.set_title("Explained variance from dim reduction")
- seaborn.despine(ax=ax_pev)
-
- adjacency_name = "_".join([dict_to_str(adjacency_settings), pca.obj_id])
-
- adjacency = self.get_adjacency(pca.reduced_space, name=adjacency_name, **adjacency_settings)
- cov_dist = np.array([i for i in adjacency.values.ravel() if np.abs(i) > 0])
- cov_cut = np.mean(cov_dist) + cov_std_cut * np.std(cov_dist)
-
- graph_settings = dict((k, settings[k]) for k in ['wt_fun', 'degree_cut', ])
- graph_settings['cov_cut'] = cov_cut
- this_graph_name = "_".join(map(dict_to_str, [pca_settings, adjacency_settings, graph_settings]))
- graph_settings['name'] = this_graph_name
-
- seaborn.kdeplot(cov_dist, ax=ax_cov)
- ax_cov.axvline(cov_cut, label='cutoff')
- ax_cov.set_title("covariance in dim reduction space")
- ax_cov.set_ylabel("density")
- ax_cov.legend()
- seaborn.despine(ax=ax_cov)
- g, pos = self.get_graph(adjacency, **graph_settings)
-
- nx.draw_networkx_nodes(g, pos, node_color=map(node_color_mapper, g.nodes()),
- node_size=map(node_size_mapper, g.nodes()),
- ax=main_ax, alpha=0.5)
-
- try:
- nx.draw_networkx_nodes(g, pos, node_color=map(lambda x: pca.X[feature_of_interest].ix[x], g.nodes()),
- cmap=plt.cm.Greys,
- node_size=map(lambda x: node_size_mapper(x) * .5, g.nodes()), ax=main_ax, alpha=1)
- except:
- pass
- nmr = lambda x:x
- labels = dict([(nm, nmr(nm)) for nm in g.nodes()])
- if draw_labels:
- nx.draw_networkx_labels(g, pos, labels = labels, ax=main_ax)
- #mst = nx.minimum_spanning_tree(g, weight='inv_weight')
- nx.draw_networkx_edges(g, pos,ax = main_ax,alpha=0.1)
- #nx.draw_networkx_edges(g, pos, edgelist=mst.edges(), edge_color="m", edge_width=200, ax=main_ax)
- main_ax.set_axis_off()
- degree = nx.degree(g)
- seaborn.kdeplot(np.array(degree.values()), ax=ax_degree)
- ax_degree.set_xlabel("degree")
- ax_degree.set_ylabel("density")
- try:
- ax_degree.axvline(x=degree[feature_of_interest], label=feature_of_interest)
- ax_degree.legend()
-
- except Exception as e:
- print e
- pass
-
- seaborn.despine(ax=ax_degree)
- #f.tight_layout(pad=5)
- if graph_file != '':
- try:
- nx.write_gml(g, graph_file)
- except Exception as e:
- print "error writing graph file:"
- print e
-
- return g, pos
-
- def draw_nonreduced_graph(self,
- degree_cut=2, cov_std_cut = 1.8,
- wt_fun = 'abs',
- featurewise=False, #else feature_components
- rpkms_not_events=False, #else event features
- feature_of_interest='RBFOX2', draw_labels=True,
- list_name=None,
- group_id=None,
- graph_file='',
- compare=""):
-
- """
- list_name - name of genelist used in making pcas
- group_id - celltype code
- x_pc - x component for PCA
- y_pc - y component for PCA
- n_pcs - n components to use for cells' covariance calculation
- cov_std_cut - covariance cutoff for edges
- pc{1-4} use these pcs in cov calculation (default True)
- degree_cut - miniumum degree for a node to be included in graph display
- wt_fun - weight function (arctan (arctan cov), sq (sq cov), abs (abs cov), arctan_sq (sqared arctan of cov))
- gene_of_interest - map a gradient representing this gene's df onto nodes
- """
-
- node_color_mapper = self._default_node_color_mapper
- node_size_mapper = self._default_node_color_mapper
- settings = locals().copy()
-
- adjacency_settings = dict(('non_reduced', True))
-
- #del settings['gene_of_interest']
- #del settings['graph_file']
- #del settings['draw_labels']
-
- f= plt.figure(figsize=(10,10))
- #gs = GridSpec(2, 2)
- plt.axis((-0.2, 1.2, -0.2, 1.2))
- main_ax = plt.gca()
- ax_cov = plt.axes([0.1, 0.1, .2, .15])
- ax_degree = plt.axes([0.9,.8,.2,.15])
-
- data = self.data_obj.df
-
-
- if featurewise:
- node_color_mapper = lambda x: 'r' if x == feature_of_interest else 'k'
- node_size_mapper = lambda x: (data.mean().ix[x]**2) + 10
- else:
- node_color_mapper = lambda x: self.data_obj.sample_descriptors.color[x]
- node_size_mapper = lambda x: 75
-
- adjacency_name = "_".join([dict_to_str(adjacency_settings)])
- #adjacency_settings['name'] = adjacency_name
-
- #import pdb
- #pdb.set_trace()
- adjacency = self.get_adjacency(data, name=adjacency_name, **adjacency_settings)
- cov_dist = np.array([i for i in adjacency.values.ravel() if np.abs(i) > 0])
- cov_cut = np.mean(cov_dist) + cov_std_cut * np.std(cov_dist)
-
- graph_settings = dict((k, settings[k]) for k in ['wt_fun', 'degree_cut', ])
- graph_settings['cov_cut'] = cov_cut
- this_graph_name = "_".join(map(dict_to_str, [adjacency_settings, graph_settings]))
- graph_settings['name'] = this_graph_name
-
- seaborn.kdeplot(cov_dist, ax=ax_cov)
- ax_cov.axvline(cov_cut, label='cutoff')
- ax_cov.set_title("covariance in original space")
- ax_cov.set_ylabel("density")
- ax_cov.legend()
- seaborn.despine(ax=ax_cov)
- g, pos = self.get_graph(adjacency, **graph_settings)
-
- nx.draw_networkx_nodes(g, pos, node_color=map(node_color_mapper, g.nodes()),
- node_size=map(node_size_mapper, g.nodes()),
- ax=main_ax, alpha=0.5)
- #nx.draw_networkx_nodes(g, pos, node_color=map(node_color_mapper, g.nodes()),
- # node_size=map(node_size_mapper, g.nodes()),
- # ax=ax4, alpha=0.5)
- try:
- nx.draw_networkx_nodes(g, pos, node_color=map(lambda x: data[feature_of_interest].ix[x], g.nodes()),
- cmap=plt.cm.Greys,
- node_size=map(lambda x: node_size_mapper(x) * .5, g.nodes()), ax=main_ax, alpha=1)
- except:
- pass
- nmr = lambda x:x
- labels = dict([(nm, nmr(nm)) for nm in g.nodes()])
- if draw_labels:
- nx.draw_networkx_labels(g, pos, labels = labels, ax=main_ax)
- #mst = nx.minimum_spanning_tree(g, weight='inv_weight')
- nx.draw_networkx_edges(g, pos,ax = main_ax,alpha=0.1)
- #nx.draw_networkx_edges(g, pos, edgelist=mst.edges(), edge_color="m", edge_width=200, ax=main_ax)
- main_ax.set_axis_off()
- degree = nx.degree(g)
- seaborn.kdeplot(np.array(degree.values()), ax=ax_degree)
- ax_degree.set_xlabel("degree")
- ax_degree.set_ylabel("density")
- try:
- ax_degree.axvline(x=degree[feature_of_interest], label=feature_of_interest)
- ax_degree.legend()
-
- except Exception as e:
- print e
- pass
-
- seaborn.despine(ax=ax_degree)
- #f.tight_layout(pad=5)
- if graph_file != '':
- try:
- nx.write_gml(g, graph_file)
- except Exception as e:
- print "error writing graph file:"
- print e
-
- return g, pos
-
-from ._frigate_compute import Predictor
-import itertools
-
-class PredictorViz(Predictor, Reduction_viz):
-
- _reducer_plotting_args = {}
- def set_reducer_plotting_args(self, rpa):
- self._reducer_plotting_args.update(rpa)
-
- def __call__(self, trait=None, ax=None, feature_score_std_cutoff=None):
-
- if trait is None:
- trait = self.traits[0]
- else:
- assert type(trait) == str or type(trait) == unicode
-
- if feature_score_std_cutoff is None:
- feature_scoring_cut_fun = self.default_classifier_scoring_cutoff_fun
- else:
- feature_scoring_cut_fun = lambda scores: np.mean(scores) + feature_score_std_cutoff*np.std(scores)
-
- if not self.has_been_fit_yet:
- self.fit_classifiers([trait])
-
- self.score_classifiers([trait], score_cutoff_fun=feature_scoring_cut_fun)
-
- from matplotlib.gridspec import GridSpec, GridSpecFromSubplotSpec
-
- import matplotlib.pyplot as plt
- gs_x = 18
- gs_y = 12
-
- if ax is None:
- fig, ax = plt.subplots(1,1,figsize=(18, 8))
- gs = GridSpec(gs_x,gs_y)
-
- else:
- gs = GridSpecFromSubplotSpec(gs_x,gs_y,ax.get_subplotspec())
- ax_pca = plt.subplot(gs[:, 2:])
-
- ax_scores= plt.subplot(gs[5:10, :2])
- ax_scores.set_xlabel("Feature Importance")
- ax_scores.set_ylabel("Density Estimate")
- self.plot_classifier_scores([trait], ax=ax_scores)
- pca = self.do_pca(trait, ax=ax_pca, show_vectors=True)
- plt.tight_layout()
- return pca
-
-
- def plot_classifier_scores(self, traits, ax=None, classifier_name=None):
- """
- plot kernel density of classifier scores and draw a vertical line where the cutoff was selected
- ax - ax to plot on. if None: plt.gca()
- """
-
-
- if classifier_name is None:
- classifier_name = self.default_classifier_name
-
- if ax==None:
- ax = plt.gca()
-
- for trait in traits:
- clf = self.classifiers_[trait][classifier_name]
- seaborn.kdeplot(clf.scores_, shade=True, ax=ax, label="%s\n%d features\noob:%.2f" % (trait,
- np.sum(clf.good_features_),
- clf.oob_score_))
- ax.axvline(x=clf.score_cutoff_)
-
- [lab.set_rotation(90) for lab in ax.get_xticklabels()]
- seaborn.despine(ax=ax)
-
-
- def generate_scatter_table(self,
- excel_out=None, external_xref=[]):
- """
- make a table to make scatterplots... maybe for plot.ly
- excelOut: full path to an excel output location for scatter data
- external_xref: list of tuples containing (attribute name, function to map row index -> an attribute)
- """
- raise NotImplementedError
- trait, classifier_name = self.attributes
- X = self.X
- sorter = np.array([np.median(i[1]) - np.median(j[1]) for (i, j) in \
- itertools.izip(X[self.y[trait]==0].iteritems(),
- X[self.y[trait]==1].iteritems())])
-
- sort_by_sorter = X.columns[np.argsort(sorter)]
- c0_values = X[sort_by_sorter][self.y[trait]==0]
- c1_values = X[sort_by_sorter][self.y[trait]==1]
-
- x = []
- s = []
- y1 = []
- y2 = []
- field_names = ['x-position', 'probe intensity', "condition0", "condition1"]
- n_default_fields = len(field_names)
- external_attrs = {}
- for external_attr_name, external_attr_fun in external_xref:
- external_attrs[external_attr_name] = []
- field_names.append(external_attr_name)
-
-
- for i, (a, b) in enumerate(itertools.izip(c0_values.iteritems(), c1_values.iteritems())):
-
- mn = np.mean(np.r_[a[1], b[1]])
- _ = [x.append(i) for _ in a[1]]
- _ = [s.append(mn) for val in a[1]]
- _ = [y1.append(val- mn) for val in a[1]]
- _ = [y2.append(np.nan) for val in a[1]]
-
- _ = [x.append(i) for _ in b[1]]
- _ = [s.append(mn) for val in b[1]]
- _ = [y1.append(np.nan) for val in b[1]]
- _ = [y2.append(val - mn) for val in b[1]]
-
-
- for external_attr_name, external_attr_fun in external_xref:
- external_attrs[external_attr_name].extend([external_attr_fun(i) for i in a[1].index])
- external_attrs[external_attr_name].extend([external_attr_fun(i) for i in b[1].index])
-
- zz = pd.DataFrame([x, s, y1, y2] + [external_attrs[attr] for attr in field_names[n_default_fields:]],
- index=field_names)
-
- if excel_out is not None:
- try:
- E = pd.ExcelWriter('%s' % excel_out)
- zz.T.to_excel(E, self.descrip)
- E.save()
- except Exception as e:
- print "excel save failed with error %s" % e
-
- return zz
-
- def check_a_feature(self, feature_name, traits=None, **vp_params):
- """Make Violin Plots for a gene/probe's value in the sets defined in sets
- feature_name - gene/probe id. must be in the index of self._parent.X
- sets - list of sample ids
- vp_params - extra parameters for violinplot
-
- returns a list of lists with values for feature_name in each set of sets
- """
- if traits is None:
- traits = self.categorical_traits
-
- for trait in traits:
- seaborn.violinplot(self.X[feature_name], linewidth=0, groupby=trait,
- alpha=0.5, bw='silverman', inner='points', names=None, **vp_params)
- seaborn.despine()
-
-
- def do_pca(self, trait, ax=None, classifier_name=None, **plotting_args):
-
- """
- plot kernel density of classifier scores and draw a vertical line where the cutoff was selected
- ax - ax to plot on. if None: plt.gca()
- """
-
- assert trait in self.traits
- assert self.has_been_fit_yet
- assert self.has_been_scored_yet
-
- if ax is None:
- ax = plt.gca()
- if classifier_name is None:
- classifier_name = self.default_classifier_name
-
- local_plotting_args = self._reducer_plotting_args
- local_plotting_args.update(plotting_args)
- pca = PCA_viz(self.X.ix[:, self.classifiers_[trait][classifier_name].good_features_], **local_plotting_args)
- pca(ax=ax)
- return pca
-
-def clusterGram(dataFrame, distance_metric = 'euclidean', linkage_method = 'average',
- outfile = None, clusterRows=True, clusterCols=True, timeSeries=False, doCovar=False,
- figsize=(8, 10), row_label_color_fun=lambda x: 'k',
- col_label_color_fun=lambda x: 'k',
- link_color_func = lambda x: 'k'):
- import scipy
- from scipy import cluster
- import matplotlib.pyplot as plt
- import matplotlib.gridspec as gridspec
- import numpy as np
- import matplotlib as mpl
- """
-
- Run hierarchical clustering on data. Creates a heatmap of cluster-ordered data
- heavy-lifting is done by:
-
- gets distances between rows/columns
-
- y_events = scipy.spatial.distance.pdist(data, distance_metric)
-
- calculates the closest rows/columns
-
- Z_events = scipy.cluster.hierarchy.linkage(y_events, linkage_method)
-
- genereates dendrogram (tree)
-
- d_events = scipy.cluster.hierarchy.dendrogram(Z_events, no_labels=True)
-
- set outfile == "None" to inibit saving an eps file (only show it, don't save it)
-
- """
- data = np.array(dataFrame)
- colLabels = dataFrame.columns
- rowLabels = dataFrame.index
- nRow, nCol = data.shape
-
- if clusterRows:
- print "getting row distance matrix"
- y_events = scipy.spatial.distance.pdist(data, distance_metric)
- print "calculating linkages"
- Z_events = scipy.cluster.hierarchy.linkage(y_events, linkage_method, metric=distance_metric)
-
- if clusterCols:
- print "getting column distance matrix"
- y_samples = scipy.spatial.distance.pdist(np.transpose(data), distance_metric)
- print "calculating linkages"
- Z_samples = scipy.cluster.hierarchy.linkage(y_samples, linkage_method, metric=distance_metric)
- else:
- if doCovar:
- raise ValueError
-
- fig = plt.figure(figsize=figsize)
-
- gs = gridspec.GridSpec(18,10)
-
- ax1 = plt.subplot(gs[1:, 0:2]) #row dendrogram
-
- ax1.set_xticklabels([])
- ax1.set_xticks([])
- ax1.set_frame_on(False)
-
- reordered = data
- event_order = range(nRow)
- if clusterRows:
- d_events = scipy.cluster.hierarchy.dendrogram(Z_events, orientation='right',
- link_color_func=link_color_func,
- labels=rowLabels)
- event_order = d_events['leaves']
- reordered = data[event_order,:]
-
- labels = ax1.get_yticklabels()
-
-
- ax2 = plt.subplot(gs[0:1, 2:9]) #column dendrogram
-
- ax2.set_yticklabels([])
- ax2.set_yticks([])
- ax2.set_frame_on(False)
-
- sample_order = range(nCol)
- if clusterCols:
- d_samples = scipy.cluster.hierarchy.dendrogram(Z_samples, labels=colLabels, leaf_rotation=90,
- link_color_func=link_color_func)
- sample_order = d_samples['leaves']
- reordered = reordered[:,sample_order]
-
- axmatrix = plt.subplot(gs[1:, 2:9])
- bds = np.max(abs(reordered))
- if timeSeries:
- norm = mpl.colors.Normalize(vmin=-bds, vmax=bds)
- else:
- norm = None
-
- if (np.max(reordered) * np.min(reordered)) > 0:
- cmap = plt.cm.Reds
- else:
- cmap= plt.cm.RdBu_r
-
- im = axmatrix.matshow(reordered, aspect='auto', origin='lower', cmap=cmap, norm=norm)
- axmatrix.set_xticks([])
- axmatrix.set_yticks([])
- axcolor = plt.subplot(gs[1:6, -1])
-
- cbTicks = [np.min(data), np.mean(data), np.max(data)]
- cb = plt.colorbar(im, cax=axcolor, ticks=cbTicks, use_gridspec=True)
- plt.draw()
- [i.set_color(row_label_color_fun(i.get_text())) for i in ax1.get_yticklabels()]
- [i.set_color(col_label_color_fun(i.get_text())) for i in ax2.get_xticklabels()]
-
- plt.tight_layout()
-
- if outfile is not None:
- fig.savefig(outfile)
- return event_order, sample_order
-
-
-class TwoWayScatterViz(TwoWayGeneComparisonLocal):
-
- def __call__(self, **kwargs):
- self.plot(**kwargs)
-
- def plot(self, ax=None):
-
- co = [] #colors container
- for label, (pVal, logratio, isSig) in self.result_.get(["pValue", "log2Ratio", "isSig"]).iterrows():
- if (pVal < self.pCut) and isSig:
- if logratio > 0:
- co.append(red)
- elif logratio < 0:
- co.append(green)
- else:
- raise Exception
- else:
- co.append(blue)
-
- if ax == None:
- ax = plt.gca()
-
- ax.set_aspect('equal')
- minVal=np.min(np.c_[self.sample1, self.sample2])
- ax.scatter(self.sample1, self.sample2, c=co, alpha=0.7, edgecolor='none')
- ax.set_xlabel("%s %s" % (self.sampleNames[0], self.dtype))
- ax.set_ylabel("%s %s" % (self.sampleNames[1], self.dtype))
- ax.set_yscale('log', basey=2)
- ax.set_xscale('log', basex=2)
- ax.set_xlim(xmin=max(minVal, 0.1))
- ax.set_ylim(ymin=max(minVal, 0.1))
- if ax == None:
- plt.tight_layout()
\ No newline at end of file
diff --git a/flotilla/src/_yacht_tutorial.py b/flotilla/src/_yacht_tutorial.py
deleted file mode 100644
index 91176f16..00000000
--- a/flotilla/src/_yacht_tutorial.py
+++ /dev/null
@@ -1,8 +0,0 @@
-__author__ = 'lovci'
-
-"""
-it has everything. cushy, for people who avoid danger. and mine doesn't exist yet.
-imports all the objects and things used in this module, like init but with cargo and projects
-
-"""
-
diff --git a/flotilla/test/README.md b/flotilla/test/README.md
new file mode 100644
index 00000000..6c9eab52
--- /dev/null
+++ b/flotilla/test/README.md
@@ -0,0 +1,23 @@
+# Flotilla Tests
+
+* All files that test something must be named `test_*.py`.
+* `conftest.py` gets auto-imported for all tests. If you want something in
+`conftest.py` to persist for all testing modules and not get rewritten or
+imported, use the decorator `@pytest.fixture(scope="module")`. Otherwise,
+if it's just used once use the decorator `@pytest.fixture`.
+* Please read these [testing guidelines](http://docs.pylonsproject
+.org/en/latest/community/testing.html) before submitting any tests
+
+## How to run:
+
+On the command line, in the flotilla dir:
+
+```
+$ py.test
+```
+
+To run a single test, specify it by name
+
+```
+$ py.test flotilla/test/test_network.py
+```
\ No newline at end of file
diff --git a/tests/test_study.py b/flotilla/test/__init__.py
similarity index 100%
rename from tests/test_study.py
rename to flotilla/test/__init__.py
diff --git a/flotilla/test/compute/test_decomposition.py b/flotilla/test/compute/test_decomposition.py
new file mode 100644
index 00000000..a2c004bd
--- /dev/null
+++ b/flotilla/test/compute/test_decomposition.py
@@ -0,0 +1,70 @@
+import numpy.testing as npt
+import pandas as pd
+import pandas.util.testing as pdt
+import pytest
+from sklearn.decomposition import PCA, NMF
+
+
+@pytest.fixture(params=[None, 2])
+def n_components(request):
+ return request.param
+
+
+class TestDataFramePCA():
+ def test_init(self, df_norm, n_components):
+ from flotilla.compute.decomposition import DataFramePCA
+
+ test_pca = DataFramePCA(df_norm, n_components=n_components)
+
+ true_pca = PCA(n_components=n_components)
+ true_pca.fit(df_norm.values)
+ pc_names = ['pc_{}'.format(i + 1) for i in
+ range(true_pca.components_.shape[0])]
+ true_pca.components_ = pd.DataFrame(true_pca.components_,
+ index=pc_names,
+ columns=df_norm.columns)
+ true_pca.explained_variance_ = pd.Series(
+ true_pca.explained_variance_, index=pc_names)
+ true_pca.explained_variance_ratio_ = pd.Series(
+ true_pca.explained_variance_ratio_, index=pc_names)
+ true_pca.reduced_space = true_pca.transform(df_norm.values)
+ true_pca.reduced_space = pd.DataFrame(true_pca.reduced_space,
+ index=df_norm.index,
+ columns=pc_names)
+
+ npt.assert_array_equal(test_pca.X, df_norm.values)
+ pdt.assert_frame_equal(test_pca.components_,
+ true_pca.components_)
+ pdt.assert_series_equal(test_pca.explained_variance_,
+ true_pca.explained_variance_)
+ pdt.assert_series_equal(test_pca.explained_variance_ratio_,
+ true_pca.explained_variance_ratio_)
+ pdt.assert_frame_equal(test_pca.reduced_space,
+ true_pca.reduced_space)
+
+
+class TestDataFrameNMF():
+ def test_init(self, df_nonneg, RANDOM_STATE):
+ from flotilla.compute.decomposition import DataFrameNMF
+
+ test_nmf = DataFrameNMF(df_nonneg, n_components=2,
+ random_state=RANDOM_STATE)
+
+ true_nmf = NMF(n_components=2, random_state=RANDOM_STATE,
+ init='nndsvd')
+ reduced_space = true_nmf.fit_transform(df_nonneg.values)
+ pc_names = ['pc_{}'.format(i + 1) for i in
+ range(true_nmf.components_.shape[0])]
+ true_nmf.reduced_space = pd.DataFrame(reduced_space,
+ index=df_nonneg.index,
+ columns=pc_names)
+ true_nmf.components_ = pd.DataFrame(true_nmf.components_,
+ index=pc_names,
+ columns=df_nonneg.columns)
+
+ npt.assert_almost_equal(test_nmf.X, df_nonneg.values, decimal=4)
+ pdt.assert_frame_equal(test_nmf.components_,
+ true_nmf.components_)
+ pdt.assert_frame_equal(test_nmf.reduced_space,
+ true_nmf.reduced_space,
+ check_less_precise=True)
diff --git a/flotilla/test/compute/test_infotheory.py b/flotilla/test/compute/test_infotheory.py
new file mode 100644
index 00000000..dce623be
--- /dev/null
+++ b/flotilla/test/compute/test_infotheory.py
@@ -0,0 +1,99 @@
+import numpy as np
+import numpy.testing as npt
+import pandas as pd
+import pandas.util.testing as pdt
+import pytest
+
+np.random.seed(0)
+
+
+@pytest.fixture
+def bins():
+ return (0, .1, .2, .3, .4, 0.5, .6, .7, .8, .9, 1)
+
+
+@pytest.fixture
+def df1():
+ return pd.DataFrame(np.random.uniform(size=200).reshape(10, 20))
+
+
+@pytest.fixture
+def df2():
+ return pd.DataFrame(np.random.uniform(size=200).reshape(10, 20))
+
+
+@pytest.fixture
+def p(df1, bins):
+ from flotilla.compute.infotheory import binify
+
+ return binify(df1, bins)
+
+
+@pytest.fixture
+def q(df2, bins):
+ from flotilla.compute.infotheory import binify
+
+ return binify(df2, bins)
+
+
+def test_bin_range_strings(bins):
+ from flotilla.compute.infotheory import bin_range_strings
+
+ bin_ranges = bin_range_strings(bins)
+ true_bin_ranges = ['{}-{}'.format(i, j) for i, j in zip(bins, bins[1:])]
+ npt.assert_equal(bin_ranges, true_bin_ranges)
+
+
+def test_binify(bins, df1):
+ from flotilla.compute.infotheory import bin_range_strings, binify
+
+ binned = binify(df1, bins)
+
+ true_binned = df1.apply(lambda x: pd.Series(np.histogram(x, bins=bins)[0]))
+ true_binned.index = bin_range_strings(bins)
+ true_binned = true_binned / true_binned.sum().astype(float)
+
+ pdt.assert_frame_equal(binned, true_binned)
+
+
+def test_kld(p, q):
+ from flotilla.compute.infotheory import kld
+
+ result = kld(p, q)
+
+ p = p.replace(0, np.nan)
+ q = q.replace(0, np.nan)
+ true_result = (np.log2(p / q) * p).sum(axis=0)
+
+ pdt.assert_series_equal(result, true_result)
+
+
+def test_jsd(p, q):
+ from flotilla.compute.infotheory import jsd, kld
+
+ result = jsd(p, q)
+
+ weight = 0.5
+ m = weight * (p + q)
+ true_result = weight * kld(p, m) + (1 - weight) * kld(q, m)
+
+ pdt.assert_series_equal(result, true_result)
+
+
+@pytest.fixture(params=[None, 2, 10])
+def base(request):
+ return request.param
+
+
+def test_entropy(p, base):
+ from flotilla.compute.infotheory import entropy
+
+ if base is not None:
+ result = entropy(p, base=base)
+ else:
+ result = entropy(p)
+ base = 2
+
+ true_result = -((np.log(p) / np.log(base)) * p).sum(axis=0)
+
+ pdt.assert_series_equal(result, true_result)
diff --git a/flotilla/test/compute/test_network.py b/flotilla/test/compute/test_network.py
new file mode 100644
index 00000000..bbafe9c5
--- /dev/null
+++ b/flotilla/test/compute/test_network.py
@@ -0,0 +1,43 @@
+import numpy as np
+import pandas as pd
+import pandas.util.testing as pdt
+import pytest
+
+
+class TestComputeNetwork:
+ @pytest.fixture
+ def networker(self):
+ from flotilla.compute.network import Networker
+
+ return Networker()
+
+ def test_init(self, networker):
+ from flotilla.visualize.color import dark2
+
+ assert networker._default_node_color_mapper('x') == dark2[0]
+ assert networker._default_node_size_mapper('x') == 300
+
+ def test_adjacency(self, base_data, networker):
+ reduced = base_data.reduce()
+ reduced.adjacency = networker.adjacency(reduced.reduced_space)
+
+ # TODO: parameterize this
+ n_pcs = 5
+ use_pc_1, use_pc_2, use_pc_3, use_pc_4 = True, True, True, True
+
+ data = reduced.reduced_space
+ total_pcs = data.shape[1]
+ use_cols = np.ones(total_pcs, dtype='bool')
+ use_cols[n_pcs:] = False
+ use_cols = use_cols * np.array(
+ [use_pc_1, use_pc_2, use_pc_3, use_pc_4] + [True, ] * (
+ total_pcs - 4))
+ selected_cols = data.loc[:, use_cols]
+ cov = np.cov(selected_cols)
+ nrow, ncol = selected_cols.shape
+ adjacency = pd.DataFrame(np.tril(cov * - (np.identity(nrow) - 1)),
+ index=selected_cols.index, columns=data.index)
+ pdt.assert_frame_equal(reduced.adjacency, adjacency)
+
+ def test_graph(self):
+ pass
diff --git a/flotilla/test/compute/test_predict.py b/flotilla/test/compute/test_predict.py
new file mode 100644
index 00000000..c9cb8971
--- /dev/null
+++ b/flotilla/test/compute/test_predict.py
@@ -0,0 +1,221 @@
+# import numpy as np
+# import numpy.testing as npt
+# import pandas as pd
+# import pandas.util.testing as pdt
+# import pytest
+# from sklearn.ensemble import ExtraTreesRegressor, ExtraTreesClassifier
+# from sklearn.preprocessing import LabelEncoder
+#
+#
+# @pytest.fixture
+# def study(shalek2013_data):
+# from flotilla.data_model import Study
+#
+# return Study(sample_metadata=shalek2013_data.metadata,
+# expression_data=shalek2013_data.expression,
+# splicing_data=shalek2013_data.splicing)
+#
+#
+# @pytest.fixture
+# def reduced(study):
+# return study.expression.reduce().df
+#
+#
+# class TestPredictorBase:
+# @pytest.fixture
+# def trait(self, study):
+# return study.experiment_design.dataset.celltype
+#
+# @pytest.fixture
+# def predictorbase(self, reduced, trait):
+# from flotilla.compute.predict import PredictorBase
+#
+# return PredictorBase(reduced, trait)
+#
+# def test_init(self, reduced, predictorbase, trait):
+# X, trait = reduced.align(trait, axis=0, join='inner')
+#
+# assert predictorbase.predictor_config is None
+# assert predictorbase.predictor_class is None
+# pdt.assert_frame_equal(predictorbase.X, X)
+# pdt.assert_series_equal(predictorbase.trait, trait)
+# assert not predictorbase.has_been_fit
+# assert not predictorbase.has_been_scored
+# assert predictorbase.default_predictor_kwargs \
+# == predictorbase.predictor_kwargs
+# assert predictorbase.default_score_cutoff_fun \
+# == predictorbase.score_cutoff_fun
+# assert predictorbase.default_predictor_scoring_fun \
+# == predictorbase.predictor_scoring_fun
+#
+# def test_fit(self, predictorbase):
+# with pytest.raises(ValueError):
+# predictorbase.fit()
+#
+# def test_score(self, predictorbase):
+# with pytest.raises(ValueError):
+# predictorbase.score()
+#
+# def test_default_score_cutoff_fun(self, predictorbase):
+# x = np.arange(10)
+# cutoff = np.mean(x) + 2 * np.std(x)
+#
+# npt.assert_approx_equal(cutoff,
+# predictorbase.default_score_cutoff_fun(x))
+#
+# def test_predictor_scoring_fun(self, predictorbase):
+# class X:
+# def feature_importances_(self):
+# pass
+#
+# x = X()
+# assert predictorbase.default_predictor_scoring_fun(x) \
+# == x.feature_importances_
+#
+#
+# class TestRegressor:
+# @pytest.fixture
+# def y(self, study):
+# return pd.Series(np.arange(study.expression.dataset.shape[0]),
+# name='dummy',
+# index=study.expression.dataset.index)
+#
+# @pytest.fixture
+# def predictor_kwargs(self):
+# return {'random_state': 2014}
+#
+# @pytest.fixture
+# def regressor(self, reduced, y, predictor_kwargs):
+# import flotilla
+#
+# return flotilla.compute.predict.Regressor(
+# reduced, y, predictor_kwargs=predictor_kwargs)
+#
+# def test_init(self, regressor, y):
+# assert regressor.predictor_class == ExtraTreesRegressor
+# pdt.assert_series_equal(regressor.y, y)
+#
+# def test_fit(self, regressor, reduced, y):
+# regressor.fit()
+#
+# regressor.predictor_config.scores_ = regressor.predictor_scoring_fun(
+# regressor
+# .predictor_config)
+#
+# true_regressor = ExtraTreesRegressor(**regressor.predictor_kwargs)
+# true_regressor.fit(reduced, y)
+# true_regressor.scores_ = regressor.predictor_scoring_fun(
+# true_regressor)
+#
+# npt.assert_array_equal(regressor.predictor_config.scores_,
+# true_regressor.scores_)
+# assert regressor.has_been_fit
+#
+# def test_score(self, regressor, reduced, y):
+# regressor.fit()
+# regressor.score()
+#
+# true_regressor = ExtraTreesRegressor(**regressor.predictor_kwargs)
+# true_regressor.fit(reduced, y)
+# scores = regressor.predictor_scoring_fun(true_regressor)
+# true_regressor.scores_ = pd.Series(scores, index=reduced.columns)
+# true_regressor.score_cutoff_ = regressor.score_cutoff_fun(
+# true_regressor.scores_)
+# true_regressor.important_features = true_regressor.scores_ > \
+# true_regressor.score_cutoff_
+# true_regressor.n_good_features = np.sum(true_regressor
+# .important_features)
+# true_regressor.subset_ = reduced.T[
+# true_regressor.important_features].T
+#
+# pdt.assert_series_equal(true_regressor.scores_,
+# regressor.predictor_config.scores_)
+# npt.assert_equal(true_regressor.score_cutoff_,
+# regressor.predictor_config.score_cutoff_)
+# pdt.assert_series_equal(true_regressor.important_features,
+# regressor.important_features_)
+# assert true_regressor.n_good_features \
+# == regressor.predictor_config.n_good_features_
+# pdt.assert_frame_equal(true_regressor.subset_,
+# regressor.predictor_config.subset_)
+# assert regressor.has_been_scored
+#
+#
+# class TestClassifier:
+#
+# @pytest.fixture
+# def trait(self, study):
+# return study.experiment_design.dataset.celltype
+#
+# @pytest.fixture
+# def y(self, trait):
+# traitset = \
+# trait.groupby(trait).describe().index.levels[0]
+# le = LabelEncoder().fit(traitset)
+# return pd.Series(data=le.transform(trait.values),
+# index=trait.index,
+# name=trait.name)
+#
+# @pytest.fixture
+# def reduced_y_aligned(self, y, reduced):
+# reduced, y = reduced.align(y, axis=0, join='inner')
+# return reduced, y
+#
+# @pytest.fixture
+# def classifier(self, reduced, trait):
+# from flotilla.compute.predict import Classifier
+#
+# return Classifier(reduced, trait)
+#
+# def test_init(self, classifier, reduced_y_aligned):
+# reduced, y = reduced_y_aligned
+# assert classifier.predictor_class == ExtraTreesClassifier
+# pdt.assert_series_equal(classifier.y, y)
+#
+# def test_fit(self, reduced_y_aligned, classifier):
+# reduced, y = reduced_y_aligned
+#
+# classifier.fit()
+# classifier.predictor_config.scores_ = \
+# classifier.predictor_scoring_fun(classifier.predictor_config)
+#
+# true_classifier = ExtraTreesClassifier(**classifier.predictor_kwargs)
+# true_classifier.fit(reduced, y)
+# true_classifier.scores_ = classifier.predictor_scoring_fun(
+# true_classifier)
+#
+# npt.assert_array_equal(classifier.predictor_config.scores_,
+# true_classifier.scores_)
+# assert classifier.has_been_fit
+#
+# def test_score(self, classifier, reduced_y_aligned):
+# reduced, y = reduced_y_aligned
+#
+# classifier.fit()
+# classifier.score()
+#
+# true_classifier = ExtraTreesClassifier(**classifier.predictor_kwargs)
+# true_classifier.fit(reduced, y)
+# scores = classifier.predictor_scoring_fun(true_classifier)
+# true_classifier.scores_ = pd.Series(scores,
+# index=reduced.columns)
+# true_classifier.score_cutoff_ = classifier.score_cutoff_fun(
+# true_classifier.scores_)
+# true_classifier.important_features = true_classifier.scores_ > \
+# true_classifier.score_cutoff_
+# true_classifier.n_good_features = np.sum(true_classifier
+# .important_features)
+# true_classifier.subset_ = reduced.T[
+# true_classifier.important_features].T
+#
+# pdt.assert_series_equal(true_classifier.scores_,
+# classifier.predictor_config.scores_)
+# npt.assert_equal(true_classifier.score_cutoff_,
+# classifier.predictor_config.score_cutoff_)
+# pdt.assert_series_equal(true_classifier.important_features,
+# classifier.important_features_)
+# assert true_classifier.n_good_features \
+# == classifier.predictor_config.n_good_features_
+# pdt.assert_frame_equal(true_classifier.subset_,
+# classifier.predictor_config.subset_)
+# assert classifier.has_been_scored
diff --git a/flotilla/test/compute/test_splicing.py b/flotilla/test/compute/test_splicing.py
new file mode 100644
index 00000000..0e9cc144
--- /dev/null
+++ b/flotilla/test/compute/test_splicing.py
@@ -0,0 +1,248 @@
+from collections import Iterable
+
+import pytest
+import numpy as np
+import numpy.testing as npt
+import pandas as pd
+import pandas.util.testing as pdt
+from scipy import stats
+from scipy.misc import logsumexp
+
+
+class TestModalityModel(object):
+ @pytest.fixture()
+ def x(self):
+ return np.arange(0, 1.1, 0.1)
+
+ @pytest.fixture(params=[1, np.arange(1, 5)])
+ def alphas(self, request):
+ return request.param
+
+ @pytest.fixture(params=[1, np.arange(1, 5)])
+ def betas(self, request):
+ return request.param
+
+ @pytest.fixture()
+ def alpha(self):
+ return np.arange(1, 5)
+
+ @pytest.fixture()
+ def beta(self):
+ return 1.
+
+ @pytest.fixture()
+ def model(self, alpha, beta):
+ from flotilla.compute.splicing import ModalityModel
+
+ return ModalityModel(alpha, beta)
+
+ def test_init(self, alphas, betas):
+ from flotilla.compute.splicing import ModalityModel
+
+ model = ModalityModel(alphas, betas)
+
+ true_alphas = alphas
+ true_betas = betas
+ if not isinstance(alphas, Iterable) and not isinstance(betas,
+ Iterable):
+ true_alphas = [alphas]
+ true_betas = [betas]
+
+ true_alphas = true_alphas if isinstance(true_alphas,
+ Iterable) else np.ones(
+ len(true_betas)) * true_alphas
+ true_betas = true_betas if isinstance(true_betas,
+ Iterable) else np.ones(
+ len(true_alphas)) * true_betas
+
+ true_rvs = [stats.beta(a, b) for a, b in
+ zip(true_alphas, true_betas)]
+ true_scores = np.arange(len(true_rvs)).astype(float) + .1
+ true_scores = true_scores / true_scores.max()
+ true_prob_parameters = true_scores / true_scores.sum()
+
+ npt.assert_array_equal(model.alphas, true_alphas)
+ npt.assert_array_equal(model.betas, true_betas)
+ npt.assert_array_equal(model.scores, true_scores)
+ npt.assert_array_equal(model.prob_parameters, true_prob_parameters)
+ for test_rv, true_rv in zip(model.rvs, true_rvs):
+ npt.assert_array_equal(test_rv.args, true_rv.args)
+
+ def test_logliks(self, x, model):
+ test_logliks = model.logliks(x)
+
+ true_x = x.copy()
+ true_x[true_x == 0] = 0.001
+ true_x[true_x == 1] = 0.999
+ true_logliks = np.array([np.log(prob) + rv.logpdf(true_x).sum()
+ for prob, rv in zip(model.prob_parameters,
+ model.rvs)])
+ npt.assert_array_equal(test_logliks, true_logliks)
+
+ def test_logsumexp_logliks(self, x, model):
+ test_logsumexp_logliks = model.logsumexp_logliks(x)
+
+ npt.assert_array_equal(test_logsumexp_logliks,
+ logsumexp(model.logliks(x)))
+
+ def test_eq(self, alphas, betas):
+ from flotilla.compute.splicing import ModalityModel
+
+ model1 = ModalityModel(alphas, betas)
+ model2 = ModalityModel(alphas, betas)
+ assert model1 == model2
+
+ def test_ne(self, alphas, betas):
+ from flotilla.compute.splicing import ModalityModel
+
+ if np.all(alphas == betas):
+ assert 1
+ return
+
+ model1 = ModalityModel(alphas, betas)
+ model2 = ModalityModel(betas, alphas)
+ assert model1 != model2
+
+
+class TestModalityEstimator(object):
+ @pytest.fixture
+ def step(self):
+ return 1
+
+ @pytest.fixture
+ def vmax(self):
+ return 10
+
+ @pytest.fixture(params=[2, 3])
+ def logbf_thresh(self, request):
+ return request.param
+
+ @pytest.fixture
+ def estimator(self, step, vmax):
+ from flotilla.compute.splicing import ModalityEstimator
+
+ return ModalityEstimator(step, vmax)
+
+ @pytest.fixture(params=['no_na', 'with_na'])
+ def event(self, request):
+ x = np.arange(0, 1.1, .1)
+ if request.param == 'no_na':
+ return x
+ elif request.param == 'with_na':
+ x[x < 0.5] = np.nan
+ return x
+
+ def test_init(self, step, vmax, logbf_thresh):
+ from flotilla.compute.splicing import ModalityEstimator, \
+ ModalityModel
+
+ estimator = ModalityEstimator(step, vmax, logbf_thresh)
+
+ true_parameters = np.arange(2, vmax + step, step).astype(float)
+ true_exclusion = ModalityModel(1, true_parameters)
+ true_inclusion = ModalityModel(true_parameters, 1)
+ true_middle = ModalityModel(true_parameters, true_parameters)
+ true_bimodal = ModalityModel(1 / true_parameters, 1 / true_parameters)
+ true_models = {'included': true_inclusion,
+ 'excluded': true_exclusion,
+ 'bimodal': true_bimodal,
+ 'middle': true_middle}
+
+ npt.assert_equal(estimator.step, step)
+ npt.assert_equal(estimator.vmax, vmax)
+ npt.assert_equal(estimator.logbf_thresh, logbf_thresh)
+ npt.assert_equal(estimator.parameters, true_parameters)
+ npt.assert_equal(estimator.exclusion_model, true_exclusion)
+ npt.assert_equal(estimator.inclusion_model, true_inclusion)
+ npt.assert_equal(estimator.middle_model, true_middle)
+ npt.assert_equal(estimator.bimodal_model, true_bimodal)
+ pdt.assert_dict_equal(estimator.models, true_models)
+
+ def test_loglik(self, event, estimator):
+ test_loglik = estimator._loglik(event)
+
+ true_loglik = dict((name, m.logliks(event))
+ for name, m in estimator.models.iteritems())
+ pdt.assert_dict_equal(test_loglik, true_loglik)
+
+ def test_logsumexp(self, event, estimator):
+ logliks = estimator._loglik(event)
+ test_logsumexp = estimator._logsumexp(logliks)
+
+ true_logsumexp = pd.Series(
+ dict((name, logsumexp(loglik))
+ for name, loglik in logliks.iteritems()))
+ true_logsumexp['uniform'] = estimator.logbf_thresh
+ pdt.assert_series_equal(test_logsumexp, true_logsumexp)
+
+ def test_guess_modality(self, event, estimator):
+ logsumexps = estimator._logsumexp(estimator._loglik(event))
+
+ test_guess_modality = estimator._guess_modality(logsumexps)
+
+ logsumexps['uniform'] = estimator.logbf_thresh
+ true_guess_modality = logsumexps.idxmax()
+
+ pdt.assert_equal(test_guess_modality, true_guess_modality)
+
+ def test_fit_transform_with_na(self, estimator, splicing_data):
+ test_fit_transform = estimator.fit_transform(splicing_data)
+
+ logsumexp_logliks = splicing_data.apply(
+ lambda x: pd.Series({k: v.logsumexp_logliks(x)
+ for k, v in estimator.models.iteritems()}),
+ axis=0)
+ logsumexp_logliks.ix['uniform'] = estimator.logbf_thresh
+ true_fit_transform = logsumexp_logliks.idxmax()
+
+ pdt.assert_series_equal(test_fit_transform, true_fit_transform)
+
+ def test_fit_transform_no_na(self, estimator, splicing_data):
+ test_fit_transform = estimator.fit_transform(splicing_data)
+
+ logsumexp_logliks = splicing_data.apply(
+ lambda x: pd.Series({k: v.logsumexp_logliks(x)
+ for k, v in estimator.models.iteritems()}),
+ axis=0)
+ logsumexp_logliks.ix['uniform'] = estimator.logbf_thresh
+ true_fit_transform = logsumexp_logliks.idxmax()
+
+ pdt.assert_series_equal(test_fit_transform, true_fit_transform)
+
+
+@pytest.fixture(params=['list', 'array', 'nan'])
+def array(request):
+ x = np.arange(0, 1.1, .1)
+ if request.param == 'list':
+ return list(x)
+ elif request.param == 'array':
+ return x
+ elif request.param == 'nan':
+ x[x > .8] = np.nan
+ return x
+
+
+def test_switchy_score(array):
+ from flotilla.compute.splicing import switchy_score
+
+ test_switchy_score = switchy_score(array)
+
+ true_array = np.array(array)
+ variance = 1 - np.std(np.sin(true_array[~np.isnan(true_array)] * np.pi))
+ mean_value = -np.mean(np.cos(true_array[~np.isnan(true_array)] * np.pi))
+ true_switchy_score = variance * mean_value
+
+ npt.assert_array_equal(test_switchy_score, true_switchy_score)
+
+
+def test_get_switchy_score_order(splicing_data):
+ from flotilla.compute.splicing import get_switchy_score_order, \
+ switchy_score
+
+ test_score_order = get_switchy_score_order(splicing_data)
+
+ switchy_scores = np.apply_along_axis(switchy_score, axis=0,
+ arr=splicing_data)
+ true_score_order = np.argsort(switchy_scores)
+
+ npt.assert_array_equal(test_score_order, true_score_order)
diff --git a/flotilla/test/conftest.py b/flotilla/test/conftest.py
new file mode 100644
index 00000000..ce1e7257
--- /dev/null
+++ b/flotilla/test/conftest.py
@@ -0,0 +1,682 @@
+"""
+This file will be auto-imported for every testing session, so you can use
+these objects and functions across test files.
+"""
+from collections import defaultdict
+import os
+
+import matplotlib as mpl
+import numpy as np
+import pytest
+import pandas as pd
+from scipy import stats
+import seaborn as sns
+
+
+@pytest.fixture(scope='module')
+def data_dir():
+ return os.path.join(os.path.abspath(os.path.dirname(__file__)),
+ 'example_data')
+
+
+@pytest.fixture(scope='module')
+def RANDOM_STATE():
+ """Consistent random state"""
+ return 0
+
+
+@pytest.fixture(scope='module')
+def n_samples():
+ """Number of samples to create example data from"""
+ return 30
+
+
+@pytest.fixture(scope='module')
+def samples(n_samples):
+ """Sample ids"""
+ return ['sample_{}'.format(i + 1) for i in np.arange(n_samples)]
+
+
+@pytest.fixture(scope='module')
+def technical_outliers(n_samples, samples):
+ """If request.param is True, return randomly chosen samples as technical
+ outliers, otherwise None"""
+ return np.random.choice(samples,
+ size=np.random.randint(1, int(n_samples / 10.)),
+ replace=False)
+
+
+@pytest.fixture(scope='module')
+def pooled(request, n_samples, samples):
+ """If request.param is True, return randomly chosen samples as pooled,
+ otherwise None"""
+ return np.random.choice(samples,
+ size=np.random.randint(1,
+ int(n_samples / 10.)),
+ replace=False)
+
+
+@pytest.fixture(scope='module')
+def outliers(request, n_samples, samples):
+ """If request.param is True, return randomly chosen samples as outliers,
+ otherwise None"""
+ return np.random.choice(samples,
+ size=np.random.randint(1,
+ int(n_samples / 10.)),
+ replace=False)
+
+
+@pytest.fixture(scope='module')
+def n_groups():
+ """Number of phenotype groups."""
+ return 2
+
+
+# @pytest.fixture(scope='module')
+# def n_groups_fixed():
+# """Fixed number of phenotype groups (3)"""
+# return 3
+
+
+@pytest.fixture(scope='module')
+def groups(n_groups):
+ """Phenotype group names"""
+ return ['group{}'.format(i + 1) for i in np.arange(n_groups)]
+
+
+# @pytest.fixture(scope='module')
+# def groups_fixed(n_groups_fixed):
+# """Phenotype group names"""
+# return ['group{}'.format(i + 1) for i in np.arange(n_groups_fixed)]
+
+
+@pytest.fixture(scope='module')
+def group_order(groups):
+ """so-called 'logical' order of groups for plotting.
+
+ To test if the user gave a specific order of the phenotypes, e.g.
+ by differentiation time
+ """
+ return np.random.permutation(groups)
+
+#
+# @pytest.fixture(scope='module')
+# def group_order_fixed(groups_fixed):
+# """so-called 'logical' order of groups for plotting.
+#
+# To test if the user gave a specific order of the phenotypes, e.g.
+# by differentiation time
+# """
+# return np.random.permutation(groups_fixed)
+
+
+@pytest.fixture(scope='module')
+def colors(n_groups):
+ """Colors to use for the samples"""
+ return map(mpl.colors.rgb2hex,
+ sns.color_palette('husl', n_colors=n_groups))
+
+
+# @pytest.fixture(scope='module')
+# def colors_fixed(n_groups_fixed):
+# """Colors to use for the samples"""
+# return map(mpl.colors.rgb2hex,
+# sns.color_palette('husl', n_colors=n_groups_fixed))
+
+
+@pytest.fixture(scope='module')
+def group_to_color(group_order, colors):
+ """Mapping of groups to colors"""
+ return dict(zip(group_order, colors))
+
+
+# @pytest.fixture(scope='module')
+# def group_to_color_fixed(group_order_fixed, colors_fixed):
+# """Mapping of groups to colors"""
+# return dict(zip(group_order_fixed, colors_fixed))
+
+
+@pytest.fixture(scope='module')
+def color_ordered(group_order, group_to_color):
+ """Colors in the order created by the groups"""
+ return [group_to_color[g] for g in group_order]
+
+#
+# @pytest.fixture(scope='module')
+# def color_ordered_fixed(group_order_fixed, group_to_color_fixed):
+# """Colors in the order created by the groups"""
+# return [group_to_color_fixed[g] for g in group_order_fixed]
+
+
+@pytest.fixture(scope='module')
+def group_to_marker(request):
+ """Mapping of groups to plotting markers"""
+ marker_iter = iter(list('ov^<>8sp*hHDd'))
+ return defaultdict(lambda: marker_iter.next())
+
+
+@pytest.fixture(scope='module')
+def group_transitions(group_order):
+ """List of pairwise transitions between phenotypes, for NMF"""
+ return zip(group_order[:-1], group_order[1:])
+
+#
+# @pytest.fixture(scope='module')
+# def group_transitions_fixed(group_order_fixed):
+# """List of pairwise transitions between phenotypes, for NMF"""
+# return zip(group_order_fixed[:-1], group_order_fixed[1:])
+
+
+# @pytest.fixture(scope='module', params=['phenotype', 'group'])
+# def metadata_phenotype_col(request):
+# """Which column in the metadata specifies the phenotype"""
+# return request.param
+
+
+@pytest.fixture(scope='module')
+def groupby(groups, samples):
+ return dict((sample, np.random.choice(groups)) for sample in samples)
+
+
+@pytest.fixture(scope='module')
+def metadata_data(groupby, samples, n_samples):
+ df = pd.DataFrame(index=samples)
+ df['phenotype'] = df.index.map(lambda x: groupby[x])
+ df['subset1'] = np.random.choice([True, False], size=n_samples)
+ return df
+
+#
+# @pytest.fixture(scope='module')
+# def metadata_data_groups_fixed(groupby_fixed, outliers, pooled, samples,
+# n_samples,
+# metadata_phenotype_col):
+# df = pd.DataFrame(index=samples)
+# if outliers is not None:
+# df['outlier'] = df.index.isin(outliers)
+# if pooled is not None:
+# df['pooled'] = df.index.isin(pooled)
+# df[metadata_phenotype_col] = df.index.map(lambda x: groupby_fixed[x])
+# df['subset1'] = np.random.choice([True, False], size=n_samples)
+# return df
+
+
+@pytest.fixture(scope='module')
+def metadata_kws(group_order, group_to_color,
+ group_to_marker):
+ kws = {}
+ # if metadata_phenotype_col != 'phenotype':
+ # kws['phenotype_col'] = metadata_phenotype_col
+ kws['phenotype_order'] = group_order
+ kws['phenotype_to_color'] = group_to_color
+ kws['phenotype_to_marker'] = group_to_marker
+ return kws
+
+#
+# @pytest.fixture(scope='module')
+# def metadata_kws_fixed(metadata_phenotype_col, group_order_fixed,
+# group_to_color_fixed):
+# kws = {}
+# if metadata_phenotype_col != 'phenotype':
+# kws['phenotype_col'] = metadata_phenotype_col
+# kws['phenotype_order'] = group_order_fixed
+# kws['phenotype_to_color'] = group_to_color_fixed
+# kws['phenotype_to_marker'] = defaultdict(lambda: 'o')
+# return kws
+
+
+@pytest.fixture(scope='module')
+def mapping_stats_number_mapped_col():
+ return 'mapped_reads'
+
+
+@pytest.fixture(scope='module')
+def mapping_stats_min_reads_default():
+ return 5e5
+
+
+@pytest.fixture(scope='module')
+def mapping_stats_kws(mapping_stats_number_mapped_col):
+ kws = {'number_mapped_col': mapping_stats_number_mapped_col}
+ # if request.param is not None:
+ # kws['min_reads'] = 1e6
+ return kws
+
+
+@pytest.fixture(scope='module')
+def mapping_stats_data(samples, technical_outliers,
+ mapping_stats_min_reads_default,
+ mapping_stats_number_mapped_col):
+ df = pd.DataFrame(index=samples)
+ df[mapping_stats_number_mapped_col] = 2 * mapping_stats_min_reads_default
+ if technical_outliers is not None:
+ df.ix[technical_outliers, mapping_stats_number_mapped_col] = \
+ .5 * mapping_stats_min_reads_default
+ return df
+
+
+@pytest.fixture(scope='module')
+def n_genes():
+ return 50
+
+
+@pytest.fixture(scope='module')
+def genes(n_genes):
+ return ['gene_{}'.format(i + 1) for i in np.arange(n_genes)]
+
+
+@pytest.fixture(scope='module')
+def n_events():
+ return 100
+
+
+@pytest.fixture(scope='module')
+def events(n_events):
+ return ['event_{}'.format(i + 1) for i in np.arange(n_events)]
+
+
+@pytest.fixture(scope='module')
+def modality_models():
+ parameter = 20.
+ rv_included = stats.beta(parameter, 1)
+ rv_excluded = stats.beta(1, parameter)
+ rv_middle = stats.beta(parameter, parameter)
+ rv_uniform = stats.uniform(0, 1)
+ rv_bimodal = stats.beta(1. / parameter, 1. / parameter)
+
+ models = {'included': rv_included,
+ 'excluded': rv_excluded,
+ 'middle': rv_middle,
+ 'uniform': rv_uniform,
+ 'bimodal': rv_bimodal}
+ return models
+
+
+@pytest.fixture(scope='module', params=[0., 1.], ids=['na_thresh0',
+ 'na_thresh1'])
+def na_thresh(request):
+ return request.param
+
+
+@pytest.fixture(scope='module')
+def gene_name():
+ return 'gene_name'
+
+
+@pytest.fixture(scope='module')
+def event_name():
+ return 'event_name'
+
+
+@pytest.fixture(scope='module')
+def gene_categories():
+ return list('ABCDE')
+
+
+@pytest.fixture(scope='module')
+def boolean_gene_categories():
+ return list('WXYZ')
+
+
+# @pytest.fixture(scope='module', params=[False, True])
+# def pooled(request):
+# return request.param
+#
+# @pytest.fixture(scope='module', params=[False, True])
+# def outlier(request):
+# return request.param
+
+@pytest.fixture(scope='module', params=[False, True],
+ ids=['renamed', 'not_renamed'])
+def renamed(request):
+ return request.param
+
+
+@pytest.fixture(scope='module')
+def expression_data(samples, genes, groupby, na_thresh):
+ df = pd.DataFrame(index=samples, columns=genes)
+
+ def dataframe_maker(df):
+ data = np.vstack([
+ np.random.lognormal(np.random.uniform(0, 5),
+ np.random.uniform(0, 2),
+ df.shape[0]) for _ in df.columns]).T
+ return pd.DataFrame(data, index=df.index, columns=df.columns)
+
+ df = pd.concat([dataframe_maker(d) for name, d in
+ df.groupby(groupby)], axis=0).sort_index()
+ if na_thresh > 0:
+ df = df.apply(lambda x: x.map(
+ lambda i: i if np.random.uniform() > np.random.uniform(0,
+ na_thresh)
+ else np.nan), axis=1)
+ return df
+
+
+@pytest.fixture(scope='module')
+def expression_data_no_na(samples, genes, groupby):
+ df = pd.DataFrame(index=samples, columns=genes)
+
+ def dataframe_maker(df):
+ data = np.vstack([
+ np.random.lognormal(np.random.uniform(0, 5),
+ np.random.uniform(0, 2),
+ df.shape[0]) for _ in df.columns]).T
+ return pd.DataFrame(data, index=df.index, columns=df.columns)
+
+ df = pd.concat([dataframe_maker(d) for name, d in
+ df.groupby(groupby)], axis=0).sort_index()
+ return df
+
+
+@pytest.fixture(scope='module')
+def expression_feature_data(genes, gene_categories,
+ boolean_gene_categories, renamed):
+ df = pd.DataFrame(index=genes)
+ if renamed:
+ df['renamed'] = df.index.map(lambda x: x.replace('gene', 'renamed'))
+ df['gene_category'] = df.index.map(lambda x:
+ np.random.choice(gene_categories))
+ for category in boolean_gene_categories:
+ p = np.random.uniform()
+ df[category] = np.random.choice([True, False], size=df.shape[0],
+ p=[p, 1 - p])
+ return df
+
+
+@pytest.fixture(scope='module')
+def expression_feature_rename_col(renamed):
+ if renamed:
+ return 'renamed'
+ else:
+ return None
+
+
+@pytest.fixture(scope='module')
+def expression_log_base():
+ return 2
+
+
+@pytest.fixture(scope='module')
+def expression_plus_one():
+ return True
+
+
+@pytest.fixture(scope='module')
+def expression_thresh(request):
+ return 2
+
+
+@pytest.fixture(scope='module')
+def expression_kws(expression_feature_data, expression_feature_rename_col,
+ expression_log_base, expression_plus_one,
+ expression_thresh):
+ kws = {}
+ kws['feature_data'] = expression_feature_data
+ kws['feature_rename_col'] = expression_feature_rename_col
+ kws['log_base'] = expression_log_base
+ kws['plus_one'] = expression_plus_one
+ kws['thresh'] = expression_thresh
+ return kws
+
+
+@pytest.fixture(scope='module')
+def true_modalities(events, modality_models, groups):
+ data = dict((e, dict((g, (np.random.choice(modality_models.keys())))
+ for g in groups)) for e in events)
+ return pd.DataFrame(data)
+
+
+# @pytest.fixture(scope='module')
+# def true_modalities_fixed(events, modality_models, groups_fixed):
+# data = dict((e, dict((g, (np.random.choice(modality_models.keys())))
+# for g in groups_fixed)) for e in events)
+# return pd.DataFrame(data)
+
+
+@pytest.fixture(scope='module')
+def splicing_data(samples, events, true_modalities, modality_models, groupby):
+ df = pd.DataFrame(index=samples, columns=events)
+
+ def dataframe_maker(group, true_modalities, modality_models, df):
+ data = np.vstack([modality_models[modality].rvs(df.shape[0])
+ for modality in true_modalities.ix[group]]).T
+ return pd.DataFrame(data, index=df.index, columns=df.columns)
+
+ df = pd.concat([dataframe_maker(group, true_modalities, modality_models,
+ d)
+ for group, d in df.groupby(groupby)], axis=0)
+ # randomly add NA since all splicing data has NAs
+ na_thresh = 0.2
+ df = df.apply(lambda x: x.map(
+ lambda i: i if np.random.uniform() > np.random.uniform(0, na_thresh)
+ else np.nan), axis=1)
+
+ def randomly_add_na(x, na_thresh):
+ if np.random.uniform() > np.random.uniform(0, na_thresh / 10):
+ return x
+ else:
+ return pd.Series(np.nan, index=x.index)
+
+ df = pd.concat([d.apply(randomly_add_na, na_thresh=na_thresh,
+ axis=1)
+ for group, d in
+ df.groupby(groupby)], axis=0)
+ return df.sort_index()
+
+
+# @pytest.fixture(scope='module')
+# def splicing_data_fixed(samples, events, true_modalities_fixed,
+# modality_models,
+# groupby_fixed):
+# df = pd.DataFrame(index=samples, columns=events)
+#
+# def dataframe_maker(group, true_modalities, modality_models, df):
+# data = np.vstack([modality_models[modality].rvs(df.shape[0])
+# for modality in true_modalities.ix[group]]).T
+# return pd.DataFrame(data, index=df.index, columns=df.columns)
+#
+# df = pd.concat([dataframe_maker(group, true_modalities_fixed,
+# modality_models, df)
+# for group, df in df.groupby(groupby_fixed)], axis=0)
+# df = df.apply(lambda x: x.map(
+# lambda i: i if np.random.uniform() > np.random.uniform()
+# else np.nan), axis=1)
+#
+# def randomly_add_na(x):
+# if np.random.uniform() > np.random.uniform(0, .1):
+# return x
+# else:
+# return pd.Series(np.nan, index=x.index)
+#
+# df = pd.concat([d.apply(randomly_add_na, axis=1)
+# for group, d in
+# df.groupby(groupby_fixed)], axis=0)
+# return df.sort_index()
+
+#
+# @pytest.fixture(scope='module')
+# def splicing_data_no_na(samples, events,
+# true_modalities, modality_models, groupby):
+# df = pd.DataFrame(index=samples, columns=events)
+#
+# def dataframe_maker(group, true_modalities, modality_models, df):
+# data = np.vstack([modality_models[modality].rvs(df.shape[0])
+# for modality in true_modalities.ix[group]]).T
+# return pd.DataFrame(data, index=df.index, columns=df.columns)
+#
+# df = pd.concat([dataframe_maker(group, true_modalities,
+# modality_models, df)
+# for group, df in df.groupby(groupby)], axis=0)
+# return df.sort_index()
+
+
+@pytest.fixture(scope='module')
+def splicing_feature_data(events, genes, gene_name, expression_feature_data,
+ splicing_feature_common_id):
+ df = pd.DataFrame(index=events)
+ df[gene_name] = df.index.map(lambda x: np.random.choice(genes))
+ df = df.join(expression_feature_data, on=splicing_feature_common_id)
+ return df
+
+
+@pytest.fixture(scope='module')
+def splicing_feature_common_id(gene_name):
+ return gene_name
+
+
+@pytest.fixture(scope='module')
+def splicing_kws(splicing_feature_data, splicing_feature_common_id,
+ gene_name):
+ return {'feature_data': splicing_feature_data,
+ 'feature_rename_col': gene_name,
+ 'feature_expression_id_col': splicing_feature_common_id}
+
+
+@pytest.fixture(scope='module')
+def genelist_path(data_dir):
+ return '{}/example_gene_list.txt'.format(data_dir)
+
+
+@pytest.fixture(scope='module')
+def genelist_dropbox_link():
+ return 'https://www.dropbox.com/s/652y6hb8zonxe4c/example_gene_list.txt' \
+ '?dl=0'
+
+
+@pytest.fixture(params=['local', 'dropbox'])
+def genelist_link(request, genelist_path, genelist_dropbox_link):
+ if request.param == 'local':
+ return genelist_path
+ elif request.param == 'dropbox':
+ return genelist_dropbox_link
+
+
+# @pytest.fixture(params=[None, 'gene_category: A',
+# 'link',
+# 'path'], scope='module')
+# def feature_subset(request, genelist_dropbox_link, genelist_path):
+# from flotilla.util import link_to_list
+#
+# name_to_location = {'link': genelist_dropbox_link,
+# 'path': genelist_path}
+#
+# if request.param is None:
+# return request.param
+# elif request.param in ('link', 'path'):
+#
+# try:
+# return link_to_list(name_to_location[request.param])
+# except subprocess.CalledProcessError:
+# # Downloading the dropbox link failed, aka not connected to the
+# # internet, so just test "None" again
+# return None
+# else:
+# # Otherwise, this is a name of a subset
+# return request.param
+
+
+@pytest.fixture(scope='module')
+def x_norm():
+ """Normally distributed numpy array"""
+ n_samples = 20
+ n_features = 50
+ x = np.random.randn(n_samples * n_features)
+ x = x.reshape(n_samples, n_features)
+ return x
+
+
+@pytest.fixture(scope='module')
+def df_norm(x_norm):
+ """Normally distributed pandas dataframe"""
+ nrow, ncol = x_norm.shape
+ index = ['sample_{0:02d}'.format(i) for i in range(nrow)]
+ columns = ['feature_{0:04d}'.format(i) for i in range(ncol)]
+ df = pd.DataFrame(x_norm, index=index, columns=columns)
+ return df
+
+
+@pytest.fixture(scope='module')
+def df_nonneg(df_norm):
+ """Non-negative data for testing NMF"""
+ return df_norm.abs()
+
+
+@pytest.fixture(scope='module', params=[0, 5])
+def metadata_minimum_samples(request):
+ return request.param
+
+
+@pytest.fixture(params=[True, False])
+def featurewise(request):
+ return request.param
+
+
+@pytest.fixture(scope='module')
+def base_data(expression_data):
+ from flotilla.data_model.base import BaseData
+
+ return BaseData(expression_data)
+
+
+@pytest.fixture(params=[None, 'half'], scope='module')
+def sample_ids(request, base_data):
+ if request.param is None:
+ return request.param
+ elif request.param == 'some':
+ half = base_data.data.shape[0] / 2
+ return base_data.data.index[:half]
+ elif request.param == 'all':
+ return base_data.data.index
+
+
+@pytest.fixture(params=[None, 'half', "all"], scope='module')
+def feature_ids(request, base_data):
+ if request.param is None:
+ return request.param
+ elif request.param == 'some':
+ half = base_data.data.shape[1] / 2
+ return base_data.data.columns[:half]
+ elif request.param == 'all':
+ return base_data.data.columns
+
+
+@pytest.fixture(params=[True, False], scope='module')
+def standardize(request):
+ return request.param
+
+
+@pytest.fixture(params=['subset1', None,
+ 'phenotype: group1',
+ '~subset1', 'ids'],
+ scope='module')
+def sample_subset(request, samples):
+ if request.param == 'ids':
+ return samples[:10]
+ else:
+ return request.param
+
+
+@pytest.fixture(
+ params=[None, 'all', 'gene_category: A', 'W', pytest.mark.xfail('asdf')],
+ ids=['none', 'all_features', 'categorical_gene_category',
+ 'boolean_gene_category', 'nonexistent_gene_category'],
+ scope='module')
+def feature_subset(request):
+ return request.param
+
+
+@pytest.fixture(scope='module')
+def splicing(splicing_data):
+ from flotilla.data_model.splicing import SplicingData
+
+ return SplicingData(splicing_data)
+
+
+@pytest.fixture(scope='module')
+def gene_ontology_data_path(data_dir):
+ return '{}/human_grch38_chr22_gene_ontology.txt'.format(data_dir)
+
+
+@pytest.fixture(scope='module')
+def gene_ontology_data(gene_ontology_data_path):
+ return pd.read_table(gene_ontology_data_path)
diff --git a/flotilla/test/data_model/test_basedata.py b/flotilla/test/data_model/test_basedata.py
new file mode 100644
index 00000000..0f829d37
--- /dev/null
+++ b/flotilla/test/data_model/test_basedata.py
@@ -0,0 +1,301 @@
+import numpy as np
+import numpy.testing as npt
+import pandas as pd
+import pandas.util.testing as pdt
+from sklearn.preprocessing import StandardScaler
+import pytest
+
+# @pytest.fixture(params=['expression', 'splicing'])
+# def data_type(request):
+# return request.param
+#
+
+# @pytest.fixture
+# def data(data_type, expression_data, splicing_data):
+# if data_type == 'expression':
+# return expression_data
+# elif data_type == 'splicing':
+# return splicing_data
+#
+# @pytest.fixture
+# def feature_data(data_type, expression_feature_data, splicing_feature_data):
+# if data_type == 'expression':
+# return expression_feature_data
+# elif data_type == 'splicing':
+# return splicing_feature_data
+#
+# @pytest.fixture
+# def thresh(data_type, expression_thresh):
+# if data_type == 'expression':
+# return expression_thresh
+# else:
+# return -np.inf
+#
+# @pytest.fixture
+# def feature_rename_col(data_type, )
+
+
+class TestBaseData:
+ def test__init(self, expression_data_no_na, outliers):
+ from flotilla.data_model.base import BaseData
+ from flotilla.compute.predict import PredictorConfigManager, \
+ PredictorDataSetManager
+
+ base_data = BaseData(expression_data_no_na, outliers=outliers)
+ outlier_samples = outliers.copy() if outliers is not None else []
+ outliers_df = expression_data_no_na.ix[outlier_samples]
+
+ feature_renamer_series = pd.Series(expression_data_no_na.columns,
+ index=expression_data_no_na.columns)
+
+ pdt.assert_frame_equal(base_data.data_original, expression_data_no_na)
+ pdt.assert_equal(base_data.feature_data, None)
+ pdt.assert_frame_equal(base_data.data, expression_data_no_na)
+ pdt.assert_series_equal(base_data.feature_renamer_series,
+ feature_renamer_series)
+ pdt.assert_frame_equal(base_data.outliers, outliers_df)
+ pdt.assert_array_equal(base_data.outlier_samples, outlier_samples)
+ assert isinstance(base_data.predictor_config_manager,
+ PredictorConfigManager)
+ assert isinstance(base_data.predictor_dataset_manager,
+ PredictorDataSetManager)
+
+ def test_feature_renamer_series_change_col(self, expression_data_no_na,
+ expression_feature_data,
+ expression_feature_rename_col,
+ n_genes):
+ from flotilla.data_model.base import BaseData
+
+ expression_feature_data = expression_feature_data.copy()
+ gene_numbers = np.arange(n_genes)
+ new_renamer = 'new_renamer'
+ expression_feature_data[new_renamer] = \
+ expression_feature_data.index.map(
+ lambda x: 'new_renamed{}'.format(
+ np.random.choice(gene_numbers)))
+
+ base_data = BaseData(expression_data_no_na,
+ feature_data=expression_feature_data,
+ feature_rename_col=expression_feature_rename_col)
+ base_data.feature_rename_col = new_renamer
+ pdt.assert_series_equal(base_data.feature_renamer_series,
+ expression_feature_data[new_renamer])
+
+ def test__init_technical_outliers(self, expression_data_no_na,
+ technical_outliers):
+ from flotilla.data_model.base import BaseData
+
+ base_data = BaseData(expression_data_no_na,
+ technical_outliers=technical_outliers)
+
+ data = expression_data_no_na.copy()
+ if technical_outliers is not None:
+ good_samples = ~data.index.isin(technical_outliers)
+ data = data.ix[good_samples]
+ pdt.assert_frame_equal(base_data.data, data)
+ pdt.assert_frame_equal(base_data.data_original,
+ expression_data_no_na)
+
+ def test__init_sample_thresholds(self, expression_data,
+ expression_thresh,
+ metadata_minimum_samples,
+ pooled):
+ from flotilla.data_model.base import BaseData
+
+ base_data = BaseData(expression_data,
+ thresh=expression_thresh,
+ minimum_samples=metadata_minimum_samples,
+ pooled=pooled)
+ data = expression_data.copy()
+ pooled_samples = pooled.copy() if pooled is not None else []
+ single_samples = data.index[~data.index.isin(pooled_samples)]
+ singles_df = data.ix[single_samples]
+
+ if expression_thresh > -np.inf or metadata_minimum_samples > 0:
+ if not singles_df.empty:
+ data = base_data._threshold(data, singles_df)
+ else:
+ data = base_data._threshold(data)
+
+ singles_df = data.ix[single_samples]
+ pooled_df = data.ix[pooled_samples]
+
+ pdt.assert_frame_equal(base_data.data_original, expression_data)
+ pdt.assert_frame_equal(base_data.data, data)
+ pdt.assert_equal(base_data.thresh, expression_thresh)
+ pdt.assert_equal(base_data.minimum_samples, metadata_minimum_samples)
+ pdt.assert_frame_equal(base_data.pooled, pooled_df)
+ pdt.assert_frame_equal(base_data.singles, singles_df)
+
+ def test__init__featuredata(self, expression_data_no_na,
+ expression_feature_data,
+ expression_feature_rename_col):
+ from flotilla.data_model.base import BaseData, \
+ subsets_from_metadata, MINIMUM_FEATURE_SUBSET
+
+ base_data = BaseData(expression_data_no_na,
+ feature_data=expression_feature_data,
+ feature_rename_col=expression_feature_rename_col)
+
+ if expression_feature_rename_col is not None:
+ feature_renamer_series = expression_feature_data[
+ expression_feature_rename_col]
+ else:
+ feature_renamer_series = pd.Series(
+ expression_feature_data.index,
+ index=expression_feature_data.index)
+ feature_subsets = subsets_from_metadata(expression_feature_data,
+ MINIMUM_FEATURE_SUBSET,
+ 'features')
+ feature_subsets['variant'] = base_data.variant
+
+ pdt.assert_frame_equal(base_data.data_original, expression_data_no_na)
+ pdt.assert_frame_equal(base_data.feature_data, expression_feature_data)
+ pdt.assert_frame_equal(base_data.data, expression_data_no_na)
+ pdt.assert_series_equal(base_data.feature_renamer_series,
+ feature_renamer_series)
+ pdt.assert_dict_equal(base_data.feature_subsets, feature_subsets)
+
+ @pytest.mark.xfail
+ def test__init_multiindex(self, df_norm):
+ from flotilla.data_model.base import BaseData
+
+ data = df_norm.copy()
+ level1 = data.columns.map(lambda x: 'level1_{}'.format(x))
+ data.columns = pd.MultiIndex.from_arrays([data.columns, level1])
+ BaseData(data)
+
+ def test__variant(self, expression_data):
+ from flotilla.data_model.base import BaseData
+
+ base_data = BaseData(expression_data)
+
+ var = expression_data.var()
+ var_cut = var.mean() + 2 * var.std()
+ variant = expression_data.columns[var > var_cut]
+
+ pdt.assert_equal(base_data._var_cut, var_cut)
+ pdt.assert_array_equal(base_data.variant, variant)
+
+ def test__subset(self, expression_data_no_na, sample_ids, feature_ids):
+ from flotilla.data_model.base import BaseData
+
+ base_data = BaseData(expression_data_no_na)
+ subset = base_data._subset(base_data.data, sample_ids=sample_ids,
+ feature_ids=feature_ids)
+ data = base_data.data
+ if feature_ids is None:
+ feature_ids = data.columns
+ else:
+ feature_ids = pd.Index(set(feature_ids).intersection(data.columns))
+ if sample_ids is None:
+ sample_ids = data.index
+ else:
+ sample_ids = pd.Index(set(sample_ids).intersection(data.index))
+
+ true_subset = data.ix[sample_ids, feature_ids]
+
+ pdt.assert_frame_equal(subset, true_subset)
+
+ def test__subset_and_standardize(self, expression_data_no_na,
+ standardize, feature_ids,
+ sample_ids):
+ from flotilla.data_model.base import BaseData
+
+ base_data = BaseData(expression_data_no_na)
+ base_data.subset, base_data.means = \
+ base_data._subset_and_standardize(base_data.data,
+ sample_ids=sample_ids,
+ feature_ids=feature_ids,
+ return_means=True,
+ standardize=standardize)
+
+ subset = base_data._subset(base_data.data, sample_ids=sample_ids,
+ feature_ids=feature_ids)
+ means = subset.mean().rename_axis(base_data.feature_renamer)
+ subset = subset.fillna(means).fillna(0)
+ subset = subset.rename_axis(base_data.feature_renamer, 1)
+
+ if standardize:
+ data = StandardScaler().fit_transform(subset)
+ else:
+ data = subset
+
+ subset_standardized = pd.DataFrame(data, index=subset.index,
+ columns=subset.columns)
+
+ pdt.assert_frame_equal(subset_standardized, base_data.subset)
+ pdt.assert_series_equal(means, base_data.means)
+
+ def test__threshold(self, expression_data_no_na, pooled):
+ from flotilla.data_model.base import BaseData
+
+ thresh = 0.5
+ minimum_samples = 5
+ base_data = BaseData(expression_data_no_na, thresh=thresh,
+ minimum_samples=minimum_samples, pooled=pooled)
+ data = expression_data_no_na.copy()
+ if pooled is not None:
+ other = base_data.singles
+ else:
+ other = data
+
+ filtered = data.ix[:, other[other > thresh].count() >= minimum_samples]
+ pdt.assert_frame_equal(base_data.data, filtered)
+
+ def test_reduce(self, expression_data_no_na, featurewise):
+ # TODO: parameterize and test with featurewise and subsets
+ from flotilla.compute.decomposition import DataFramePCA
+ from flotilla.data_model.base import BaseData
+
+ expression = BaseData(expression_data_no_na)
+ test_reduced = expression.reduce(featurewise=featurewise)
+
+ subset, means = expression._subset_and_standardize(
+ expression.data, return_means=True, standardize=True)
+
+ if featurewise:
+ subset = subset.T
+
+ true_reduced = DataFramePCA(subset)
+ true_reduced.means = means
+
+ pdt.assert_frame_equal(test_reduced.X, subset)
+ npt.assert_array_equal(test_reduced.reduced_space,
+ true_reduced.reduced_space)
+ pdt.assert_series_equal(test_reduced.means,
+ true_reduced.means)
+
+ def test_feature_subset_to_feature_ids(self, expression_data_no_na,
+ expression_feature_data,
+ feature_subset):
+ from flotilla.data_model.base import BaseData
+
+ expression = BaseData(expression_data_no_na,
+ feature_data=expression_feature_data)
+ test_feature_ids = expression.feature_subset_to_feature_ids(
+ feature_subset, rename=False)
+
+ true_feature_ids = pd.Index([])
+ if feature_subset is not None:
+ try:
+ if feature_subset in expression.feature_subsets:
+ true_feature_ids = expression.feature_subsets[
+ feature_subset]
+ elif feature_subset.startswith('all'):
+ true_feature_ids = expression.data.columns
+ except TypeError:
+ if not isinstance(feature_subset, str):
+ feature_ids = feature_subset
+ n_custom = expression.feature_data.columns.map(
+ lambda x: x.startswith('custom')).sum()
+ ind = 'custom_{}'.format(n_custom + 1)
+ expression.feature_data[ind] = \
+ expression.feature_data.index.isin(feature_ids)
+ else:
+ raise ValueError(
+ "There are no {} features in this data: "
+ "{}".format(feature_subset, self))
+ else:
+ true_feature_ids = expression.data.columns
+ pdt.assert_array_equal(test_feature_ids, true_feature_ids)
diff --git a/flotilla/test/data_model/test_expressiondata.py b/flotilla/test/data_model/test_expressiondata.py
new file mode 100644
index 00000000..ed907cd2
--- /dev/null
+++ b/flotilla/test/data_model/test_expressiondata.py
@@ -0,0 +1,30 @@
+import numpy as np
+import pandas.util.testing as pdt
+
+
+class TestExpressionData:
+ def test_init(self, expression_data_no_na,
+ expression_log_base,
+ expression_plus_one, expression_thresh):
+ from flotilla.data_model import ExpressionData
+
+ expression = ExpressionData(expression_data_no_na.copy(),
+ log_base=expression_log_base,
+ plus_one=expression_plus_one,
+ thresh=expression_thresh)
+ data = expression_data_no_na.copy()
+ thresh = float(expression_thresh)
+
+ if expression_plus_one:
+ data += 1
+ thresh += 1
+
+ if expression_log_base is not None:
+ data = np.divide(np.log(data), np.log(expression_log_base))
+
+ pdt.assert_equal(expression.plus_one, expression_plus_one)
+ pdt.assert_equal(expression.log_base, expression_log_base)
+ pdt.assert_equal(expression.thresh, thresh)
+ pdt.assert_frame_equal(expression.data_original,
+ expression_data_no_na)
+ pdt.assert_frame_equal(expression.data, data)
diff --git a/flotilla/test/data_model/test_geneontologydata.py b/flotilla/test/data_model/test_geneontologydata.py
new file mode 100644
index 00000000..943bfe8b
--- /dev/null
+++ b/flotilla/test/data_model/test_geneontologydata.py
@@ -0,0 +1,356 @@
+from collections import defaultdict
+
+import pandas as pd
+import pandas.util.testing as pdt
+import pytest
+from scipy.stats import hypergeom
+
+
+class TestGeneOntologyData(object):
+
+ @pytest.fixture
+ def gene_ontology(self, gene_ontology_data):
+ from flotilla import GeneOntologyData
+
+ return GeneOntologyData(gene_ontology_data)
+
+ def test_init(self, gene_ontology_data, gene_ontology):
+ true_data = gene_ontology_data.dropna()
+ true_all_genes = true_data['Ensembl Gene ID'].unique()
+ true_ontology = defaultdict(dict)
+
+ for go, df in true_data.groupby('GO Term Accession'):
+ true_ontology[go]['genes'] = set(df['Ensembl Gene ID'])
+ true_ontology[go]['name'] = df['GO Term Name'].values[0]
+ true_ontology[go]['domain'] = df['GO domain'].values[0]
+ true_ontology[go]['n_genes'] = len(true_ontology[go]['genes'])
+
+ pdt.assert_frame_equal(true_data, gene_ontology.data)
+ pdt.assert_array_equal(sorted(true_all_genes),
+ sorted(gene_ontology.all_genes))
+
+ pdt.assert_contains_all(true_ontology.keys(), gene_ontology.ontology)
+ pdt.assert_contains_all(gene_ontology.ontology.keys(), true_ontology)
+
+ for go, true_attributes in true_ontology.items():
+ test_attributes = gene_ontology.ontology[go]
+ true_genes = sorted(true_attributes['genes'])
+ test_genes = sorted(test_attributes['genes'])
+ pdt.assert_array_equal(true_genes, test_genes)
+ pdt.assert_equal(true_attributes['name'], test_attributes['name'])
+ pdt.assert_equal(true_attributes['domain'],
+ test_attributes['domain'])
+ pdt.assert_equal(true_attributes['n_genes'],
+ test_attributes['n_genes'])
+
+ def test_enrichment(self, gene_ontology):
+ features_of_interest = gene_ontology.all_genes[:10]
+ test_enrichment_df = gene_ontology.enrichment(features_of_interest)
+
+ p_value_cutoff = 1000000
+ min_feature_size = 3
+ min_background_size = 5
+ cross_reference = {}
+ domains = gene_ontology.domains
+ background = gene_ontology.all_genes
+ n_all_genes = len(background)
+ n_features_of_interest = len(features_of_interest)
+ enrichment = defaultdict(dict)
+
+ for go_term, go_genes in gene_ontology.ontology.items():
+ if go_genes['domain'] not in domains:
+ continue
+
+ features_in_go = go_genes['genes'].intersection(
+ features_of_interest)
+ background_in_go = go_genes['genes'].intersection(background)
+ too_few_features = len(features_in_go) < min_feature_size
+ too_few_background = len(background_in_go) < min_background_size
+ if too_few_features or too_few_background:
+ continue
+
+ # Survival function is more accurate on small p-values
+ p_value = hypergeom.sf(len(features_in_go), n_all_genes,
+ len(background_in_go),
+ n_features_of_interest)
+ p_value = 0 if p_value < 0 else p_value
+ symbols = [cross_reference[f] if f in cross_reference else f for f
+ in features_in_go]
+ enrichment['p_value'][go_term] = p_value
+ enrichment['n_features_of_interest_in_go_term'][go_term] = len(
+ features_in_go)
+ enrichment['n_background_in_go_term'][go_term] = len(
+ background_in_go)
+ enrichment['n_features_total_in_go_term'][go_term] = len(
+ go_genes['genes'])
+ enrichment['features_of_interest_in_go_term'][
+ go_term] = ','.join(features_in_go)
+ enrichment['features_of_interest_in_go_term_gene_symbols'][
+ go_term] = ','.join(symbols)
+ enrichment['go_domain'][go_term] = go_genes['domain']
+ enrichment['go_name'][go_term] = go_genes['name']
+ enrichment_df = pd.DataFrame(enrichment)
+
+ # Bonferonni correction
+ enrichment_df['bonferonni_corrected_p_value'] = \
+ enrichment_df.p_value * enrichment_df.shape[0]
+ ind = enrichment_df['bonferonni_corrected_p_value'] < p_value_cutoff
+ enrichment_df = enrichment_df.ix[ind]
+ true_enrichment_df = enrichment_df.sort(columns=['p_value'])
+
+ pdt.assert_frame_equal(test_enrichment_df, true_enrichment_df)
+
+ @pytest.mark.xfail
+ def test_invalid_background(self, gene_ontology):
+ features_of_interest = gene_ontology.all_genes[:10]
+ background = [f + '_asdf' for f in features_of_interest]
+ gene_ontology.enrichment(features_of_interest,
+ background=background)
+
+ @pytest.mark.xfail
+ def test_invalid_features(self, gene_ontology):
+ features_of_interest = gene_ontology.all_genes[:10]
+ features_of_interest = [f + '_asdf' for f in features_of_interest]
+ background = [f + '_asdf' for f in gene_ontology.all_genes[:20]]
+ gene_ontology.enrichment(features_of_interest,
+ background=background)
+
+ @pytest.mark.xfail
+ def test_invalid_domain_str(self, gene_ontology):
+ features_of_interest = gene_ontology.all_genes[:10]
+ gene_ontology.enrichment(features_of_interest,
+ domain='fake_domain')
+
+ @pytest.mark.xfail
+ def test_invalid_domain_iterable(self, gene_ontology):
+ features_of_interest = gene_ontology.all_genes[:10]
+ gene_ontology.enrichment(features_of_interest,
+ domain=['fake_domain1', 'fake_domain2'])
+
+ def test_custom_domain_str(self, gene_ontology):
+ features_of_interest = gene_ontology.all_genes[:10]
+
+ domain = 'cellular_component'
+
+ test_enrichment_df = gene_ontology.enrichment(features_of_interest,
+ domain=domain)
+ domains = frozenset([domain])
+ p_value_cutoff = 1000000
+ min_feature_size = 3
+ min_background_size = 5
+ cross_reference = {}
+ background = gene_ontology.all_genes
+ n_all_genes = len(background)
+ n_features_of_interest = len(features_of_interest)
+ enrichment = defaultdict(dict)
+
+ for go_term, go_genes in gene_ontology.ontology.items():
+ if go_genes['domain'] not in domains:
+ continue
+
+ features_in_go = go_genes['genes'].intersection(
+ features_of_interest)
+ background_in_go = go_genes['genes'].intersection(background)
+ too_few_features = len(features_in_go) < min_feature_size
+ too_few_background = len(background_in_go) < min_background_size
+ if too_few_features or too_few_background:
+ continue
+
+ # Survival function is more accurate on small p-values
+ p_value = hypergeom.sf(len(features_in_go), n_all_genes,
+ len(background_in_go),
+ n_features_of_interest)
+ p_value = 0 if p_value < 0 else p_value
+ symbols = [cross_reference[f] if f in cross_reference else f for f
+ in features_in_go]
+ enrichment['p_value'][go_term] = p_value
+ enrichment['n_features_of_interest_in_go_term'][go_term] = len(
+ features_in_go)
+ enrichment['n_background_in_go_term'][go_term] = len(
+ background_in_go)
+ enrichment['n_features_total_in_go_term'][go_term] = len(
+ go_genes['genes'])
+ enrichment['features_of_interest_in_go_term'][
+ go_term] = ','.join(features_in_go)
+ enrichment['features_of_interest_in_go_term_gene_symbols'][
+ go_term] = ','.join(symbols)
+ enrichment['go_domain'][go_term] = go_genes['domain']
+ enrichment['go_name'][go_term] = go_genes['name']
+ enrichment_df = pd.DataFrame(enrichment)
+
+ # Bonferonni correction
+ enrichment_df['bonferonni_corrected_p_value'] = \
+ enrichment_df.p_value * enrichment_df.shape[0]
+ ind = enrichment_df['bonferonni_corrected_p_value'] < p_value_cutoff
+ enrichment_df = enrichment_df.ix[ind]
+ true_enrichment_df = enrichment_df.sort(columns=['p_value'])
+
+ pdt.assert_frame_equal(test_enrichment_df, true_enrichment_df)
+
+ def test_custom_domain_iterable(self, gene_ontology):
+ features_of_interest = gene_ontology.all_genes[:10]
+
+ domain = ['cellular_component', 'molecular_function']
+
+ test_enrichment_df = gene_ontology.enrichment(features_of_interest,
+ domain=domain)
+
+ domains = frozenset(domain)
+ p_value_cutoff = 1000000
+ min_feature_size = 3
+ min_background_size = 5
+ cross_reference = {}
+ background = gene_ontology.all_genes
+ n_all_genes = len(background)
+ n_features_of_interest = len(features_of_interest)
+ enrichment = defaultdict(dict)
+
+ for go_term, go_genes in gene_ontology.ontology.items():
+ if go_genes['domain'] not in domains:
+ continue
+
+ features_in_go = go_genes['genes'].intersection(
+ features_of_interest)
+ background_in_go = go_genes['genes'].intersection(background)
+ too_few_features = len(features_in_go) < min_feature_size
+ too_few_background = len(background_in_go) < min_background_size
+ if too_few_features or too_few_background:
+ continue
+
+ # Survival function is more accurate on small p-values
+ p_value = hypergeom.sf(len(features_in_go), n_all_genes,
+ len(background_in_go),
+ n_features_of_interest)
+ p_value = 0 if p_value < 0 else p_value
+ symbols = [cross_reference[f] if f in cross_reference else f for f
+ in features_in_go]
+ enrichment['p_value'][go_term] = p_value
+ enrichment['n_features_of_interest_in_go_term'][go_term] = len(
+ features_in_go)
+ enrichment['n_background_in_go_term'][go_term] = len(
+ background_in_go)
+ enrichment['n_features_total_in_go_term'][go_term] = len(
+ go_genes['genes'])
+ enrichment['features_of_interest_in_go_term'][
+ go_term] = ','.join(features_in_go)
+ enrichment['features_of_interest_in_go_term_gene_symbols'][
+ go_term] = ','.join(symbols)
+ enrichment['go_domain'][go_term] = go_genes['domain']
+ enrichment['go_name'][go_term] = go_genes['name']
+ enrichment_df = pd.DataFrame(enrichment)
+
+ # Bonferonni correction
+ enrichment_df['bonferonni_corrected_p_value'] = \
+ enrichment_df.p_value * enrichment_df.shape[0]
+ ind = enrichment_df['bonferonni_corrected_p_value'] < p_value_cutoff
+ enrichment_df = enrichment_df.ix[ind]
+ true_enrichment_df = enrichment_df.sort(columns=['p_value'])
+
+ pdt.assert_frame_equal(test_enrichment_df, true_enrichment_df)
+
+ def test_too_few_features(self, gene_ontology):
+ features_of_interest = gene_ontology.all_genes[:3]
+ test_enrichment_df = gene_ontology.enrichment(features_of_interest)
+
+ domains = gene_ontology.domains
+ p_value_cutoff = 1000000
+ min_feature_size = 3
+ min_background_size = 5
+ cross_reference = {}
+ background = gene_ontology.all_genes
+ n_all_genes = len(background)
+ n_features_of_interest = len(features_of_interest)
+ enrichment = defaultdict(dict)
+
+ for go_term, go_genes in gene_ontology.ontology.items():
+ if go_genes['domain'] not in domains:
+ continue
+
+ features_in_go = go_genes['genes'].intersection(
+ features_of_interest)
+ background_in_go = go_genes['genes'].intersection(background)
+ too_few_features = len(features_in_go) < min_feature_size
+ too_few_background = len(background_in_go) < min_background_size
+ if too_few_features or too_few_background:
+ continue
+
+ # Survival function is more accurate on small p-values
+ p_value = hypergeom.sf(len(features_in_go), n_all_genes,
+ len(background_in_go),
+ n_features_of_interest)
+ p_value = 0 if p_value < 0 else p_value
+ symbols = [cross_reference[f] if f in cross_reference else f for f
+ in features_in_go]
+ enrichment['p_value'][go_term] = p_value
+ enrichment['n_features_of_interest_in_go_term'][go_term] = len(
+ features_in_go)
+ enrichment['n_background_in_go_term'][go_term] = len(
+ background_in_go)
+ enrichment['n_features_total_in_go_term'][go_term] = len(
+ go_genes['genes'])
+ enrichment['features_of_interest_in_go_term'][
+ go_term] = ','.join(features_in_go)
+ enrichment['features_of_interest_in_go_term_gene_symbols'][
+ go_term] = ','.join(symbols)
+ enrichment['go_domain'][go_term] = go_genes['domain']
+ enrichment['go_name'][go_term] = go_genes['name']
+ enrichment_df = pd.DataFrame(enrichment)
+
+ # Bonferonni correction
+ enrichment_df['bonferonni_corrected_p_value'] = \
+ enrichment_df.p_value * enrichment_df.shape[0]
+ ind = enrichment_df['bonferonni_corrected_p_value'] < p_value_cutoff
+ enrichment_df = enrichment_df.ix[ind]
+ true_enrichment_df = enrichment_df.sort(columns=['p_value'])
+
+ pdt.assert_frame_equal(test_enrichment_df, true_enrichment_df)
+
+ def test_no_enrichment(self, gene_ontology):
+ features_of_interest = gene_ontology.all_genes[:2]
+ test_enrichment_df = gene_ontology.enrichment(features_of_interest)
+
+ domains = gene_ontology.domains
+ min_feature_size = 3
+ min_background_size = 5
+ cross_reference = {}
+ background = gene_ontology.all_genes
+ n_all_genes = len(background)
+ n_features_of_interest = len(features_of_interest)
+ enrichment = defaultdict(dict)
+
+ for go_term, go_genes in gene_ontology.ontology.items():
+ if go_genes['domain'] not in domains:
+ continue
+
+ features_in_go = go_genes['genes'].intersection(
+ features_of_interest)
+ background_in_go = go_genes['genes'].intersection(background)
+ too_few_features = len(features_in_go) < min_feature_size
+ too_few_background = len(background_in_go) < min_background_size
+ if too_few_features or too_few_background:
+ continue
+
+ # Survival function is more accurate on small p-values
+ p_value = hypergeom.sf(len(features_in_go), n_all_genes,
+ len(background_in_go),
+ n_features_of_interest)
+ p_value = 0 if p_value < 0 else p_value
+ symbols = [cross_reference[f] if f in cross_reference else f for f
+ in features_in_go]
+ enrichment['p_value'][go_term] = p_value
+ enrichment['n_features_of_interest_in_go_term'][go_term] = len(
+ features_in_go)
+ enrichment['n_background_in_go_term'][go_term] = len(
+ background_in_go)
+ enrichment['n_features_total_in_go_term'][go_term] = len(
+ go_genes['genes'])
+ enrichment['features_of_interest_in_go_term'][
+ go_term] = ','.join(features_in_go)
+ enrichment['features_of_interest_in_go_term_gene_symbols'][
+ go_term] = ','.join(symbols)
+ enrichment['go_domain'][go_term] = go_genes['domain']
+ enrichment['go_name'][go_term] = go_genes['name']
+ true_enrichment_df = pd.DataFrame(enrichment)
+
+ assert true_enrichment_df.empty
+ assert test_enrichment_df is None
diff --git a/flotilla/test/data_model/test_mappingstatsdata.py b/flotilla/test/data_model/test_mappingstatsdata.py
new file mode 100644
index 00000000..c70b81ed
--- /dev/null
+++ b/flotilla/test/data_model/test_mappingstatsdata.py
@@ -0,0 +1,36 @@
+import numpy.testing as npt
+import pandas.util.testing as pdt
+import pytest
+
+
+class TestMappingStatsData(object):
+ @pytest.fixture
+ def mapping_stats(self, mapping_stats_data, mapping_stats_kws):
+ from flotilla.data_model.quality_control import MappingStatsData
+
+ return MappingStatsData(mapping_stats_data, **mapping_stats_kws)
+
+ def test__init(self, mapping_stats, mapping_stats_kws):
+ from flotilla.data_model.quality_control import MIN_READS
+
+ min_reads = mapping_stats_kws.get('min_reads', MIN_READS)
+ number_mapped_col = mapping_stats_kws.get('number_mapped_col')
+
+ npt.assert_equal(mapping_stats.min_reads, min_reads)
+ npt.assert_equal(mapping_stats.number_mapped_col, number_mapped_col)
+
+ def test_number_mapped(self, mapping_stats, mapping_stats_data,
+ mapping_stats_kws):
+ number_mapped_col = mapping_stats_kws.get('number_mapped_col')
+ number_mapped = mapping_stats_data[number_mapped_col]
+ pdt.assert_series_equal(mapping_stats.number_mapped, number_mapped)
+
+ def test_too_few_mapped(self, mapping_stats, mapping_stats_data,
+ mapping_stats_kws):
+ from flotilla.data_model.quality_control import MIN_READS
+
+ min_reads = mapping_stats_kws.get('min_reads', MIN_READS)
+ number_mapped_col = mapping_stats_kws.get('number_mapped_col')
+ number_mapped = mapping_stats_data[number_mapped_col]
+ too_few_mapped = number_mapped.index[number_mapped < min_reads]
+ pdt.assert_array_equal(mapping_stats.too_few_mapped, too_few_mapped)
diff --git a/flotilla/test/data_model/test_metadata.py b/flotilla/test/data_model/test_metadata.py
new file mode 100644
index 00000000..562ec753
--- /dev/null
+++ b/flotilla/test/data_model/test_metadata.py
@@ -0,0 +1,142 @@
+from collections import defaultdict
+
+import matplotlib as mpl
+import numpy as np
+import pandas as pd
+import pytest
+import seaborn as sns
+import pandas.util.testing as pdt
+
+
+class TestMetaData(object):
+ n = 20
+ index = ['sample_{}'.format(i + 1) for i in range(n)]
+ metadata = pd.DataFrame(index=index)
+ phenotype_col = 'phenotype'
+ metadata[phenotype_col] = np.random.choice(list('ABC'), size=20)
+ metadata['subset1'] = np.random.choice([True, False], size=20)
+
+ kws = {'minimum_sample_subset': 2,
+ 'phenotype_col': phenotype_col}
+
+ @pytest.fixture(params=[None, list('CAB')])
+ def phenotype_order(self, request):
+ return request.param
+
+ @pytest.fixture(params=[None, {'A': 'blue', 'B': 'red', 'C': 'green'},
+ {'A': '#f66ab5', 'B': '#f77189', 'C': '#6cad31'}])
+ def phenotype_to_color(self, request):
+ return request.param
+
+ @pytest.fixture(params=[None, {'A': '*', 'B': 'o', 'C': 's'}])
+ def phenotype_to_marker(self, request):
+ return request.param
+
+ def test_init(self, phenotype_order, phenotype_to_color,
+ phenotype_to_marker):
+ from flotilla.data_model.metadata import MetaData
+ from flotilla.data_model.base import subsets_from_metadata
+ from flotilla.visualize.color import str_to_color
+
+ test_metadata = MetaData(self.metadata,
+ phenotype_order=phenotype_order,
+ phenotype_to_color=phenotype_to_color,
+ phenotype_to_marker=phenotype_to_marker,
+ **self.kws)
+
+ if phenotype_order is None:
+ true_phenotype_order = list(sorted(
+ test_metadata.unique_phenotypes))
+ else:
+ true_phenotype_order = phenotype_order
+
+ if phenotype_to_color is None:
+ default_phenotype_to_color = \
+ test_metadata._default_phenotype_to_color
+ true_phenotype_to_color = dict(
+ (k, default_phenotype_to_color[k])
+ for k in true_phenotype_order)
+ else:
+ true_phenotype_to_color = {}
+ for phenotype, color in phenotype_to_color.iteritems():
+ try:
+ color = str_to_color[color]
+ except KeyError:
+ pass
+ true_phenotype_to_color[phenotype] = color
+
+ if phenotype_to_marker is None:
+ true_phenotype_to_marker = dict.fromkeys(
+ test_metadata.unique_phenotypes, 'o')
+ else:
+ true_phenotype_to_marker = phenotype_to_marker
+
+ true_phenotype_transitions = zip(true_phenotype_order[:-1],
+ true_phenotype_order[1:])
+ true_unique_phenotypes = self.metadata[self.phenotype_col].unique()
+ true_n_phenotypes = len(true_unique_phenotypes)
+
+ true_colors = map(mpl.colors.rgb2hex,
+ sns.color_palette('husl',
+ n_colors=true_n_phenotypes))
+ colors = iter(true_colors)
+ true_default_phenotype_to_color = defaultdict(lambda: colors.next())
+
+ true_sample_id_to_phenotype = self.metadata[self.phenotype_col]
+ true_phenotype_color_order = [true_phenotype_to_color[p]
+ for p in true_phenotype_order]
+ true_sample_id_to_color = \
+ dict((i, true_phenotype_to_color[true_sample_id_to_phenotype[i]])
+ for i in self.metadata.index)
+
+ true_sample_subsets = subsets_from_metadata(
+ self.metadata, self.kws['minimum_sample_subset'], 'samples')
+
+ pdt.assert_frame_equal(test_metadata.data, self.metadata)
+ pdt.assert_series_equal(test_metadata.sample_id_to_phenotype,
+ true_sample_id_to_phenotype)
+ pdt.assert_array_equal(test_metadata.unique_phenotypes,
+ true_unique_phenotypes)
+ pdt.assert_array_equal(test_metadata.n_phenotypes,
+ len(true_unique_phenotypes))
+ pdt.assert_array_equal(test_metadata._default_phenotype_order,
+ list(sorted(true_unique_phenotypes)))
+ pdt.assert_array_equal(test_metadata.phenotype_order,
+ true_phenotype_order)
+ pdt.assert_array_equal(test_metadata.phenotype_transitions,
+ true_phenotype_transitions)
+ pdt.assert_array_equal(test_metadata._colors, true_colors)
+ pdt.assert_array_equal(test_metadata._default_phenotype_to_color,
+ true_default_phenotype_to_color)
+ pdt.assert_dict_equal(test_metadata.phenotype_to_color,
+ true_phenotype_to_color)
+ pdt.assert_dict_equal(test_metadata.phenotype_to_marker,
+ true_phenotype_to_marker)
+ pdt.assert_array_equal(test_metadata.phenotype_color_order,
+ true_phenotype_color_order)
+ pdt.assert_dict_equal(test_metadata.sample_id_to_color,
+ true_sample_id_to_color)
+ pdt.assert_dict_equal(test_metadata.sample_subsets,
+ true_sample_subsets)
+
+ def test_change_phenotype_col(self, phenotype_order, phenotype_to_color,
+ phenotype_to_marker):
+ from flotilla.data_model.metadata import MetaData
+
+ metadata = self.metadata.copy()
+ metadata['phenotype2'] = np.random.choice(list('QXYZ'), size=self.n)
+
+ test_metadata = MetaData(metadata, phenotype_order,
+ phenotype_to_color,
+ phenotype_to_marker,
+ phenotype_col='phenotype')
+ test_metadata.phenotype_col = 'phenotype2'
+
+ pdt.assert_array_equal(test_metadata.unique_phenotypes,
+ metadata.phenotype2.unique())
+ pdt.assert_contains_all(metadata.phenotype2.unique(),
+ test_metadata.phenotype_to_color)
+ pdt.assert_contains_all(metadata.phenotype2.unique(),
+ test_metadata.phenotype_to_marker)
+ pdt.assert_array_equal(test_metadata.phenotype_order,
+ list(sorted(metadata.phenotype2.unique())))
diff --git a/flotilla/test/data_model/test_splicingdata.py b/flotilla/test/data_model/test_splicingdata.py
new file mode 100644
index 00000000..194757d8
--- /dev/null
+++ b/flotilla/test/data_model/test_splicingdata.py
@@ -0,0 +1,345 @@
+"""
+This tests whether the SplicingData object was created correctly. No
+computation or visualization tests yet.
+"""
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import pandas.util.testing as pdt
+import pytest
+
+
+@pytest.fixture(params=[None, 10], ids=['n_none', 'n_10'])
+def n(request):
+ return request.param
+
+
+class TestSplicingData:
+
+ # @pytest.fixture(params=[None, 100])
+ # def data_for_binned_nmf_reduced(self, request, splicing):
+ # if request.param is None:
+ # return None
+ # else:
+ # psi = copy.deepcopy(splicing.data)
+ # max_index = np.prod(map(lambda x: x - 1, psi.shape))
+ # random_flat_indices = np.random.randint(0, max_index, 100)
+ # psi.values[
+ # np.unravel_index(random_flat_indices, psi.shape)] = np.nan
+ # return psi
+
+ # def test_modality_assignments(self, splicing, groupby, true_modalities):
+ # assignments = splicing.modalities(groupby=groupby)
+ #
+ # pdt.assert_frame_equal(assignments.sort_index(axis=1),
+ # true_modalities.sort_index(axis=1))
+
+ @pytest.fixture(params=['groupby_real', 'groupby_none'])
+ def groupby_params(self, request, groupby):
+ if request.param == 'groupby_real':
+ return groupby
+ elif request.param == 'groupby_none':
+ return None
+
+ @pytest.fixture(params=[0.2, 5])
+ def min_samples(self, request):
+ return request.param
+
+ @pytest.fixture(params=[True, False])
+ def percentages(self, request):
+ return request.param
+
+ @pytest.fixture(params=[True, False])
+ def rename(self, request):
+ return request.param
+
+ @pytest.fixture(params=[True, False])
+ def return_means(self, request):
+ return request.param
+
+ def test__subset_and_standardize(self, splicing):
+ test_subset = splicing._subset_and_standardize(splicing.data)
+
+ true_subset = splicing._subset(splicing.data)
+ true_subset = true_subset.dropna(how='all', axis=1).dropna(how='all',
+ axis=0)
+
+ true_subset = true_subset.fillna(0.5)
+ true_subset = -2 * np.arccos(true_subset*2-1) + np.pi
+
+ pdt.assert_frame_equal(test_subset, true_subset)
+
+ def test__subset_and_standardize_rename_means(self, splicing, rename):
+ test_subset, test_means = splicing._subset_and_standardize(
+ splicing.data, return_means=True, rename=rename)
+
+ true_subset = splicing._subset(splicing.data)
+ true_subset = true_subset.dropna(how='all', axis=1).dropna(how='all',
+ axis=0)
+
+ true_subset = true_subset.fillna(0.5)
+ true_subset = -2 * np.arccos(true_subset*2-1) + np.pi
+
+ true_means = true_subset.mean()
+
+ if rename:
+ true_means = true_means.rename_axis(splicing.feature_renamer)
+ true_subset = true_subset.rename_axis(
+ splicing.feature_renamer, 1)
+
+ pdt.assert_frame_equal(test_subset, true_subset)
+ pdt.assert_series_equal(test_means, true_means)
+
+ def test_binify(self, splicing):
+ from flotilla.compute.infotheory import binify
+
+ test_binned = splicing.binify(splicing.data)
+
+ true_binned = binify(splicing.data, splicing.bins)
+ true_binned = true_binned.dropna(how='all', axis=1)
+
+ pdt.assert_frame_equal(test_binned, true_binned)
+
+ def test_binned_nmf_reduced(self, splicing):
+ test_binned_nmf_reduced = splicing.binned_nmf_reduced()
+
+ data = splicing.data
+ binned = splicing.binify(data)
+ true_binned_nmf_reduced = splicing.nmf.transform(binned.T)
+
+ pdt.assert_frame_equal(
+ test_binned_nmf_reduced.sort_index(axis=0).sort_index(axis=1),
+ true_binned_nmf_reduced.sort_index(axis=0).sort_index(axis=1))
+
+ def test_nmf_space_positions(self, splicing, groupby, n):
+ if n is None:
+ n = 0.5
+
+ def thresh(x):
+ return n * x.shape[0]
+
+ test_positions = splicing.nmf_space_positions(groupby)
+ else:
+ test_positions = splicing.nmf_space_positions(groupby, n=n)
+
+ def thresh(x):
+ return n
+
+ grouped = splicing.singles.groupby(groupby)
+ at_least_n_per_group_per_event = pd.concat(
+ [df.dropna(thresh=thresh(df), axis=1) for name, df in grouped])
+ df = at_least_n_per_group_per_event.groupby(groupby).apply(
+ lambda x: splicing.binned_nmf_reduced(data=x))
+ df = df.swaplevel(0, 1)
+ true_positions = df.sort_index()
+
+ pdt.assert_frame_equal(test_positions, true_positions)
+
+ def test_transition_distances(self, splicing, groupby, group_transitions):
+ nmf_positions = splicing.nmf_space_positions(groupby=groupby)
+
+ test_distances = splicing.transition_distances(nmf_positions,
+ group_transitions)
+
+ nmf_positions.index = nmf_positions.index.droplevel(0)
+ true_distances = pd.Series(index=group_transitions)
+ for transition in group_transitions:
+ try:
+ phenotype1, phenotype2 = transition
+ norm = np.linalg.norm(
+ nmf_positions.ix[phenotype2] - nmf_positions.ix[
+ phenotype1])
+ # print phenotype1, phenotype2, norm
+ true_distances[transition] = norm
+ except KeyError:
+ pass
+
+ pdt.assert_series_equal(test_distances, true_distances)
+
+ def test_nmf_space_transitions(self, splicing, groupby, group_transitions):
+ nmf_space_positions = splicing.nmf_space_positions(
+ groupby=groupby)
+
+ test_transitions = splicing.nmf_space_transitions(
+ groupby, group_transitions)
+
+ nmf_space_positions = nmf_space_positions.groupby(
+ level=0, axis=0).filter(lambda x: len(x) > 1)
+
+ nmf_space_transitions = nmf_space_positions.groupby(
+ level=0, axis=0, as_index=True, group_keys=False).apply(
+ splicing.transition_distances,
+ transitions=group_transitions)
+
+ # Remove any events that didn't have phenotype pairs from
+ # the transitions
+ true_transitions = nmf_space_transitions.dropna(how='all', axis=0)
+
+ pdt.assert_frame_equal(test_transitions, true_transitions)
+
+ # @pytest.mark.parameterize('n_groups', 2)
+ def test_big_nmf_space_transitions(self, splicing, groupby,
+ group_transitions):
+ test_big_transitions = splicing.big_nmf_space_transitions(
+ groupby, group_transitions)
+
+ nmf_space_transitions = splicing.nmf_space_transitions(
+ groupby, group_transitions)
+
+ # get the mean and standard dev of the whole array
+ n = nmf_space_transitions.count().sum()
+ mean = nmf_space_transitions.sum().sum() / n
+ std = np.sqrt(np.square(nmf_space_transitions - mean).sum().sum() / n)
+
+ true_big_transitions = nmf_space_transitions[
+ nmf_space_transitions > (mean + std)].dropna(how='all')
+
+ pdt.assert_frame_equal(test_big_transitions, true_big_transitions)
+
+ # @pytest.mark.parameterize('n_groups', 2)
+ def test_is_nmf_space_x_axis_included(self, splicing, groupby):
+ test_is_nmf_space_x_axis_included = \
+ splicing._is_nmf_space_x_axis_excluded(groupby)
+
+ nmf_space_positions = splicing.nmf_space_positions(groupby)
+
+ # Get the correct included/excluded labeling for the x and y axes
+ event, phenotype = nmf_space_positions.pc_1.argmax()
+ top_pc1_samples = splicing.data.groupby(groupby).groups[
+ phenotype]
+
+ data = splicing._subset(splicing.data, sample_ids=top_pc1_samples)
+ binned = splicing.binify(data)
+ true_is_nmf_space_x_axis_included = bool(binned[event][0])
+
+ pdt.assert_equal(test_is_nmf_space_x_axis_included,
+ true_is_nmf_space_x_axis_included)
+
+ # @pytest.mark.parameterize('n_groups', 2)
+ def test_nmf_space_xlabel(self, splicing, groupby):
+ test_xlabel = splicing._nmf_space_xlabel(groupby)
+
+ if splicing._is_nmf_space_x_axis_excluded(groupby):
+ true_xlabel = splicing.excluded_label
+ else:
+ true_xlabel = splicing.included_label
+
+ pdt.assert_equal(test_xlabel, true_xlabel)
+
+ # @pytest.mark.parameterize('n_groups', 2)
+ def test_nmf_space_ylabel(self, splicing, groupby):
+ test_ylabel = splicing._nmf_space_ylabel(groupby)
+
+ if splicing._is_nmf_space_x_axis_excluded(groupby):
+ true_ylabel = splicing.included_label
+ else:
+ true_ylabel = splicing.excluded_label
+
+ pdt.assert_equal(test_ylabel, true_ylabel)
+
+ # @pytest.mark.parameterize(n_groups=3)
+ def test_plot_big_nmf_space(self, splicing,
+ groupby, group_to_color,
+ group_order, group_transitions,
+ color_ordered, group_to_marker):
+ splicing.plot_big_nmf_space_transitions(
+ groupby, group_transitions, group_order,
+ color_ordered, group_to_color, group_to_marker)
+ plt.close('all')
+
+ def test_modality_assignments(self, splicing, groupby_params,
+ min_samples):
+ sample_ids = None
+ feature_ids = None
+ test_modality_assignments = splicing.modality_assignments(
+ sample_ids=sample_ids, feature_ids=feature_ids,
+ groupby=groupby_params, min_samples=min_samples)
+
+ data = splicing._subset(splicing.data, sample_ids, feature_ids,
+ require_min_samples=False)
+ if groupby_params is None:
+ groupby_copy = pd.Series('all', index=data.index)
+ else:
+ groupby_copy = groupby_params
+
+ grouped = data.groupby(groupby_copy)
+ if isinstance(min_samples, int):
+ thresh = splicing._thresh_int
+ elif isinstance(min_samples, float):
+ thresh = splicing._thresh_float
+ else:
+ raise TypeError('Threshold for minimum samples for modality '
+ 'detection can only be int or float, '
+ 'not {}'.format(type(min_samples)))
+ data = pd.concat([df.dropna(thresh=thresh(df, min_samples), axis=1)
+ for name, df in grouped])
+ true_assignments = data.groupby(groupby_copy).apply(
+ splicing.modality_estimator.fit_transform)
+
+ pdt.assert_frame_equal(test_modality_assignments, true_assignments)
+
+ @pytest.mark.xfail
+ def test_modality_assignments_all_inputs_not_none(self, splicing,
+ groupby):
+ sample_ids = None
+ feature_ids = None
+ splicing.modality_assignments(
+ sample_ids=sample_ids, feature_ids=feature_ids,
+ data=splicing.singles,
+ groupby=groupby)
+
+ @pytest.mark.xfail
+ def test_modality_assignments_invalid_thresh(self, splicing,
+ groupby):
+ sample_ids = None
+ feature_ids = None
+ splicing.modality_assignments(
+ sample_ids=sample_ids, feature_ids=feature_ids, min_samples=None,
+ groupby=groupby)
+
+ def test_modality_counts(self, splicing):
+ sample_ids = None
+ feature_ids = None
+ test_modality_counts = splicing.modality_counts(
+ sample_ids=sample_ids, feature_ids=feature_ids)
+
+ assignments = splicing.modality_assignments(sample_ids,
+ feature_ids)
+ true_counts = assignments.apply(lambda x: x.groupby(x).size(), axis=1)
+ pdt.assert_frame_equal(test_modality_counts, true_counts)
+
+ def test_plot_modalities_bars(self, splicing, groupby,
+ group_to_color, percentages):
+ splicing.plot_modalities_bars(
+ groupby=groupby, percentages=percentages,
+ phenotype_to_color=group_to_color)
+
+ def test_plot_modalities_reduced(self, splicing, groupby,
+ group_to_color):
+ splicing.plot_modalities_bars(
+ groupby=groupby, phenotype_to_color=group_to_color)
+
+ def test_plot_modalities_lavalamps(self, splicing, groupby,
+ group_to_color):
+ splicing.plot_modalities_lavalamps(
+ groupby=groupby, phenotype_to_color=group_to_color)
+
+ def test_plot_feature(self, splicing):
+ splicing.plot_feature(splicing.data.columns[0])
+
+ def test_plot_lavalamp(self, splicing, group_to_color):
+ splicing.plot_lavalamp(group_to_color)
+
+ def test_plot_two_features(self, splicing, groupby,
+ group_to_color):
+ features = splicing.data.columns[splicing.data.count() > 10]
+ feature1 = features[0]
+ feature2 = features[1]
+ splicing.plot_two_features(feature1, feature2, groupby=groupby,
+ label_to_color=group_to_color)
+
+ def test_plot_two_samples(self, splicing):
+ samples = splicing.data.index[splicing.data.T.count() > 10]
+ sample1 = samples[0]
+ sample2 = samples[1]
+ splicing.plot_two_samples(sample1, sample2)
diff --git a/flotilla/test/data_model/test_study.py b/flotilla/test/data_model/test_study.py
new file mode 100644
index 00000000..2c1948a2
--- /dev/null
+++ b/flotilla/test/data_model/test_study.py
@@ -0,0 +1,679 @@
+"""
+This tests whether the Study object was created correctly. No
+computation or visualization tests yet.
+"""
+from collections import Iterable
+import json
+
+import matplotlib.pyplot as plt
+import numpy as np
+import numpy.testing as npt
+import pandas as pd
+import pandas.util.testing as pdt
+import pytest
+import semantic_version
+
+
+@pytest.fixture(params=['expression', 'splicing'])
+def data_type(request):
+ return request.param
+
+
+@pytest.fixture(params=[None, 'subset1'],
+ ids=['color_samples_by_none', 'color_samples_by_subset1'])
+def color_samples_by(request, metadata_phenotype_col):
+ if request.param == 'phenotype':
+ return metadata_phenotype_col
+ else:
+ return request.param
+
+
+class TestStudy(object):
+ # @pytest.fixture
+ # def n_groups(self):
+ # return 3
+
+ @pytest.fixture
+ def study(self, metadata_data, metadata_kws,
+ mapping_stats_data, mapping_stats_kws,
+ expression_data, expression_kws,
+ splicing_data, splicing_kws,
+ gene_ontology_data):
+ from flotilla.data_model import Study
+
+ kwargs = {}
+ metadata = metadata_data.copy()
+ splicing = splicing_data.copy()
+ expression = expression_data.copy()
+ mapping_stats = mapping_stats_data.copy()
+ gene_ontology = gene_ontology_data.copy()
+
+ kw_pairs = (('metadata', metadata_kws),
+ ('mapping_stats', mapping_stats_kws),
+ ('expression', expression_kws),
+ ('splicing', splicing_kws))
+ for data_type, kws in kw_pairs:
+ for kw_name, kw_value in kws.iteritems():
+ kwargs['{}_{}'.format(data_type, kw_name)] = kw_value
+
+ return Study(metadata,
+ mapping_stats_data=mapping_stats,
+ expression_data=expression,
+ splicing_data=splicing,
+ gene_ontology_data=gene_ontology, **kwargs)
+
+ def test_init(self, metadata_data):
+ from flotilla import Study
+
+ metadata = metadata_data.copy()
+ study = Study(metadata)
+
+ metadata['outlier'] = False
+
+ true_default_sample_subsets = list(sorted(list(set(
+ study.metadata.sample_subsets.keys()).difference(
+ set(study.default_sample_subset)))))
+ true_default_sample_subsets.insert(0, study.default_sample_subset)
+
+ pdt.assert_frame_equal(study.metadata.data, metadata)
+ pdt.assert_equal(study.version, '0.1.0')
+ pdt.assert_equal(study.pooled, None)
+ pdt.assert_equal(study.technical_outliers, None)
+ pdt.assert_equal(study.phenotype_col, study.metadata.phenotype_col)
+ pdt.assert_equal(study.phenotype_order, study.metadata.phenotype_order)
+ pdt.assert_equal(study.phenotype_to_color,
+ study.metadata.phenotype_to_color)
+ pdt.assert_equal(study.phenotype_to_marker,
+ study.metadata.phenotype_to_marker)
+ pdt.assert_series_equal(study.sample_id_to_phenotype,
+ study.metadata.sample_id_to_phenotype)
+ pdt.assert_series_equal(study.sample_id_to_color,
+ study.metadata.sample_id_to_color)
+ pdt.assert_array_equal(study.phenotype_transitions,
+ study.metadata.phenotype_transitions)
+ pdt.assert_array_equal(study.phenotype_color_ordered,
+ study.metadata.phenotype_color_order)
+ pdt.assert_equal(study.default_sample_subset, 'all_samples')
+ pdt.assert_equal(study.default_feature_subset, 'variant')
+ pdt.assert_array_equal(study.default_sample_subsets,
+ true_default_sample_subsets)
+ pdt.assert_dict_equal(study.default_feature_subsets, {})
+
+ @pytest.mark.xfail
+ def test_setattr(self, metadata_data):
+ # warnings.simplefilter("error")
+
+ from flotilla import Study
+
+ study = Study(metadata_data.copy())
+
+ study.pooled = 'asdf'
+ # warnings.simplefilter('default')
+
+ def test_init_metdadata_kws(self, metadata_data, metadata_kws):
+ # Also need to check for when these are NAs
+ from flotilla import Study
+
+ kws = dict(('metadata_'+k, v) for k, v in metadata_kws.items())
+ study = Study(metadata_data, **kws)
+
+ pdt.assert_frame_equal(study.metadata.data,
+ metadata_data)
+ pdt.assert_equal(study.version, '0.1.0')
+ npt.assert_equal(study.pooled, None)
+ # npt.assert_equal(study.outliers, None)
+
+ def test_init_pooled(self, metadata_data,
+ metadata_kws,
+ pooled):
+ from flotilla import Study
+ metadata = metadata_data.copy()
+
+ kws = dict(('metadata_'+k, v) for k, v in metadata_kws.items())
+ metadata['pooled'] = metadata.index.isin(pooled)
+
+ study = Study(metadata, **kws)
+
+ npt.assert_array_equal(sorted(study.pooled), sorted(pooled))
+
+ def test_init_bad_pooled(self, metadata_data, metadata_kws, pooled):
+ from flotilla import Study
+
+ metadata = metadata_data.copy()
+
+ kws = dict(('metadata_' + k, v) for k, v in metadata_kws.items())
+ metadata['pooled_asdf'] = metadata.index.isin(pooled)
+
+ study = Study(metadata, **kws)
+
+ true_pooled = None
+ if study.metadata.pooled_col is not None:
+ if study.metadata.pooled_col in study.metadata.data:
+ try:
+ true_pooled = study.metadata.data.index[
+ study.metadata.data[
+ study.metadata.pooled_col].astype(bool)]
+ except KeyError:
+ true_pooled = None
+
+ npt.assert_equal(study.pooled, true_pooled)
+
+ def test_init_outlier(self, metadata_data, metadata_kws, outliers):
+ from flotilla import Study
+
+ metadata = metadata_data.copy()
+
+ kws = dict(('metadata_' + k, v) for k, v in metadata_kws.items())
+ metadata['outlier'] = metadata.index.isin(outliers)
+
+ study = Study(metadata, **kws)
+
+ npt.assert_array_equal(study.metadata.data, metadata)
+
+ def test_init_technical_outlier(self, metadata_data, metadata_kws,
+ technical_outliers, mapping_stats_data,
+ mapping_stats_kws):
+ from flotilla import Study
+
+ metadata = metadata_data.copy()
+
+ kw_pairs = (('metadata', metadata_kws),
+ ('mapping_stats', mapping_stats_kws))
+ kwargs = {}
+ for name, kws in kw_pairs:
+ for k, v in kws.items():
+ kwargs['{}_{}'.format(name, k)] = v
+ study = Study(metadata, mapping_stats_data=mapping_stats_data,
+ **kwargs)
+ pdt.assert_array_equal(sorted(study.technical_outliers),
+ sorted(technical_outliers))
+
+ def test_init_expression(self, metadata_data, metadata_kws,
+ expression_data, expression_kws):
+ from flotilla import Study
+
+ metadata = metadata_data.copy()
+ expression = expression_data.copy()
+
+ kw_pairs = (('metadata', metadata_kws),
+ ('expression', expression_kws))
+ kwargs = {}
+ for name, kws in kw_pairs:
+ for k, v in kws.items():
+ kwargs['{}_{}'.format(name, k)] = v
+ study = Study(metadata, expression_data=expression,
+ **kwargs)
+ pdt.assert_array_equal(study.expression.data_original,
+ expression_data)
+
+ def test_init_splicing(self, metadata_data, metadata_kws,
+ splicing_data, splicing_kws):
+ from flotilla import Study
+
+ metadata = metadata_data.copy()
+ splicing = splicing_data.copy()
+
+ kw_pairs = (('metadata', metadata_kws),
+ ('splicing', splicing_kws))
+ kwargs = {}
+ for name, kws in kw_pairs:
+ for k, v in kws.items():
+ kwargs['{}_{}'.format(name, k)] = v
+ study = Study(metadata, splicing_data=splicing,
+ **kwargs)
+ pdt.assert_array_equal(study.splicing.data_original,
+ splicing_data)
+
+ def test_feature_subset_to_feature_ids(self, study, data_type,
+ feature_subset):
+ test_feature_subset = study.feature_subset_to_feature_ids(
+ data_type, feature_subset)
+ if 'expression'.startswith(data_type):
+ true_feature_subset = \
+ study.expression.feature_subset_to_feature_ids(feature_subset,
+ rename=False)
+ elif 'splicing'.startswith(data_type):
+ true_feature_subset = study.splicing.feature_subset_to_feature_ids(
+ feature_subset, rename=False)
+ pdt.assert_array_equal(test_feature_subset, true_feature_subset)
+
+ def test_sample_subset_to_sample_ids(self, study, sample_subset):
+ test_sample_subset = study.sample_subset_to_sample_ids(sample_subset)
+
+ try:
+ true_sample_subset = study.metadata.sample_subsets[sample_subset]
+ except (KeyError, TypeError):
+ try:
+ ind = study.metadata.sample_id_to_phenotype == sample_subset
+ if ind.sum() > 0:
+ true_sample_subset = \
+ study.metadata.sample_id_to_phenotype.index[ind]
+ else:
+ if sample_subset is None or 'all_samples'.startswith(
+ sample_subset):
+ sample_ind = np.ones(study.metadata.data.shape[0],
+ dtype=bool)
+ elif sample_subset.startswith("~"):
+ sample_ind = ~pd.Series(
+ study.metadata.data[sample_subset.lstrip("~")],
+ dtype='bool')
+
+ else:
+ sample_ind = pd.Series(
+ study.metadata.data[sample_subset], dtype='bool')
+ true_sample_subset = study.metadata.data.index[sample_ind]
+ except (AttributeError, ValueError):
+ true_sample_subset = sample_subset
+
+ pdt.assert_array_equal(true_sample_subset, test_sample_subset)
+
+ def test_filter_splicing_on_expression(self, study):
+ expression_thresh = 5
+ sample_subset = None
+ test_filtered_splicing = study.filter_splicing_on_expression(
+ expression_thresh)
+ columns = study._maybe_get_axis_name(study.splicing.data, axis=1,
+ alt_name=study._event_name)
+
+ index = study._maybe_get_axis_name(study.splicing.data, axis=0,
+ alt_name=study._sample_id)
+
+ sample_ids = study.sample_subset_to_sample_ids(sample_subset)
+ splicing_with_expression = \
+ study.tidy_splicing_with_expression.ix[
+ study.tidy_splicing_with_expression.sample_id.isin(
+ sample_ids)]
+ ind = splicing_with_expression.expression >= expression_thresh
+ splicing_high_expression = splicing_with_expression.ix[ind]
+ splicing_high_expression = \
+ splicing_high_expression.reset_index().dropna()
+
+ if isinstance(columns, list) or isinstance(index, list):
+ true_filtered_splicing = splicing_high_expression.pivot_table(
+ columns=columns, index=index, values='psi')
+ else:
+ true_filtered_splicing = splicing_high_expression.pivot(
+ columns=columns, index=index, values='psi')
+ pdt.assert_frame_equal(true_filtered_splicing, test_filtered_splicing)
+
+ def test_plot_pca(self, study, data_type):
+ study.plot_pca(feature_subset='all', data_type=data_type)
+ plt.close('all')
+
+ # Too few features to test graph or classifier
+ # def test_plot_graph(self, study, data_type):
+ # study.plot_graph(feature_subset='all', data_type=data_type)
+ # plt.close('all')
+
+ # def test_plot_classifier(self, study, data_type):
+ # trait = study.metadata.phenotype_col
+ # study.plot_classifier(trait, feature_subset='all',
+ # data_type=data_type)
+ # plt.close('all')
+
+ def test_plot_clustermap(self, study, data_type):
+ study.plot_clustermap(feature_subset='all', data_type=data_type)
+ plt.close('all')
+
+ def test_plot_correlations(self, study, featurewise, data_type):
+ study.plot_correlations(feature_subset='all', featurewise=featurewise,
+ data_type=data_type)
+ plt.close('all')
+
+ def test_plot_lavalamps(self, study):
+ study.plot_lavalamps()
+ plt.close('all')
+
+ def test_plot_big_nmf_space_transitions(self, study):
+ study.plot_big_nmf_space_transitions('splicing')
+ plt.close('all')
+
+ def test_plot_two_samples(self, study, data_type):
+ sample1 = study.expression.data.index[0]
+ sample2 = study.expression.data.index[-1]
+ study.plot_two_samples(sample1, sample2, data_type=data_type)
+
+ def test_plot_two_features(self, study, data_type):
+ if data_type == 'expression':
+ feature1 = study.expression.data.columns[0]
+ feature2 = study.expression.data.columns[-1]
+ elif data_type == 'splicing':
+ feature1 = study.splicing.data.columns[0]
+ feature2 = study.splicing.data.columns[-1]
+ study.plot_two_features(feature1, feature2, data_type=data_type)
+
+ @pytest.fixture(params=[None, 'gene'])
+ def gene_of_interest(self, request, genes):
+ if request is not None:
+ return genes[0]
+ else:
+ return request.param
+
+ # def test_plot_graph(self, study, gene_of_interest, featurewise):
+ # study.plot_graph(feature_of_interest=gene_of_interest,
+ # feature_subset='all', featurewise=featurewise)
+ # plt.close('all')
+ #
+ # # def test_plot_classifier(self, study):
+ # # study.plot_classifier(study.metadata.phenotype_col,
+ # # feature_subset='all')
+ # # plt.close('all')
+ # #
+ # # def test_plot_classifier_splicing(self, study):
+ # # study.plot_classifier(study.metadata.phenotype_col,
+ # # feature_subset='all',
+ # # data_type='splicing')
+ # # plt.close('all')
+ #
+ # def test_plot_clustermap(self, study):
+ # study.plot_clustermap(feature_subset='all')
+ # plt.close('all')
+ #
+ # def test_plot_clustermap_splicing(self, study):
+ # study.plot_clustermap(feature_subset='all',
+ # data_type='splicing')
+ # plt.close('all')
+ #
+ # def test_plot_correlations(self, study, featurewise):
+ # study.plot_correlations(featurewise=featurewise,
+ # feature_subset='all')
+ # plt.close('all')
+ #
+ # def test_plot_correlations_splicing(self, study, featurewise):
+ # study.plot_correlations(featurewise=featurewise,
+ # data_type='splicing',
+ # feature_subset='all')
+ # plt.close('all')
+ #
+ # def test_tidy_splicing_with_expression(self, study):
+ # test = study.tidy_splicing_with_expression
+ #
+ # common_id = 'common_id'
+ # sample_id = 'sample_id'
+ # event_name = 'event_name'
+ #
+ # splicing_common_id = study.splicing.feature_data[
+ # study.splicing.feature_expression_id_col]
+ #
+ # # Tidify splicing
+ # splicing = study.splicing.data
+ # splicing_index_name = study._maybe_get_axis_name(splicing, axis=0)
+ # splicing_columns_name = study._maybe_get_axis_name(splicing, axis=1)
+ #
+ # splicing_tidy = pd.melt(splicing.reset_index(),
+ # id_vars=splicing_index_name,
+ # value_name='psi',
+ # var_name=splicing_columns_name)
+ # rename_columns = {}
+ # if splicing_index_name == 'index':
+ # rename_columns[splicing_index_name] = sample_id
+ # if splicing_columns_name == 'columns':
+ # rename_columns[splicing_columns_name] = event_name
+ # splicing_columns_name = event_name
+ # splicing_tidy = splicing_tidy.rename(columns=rename_columns)
+ #
+ # # Create a column of the common id on which to join splicing
+ # # and expression
+ # splicing_names = splicing_tidy[splicing_columns_name]
+ # if isinstance(splicing_names, pd.Series):
+ # splicing_tidy[common_id] = splicing_tidy[
+ # splicing_columns_name].map(splicing_common_id)
+ # else:
+ # splicing_tidy[common_id] = [
+ # study.splicing.feature_renamer(x)
+ # for x in splicing_names.itertuples(index=False)]
+ #
+ # splicing_tidy = splicing_tidy.dropna()
+ #
+ # # Tidify expression
+ # expression = study.expression.data_original
+ # expression_index_name = study._maybe_get_axis_name(expression,
+ # axis=0)
+ # expression_columns_name = study._maybe_get_axis_name(expression,
+ # axis=1)
+ #
+ # expression_tidy = pd.melt(expression.reset_index(),
+ # id_vars=expression_index_name,
+ # value_name='expression',
+ # var_name=common_id)
+ # # This will only do anything if there is a column named "index" so
+ # # no need to check anything
+ # expression_tidy = expression_tidy.rename(
+ # columns={'index': sample_id})
+ # expression_tidy = expression_tidy.dropna()
+ #
+ # splicing_tidy.set_index([sample_id, common_id], inplace=True)
+ # expression_tidy.set_index([sample_id, common_id], inplace=True)
+ #
+ # true = splicing_tidy.join(expression_tidy, how='inner').reset_index()
+ #
+ # pdt.assert_frame_equal(test, true)
+
+ #
+ #
+ # @pytest.fixture(params=[None, 'pooled_col', 'phenotype_col'])
+ # def metadata_none_key(self, request):
+ # return request.param
+ #
+ # @pytest.fixture(params=[None])
+ # def expression_none_key(self, request):
+ # return request.param
+ #
+ # @pytest.fixture(params=[None,
+ # pytest.mark.xfail('feature_rename_col')])
+ # def splicing_none_key(self, request):
+ # return request.param
+ #
+ # @pytest.fixture
+ # def datapackage(self, shalek2013_datapackage, metadata_none_key,
+ # expression_none_key, splicing_none_key, monkeypatch):
+ # datapackage = copy.deepcopy(shalek2013_datapackage)
+ # datatype_to_key = {'metadata': metadata_none_key,
+ # 'expression': expression_none_key,
+ # 'splicing': splicing_none_key}
+ # for datatype, key in datatype_to_key.iteritems():
+ # if key is not None:
+ # resource = name_to_resource(datapackage, datatype)
+ # if key in resource:
+ # monkeypatch.delitem(resource, key, raising=False)
+ # return datapackage
+ #
+ # @pytest.fixture
+ # def datapackage_dir(self, shalek2013_datapackage_path):
+ # return os.path.dirname(shalek2013_datapackage_path)
+ #
+ # # def test_from_datapackage(self, datapackage, datapackage_dir):
+ # # import flotilla
+ # #
+ # # study = flotilla.Study.from_datapackage(
+ # # datapackage, datapackage_dir, load_species_data=False)
+ # #
+ # # metadata_resource = get_resource_from_name(
+ # # datapackage, 'metadata')
+ # # expression_resource = get_resource_from_name(datapackage,
+ # # 'expression')
+ # # splicing_resource = get_resource_from_name(datapackage,
+ # # 'splicing')
+ # #
+ # # phenotype_col = 'phenotype' if 'phenotype_col' \
+ # # not in metadata_resource else \
+ # # metadata_resource['phenotype_col']
+ # # pooled_col = 'pooled' if 'pooled_col' not in
+ # # metadata_resource else \
+ # # metadata_resource['pooled_col']
+ # # expression_feature_rename_col = None if \
+ # # 'feature_rename_col' not in expression_resource \
+ # # else expression_resource['feature_rename_col']
+ # # splicing_feature_rename_col = 'gene_name' if \
+ # # 'feature_rename_col' not in splicing_resource \
+ # # else splicing_resource['feature_rename_col']
+ # #
+ # # assert study.metadata.phenotype_col == phenotype_col
+ # # assert study.metadata.pooled_col == pooled_col
+ # # assert study.expression.feature_rename_col \
+ # # == expression_feature_rename_col
+ # # assert study.splicing.feature_rename_col \
+ # # == splicing_feature_rename_col
+
+ @staticmethod
+ def get_data_eval_command(data_type, attribute):
+ if 'feature' in data_type:
+ # Feature data doesn't have "data_original", only "data"
+ if attribute == 'data_original':
+ attribute = 'data'
+ command = 'study.{}.feature_{}'.format(
+ data_type.split('_feature')[0], attribute)
+ else:
+ command = 'study.{}.{}'.format(data_type, attribute)
+ return command
+
+ def test_save(self, study, tmpdir):
+ from flotilla.datapackage import name_to_resource
+
+ study_name = 'test_save'
+ study.supplemental.expression_corr = study.expression.data.corr()
+ study.save(study_name, flotilla_dir=tmpdir)
+
+ assert len(tmpdir.listdir()) == 1
+ save_dir = tmpdir.listdir()[0]
+
+ with open('{}/datapackage.json'.format(save_dir)) as f:
+ test_datapackage = json.load(f)
+
+ assert study_name == save_dir.purebasename
+
+ # resource_keys_to_ignore = ('compression', 'format', 'path',
+ # 'url')
+ keys_from_study = {'splicing': [],
+ 'expression': ['thresh',
+ 'log_base',
+ 'plus_one'],
+ 'metadata': ['phenotype_order',
+ 'phenotype_to_color',
+ 'phenotype_col',
+ 'phenotype_to_marker',
+ 'pooled_col',
+ 'minimum_samples'],
+ 'mapping_stats': ['number_mapped_col',
+ 'min_reads'],
+ 'expression_feature': ['rename_col',
+ 'ignore_subset_cols'],
+ 'splicing_feature': ['rename_col',
+ 'ignore_subset_cols',
+ 'expression_id_col'],
+ 'gene_ontology': []}
+ resource_names = keys_from_study.keys()
+
+ # Add auto-generated attributes into the true datapackage
+ for name, keys in keys_from_study.iteritems():
+ resource = name_to_resource(test_datapackage, name)
+ for key in keys:
+ command = self.get_data_eval_command(name, key)
+ test_value = resource[key]
+ true_value = eval(command)
+ if isinstance(test_value, dict):
+ pdt.assert_dict_equal(test_value, true_value)
+ elif isinstance(test_value, Iterable):
+ pdt.assert_array_equal(test_value, true_value)
+
+ for name in resource_names:
+ resource = name_to_resource(test_datapackage, name)
+ path = '{}.csv.gz'.format(name)
+ assert resource['path'] == path
+ test_df = pd.read_csv('{}/{}/{}'.format(tmpdir, study_name, path),
+ index_col=0, compression='gzip')
+ command = self.get_data_eval_command(name, 'data_original')
+ true_df = eval(command)
+ pdt.assert_frame_equal(test_df, true_df)
+
+ version = semantic_version.Version(study.version)
+ version.patch += 1
+ assert str(version) == test_datapackage['datapackage_version']
+ assert study_name == test_datapackage['name']
+
+ def test_save_supplemental(self, study, tmpdir):
+ from flotilla.datapackage import name_to_resource
+
+ study_name = 'test_save_supplemental'
+ study.supplemental.expression_corr = study.expression.data.corr()
+ study.save(study_name, flotilla_dir=tmpdir)
+
+ assert len(tmpdir.listdir()) == 1
+ save_dir = tmpdir.listdir()[0]
+
+ with open('{}/datapackage.json'.format(save_dir)) as f:
+ test_datapackage = json.load(f)
+
+ supplemental = name_to_resource(test_datapackage, 'supplemental')
+ for resource in supplemental['resources']:
+ name = resource['name']
+ path = '{}.csv.gz'.format(name)
+ assert resource['path'] == path
+ full_path = '{}/{}/{}'.format(tmpdir, study_name, path)
+ test_df = pd.read_csv(full_path, index_col=0, compression='gzip')
+ command = self.get_data_eval_command('supplemental', name)
+ true_df = eval(command)
+ pdt.assert_frame_equal(test_df, true_df)
+
+ version = semantic_version.Version(study.version)
+ version.patch += 1
+ assert str(version) == test_datapackage['datapackage_version']
+ assert study_name == test_datapackage['name']
+
+ def test_embark_supplemental(self, study, tmpdir):
+ import flotilla
+
+ study_name = 'test_save_supplemental'
+ study.supplemental.expression_corr = study.expression.data.corr()
+ study.save(study_name, flotilla_dir=tmpdir)
+
+ study2 = flotilla.embark(study_name, flotilla_dir=tmpdir)
+ pdt.assert_frame_equal(study2.supplemental.expression_corr,
+ study.supplemental.expression_corr)
+
+ # datapackage_keys_to_ignore = ['name', 'datapackage_version',
+ # 'resources']
+ # datapackages = (true_datapackage, test_datapackage)
+
+ # for name in resource_names:
+ # for datapackage in datapackages:
+ # resource = name_to_resource(datapackage, name)
+ # for key in resource_keys_to_ignore:
+ # monkeypatch.delitem(resource, key, raising=False)
+
+ # # Have to check for resources separately because they could be
+ # # in any order, it just matters that the contents are equal
+ # # sorted_true = sorted(true_datapackage['resources'],
+ # # key=lambda x: x['name'])
+ # sorted_test = sorted(test_datapackage['resources'],
+ # key=lambda x: x['name'])
+ # for i in range(len(sorted_true)):
+ # pdt.assert_equal(sorted(sorted_true[i].items()),
+ # sorted(sorted_test[i].items()))
+ #
+ # for key in datapackage_keys_to_ignore:
+ # for datapackage in datapackages:
+ # monkeypatch.delitem(datapackage, key)
+
+ # pdt.assert_dict_equal(test_datapackage)
+
+ # Temporary commenting out while chr22 dataset is down
+ # def test_nmf_space_positions(self, chr22):
+ # test_positions = chr22.nmf_space_positions()
+ #
+ # true_positions = chr22.splicing.nmf_space_positions(
+ # groupby=chr22.sample_id_to_phenotype)
+ #
+ # pdt.assert_frame_equal(test_positions, true_positions)
+
+# def test_write_package(tmpdir):
+# from flotilla.data_model import StudyFactory
+#
+# new_study = StudyFactory()
+# new_study.experiment_design_data = None
+# new_study.event_metadata = None
+# new_study.expression_metadata = None
+# new_study.expression_df = None
+# new_study.splicing_df = None
+# new_study.event_metadata = None
+# new_study.write_package('test_package', where=tmpdir, install=False)
diff --git a/flotilla/test/example_data/example_gene_list.txt b/flotilla/test/example_data/example_gene_list.txt
new file mode 100644
index 00000000..3fe4a73e
--- /dev/null
+++ b/flotilla/test/example_data/example_gene_list.txt
@@ -0,0 +1,945 @@
+1110018G07RIK
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+ACPP
+ACSL1
+ACVR1
+ADAMTS4
+ADAP2
+ADAR
+ADHFE1
+ADORA2A
+AFF1
+AFTPH
+AGRN
+AI607873
+AIDA
+AIM1
+AIM2
+AK014726
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+AK163103
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+AK202078
+AK202663
+AK204799
+AK217863
+AKAP12
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+ALCAM
+ALDH1A2
+ALDH1B1
+ALPK2
+AMIGO3
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+ANKRD17
+ANKRD33B
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+ATP10A
+ATP13A4
+AW011738
+AW112010
+AY096003
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+BATF
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+DAXX
+DCBLD2
+DCK
+DCP2
+DCUN1D3
+DDX58
+DDX60
+DENND3
+DENND5A
+DGAT2
+DGKH
+DHX58
+DIRC2
+DLGAP4
+DLL1
+DNAJB6
+DNAJC13
+DNASE1L3
+DOK1
+DOK7
+DPYS
+DTX3L
+DUSP14
+DUSP16
+DUSP2
+DUSP5
+DYRK2
+E030037K03RIK
+E330016A19RIK
+EBI3
+EDN1
+EHD1
+EHD4
+EIF2AK2
+EIF2C3
+ELL2
+ENG
+ENPP4
+EPHA4
+EPSTI1
+EREG
+ETNK1
+ETS2
+ETV3
+EXPI
+EXT1
+F10
+F3
+FAAH
+FABP3
+FAM102B
+FAM129A
+FAM177A
+FAM26F
+FAM46A
+FAM46C
+FAM53C
+FAM82A2
+FBXW17
+FCGR4
+FGFBP3
+FGL2
+FILIP1L
+FLNB
+FLT4
+FMNL2
+FNBP1L
+FNDC3A
+FNDC7
+FPR1
+FPR2
+FRMD4A
+FRMD4B
+FSCN1
+FST
+FSTL1
+FTSJD2
+FURIN
+FZD1
+FZD5
+G530011O06RIK
+GADD45B
+GBGT1
+GBP2
+GBP3
+GBP4
+GBP5
+GBP6
+GBP9
+GCA
+GCH1
+GCNT2
+GEM
+GFI1
+GJA1
+GM10825
+GM11435
+GM12185
+GM12250
+GM14005
+GM14047
+GM14446
+GM1604B
+GM4841
+GM4902
+GM4951
+GM5431
+GM6377
+GM6548
+GM6644
+GM6904
+GM7609
+GM8221
+GM8773
+GM8898
+GM8979
+GM9766
+GMPPB
+GNA13
+GNB4
+GOLGA3
+GPD2
+GPR126
+GPR141
+GPR31C
+GPR33
+GPR84
+GRAP
+GTPBP2
+GVIN1
+GYK
+GYPC
+H1F0
+H2-M2
+H2-Q7
+H2-T10
+H2-T23
+H2-T24
+H3F3B
+HAMP
+HAMP2
+HAS1
+HBEGF
+HCK
+HDC
+HERC6
+HIF1A
+HIPK2
+HIVEP3
+HK2
+HK3
+HMGN3
+HOMER1
+HRC
+HSH2D
+HTR7
+HTRA1
+HTRA4
+I830012O16RIK
+IER3
+IFI203
+IFI204
+IFI205
+IFI35
+IFI44
+IFI47
+IFIH1
+IFIT1
+IFIT2
+IFIT3
+IFITM3
+IFNA9
+IFNB1
+IFRG15
+IGF2BP2
+IGSF5
+IGTP
+IIGP1
+IKZF1
+IKZF4
+IL10
+IL10RA
+IL12A
+IL12B
+IL12RB1
+IL12RB2
+IL15
+IL15RA
+IL18
+IL18BP
+IL19
+IL1A
+IL1B
+IL1F6
+IL1RN
+IL20RB
+IL23A
+IL23R
+IL27
+IL2RA
+IL33
+IL6
+INHBA
+INPP5B
+INTU
+IQCJ
+IQSEC2
+IRAK-2
+IRAK3
+IRF1
+IRF7
+IRF8
+IRF9
+IRG1
+IRGM1
+IRGM2
+ISG15
+ISG20
+ITGA4
+ITGA5
+ITGAV
+ITGB8
+ITPKB
+JAG1
+JAK2
+JAM2
+JUNB
+KALRN
+KAT2B
+KATNA1
+KCNA3
+KCTD14
+KDM4A
+KDR
+KLF6
+KLF8
+KLRK1
+KPNA3
+KREMEN1
+KTN1
+KYNU
+LAD1
+LAG3
+LAP3
+LARP1
+LASS6
+LCK
+LCN2
+LCP2
+LGALS3BP
+LGALS9
+LHX2
+LIPG
+LMO4
+LNP
+LNPEP
+LOX
+LOXL3
+LPAR1
+LRCH1
+LRCH3
+LRP11
+LRRC32
+LRRK2
+LTA
+LY6A
+LY6I
+LY75
+LYRM1
+LZTFL1
+MACC1
+MAF
+MAFK
+MALL
+MALT1
+MAP2K1
+MAP3K8
+MAPKAPK2
+MAPKBP1
+41338
+MARCKSL1
+MARCO
+MAX
+MDFIC
+MDM2
+MED13
+MEFV
+MERTK
+MET
+MFLJ00294
+MFSD7
+MID1
+MIER3
+MINA
+MINPP1
+MITD1
+MITF
+MKIAA0694
+MKIAA0696
+MKIAA1386
+MKIAA1673
+MKIAA1823
+MKIAA1994
+MLKL
+MMP13
+MMP14
+MMP25
+MNDAL
+MOV10
+MPA2L
+MPP1
+MS4A4B
+MS4A4C
+MS4A6B
+MS4A6C
+MS4A6D
+MT2
+MTDH
+MTHFR
+MTMR14
+MTMR7
+MTPN
+MTUS1
+MX1
+MX2
+MXD1
+MYD88
+NAA25
+NAMPT
+NCOA5
+NCOA7
+NDRG1
+NDRG2
+NFKB1
+NFKB2
+NFKBIA
+NFKBIB
+NFKBIL1
+NFKBIZ
+NFXL1
+NIACR1
+NIPAL1
+NLGN2
+NLRC5
+NLRP3
+NMI
+NNAT
+NOD1
+NOD2
+NOS2
+NOTCH2
+NPPC
+NPY
+NR4A2
+NR4A3
+NRP2
+NT5C1A
+NT5C3
+NTNG2
+NTS
+NUB1
+NUP62-IL4I1
+OAS1A
+OAS1B
+OAS1G
+OAS2
+OAS3
+OASL1
+OASL2
+OGFR
+OLFR110
+OLFR111
+OLFR56
+OLR1
+OPRD1
+OPTN
+ORM1
+OSGIN2
+OSM
+OTUD1
+P2RY14
+P4HA1
+PALLD
+PALM2
+PAPD4
+PAPD7
+PARP10
+PARP11
+PARP12
+PARP14
+PARP8
+PARP9
+PCDH7
+PCGF5
+PDE11A
+PDE4B
+PDE7B
+PDGFRB
+PDZK1IP1
+PELI1
+PENK
+PFKP
+PHC2
+PHF11
+PHLPP1
+PI4K2A
+PI4K2B
+PIK3AP1
+PIK3R5
+PIM1
+PLA1A
+PLA2G16
+PLAC8
+PLAGL1
+PLAGL2
+PLAT
+PLEK
+PLEKHA4
+PLEKHF2
+PLEKHN1
+PLK2
+PLOD2
+PMEPA1
+PML
+PNP
+PNP2
+PNPT1
+POU3F1
+PPA1
+PPAP2B
+PPFIA1
+PPFIA3
+PPM1K
+PPP1R15A
+PPP1R15B
+PPP2R5A
+PPP4R2
+PRDM9
+PRKX
+PROCR
+PRPF38A
+PSMB10
+PSMB9
+PSME2
+PTCH1
+PTGES
+PTGS2
+PTPRJ
+PTTG1
+PTX3
+PVR
+PVRL2
+PVRL4
+PYDC4
+PYHIN1
+RAB12
+RAB20
+RAB21
+RAB22A
+RAB32
+RAB8B
+RALGDS
+RANBP2
+RAP1B
+RAP2C
+RAPGEF2
+RARS
+RASA4
+RASGEF1B
+RBM43
+RBM7
+RBMS2
+RCN1
+REL
+RFFL
+RGL1
+RGS1
+RGS14
+RHBDF2
+RHOB
+RILPL1
+RILPL2
+RIN2
+RIPK2
+RNASE10
+RNASET2A
+RNASET2B
+RND1
+RND3
+RNF114
+RNF135
+RNF139
+RNF19B
+RNF213
+RNF24
+RNF31
+RSAD2
+RSBN1
+RTP4
+S1PR3
+SAA3
+SAMD9L
+SAMHD1
+SAP30
+SBDS
+SCARF1
+SCO1
+SDC4
+SDCBP2
+SEC24B
+SEMA7A
+SERPINA3G
+SERPINA3I
+SERPINB2
+SERPINB9
+SERPINB9B
+SERPINE1
+SETDB2
+SGCB
+SGK3
+SH3BP5
+SHISA3
+SIPA1L1
+SIX1
+SKIL
+SLAMF1
+SLAMF7
+SLC11A2
+SLC12A6
+SLC15A3
+SLC16A10
+SLC1A2
+SLC25A22
+SLC25A25
+SLC28A2
+SLC2A6
+SLC30A4
+SLC39A2
+SLC3A2
+SLC44A1
+SLC7A11
+SLC7A2
+SLC7A8
+SLCO3A1
+SLFN1
+SLFN10-PS
+SLFN2
+SLFN3
+SLFN4
+SLFN5
+SLFN8
+SLFN9
+SLPI
+SMG7
+SNN
+SNTB1
+SNX10
+SOCS1
+SOCS3
+SOCS7
+SOD2
+SORBS1
+SP100
+SP110
+SP140
+SPRED1
+SPSB1
+SRC
+SRGN
+ST3GAL1
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+ST3GAL6
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+STARD3
+STAT1
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+STAT3
+STAT5A
+STK39
+STX11
+STXBP1
+STXBP3A
+SVCT2
+SYNE2
+TAGAP
+TANK
+TAP1
+TAP2
+TAPBP
+TAPBPL
+TARM1
+TBC1D1
+TBC1D13
+TCF4
+TCP10C
+TDRD7
+TET2
+TEX12
+TFG
+TGM2
+TGTP2
+THBS4
+TIMP1
+TIPARP
+TLE3
+TLK2
+TLR1
+TLR3
+TLR6
+TLR7
+TMCC3
+TMCO3
+TMEM106A
+TMEM140
+TMEM171
+TMEM184B
+TMEM2
+TMEM39A
+TMEM67
+TMOD3
+TMTC2
+TNC
+TNF
+TNFAIP2
+TNFAIP3
+TNFRSF1B
+TNFSF10
+TNFSF15
+TNFSF4
+TNFSF8
+TNFSF9
+TNIP1
+TNS3
+TOR1AIP1
+TOR1AIP2
+TOR3A
+TPBG
+TPR
+TPST1
+TRA2A
+TRAF1
+TRAFD1
+TREX1
+TRIM21
+TRIM25
+TRIM26
+TRIM30A
+TRIM30D
+TRIM34
+TRIM5
+TRIM56
+TRIOBP
+TRMT61B
+TRP53I11
+TTC39C
+TTLL2
+U90926
+UBA7
+UBE2L6
+UBR4
+UBXN2A
+UNC93A
+UPP1
+URGCP
+USP12
+USP18
+USP25
+USP42
+VCAM1
+VCAN
+VCPIP1
+VNN3
+VPS54
+WARS
+WDR37
+WDR59
+WHAMM
+WHSC1L1
+XAF1
+XCR1
+XKR8
+XRN1
+ZBP1
+ZC3H12A
+ZC3H12C
+ZC3HAV1
+ZCCHC2
+ZCCHC6
+ZDHHC21
+ZFP36
+ZFP558
+ZFP800
+ZFP811
+ZHX2
+ZNFX1
+ZUFSP
diff --git a/flotilla/test/example_data/human_grch38_chr22_gene_ontology.txt b/flotilla/test/example_data/human_grch38_chr22_gene_ontology.txt
new file mode 100644
index 00000000..d5bf1039
--- /dev/null
+++ b/flotilla/test/example_data/human_grch38_chr22_gene_ontology.txt
@@ -0,0 +1,8467 @@
+Ensembl Gene ID GO Term Accession GO Term Name GO Term Definition GO domain
+ENSG00000185264
+ENSG00000184571 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000184571 GO:0030154 cell differentiation "The process in which relatively unspecialized cells, e.g. embryonic or regenerative cells, acquire specialized structural and/or functional features that characterize the cells, tissues, or organs of the mature organism or some other relatively stable phase of the organism's life history. Differentiation includes the processes involved in commitment of a cell to a specific fate and its subsequent development to the mature state." [ISBN:0198506732] biological_process
+ENSG00000184571 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000184571 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000184571 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000184571 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000184571 GO:0006417 regulation of translation "Any process that modulates the frequency, rate or extent of the chemical reactions and pathways resulting in the formation of proteins by the translation of mRNA." [GOC:isa_complete] biological_process
+ENSG00000184571 GO:0007275 multicellular organismal development "The biological process whose specific outcome is the progression of a multicellular organism over time from an initial condition (e.g. a zygote or a young adult) to a later condition (e.g. a multicellular animal or an aged adult)." [GOC:dph, GOC:ems, GOC:isa_complete, GOC:tb] biological_process
+ENSG00000184571 GO:0007283 spermatogenesis "The process of formation of spermatozoa, including spermatocytogenesis and spermiogenesis." [GOC:jid, ISBN:9780878933846] biological_process
+ENSG00000184571 GO:0031047 gene silencing by RNA "Any process in which RNA molecules inactivate expression of target genes." [GOC:dph, GOC:mah, GOC:tb, PMID:15020054] biological_process
+ENSG00000184571 GO:0051321 meiotic cell cycle "Progression through the phases of the meiotic cell cycle, in which canonically a cell replicates to produce four offspring with half the chromosomal content of the progenitor cell via two nuclear divisions." [GOC:ai] biological_process
+ENSG00000184571 GO:0007049 cell cycle "The progression of biochemical and morphological phases and events that occur in a cell during successive cell replication or nuclear replication events. Canonically, the cell cycle comprises the replication and segregation of genetic material followed by the division of the cell, but in endocycles or syncytial cells nuclear replication or nuclear division may not be followed by cell division." [GOC:go_curators, GOC:mtg_cell_cycle] biological_process
+ENSG00000184571 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000184571 GO:0003676 nucleic acid binding "Interacting selectively and non-covalently with any nucleic acid." [GOC:jl] molecular_function
+ENSG00000099998 GO:0003840 gamma-glutamyltransferase activity "Catalysis of the reaction: (5-L-glutamyl)-peptide + an amino acid = peptide + 5-L-glutamyl-amino acid." [EC:2.3.2.2] molecular_function
+ENSG00000099998 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000099998 GO:0006508 proteolysis "The hydrolysis of proteins into smaller polypeptides and/or amino acids by cleavage of their peptide bonds." [GOC:bf, GOC:mah] biological_process
+ENSG00000099998 GO:0006520 cellular amino acid metabolic process "The chemical reactions and pathways involving amino acids, carboxylic acids containing one or more amino groups, as carried out by individual cells." [CHEBI:33709, GOC:curators, ISBN:0198506732] biological_process
+ENSG00000099998 GO:0006691 leukotriene metabolic process "The chemical reactions and pathways involving leukotriene, a pharmacologically active substance derived from a polyunsaturated fatty acid, such as arachidonic acid." [GOC:ma] biological_process
+ENSG00000099998 GO:0006749 glutathione metabolic process "The chemical reactions and pathways involving glutathione, the tripeptide glutamylcysteinylglycine, which acts as a coenzyme for some enzymes and as an antioxidant in the protection of sulfhydryl groups in enzymes and other proteins; it has a specific role in the reduction of hydrogen peroxide (H2O2) and oxidized ascorbate, and it participates in the gamma-glutamyl cycle." [CHEBI:16856, ISBN:0198506732] biological_process
+ENSG00000099998 GO:0006750 glutathione biosynthetic process "The chemical reactions and pathways resulting in the formation of glutathione, the tripeptide glutamylcysteinylglycine, which acts as a coenzyme for some enzymes and as an antioxidant in the protection of sulfhydryl groups in enzymes and other proteins." [CHEBI:16856, GOC:ai, GOC:al, GOC:pde, ISBN:0198506732] biological_process
+ENSG00000099998 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000099998 GO:0019369 arachidonic acid metabolic process "The chemical reactions and pathways involving arachidonic acid, a straight chain fatty acid with 20 carbon atoms and four double bonds per molecule. Arachidonic acid is the all-Z-(5,8,11,14)-isomer." [ISBN:0198506732] biological_process
+ENSG00000099998 GO:0019370 leukotriene biosynthetic process "The chemical reactions and pathways resulting in the formation of leukotriene, a pharmacologically active substance derived from a polyunsaturated fatty acid, such as arachidonic acid." [GOC:go_curators] biological_process
+ENSG00000099998 GO:0031362 anchored component of external side of plasma membrane "The component of the plasma membrane consisting of the gene products that are tethered to the membrane only by a covalently attached anchor, such as a lipid group embedded in the membrane. Gene products with peptide sequences that are embedded in the membrane are excluded from this grouping." [GOC:dos, GOC:mah] cellular_component
+ENSG00000099998 GO:0036374 glutathione hydrolase activity "Catalysis of the reaction: glutathione + H2O = L-cysteinylglycine + L-glutamate." [EC:3.4.19.13, GOC:imk] molecular_function
+ENSG00000099998 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000099998 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000099998 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000099998 GO:0008233 peptidase activity "Catalysis of the hydrolysis of a peptide bond. A peptide bond is a covalent bond formed when the carbon atom from the carboxyl group of one amino acid shares electrons with the nitrogen atom from the amino group of a second amino acid." [GOC:jl, ISBN:0815332181] molecular_function
+ENSG00000099998 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000099998 GO:0006629 lipid metabolic process "The chemical reactions and pathways involving lipids, compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent. Includes fatty acids; neutral fats, other fatty-acid esters, and soaps; long-chain (fatty) alcohols and waxes; sphingoids and other long-chain bases; glycolipids, phospholipids and sphingolipids; and carotenes, polyprenols, sterols, terpenes and other isoprenoids." [GOC:ma] biological_process
+ENSG00000099998 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000099998 GO:0006790 sulfur compound metabolic process "The chemical reactions and pathways involving the nonmetallic element sulfur or compounds that contain sulfur, such as the amino acids methionine and cysteine or the tripeptide glutathione." [GOC:ai] biological_process
+ENSG00000099998 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000099998 GO:0016746 transferase activity, transferring acyl groups "Catalysis of the transfer of an acyl group from one compound (donor) to another (acceptor)." [GOC:jl, ISBN:0198506732] molecular_function
+ENSG00000099998 GO:0006954 inflammatory response "The immediate defensive reaction (by vertebrate tissue) to infection or injury caused by chemical or physical agents. The process is characterized by local vasodilation, extravasation of plasma into intercellular spaces and accumulation of white blood cells and macrophages." [GO_REF:0000022, GOC:mtg_15nov05, ISBN:0198506732] biological_process
+ENSG00000100285 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100285 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100285 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100285 GO:0007010 cytoskeleton organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of cytoskeletal structures." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100285 GO:0022607 cellular component assembly "The aggregation, arrangement and bonding together of a cellular component." [GOC:isa_complete] biological_process
+ENSG00000100285 GO:0030674 protein binding, bridging "The binding activity of a molecule that brings together two or more protein molecules, or a protein and another macromolecule or complex, through a selective, non-covalent, often stoichiometric interaction, permitting those molecules to function in a coordinated way." [GOC:bf, GOC:mah, GOC:vw] molecular_function
+ENSG00000100285 GO:0019899 enzyme binding "Interacting selectively and non-covalently with any enzyme." [GOC:jl] molecular_function
+ENSG00000100285 GO:0008092 cytoskeletal protein binding "Interacting selectively and non-covalently with any protein component of any cytoskeleton (actin, microtubule, or intermediate filament cytoskeleton)." [GOC:mah] molecular_function
+ENSG00000100285 GO:0008219 cell death "Any biological process that results in permanent cessation of all vital functions of a cell. A cell should be considered dead when any one of the following molecular or morphological criteria is met: (1) the cell has lost the integrity of its plasma membrane; (2) the cell, including its nucleus, has undergone complete fragmentation into discrete bodies (frequently referred to as \"apoptotic bodies\"); and/or (3) its corpse (or its fragments) have been engulfed by an adjacent cell in vivo." [GOC:mah, GOC:mtg_apoptosis] biological_process
+ENSG00000100285 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100285 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100285 GO:0005198 structural molecule activity "The action of a molecule that contributes to the structural integrity of a complex or assembly within or outside a cell." [GOC:mah] molecular_function
+ENSG00000100285 GO:0005200 structural constituent of cytoskeleton "The action of a molecule that contributes to the structural integrity of a cytoskeletal structure." [GOC:mah] molecular_function
+ENSG00000100285 GO:0005883 neurofilament "A type of intermediate filament found in the core of neuronal axons. Neurofilaments are heteropolymers composed of three type IV polypeptides: NF-L, NF-M, and NF-H (for low, middle, and high molecular weight). Neurofilaments are responsible for the radial growth of an axon and determine axonal diameter." [ISBN:0198506732, ISBN:0716731363, ISBN:0815316194] cellular_component
+ENSG00000100285 GO:0007409 axonogenesis "De novo generation of a long process of a neuron, that carries efferent (outgoing) action potentials from the cell body towards target cells. Refers to the morphogenesis or creation of shape or form of the developing axon." [GOC:dph, GOC:jid, GOC:pg, GOC:pr, ISBN:0198506732] biological_process
+ENSG00000100285 GO:0008017 microtubule binding "Interacting selectively and non-covalently with microtubules, filaments composed of tubulin monomers." [GOC:krc] molecular_function
+ENSG00000100285 GO:0019894 kinesin binding "Interacting selectively and non-covalently and stoichiometrically with kinesin, a member of a superfamily of microtubule-based motor proteins that perform force-generating tasks such as organelle transport and chromosome segregation." [GOC:curators, PMID:8606779] molecular_function
+ENSG00000100285 GO:0019901 protein kinase binding "Interacting selectively and non-covalently with a protein kinase, any enzyme that catalyzes the transfer of a phosphate group, usually from ATP, to a protein substrate." [GOC:jl] molecular_function
+ENSG00000100285 GO:0030031 cell projection assembly "Formation of a prolongation or process extending from a cell, e.g. a flagellum or axon." [GOC:jl, GOC:mah, http://www.cogsci.princeton.edu/~wn/] biological_process
+ENSG00000100285 GO:0030424 axon "The long process of a neuron that conducts nerve impulses, usually away from the cell body to the terminals and varicosities, which are sites of storage and release of neurotransmitter." [GOC:nln, ISBN:0198506732] cellular_component
+ENSG00000100285 GO:0033693 neurofilament bundle assembly "The assembly of neurofilaments into bundles, in which the filaments are longitudinally oriented, with numerous crossbridges between them. Neurofilament bundles may be cross-linked to each other, to membrane-bounded organelles or other cytoskeletal structures such as microtubules." [PMID:11034913, PMID:11264295] biological_process
+ENSG00000100285 GO:0045502 dynein binding "Interacting selectively and non-covalently with dynein, the multisubunit protein complex that is associated with microtubules." [GOC:bf] molecular_function
+ENSG00000100285 GO:0061564 axon development "The progression of an axon over time. Covers axonogenesis (de novo generation of an axon) and axon regeneration (regrowth), as well as processes pertaining to the progression of the axon over time (fasciculation and defasciculation)." [GOC:dph, GOC:pg, GOC:pr] biological_process
+ENSG00000100285 GO:0097418 neurofibrillary tangle "Intracellular mass of paired, helically wound protein filaments (also called PHF) lying in the cytoplasm of neuronal cell bodies and neuritic cell processes. Neurofibrillary tangles contain an abnormally phosphorylated form of a microtubule-associated protein, tau. The shape of these inclusions may resemble a flame or a star." [NIF_Subcellular:nlx_subcell_20090201, NIF_Subcellular:nlx_subcell_20090202, NIF_Subcellular:sao2409833926] cellular_component
+ENSG00000100285 GO:1902513 regulation of organelle transport along microtubule "Any process that modulates the frequency, rate or extent of organelle transport along microtubule." [GOC:dph, GOC:TermGenie, PMID:21147087] biological_process
+ENSG00000100285 GO:0005882 intermediate filament "A cytoskeletal structure that forms a distinct elongated structure, characteristically 10 nm in diameter, that occurs in the cytoplasm of eukaryotic cells. Intermediate filaments form a fibrous system, composed of chemically heterogeneous subunits and involved in mechanically integrating the various components of the cytoplasmic space. Intermediate filaments may be divided into five chemically distinct classes: Type I, acidic keratins; Type II, basic keratins; Type III, including desmin, vimentin and others; Type IV, neurofilaments and related filaments; and Type V, lamins." [http://www.cytochemistry.net/Cell-biology/intermediate_filaments.htm, ISBN:0198506732] cellular_component
+ENSG00000100285 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000100285 GO:0000226 microtubule cytoskeleton organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of cytoskeletal structures comprising microtubules and their associated proteins." [GOC:mah] biological_process
+ENSG00000100285 GO:0045104 intermediate filament cytoskeleton organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of cytoskeletal structures comprising intermediate filaments and their associated proteins." [GOC:ai] biological_process
+ENSG00000100285 GO:0045110 intermediate filament bundle assembly "The formation of the bundles of intermediate filaments. Intermediate filament-associated proteins (IFAPs) cross-link intermediate filaments with one another, forming a bundle or a network, and with other cell structures, including the plasma membrane. The organization of intermediate filaments and their supportive function in various cells types depends in large part on their linkage to other cell structures via IFAPs." [ISBN:0716731363] biological_process
+ENSG00000100285 GO:0060052 neurofilament cytoskeleton organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of cytoskeletal structures comprising neurofilaments and their associated proteins." [GOC:dph] biological_process
+ENSG00000100285 GO:0048936 peripheral nervous system neuron axonogenesis "Generation of a long process from a neuron whose cell body resides in the peripheral nervous system. The axon carries action potential from the cell body towards target cells." [GOC:dgh] biological_process
+ENSG00000128342 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000128342 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000128342 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000128342 GO:0002376 immune system process "Any process involved in the development or functioning of the immune system, an organismal system for calibrated responses to potential internal or invasive threats." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05] biological_process
+ENSG00000128342 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000128342 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000128342 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000128342 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000128342 GO:0005615 extracellular space "That part of a multicellular organism outside the cells proper, usually taken to be outside the plasma membranes, and occupied by fluid." [ISBN:0198547684] cellular_component
+ENSG00000128342 GO:0000988 protein binding transcription factor activity "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules), in order to modulate transcription. A protein binding transcription factor may or may not also interact with the template nucleic acid (either DNA or RNA) as well." [GOC:txnOH] molecular_function
+ENSG00000128342 GO:0001135 RNA polymerase II transcription factor recruiting transcription factor activity "The function of binding to an RNA polymerase II (RNAP II) transcription factor and recruiting it to the transcription machinery complex in order to modulate transcription by RNAP II." [GOC:txnOH, PMID:16858867] molecular_function
+ENSG00000128342 GO:0005102 receptor binding "Interacting selectively and non-covalently with one or more specific sites on a receptor molecule, a macromolecule that undergoes combination with a hormone, neurotransmitter, drug or intracellular messenger to initiate a change in cell function." [GOC:bf, GOC:ceb, ISBN:0198506732] molecular_function
+ENSG00000128342 GO:0005125 cytokine activity "Functions to control the survival, growth, differentiation and effector function of tissues and cells." [ISBN:0198599471] molecular_function
+ENSG00000128342 GO:0005146 leukemia inhibitory factor receptor binding "Interacting selectively and non-covalently with the leukemia inhibitory factor receptor." [GOC:ai] molecular_function
+ENSG00000128342 GO:0006366 transcription from RNA polymerase II promoter "The synthesis of RNA from a DNA template by RNA polymerase II, originating at an RNA polymerase II promoter. Includes transcription of messenger RNA (mRNA) and certain small nuclear RNAs (snRNAs)." [GOC:jl, GOC:txnOH, ISBN:0321000382] biological_process
+ENSG00000128342 GO:0006955 immune response "Any immune system process that functions in the calibrated response of an organism to a potential internal or invasive threat." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05] biological_process
+ENSG00000128342 GO:0007275 multicellular organismal development "The biological process whose specific outcome is the progression of a multicellular organism over time from an initial condition (e.g. a zygote or a young adult) to a later condition (e.g. a multicellular animal or an aged adult)." [GOC:dph, GOC:ems, GOC:isa_complete, GOC:tb] biological_process
+ENSG00000128342 GO:0008083 growth factor activity "The function that stimulates a cell to grow or proliferate. Most growth factors have other actions besides the induction of cell growth or proliferation." [ISBN:0815316194] molecular_function
+ENSG00000128342 GO:0008284 positive regulation of cell proliferation "Any process that activates or increases the rate or extent of cell proliferation." [GOC:go_curators] biological_process
+ENSG00000128342 GO:0033138 positive regulation of peptidyl-serine phosphorylation "Any process that activates or increases the frequency, rate or extent of the phosphorylation of peptidyl-serine." [GOC:mah] biological_process
+ENSG00000128342 GO:0033141 positive regulation of peptidyl-serine phosphorylation of STAT protein "Any process that activates or increases the frequency, rate or extent of the phosphorylation of a serine residue of a STAT (Signal Transducer and Activator of Transcription) protein." [GOC:mah] biological_process
+ENSG00000128342 GO:0042511 positive regulation of tyrosine phosphorylation of Stat1 protein "Any process that activates or increases the frequency, rate or extent of the introduction of a phosphate group to a tyrosine residue of a Stat1 protein." [GOC:jl, PMID:10918594] biological_process
+ENSG00000128342 GO:0042517 positive regulation of tyrosine phosphorylation of Stat3 protein "Any process that activates or increases the frequency, rate or extent of the introduction of a phosphate group to a tyrosine residue of a Stat3 protein." [GOC:jl, PMID:11426647] biological_process
+ENSG00000128342 GO:0043410 positive regulation of MAPK cascade "Any process that activates or increases the frequency, rate or extent of signal transduction mediated by the MAPK cascade." [GOC:go_curators] biological_process
+ENSG00000128342 GO:0045651 positive regulation of macrophage differentiation "Any process that activates or increases the frequency, rate or extent of macrophage differentiation." [GOC:go_curators] biological_process
+ENSG00000128342 GO:0045944 positive regulation of transcription from RNA polymerase II promoter "Any process that activates or increases the frequency, rate or extent of transcription from an RNA polymerase II promoter." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000128342 GO:0046888 negative regulation of hormone secretion "Any process that stops, prevents, or reduces the frequency, rate or extent of the regulated release of a hormone from a cell." [GOC:ai] biological_process
+ENSG00000128342 GO:0048861 leukemia inhibitory factor signaling pathway "Any series of molecular signals initiated by the binding of leukemia inhibitory factor to a receptor on the surface of the target cell, and ending with regulation of a downstream cellular process, e.g. transcription." [GOC:devbiol, GOC:signaling] biological_process
+ENSG00000128342 GO:0050731 positive regulation of peptidyl-tyrosine phosphorylation "Any process that activates or increases the frequency, rate or extent of the phosphorylation of peptidyl-tyrosine." [GOC:ai] biological_process
+ENSG00000128342 GO:0072108 positive regulation of mesenchymal to epithelial transition involved in metanephros morphogenesis "Any process that increases the rate, frequency or extent of the transition where a mesenchymal cell establishes apical/basolateral polarity, forms intercellular adhesive junctions, synthesizes basement membrane components and becomes an epithelial cell that will contribute to the shaping of the metanephros." [GOC:mtg_kidney_jan10] biological_process
+ENSG00000128342 GO:0072307 regulation of metanephric nephron tubule epithelial cell differentiation "Any process that modulates the frequency, rate or extent of metanephric nephron tubule epithelial cell differentiation." [GOC:mtg_kidney_jan10] biological_process
+ENSG00000128342 GO:1900182 positive regulation of protein localization to nucleus "Any process that activates or increases the frequency, rate or extent of protein localization to nucleus." [GOC:TermGenie] biological_process
+ENSG00000128342 GO:1901676 positive regulation of histone H3-K27 acetylation "Any process that activates or increases the frequency, rate or extent of histone H3-K27 acetylation." [GOC:BHF, GOC:TermGenie] biological_process
+ENSG00000128342 GO:0005576 extracellular region "The space external to the outermost structure of a cell. For cells without external protective or external encapsulating structures this refers to space outside of the plasma membrane. This term covers the host cell environment outside an intracellular parasite." [GOC:go_curators] cellular_component
+ENSG00000128342 GO:0048863 stem cell differentiation "The process in which a relatively unspecialized cell acquires specialized features of a stem cell. A stem cell is a cell that retains the ability to divide and proliferate throughout life to provide progenitor cells that can differentiate into specialized cells." [CL:0000034, GOC:isa_complete] biological_process
+ENSG00000128342 GO:0008285 negative regulation of cell proliferation "Any process that stops, prevents or reduces the rate or extent of cell proliferation." [GOC:go_curators] biological_process
+ENSG00000128342 GO:0007566 embryo implantation "Attachment of the blastocyst to the uterine lining." [GOC:isa_complete, http://www.medterms.com] biological_process
+ENSG00000128342 GO:0048286 lung alveolus development "The process whose specific outcome is the progression of the alveolus over time, from its formation to the mature structure. The alveolus is a sac for holding air in the lungs; formed by the terminal dilation of air passageways." [GOC:mtg_lung, PMID:9751757] biological_process
+ENSG00000128342 GO:0045595 regulation of cell differentiation "Any process that modulates the frequency, rate or extent of cell differentiation, the process in which relatively unspecialized cells acquire specialized structural and functional features." [GOC:go_curators] biological_process
+ENSG00000128342 GO:0019827 stem cell maintenance "The process by which an organism or tissue maintains a population of stem cells of a single type. This can be achieved by a number of mechanisms: stem cell asymmetric division maintains stem cell numbers; stem cell symmetric division increases them; maintenance of a stem cell niche maintains the conditions for commitment to the stem cell fate for some types of stem cell; stem cells may arise de novo from other cell types. " [GOC:mah, ISBN:0878932437] biological_process
+ENSG00000128342 GO:0030324 lung development "The process whose specific outcome is the progression of the lung over time, from its formation to the mature structure. In all air-breathing vertebrates the lungs are developed from the ventral wall of the oesophagus as a pouch which divides into two sacs. In amphibians and many reptiles the lungs retain very nearly this primitive sac-like character, but in the higher forms the connection with the esophagus becomes elongated into the windpipe and the inner walls of the sacs become more and more divided, until, in the mammals, the air spaces become minutely divided into tubes ending in small air cells, in the walls of which the blood circulates in a fine network of capillaries. In mammals the lungs are more or less divided into lobes, and each lung occupies a separate cavity in the thorax." [GOC:jid, UBERON:0002048] biological_process
+ENSG00000128342 GO:0060135 maternal process involved in female pregnancy "A reproductive process occurring in the mother that allows an embryo or fetus to develop within it." [GOC:dph] biological_process
+ENSG00000128342 GO:0001974 blood vessel remodeling "The reorganization or renovation of existing blood vessels." [GOC:hjd] biological_process
+ENSG00000128342 GO:0060426 lung vasculature development "The biological process whose specific outcome is the progression of a lung vasculature from an initial condition to its mature state. This process begins with the formation of the lung vasculature and ends with the mature structure. The lung vasculature is composed of the tubule structures that carry blood or lymph in the lungs." [GOC:dph, GOC:mtg_lung] biological_process
+ENSG00000128342 GO:0048666 neuron development "The process whose specific outcome is the progression of a neuron over time, from initial commitment of the cell to a specific fate, to the fully functional differentiated cell." [GOC:dph] biological_process
+ENSG00000128342 GO:0070373 negative regulation of ERK1 and ERK2 cascade "Any process that stops, prevents, or reduces the frequency, rate or extent of signal transduction mediated by the ERK1 and ERK2 cascade." [GOC:add, ISBN:0121245462, ISBN:0896039986] biological_process
+ENSG00000128342 GO:0048644 muscle organ morphogenesis "The process in which the anatomical structures of muscle are generated and organized." [GOC:jid] biological_process
+ENSG00000128342 GO:0060707 trophoblast giant cell differentiation "The process in which a relatively unspecialized cell acquires specialized features of a trophoblast giant cell of the placenta. Trophoblast giant cells are the cell of the placenta that line the maternal decidua." [GOC:dph, PMID:16269175] biological_process
+ENSG00000128342 GO:0046697 decidualization "The cellular and vascular changes occurring in the endometrium of the pregnant uterus just after the onset of blastocyst implantation. This process involves the proliferation and differentiation of the fibroblast-like endometrial stromal cells into large, polyploid decidual cells that eventually form the maternal component of the placenta." [ISBN:0721662544, PMID:11133685] biological_process
+ENSG00000128342 GO:0060463 lung lobe morphogenesis "The process in which the anatomical structures of a lung lobe are generated and organized. A lung lobe is a projection that extends from the lung." [GOC:dph] biological_process
+ENSG00000128342 GO:0060708 spongiotrophoblast differentiation "The process in which a relatively unspecialized cell of the ectoplacental cone acquires specialized features of a spongiotrophoblast of the placenta. A spongiotrophoblast cell is a basophilic cell." [GOC:dph, PMID:16269175] biological_process
+ENSG00000128342 GO:0042503 tyrosine phosphorylation of Stat3 protein "The process of introducing a phosphate group to a tyrosine residue of a Stat3 protein." [GOC:jl, PMID:11426647] biological_process
+ENSG00000128342 GO:0045835 negative regulation of meiosis "Any process that stops, prevents, or reduces the frequency, rate or extent of meiosis." [GOC:go_curators] biological_process
+ENSG00000128342 GO:0048711 positive regulation of astrocyte differentiation "Any process that activates or increases the frequency, rate or extent of astrocyte differentiation." [GOC:vp, PMID:15139015] biological_process
+ENSG00000186732 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000186732 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000186732 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000186732 GO:0008152 metabolic process "The chemical reactions and pathways, including anabolism and catabolism, by which living organisms transform chemical substances. Metabolic processes typically transform small molecules, but also include macromolecular processes such as DNA repair and replication, and protein synthesis and degradation." [GOC:go_curators, ISBN:0198547684] biological_process
+ENSG00000186732 GO:0016787 hydrolase activity "Catalysis of the hydrolysis of various bonds, e.g. C-O, C-N, C-C, phosphoric anhydride bonds, etc. Hydrolase is the systematic name for any enzyme of EC class 3." [ISBN:0198506732] molecular_function
+ENSG00000186732
+ENSG00000100024
+ENSG00000100024 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100024 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100024 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000100024 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100024 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100024 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000100024 GO:0034655 nucleobase-containing compound catabolic process "The chemical reactions and pathways resulting in the breakdown of nucleobases, nucleosides, nucleotides and nucleic acids." [GOC:mah] biological_process
+ENSG00000100024 GO:0006520 cellular amino acid metabolic process "The chemical reactions and pathways involving amino acids, carboxylic acids containing one or more amino groups, as carried out by individual cells." [CHEBI:33709, GOC:curators, ISBN:0198506732] biological_process
+ENSG00000100024 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000100024 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100024 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000100024 GO:0016810 hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds "Catalysis of the hydrolysis of any carbon-nitrogen bond, C-N, with the exception of peptide bonds." [GOC:jl] molecular_function
+ENSG00000100024 GO:0003837 beta-ureidopropionase activity "Catalysis of the reaction: N-carbamoyl-beta-alanine + H2O = beta-alanine + CO2 + NH3." [EC:3.5.1.6] molecular_function
+ENSG00000100024 GO:0006206 pyrimidine nucleobase metabolic process "The chemical reactions and pathways involving pyrimidine nucleobases, 1,3-diazine, organic nitrogenous bases." [CHEBI:26432, GOC:go_curators] biological_process
+ENSG00000100024 GO:0019483 beta-alanine biosynthetic process "The chemical reactions and pathways resulting in the formation of beta-alanine (3-aminopropanoic acid), an achiral amino acid and an isomer of alanine. It occurs free (e.g. in brain) and in combination (e.g. in pantothenate) but it is not a constituent of proteins." [GOC:jl, ISBN:0198506732] biological_process
+ENSG00000100024 GO:0046135 pyrimidine nucleoside catabolic process "The chemical reactions and pathways resulting in the breakdown of one of a family of organic molecules consisting of a pyrimidine base covalently bonded to a sugar ribose (a ribonucleoside) or deoxyribose (a deoxyribonucleoside)." [GOC:ai] biological_process
+ENSG00000100024 GO:0046872 metal ion binding "Interacting selectively and non-covalently with any metal ion." [GOC:ai] molecular_function
+ENSG00000100024 GO:0055086 nucleobase-containing small molecule metabolic process "The cellular chemical reactions and pathways involving a nucleobase-containing small molecule: a nucleobase, a nucleoside, or a nucleotide." [GOC:vw] biological_process
+ENSG00000100024 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000100024 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100024 GO:0006807 nitrogen compound metabolic process "The chemical reactions and pathways involving organic or inorganic compounds that contain nitrogen, including (but not limited to) nitrogen fixation, nitrification, denitrification, assimilatory/dissimilatory nitrate reduction and the interconversion of nitrogenous organic matter and ammonium." [CHEBI:51143, GOC:go_curators, GOC:jl, ISBN:0198506732] biological_process
+ENSG00000186951
+ENSG00000186951 GO:0030522 intracellular receptor signaling pathway "Any series of molecular signals initiated by a ligand binding to an receptor located within a cell." [GOC:bf, GOC:mah] biological_process
+ENSG00000186951 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000186951 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000186951 GO:0004879 ligand-activated sequence-specific DNA binding RNA polymerase II transcription factor activity "Combining with a signal and transmitting the signal to the transcriptional machinery by interacting selectively and non-covalently with a specific DNA sequence in order to modulate transcription by RNA polymerase II." [GOC:signaling, GOC:txnOH] molecular_function
+ENSG00000186951 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000186951 GO:0006355 regulation of transcription, DNA-templated "Any process that modulates the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000186951 GO:0008270 zinc ion binding "Interacting selectively and non-covalently with zinc (Zn) ions." [GOC:ai] molecular_function
+ENSG00000186951 GO:0043565 sequence-specific DNA binding "Interacting selectively and non-covalently with DNA of a specific nucleotide composition, e.g. GC-rich DNA binding, or with a specific sequence motif or type of DNA e.g. promotor binding or rDNA binding." [GOC:jl] molecular_function
+ENSG00000186951 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000186951 GO:0003677 DNA binding "Any molecular function by which a gene product interacts selectively and non-covalently with DNA (deoxyribonucleic acid)." [GOC:dph, GOC:jl, GOC:tb, GOC:vw] molecular_function
+ENSG00000186951 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000186951 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000186951 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000186951 GO:0001071 nucleic acid binding transcription factor activity "Interacting selectively and non-covalently with a DNA or RNA sequence in order to modulate transcription. The transcription factor may or may not also interact selectively with a protein or macromolecular complex." [GOC:txnOH] molecular_function
+ENSG00000186951 GO:0003700 sequence-specific DNA binding transcription factor activity "Interacting selectively and non-covalently with a specific DNA sequence in order to modulate transcription. The transcription factor may or may not also interact selectively with a protein or macromolecular complex." [GOC:curators, GOC:txnOH] molecular_function
+ENSG00000186951 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000186951 GO:0006629 lipid metabolic process "The chemical reactions and pathways involving lipids, compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent. Includes fatty acids; neutral fats, other fatty-acid esters, and soaps; long-chain (fatty) alcohols and waxes; sphingoids and other long-chain bases; glycolipids, phospholipids and sphingolipids; and carotenes, polyprenols, sterols, terpenes and other isoprenoids." [GOC:ma] biological_process
+ENSG00000186951 GO:0019899 enzyme binding "Interacting selectively and non-covalently with any enzyme." [GOC:jl] molecular_function
+ENSG00000186951 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000186951 GO:0008289 lipid binding "Interacting selectively and non-covalently with a lipid." [GOC:ai] molecular_function
+ENSG00000186951 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000186951 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000186951 GO:0005654 nucleoplasm "That part of the nuclear content other than the chromosomes or the nucleolus." [GOC:ma, ISBN:0124325653] cellular_component
+ENSG00000186951 GO:0004871 signal transducer activity "Conveys a signal across a cell to trigger a change in cell function or state. A signal is a physical entity or change in state that is used to transfer information in order to trigger a response." [GOC:go_curators] molecular_function
+ENSG00000186951 GO:0008134 transcription factor binding "Interacting selectively and non-covalently with a transcription factor, any protein required to initiate or regulate transcription." [ISBN:0198506732] molecular_function
+ENSG00000186951 GO:0000122 negative regulation of transcription from RNA polymerase II promoter "Any process that stops, prevents, or reduces the frequency, rate or extent of transcription from an RNA polymerase II promoter." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000186951 GO:0000978 RNA polymerase II core promoter proximal region sequence-specific DNA binding "Interacting selectively and non-covalently with a sequence of DNA that is in cis with and relatively close to a core promoter for RNA polymerase II." [GOC:txnOH] molecular_function
+ENSG00000186951 GO:0001077 RNA polymerase II core promoter proximal region sequence-specific DNA binding transcription factor activity involved in positive regulation of transcription "Interacting selectively and non-covalently with a sequence of DNA that is in cis with and relatively close to a core promoter for RNA polymerase II (RNAP II) in order to activate or increase the frequency, rate or extent of transcription from the RNAP II promoter." [GOC:txnOH] molecular_function
+ENSG00000186951 GO:0001078 RNA polymerase II core promoter proximal region sequence-specific DNA binding transcription factor activity involved in negative regulation of transcription "Interacting selectively and non-covalently with a sequence of DNA that is in cis with and relatively close to a core promoter for RNA polymerase II (RNAP II) in order to stop, prevent, or reduce the frequency, rate or extent of transcription from an RNA polymerase II promoter." [GOC:txnOH] molecular_function
+ENSG00000186951 GO:0001103 RNA polymerase II repressing transcription factor binding "Interacting selectively and non-covalently with an RNA polymerase II transcription repressing factor, a protein involved in negative regulation of transcription." [GOC:txnOH] molecular_function
+ENSG00000186951 GO:0003707 steroid hormone receptor activity "Combining with a steroid hormone and transmitting the signal within the cell to initiate a change in cell activity or function." [GOC:signaling, PMID:14708019] molecular_function
+ENSG00000186951 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000186951 GO:0006367 transcription initiation from RNA polymerase II promoter "Any process involved in the assembly of the RNA polymerase II preinitiation complex (PIC) at an RNA polymerase II promoter region of a DNA template, resulting in the subsequent synthesis of RNA from that promoter. The initiation phase includes PIC assembly and the formation of the first few bonds in the RNA chain, including abortive initiation, which occurs when the first few nucleotides are repeatedly synthesized and then released. Promoter clearance, or release, is the transition between the initiation and elongation phases of transcription." [GOC:mah, GOC:txnOH] biological_process
+ENSG00000186951 GO:0008144 drug binding "Interacting selectively and non-covalently with a drug, any naturally occurring or synthetic substance, other than a nutrient, that, when administered or applied to an organism, affects the structure or functioning of the organism; in particular, any such substance used in the diagnosis, prevention, or treatment of disease." [GOC:jl, ISBN:0198506732] molecular_function
+ENSG00000186951 GO:0010467 gene expression "The process in which a gene's sequence is converted into a mature gene product or products (proteins or RNA). This includes the production of an RNA transcript as well as any processing to produce a mature RNA product or an mRNA (for protein-coding genes) and the translation of that mRNA into protein. Some protein processing events may be included when they are required to form an active form of a product from an inactive precursor form." [GOC:dph, GOC:tb] biological_process
+ENSG00000186951 GO:0010745 negative regulation of macrophage derived foam cell differentiation "Any process that decreases the rate, frequency or extent of macrophage derived foam cell differentiation. Macrophage derived foam cell differentiation is the process in which a macrophage acquires the specialized features of a foam cell. A foam cell is a type of cell containing lipids in small vacuoles and typically seen in atherosclerotic lesions, as well as other conditions." [GOC:add, GOC:BHF, GOC:dph, GOC:tb] biological_process
+ENSG00000186951 GO:0010871 negative regulation of receptor biosynthetic process "Any process that decreases the frequency or rate of receptor biosynthesis. Receptor biosynthesis is the collection of chemical reactions and pathways resulting in the formation of a receptor molecule, a macromolecule that undergoes combination with a hormone, neurotransmitter, drug or intracellular messenger to initiate a change in cell function." [GOC:BHF, GOC:tb] biological_process
+ENSG00000186951 GO:0010887 negative regulation of cholesterol storage "Any process that decreases the rate or extent of cholesterol storage. Cholesterol storage is the accumulation and maintenance in cells or tissues of cholesterol, cholest-5-en-3 beta-ol, the principal sterol of vertebrates and the precursor of many steroids, including bile acids and steroid hormones." [GOC:BHF, GOC:dph, GOC:tb] biological_process
+ENSG00000186951 GO:0010891 negative regulation of sequestering of triglyceride "Any process that modulates the rate, frequency or extent of sequestering of triglyceride. Triglyceride sequestration is the process of binding or confining any triester of glycerol such that it is separated from other components of a biological system." [GOC:BHF, GOC:dph, GOC:tb] biological_process
+ENSG00000186951 GO:0015908 fatty acid transport "The directed movement of fatty acids into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore. Fatty acids are aliphatic monocarboxylic acids liberated from naturally occurring fats and oils by hydrolysis." [GOC:ai] biological_process
+ENSG00000186951 GO:0031624 ubiquitin conjugating enzyme binding "Interacting selectively and non-covalently with a ubiquitin conjugating enzyme, any of the E2 proteins." [GOC:vp] molecular_function
+ENSG00000186951 GO:0032000 positive regulation of fatty acid beta-oxidation "Any process that activates or increases the frequency, rate or extent of fatty acid beta-oxidation." [GOC:mah] biological_process
+ENSG00000186951 GO:0032099 negative regulation of appetite "Any process that reduces appetite." [GOC:add] biological_process
+ENSG00000186951 GO:0032922 circadian regulation of gene expression "Any process that modulates the frequency, rate or extent of gene expression such that an expression pattern recurs with a regularity of approximately 24 hours." [GOC:mah] biological_process
+ENSG00000186951 GO:0042752 regulation of circadian rhythm "Any process that modulates the frequency, rate or extent of a circadian rhythm. A circadian rhythm is a biological process in an organism that recurs with a regularity of approximately 24 hours." [GOC:dph, GOC:jl, GOC:tb] biological_process
+ENSG00000186951 GO:0043401 steroid hormone mediated signaling pathway "A series of molecular signals mediated by a steroid hormone binding to a receptor." [PMID:12606724] biological_process
+ENSG00000186951 GO:0044255 cellular lipid metabolic process "The chemical reactions and pathways involving lipids, as carried out by individual cells." [GOC:jl] biological_process
+ENSG00000186951 GO:0045820 negative regulation of glycolytic process "Any process that stops, prevents, or reduces the frequency, rate or extent of glycolysis." [GOC:go_curators] biological_process
+ENSG00000186951 GO:0045893 positive regulation of transcription, DNA-templated "Any process that activates or increases the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000186951 GO:0045944 positive regulation of transcription from RNA polymerase II promoter "Any process that activates or increases the frequency, rate or extent of transcription from an RNA polymerase II promoter." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000186951 GO:0046321 positive regulation of fatty acid oxidation "Any process that activates or increases the frequency, rate or extent of fatty acid oxidation." [GOC:ai] biological_process
+ENSG00000186951 GO:0072363 regulation of glycolytic by positive regulation of transcription from RNA polymerase II promoter "Any process that modulates the frequency, rate or extent of glycolysis by activating or increasing the frequency, rate or extent of transcription from an RNA polymerase II promoter." [GOC:BHF, GOC:mah] biological_process
+ENSG00000186951 GO:0072366 regulation of cellular ketone metabolic process by positive regulation of transcription from RNA polymerase II promoter "Any process that modulates the frequency, rate or extent of a cellular ketone metabolic process by activating or increasing the frequency, rate or extent of transcription from an RNA polymerase II promoter." [GOC:BHF, GOC:mah] biological_process
+ENSG00000186951 GO:0072369 regulation of lipid transport by positive regulation of transcription from RNA polymerase II promoter "Any process that modulates the frequency, rate or extent of lipid transport by activating or increasing the frequency, rate or extent of transcription from an RNA polymerase II promoter." [GOC:BHF, GOC:mah] biological_process
+ENSG00000186951 GO:0010468 regulation of gene expression "Any process that modulates the frequency, rate or extent of gene expression. Gene expression is the process in which a gene's coding sequence is converted into a mature gene product or products (proteins or RNA). This includes the production of an RNA transcript as well as any processing to produce a mature RNA product or an mRNA (for protein-coding genes) and the translation of that mRNA into protein. Some protein processing events may be included when they are required to form an active form of a product from an inactive precursor form." [GOC:dph, GOC:tb] biological_process
+ENSG00000186951 GO:0001190 RNA polymerase II transcription factor binding transcription factor activity involved in positive regulation of transcription "Interacting selectively and non-covalently with an RNA polymerase II transcription factor, which may be a single protein or a complex, in order to increase the frequency, rate or extent of transcription from an RNA polymerase II promoter. A protein binding transcription factor may or may not also interact with the template nucleic acid (either DNA or RNA) as well." [GOC:txnOH] molecular_function
+ENSG00000186951 GO:0042060 wound healing "The series of events that restore integrity to a damaged tissue, following an injury." [GOC:bf, PMID:15269788] biological_process
+ENSG00000186951 GO:0004872 receptor activity "Combining with an extracellular or intracellular messenger to initiate a change in cell activity." [GOC:ceb, ISBN:0198506732] molecular_function
+ENSG00000186951 GO:0008544 epidermis development "The process whose specific outcome is the progression of the epidermis over time, from its formation to the mature structure. The epidermis is the outer epithelial layer of a plant or animal, it may be a single layer that produces an extracellular material (e.g. the cuticle of arthropods) or a complex stratified squamous epithelium, as in the case of many vertebrate species." [GOC:go_curators, UBERON:0001003] biological_process
+ENSG00000186951 GO:0070166 enamel mineralization "The process in which calcium salts, mainly carbonated hydroxyapatite, are deposited in tooth enamel." [GOC:BHF, GOC:mah, GOC:sl, PMID:10206335, PMID:16931858, PMID:21196346] biological_process
+ENSG00000186951 GO:0019217 regulation of fatty acid metabolic process "Any process that modulates the frequency, rate or extent of the chemical reactions and pathways involving fatty acids." [GOC:go_curators] biological_process
+ENSG00000186951 GO:0045722 positive regulation of gluconeogenesis "Any process that activates or increases the frequency, rate or extent of gluconeogenesis." [GOC:go_curators] biological_process
+ENSG00000186951 GO:0032403 protein complex binding "Interacting selectively and non-covalently with any protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:mah] molecular_function
+ENSG00000186951 GO:0019904 protein domain specific binding "Interacting selectively and non-covalently with a specific domain of a protein." [GOC:go_curators] molecular_function
+ENSG00000186951 GO:0019902 phosphatase binding "Interacting selectively and non-covalently with any phosphatase." [GOC:jl] molecular_function
+ENSG00000186951 GO:0045776 negative regulation of blood pressure "Any process in which the force of blood traveling through the circulatory system is decreased." [GOC:go_curators, GOC:mtg_cardio] biological_process
+ENSG00000186951 GO:0001666 response to hypoxia "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a stimulus indicating lowered oxygen tension. Hypoxia, defined as a decline in O2 levels below normoxic levels of 20.8 - 20.95%, results in metabolic adaptation at both the cellular and organismal level." [GOC:hjd] biological_process
+ENSG00000186951 GO:1901215 negative regulation of neuron death "Any process that stops, prevents or reduces the frequency, rate or extent of neuron death." [GOC:rph, GOC:TermGenie] biological_process
+ENSG00000186951 GO:0007507 heart development "The process whose specific outcome is the progression of the heart over time, from its formation to the mature structure. The heart is a hollow, muscular organ, which, by contracting rhythmically, keeps up the circulation of the blood." [GOC:jid, UBERON:0000948] biological_process
+ENSG00000186951 GO:0032868 response to insulin "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of an insulin stimulus. Insulin is a polypeptide hormone produced by the islets of Langerhans of the pancreas in mammals, and by the homologous organs of other organisms." [GOC:mah, ISBN:0198506732] biological_process
+ENSG00000186951 GO:0042157 lipoprotein metabolic process "The chemical reactions and pathways involving any conjugated, water-soluble protein in which the nonprotein group consists of a lipid or lipids." [ISBN:0198506732] biological_process
+ENSG00000186951 GO:0097371 MDM2/MDM4 family protein binding "Interacting selectively and non-covalently with any isoform of the MDM2/MDM4 protein family, comprising negative regulators of p53." [InterPro:IPR016495] molecular_function
+ENSG00000186951 GO:0051525 NFAT protein binding "Interacting selectively and non-covalently with NFAT (nuclear factor of activated T cells) proteins, a family of transcription factors. NFAT proteins have crucial roles in the development and function of the immune system." [PMID:15928679] molecular_function
+ENSG00000186951 GO:2000678 negative regulation of transcription regulatory region DNA binding "Any process that stops, prevents or reduces the frequency, rate or extent of transcription regulatory region DNA binding." [GOC:obol] biological_process
+ENSG00000186951 GO:0035095 behavioral response to nicotine "Any process that results in a change in the behavior of an organism as a result of a nicotine stimulus." [GOC:bf, ISBN:0198506732] biological_process
+ENSG00000186951 GO:0032091 negative regulation of protein binding "Any process that stops, prevents, or reduces the frequency, rate or extent of protein binding." [GOC:mah] biological_process
+ENSG00000186951 GO:0006631 fatty acid metabolic process "The chemical reactions and pathways involving fatty acids, aliphatic monocarboxylic acids liberated from naturally occurring fats and oils by hydrolysis." [ISBN:0198547684] biological_process
+ENSG00000099977 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000099977 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000099977 GO:0019748 secondary metabolic process "The chemical reactions and pathways resulting in many of the chemical changes of compounds that are not necessarily required for growth and maintenance of cells, and are often unique to a taxon. In multicellular organisms secondary metabolism is generally carried out in specific cell types, and may be useful for the organism as a whole. In unicellular organisms, secondary metabolism is often used for the production of antibiotics or for the utilization and acquisition of unusual nutrients." [GOC:go_curators] biological_process
+ENSG00000099977 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000099977 GO:0016829 lyase activity "Catalysis of the cleavage of C-C, C-O, C-N and other bonds by other means than by hydrolysis or oxidation, or conversely adding a group to a double bond. They differ from other enzymes in that two substrates are involved in one reaction direction, but only one in the other direction. When acting on the single substrate, a molecule is eliminated and this generates either a new double bond or a new ring." [EC:4.-.-.-, ISBN:0198547684] molecular_function
+ENSG00000099977 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000099977 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000099977 GO:0016853 isomerase activity "Catalysis of the geometric or structural changes within one molecule. Isomerase is the systematic name for any enzyme of EC class 5." [ISBN:0198506732] molecular_function
+ENSG00000099977 GO:0004167 dopachrome isomerase activity "Catalysis of the reaction: L-dopachrome = 5,6-dihydroxyindole-2-carboxylate." [EC:5.3.3.12, RHEA:13044] molecular_function
+ENSG00000099977 GO:0033981 D-dopachrome decarboxylase activity "Catalysis of the reaction: D-dopachrome + H(+) = 5,6-dihydroxyindole + CO(2)." [EC:4.1.1.84, RHEA:18444] molecular_function
+ENSG00000099977 GO:0042438 melanin biosynthetic process "The chemical reactions and pathways resulting in the formation of melanins, pigments largely of animal origin. High molecular weight polymers of indole quinone, they are irregular polymeric structures and are divided into three groups: allomelanins in the plant kingdom and eumelanins and phaeomelanins in the animal kingdom." [CHEBI:25179, GOC:curators] biological_process
+ENSG00000099977 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000099977 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000099977
+ENSG00000280080
+ENSG00000211658 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000211658 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000280178
+ENSG00000128383 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000128383 GO:0006259 DNA metabolic process "Any cellular metabolic process involving deoxyribonucleic acid. This is one of the two main types of nucleic acid, consisting of a long, unbranched macromolecule formed from one, or more commonly, two, strands of linked deoxyribonucleotides." [ISBN:0198506732] biological_process
+ENSG00000128383 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000128383 GO:0002376 immune system process "Any process involved in the development or functioning of the immune system, an organismal system for calibrated responses to potential internal or invasive threats." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05] biological_process
+ENSG00000128383 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000128383 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000128383 GO:0016810 hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds "Catalysis of the hydrolysis of any carbon-nitrogen bond, C-N, with the exception of peptide bonds." [GOC:jl] molecular_function
+ENSG00000128383 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000128383 GO:0034655 nucleobase-containing compound catabolic process "The chemical reactions and pathways resulting in the breakdown of nucleobases, nucleosides, nucleotides and nucleic acids." [GOC:mah] biological_process
+ENSG00000128383 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000128383 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000128383 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000128383 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000128383 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000128383 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000128383 GO:0004126 cytidine deaminase activity "Catalysis of the reaction: cytidine + H2O = uridine + NH3." [EC:3.5.4.5] molecular_function
+ENSG00000128383 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000128383 GO:0008270 zinc ion binding "Interacting selectively and non-covalently with zinc (Zn) ions." [GOC:ai] molecular_function
+ENSG00000128383 GO:0009972 cytidine deamination "The removal of amino group in the presence of water." [GOC:sm] biological_process
+ENSG00000128383 GO:0010529 negative regulation of transposition "Any process that decreases the frequency, rate or extent of transposition. Transposition results in the movement of discrete segments of DNA between nonhomologous sites." [GOC:dph, GOC:tb] biological_process
+ENSG00000128383 GO:0044356 clearance of foreign intracellular DNA by conversion of DNA cytidine to uridine "A defense process that protects an organism from invading foreign DNA. The process begins by the deamination of foreign double-stranded DNA cytidines to uridines. These atypical DNA nucleosides are then converted by a uracil DNA glycosylase to abasic lesions, and the process ends with the degradation of the foreign DNA." [GO:jl, PMID:20062055] biological_process
+ENSG00000128383 GO:0045071 negative regulation of viral genome replication "Any process that stops, prevents, or reduces the frequency, rate or extent of viral genome replication." [GOC:go_curators] biological_process
+ENSG00000128383 GO:0045087 innate immune response "Innate immune responses are defense responses mediated by germline encoded components that directly recognize components of potential pathogens." [GO_REF:0000022, GOC:add, GOC:ebc, GOC:mtg_15nov05, GOC:mtg_sensu] biological_process
+ENSG00000128383 GO:0047844 deoxycytidine deaminase activity "Catalysis of the reaction: deoxycytidine + H2O = deoxyuridine + NH3." [EC:3.5.4.14, MetaCyc:DEOXYCYTIDINE-DEAMINASE-RXN] molecular_function
+ENSG00000128383 GO:0051607 defense response to virus "Reactions triggered in response to the presence of a virus that act to protect the cell or organism." [GOC:ai] biological_process
+ENSG00000128383 GO:0070383 DNA cytosine deamination "The removal of an amino group from a cytosine residue in DNA, forming a uracil residue." [GOC:mah] biological_process
+ENSG00000128383 GO:0071466 cellular response to xenobiotic stimulus "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a xenobiotic compound stimulus. Xenobiotic compounds are compounds foreign to living organisms." [GOC:mah] biological_process
+ENSG00000128383 GO:0080111 DNA demethylation "The removal of a methyl group from one or more nucleotides within an DNA molecule." [PMID:17208187] biological_process
+ENSG00000128383 GO:0016814 hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds, in cyclic amidines "Catalysis of the hydrolysis of any non-peptide carbon-nitrogen bond in a cyclic amidine, a compound of the form R-C(=NH)-NH2." [ISBN:0198506732] molecular_function
+ENSG00000128383 GO:0003824 catalytic activity "Catalysis of a biochemical reaction at physiological temperatures. In biologically catalyzed reactions, the reactants are known as substrates, and the catalysts are naturally occurring macromolecular substances known as enzymes. Enzymes possess specific binding sites for substrates, and are usually composed wholly or largely of protein, but RNA that has catalytic activity (ribozyme) is often also regarded as enzymatic." [ISBN:0198506732] molecular_function
+ENSG00000128340 GO:0005525 GTP binding "Interacting selectively and non-covalently with GTP, guanosine triphosphate." [GOC:ai] molecular_function
+ENSG00000128340 GO:0007264 small GTPase mediated signal transduction "Any series of molecular signals in which a small monomeric GTPase relays one or more of the signals." [GOC:mah] biological_process
+ENSG00000128340 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000128340 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000128340 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000128340 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000128340 GO:0007155 cell adhesion "The attachment of a cell, either to another cell or to an underlying substrate such as the extracellular matrix, via cell adhesion molecules." [GOC:hb, GOC:pf] biological_process
+ENSG00000128340 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000128340 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000128340 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000128340 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000128340 GO:0005925 focal adhesion "Small region on the surface of a cell that anchors the cell to the extracellular matrix and that forms a point of termination of actin filaments." [ISBN:0124325653, ISBN:0815316208] cellular_component
+ENSG00000128340 GO:0007015 actin filament organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of cytoskeletal structures comprising actin filaments. Includes processes that control the spatial distribution of actin filaments, such as organizing filaments into meshworks, bundles, or other structures, as by cross-linking." [GOC:mah] biological_process
+ENSG00000128340 GO:0007411 axon guidance "The chemotaxis process that directs the migration of an axon growth cone to a specific target site in response to a combination of attractive and repulsive cues." [ISBN:0878932437] biological_process
+ENSG00000128340 GO:0007596 blood coagulation "The sequential process in which the multiple coagulation factors of the blood interact, ultimately resulting in the formation of an insoluble fibrin clot; it may be divided into three stages: stage 1, the formation of intrinsic and extrinsic prothrombin converting principle; stage 2, the formation of thrombin; stage 3, the formation of stable fibrin polymers." [http://www.graylab.ac.uk/omd/, ISBN:0198506732] biological_process
+ENSG00000128340 GO:0010310 regulation of hydrogen peroxide metabolic process "Any process that modulates the frequency, rate or extent of the chemical reactions and pathways involving hydrogen peroxide." [PMID:14765119] biological_process
+ENSG00000128340 GO:0010592 positive regulation of lamellipodium assembly "Any process that increases the rate, frequency or extent of the formation of a lamellipodium, a thin sheetlike extension of the surface of a migrating cell." [GOC:dph, GOC:tb] biological_process
+ENSG00000128340 GO:0010810 regulation of cell-substrate adhesion "Any process that modulates the frequency, rate or extent of cell-substrate adhesion. Cell-substrate adhesion is the attachment of a cell to the underlying substrate via adhesion molecules." [GOC:dph, GOC:pf, GOC:tb] biological_process
+ENSG00000128340 GO:0030027 lamellipodium "A thin sheetlike process extended by the leading edge of a crawling fibroblast; contains a dense meshwork of actin filaments." [ISBN:0815316194] cellular_component
+ENSG00000128340 GO:0030168 platelet activation "A series of progressive, overlapping events triggered by exposure of the platelets to subendothelial tissue. These events include shape change, adhesiveness, aggregation, and release reactions. When carried through to completion, these events lead to the formation of a stable hemostatic plug." [http://www.graylab.ac.uk/omd/] biological_process
+ENSG00000128340 GO:0051056 regulation of small GTPase mediated signal transduction "Any process that modulates the frequency, rate or extent of small GTPase mediated signal transduction." [GOC:go_curators] biological_process
+ENSG00000128340 GO:0060263 regulation of respiratory burst "Any process that modulates the rate frequency or extent of a phase of elevated metabolic activity, during which oxygen consumption increases; this leads to the production, by an NADH dependent system, of hydrogen peroxide (H2O2), superoxide anions and hydroxyl radicals." [GOC:dph, GOC:tb] biological_process
+ENSG00000128340 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000128340 GO:0071593 lymphocyte aggregation "The adhesion of one lymphocyte to one or more other lymphocytes via adhesion molecules." [GOC:sl] biological_process
+ENSG00000128340 GO:0090023 positive regulation of neutrophil chemotaxis "Any process that increases the frequency, rate, or extent of neutrophil chemotaxis. Neutrophil chemotaxis is the directed movement of a neutrophil cell, the most numerous polymorphonuclear leukocyte found in the blood, in response to an external stimulus, usually an infection or wounding." [GOC:dph, GOC:tb] biological_process
+ENSG00000128340 GO:1902622 regulation of neutrophil migration "Any process that modulates the frequency, rate or extent of neutrophil migration." [GO_REF:0000058, GOC:TermGenie, PMID:1826836] biological_process
+ENSG00000128340 GO:0005884 actin filament "A filamentous structure formed of a two-stranded helical polymer of the protein actin and associated proteins. Actin filaments are a major component of the contractile apparatus of skeletal muscle and the microfilaments of the cytoskeleton of eukaryotic cells. The filaments, comprising polymerized globular actin molecules, appear as flexible structures with a diameter of 5-9 nm. They are organized into a variety of linear bundles, two-dimensional networks, and three dimensional gels. In the cytoskeleton they are most highly concentrated in the cortex of the cell just beneath the plasma membrane." [GOC:mah, ISBN:0198506732, PMID:10666339] cellular_component
+ENSG00000128340 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000128340 GO:0003924 GTPase activity "Catalysis of the reaction: GTP + H2O = GDP + phosphate." [ISBN:0198547684] molecular_function
+ENSG00000128340 GO:0006184 GTP catabolic process "The chemical reactions and pathways resulting in the breakdown of GTP, guanosine triphosphate." [ISBN:0198506732] biological_process
+ENSG00000128340 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000128340 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000128340 GO:0034655 nucleobase-containing compound catabolic process "The chemical reactions and pathways resulting in the breakdown of nucleobases, nucleosides, nucleotides and nucleic acids." [GOC:mah] biological_process
+ENSG00000128340 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000128340 GO:0006886 intracellular protein transport "The directed movement of proteins in a cell, including the movement of proteins between specific compartments or structures within a cell, such as organelles of a eukaryotic cell." [GOC:mah] biological_process
+ENSG00000128340 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000128340 GO:0006913 nucleocytoplasmic transport "The directed movement of molecules between the nucleus and the cytoplasm." [GOC:go_curators] biological_process
+ENSG00000128340 GO:0005622 intracellular "The living contents of a cell; the matter contained within (but not including) the plasma membrane, usually taken to exclude large vacuoles and masses of secretory or ingested material. In eukaryotes it includes the nucleus and cytoplasm." [ISBN:0198506732] cellular_component
+ENSG00000128340 GO:0015031 protein transport "The directed movement of proteins into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:ai] biological_process
+ENSG00000128340 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000128340 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000128340 GO:0007186 G-protein coupled receptor signaling pathway "A series of molecular signals that proceeds with an activated receptor promoting the exchange of GDP for GTP on the alpha-subunit of an associated heterotrimeric G-protein complex. The GTP-bound activated alpha-G-protein then dissociates from the beta- and gamma-subunits to further transmit the signal within the cell. The pathway begins with receptor-ligand interaction, or for basal GPCR signaling the pathway begins with the receptor activating its G protein in the absence of an agonist, and ends with regulation of a downstream cellular process, e.g. transcription." [GOC:bf, GOC:mah, Wikipedia:G_protein-coupled_receptor] biological_process
+ENSG00000128340 GO:0008284 positive regulation of cell proliferation "Any process that activates or increases the rate or extent of cell proliferation." [GOC:go_curators] biological_process
+ENSG00000128340 GO:0006935 chemotaxis "The directed movement of a motile cell or organism, or the directed growth of a cell guided by a specific chemical concentration gradient. Movement may be towards a higher concentration (positive chemotaxis) or towards a lower concentration (negative chemotaxis)." [ISBN:0198506732] biological_process
+ENSG00000128340 GO:0030036 actin cytoskeleton organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of cytoskeletal structures comprising actin filaments and their associated proteins." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000128340 GO:0005635 nuclear envelope "The double lipid bilayer enclosing the nucleus and separating its contents from the rest of the cytoplasm; includes the intermembrane space, a gap of width 20-40 nm (also called the perinuclear space)." [ISBN:0198547684] cellular_component
+ENSG00000128340 GO:0030031 cell projection assembly "Formation of a prolongation or process extending from a cell, e.g. a flagellum or axon." [GOC:jl, GOC:mah, http://www.cogsci.princeton.edu/~wn/] biological_process
+ENSG00000128340 GO:0045453 bone resorption "The process in which specialized cells known as osteoclasts degrade the organic and inorganic portions of bone, and endocytose and transport the degradation products." [GOC:mah, PMID:10968780] biological_process
+ENSG00000128322 GO:0006955 immune response "Any immune system process that functions in the calibrated response of an organism to a potential internal or invasive threat." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05] biological_process
+ENSG00000128322 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000128322 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000128322 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000128322 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000128322 GO:0002376 immune system process "Any process involved in the development or functioning of the immune system, an organismal system for calibrated responses to potential internal or invasive threats." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05] biological_process
+ENSG00000128322 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000128322 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000128322 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000211642 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000211642 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000211637 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000211637 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000211677 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000211677 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100342 GO:0005576 extracellular region "The space external to the outermost structure of a cell. For cells without external protective or external encapsulating structures this refers to space outside of the plasma membrane. This term covers the host cell environment outside an intracellular parasite." [GOC:go_curators] cellular_component
+ENSG00000100342 GO:0006869 lipid transport "The directed movement of lipids into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore. Lipids are compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent." [ISBN:0198506732] biological_process
+ENSG00000100342 GO:0008289 lipid binding "Interacting selectively and non-covalently with a lipid." [GOC:ai] molecular_function
+ENSG00000100342 GO:0042157 lipoprotein metabolic process "The chemical reactions and pathways involving any conjugated, water-soluble protein in which the nonprotein group consists of a lipid or lipids." [ISBN:0198506732] biological_process
+ENSG00000100342 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100342 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100342 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100342 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100342 GO:0002376 immune system process "Any process involved in the development or functioning of the immune system, an organismal system for calibrated responses to potential internal or invasive threats." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05] biological_process
+ENSG00000100342 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000100342 GO:0006629 lipid metabolic process "The chemical reactions and pathways involving lipids, compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent. Includes fatty acids; neutral fats, other fatty-acid esters, and soaps; long-chain (fatty) alcohols and waxes; sphingoids and other long-chain bases; glycolipids, phospholipids and sphingolipids; and carotenes, polyprenols, sterols, terpenes and other isoprenoids." [GOC:ma] biological_process
+ENSG00000100342 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000100342 GO:0005615 extracellular space "That part of a multicellular organism outside the cells proper, usually taken to be outside the plasma membranes, and occupied by fluid." [ISBN:0198547684] cellular_component
+ENSG00000100342 GO:0022857 transmembrane transporter activity "Enables the transfer of a substance from one side of a membrane to the other." [GOC:mtg_transport, ISBN:0815340729] molecular_function
+ENSG00000100342 GO:0005254 chloride channel activity "Catalysis of facilitated diffusion of a chloride (by an energy-independent process) involving passage through a transmembrane aqueous pore or channel without evidence for a carrier-mediated mechanism." [GOC:mtg_transport, GOC:pr, ISBN:0815340729] molecular_function
+ENSG00000100342 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100342 GO:0006821 chloride transport "The directed movement of chloride into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:krc] biological_process
+ENSG00000100342 GO:0008203 cholesterol metabolic process "The chemical reactions and pathways involving cholesterol, cholest-5-en-3 beta-ol, the principal sterol of vertebrates and the precursor of many steroids, including bile acids and steroid hormones. It is a component of the plasma membrane lipid bilayer and of plasma lipoproteins and can be found in all animal tissues." [ISBN:0198506732] biological_process
+ENSG00000100342 GO:0019835 cytolysis "The rupture of cell membranes and the loss of cytoplasm." [UniProtKB-KW:KW-0204] biological_process
+ENSG00000100342 GO:0031224 intrinsic component of membrane "The component of a membrane consisting of the gene products having some covalently attached portion, for example part of a peptide sequence or some other covalently attached group such as a GPI anchor, which spans or is embedded in one or both leaflets of the membrane." [GOC:mah] cellular_component
+ENSG00000100342 GO:0031640 killing of cells of other organism "Any process in an organism that results in the killing of cells of another organism, including in some cases the death of the other organism. Killing here refers to the induction of death in one cell by another cell, not cell-autonomous death due to internal or other environmental conditions." [GOC:add] biological_process
+ENSG00000100342 GO:0034361 very-low-density lipoprotein particle "A triglyceride-rich lipoprotein particle that is typically composed of APOB100, APOE and APOCs and has a density of about 1.006 g/ml and a diameter of between 20-80 nm. It is found in blood and transports endogenous products (newly synthesized cholesterol and triglycerides) from the liver." [GOC:BHF, GOC:expert_pt, GOC:mah, GOC:rl] cellular_component
+ENSG00000100342 GO:0034364 high-density lipoprotein particle "A lipoprotein particle with a high density (typically 1.063-1.21 g/ml) and a diameter of 5-10 nm that contains APOAs and may contain APOCs and APOE; found in blood and carries lipids from body tissues to the liver as part of the reverse cholesterol transport process." [GOC:BHF, GOC:expert_pt, GOC:mah, GOC:pde, GOC:rl] cellular_component
+ENSG00000100342 GO:0045087 innate immune response "Innate immune responses are defense responses mediated by germline encoded components that directly recognize components of potential pathogens." [GO_REF:0000022, GOC:add, GOC:ebc, GOC:mtg_15nov05, GOC:mtg_sensu] biological_process
+ENSG00000100342 GO:0072562 blood microparticle "A phospholipid microvesicle that is derived from any of several cell types, such as platelets, blood cells, endothelial cells, or others, and contains membrane receptors as well as other proteins characteristic of the parental cell. Microparticles are heterogeneous in size, and are characterized as microvesicles free of nucleic acids." [GOC:BHF, GOC:mah, PMID:16373184] cellular_component
+ENSG00000100342 GO:1902476 chloride transmembrane transport "The directed movement of chloride across a membrane." [GOC:TermGenie, GOC:vw] biological_process
+ENSG00000100342 GO:0055085 transmembrane transport "The process in which a solute is transported from one side of a membrane to the other." [GOC:dph, GOC:jid] biological_process
+ENSG00000100342
+ENSG00000179750 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000179750 GO:0008152 metabolic process "The chemical reactions and pathways, including anabolism and catabolism, by which living organisms transform chemical substances. Metabolic processes typically transform small molecules, but also include macromolecular processes such as DNA repair and replication, and protein synthesis and degradation." [GOC:go_curators, ISBN:0198547684] biological_process
+ENSG00000179750 GO:0008270 zinc ion binding "Interacting selectively and non-covalently with zinc (Zn) ions." [GOC:ai] molecular_function
+ENSG00000179750 GO:0010529 negative regulation of transposition "Any process that decreases the frequency, rate or extent of transposition. Transposition results in the movement of discrete segments of DNA between nonhomologous sites." [GOC:dph, GOC:tb] biological_process
+ENSG00000179750 GO:0044822 poly(A) RNA binding "Interacting non-covalently with a poly(A) RNA, a RNA molecule which has a tail of adenine bases." [GOC:jl] molecular_function
+ENSG00000179750 GO:0045087 innate immune response "Innate immune responses are defense responses mediated by germline encoded components that directly recognize components of potential pathogens." [GO_REF:0000022, GOC:add, GOC:ebc, GOC:mtg_15nov05, GOC:mtg_sensu] biological_process
+ENSG00000179750 GO:0047844 deoxycytidine deaminase activity "Catalysis of the reaction: deoxycytidine + H2O = deoxyuridine + NH3." [EC:3.5.4.14, MetaCyc:DEOXYCYTIDINE-DEAMINASE-RXN] molecular_function
+ENSG00000179750 GO:0051607 defense response to virus "Reactions triggered in response to the presence of a virus that act to protect the cell or organism." [GOC:ai] biological_process
+ENSG00000179750 GO:0002376 immune system process "Any process involved in the development or functioning of the immune system, an organismal system for calibrated responses to potential internal or invasive threats." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05] biological_process
+ENSG00000179750 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000179750 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000179750 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000179750 GO:0016810 hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds "Catalysis of the hydrolysis of any carbon-nitrogen bond, C-N, with the exception of peptide bonds." [GOC:jl] molecular_function
+ENSG00000179750 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000179750 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000179750 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000179750 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000179750 GO:0016814 hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds, in cyclic amidines "Catalysis of the hydrolysis of any non-peptide carbon-nitrogen bond in a cyclic amidine, a compound of the form R-C(=NH)-NH2." [ISBN:0198506732] molecular_function
+ENSG00000179750 GO:0003824 catalytic activity "Catalysis of a biochemical reaction at physiological temperatures. In biologically catalyzed reactions, the reactants are known as substrates, and the catalysts are naturally occurring macromolecular substances known as enzymes. Enzymes possess specific binding sites for substrates, and are usually composed wholly or largely of protein, but RNA that has catalytic activity (ribozyme) is often also regarded as enzymatic." [ISBN:0198506732] molecular_function
+ENSG00000186716 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000186716 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000186716 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000186716 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000186716 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000186716 GO:0016301 kinase activity "Catalysis of the transfer of a phosphate group, usually from ATP, to a substrate molecule." [ISBN:0198506732] molecular_function
+ENSG00000186716 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000186716 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000186716 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000186716 GO:0030234 enzyme regulator activity "Binds to and modulates the activity of an enzyme." [GOC:mah] molecular_function
+ENSG00000186716 GO:0004674 protein serine/threonine kinase activity "Catalysis of the reactions: ATP + protein serine = ADP + protein serine phosphate, and ATP + protein threonine = ADP + protein threonine phosphate." [GOC:bf] molecular_function
+ENSG00000186716 GO:0004713 protein tyrosine kinase activity "Catalysis of the reaction: ATP + a protein tyrosine = ADP + protein tyrosine phosphate." [EC:2.7.10] molecular_function
+ENSG00000186716 GO:0005089 Rho guanyl-nucleotide exchange factor activity "Stimulates the exchange of guanyl nucleotides associated with a GTPase of the Rho family. Under normal cellular physiological conditions, the concentration of GTP is higher than that of GDP, favoring the replacement of GDP by GTP in association with the GTPase." [GOC:mah] molecular_function
+ENSG00000186716 GO:0005096 GTPase activator activity "Increases the activity of a GTPase, an enzyme that catalyzes the hydrolysis of GTP." [GOC:mah] molecular_function
+ENSG00000186716 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000186716 GO:0005524 ATP binding "Interacting selectively and non-covalently with ATP, adenosine 5'-triphosphate, a universally important coenzyme and enzyme regulator." [ISBN:0198506732] molecular_function
+ENSG00000186716 GO:0006468 protein phosphorylation "The process of introducing a phosphate group on to a protein." [GOC:hb] biological_process
+ENSG00000186716 GO:0007264 small GTPase mediated signal transduction "Any series of molecular signals in which a small monomeric GTPase relays one or more of the signals." [GOC:mah] biological_process
+ENSG00000186716 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000186716 GO:0018108 peptidyl-tyrosine phosphorylation "The phosphorylation of peptidyl-tyrosine to form peptidyl-O4'-phospho-L-tyrosine." [RESID:AA0039] biological_process
+ENSG00000186716 GO:0051056 regulation of small GTPase mediated signal transduction "Any process that modulates the frequency, rate or extent of small GTPase mediated signal transduction." [GOC:go_curators] biological_process
+ENSG00000186716 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000186716 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000186716 GO:0035023 regulation of Rho protein signal transduction "Any process that modulates the frequency, rate or extent of Rho protein signal transduction." [GOC:bf] biological_process
+ENSG00000186716 GO:0005622 intracellular "The living contents of a cell; the matter contained within (but not including) the plasma membrane, usually taken to exclude large vacuoles and masses of secretory or ingested material. In eukaryotes it includes the nucleus and cytoplasm." [ISBN:0198506732] cellular_component
+ENSG00000186716 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000186716 GO:0007420 brain development "The process whose specific outcome is the progression of the brain over time, from its formation to the mature structure. Brain development begins with patterning events in the neural tube and ends with the mature structure that is the center of thought and emotion. The brain is responsible for the coordination and control of bodily activities and the interpretation of information from the senses (sight, hearing, smell, etc.)." [GOC:dph, GOC:jid, GOC:tb, UBERON:0000955] biological_process
+ENSG00000186716 GO:0042472 inner ear morphogenesis "The process in which the anatomical structures of the inner ear are generated and organized. The inner ear is the structure in vertebrates that contains the organs of balance and hearing. It consists of soft hollow sensory structures (the membranous labyrinth) containing fluid (endolymph) surrounded by fluid (perilymph) and encased in a bony cavity (the bony labyrinth). It consists of two chambers, the sacculus and utriculus, from which arise the cochlea and semicircular canals respectively." [GOC:jl, ISBN:0192801023] biological_process
+ENSG00000186716 GO:0030036 actin cytoskeleton organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of cytoskeletal structures comprising actin filaments and their associated proteins." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000186716 GO:0030675 Rac GTPase activator activity "Increases the rate of GTP hydrolysis by a GTPase of the Rac family." [GOC:mah] molecular_function
+ENSG00000186716 GO:0032855 positive regulation of Rac GTPase activity "Any process that activates or increases the activity of a GTPase of the Rac family." [GOC:mah] biological_process
+ENSG00000186716 GO:0050885 neuromuscular process controlling balance "Any process that an organism uses to control its balance, the orientation of the organism (or the head of the organism) in relation to the source of gravity. In humans and animals, balance is perceived through visual cues, the labyrinth system of the inner ears and information from skin pressure receptors and muscle and joint receptors." [GOC:ai, GOC:dph, http://www.onelook.com/] biological_process
+ENSG00000186716 GO:0032496 response to lipopolysaccharide "Any process that results in a change in state or activity of an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a lipopolysaccharide stimulus; lipopolysaccharide is a major component of the cell wall of gram-negative bacteria." [GOC:add, ISBN:0721601464] biological_process
+ENSG00000186716 GO:0050766 positive regulation of phagocytosis "Any process that activates or increases the frequency, rate or extent of phagocytosis." [GOC:ai] biological_process
+ENSG00000186716 GO:0051726 regulation of cell cycle "Any process that modulates the rate or extent of progression through the cell cycle." [GOC:ai, GOC:dph, GOC:tb] biological_process
+ENSG00000186716 GO:0030336 negative regulation of cell migration "Any process that stops, prevents, or reduces the frequency, rate or extent of cell migration." [GOC:go_curators] biological_process
+ENSG00000186716 GO:0050728 negative regulation of inflammatory response "Any process that stops, prevents, or reduces the frequency, rate or extent of the inflammatory response." [GOC:ai] biological_process
+ENSG00000186716 GO:0043314 negative regulation of neutrophil degranulation "Any process that stops, prevents, or reduces the rate of neutrophil degranulation." [ISBN:0781735149] biological_process
+ENSG00000186716 GO:0019899 enzyme binding "Interacting selectively and non-covalently with any enzyme." [GOC:jl] molecular_function
+ENSG00000186716 GO:0046777 protein autophosphorylation "The phosphorylation by a protein of one or more of its own amino acid residues (cis-autophosphorylation), or residues on an identical protein (trans-autophosphorylation)." [ISBN:0198506732] biological_process
+ENSG00000186716 GO:0048008 platelet-derived growth factor receptor signaling pathway "The series of molecular signals generated as a consequence of a platelet-derived growth factor receptor binding to one of its physiological ligands." [GOC:ceb] biological_process
+ENSG00000186716 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000186716 GO:0032321 positive regulation of Rho GTPase activity "Any process that activates or increases the activity of a GTPase of the Rho family." [GOC:mah] biological_process
+ENSG00000186716 GO:0035556 intracellular signal transduction "The process in which a signal is passed on to downstream components within the cell, which become activated themselves to further propagate the signal and finally trigger a change in the function or state of the cell." [GOC:bf, GOC:jl, GOC:signaling, ISBN:3527303782] biological_process
+ENSG00000069998
+ENSG00000069998 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000100297 GO:0003677 DNA binding "Any molecular function by which a gene product interacts selectively and non-covalently with DNA (deoxyribonucleic acid)." [GOC:dph, GOC:jl, GOC:tb, GOC:vw] molecular_function
+ENSG00000100297 GO:0005524 ATP binding "Interacting selectively and non-covalently with ATP, adenosine 5'-triphosphate, a universally important coenzyme and enzyme regulator." [ISBN:0198506732] molecular_function
+ENSG00000100297 GO:0006260 DNA replication "The cellular metabolic process in which a cell duplicates one or more molecules of DNA. DNA replication begins when specific sequences, known as origins of replication, are recognized and bound by initiation proteins, and ends when the original DNA molecule has been completely duplicated and the copies topologically separated. The unit of replication usually corresponds to the genome of the cell, an organelle, or a virus. The template for replication can either be an existing DNA molecule or RNA." [GOC:mah] biological_process
+ENSG00000100297 GO:0006259 DNA metabolic process "Any cellular metabolic process involving deoxyribonucleic acid. This is one of the two main types of nucleic acid, consisting of a long, unbranched macromolecule formed from one, or more commonly, two, strands of linked deoxyribonucleotides." [ISBN:0198506732] biological_process
+ENSG00000100297 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100297 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000100297 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100297 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100297 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100297 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100297 GO:0005654 nucleoplasm "That part of the nuclear content other than the chromosomes or the nucleolus." [GOC:ma, ISBN:0124325653] cellular_component
+ENSG00000100297 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100297 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100297 GO:0004386 helicase activity "Catalysis of the reaction: NTP + H2O = NDP + phosphate, to drive the unwinding of a DNA or RNA helix." [GOC:mah, ISBN:0198506732] molecular_function
+ENSG00000100297 GO:0007049 cell cycle "The progression of biochemical and morphological phases and events that occur in a cell during successive cell replication or nuclear replication events. Canonically, the cell cycle comprises the replication and segregation of genetic material followed by the division of the cell, but in endocycles or syncytial cells nuclear replication or nuclear division may not be followed by cell division." [GOC:go_curators, GOC:mtg_cell_cycle] biological_process
+ENSG00000100297 GO:0000082 G1/S transition of mitotic cell cycle "The mitotic cell cycle transition by which a cell in G1 commits to S phase. The process begins with the build up of G1 cyclin-dependent kinase (G1 CDK), resulting in the activation of transcription of G1 cyclins. The process ends with the positive feedback of the G1 cyclins on the G1 CDK which commits the cell to S phase, in which DNA replication is initiated." [GOC:mtg_cell_cycle] biological_process
+ENSG00000100297 GO:0000278 mitotic cell cycle "Progression through the phases of the mitotic cell cycle, the most common eukaryotic cell cycle, which canonically comprises four successive phases called G1, S, G2, and M and includes replication of the genome and the subsequent segregation of chromosomes into daughter cells. In some variant cell cycles nuclear replication or nuclear division may not be followed by cell division, or G1 and G2 phases may be absent." [GOC:mah, ISBN:0815316194, Reactome:69278] biological_process
+ENSG00000100297 GO:0003678 DNA helicase activity "Catalysis of the reaction: NTP + H2O = NDP + phosphate, to drive the unwinding of a DNA helix." [GOC:jl, GOC:mah] molecular_function
+ENSG00000100297 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100297 GO:0006270 DNA replication initiation "The process in which DNA-dependent DNA replication is started; this involves the separation of a stretch of the DNA double helix, the recruitment of DNA polymerases and the initiation of polymerase action." [ISBN:071673706X, ISBN:0815316194] biological_process
+ENSG00000100297 GO:0006271 DNA strand elongation involved in DNA replication "The process in which a DNA strand is synthesized from template DNA during replication by the action of polymerases, which add nucleotides to the 3' end of the nascent DNA strand." [ISBN:071673706X, ISBN:0815316194] biological_process
+ENSG00000100297 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000100297 GO:0032508 DNA duplex unwinding "The process in which interchain hydrogen bonds between two strands of DNA are broken or 'melted', generating a region of unpaired single strands." [GOC:isa_complete, GOC:mah] biological_process
+ENSG00000100297 GO:0042555 MCM complex "A hexameric protein complex required for the initiation and regulation of DNA replication." [GOC:jl, PMID:11282021] cellular_component
+ENSG00000100297 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100297
+ENSG00000100297 GO:0051301 cell division "The process resulting in division and partitioning of components of a cell to form more cells; may or may not be accompanied by the physical separation of a cell into distinct, individually membrane-bounded daughter cells." [GOC:di, GOC:go_curators, GOC:pr] biological_process
+ENSG00000279219
+ENSG00000177989
+ENSG00000198445 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000198445 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000198445 GO:0022857 transmembrane transporter activity "Enables the transfer of a substance from one side of a membrane to the other." [GOC:mtg_transport, ISBN:0815340729] molecular_function
+ENSG00000198445 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000198445 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000198445 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000198445 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000198445 GO:0005253 anion channel activity "Catalysis of the energy-independent passage of anions across a lipid bilayer down a concentration gradient." [GOC:dph, GOC:mtg_transport, ISBN:0815340729] molecular_function
+ENSG00000198445 GO:0005524 ATP binding "Interacting selectively and non-covalently with ATP, adenosine 5'-triphosphate, a universally important coenzyme and enzyme regulator." [ISBN:0198506732] molecular_function
+ENSG00000198445 GO:0006820 anion transport "The directed movement of anions, atoms or small molecules with a net negative charge, into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:ai] biological_process
+ENSG00000198445 GO:0015269 calcium-activated potassium channel activity "Catalysis of the calcium concentration-regulatable energy-independent passage of potassium ions across a lipid bilayer down a concentration gradient." [GOC:dph, GOC:mtg_transport] molecular_function
+ENSG00000198445 GO:0044267 cellular protein metabolic process "The chemical reactions and pathways involving a specific protein, rather than of proteins in general, occurring at the level of an individual cell. Includes cellular protein modification." [GOC:jl] biological_process
+ENSG00000198445 GO:0071805 potassium ion transmembrane transport "A process in which a potassium ion is transported from one side of a membrane to the other." [GOC:mah] biological_process
+ENSG00000198445 GO:0055085 transmembrane transport "The process in which a solute is transported from one side of a membrane to the other." [GOC:dph, GOC:jid] biological_process
+ENSG00000211639 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000211639 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000198911 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000198911 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000198911 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000198911 GO:0003677 DNA binding "Any molecular function by which a gene product interacts selectively and non-covalently with DNA (deoxyribonucleic acid)." [GOC:dph, GOC:jl, GOC:tb, GOC:vw] molecular_function
+ENSG00000198911 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000198911 GO:0006629 lipid metabolic process "The chemical reactions and pathways involving lipids, compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent. Includes fatty acids; neutral fats, other fatty-acid esters, and soaps; long-chain (fatty) alcohols and waxes; sphingoids and other long-chain bases; glycolipids, phospholipids and sphingolipids; and carotenes, polyprenols, sterols, terpenes and other isoprenoids." [GOC:ma] biological_process
+ENSG00000198911 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000198911 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000198911 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000198911 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000198911 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000198911 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000198911 GO:0005783 endoplasmic reticulum "The irregular network of unit membranes, visible only by electron microscopy, that occurs in the cytoplasm of many eukaryotic cells. The membranes form a complex meshwork of tubular channels, which are often expanded into slitlike cavities called cisternae. The ER takes two forms, rough (or granular), with ribosomes adhering to the outer surface, and smooth (with no ribosomes attached)." [ISBN:0198506732] cellular_component
+ENSG00000198911 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000198911 GO:0005730 nucleolus "A small, dense body one or more of which are present in the nucleus of eukaryotic cells. It is rich in RNA and protein, is not bounded by a limiting membrane, and is not seen during mitosis. Its prime function is the transcription of the nucleolar DNA into 45S ribosomal-precursor RNA, the processing of this RNA into 5.8S, 18S, and 28S components of ribosomal RNA, and the association of these components with 5S RNA and proteins synthesized outside the nucleolus. This association results in the formation of ribonucleoprotein precursors; these pass into the cytoplasm and mature into the 40S and 60S subunits of the ribosome." [ISBN:0198506732] cellular_component
+ENSG00000198911 GO:0005654 nucleoplasm "That part of the nuclear content other than the chromosomes or the nucleolus." [GOC:ma, ISBN:0124325653] cellular_component
+ENSG00000198911 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000198911 GO:0000122 negative regulation of transcription from RNA polymerase II promoter "Any process that stops, prevents, or reduces the frequency, rate or extent of transcription from an RNA polymerase II promoter." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000198911 GO:0000139 Golgi membrane "The lipid bilayer surrounding any of the compartments of the Golgi apparatus." [GOC:mah] cellular_component
+ENSG00000198911 GO:0000978 RNA polymerase II core promoter proximal region sequence-specific DNA binding "Interacting selectively and non-covalently with a sequence of DNA that is in cis with and relatively close to a core promoter for RNA polymerase II." [GOC:txnOH] molecular_function
+ENSG00000198911 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000198911 GO:0005789 endoplasmic reticulum membrane "The lipid bilayer surrounding the endoplasmic reticulum." [GOC:mah] cellular_component
+ENSG00000198911 GO:0006351 transcription, DNA-templated "The cellular synthesis of RNA on a template of DNA." [GOC:jl, GOC:txnOH] biological_process
+ENSG00000198911 GO:0008022 protein C-terminus binding "Interacting selectively and non-covalently with a protein C-terminus, the end of any peptide chain at which the 1-carboxy function of a constituent amino acid is not attached in peptide linkage to another amino-acid residue." [ISBN:0198506732] molecular_function
+ENSG00000198911 GO:0008203 cholesterol metabolic process "The chemical reactions and pathways involving cholesterol, cholest-5-en-3 beta-ol, the principal sterol of vertebrates and the precursor of many steroids, including bile acids and steroid hormones. It is a component of the plasma membrane lipid bilayer and of plasma lipoproteins and can be found in all animal tissues." [ISBN:0198506732] biological_process
+ENSG00000198911 GO:0010886 positive regulation of cholesterol storage "Any process that increases the rate or extent of cholesterol storage. Cholesterol storage is the accumulation and maintenance in cells or tissues of cholesterol, cholest-5-en-3 beta-ol, the principal sterol of vertebrates and the precursor of many steroids, including bile acids and steroid hormones." [GOC:BHF, GOC:dph, GOC:tb] biological_process
+ENSG00000198911 GO:0031410 cytoplasmic vesicle "A vesicle formed of membrane or protein, found in the cytoplasm of a cell." [GOC:mah] cellular_component
+ENSG00000198911 GO:0032937 SREBP-SCAP-Insig complex "A protein complex formed by the association of sterol regulatory element binding protein (SREBP), SREBP-cleavage-activating protein (SCAP), and an Insig protein (Insig-1 or Insig-2) in the ER membrane." [PMID:12923525] cellular_component
+ENSG00000198911 GO:0044255 cellular lipid metabolic process "The chemical reactions and pathways involving lipids, as carried out by individual cells." [GOC:jl] biological_process
+ENSG00000198911 GO:0045944 positive regulation of transcription from RNA polymerase II promoter "Any process that activates or increases the frequency, rate or extent of transcription from an RNA polymerase II promoter." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000198911 GO:0046983 protein dimerization activity "The formation of a protein dimer, a macromolecular structure consists of two noncovalently associated identical or nonidentical subunits." [ISBN:0198506732] molecular_function
+ENSG00000198911 GO:0055098 response to low-density lipoprotein particle "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a low-density lipoprotein particle stimulus." [GOC:BHF, GOC:rl] biological_process
+ENSG00000198911 GO:0070888 E-box binding "Interacting selectively and non-covalently with an E-box, a DNA motif with the consensus sequence CANNTG that is found in the promoters of a wide array of genes expressed in neurons, muscle and other tissues." [GOC:BHF, GOC:vk, PMID:11812799] molecular_function
+ENSG00000198911 GO:0071499 cellular response to laminar fluid shear stress "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a laminar fluid shear stress stimulus. Laminar fluid flow is the force acting on an object in a system where the fluid is moving across a solid surface in parallel layers." [GOC:mah] biological_process
+ENSG00000198911 GO:0072368 regulation of lipid transport by negative regulation of transcription from RNA polymerase II promoter "Any process that modulates the frequency, rate or extent of lipid transport by stopping, preventing, or reducing the frequency, rate or extent of transcription from an RNA polymerase II promoter." [GOC:BHF, GOC:mah] biological_process
+ENSG00000198911 GO:0090370 negative regulation of cholesterol efflux "Any process that decreases the frequency, rate or extent of cholesterol efflux. Cholesterol efflux is the directed movement of cholesterol, cholest-5-en-3-beta-ol, out of a cell or organelle." [GOC:dph, GOC:tb, GOC:yaf] biological_process
+ENSG00000198911 GO:0003700 sequence-specific DNA binding transcription factor activity "Interacting selectively and non-covalently with a specific DNA sequence in order to modulate transcription. The transcription factor may or may not also interact selectively with a protein or macromolecular complex." [GOC:curators, GOC:txnOH] molecular_function
+ENSG00000198911 GO:0044212 transcription regulatory region DNA binding "Interacting selectively and non-covalently with a DNA region that regulates the transcription of a region of DNA, which may be a gene, cistron, or operon. Binding may occur as a sequence specific interaction or as an interaction observed only once a factor has been recruited to the DNA by other factors." [GOC:jl, GOC:txnOH, SO:0005836] molecular_function
+ENSG00000198911 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000198911 GO:0006355 regulation of transcription, DNA-templated "Any process that modulates the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000198911 GO:2000188 regulation of cholesterol homeostasis "Any process that modulates the frequency, rate or extent of cholesterol homeostasis." [GOC:BHF] biological_process
+ENSG00000198911
+ENSG00000211645 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000211645 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000278881 GO:0005576 extracellular region "The space external to the outermost structure of a cell. For cells without external protective or external encapsulating structures this refers to space outside of the plasma membrane. This term covers the host cell environment outside an intracellular parasite." [GOC:go_curators] cellular_component
+ENSG00000278881 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000185666 GO:0007269 neurotransmitter secretion "The regulated release of neurotransmitter into the synaptic cleft. A neurotransmitter is any of a group of substances that are released on excitation from the axon terminal of a presynaptic neuron of the central or peripheral nervous system and travel across the synaptic cleft to either excite or inhibit the target cell. Among the many substances that have the properties of a neurotransmitter are acetylcholine, noradrenaline, adrenaline, dopamine, glycine, gamma-aminobutyrate, glutamic acid, substance P, enkephalins, endorphins and serotonin." [CHEBI:25512, GOC:dph] biological_process
+ENSG00000185666 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000185666 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000185666 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000185666 GO:0016023 cytoplasmic membrane-bounded vesicle "A membrane-bounded vesicle found in the cytoplasm of the cell." [GOC:ai, GOC:mah] cellular_component
+ENSG00000185666 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000185666 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000185666 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000185666 GO:0003824 catalytic activity "Catalysis of a biochemical reaction at physiological temperatures. In biologically catalyzed reactions, the reactants are known as substrates, and the catalysts are naturally occurring macromolecular substances known as enzymes. Enzymes possess specific binding sites for substrates, and are usually composed wholly or largely of protein, but RNA that has catalytic activity (ribozyme) is often also regarded as enzymatic." [ISBN:0198506732] molecular_function
+ENSG00000185666 GO:0005524 ATP binding "Interacting selectively and non-covalently with ATP, adenosine 5'-triphosphate, a universally important coenzyme and enzyme regulator." [ISBN:0198506732] molecular_function
+ENSG00000185666 GO:0008021 synaptic vesicle "A secretory organelle, some 50 nm in diameter, of presynaptic nerve terminals; accumulates in high concentrations of neurotransmitters and secretes these into the synaptic cleft by fusion with the 'active zone' of the presynaptic plasma membrane." [PMID:10099709] cellular_component
+ENSG00000185666 GO:0008152 metabolic process "The chemical reactions and pathways, including anabolism and catabolism, by which living organisms transform chemical substances. Metabolic processes typically transform small molecules, but also include macromolecular processes such as DNA repair and replication, and protein synthesis and degradation." [GOC:go_curators, ISBN:0198547684] biological_process
+ENSG00000185666 GO:0030054 cell junction "A cellular component that forms a specialized region of connection between two cells or between a cell and the extracellular matrix. At a cell junction, anchoring proteins extend through the plasma membrane to link cytoskeletal proteins in one cell to cytoskeletal proteins in neighboring cells or to proteins in the extracellular matrix." [GOC:mah, http://www.vivo.colostate.edu/hbooks/cmb/cells/pmemb/junctions_a.html, ISBN:0198506732] cellular_component
+ENSG00000185666 GO:0032228 regulation of synaptic transmission, GABAergic "Any process that modulates the frequency, rate or extent of GABAergic synaptic transmission, the process of communication from a neuron to another neuron across a synapse using the neurotransmitter gamma-aminobutyric acid (GABA)." [GOC:mah] biological_process
+ENSG00000185666 GO:0045202 synapse "The junction between a nerve fiber of one neuron and another neuron or muscle fiber or glial cell; the site of interneuronal communication. As the nerve fiber approaches the synapse it enlarges into a specialized structure, the presynaptic nerve ending, which contains mitochondria and synaptic vesicles. At the tip of the nerve ending is the presynaptic membrane; facing it, and separated from it by a minute cleft (the synaptic cleft) is a specialized area of membrane on the receiving cell, known as the postsynaptic membrane. In response to the arrival of nerve impulses, the presynaptic nerve ending secretes molecules of neurotransmitters into the synaptic cleft. These diffuse across the cleft and transmit the signal to the postsynaptic membrane." [ISBN:0198506732] cellular_component
+ENSG00000185666 GO:0030672 synaptic vesicle membrane "The lipid bilayer surrounding a synaptic vesicle." [GOC:mah] cellular_component
+ENSG00000185666 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000185666
+ENSG00000167077 GO:0005488 binding "The selective, non-covalent, often stoichiometric, interaction of a molecule with one or more specific sites on another molecule." [GOC:ceb, GOC:mah, ISBN:0198506732] molecular_function
+ENSG00000167077 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000167077 GO:0007286 spermatid development "The process whose specific outcome is the progression of a spermatid over time, from its formation to the mature structure." [GOC:dph, GOC:go_curators] biological_process
+ENSG00000167077 GO:0007126 meiotic nuclear division "One of the two nuclear divisions that occur as part of the meiotic cell cycle." [GOC:dph, GOC:mah, PMID:9334324] biological_process
+ENSG00000167077 GO:0007276 gamete generation "The generation and maintenance of gametes in a multicellular organism. A gamete is a haploid reproductive cell." [GOC:ems, GOC:mtg_sensu] biological_process
+ENSG00000167077 GO:0007141 male meiosis I "A cell cycle process comprising the steps by which a cell progresses through male meiosis I, the first meiotic division in the male germline." [GOC:dph, GOC:mah] biological_process
+ENSG00000167077
+ENSG00000100364
+ENSG00000100023 GO:0002376 immune system process "Any process involved in the development or functioning of the immune system, an organismal system for calibrated responses to potential internal or invasive threats." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05] biological_process
+ENSG00000100023 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100023 GO:0040011 locomotion "Self-propelled movement of a cell or organism from one location to another." [GOC:dgh] biological_process
+ENSG00000100023 GO:0048870 cell motility "Any process involved in the controlled self-propelled movement of a cell that results in translocation of the cell from one place to another." [GOC:dgh, GOC:dph, GOC:isa_complete, GOC:mlg] biological_process
+ENSG00000100023 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100023 GO:0006457 protein folding "The process of assisting in the covalent and noncovalent assembly of single chain polypeptides or multisubunit complexes into the correct tertiary structure." [GOC:go_curators, GOC:rb] biological_process
+ENSG00000100023 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100023 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000100023 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100023 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100023 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100023 GO:0016853 isomerase activity "Catalysis of the geometric or structural changes within one molecule. Isomerase is the systematic name for any enzyme of EC class 5." [ISBN:0198506732] molecular_function
+ENSG00000100023 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000100023 GO:0000209 protein polyubiquitination "Addition of multiple ubiquitin groups to a protein, forming a ubiquitin chain." [ISBN:0815316194] biological_process
+ENSG00000100023 GO:0000413 protein peptidyl-prolyl isomerization "The modification of a protein by cis-trans isomerization of a proline residue." [GOC:krc, PMID:16959570] biological_process
+ENSG00000100023 GO:0003755 peptidyl-prolyl cis-trans isomerase activity "Catalysis of the reaction: peptidyl-proline (omega=180) = peptidyl-proline (omega=0)." [EC:5.2.1.8] molecular_function
+ENSG00000100023 GO:0004842 ubiquitin-protein transferase activity "Catalysis of the transfer of ubiquitin from one protein to another via the reaction X-Ub + Y --> Y-Ub + X, where both X-Ub and Y-Ub are covalent linkages." [GOC:BioGRID, GOC:jh2, PMID:9635407] molecular_function
+ENSG00000100023 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100023 GO:0005796 Golgi lumen "The volume enclosed by the membranes of any cisterna or subcompartment of the Golgi apparatus, including the cis- and trans-Golgi networks." [GOC:mah] cellular_component
+ENSG00000100023 GO:0007596 blood coagulation "The sequential process in which the multiple coagulation factors of the blood interact, ultimately resulting in the formation of an insoluble fibrin clot; it may be divided into three stages: stage 1, the formation of intrinsic and extrinsic prothrombin converting principle; stage 2, the formation of thrombin; stage 3, the formation of stable fibrin polymers." [http://www.graylab.ac.uk/omd/, ISBN:0198506732] biological_process
+ENSG00000100023 GO:0034450 ubiquitin-ubiquitin ligase activity "Isoenergetic transfer of ubiquitin from one protein to an existing ubiquitin chain via the reaction X-ubiquitin + Y-ubiquitin -> Y-ubiquitin-ubiquitin + X, where both the X-ubiquitin and Y-ubiquitin-ubiquitin linkages are thioester bonds between the C-terminal glycine of ubiquitin and a sulfhydryl side group of a cysteine residue." [GOC:mah, GOC:mcc, PMID:10089879, PMID:17190603] molecular_function
+ENSG00000100023 GO:0050900 leukocyte migration "The movement of a leukocyte within or between different tissues and organs of the body." [GOC:add, ISBN:0781735149, PMID:14680625, PMID:14708592, PMID:7507411, PMID:8600538] biological_process
+ENSG00000100023 GO:0072659 protein localization to plasma membrane "A process in which a protein is transported to, or maintained in, a specific location in the plasma membrane." [GOC:mah] biological_process
+ENSG00000100023 GO:0007009 plasma membrane organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of the plasma membrane." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100023 GO:0061024 membrane organization "A process which results in the assembly, arrangement of constituent parts, or disassembly of a membrane. A membrane is a double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:dph, GOC:tb] biological_process
+ENSG00000100023 GO:0016567 protein ubiquitination "The process in which one or more ubiquitin groups are added to a protein." [GOC:ai] biological_process
+ENSG00000100023
+ENSG00000100221 GO:0006508 proteolysis "The hydrolysis of proteins into smaller polypeptides and/or amino acids by cleavage of their peptide bonds." [GOC:bf, GOC:mah] biological_process
+ENSG00000100221 GO:0008242 omega peptidase activity "Catalysis of the removal of terminal peptide residues that are substituted, cyclized or linked by isopeptide bonds (peptide linkages other than those of alpha-carboxyl to alpha-amino groups)." [EC:3.4.19.-] molecular_function
+ENSG00000100221 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100221 GO:0008233 peptidase activity "Catalysis of the hydrolysis of a peptide bond. A peptide bond is a covalent bond formed when the carbon atom from the carboxyl group of one amino acid shares electrons with the nitrogen atom from the amino group of a second amino acid." [GOC:jl, ISBN:0815332181] molecular_function
+ENSG00000100221 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100221 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100221 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000100221 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100221 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000100345 GO:0003774 motor activity "Catalysis of movement along a polymeric molecule such as a microfilament or microtubule, coupled to the hydrolysis of a nucleoside triphosphate." [GOC:mah, ISBN:0815316194] molecular_function
+ENSG00000100345 GO:0005524 ATP binding "Interacting selectively and non-covalently with ATP, adenosine 5'-triphosphate, a universally important coenzyme and enzyme regulator." [ISBN:0198506732] molecular_function
+ENSG00000100345 GO:0008152 metabolic process "The chemical reactions and pathways, including anabolism and catabolism, by which living organisms transform chemical substances. Metabolic processes typically transform small molecules, but also include macromolecular processes such as DNA repair and replication, and protein synthesis and degradation." [GOC:go_curators, ISBN:0198547684] biological_process
+ENSG00000100345 GO:0051015 actin filament binding "Interacting selectively and non-covalently with an actin filament, also known as F-actin, a helical filamentous polymer of globular G-actin subunits." [ISBN:0198506732] molecular_function
+ENSG00000100345 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100345 GO:0008092 cytoskeletal protein binding "Interacting selectively and non-covalently with any protein component of any cytoskeleton (actin, microtubule, or intermediate filament cytoskeleton)." [GOC:mah] molecular_function
+ENSG00000100345 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100345 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100345 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100345 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100345 GO:0007155 cell adhesion "The attachment of a cell, either to another cell or to an underlying substrate such as the extracellular matrix, via cell adhesion molecules." [GOC:hb, GOC:pf] biological_process
+ENSG00000100345 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100345 GO:0002376 immune system process "Any process involved in the development or functioning of the immune system, an organismal system for calibrated responses to potential internal or invasive threats." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05] biological_process
+ENSG00000100345 GO:0040011 locomotion "Self-propelled movement of a cell or organism from one location to another." [GOC:dgh] biological_process
+ENSG00000100345 GO:0048870 cell motility "Any process involved in the controlled self-propelled movement of a cell that results in translocation of the cell from one place to another." [GOC:dgh, GOC:dph, GOC:isa_complete, GOC:mlg] biological_process
+ENSG00000100345 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000100345 GO:0007010 cytoskeleton organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of cytoskeletal structures." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100345 GO:0016887 ATPase activity "Catalysis of the reaction: ATP + H2O = ADP + phosphate + 2 H+. May or may not be coupled to another reaction." [EC:3.6.1.3, GOC:jl] molecular_function
+ENSG00000100345 GO:0030154 cell differentiation "The process in which relatively unspecialized cells, e.g. embryonic or regenerative cells, acquire specialized structural and/or functional features that characterize the cells, tissues, or organs of the mature organism or some other relatively stable phase of the organism's life history. Differentiation includes the processes involved in commitment of a cell to a specific fate and its subsequent development to the mature state." [ISBN:0198506732] biological_process
+ENSG00000100345 GO:0048646 anatomical structure formation involved in morphogenesis "The developmental process pertaining to the initial formation of an anatomical structure from unspecified parts. This process begins with the specific processes that contribute to the appearance of the discrete structure and ends when the structural rudiment is recognizable. An anatomical structure is any biological entity that occupies space and is distinguished from its surroundings. Anatomical structures can be macroscopic such as a carpel, or microscopic such as an acrosome." [GOC:dph, GOC:jid, GOC:tb] biological_process
+ENSG00000100345 GO:0005856 cytoskeleton "Any of the various filamentous elements that form the internal framework of cells, and typically remain after treatment of the cells with mild detergent to remove membrane constituents and soluble components of the cytoplasm. The term embraces intermediate filaments, microfilaments, microtubules, the microtrabecular lattice, and other structures characterized by a polymeric filamentous nature and long-range order within the cell. The various elements of the cytoskeleton not only serve in the maintenance of cellular shape but also have roles in other cellular functions, including cellular movement, cell division, endocytosis, and movement of organelles." [GOC:mah, ISBN:0198547684, PMID:16959967] cellular_component
+ENSG00000100345 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100345 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000100345 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000100345 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100345 GO:0034655 nucleobase-containing compound catabolic process "The chemical reactions and pathways resulting in the breakdown of nucleobases, nucleosides, nucleotides and nucleic acids." [GOC:mah] biological_process
+ENSG00000100345 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000100345 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000100345 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000100345 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100345 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100345 GO:0000146 microfilament motor activity "Catalysis of movement along a microfilament, coupled to the hydrolysis of a nucleoside triphosphate (usually ATP)." [GOC:mah, ISBN:0815316194] molecular_function
+ENSG00000100345 GO:0000910 cytokinesis "The division of the cytoplasm and the plasma membrane of a cell and its separation into two daughter cells." [GOC:mtg_cell_cycle] biological_process
+ENSG00000100345 GO:0001525 angiogenesis "Blood vessel formation when new vessels emerge from the proliferation of pre-existing blood vessels." [ISBN:0878932453] biological_process
+ENSG00000100345 GO:0001725 stress fiber "A contractile actin filament bundle that consists of short actin filaments with alternating polarity, cross-linked by alpha-actinin and possibly other actin bundling proteins, and with myosin present in a periodic distribution along the fiber." [PMID:16651381] cellular_component
+ENSG00000100345 GO:0001726 ruffle "Projection at the leading edge of a crawling cell; the protrusions are supported by a microfilament meshwork." [ISBN:0124325653] cellular_component
+ENSG00000100345 GO:0001931 uropod "A membrane projection with related cytoskeletal components at the trailing edge of a cell in the process of migrating or being activated, found on the opposite side of the cell from the leading edge or immunological synapse, respectively." [GOC:add, ISBN:0781735149, PMID:12714569, PMID:12787750] cellular_component
+ENSG00000100345 GO:0003779 actin binding "Interacting selectively and non-covalently with monomeric or multimeric forms of actin, including actin filaments." [GOC:clt] molecular_function
+ENSG00000100345 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100345 GO:0005516 calmodulin binding "Interacting selectively and non-covalently with calmodulin, a calcium-binding protein with many roles, both in the calcium-bound and calcium-free states." [GOC:krc] molecular_function
+ENSG00000100345 GO:0005826 actomyosin contractile ring "A cytoskeletal structure composed of actin filaments and myosin that forms beneath the plasma membrane of many cells, including animal cells and yeast cells, in a plane perpendicular to the axis of the spindle, i.e. the cell division plane. Ring contraction is associated with centripetal growth of the membrane that divides the cytoplasm of the two daughter cells. In animal cells, the contractile ring is located inside the plasma membrane at the location of the cleavage furrow. In budding fungal cells, e.g. mitotic S. cerevisiae cells, the contractile ring forms beneath the plasma membrane at the mother-bud neck before mitosis." [GOC:expert_jrp, GOC:sgd_curators, ISBN:0805319409, ISBN:0815316194] cellular_component
+ENSG00000100345 GO:0006200 ATP catabolic process "The chemical reactions and pathways resulting in the breakdown of ATP, adenosine 5'-triphosphate, a universally important coenzyme and enzyme regulator." [GOC:ai] biological_process
+ENSG00000100345 GO:0006509 membrane protein ectodomain proteolysis "The proteolytic cleavage of transmembrane proteins and release of their ectodomain (extracellular domain)." [GOC:jl, http://www.copewithcytokines.de/] biological_process
+ENSG00000100345 GO:0007229 integrin-mediated signaling pathway "A series of molecular signals initiated by the binding of extracellular ligand to an integrin on the surface of a target cell, and ending with regulation of a downstream cellular process, e.g. transcription." [GOC:mah, GOC:signaling] biological_process
+ENSG00000100345 GO:0007411 axon guidance "The chemotaxis process that directs the migration of an axon growth cone to a specific target site in response to a combination of attractive and repulsive cues." [ISBN:0878932437] biological_process
+ENSG00000100345 GO:0008360 regulation of cell shape "Any process that modulates the surface configuration of a cell." [GOC:dph, GOC:go_curators, GOC:tb] biological_process
+ENSG00000100345 GO:0015031 protein transport "The directed movement of proteins into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:ai] biological_process
+ENSG00000100345 GO:0015629 actin cytoskeleton "The part of the cytoskeleton (the internal framework of a cell) composed of actin and associated proteins. Includes actin cytoskeleton-associated complexes." [GOC:jl, ISBN:0395825172, ISBN:0815316194] cellular_component
+ENSG00000100345 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000100345 GO:0016460 myosin II complex "A myosin complex containing two class II myosin heavy chains, two myosin essential light chains and two myosin regulatory light chains. Also known as classical myosin or conventional myosin, the myosin II class includes the major muscle myosin of vertebrate and invertebrate muscle, and is characterized by alpha-helical coiled coil tails that self assemble to form a variety of filament structures." [http://www.mrc-lmb.cam.ac.uk/myosin/Review/Reviewframeset.html, ISBN:96235764] cellular_component
+ENSG00000100345 GO:0030048 actin filament-based movement "Movement of organelles or other particles along actin filaments, or sliding of actin filaments past each other, mediated by motor proteins." [GOC:BHF, GOC:mah] biological_process
+ENSG00000100345 GO:0030220 platelet formation "The process in which platelets bud from long processes extended by megakaryocytes." [GOC:mah, ISBN:0815316194] biological_process
+ENSG00000100345 GO:0030224 monocyte differentiation "The process in which a relatively unspecialized myeloid precursor cell acquires the specialized features of a monocyte." [GOC:mah] biological_process
+ENSG00000100345 GO:0030898 actin-dependent ATPase activity "Catalysis of the reaction: ATP + H2O = ADP + phosphate. This reaction requires the presence of an actin filament to accelerate release of ADP and phosphate." [GOC:mah] molecular_function
+ENSG00000100345 GO:0031032 actomyosin structure organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of cytoskeletal structures containing both actin and myosin or paramyosin. The myosin may be organized into filaments." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100345 GO:0031252 cell leading edge "The area of a motile cell closest to the direction of movement." [GOC:pg] cellular_component
+ENSG00000100345 GO:0031532 actin cytoskeleton reorganization "A process that is carried out at the cellular level which results in dynamic structural changes to the arrangement of constituent parts of cytoskeletal structures comprising actin filaments and their associated proteins." [GOC:ecd, GOC:mah] biological_process
+ENSG00000100345 GO:0032154 cleavage furrow "In animal cells, the first sign of cleavage, or cytokinesis, is the appearance of a shallow groove in the cell surface near the old metaphase plate. A contractile ring containing actin and myosin is located just inside the plasma membrane at the location of the furrow. Ring contraction is associated with centripetal growth of the membrane that deepens the cleavage furrow and divides the cytoplasm of the two daughter cells. While the term 'cleavage furrow' was initially associated with animal cells, such a structure occurs in many other types of cells, including unicellular protists." [ISBN:0805319409] cellular_component
+ENSG00000100345 GO:0038032 termination of G-protein coupled receptor signaling pathway "The signaling process in which G-protein coupled receptor signaling is brought to an end. For example, through the action of GTPase-activating proteins (GAPs) that act to accelerate hydrolysis of GTP to GDP on G-alpha proteins, thereby terminating the transduced signal." [GOC:bf, GOC:signaling] biological_process
+ENSG00000100345 GO:0042641 actomyosin "Any complex of actin, myosin, and accessory proteins." [GOC:go_curators] cellular_component
+ENSG00000100345 GO:0042803 protein homodimerization activity "Interacting selectively and non-covalently with an identical protein to form a homodimer." [GOC:jl] molecular_function
+ENSG00000100345 GO:0043495 protein anchor "Interacting selectively and non-covalently with both a protein or protein complex and a membrane, in order to maintain the localization of the protein at a specific location on the membrane." [GOC:go_curators] molecular_function
+ENSG00000100345 GO:0043531 ADP binding "Interacting selectively and non-covalently with ADP, adenosine 5'-diphosphate." [GOC:jl] molecular_function
+ENSG00000100345 GO:0043534 blood vessel endothelial cell migration "The orderly movement of an endothelial cell into the extracellular matrix in order to form new blood vessels during angiogenesis." [PMID:11166264] biological_process
+ENSG00000100345 GO:0044822 poly(A) RNA binding "Interacting non-covalently with a poly(A) RNA, a RNA molecule which has a tail of adenine bases." [GOC:jl] molecular_function
+ENSG00000100345 GO:0050900 leukocyte migration "The movement of a leukocyte within or between different tissues and organs of the body." [GOC:add, ISBN:0781735149, PMID:14680625, PMID:14708592, PMID:7507411, PMID:8600538] biological_process
+ENSG00000100345 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000100345 GO:0070527 platelet aggregation "The adhesion of one platelet to one or more other platelets via adhesion molecules." [GOC:BHF, GOC:vk] biological_process
+ENSG00000100345 GO:0097513 myosin II filament "A bipolar filament composed of myosin II molecules." [GOC:cjm, GOC:mah] cellular_component
+ENSG00000100345 GO:0001772 immunological synapse "An area of close contact between a lymphocyte (T-, B-, or natural killer cell) and a target cell formed through the clustering of particular signaling and adhesion molecules and their associated membrane rafts on both the lymphocyte and the target cell and facilitating activation of the lymphocyte, transfer of membrane from the target cell to the lymphocyte, and in some situations killing of the target cell through release of secretory granules and/or death-pathway ligand-receptor interaction." [GOC:mgi_curators, PMID:11244041, PMID:11376300] cellular_component
+ENSG00000100345 GO:0008180 COP9 signalosome "A protein complex that catalyzes the deneddylation of proteins, including the cullin component of SCF ubiquitin E3 ligase; deneddylation increases the activity of cullin family ubiquitin ligases. The signalosome is involved in many regulatory process, including some which control development, in many species; also regulates photomorphogenesis in plants; in many species its subunits are highly similar to those of the proteasome." [PMID:11019806, PMID:12186635, PMID:14570571] cellular_component
+ENSG00000100345 GO:0008305 integrin complex "A protein complex that is composed of one alpha subunit and one beta subunit, both of which are members of the integrin superfamily of cell adhesion receptors; the complex spans the plasma membrane and binds to extracellular matrix ligands, cell-surface ligands, and soluble ligands." [PMID:17543136] cellular_component
+ENSG00000100345 GO:0016459 myosin complex "A protein complex, formed of one or more myosin heavy chains plus associated light chains and other proteins, that functions as a molecular motor; uses the energy of ATP hydrolysis to move actin filaments or to move vesicles or other cargo on fixed actin filaments; has magnesium-ATPase activity and binds actin. Myosin classes are distinguished based on sequence features of the motor, or head, domain, but also have distinct tail regions that are believed to bind specific cargoes." [GOC:mah, http://www.mrc-lmb.cam.ac.uk/myosin/Review/Reviewframeset.html, ISBN:96235764] cellular_component
+ENSG00000100345 GO:0000166 nucleotide binding "Interacting selectively and non-covalently with a nucleotide, any compound consisting of a nucleoside that is esterified with (ortho)phosphate or an oligophosphate at any hydroxyl group on the ribose or deoxyribose." [GOC:mah, ISBN:0198547684] molecular_function
+ENSG00000100345 GO:0003700 sequence-specific DNA binding transcription factor activity "Interacting selectively and non-covalently with a specific DNA sequence in order to modulate transcription. The transcription factor may or may not also interact selectively with a protein or macromolecular complex." [GOC:curators, GOC:txnOH] molecular_function
+ENSG00000100345 GO:0001071 nucleic acid binding transcription factor activity "Interacting selectively and non-covalently with a DNA or RNA sequence in order to modulate transcription. The transcription factor may or may not also interact selectively with a protein or macromolecular complex." [GOC:txnOH] molecular_function
+ENSG00000100345 GO:0004871 signal transducer activity "Conveys a signal across a cell to trigger a change in cell function or state. A signal is a physical entity or change in state that is used to transfer information in order to trigger a response." [GOC:go_curators] molecular_function
+ENSG00000100345 GO:0006355 regulation of transcription, DNA-templated "Any process that modulates the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000100345 GO:0016337 single organismal cell-cell adhesion "The attachment of one cell to another cell via adhesion molecules, where both cells are part of the same organism." [GOC:hb] biological_process
+ENSG00000100345 GO:0001701 in utero embryonic development "The process whose specific outcome is the progression of the embryo in the uterus over time, from formation of the zygote in the oviduct, to birth. An example of this process is found in Mus musculus." [GOC:go_curators, GOC:mtg_sensu] biological_process
+ENSG00000100345 GO:0030863 cortical cytoskeleton "The portion of the cytoskeleton that lies just beneath the plasma membrane." [GOC:mah] cellular_component
+ENSG00000100345 GO:0005938 cell cortex "The region of a cell that lies just beneath the plasma membrane and often, but not always, contains a network of actin filaments and associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000100345 GO:0031594 neuromuscular junction "The junction between the axon of a motor neuron and a muscle fiber. In response to the arrival of action potentials, the presynaptic button releases molecules of neurotransmitters into the synaptic cleft. These diffuse across the cleft and transmit the signal to the postsynaptic membrane of the muscle fiber, leading to a change in post-synaptic potential." [GOC:nln] cellular_component
+ENSG00000100345 GO:0005913 cell-cell adherens junction "An adherens junction which connects a cell to another cell." [GOC:hb] cellular_component
+ENSG00000100345 GO:0006928 cellular component movement "The directed, self-propelled movement of a cellular component without the involvement of an external agent such as a transporter or a pore." [GOC:dgh, GOC:dph, GOC:jl, GOC:mlg] biological_process
+ENSG00000100345 GO:0005819 spindle "The array of microtubules and associated molecules that forms between opposite poles of a eukaryotic cell during mitosis or meiosis and serves to move the duplicated chromosomes apart." [ISBN:0198547684] cellular_component
+ENSG00000100345 GO:0000904 cell morphogenesis involved in differentiation "The change in form (cell shape and size) that occurs when relatively unspecialized cells, e.g. embryonic or regenerative cells, acquire specialized structural and/or functional features that characterize the cells, tissues, or organs of the mature organism or some other relatively stable phase of the organism's life history." [GOC:go_curators] biological_process
+ENSG00000100345 GO:0032796 uropod organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of a uropod, a rigid membrane projection with related cytoskeletal components at the trailing edge of a lymphocyte or other cell in the process of migrating or being activated." [GOC:add, ISBN:0781735149, PMID:12714569, PMID:12787750] biological_process
+ENSG00000100345 GO:0007520 myoblast fusion "A process in which non-proliferating myoblasts fuse to existing fibers or to myotubes to form new fibers. A myoblast is a mononucleate cell type that, by fusion with other myoblasts, gives rise to the myotubes that eventually develop into skeletal muscle fibers." [CL:0000056, GOC:mtg_muscle] biological_process
+ENSG00000100345 GO:0001768 establishment of T cell polarity "The directed orientation of T cell signaling molecules and associated membrane rafts towards a chemokine gradient or a contact point with antigen presenting cell." [GOC:mgi_curators, PMID:11244041, PMID:12615889] biological_process
+ENSG00000100345 GO:0000212 meiotic spindle organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of the microtubule spindle during a meiotic cell cycle." [GOC:mah] biological_process
+ENSG00000100345 GO:0051295 establishment of meiotic spindle localization "The cell cycle process in which the directed movement of the meiotic spindle to a specific location in the cell occurs." [GOC:ai] biological_process
+ENSG00000239713 GO:0002376 immune system process "Any process involved in the development or functioning of the immune system, an organismal system for calibrated responses to potential internal or invasive threats." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05] biological_process
+ENSG00000239713 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000239713 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000239713 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000239713 GO:0016810 hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds "Catalysis of the hydrolysis of any carbon-nitrogen bond, C-N, with the exception of peptide bonds." [GOC:jl] molecular_function
+ENSG00000239713 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000239713 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000239713 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000239713 GO:0044403 symbiosis, encompassing mutualism through parasitism "An interaction between two organisms living together in more or less intimate association. The term host is usually used for the larger (macro) of the two members of a symbiosis. The smaller (micro) member is called the symbiont organism. Microscopic symbionts are often referred to as endosymbionts. The various forms of symbiosis include parasitism, in which the association is disadvantageous or destructive to one of the organisms; mutualism, in which the association is advantageous, or often necessary to one or both and not harmful to either; and commensalism, in which one member of the association benefits while the other is not affected. However, mutualism, parasitism, and commensalism are often not discrete categories of interactions and should rather be perceived as a continuum of interaction ranging from parasitism to mutualism. In fact, the direction of a symbiotic interaction can change during the lifetime of the symbionts due to developmental changes as well as changes in the biotic/abiotic environment in which the interaction occurs." [GOC:cc, http://www.free-definition.com] biological_process
+ENSG00000239713 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000239713 GO:0034655 nucleobase-containing compound catabolic process "The chemical reactions and pathways resulting in the breakdown of nucleobases, nucleosides, nucleotides and nucleic acids." [GOC:mah] biological_process
+ENSG00000239713 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000239713 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000239713 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000239713 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000239713 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000239713 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000239713 GO:0000932 cytoplasmic mRNA processing body "A focus in the cytoplasm where mRNAs may become inactivated by decapping or some other mechanism. mRNA processing and binding proteins are localized to these foci." [GOC:clt, PMID:12730603] cellular_component
+ENSG00000239713 GO:0002230 positive regulation of defense response to virus by host "Any host process that results in the promotion of antiviral immune response mechanisms, thereby limiting viral replication." [GOC:add, GOC:dph, GOC:tb, ISBN:0781735149] biological_process
+ENSG00000239713 GO:0004126 cytidine deaminase activity "Catalysis of the reaction: cytidine + H2O = uridine + NH3." [EC:3.5.4.5] molecular_function
+ENSG00000239713 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000239713 GO:0008270 zinc ion binding "Interacting selectively and non-covalently with zinc (Zn) ions." [GOC:ai] molecular_function
+ENSG00000239713 GO:0009972 cytidine deamination "The removal of amino group in the presence of water." [GOC:sm] biological_process
+ENSG00000239713 GO:0010529 negative regulation of transposition "Any process that decreases the frequency, rate or extent of transposition. Transposition results in the movement of discrete segments of DNA between nonhomologous sites." [GOC:dph, GOC:tb] biological_process
+ENSG00000239713 GO:0016032 viral process "A multi-organism process in which a virus is a participant. The other participant is the host. Includes infection of a host cell, replication of the viral genome, and assembly of progeny virus particles. In some cases the viral genetic material may integrate into the host genome and only subsequently, under particular circumstances, 'complete' its life cycle." [GOC:bf, GOC:jl, GOC:mah] biological_process
+ENSG00000239713 GO:0016553 base conversion or substitution editing "Any base modification or substitution events that result in alterations in the coding potential or structural properties of RNAs as a result of changes in the base-pairing properties of the modified ribonucleoside(s)." [PMID:11092837] biological_process
+ENSG00000239713 GO:0030529 ribonucleoprotein complex "A macromolecular complex containing both protein and RNA molecules." [GOC:krc] cellular_component
+ENSG00000239713 GO:0030895 apolipoprotein B mRNA editing enzyme complex "Protein complex that mediates editing of the mRNA encoding apolipoprotein B; catalyzes the deamination of C to U (residue 6666 in the human mRNA). Contains a catalytic subunit, APOBEC-1, and other proteins (e.g. human ASP; rat ASP and KSRP)." [PMID:10781591] cellular_component
+ENSG00000239713 GO:0042803 protein homodimerization activity "Interacting selectively and non-covalently with an identical protein to form a homodimer." [GOC:jl] molecular_function
+ENSG00000239713 GO:0045071 negative regulation of viral genome replication "Any process that stops, prevents, or reduces the frequency, rate or extent of viral genome replication." [GOC:go_curators] biological_process
+ENSG00000239713 GO:0045087 innate immune response "Innate immune responses are defense responses mediated by germline encoded components that directly recognize components of potential pathogens." [GO_REF:0000022, GOC:add, GOC:ebc, GOC:mtg_15nov05, GOC:mtg_sensu] biological_process
+ENSG00000239713 GO:0045869 negative regulation of single stranded viral RNA replication via double stranded DNA intermediate "Any process that stops, prevents, or reduces the frequency, rate or extent of single stranded viral RNA replication via double stranded DNA intermediate." [GOC:go_curators] biological_process
+ENSG00000239713 GO:0047844 deoxycytidine deaminase activity "Catalysis of the reaction: deoxycytidine + H2O = deoxyuridine + NH3." [EC:3.5.4.14, MetaCyc:DEOXYCYTIDINE-DEAMINASE-RXN] molecular_function
+ENSG00000239713 GO:0048525 negative regulation of viral process "Any process that stops, prevents, or reduces the frequency, rate or extent of a multi-organism process in which a virus is a participant." [GOC:bf, GOC:jl] biological_process
+ENSG00000239713 GO:0051607 defense response to virus "Reactions triggered in response to the presence of a virus that act to protect the cell or organism." [GOC:ai] biological_process
+ENSG00000239713 GO:0070383 DNA cytosine deamination "The removal of an amino group from a cytosine residue in DNA, forming a uracil residue." [GOC:mah] biological_process
+ENSG00000239713 GO:0006259 DNA metabolic process "Any cellular metabolic process involving deoxyribonucleic acid. This is one of the two main types of nucleic acid, consisting of a long, unbranched macromolecule formed from one, or more commonly, two, strands of linked deoxyribonucleotides." [ISBN:0198506732] biological_process
+ENSG00000239713 GO:0016814 hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds, in cyclic amidines "Catalysis of the hydrolysis of any non-peptide carbon-nitrogen bond in a cyclic amidine, a compound of the form R-C(=NH)-NH2." [ISBN:0198506732] molecular_function
+ENSG00000239713 GO:0003824 catalytic activity "Catalysis of a biochemical reaction at physiological temperatures. In biologically catalyzed reactions, the reactants are known as substrates, and the catalysts are naturally occurring macromolecular substances known as enzymes. Enzymes possess specific binding sites for substrates, and are usually composed wholly or largely of protein, but RNA that has catalytic activity (ribozyme) is often also regarded as enzymatic." [ISBN:0198506732] molecular_function
+ENSG00000100253 GO:0004033 aldo-keto reductase (NADP) activity "Catalysis of the reaction: an alcohol + NADP+ = an aldehyde or a ketone + NADPH + H+." [GOC:ai] molecular_function
+ENSG00000100253 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100253 GO:0008199 ferric iron binding "Interacting selectively and non-covalently with ferric iron, Fe(III)." [GOC:ai] molecular_function
+ENSG00000100253 GO:0016234 inclusion body "A discrete intracellular part formed of aggregated molecules such as proteins or other biopolymers." [GOC:mah, PMID:11121744] cellular_component
+ENSG00000100253 GO:0016701 oxidoreductase activity, acting on single donors with incorporation of molecular oxygen "Catalysis of an oxidation-reduction (redox) reaction in which hydrogen or electrons are transferred from one donor, and molecular oxygen is incorporated into a donor." [GOC:mah] molecular_function
+ENSG00000100253 GO:0019310 inositol catabolic process "The chemical reactions and pathways resulting in the breakdown of inositol, 1,2,3,4,5,6-cyclohexanehexol, a growth factor for animals and microorganisms." [CHEBI:24848, GOC:go_curators] biological_process
+ENSG00000100253 GO:0050113 inositol oxygenase activity "Catalysis of the reaction: myo-inositol + O(2) = D-glucuronate + H(2)O + H(+)." [EC:1.13.99.1, RHEA:23699] molecular_function
+ENSG00000100253 GO:0055114 oxidation-reduction process "A metabolic process that results in the removal or addition of one or more electrons to or from a substance, with or without the concomitant removal or addition of a proton or protons." [GOC:dhl, GOC:ecd, GOC:jh2, GOC:jid, GOC:mlg, GOC:rph] biological_process
+ENSG00000100253 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000100253 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100253 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100253 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100253 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100253 GO:0016491 oxidoreductase activity "Catalysis of an oxidation-reduction (redox) reaction, a reversible chemical reaction in which the oxidation state of an atom or atoms within a molecule is altered. One substrate acts as a hydrogen or electron donor and becomes oxidized, while the other acts as hydrogen or electron acceptor and becomes reduced." [GOC:go_curators] molecular_function
+ENSG00000100253 GO:0005975 carbohydrate metabolic process "The chemical reactions and pathways involving carbohydrates, any of a group of organic compounds based of the general formula Cx(H2O)y. Includes the formation of carbohydrate derivatives by the addition of a carbohydrate residue to another molecule." [GOC:mah, ISBN:0198506732] biological_process
+ENSG00000100253 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000100253 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000100253 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100253 GO:0005506 iron ion binding "Interacting selectively and non-covalently with iron (Fe) ions." [GOC:ai] molecular_function
+ENSG00000100253 GO:0016651 oxidoreductase activity, acting on NAD(P)H "Catalysis of an oxidation-reduction (redox) reaction in which NADH or NADPH acts as a hydrogen or electron donor and reduces a hydrogen or electron acceptor." [GOC:ai] molecular_function
+ENSG00000100253 GO:0050661 NADP binding "Interacting selectively and non-covalently with nicotinamide-adenine dinucleotide phosphate, a coenzyme involved in many redox and biosynthetic reactions; binding may be to either the oxidized form, NADP+, or the reduced form, NADPH." [GOC:ai] molecular_function
+ENSG00000100234 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100234 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100234 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100234 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100234 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100234 GO:0030234 enzyme regulator activity "Binds to and modulates the activity of an enzyme." [GOC:mah] molecular_function
+ENSG00000100234 GO:0050877 neurological system process "A organ system process carried out by any of the organs or tissues of neurological system." [GOC:ai, GOC:mtg_cardio] biological_process
+ENSG00000100234 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100234 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100234 GO:0007601 visual perception "The series of events required for an organism to receive a visual stimulus, convert it to a molecular signal, and recognize and characterize the signal. Visual stimuli are detected in the form of photons and are processed to form an image." [GOC:ai] biological_process
+ENSG00000100234 GO:0008191 metalloendopeptidase inhibitor activity "Stops, prevents or reduces the activity of metalloendopeptidases, enzymes that catalyze the hydrolysis of nonterminal peptide bonds in a polypeptide chain and contain a chelated metal ion at their active sites which is essential to their catalytic activity." [GOC:ai] molecular_function
+ENSG00000100234 GO:0010951 negative regulation of endopeptidase activity "Any process that decreases the frequency, rate or extent of endopeptidase activity, the endohydrolysis of peptide bonds within proteins." [GOC:dph, GOC:tb] biological_process
+ENSG00000100234 GO:0046872 metal ion binding "Interacting selectively and non-covalently with any metal ion." [GOC:ai] molecular_function
+ENSG00000100234 GO:0051045 negative regulation of membrane protein ectodomain proteolysis "Any process that stops, prevents, or reduces the frequency, rate or extent of membrane protein ectodomain proteolysis." [GOC:ai] biological_process
+ENSG00000100234 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000100234 GO:0031012 extracellular matrix "A structure lying external to one or more cells, which provides structural support for cells or tissues; may be completely external to the cell (as in animals and bacteria) or be part of the cell (as in plants)." [GOC:mah, NIF_Subcellular:nlx_subcell_20090513] cellular_component
+ENSG00000100234 GO:0005604 basement membrane "A thin layer of dense material found in various animal tissues interposed between the cells and the adjacent connective tissue. It consists of the basal lamina plus an associated layer of reticulin fibers." [ISBN:0198547684] cellular_component
+ENSG00000100234 GO:0071310 cellular response to organic substance "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of an organic substance stimulus." [GOC:mah] biological_process
+ENSG00000128276 GO:0008270 zinc ion binding "Interacting selectively and non-covalently with zinc (Zn) ions." [GOC:ai] molecular_function
+ENSG00000128276 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000128276 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000128276 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100249
+ENSG00000183762 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000183762 GO:0007154 cell communication "Any process that mediates interactions between a cell and its surroundings. Encompasses interactions such as signaling or attachment between one cell and another cell, between a cell and an extracellular matrix, or between a cell and any other aspect of its environment." [GOC:mah] biological_process
+ENSG00000183762 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000183762 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000183762 GO:0016055 Wnt signaling pathway "The series of molecular signals initiated by binding of a Wnt protein to a frizzled family receptor on the surface of the target cell and ending with a change in cell state." [GOC:dph, GOC:go_curators, PMID:11532397] biological_process
+ENSG00000183762 GO:0060828 regulation of canonical Wnt signaling pathway "Any process that modulates the rate, frequency, or extent of the Wnt signaling pathway through beta-catenin, the series of molecular signals initiated by binding of a Wnt protein to a frizzled family receptor on the surface of the target cell, followed by propagation of the signal via beta-catenin, and ending with a change in transcription of target genes." [GOC:dph, GOC:sdb_2009, GOC:tb] biological_process
+ENSG00000183762 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000183762 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000183762 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000183762 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000183762 GO:0006952 defense response "Reactions, triggered in response to the presence of a foreign body or the occurrence of an injury, which result in restriction of damage to the organism attacked or prevention/recovery from the infection caused by the attack." [GOC:go_curators] biological_process
+ENSG00000183762 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000187860
+ENSG00000187860 GO:0016192 vesicle-mediated transport "A cellular transport process in which transported substances are moved in membrane-bounded vesicles; transported substances are enclosed in the vesicle lumen or located in the vesicle membrane. The process begins with a step that directs a substance to the forming vesicle, and includes vesicle budding and coating. Vesicles are then targeted to, and fuse with, an acceptor membrane." [GOC:ai, GOC:mah, ISBN:08789310662000] biological_process
+ENSG00000187860 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000187860 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000187860 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000187860 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100109 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100109 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100109 GO:0048856 anatomical structure development "The biological process whose specific outcome is the progression of an anatomical structure from an initial condition to its mature state. This process begins with the formation of the structure and ends with the mature structure, whatever form that may be including its natural destruction. An anatomical structure is any biological entity that occupies space and is distinguished from its surroundings. Anatomical structures can be macroscopic such as a carpel, or microscopic such as an acrosome." [GO_REF:0000021, GOC:mtg_15jun06] biological_process
+ENSG00000100109 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100109 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100109 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100109 GO:0003677 DNA binding "Any molecular function by which a gene product interacts selectively and non-covalently with DNA (deoxyribonucleic acid)." [GOC:dph, GOC:jl, GOC:tb, GOC:vw] molecular_function
+ENSG00000100109 GO:0006397 mRNA processing "Any process involved in the conversion of a primary mRNA transcript into one or more mature mRNA(s) prior to translation into polypeptide." [GOC:mah] biological_process
+ENSG00000100109 GO:0000390 spliceosomal complex disassembly "Disassembly of a spliceosomal complex with the ATP-dependent release of the product RNAs, one of which is composed of the joined exons. In cis splicing, the other product is the excised sequence, often a single intron, in a lariat structure." [GOC:krc, ISBN:0879695897] biological_process
+ENSG00000100109 GO:0000398 mRNA splicing, via spliceosome "The joining together of exons from one or more primary transcripts of messenger RNA (mRNA) and the excision of intron sequences, via a spliceosomal mechanism, so that mRNA consisting only of the joined exons is produced." [GOC:krc, ISBN:0198506732, ISBN:0879695897] biological_process
+ENSG00000100109 GO:0005681 spliceosomal complex "Any of a series of ribonucleoprotein complexes that contain RNA and small nuclear ribonucleoproteins (snRNPs), and are formed sequentially during the splicing of a messenger RNA primary transcript to excise an intron." [GOC:mah, ISBN:0198547684, PMID:19239890] cellular_component
+ENSG00000100109 GO:0006355 regulation of transcription, DNA-templated "Any process that modulates the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000100109 GO:0006396 RNA processing "Any process involved in the conversion of one or more primary RNA transcripts into one or more mature RNA molecules." [GOC:mah] biological_process
+ENSG00000100109 GO:0016607 nuclear speck "A discrete extra-nucleolar subnuclear domain, 20-50 in number, in which splicing factors are seen to be localized by immunofluorescence microscopy." [http://www.cellnucleus.com/] cellular_component
+ENSG00000100109 GO:0031214 biomineral tissue development "Formation of hard tissues that consist mainly of inorganic compounds, and also contain a small amounts of organic matrices that are believed to play important roles in their formation." [PMID:15132736] biological_process
+ENSG00000100109 GO:0071008 U2-type post-mRNA release spliceosomal complex "A spliceosomal complex that is formed following the release of the spliced product from the post-spliceosomal complex and contains the excised intron and the U2, U5 and U6 snRNPs." [GOC:ab, GOC:krc, GOC:mah, ISBN:0879695897, ISBN:0879697393, PMID:19239890] cellular_component
+ENSG00000100109 GO:0071013 catalytic step 2 spliceosome "A spliceosomal complex that contains three snRNPs, including U5, bound to a splicing intermediate in which the first catalytic cleavage of the 5' splice site has occurred. The precise subunit composition differs significantly from that of the catalytic step 1, or activated, spliceosome, and includes many proteins in addition to those found in the associated snRNPs." [GOC:ab, GOC:krc, GOC:mah, ISBN:0879695897, ISBN:0879697393, PMID:18322460, PMID:19239890] cellular_component
+ENSG00000100109 GO:0003676 nucleic acid binding "Interacting selectively and non-covalently with any nucleic acid." [GOC:jl] molecular_function
+ENSG00000100109 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100109 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100109 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100109 GO:0005578 proteinaceous extracellular matrix "A layer consisting mainly of proteins (especially collagen) and glycosaminoglycans (mostly as proteoglycans) that forms a sheet underlying or overlying cells such as endothelial and epithelial cells. The proteins are secreted by cells in the vicinity. An example of this component is found in Mus musculus." [GOC:mtg_sensu, ISBN:0198547684] cellular_component
+ENSG00000100109
+ENSG00000280363
+ENSG00000187045 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000187045 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000187045 GO:0030198 extracellular matrix organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of an extracellular matrix." [GOC:mah] biological_process
+ENSG00000187045 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000187045 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000187045 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000187045 GO:0008233 peptidase activity "Catalysis of the hydrolysis of a peptide bond. A peptide bond is a covalent bond formed when the carbon atom from the carboxyl group of one amino acid shares electrons with the nitrogen atom from the amino group of a second amino acid." [GOC:jl, ISBN:0815332181] molecular_function
+ENSG00000187045 GO:0048646 anatomical structure formation involved in morphogenesis "The developmental process pertaining to the initial formation of an anatomical structure from unspecified parts. This process begins with the specific processes that contribute to the appearance of the discrete structure and ends when the structural rudiment is recognizable. An anatomical structure is any biological entity that occupies space and is distinguished from its surroundings. Anatomical structures can be macroscopic such as a carpel, or microscopic such as an acrosome." [GOC:dph, GOC:jid, GOC:tb] biological_process
+ENSG00000187045 GO:0001525 angiogenesis "Blood vessel formation when new vessels emerge from the proliferation of pre-existing blood vessels." [ISBN:0878932453] biological_process
+ENSG00000187045 GO:0004252 serine-type endopeptidase activity "Catalysis of the hydrolysis of internal, alpha-peptide bonds in a polypeptide chain by a catalytic mechanism that involves a catalytic triad consisting of a serine nucleophile that is activated by a proton relay involving an acidic residue (e.g. aspartate or glutamate) and a basic residue (usually histidine)." [GOC:mah, http://merops.sanger.ac.uk/about/glossary.htm#CATTYPE, ISBN:0716720094] molecular_function
+ENSG00000187045 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000187045 GO:0006508 proteolysis "The hydrolysis of proteins into smaller polypeptides and/or amino acids by cleavage of their peptide bonds." [GOC:bf, GOC:mah] biological_process
+ENSG00000187045 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000187045 GO:0035556 intracellular signal transduction "The process in which a signal is passed on to downstream components within the cell, which become activated themselves to further propagate the signal and finally trigger a change in the function or state of the cell." [GOC:bf, GOC:jl, GOC:signaling, ISBN:3527303782] biological_process
+ENSG00000187045 GO:0042730 fibrinolysis "A process that solubilizes fibrin in the bloodstream of a multicellular organism, chiefly by the proteolytic action of plasmin." [GOC:jl, PMID:15842654] biological_process
+ENSG00000187045 GO:0055072 iron ion homeostasis "Any process involved in the maintenance of an internal steady state of iron ions within an organism or cell." [GOC:ai, GOC:jid, GOC:mah] biological_process
+ENSG00000187045 GO:0042592 homeostatic process "Any biological process involved in the maintenance of an internal steady state." [GOC:jl, ISBN:0395825172] biological_process
+ENSG00000187045 GO:0003824 catalytic activity "Catalysis of a biochemical reaction at physiological temperatures. In biologically catalyzed reactions, the reactants are known as substrates, and the catalysts are naturally occurring macromolecular substances known as enzymes. Enzymes possess specific binding sites for substrates, and are usually composed wholly or largely of protein, but RNA that has catalytic activity (ribozyme) is often also regarded as enzymatic." [ISBN:0198506732] molecular_function
+ENSG00000187045
+ENSG00000187045 GO:0030514 negative regulation of BMP signaling pathway "Any process that stops, prevents, or reduces the frequency, rate or extent of the BMP signaling pathway." [GOC:go_curators] biological_process
+ENSG00000100302 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100302 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100302 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000100302 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000100302 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100302 GO:0034655 nucleobase-containing compound catabolic process "The chemical reactions and pathways resulting in the breakdown of nucleobases, nucleosides, nucleotides and nucleic acids." [GOC:mah] biological_process
+ENSG00000100302 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000100302 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100302 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000100302 GO:0003924 GTPase activity "Catalysis of the reaction: GTP + H2O = GDP + phosphate." [ISBN:0198547684] molecular_function
+ENSG00000100302 GO:0006184 GTP catabolic process "The chemical reactions and pathways resulting in the breakdown of GTP, guanosine triphosphate." [ISBN:0198506732] biological_process
+ENSG00000100302 GO:0007264 small GTPase mediated signal transduction "Any series of molecular signals in which a small monomeric GTPase relays one or more of the signals." [GOC:mah] biological_process
+ENSG00000100302 GO:0007626 locomotory behavior "The specific movement from place to place of an organism in response to external or internal stimuli. Locomotion of a whole organism in a manner dependent upon some combination of that organism's internal state and external conditions." [GOC:dph] biological_process
+ENSG00000100302 GO:0031681 G-protein beta-subunit binding "Interacting selectively and non-covalently with a G-protein beta subunit." [GOC:mah] molecular_function
+ENSG00000100302 GO:0033235 positive regulation of protein sumoylation "Any process that activates or increases the frequency, rate or extent of the addition of SUMO groups to a protein." [GOC:mah] biological_process
+ENSG00000100302 GO:0043949 regulation of cAMP-mediated signaling "Any process which modulates the frequency, rate or extent of cAMP-mediated signaling, a series of molecular signals in which a cell uses cyclic AMP to convert an extracellular signal into a response." [GOC:jl] biological_process
+ENSG00000100302 GO:0051897 positive regulation of protein kinase B signaling "Any process that activates or increases the frequency, rate or extent of protein kinase B signaling, a series of reactions mediated by the intracellular serine/threonine kinase protein kinase B." [GOC:ai] biological_process
+ENSG00000100302 GO:0005525 GTP binding "Interacting selectively and non-covalently with GTP, guanosine triphosphate." [GOC:ai] molecular_function
+ENSG00000100302 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100302 GO:0006886 intracellular protein transport "The directed movement of proteins in a cell, including the movement of proteins between specific compartments or structures within a cell, such as organelles of a eukaryotic cell." [GOC:mah] biological_process
+ENSG00000100302 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100302 GO:0006913 nucleocytoplasmic transport "The directed movement of molecules between the nucleus and the cytoplasm." [GOC:go_curators] biological_process
+ENSG00000100302 GO:0005622 intracellular "The living contents of a cell; the matter contained within (but not including) the plasma membrane, usually taken to exclude large vacuoles and masses of secretory or ingested material. In eukaryotes it includes the nucleus and cytoplasm." [ISBN:0198506732] cellular_component
+ENSG00000100302 GO:0015031 protein transport "The directed movement of proteins into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:ai] biological_process
+ENSG00000100302 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000100302 GO:0001963 synaptic transmission, dopaminergic "The process of communication from a neuron to another neuron across a synapse using the neurotransmitter dopamine." [GOC:dph] biological_process
+ENSG00000100302 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100302 GO:0031624 ubiquitin conjugating enzyme binding "Interacting selectively and non-covalently with a ubiquitin conjugating enzyme, any of the E2 proteins." [GOC:vp] molecular_function
+ENSG00000100302 GO:0031397 negative regulation of protein ubiquitination "Any process that stops, prevents, or reduces the frequency, rate or extent of the addition of ubiquitin groups to a protein." [GOC:mah] biological_process
+ENSG00000100359 GO:0005097 Rab GTPase activator activity "Increases the rate of GTP hydrolysis by a GTPase of the Rab family." [GOC:mah] molecular_function
+ENSG00000100359 GO:0032851 positive regulation of Rab GTPase activity "Any process that activates or increases the activity of a GTPase of the Rab family." [GOC:mah] biological_process
+ENSG00000100359 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100359 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100359 GO:0030234 enzyme regulator activity "Binds to and modulates the activity of an enzyme." [GOC:mah] molecular_function
+ENSG00000100359 GO:0032313 regulation of Rab GTPase activity "Any process that modulates the activity of a GTPase of the Rab family." [GOC:mah] biological_process
+ENSG00000100359 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000100359 GO:0019899 enzyme binding "Interacting selectively and non-covalently with any enzyme." [GOC:jl] molecular_function
+ENSG00000100359 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100359 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000100359 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100359 GO:0007050 cell cycle arrest "A regulatory process that halts progression through the cell cycle during one of the normal phases (G1, S, G2, M)." [GOC:dph, GOC:mah, GOC:tb] biological_process
+ENSG00000100359 GO:0017137 Rab GTPase binding "Interacting selectively and non-covalently with Rab protein, any member of the Rab subfamily of the Ras superfamily of monomeric GTPases." [GOC:mah] molecular_function
+ENSG00000100359 GO:0032483 regulation of Rab protein signal transduction "Any process that modulates the frequency, rate or extent of Rab protein signal transduction." [GOC:mah] biological_process
+ENSG00000100359 GO:0032486 Rap protein signal transduction "A series of molecular signals within the cell that are mediated by a member of the Rap family of proteins switching to a GTP-bound active state." [GOC:mah] biological_process
+ENSG00000100359 GO:0033126 positive regulation of GTP catabolic process "Any process that activates or increases the frequency, rate or extent of the chemical reactions and pathways resulting in the breakdown of GTP, guanosine triphosphate." [GOC:mah] biological_process
+ENSG00000100359 GO:0048227 plasma membrane to endosome transport "Transport of a vesicle from the plasma membrane to the endosome." [GOC:jid] biological_process
+ENSG00000100359 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100359 GO:0016192 vesicle-mediated transport "A cellular transport process in which transported substances are moved in membrane-bounded vesicles; transported substances are enclosed in the vesicle lumen or located in the vesicle membrane. The process begins with a step that directs a substance to the forming vesicle, and includes vesicle budding and coating. Vesicles are then targeted to, and fuse with, an acceptor membrane." [GOC:ai, GOC:mah, ISBN:08789310662000] biological_process
+ENSG00000100359
+ENSG00000205853
+ENSG00000100077 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100077 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100077 GO:0016192 vesicle-mediated transport "A cellular transport process in which transported substances are moved in membrane-bounded vesicles; transported substances are enclosed in the vesicle lumen or located in the vesicle membrane. The process begins with a step that directs a substance to the forming vesicle, and includes vesicle budding and coating. Vesicles are then targeted to, and fuse with, an acceptor membrane." [GOC:ai, GOC:mah, ISBN:08789310662000] biological_process
+ENSG00000100077 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000100077 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000100077 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100077 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100077 GO:0016301 kinase activity "Catalysis of the transfer of a phosphate group, usually from ATP, to a substrate molecule." [ISBN:0198506732] molecular_function
+ENSG00000100077 GO:0004672 protein kinase activity "Catalysis of the phosphorylation of an amino acid residue in a protein, usually according to the reaction: a protein + ATP = a phosphoprotein + ADP." [MetaCyc:PROTEIN-KINASE-RXN] molecular_function
+ENSG00000100077 GO:0005524 ATP binding "Interacting selectively and non-covalently with ATP, adenosine 5'-triphosphate, a universally important coenzyme and enzyme regulator." [ISBN:0198506732] molecular_function
+ENSG00000100077 GO:0006468 protein phosphorylation "The process of introducing a phosphate group on to a protein." [GOC:hb] biological_process
+ENSG00000100077 GO:0031623 receptor internalization "A receptor-mediated endocytosis process that results in the movement of receptors from the plasma membrane to the inside of the cell. The process begins when cell surface receptors are monoubiquitinated following ligand-induced activation. Receptors are subsequently taken up into endocytic vesicles from where they are either targeted to the lysosome or vacuole for degradation or recycled back to the plasma membrane." [GOC:bf, GOC:mah, GOC:signaling, PMID:15006537, PMID:19643732] biological_process
+ENSG00000100077 GO:0038032 termination of G-protein coupled receptor signaling pathway "The signaling process in which G-protein coupled receptor signaling is brought to an end. For example, through the action of GTPase-activating proteins (GAPs) that act to accelerate hydrolysis of GTP to GDP on G-alpha proteins, thereby terminating the transduced signal." [GOC:bf, GOC:signaling] biological_process
+ENSG00000100077 GO:0047696 beta-adrenergic receptor kinase activity "Catalysis of the reaction: ATP + beta-adrenergic receptor = ADP + phospho-beta-adrenergic receptor." [EC:2.7.11.15, MetaCyc:BETA-ADRENERGIC-RECEPTOR-KINASE-RXN] molecular_function
+ENSG00000100077 GO:0008277 regulation of G-protein coupled receptor protein signaling pathway "Any process that modulates the frequency, rate or extent of G-protein coupled receptor protein signaling pathway activity." [GOC:go_curators] biological_process
+ENSG00000100077 GO:0004674 protein serine/threonine kinase activity "Catalysis of the reactions: ATP + protein serine = ADP + protein serine phosphate, and ATP + protein threonine = ADP + protein threonine phosphate." [GOC:bf] molecular_function
+ENSG00000100077 GO:0016772 transferase activity, transferring phosphorus-containing groups "Catalysis of the transfer of a phosphorus-containing group from one compound (donor) to another (acceptor)." [GOC:jl, ISBN:0198506732] molecular_function
+ENSG00000100077 GO:0004713 protein tyrosine kinase activity "Catalysis of the reaction: ATP + a protein tyrosine = ADP + protein tyrosine phosphate." [EC:2.7.10] molecular_function
+ENSG00000100077 GO:0004703 G-protein coupled receptor kinase activity "Catalysis of the reaction: ATP + G-protein coupled receptor = ADP + G-protein coupled receptor phosphate." [GOC:dph] molecular_function
+ENSG00000214491 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000214491 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000214491 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000214491 GO:0005622 intracellular "The living contents of a cell; the matter contained within (but not including) the plasma membrane, usually taken to exclude large vacuoles and masses of secretory or ingested material. In eukaryotes it includes the nucleus and cytoplasm." [ISBN:0198506732] cellular_component
+ENSG00000214491 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000214491 GO:0005215 transporter activity "Enables the directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells." [GOC:ai, GOC:dgf] molecular_function
+ENSG00000214491 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000214491
+ENSG00000183579 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000183579 GO:0008283 cell proliferation "The multiplication or reproduction of cells, resulting in the expansion of a cell population." [GOC:mah, GOC:mb] biological_process
+ENSG00000183579 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000183579 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000183579 GO:0016874 ligase activity "Catalysis of the joining of two substances, or two groups within a single molecule, with the concomitant hydrolysis of the diphosphate bond in ATP or a similar triphosphate." [EC:6, GOC:mah] molecular_function
+ENSG00000183579 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000183579 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000183579 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000183579 GO:0004842 ubiquitin-protein transferase activity "Catalysis of the transfer of ubiquitin from one protein to another via the reaction X-Ub + Y --> Y-Ub + X, where both X-Ub and Y-Ub are covalent linkages." [GOC:BioGRID, GOC:jh2, PMID:9635407] molecular_function
+ENSG00000183579 GO:0005109 frizzled binding "Interacting selectively and non-covalently with the frizzled (fz) receptor." [GOC:ceb, PR:000001315] molecular_function
+ENSG00000183579 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000183579 GO:0005887 integral component of plasma membrane "The component of the plasma membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000183579 GO:0006511 ubiquitin-dependent protein catabolic process "The chemical reactions and pathways resulting in the breakdown of a protein or peptide by hydrolysis of its peptide bonds, initiated by the covalent attachment of a ubiquitin group, or multiple ubiquitin groups, to the protein." [GOC:go_curators] biological_process
+ENSG00000183579 GO:0008270 zinc ion binding "Interacting selectively and non-covalently with zinc (Zn) ions." [GOC:ai] molecular_function
+ENSG00000183579 GO:0016567 protein ubiquitination "The process in which one or more ubiquitin groups are added to a protein." [GOC:ai] biological_process
+ENSG00000183579 GO:0038018 Wnt receptor catabolic process "The chemical reactions and pathways resulting in the breakdown of a Wnt receptor. Internalized Wnt receptors can be recycled to the plasma membrane or sorted to lysosomes for protein degradation." [GOC:BHF, GOC:rl, GOC:signaling, PMID:19643732] biological_process
+ENSG00000183579 GO:0072089 stem cell proliferation "The multiplication or reproduction of stem cells, resulting in the expansion of a stem cell population. A stem cell is a cell that retains the ability to divide and proliferate throughout life to provide progenitor cells that can differentiate into specialized cells." [GOC:mtg_kidney_jan10] biological_process
+ENSG00000183579 GO:0090090 negative regulation of canonical Wnt signaling pathway "Any process that decreases the rate, frequency, or extent of the Wnt signaling pathway through beta-catenin, the series of molecular signals initiated by binding of a Wnt protein to a frizzled family receptor on the surface of the target cell, followed by propagation of the signal via beta-catenin, and ending with a change in transcription of target genes." [GOC:dph, GOC:tb] biological_process
+ENSG00000183579 GO:2000051 negative regulation of non-canonical Wnt signaling pathway "Any process that stops, prevents, or reduces the frequency, rate or extent of non-canonical Wnt signaling pathway." [GOC:obol, GOC:yaf] biological_process
+ENSG00000183579 GO:0046872 metal ion binding "Interacting selectively and non-covalently with any metal ion." [GOC:ai] molecular_function
+ENSG00000183579 GO:0060070 canonical Wnt signaling pathway "The series of molecular signals initiated by binding of a Wnt protein to a frizzled family receptor on the surface of the target cell, followed by propagation of the signal via beta-catenin, and ending with a change in transcription of target genes. In this pathway, the activated receptor signals via downstream effectors that result in the inhibition of beta-catenin phosphorylation, thereby preventing degradation of beta-catenin. Stabilized beta-catenin can then accumulate and travel to the nucleus to trigger changes in transcription of target genes." [GOC:bf, GOC:dph, PMID:11532397, PMID:19619488] biological_process
+ENSG00000183579 GO:0060071 Wnt signaling pathway, planar cell polarity pathway "The series of molecular signals initiated by binding of a Wnt protein to a receptor on the surface of the target cell where activated receptors signal via downstream effectors including C-Jun N-terminal kinase (JNK) to modulate cytoskeletal elements and control cell polarity." [GOC:bf, GOC:dph, PMID:11532397] biological_process
+ENSG00000183579 GO:0030178 negative regulation of Wnt signaling pathway "Any process that stops, prevents, or reduces the frequency, rate or extent of the Wnt signaling pathway." [GOC:dph, GOC:go_curators, GOC:tb] biological_process
+ENSG00000008735 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000008735 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000008735 GO:0050877 neurological system process "A organ system process carried out by any of the organs or tissues of neurological system." [GOC:ai, GOC:mtg_cardio] biological_process
+ENSG00000008735 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000008735 GO:0030234 enzyme regulator activity "Binds to and modulates the activity of an enzyme." [GOC:mah] molecular_function
+ENSG00000008735 GO:0019899 enzyme binding "Interacting selectively and non-covalently with any enzyme." [GOC:jl] molecular_function
+ENSG00000008735 GO:0008092 cytoskeletal protein binding "Interacting selectively and non-covalently with any protein component of any cytoskeleton (actin, microtubule, or intermediate filament cytoskeleton)." [GOC:mah] molecular_function
+ENSG00000008735 GO:0006461 protein complex assembly "The aggregation, arrangement and bonding together of a set of components to form a protein complex." [GOC:ai] biological_process
+ENSG00000008735 GO:0022607 cellular component assembly "The aggregation, arrangement and bonding together of a cellular component." [GOC:isa_complete] biological_process
+ENSG00000008735 GO:0065003 macromolecular complex assembly "The aggregation, arrangement and bonding together of a set of macromolecules to form a complex." [GOC:jl] biological_process
+ENSG00000008735 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000008735 GO:0005198 structural molecule activity "The action of a molecule that contributes to the structural integrity of a complex or assembly within or outside a cell." [GOC:mah] molecular_function
+ENSG00000008735 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000008735 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000008735 GO:0000165 MAPK cascade "An intracellular protein kinase cascade containing at least a MAPK, a MAPKK and a MAP3K. The cascade can also contain two additional tiers: the upstream MAP4K and the downstream MAP Kinase-activated kinase (MAPKAPK). The kinases in each tier phosphorylate and activate the kinases in the downstream tier to transmit a signal within a cell." [GOC:bf, GOC:mtg_signaling_feb11, PMID:20811974, PMID:9561267] biological_process
+ENSG00000008735 GO:0001540 beta-amyloid binding "Interacting selectively and non-covalently with beta-amyloid peptide/protein and/or its precursor." [GOC:hjd] molecular_function
+ENSG00000008735 GO:0001662 behavioral fear response "An acute behavioral change resulting from a perceived external threat." [GOC:dph, PMID:9920659] biological_process
+ENSG00000008735 GO:0005078 MAP-kinase scaffold activity "The binding activity of a molecule that functions as a physical support for the assembly of a multiprotein mitogen-activated protein kinase (MAPK) complex. Binds multiple kinases of the MAPKKK cascade, and also upstream signaling proteins, permitting those molecules to function in a coordinated way. Bringing together multiple enzymes and their substrates enables the signal to be transduced quickly and efficiently." [PMID:12511654, PMID:15213240, PMID:9405336] molecular_function
+ENSG00000008735 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000008735 GO:0007172 signal complex assembly "The aggregation, arrangement and bonding together of a set of components to form a complex capable of relaying a signal within a cell." [GOC:bf, GOC:signaling, PMID:9646862] biological_process
+ENSG00000008735 GO:0009967 positive regulation of signal transduction "Any process that activates or increases the frequency, rate or extent of signal transduction." [GOC:sm] biological_process
+ENSG00000008735 GO:0010469 regulation of receptor activity "Any process that modulates the frequency, rate or extent of receptor activity. Receptor activity is when a molecule combines with an extracellular or intracellular messenger to initiate a change in cell activity." [GOC:dph, GOC:tb] biological_process
+ENSG00000008735 GO:0019894 kinesin binding "Interacting selectively and non-covalently and stoichiometrically with kinesin, a member of a superfamily of microtubule-based motor proteins that perform force-generating tasks such as organelle transport and chromosome segregation." [GOC:curators, PMID:8606779] molecular_function
+ENSG00000008735 GO:0019901 protein kinase binding "Interacting selectively and non-covalently with a protein kinase, any enzyme that catalyzes the transfer of a phosphate group, usually from ATP, to a protein substrate." [GOC:jl] molecular_function
+ENSG00000008735 GO:0030295 protein kinase activator activity "Increases the activity of a protein kinase, an enzyme which phosphorylates a protein." [GOC:ai] molecular_function
+ENSG00000008735 GO:0035176 social behavior "Behavior directed towards society, or taking place between members of the same species. Occurs predominantly, or only, in individuals that are part of a group." [GOC:jh2, PMID:12848939, Wikipedia:Social_behavior] biological_process
+ENSG00000008735 GO:0045860 positive regulation of protein kinase activity "Any process that activates or increases the frequency, rate or extent of protein kinase activity." [GOC:go_curators] biological_process
+ENSG00000008735 GO:0046328 regulation of JNK cascade "Any process that modulates the frequency, rate or extent of signal transduction mediated by the JNK cascade." [GOC:bf] biological_process
+ENSG00000008735 GO:0046958 nonassociative learning "A simple form of learning whereby the repeated presence of a stimulus leads to a change in the probability or strength of the response to that stimulus. There is no association of one type of stimulus with another, rather it is a generalized response to the environment." [ISBN:0582227089] biological_process
+ENSG00000008735 GO:0048813 dendrite morphogenesis "The process in which the anatomical structures of a dendrite are generated and organized. A dendrite is a freely branching protoplasmic process of a nerve cell." [GOC:jl, ISBN:0198506732] biological_process
+ENSG00000008735 GO:0051966 regulation of synaptic transmission, glutamatergic "Any process that modulates the frequency, rate or extent of glutamatergic synaptic transmission, the process of communication from a neuron to another neuron across a synapse using the neurotransmitter glutamate." [GOC:ai] biological_process
+ENSG00000008735 GO:0060079 regulation of excitatory postsynaptic membrane potential "Any process that modulates the establishment or extent of the excitatory postsynaptic potential (EPSP) which is a temporary increase in postsynaptic potential due to the flow of positively charged ions into the postsynaptic cell. The flow of ions that causes an EPSP is an excitatory postsynaptic current (EPSC) and makes it easier for the neuron to fire an action potential." [GOC:dph, GOC:ef] biological_process
+ENSG00000008735 GO:0097481 neuronal postsynaptic density "A postsynaptic density that is part of a neuron." [GOC:BHF, GOC:pr, GOC:rl] cellular_component
+ENSG00000008735 GO:2000310 regulation of N-methyl-D-aspartate selective glutamate receptor activity "Any process that modulates the frequency, rate or extent of N-methyl-D-aspartate selective glutamate receptor activity." [GOC:BHF] biological_process
+ENSG00000008735 GO:2000311 regulation of alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionate selective glutamate receptor activity "Any process that modulates the frequency, rate or extent of alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionate selective glutamate receptor activity." [GOC:BHF] biological_process
+ENSG00000008735 GO:0043025 neuronal cell body "The portion of a neuron that includes the nucleus, but excludes cell projections such as axons and dendrites." [GOC:go_curators] cellular_component
+ENSG00000008735 GO:0007254 JNK cascade "An intracellular protein kinase cascade containing at least a JNK (a MAPK), a JNKK (a MAPKK) and a JUN3K (a MAP3K). The cascade can also contain two additional tiers: the upstream MAP4K and the downstream MAP Kinase-activated kinase (MAPKAPK). The kinases in each tier phosphorylate and activate the kinases in the downstream tier to transmit a signal within a cell." [GOC:bf, GOC:signaling, PMID:11790549, PMID:20811974] biological_process
+ENSG00000008735 GO:0007617 mating behavior "The behavioral interactions between organisms for the purpose of mating, or sexual reproduction resulting in the formation of zygotes." [GOC:ai, GOC:dph] biological_process
+ENSG00000008735 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000008735 GO:0032403 protein complex binding "Interacting selectively and non-covalently with any protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:mah] molecular_function
+ENSG00000008735 GO:0032874 positive regulation of stress-activated MAPK cascade "Any process that activates or increases the frequency, rate or extent of signal transduction mediated by the stress-activated MAPK cascade." [GOC:mah] biological_process
+ENSG00000100425 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100425 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100425 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000100425 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100425 GO:0042393 histone binding "Interacting selectively and non-covalently with a histone, any of a group of water-soluble proteins found in association with the DNA of plant and animal chromosomes. They are involved in the condensation and coiling of chromosomes during cell division and have also been implicated in nonspecific suppression of gene activity." [GOC:jl] molecular_function
+ENSG00000100425 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100425 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100425 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100425 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100425 GO:0008270 zinc ion binding "Interacting selectively and non-covalently with zinc (Zn) ions." [GOC:ai] molecular_function
+ENSG00000100425 GO:0043966 histone H3 acetylation "The modification of histone H3 by the addition of an acetyl group." [GOC:jl] biological_process
+ENSG00000100425 GO:0070776 MOZ/MORF histone acetyltransferase complex "A histone acetyltransferase complex that has histone H3 acetyltransferase and coactivator activities. Subunits of the human complex include MYST3/MOZ, MYST4/MORF, ING5, EAF6 and one of BRPF1, BRD1/BRPF2 and BRPF3." [PMID:18794358] cellular_component
+ENSG00000100425 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000211678
+ENSG00000100104 GO:0048511 rhythmic process "Any process pertinent to the generation and maintenance of rhythms in the physiology of an organism." [GOC:jid] biological_process
+ENSG00000100104 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100104
+ENSG00000100030 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100030 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100030 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100030 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100030 GO:0016192 vesicle-mediated transport "A cellular transport process in which transported substances are moved in membrane-bounded vesicles; transported substances are enclosed in the vesicle lumen or located in the vesicle membrane. The process begins with a step that directs a substance to the forming vesicle, and includes vesicle budding and coating. Vesicles are then targeted to, and fuse with, an acceptor membrane." [GOC:ai, GOC:mah, ISBN:08789310662000] biological_process
+ENSG00000100030 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000100030 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000100030 GO:0002376 immune system process "Any process involved in the development or functioning of the immune system, an organismal system for calibrated responses to potential internal or invasive threats." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05] biological_process
+ENSG00000100030 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100030 GO:0019899 enzyme binding "Interacting selectively and non-covalently with any enzyme." [GOC:jl] molecular_function
+ENSG00000100030 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000100030 GO:0044403 symbiosis, encompassing mutualism through parasitism "An interaction between two organisms living together in more or less intimate association. The term host is usually used for the larger (macro) of the two members of a symbiosis. The smaller (micro) member is called the symbiont organism. Microscopic symbionts are often referred to as endosymbionts. The various forms of symbiosis include parasitism, in which the association is disadvantageous or destructive to one of the organisms; mutualism, in which the association is advantageous, or often necessary to one or both and not harmful to either; and commensalism, in which one member of the association benefits while the other is not affected. However, mutualism, parasitism, and commensalism are often not discrete categories of interactions and should rather be perceived as a continuum of interaction ranging from parasitism to mutualism. In fact, the direction of a symbiotic interaction can change during the lifetime of the symbionts due to developmental changes as well as changes in the biotic/abiotic environment in which the interaction occurs." [GOC:cc, http://www.free-definition.com] biological_process
+ENSG00000100030 GO:0005856 cytoskeleton "Any of the various filamentous elements that form the internal framework of cells, and typically remain after treatment of the cells with mild detergent to remove membrane constituents and soluble components of the cytoplasm. The term embraces intermediate filaments, microfilaments, microtubules, the microtrabecular lattice, and other structures characterized by a polymeric filamentous nature and long-range order within the cell. The various elements of the cytoskeleton not only serve in the maintenance of cellular shape but also have roles in other cellular functions, including cellular movement, cell division, endocytosis, and movement of organelles." [GOC:mah, ISBN:0198547684, PMID:16959967] cellular_component
+ENSG00000100030 GO:0016301 kinase activity "Catalysis of the transfer of a phosphate group, usually from ATP, to a substrate molecule." [ISBN:0198506732] molecular_function
+ENSG00000100030 GO:0007267 cell-cell signaling "Any process that mediates the transfer of information from one cell to another." [GOC:mah] biological_process
+ENSG00000100030 GO:0007049 cell cycle "The progression of biochemical and morphological phases and events that occur in a cell during successive cell replication or nuclear replication events. Canonically, the cell cycle comprises the replication and segregation of genetic material followed by the division of the cell, but in endocycles or syncytial cells nuclear replication or nuclear division may not be followed by cell division." [GOC:go_curators, GOC:mtg_cell_cycle] biological_process
+ENSG00000100030 GO:0040011 locomotion "Self-propelled movement of a cell or organism from one location to another." [GOC:dgh] biological_process
+ENSG00000100030 GO:0008219 cell death "Any biological process that results in permanent cessation of all vital functions of a cell. A cell should be considered dead when any one of the following molecular or morphological criteria is met: (1) the cell has lost the integrity of its plasma membrane; (2) the cell, including its nucleus, has undergone complete fragmentation into discrete bodies (frequently referred to as \"apoptotic bodies\"); and/or (3) its corpse (or its fragments) have been engulfed by an adjacent cell in vivo." [GOC:mah, GOC:mtg_apoptosis] biological_process
+ENSG00000100030 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000100030 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100030 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000100030 GO:0005794 Golgi apparatus "A compound membranous cytoplasmic organelle of eukaryotic cells, consisting of flattened, ribosome-free vesicles arranged in a more or less regular stack. The Golgi apparatus differs from the endoplasmic reticulum in often having slightly thicker membranes, appearing in sections as a characteristic shallow semicircle so that the convex side (cis or entry face) abuts the endoplasmic reticulum, secretory vesicles emerging from the concave side (trans or exit face). In vertebrate cells there is usually one such organelle, while in invertebrates and plants, where they are known usually as dictyosomes, there may be several scattered in the cytoplasm. The Golgi apparatus processes proteins produced on the ribosomes of the rough endoplasmic reticulum; such processing includes modification of the core oligosaccharides of glycoproteins, and the sorting and packaging of proteins for transport to a variety of cellular locations. Three different regions of the Golgi are now recognized both in terms of structure and function: cis, in the vicinity of the cis face, trans, in the vicinity of the trans face, and medial, lying between the cis and trans regions." [ISBN:0198506732] cellular_component
+ENSG00000100030 GO:0005768 endosome "A membrane-bounded organelle to which materials ingested by endocytosis are delivered." [ISBN:0198506732, PMID:19696797] cellular_component
+ENSG00000100030 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000100030 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100030 GO:0005654 nucleoplasm "That part of the nuclear content other than the chromosomes or the nucleolus." [GOC:ma, ISBN:0124325653] cellular_component
+ENSG00000100030 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100030 GO:0004871 signal transducer activity "Conveys a signal across a cell to trigger a change in cell function or state. A signal is a physical entity or change in state that is used to transfer information in order to trigger a response." [GOC:go_curators] molecular_function
+ENSG00000100030 GO:0003677 DNA binding "Any molecular function by which a gene product interacts selectively and non-covalently with DNA (deoxyribonucleic acid)." [GOC:dph, GOC:jl, GOC:tb, GOC:vw] molecular_function
+ENSG00000100030 GO:0000165 MAPK cascade "An intracellular protein kinase cascade containing at least a MAPK, a MAPKK and a MAP3K. The cascade can also contain two additional tiers: the upstream MAP4K and the downstream MAP Kinase-activated kinase (MAPKAPK). The kinases in each tier phosphorylate and activate the kinases in the downstream tier to transmit a signal within a cell." [GOC:bf, GOC:mtg_signaling_feb11, PMID:20811974, PMID:9561267] biological_process
+ENSG00000100030 GO:0000186 activation of MAPKK activity "The initiation of the activity of the inactive enzyme MAP kinase kinase (MAPKK)." [PMID:9561267] biological_process
+ENSG00000100030 GO:0000187 activation of MAPK activity "The initiation of the activity of the inactive enzyme MAP kinase (MAPK)." [PMID:9561267] biological_process
+ENSG00000100030 GO:0002224 toll-like receptor signaling pathway "Any series of molecular signals generated as a consequence of binding to a toll-like receptor. Toll-like receptors directly bind pattern motifs from a variety of microbial sources to initiate innate immune response." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05, ISBN:0781735149, PMID:12467241, PMID:12524386, PMID:12855817, PMID:15585605, PMID:15728447] biological_process
+ENSG00000100030 GO:0002755 MyD88-dependent toll-like receptor signaling pathway "Any series of molecular signals generated as a consequence of binding to a toll-like receptor where the MyD88 adaptor molecule mediates transduction of the signal. Toll-like receptors directly bind pattern motifs from a variety of microbial sources to initiate innate immune response." [GOC:add, ISBN:0781735149, PMID:12467241, PMID:12524386, PMID:12855817, PMID:15585605, PMID:15728447] biological_process
+ENSG00000100030 GO:0002756 MyD88-independent toll-like receptor signaling pathway "Any series of molecular signals generated as a consequence of binding to a toll-like receptor not relying on the MyD88 adaptor molecule. Toll-like receptors directly bind pattern motifs from a variety of microbial sources to initiate innate immune response." [GOC:add, ISBN:0781735149, PMID:12467241, PMID:12524386, PMID:12855817, PMID:15585605, PMID:15728447] biological_process
+ENSG00000100030 GO:0004674 protein serine/threonine kinase activity "Catalysis of the reactions: ATP + protein serine = ADP + protein serine phosphate, and ATP + protein threonine = ADP + protein threonine phosphate." [GOC:bf] molecular_function
+ENSG00000100030 GO:0004707 MAP kinase activity "Catalysis of the reaction: protein + ATP = protein phosphate + ADP. This reaction is the phosphorylation of proteins. Mitogen-activated protein kinase; a family of protein kinases that perform a crucial step in relaying signals from the plasma membrane to the nucleus. They are activated by a wide range of proliferation- or differentiation-inducing signals; activation is strong with agonists such as polypeptide growth factors and tumor-promoting phorbol esters, but weak (in most cell backgrounds) by stress stimuli." [GOC:ma, ISBN:0198547684] molecular_function
+ENSG00000100030 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100030 GO:0005769 early endosome "A membrane-bounded organelle that receives incoming material from primary endocytic vesicles that have been generated by clathrin-dependent and clathrin-independent endocytosis; vesicles fuse with the early endosome to deliver cargo for sorting into recycling or degradation pathways." [GOC:mah, NIF_Subcellular:nlx_subcell_20090701, PMID:19696797] cellular_component
+ENSG00000100030 GO:0005770 late endosome "A prelysosomal endocytic organelle differentiated from early endosomes by lower lumenal pH and different protein composition. Late endosomes are more spherical than early endosomes and are mostly juxtanuclear, being concentrated near the microtubule organizing center." [NIF_Subcellular:nlx_subcell_20090702, PMID:11964142, PMID:2557062] cellular_component
+ENSG00000100030 GO:0005901 caveola "A membrane raft that forms small pit, depression, or invagination that communicates with the outside of a cell and extends inward, indenting the cytoplasm and the cell membrane. Examples include any of the minute pits or incuppings of the cell membrane formed during pinocytosis. Such caveolae may be pinched off to form free vesicles within the cytoplasm." [GOC:mah, ISBN:0721662544, PMID:16645198] cellular_component
+ENSG00000100030 GO:0005925 focal adhesion "Small region on the surface of a cell that anchors the cell to the extracellular matrix and that forms a point of termination of actin filaments." [ISBN:0124325653, ISBN:0815316208] cellular_component
+ENSG00000100030 GO:0006351 transcription, DNA-templated "The cellular synthesis of RNA on a template of DNA." [GOC:jl, GOC:txnOH] biological_process
+ENSG00000100030 GO:0006915 apoptotic process "A programmed cell death process which begins when a cell receives an internal (e.g. DNA damage) or external signal (e.g. an extracellular death ligand), and proceeds through a series of biochemical events (signaling pathway phase) which trigger an execution phase. The execution phase is the last step of an apoptotic process, and is typically characterized by rounding-up of the cell, retraction of pseudopodes, reduction of cellular volume (pyknosis), chromatin condensation, nuclear fragmentation (karyorrhexis), plasma membrane blebbing and fragmentation of the cell into apoptotic bodies. When the execution phase is completed, the cell has died." [GOC:dhl, GOC:ecd, GOC:go_curators, GOC:mtg_apoptosis, GOC:tb, ISBN:0198506732, PMID:18846107, PMID:21494263] biological_process
+ENSG00000100030 GO:0006935 chemotaxis "The directed movement of a motile cell or organism, or the directed growth of a cell guided by a specific chemical concentration gradient. Movement may be towards a higher concentration (positive chemotaxis) or towards a lower concentration (negative chemotaxis)." [ISBN:0198506732] biological_process
+ENSG00000100030 GO:0007173 epidermal growth factor receptor signaling pathway "A series of molecular signals initiated by binding of a ligand to the tyrosine kinase receptor EGFR (ERBB1) on the surface of a cell. The pathway ends with regulation of a downstream cellular process, e.g. transcription." [GOC:ceb, PR:000006933] biological_process
+ENSG00000100030 GO:0007264 small GTPase mediated signal transduction "Any series of molecular signals in which a small monomeric GTPase relays one or more of the signals." [GOC:mah] biological_process
+ENSG00000100030 GO:0007265 Ras protein signal transduction "A series of molecular signals within the cell that are mediated by a member of the Ras superfamily of proteins switching to a GTP-bound active state." [GOC:bf] biological_process
+ENSG00000100030 GO:0007268 synaptic transmission "The process of communication from a neuron to a target (neuron, muscle, or secretory cell) across a synapse." [GOC:jl, MeSH:D009435] biological_process
+ENSG00000100030 GO:0007411 axon guidance "The chemotaxis process that directs the migration of an axon growth cone to a specific target site in response to a combination of attractive and repulsive cues." [ISBN:0878932437] biological_process
+ENSG00000100030 GO:0007596 blood coagulation "The sequential process in which the multiple coagulation factors of the blood interact, ultimately resulting in the formation of an insoluble fibrin clot; it may be divided into three stages: stage 1, the formation of intrinsic and extrinsic prothrombin converting principle; stage 2, the formation of thrombin; stage 3, the formation of stable fibrin polymers." [http://www.graylab.ac.uk/omd/, ISBN:0198506732] biological_process
+ENSG00000100030 GO:0008286 insulin receptor signaling pathway "The series of molecular signals generated as a consequence of the insulin receptor binding to insulin." [GOC:ceb] biological_process
+ENSG00000100030 GO:0008353 RNA polymerase II carboxy-terminal domain kinase activity "Catalysis of the reaction: ATP + (DNA-directed RNA polymerase II) = ADP + phospho-(DNA-directed RNA polymerase II); phosphorylation occurs on residues in the carboxy-terminal domain (CTD) repeats." [EC:2.7.11.23, GOC:mah] molecular_function
+ENSG00000100030 GO:0008543 fibroblast growth factor receptor signaling pathway "The series of molecular signals generated as a consequence of a fibroblast growth factor receptor binding to one of its physiological ligands." [GOC:ceb] biological_process
+ENSG00000100030 GO:0010800 positive regulation of peptidyl-threonine phosphorylation "Any process that increases the frequency, rate or extent of peptidyl-threonine phosphorylation. Peptidyl-threonine phosphorylation is the phosphorylation of peptidyl-threonine to form peptidyl-O-phospho-L-threonine." [GOC:dph, GOC:tb] biological_process
+ENSG00000100030 GO:0015630 microtubule cytoskeleton "The part of the cytoskeleton (the internal framework of a cell) composed of microtubules and associated proteins." [GOC:jl, ISBN:0395825172] cellular_component
+ENSG00000100030 GO:0016032 viral process "A multi-organism process in which a virus is a participant. The other participant is the host. Includes infection of a host cell, replication of the viral genome, and assembly of progeny virus particles. In some cases the viral genetic material may integrate into the host genome and only subsequently, under particular circumstances, 'complete' its life cycle." [GOC:bf, GOC:jl, GOC:mah] biological_process
+ENSG00000100030 GO:0018105 peptidyl-serine phosphorylation "The phosphorylation of peptidyl-serine to form peptidyl-O-phospho-L-serine." [RESID:AA0037] biological_process
+ENSG00000100030 GO:0018107 peptidyl-threonine phosphorylation "The phosphorylation of peptidyl-threonine to form peptidyl-O-phospho-L-threonine." [RESID:AA0038] biological_process
+ENSG00000100030 GO:0019902 phosphatase binding "Interacting selectively and non-covalently with any phosphatase." [GOC:jl] molecular_function
+ENSG00000100030 GO:0030168 platelet activation "A series of progressive, overlapping events triggered by exposure of the platelets to subendothelial tissue. These events include shape change, adhesiveness, aggregation, and release reactions. When carried through to completion, these events lead to the formation of a stable hemostatic plug." [http://www.graylab.ac.uk/omd/] biological_process
+ENSG00000100030 GO:0031647 regulation of protein stability "Any process that affects the structure and integrity of a protein by altering the likelihood of its degradation or aggregation." [GOC:dph, GOC:mah, GOC:tb] biological_process
+ENSG00000100030 GO:0032872 regulation of stress-activated MAPK cascade "Any process that modulates the frequency, rate or extent of signal transduction mediated by the stress-activated MAPK cascade." [GOC:mah] biological_process
+ENSG00000100030 GO:0034134 toll-like receptor 2 signaling pathway "Any series of molecular signals generated as a consequence of binding to toll-like receptor 2." [GOC:add, PMID:16551253, PMID:17328678] biological_process
+ENSG00000100030 GO:0034138 toll-like receptor 3 signaling pathway "Any series of molecular signals generated as a consequence of binding to toll-like receptor 3." [GOC:add, PMID:16551253, PMID:17328678] biological_process
+ENSG00000100030 GO:0034142 toll-like receptor 4 signaling pathway "Any series of molecular signals generated as a consequence of binding to toll-like receptor 4." [GOC:add, PMID:16551253, PMID:17328678] biological_process
+ENSG00000100030 GO:0034146 toll-like receptor 5 signaling pathway "Any series of molecular signals generated as a consequence of binding to toll-like receptor 5." [GOC:add, PMID:16551253, PMID:17328678] biological_process
+ENSG00000100030 GO:0034162 toll-like receptor 9 signaling pathway "Any series of molecular signals generated as a consequence of binding to toll-like receptor 9." [GOC:add, PMID:16551253, PMID:17328678] biological_process
+ENSG00000100030 GO:0034166 toll-like receptor 10 signaling pathway "Any series of molecular signals generated as a consequence of binding to toll-like receptor 10." [GOC:add, PMID:16551253, PMID:17328678] biological_process
+ENSG00000100030 GO:0035666 TRIF-dependent toll-like receptor signaling pathway "Any series of molecular signals generated as a consequence of binding to a toll-like receptor where the TRIF adaptor mediates transduction of the signal. Toll-like receptors directly bind pattern motifs from a variety of microbial sources to initiate innate immune response." [GOC:BHF, PMID:12855817] biological_process
+ENSG00000100030 GO:0038095 Fc-epsilon receptor signaling pathway "A series of molecular signals initiated by the binding of the Fc portion of immunoglobulin E (IgE) to an Fc-epsilon receptor on the surface of a signal-receiving cell, and ending with regulation of a downstream cellular process, e.g. transcription. The Fc portion of an immunoglobulin is its C-terminal constant region." [GOC:phg, PMID:12413516, PMID:15048725] biological_process
+ENSG00000100030 GO:0038096 Fc-gamma receptor signaling pathway involved in phagocytosis "An Fc-gamma receptor signaling pathway that contributes to the endocytic engulfment of external particulate material by phagocytes." [GOC:phg, PMID:12488490, PMID:15466916] biological_process
+ENSG00000100030 GO:0038123 toll-like receptor TLR1:TLR2 signaling pathway "A series of molecular signals initiated by the binding of a heterodimeric TLR1:TLR2 complex to one of it's physiological ligands, followed by transmission of the signal by the activated receptor, and ending with regulation of a downstream cellular process, e.g. transcription." [GOC:nhn, GOC:signaling, PMID:17318230] biological_process
+ENSG00000100030 GO:0038124 toll-like receptor TLR6:TLR2 signaling pathway "A series of molecular signals initiated by the binding of a heterodimeric TLR6:TLR2 complex to one of it's physiological ligands, followed by transmission of the signal by the activated receptor, and ending with regulation of a downstream cellular process, e.g. transcription." [GOC:nhn, GOC:signaling, PMID:17318230] biological_process
+ENSG00000100030 GO:0038127 ERBB signaling pathway "A series of molecular signals initiated by binding of a ligand to a member of the ERBB family of receptor tyrosine kinases on the surface of a cell, and ending with regulation of a downstream cellular process, e.g. transcription." [GOC:jc, PMID:16460914, PR:000001812, Wikipedia:ErbB] biological_process
+ENSG00000100030 GO:0045087 innate immune response "Innate immune responses are defense responses mediated by germline encoded components that directly recognize components of potential pathogens." [GO_REF:0000022, GOC:add, GOC:ebc, GOC:mtg_15nov05, GOC:mtg_sensu] biological_process
+ENSG00000100030 GO:0048011 neurotrophin TRK receptor signaling pathway "A series of molecular signals initiated by the binding of a neurotrophin to a receptor on the surface of the target cell where the receptor possesses tyrosine kinase activity, and ending with regulation of a downstream cellular process, e.g. transcription." [GOC:bf, GOC:ceb, GOC:jc, GOC:signaling, PMID:12065629, Wikipedia:Trk_receptor] biological_process
+ENSG00000100030 GO:0051090 regulation of sequence-specific DNA binding transcription factor activity "Any process that modulates the frequency, rate or extent of the activity of a transcription factor, any factor involved in the initiation or regulation of transcription." [GOC:ai] biological_process
+ENSG00000100030 GO:0051403 stress-activated MAPK cascade "A series of molecular signals in which a stress-activated MAP kinase cascade relays one or more of the signals; MAP kinase cascades involve at least three protein kinase activities and culminate in the phosphorylation and activation of a MAP kinase." [GOC:ai, PMID:15936270] biological_process
+ENSG00000100030 GO:0051493 regulation of cytoskeleton organization "Any process that modulates the frequency, rate or extent of the formation, arrangement of constituent parts, or disassembly of cytoskeletal structures." [GOC:ai] biological_process
+ENSG00000100030 GO:0060397 JAK-STAT cascade involved in growth hormone signaling pathway "The process in which STAT proteins (Signal Transducers and Activators of Transcription) are activated by members of the JAK (janus activated kinase) family of tyrosine kinases, following the binding of physiological ligands to the growth hormone receptor. Once activated, STATs dimerize and translocate to the nucleus and modulate the expression of target genes." [GOC:BHF, GOC:dph, PMID:11445442] biological_process
+ENSG00000100030 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000100030 GO:0070371 ERK1 and ERK2 cascade "An intracellular protein kinase cascade containing at least ERK1 or ERK2 (MAPKs), a MEK (a MAPKK) and a MAP3K. The cascade can also contain two additional tiers: the upstream MAP4K and the downstream MAP Kinase-activated kinase (MAPKAPK). The kinases in each tier phosphorylate and activate the kinases in the downstream tier to transmit a signal within a cell." [GOC:add, GOC:signaling, ISBN:0121245462, ISBN:0896039986, PMID:20811974] biological_process
+ENSG00000100030 GO:0070849 response to epidermal growth factor "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of an epidermal growth factor stimulus." [GOC:BHF, GOC:mah] biological_process
+ENSG00000100030 GO:0072584 caveolin-mediated endocytosis "An endocytosis process that begins when material is taken up into plasma membrane caveolae, which then pinch off to form endocytic caveolar carriers." [GOC:BHF, GOC:mah, PMID:17318224, PMID:18498251, PMID:8970738, PMID:9234965] biological_process
+ENSG00000100030 GO:0072686 mitotic spindle "A spindle that forms as part of mitosis. Mitotic and meiotic spindles contain distinctive complements of proteins associated with microtubules." [GOC:mah, GOC:vw, PMID:11408572, PMID:18367542, PMID:8027178] cellular_component
+ENSG00000100030 GO:0090170 regulation of Golgi inheritance "Any process that modulates the rate, frequency or extent of Golgi inheritance. Golgi inheritance is the partitioning of Golgi apparatus between daughter cells at cell division." [GOC:ascb_2009, GOC:dph, GOC:tb] biological_process
+ENSG00000100030 GO:2000641 regulation of early endosome to late endosome transport "Any process that modulates the frequency, rate or extent of early endosome to late endosome transport." [GOC:BHF] biological_process
+ENSG00000100030 GO:0004672 protein kinase activity "Catalysis of the phosphorylation of an amino acid residue in a protein, usually according to the reaction: a protein + ATP = a phosphoprotein + ADP." [MetaCyc:PROTEIN-KINASE-RXN] molecular_function
+ENSG00000100030 GO:0005524 ATP binding "Interacting selectively and non-covalently with ATP, adenosine 5'-triphosphate, a universally important coenzyme and enzyme regulator." [ISBN:0198506732] molecular_function
+ENSG00000100030 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100030 GO:0006468 protein phosphorylation "The process of introducing a phosphate group on to a protein." [GOC:hb] biological_process
+ENSG00000100030 GO:0016772 transferase activity, transferring phosphorus-containing groups "Catalysis of the transfer of a phosphorus-containing group from one compound (donor) to another (acceptor)." [GOC:jl, ISBN:0198506732] molecular_function
+ENSG00000100030 GO:0004713 protein tyrosine kinase activity "Catalysis of the reaction: ATP + a protein tyrosine = ADP + protein tyrosine phosphate." [EC:2.7.10] molecular_function
+ENSG00000100030 GO:0050852 T cell receptor signaling pathway "A series of molecular signals initiated by the cross-linking of an antigen receptor on a T cell." [GOC:add] biological_process
+ENSG00000100030 GO:0097011 cellular response to granulocyte macrophage colony-stimulating factor stimulus "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a granulocyte macrophage colony-stimulating factor stimulus." [GOC:BHF, GOC:ebc, PMID:7901744] biological_process
+ENSG00000100030 GO:0032496 response to lipopolysaccharide "Any process that results in a change in state or activity of an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a lipopolysaccharide stimulus; lipopolysaccharide is a major component of the cell wall of gram-negative bacteria." [GOC:add, ISBN:0721601464] biological_process
+ENSG00000100030 GO:0060716 labyrinthine layer blood vessel development "The process whose specific outcome is the progression of a blood vessel of the labyrinthine layer of the placenta over time, from its formation to the mature structure. The embryonic vessels grow through the layer to come in close contact with the maternal blood supply." [GOC:dph] biological_process
+ENSG00000100030 GO:0009887 organ morphogenesis "Morphogenesis of an organ. An organ is defined as a tissue or set of tissues that work together to perform a specific function or functions. Morphogenesis is the process in which anatomical structures are generated and organized. Organs are commonly observed as visibly distinct structures, but may also exist as loosely associated clusters of cells that work together to perform a specific function or functions." [GOC:dgh, GOC:go_curators, ISBN:0471245208, ISBN:0721662544] biological_process
+ENSG00000100030 GO:0043330 response to exogenous dsRNA "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of an exogenous double-stranded RNA stimulus." [GOC:go_curators] biological_process
+ENSG00000100030 GO:0045596 negative regulation of cell differentiation "Any process that stops, prevents, or reduces the frequency, rate or extent of cell differentiation." [GOC:go_curators] biological_process
+ENSG00000100030 GO:0006974 cellular response to DNA damage stimulus "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a stimulus indicating damage to its DNA from environmental insults or errors during metabolism." [GOC:go_curators] biological_process
+ENSG00000100030 GO:0050853 B cell receptor signaling pathway "A series of molecular signals initiated by the cross-linking of an antigen receptor on a B cell." [GOC:add] biological_process
+ENSG00000100030 GO:0031663 lipopolysaccharide-mediated signaling pathway "A series of molecular signals initiated by the binding of a lipopolysaccharide (LPS) to a receptor on the surface of a target cell, and ending with regulation of a downstream cellular process, e.g. transcription. Lipopolysaccharides are major components of the outer membrane of Gram-negative bacteria, making them prime targets for recognition by the immune system." [GOC:mah, GOC:signaling, PMID:15379975] biological_process
+ENSG00000100030 GO:0033598 mammary gland epithelial cell proliferation "The multiplication or reproduction of mammary gland epithelial cells, resulting in the expansion of a cell population. Mammary gland epithelial cells make up the covering of surfaces of the mammary gland. The mammary gland is a large compound sebaceous gland that in female mammals is modified to secrete milk." [GOC:dph, GOC:mah] biological_process
+ENSG00000100030 GO:0001784 phosphotyrosine binding "Interacting selectively and non-covalently with a phosphorylated tyrosine residue within a protein." [PMID:14636584] molecular_function
+ENSG00000100030 GO:0019858 cytosine metabolic process "The chemical reactions and pathways involving cytosine, 4-amino-2-hydroxypyrimidine, a pyrimidine derivative that is one of the five main bases found in nucleic acids; it occurs widely in cytidine derivatives." [GOC:ai] biological_process
+ENSG00000100030 GO:0031143 pseudopodium "A temporary protrusion or retractile process of a cell, associated with flowing movements of the protoplasm, and serving for locomotion and feeding." [ISBN:0198506732] cellular_component
+ENSG00000100030 GO:0008134 transcription factor binding "Interacting selectively and non-covalently with a transcription factor, any protein required to initiate or regulate transcription." [ISBN:0198506732] molecular_function
+ENSG00000100030 GO:0030424 axon "The long process of a neuron that conducts nerve impulses, usually away from the cell body to the terminals and varicosities, which are sites of storage and release of neurotransmitter." [GOC:nln, ISBN:0198506732] cellular_component
+ENSG00000100030 GO:0035556 intracellular signal transduction "The process in which a signal is passed on to downstream components within the cell, which become activated themselves to further propagate the signal and finally trigger a change in the function or state of the cell." [GOC:bf, GOC:jl, GOC:signaling, ISBN:3527303782] biological_process
+ENSG00000100030 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100030 GO:0019901 protein kinase binding "Interacting selectively and non-covalently with a protein kinase, any enzyme that catalyzes the transfer of a phosphate group, usually from ATP, to a protein substrate." [GOC:jl] molecular_function
+ENSG00000100030 GO:0008284 positive regulation of cell proliferation "Any process that activates or increases the rate or extent of cell proliferation." [GOC:go_curators] biological_process
+ENSG00000100030 GO:0031435 mitogen-activated protein kinase kinase kinase binding "Interacting selectively and non-covalently with a mitogen-activated protein kinase kinase kinase, any protein that can phosphorylate a MAP kinase kinase." [GOC:bf] molecular_function
+ENSG00000100030 GO:0043204 perikaryon "The portion of the cell soma (cell body) that excludes the nucleus." [GOC:jl] cellular_component
+ENSG00000100030 GO:0030335 positive regulation of cell migration "Any process that activates or increases the frequency, rate or extent of cell migration." [GOC:go_curators] biological_process
+ENSG00000100030 GO:0043627 response to estrogen "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of stimulus by an estrogen, C18 steroid hormones that can stimulate the development of female sexual characteristics." [GOC:jl, ISBN:0198506732] biological_process
+ENSG00000100030 GO:0045893 positive regulation of transcription, DNA-templated "Any process that activates or increases the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000100030 GO:0000189 MAPK import into nucleus "The directed movement of a MAP kinase to the nucleus upon activation." [PMID:9561267] biological_process
+ENSG00000100030 GO:0071310 cellular response to organic substance "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of an organic substance stimulus." [GOC:mah] biological_process
+ENSG00000100030 GO:0009636 response to toxic substance "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a toxic stimulus." [GOC:lr] biological_process
+ENSG00000100030 GO:0019233 sensory perception of pain "The series of events required for an organism to receive a painful stimulus, convert it to a molecular signal, and recognize and characterize the signal. Pain is medically defined as the physical sensation of discomfort or distress caused by injury or illness, so can hence be described as a harmful stimulus which signals current (or impending) tissue damage. Pain may come from extremes of temperature, mechanical damage, electricity or from noxious chemical substances. This is a neurological process." [http://www.onelook.com/] biological_process
+ENSG00000100030 GO:0045727 positive regulation of translation "Any process that activates or increases the frequency, rate or extent of the chemical reactions and pathways resulting in the formation of proteins by the translation of mRNA." [GOC:dph, GOC:go_curators, GOC:tb] biological_process
+ENSG00000100030 GO:0032839 dendrite cytoplasm "All of the contents of a dendrite, excluding the surrounding plasma membrane." [GOC:mah] cellular_component
+ENSG00000249590
+ENSG00000249590 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000249590 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000249590 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000249590 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000211679 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000211679 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100325 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100325 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000100325 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100325 GO:0006351 transcription, DNA-templated "The cellular synthesis of RNA on a template of DNA." [GOC:jl, GOC:txnOH] biological_process
+ENSG00000100325 GO:0006355 regulation of transcription, DNA-templated "Any process that modulates the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000100325 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100325 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100325
+ENSG00000130489 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000130489 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000130489 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000130489 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000130489 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000130489 GO:0042592 homeostatic process "Any biological process involved in the maintenance of an internal steady state." [GOC:jl, ISBN:0395825172] biological_process
+ENSG00000130489 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000130489 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000130489 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000130489 GO:0048856 anatomical structure development "The biological process whose specific outcome is the progression of an anatomical structure from an initial condition to its mature state. This process begins with the formation of the structure and ends with the mature structure, whatever form that may be including its natural destruction. An anatomical structure is any biological entity that occupies space and is distinguished from its surroundings. Anatomical structures can be macroscopic such as a carpel, or microscopic such as an acrosome." [GO_REF:0000021, GOC:mtg_15jun06] biological_process
+ENSG00000130489 GO:0001654 eye development "The process whose specific outcome is the progression of the eye over time, from its formation to the mature structure. The eye is the organ of sight." [GOC:jid, GOC:jl] biological_process
+ENSG00000130489 GO:0005507 copper ion binding "Interacting selectively and non-covalently with copper (Cu) ions." [GOC:ai] molecular_function
+ENSG00000130489 GO:0005743 mitochondrial inner membrane "The inner, i.e. lumen-facing, lipid bilayer of the mitochondrial envelope. It is highly folded to form cristae." [GOC:ai] cellular_component
+ENSG00000130489 GO:0006825 copper ion transport "The directed movement of copper (Cu) ions into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:ai] biological_process
+ENSG00000130489 GO:0006878 cellular copper ion homeostasis "Any process involved in the maintenance of an internal steady state of copper ions at the level of a cell." [GOC:ai, GOC:mah] biological_process
+ENSG00000130489 GO:0030016 myofibril "The contractile element of skeletal and cardiac muscle; a long, highly organized bundle of actin, myosin, and other proteins that contracts by a sliding filament mechanism." [ISBN:0815316194] cellular_component
+ENSG00000130489 GO:0055114 oxidation-reduction process "A metabolic process that results in the removal or addition of one or more electrons to or from a substance, with or without the concomitant removal or addition of a proton or protons." [GOC:dhl, GOC:ecd, GOC:jh2, GOC:jid, GOC:mlg, GOC:rph] biological_process
+ENSG00000130489 GO:0008535 respiratory chain complex IV assembly "The aggregation, arrangement and bonding together of a set of components to form respiratory chain complex IV (also known as cytochrome c oxidase), the terminal member of the respiratory chain of the mitochondrion and some aerobic bacteria. Cytochrome c oxidases are multi-subunit enzymes containing from 13 subunits in the mammalian mitochondrial form to 3-4 subunits in the bacterial forms." [GOC:jl, http://www.med.wright.edu/bmb/lp/lplab.htm] biological_process
+ENSG00000130489 GO:0006461 protein complex assembly "The aggregation, arrangement and bonding together of a set of components to form a protein complex." [GOC:ai] biological_process
+ENSG00000130489 GO:0022607 cellular component assembly "The aggregation, arrangement and bonding together of a cellular component." [GOC:isa_complete] biological_process
+ENSG00000130489 GO:0065003 macromolecular complex assembly "The aggregation, arrangement and bonding together of a set of macromolecules to form a complex." [GOC:jl] biological_process
+ENSG00000130489 GO:0001701 in utero embryonic development "The process whose specific outcome is the progression of the embryo in the uterus over time, from formation of the zygote in the oviduct, to birth. An example of this process is found in Mus musculus." [GOC:go_curators, GOC:mtg_sensu] biological_process
+ENSG00000130489 GO:0014823 response to activity "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of an activity stimulus." [GOC:mtg_muscle] biological_process
+ENSG00000130489 GO:0022904 respiratory electron transport chain "A process in which a series of electron carriers operate together to transfer electrons from donors such as NADH and FADH2 to any of several different terminal electron acceptors to generate a transmembrane electrochemical gradient." [GOC:mtg_electron_transport, ISBN:0716720094] biological_process
+ENSG00000130489 GO:0055070 copper ion homeostasis "Any process involved in the maintenance of an internal steady state of copper ions within an organism or cell." [GOC:ai, GOC:jid, GOC:mah] biological_process
+ENSG00000130489 GO:0003012 muscle system process "A organ system process carried out at the level of a muscle. Muscle tissue is composed of contractile cells or fibers." [GOC:mtg_cardio] biological_process
+ENSG00000100321 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000100321 GO:0030054 cell junction "A cellular component that forms a specialized region of connection between two cells or between a cell and the extracellular matrix. At a cell junction, anchoring proteins extend through the plasma membrane to link cytoskeletal proteins in one cell to cytoskeletal proteins in neighboring cells or to proteins in the extracellular matrix." [GOC:mah, http://www.vivo.colostate.edu/hbooks/cmb/cells/pmemb/junctions_a.html, ISBN:0198506732] cellular_component
+ENSG00000100321 GO:0048169 regulation of long-term neuronal synaptic plasticity "A process that modulates long-term neuronal synaptic plasticity, the ability of neuronal synapses to change long-term as circumstances require. Long-term neuronal synaptic plasticity generally involves increase or decrease in actual synapse numbers." [GOC:jid, http://www.mercksource.com, PMID:11891290] biological_process
+ENSG00000100321 GO:0048172 regulation of short-term neuronal synaptic plasticity "A process that modulates short-term neuronal synaptic plasticity, the ability of neuronal synapses to change in the short-term as circumstances require. Short-term neuronal synaptic plasticity generally involves increasing or decreasing synaptic sensitivity." [GOC:jid, http://www.mercksource.com, PMID:11891290] biological_process
+ENSG00000100321 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100321 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100321 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000100321 GO:0008021 synaptic vesicle "A secretory organelle, some 50 nm in diameter, of presynaptic nerve terminals; accumulates in high concentrations of neurotransmitters and secretes these into the synaptic cleft by fusion with the 'active zone' of the presynaptic plasma membrane." [PMID:10099709] cellular_component
+ENSG00000100321 GO:0006605 protein targeting "The process of targeting specific proteins to particular membrane-bounded subcellular organelles. Usually requires an organelle specific protein sequence motif." [GOC:ma] biological_process
+ENSG00000100321 GO:0030672 synaptic vesicle membrane "The lipid bilayer surrounding a synaptic vesicle." [GOC:mah] cellular_component
+ENSG00000223350 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000223350 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100099 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100099 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100099 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100099 GO:0016023 cytoplasmic membrane-bounded vesicle "A membrane-bounded vesicle found in the cytoplasm of the cell." [GOC:ai, GOC:mah] cellular_component
+ENSG00000100099 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100099 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100099 GO:0006605 protein targeting "The process of targeting specific proteins to particular membrane-bounded subcellular organelles. Usually requires an organelle specific protein sequence motif." [GOC:ma] biological_process
+ENSG00000100099 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100099 GO:0005764 lysosome "A small lytic vacuole that has cell cycle-independent morphology and is found in most animal cells and that contains a variety of hydrolases, most of which have their maximal activities in the pH range 5-6. The contained enzymes display latency if properly isolated. About 40 different lysosomal hydrolases are known and lysosomes have a great variety of morphologies and functions." [GOC:mah, ISBN:0198506732] cellular_component
+ENSG00000100099 GO:0005773 vacuole "A closed structure, found only in eukaryotic cells, that is completely surrounded by unit membrane and contains liquid material. Cells contain one or several vacuoles, that may have different functions from each other. Vacuoles have a diverse array of functions. They can act as a storage organelle for nutrients or waste products, as a degradative compartment, as a cost-effective way of increasing cell size, and as a homeostatic regulator controlling both turgor pressure and pH of the cytosol." [GOC:mtg_sensu, ISBN:0198506732] cellular_component
+ENSG00000100099 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100099 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100099 GO:0007040 lysosome organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of a lysosome. A lysosome is a cytoplasmic, membrane-bounded organelle that is found in most animal cells and that contains a variety of hydrolases." [GOC:mah] biological_process
+ENSG00000100099 GO:0007599 hemostasis "The stopping of bleeding (loss of body fluid) or the arrest of the circulation to an organ or part." [ISBN:0198506732] biological_process
+ENSG00000100099 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000100099 GO:0031085 BLOC-3 complex "A protein complex required for the biogenesis of specialized organelles of the endosomal-lysosomal system, such as melanosomes and platelet dense granules. The human complex contains the Hps1 and Hps4 proteins." [PMID:12756248] cellular_component
+ENSG00000100099 GO:0042470 melanosome "A tissue-specific, membrane-bounded cytoplasmic organelle within which melanin pigments are synthesized and stored. Melanosomes are synthesized in melanocyte cells." [GOC:jl, PMID:11584301] cellular_component
+ENSG00000100099 GO:0042803 protein homodimerization activity "Interacting selectively and non-covalently with an identical protein to form a homodimer." [GOC:jl] molecular_function
+ENSG00000100099 GO:0042827 platelet dense granule "Electron-dense granule occurring in blood platelets that stores and secretes adenosine nucleotides and serotonin. They contain a highly condensed core consisting of serotonin, histamine, calcium, magnesium, ATP, ADP, pyrophosphate and membrane lysosomal proteins." [GOC:jl, http://www.mercksource.com/, PMID:10403682, PMID:11487378] cellular_component
+ENSG00000100099 GO:0046983 protein dimerization activity "The formation of a protein dimer, a macromolecular structure consists of two noncovalently associated identical or nonidentical subunits." [ISBN:0198506732] molecular_function
+ENSG00000100099 GO:0048075 positive regulation of eye pigmentation "Any process that activates or increases the frequency, rate or extent of establishment of a pattern of pigment in the eye of an organism." [GOC:jid] biological_process
+ENSG00000100099 GO:0050821 protein stabilization "Any process involved in maintaining the structure and integrity of a protein and preventing it from degradation or aggregation." [GOC:ai] biological_process
+ENSG00000100099 GO:0030318 melanocyte differentiation "The process in which a relatively unspecialized cell acquires specialized features of a melanocyte." [GOC:mah] biological_process
+ENSG00000100099 GO:0007596 blood coagulation "The sequential process in which the multiple coagulation factors of the blood interact, ultimately resulting in the formation of an insoluble fibrin clot; it may be divided into three stages: stage 1, the formation of intrinsic and extrinsic prothrombin converting principle; stage 2, the formation of thrombin; stage 3, the formation of stable fibrin polymers." [http://www.graylab.ac.uk/omd/, ISBN:0198506732] biological_process
+ENSG00000100099 GO:0006996 organelle organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of an organelle within a cell. An organelle is an organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton. Excludes the plasma membrane." [GOC:mah] biological_process
+ENSG00000100099
+ENSG00000099999
+ENSG00000099999 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000099999 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000099999 GO:0008270 zinc ion binding "Interacting selectively and non-covalently with zinc (Zn) ions." [GOC:ai] molecular_function
+ENSG00000099999 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000099999 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000099999 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000099999 GO:0046872 metal ion binding "Interacting selectively and non-covalently with any metal ion." [GOC:ai] molecular_function
+ENSG00000128310 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000128310 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000128310 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000128310 GO:0050877 neurological system process "A organ system process carried out by any of the organs or tissues of neurological system." [GOC:ai, GOC:mtg_cardio] biological_process
+ENSG00000128310 GO:0007267 cell-cell signaling "Any process that mediates the transfer of information from one cell to another." [GOC:mah] biological_process
+ENSG00000128310 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000128310 GO:0004871 signal transducer activity "Conveys a signal across a cell to trigger a change in cell function or state. A signal is a physical entity or change in state that is used to transfer information in order to trigger a response." [GOC:go_curators] molecular_function
+ENSG00000128310 GO:0004966 galanin receptor activity "Combining with galanin to initiate a change in cell activity." [GOC:ai] molecular_function
+ENSG00000128310 GO:0007194 negative regulation of adenylate cyclase activity "Any process that stops, prevents, or reduces the frequency, rate or extent of adenylate cyclase activity." [GOC:go_curators] biological_process
+ENSG00000128310 GO:0007268 synaptic transmission "The process of communication from a neuron to a target (neuron, muscle, or secretory cell) across a synapse." [GOC:jl, MeSH:D009435] biological_process
+ENSG00000128310 GO:0007611 learning or memory "The acquisition and processing of information and/or the storage and retrieval of this information over time." [GOC:jid, PMID:8938125] biological_process
+ENSG00000128310 GO:0007631 feeding behavior "Behavior associated with the intake of food." [GOC:mah] biological_process
+ENSG00000128310 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000128310 GO:0017046 peptide hormone binding "Interacting selectively and non-covalently with any peptide with hormonal activity in animals." [GOC:jl, ISBN:0198506732] molecular_function
+ENSG00000128310 GO:0045944 positive regulation of transcription from RNA polymerase II promoter "Any process that activates or increases the frequency, rate or extent of transcription from an RNA polymerase II promoter." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000128310 GO:0004930 G-protein coupled receptor activity "Combining with an extracellular signal and transmitting the signal across the membrane by activating an associated G-protein; promotes the exchange of GDP for GTP on the alpha subunit of a heterotrimeric G-protein complex." [GOC:bf, http://www.iuphar-db.org, Wikipedia:GPCR] molecular_function
+ENSG00000128310 GO:0007186 G-protein coupled receptor signaling pathway "A series of molecular signals that proceeds with an activated receptor promoting the exchange of GDP for GTP on the alpha-subunit of an associated heterotrimeric G-protein complex. The GTP-bound activated alpha-G-protein then dissociates from the beta- and gamma-subunits to further transmit the signal within the cell. The pathway begins with receptor-ligand interaction, or for basal GPCR signaling the pathway begins with the receptor activating its G protein in the absence of an agonist, and ends with regulation of a downstream cellular process, e.g. transcription." [GOC:bf, GOC:mah, Wikipedia:G_protein-coupled_receptor] biological_process
+ENSG00000128310 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000128310 GO:0007218 neuropeptide signaling pathway "The series of molecular signals generated as a consequence of a peptide neurotransmitter binding to a cell surface receptor." [GOC:mah, ISBN:0815316194] biological_process
+ENSG00000128310 GO:0007193 adenylate cyclase-inhibiting G-protein coupled receptor signaling pathway "The series of molecular signals generated as a consequence of a G-protein coupled receptor binding to its physiological ligand, where the pathway proceeds through inhibition of adenylyl cyclase activity and a subsequent decrease in the concentration of cyclic AMP (cAMP)." [GOC:dph, GOC:mah, GOC:signaling, GOC:tb, ISBN:0815316194] biological_process
+ENSG00000128310 GO:0030818 negative regulation of cAMP biosynthetic process "Any process that stops, prevents, or reduces the frequency, rate or extent of the chemical reactions and pathways resulting in the formation of the nucleotide cAMP (cyclic AMP, adenosine 3',5'-cyclophosphate)." [GOC:mah] biological_process
+ENSG00000205643
+ENSG00000100316 GO:0003735 structural constituent of ribosome "The action of a molecule that contributes to the structural integrity of the ribosome." [GOC:mah] molecular_function
+ENSG00000100316 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100316 GO:0005730 nucleolus "A small, dense body one or more of which are present in the nucleus of eukaryotic cells. It is rich in RNA and protein, is not bounded by a limiting membrane, and is not seen during mitosis. Its prime function is the transcription of the nucleolar DNA into 45S ribosomal-precursor RNA, the processing of this RNA into 5.8S, 18S, and 28S components of ribosomal RNA, and the association of these components with 5S RNA and proteins synthesized outside the nucleolus. This association results in the formation of ribonucleoprotein precursors; these pass into the cytoplasm and mature into the 40S and 60S subunits of the ribosome." [ISBN:0198506732] cellular_component
+ENSG00000100316 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100316 GO:0005840 ribosome "An intracellular organelle, about 200 A in diameter, consisting of RNA and protein. It is the site of protein biosynthesis resulting from translation of messenger RNA (mRNA). It consists of two subunits, one large and one small, each containing only protein and RNA. Both the ribosome and its subunits are characterized by their sedimentation coefficients, expressed in Svedberg units (symbol: S). Hence, the prokaryotic ribosome (70S) comprises a large (50S) subunit and a small (30S) subunit, while the eukaryotic ribosome (80S) comprises a large (60S) subunit and a small (40S) subunit. Two sites on the ribosomal large subunit are involved in translation, namely the aminoacyl site (A site) and peptidyl site (P site). Ribosomes from prokaryotes, eukaryotes, mitochondria, and chloroplasts have characteristically distinct ribosomal proteins." [ISBN:0198506732] cellular_component
+ENSG00000100316 GO:0006412 translation "The cellular metabolic process in which a protein is formed, using the sequence of a mature mRNA molecule to specify the sequence of amino acids in a polypeptide chain. Translation is mediated by the ribosome, and begins with the formation of a ternary complex between aminoacylated initiator methionine tRNA, GTP, and initiation factor 2, which subsequently associates with the small subunit of the ribosome and an mRNA. Translation ends with the release of a polypeptide chain from the ribosome." [GOC:go_curators] biological_process
+ENSG00000100316 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100316 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000100316 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100316 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100316 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100316 GO:0005198 structural molecule activity "The action of a molecule that contributes to the structural integrity of a complex or assembly within or outside a cell." [GOC:mah] molecular_function
+ENSG00000100316 GO:0005622 intracellular "The living contents of a cell; the matter contained within (but not including) the plasma membrane, usually taken to exclude large vacuoles and masses of secretory or ingested material. In eukaryotes it includes the nucleus and cytoplasm." [ISBN:0198506732] cellular_component
+ENSG00000100316 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000100316 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100316 GO:0044403 symbiosis, encompassing mutualism through parasitism "An interaction between two organisms living together in more or less intimate association. The term host is usually used for the larger (macro) of the two members of a symbiosis. The smaller (micro) member is called the symbiont organism. Microscopic symbionts are often referred to as endosymbionts. The various forms of symbiosis include parasitism, in which the association is disadvantageous or destructive to one of the organisms; mutualism, in which the association is advantageous, or often necessary to one or both and not harmful to either; and commensalism, in which one member of the association benefits while the other is not affected. However, mutualism, parasitism, and commensalism are often not discrete categories of interactions and should rather be perceived as a continuum of interaction ranging from parasitism to mutualism. In fact, the direction of a symbiotic interaction can change during the lifetime of the symbionts due to developmental changes as well as changes in the biotic/abiotic environment in which the interaction occurs." [GOC:cc, http://www.free-definition.com] biological_process
+ENSG00000100316 GO:0006605 protein targeting "The process of targeting specific proteins to particular membrane-bounded subcellular organelles. Usually requires an organelle specific protein sequence motif." [GOC:ma] biological_process
+ENSG00000100316 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100316 GO:0061024 membrane organization "A process which results in the assembly, arrangement of constituent parts, or disassembly of a membrane. A membrane is a double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:dph, GOC:tb] biological_process
+ENSG00000100316 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000100316 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000100316 GO:0034655 nucleobase-containing compound catabolic process "The chemical reactions and pathways resulting in the breakdown of nucleobases, nucleosides, nucleotides and nucleic acids." [GOC:mah] biological_process
+ENSG00000100316 GO:0000184 nuclear-transcribed mRNA catabolic process, nonsense-mediated decay "The nonsense-mediated decay pathway for nuclear-transcribed mRNAs degrades mRNAs in which an amino-acid codon has changed to a nonsense codon; this prevents the translation of such mRNAs into truncated, and potentially harmful, proteins." [GOC:krc, GOC:ma, PMID:10025395] biological_process
+ENSG00000100316 GO:0005925 focal adhesion "Small region on the surface of a cell that anchors the cell to the extracellular matrix and that forms a point of termination of actin filaments." [ISBN:0124325653, ISBN:0815316208] cellular_component
+ENSG00000100316 GO:0006413 translational initiation "The process preceding formation of the peptide bond between the first two amino acids of a protein. This includes the formation of a complex of the ribosome, mRNA, and an initiation complex that contains the first aminoacyl-tRNA." [ISBN:019879276X] biological_process
+ENSG00000100316 GO:0006414 translational elongation "The successive addition of amino acid residues to a nascent polypeptide chain during protein biosynthesis." [GOC:ems] biological_process
+ENSG00000100316 GO:0006415 translational termination "The process resulting in the release of a polypeptide chain from the ribosome, usually in response to a termination codon (UAA, UAG, or UGA in the universal genetic code)." [GOC:hjd, ISBN:019879276X] biological_process
+ENSG00000100316 GO:0006614 SRP-dependent cotranslational protein targeting to membrane "The targeting of proteins to a membrane that occurs during translation and is dependent upon two key components, the signal-recognition particle (SRP) and the SRP receptor. SRP is a cytosolic particle that transiently binds to the endoplasmic reticulum (ER) signal sequence in a nascent protein, to the large ribosomal unit, and to the SRP receptor in the ER membrane." [ISBN:0716731363] biological_process
+ENSG00000100316 GO:0010467 gene expression "The process in which a gene's sequence is converted into a mature gene product or products (proteins or RNA). This includes the production of an RNA transcript as well as any processing to produce a mature RNA product or an mRNA (for protein-coding genes) and the translation of that mRNA into protein. Some protein processing events may be included when they are required to form an active form of a product from an inactive precursor form." [GOC:dph, GOC:tb] biological_process
+ENSG00000100316 GO:0016032 viral process "A multi-organism process in which a virus is a participant. The other participant is the host. Includes infection of a host cell, replication of the viral genome, and assembly of progeny virus particles. In some cases the viral genetic material may integrate into the host genome and only subsequently, under particular circumstances, 'complete' its life cycle." [GOC:bf, GOC:jl, GOC:mah] biological_process
+ENSG00000100316 GO:0016070 RNA metabolic process "The cellular chemical reactions and pathways involving RNA, ribonucleic acid, one of the two main type of nucleic acid, consisting of a long, unbranched macromolecule formed from ribonucleotides joined in 3',5'-phosphodiester linkage." [ISBN:0198506732] biological_process
+ENSG00000100316 GO:0016071 mRNA metabolic process "The chemical reactions and pathways involving mRNA, messenger RNA, which is responsible for carrying the coded genetic 'message', transcribed from DNA, to sites of protein assembly at the ribosomes." [ISBN:0198506732] biological_process
+ENSG00000100316 GO:0019058 viral life cycle "A set of processes which all viruses follow to ensure survival; includes attachment and entry of the virus particle, decoding of genome information, translation of viral mRNA by host ribosomes, genome replication, and assembly and release of viral particles containing the genome." [ISBN:1555811272] biological_process
+ENSG00000100316 GO:0019083 viral transcription "The process by which a viral genome, or part of a viral genome, is transcribed within the host cell." [GOC:jl, ISBN:0781702534] biological_process
+ENSG00000100316 GO:0022625 cytosolic large ribosomal subunit "The large subunit of a ribosome located in the cytosol." [GOC:mtg_sensu] cellular_component
+ENSG00000100316 GO:0044267 cellular protein metabolic process "The chemical reactions and pathways involving a specific protein, rather than of proteins in general, occurring at the level of an individual cell. Includes cellular protein modification." [GOC:jl] biological_process
+ENSG00000100316 GO:0044822 poly(A) RNA binding "Interacting non-covalently with a poly(A) RNA, a RNA molecule which has a tail of adenine bases." [GOC:jl] molecular_function
+ENSG00000100316 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000100316 GO:0071353 cellular response to interleukin-4 "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of an interleukin-4 stimulus." [GOC:mah] biological_process
+ENSG00000099954 GO:0021915 neural tube development "The process whose specific outcome is the progression of the neural tube over time, from its formation to the mature structure. The mature structure of the neural tube exists when the tube has been segmented into the forebrain, midbrain, hindbrain and spinal cord regions. In addition neural crest has budded away from the epithelium." [GO_REF:0000021, GOC:cls, GOC:dgh, GOC:dph, GOC:jid, GOC:mtg_15jun06] biological_process
+ENSG00000099954 GO:0043044 ATP-dependent chromatin remodeling "Dynamic structural changes to eukaryotic chromatin that require energy from the hydrolysis of ATP, ranging from local changes necessary for transcriptional regulation to global changes necessary for chromosome segregation, mediated by ATP-dependent chromatin-remodelling factors." [GOC:jl, PMID:12042764] biological_process
+ENSG00000099954 GO:0090537 CERF complex "An ISWI complex that contains an ATPase subunit of the ISWI family (specifically SNF2L in mammals, which contain two ISWI homologs) and a CECR2 homolog. In mammals, CERF is involved in regulation of transcription from RNA polymerase II promoters." [GOC:krc] cellular_component
+ENSG00000099954 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000099954 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000099954 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000099954 GO:0048856 anatomical structure development "The biological process whose specific outcome is the progression of an anatomical structure from an initial condition to its mature state. This process begins with the formation of the structure and ends with the mature structure, whatever form that may be including its natural destruction. An anatomical structure is any biological entity that occupies space and is distinguished from its surroundings. Anatomical structures can be macroscopic such as a carpel, or microscopic such as an acrosome." [GO_REF:0000021, GOC:mtg_15jun06] biological_process
+ENSG00000099954 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000099954 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000099954
+ENSG00000099954 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000099954 GO:0016192 vesicle-mediated transport "A cellular transport process in which transported substances are moved in membrane-bounded vesicles; transported substances are enclosed in the vesicle lumen or located in the vesicle membrane. The process begins with a step that directs a substance to the forming vesicle, and includes vesicle budding and coating. Vesicles are then targeted to, and fuse with, an acceptor membrane." [GOC:ai, GOC:mah, ISBN:08789310662000] biological_process
+ENSG00000099954 GO:0007010 cytoskeleton organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of cytoskeletal structures." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000099954 GO:0006259 DNA metabolic process "Any cellular metabolic process involving deoxyribonucleic acid. This is one of the two main types of nucleic acid, consisting of a long, unbranched macromolecule formed from one, or more commonly, two, strands of linked deoxyribonucleotides." [ISBN:0198506732] biological_process
+ENSG00000099954 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000099954 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000099954 GO:0034655 nucleobase-containing compound catabolic process "The chemical reactions and pathways resulting in the breakdown of nucleobases, nucleosides, nucleotides and nucleic acids." [GOC:mah] biological_process
+ENSG00000099954 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000099954 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000099954 GO:0000910 cytokinesis "The division of the cytoplasm and the plasma membrane of a cell and its separation into two daughter cells." [GOC:mtg_cell_cycle] biological_process
+ENSG00000099954 GO:0006309 apoptotic DNA fragmentation "The cleavage of DNA during apoptosis, which usually occurs in two stages: cleavage into fragments of about 50 kbp followed by cleavage between nucleosomes to yield 200 bp fragments." [GOC:dph, GOC:mah, GOC:mtg_apoptosis, GOC:tb, ISBN:0721639976, PMID:15723341, PMID:23379520] biological_process
+ENSG00000099954 GO:0097194 execution phase of apoptosis "A stage of the apoptotic process that starts with the controlled breakdown of the cell through the action of effector caspases or other effector molecules (e.g. cathepsins, calpains etc.). Key steps of the execution phase are rounding-up of the cell, retraction of pseudopodes, reduction of cellular volume (pyknosis), chromatin condensation, nuclear fragmentation (karyorrhexis), plasma membrane blebbing and fragmentation of the cell into apoptotic bodies. When the execution phase is completed, the cell has died." [GOC:mtg_apoptosis, PMID:21760595] biological_process
+ENSG00000099954 GO:0007338 single fertilization "The union of male and female gametes to form a zygote." [GOC:ems, GOC:mtg_sensu] biological_process
+ENSG00000099954 GO:0001843 neural tube closure "The last step in the formation of the neural tube, where the paired neural folds are brought together and fuse at the dorsal midline." [GOC:dph, ISBN:0878932437] biological_process
+ENSG00000099954 GO:0060122 inner ear receptor stereocilium organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of a stereocilium. A stereocilium is an actin-based protrusion from the apical surface of inner ear receptor cells." [GOC:dph] biological_process
+ENSG00000099954 GO:0090102 cochlea development "The progression of the cochlea over time from its formation to the mature structure. The cochlea is the snail-shaped portion of the inner ear that is responsible for the detection of sound." [GOC:dph, GOC:tb] biological_process
+ENSG00000099954 GO:0005719 nuclear euchromatin "The dispersed less dense form of chromatin in the interphase nucleus. It exists in at least two forms, a some being in the form of transcriptionally active chromatin which is the least condensed, while the rest is inactive euchromatin which is more condensed than active chromatin but less condensed than heterochromatin." [ISBN:0198506732] cellular_component
+ENSG00000099954 GO:0001842 neural fold formation "The process in which the neural fold is formed. The edges of the neural plate thicken and move up to form a U-shaped structure called the neural groove." [GOC:dph, ISBN:0878932437] biological_process
+ENSG00000099954 GO:0031010 ISWI-type complex "Any nuclear protein complex that contains an ATPase subunit of the imitation switch (ISWI) family. ISWI ATPases are involved in assembling chromatin and in sliding and spacing nucleosomes to regulate transcription of nuclear RNA polymerases I, II, and III and also DNA replication, recombination and repair." [GOC:krc, GOC:mah, PMID:15020051, PMID:15284901, PMID:16568949, PMID:21810179] cellular_component
+ENSG00000100365 GO:0035091 phosphatidylinositol binding "Interacting selectively and non-covalently with any inositol-containing glycerophospholipid, i.e. phosphatidylinositol (PtdIns) and its phosphorylated derivatives." [GOC:bf, ISBN:0198506732, PMID:11395417] molecular_function
+ENSG00000100365 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100365 GO:0008289 lipid binding "Interacting selectively and non-covalently with a lipid." [GOC:ai] molecular_function
+ENSG00000100365 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100365 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100365 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100365 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100365 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100365 GO:0002376 immune system process "Any process involved in the development or functioning of the immune system, an organismal system for calibrated responses to potential internal or invasive threats." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05] biological_process
+ENSG00000100365 GO:0005764 lysosome "A small lytic vacuole that has cell cycle-independent morphology and is found in most animal cells and that contains a variety of hydrolases, most of which have their maximal activities in the pH range 5-6. The contained enzymes display latency if properly isolated. About 40 different lysosomal hydrolases are known and lysosomes have a great variety of morphologies and functions." [GOC:mah, ISBN:0198506732] cellular_component
+ENSG00000100365 GO:0005773 vacuole "A closed structure, found only in eukaryotic cells, that is completely surrounded by unit membrane and contains liquid material. Cells contain one or several vacuoles, that may have different functions from each other. Vacuoles have a diverse array of functions. They can act as a storage organelle for nutrients or waste products, as a degradative compartment, as a cost-effective way of increasing cell size, and as a homeostatic regulator controlling both turgor pressure and pH of the cytosol." [GOC:mtg_sensu, ISBN:0198506732] cellular_component
+ENSG00000100365 GO:0016023 cytoplasmic membrane-bounded vesicle "A membrane-bounded vesicle found in the cytoplasm of the cell." [GOC:ai, GOC:mah] cellular_component
+ENSG00000100365 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100365 GO:0030234 enzyme regulator activity "Binds to and modulates the activity of an enzyme." [GOC:mah] molecular_function
+ENSG00000100365 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000100365 GO:0002474 antigen processing and presentation of peptide antigen via MHC class I "The process in which an antigen-presenting cell expresses a peptide antigen on its cell surface in association with an MHC class I protein complex. Class I here refers to classical class I molecules." [GOC:add, ISBN:0781735149, PMID:15224092, PMID:15771591] biological_process
+ENSG00000100365 GO:0002479 antigen processing and presentation of exogenous peptide antigen via MHC class I, TAP-dependent "The process in which an antigen-presenting cell expresses a peptide antigen of exogenous origin on its cell surface in association with an MHC class I protein complex following intracellular transport via a TAP (transporter associated with antigen processing) pathway. The peptide is typically a fragment of a larger exogenous protein which has been degraded within the cell and is dependent on TAP transport from the cytosol to ER for association with the MHC class I molecule. Class I here refers to classical class I molecules." [GOC:add, PMID:15224093, PMID:15771591, PMID:16181335] biological_process
+ENSG00000100365 GO:0006955 immune response "Any immune system process that functions in the calibrated response of an organism to a potential internal or invasive threat." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05] biological_process
+ENSG00000100365 GO:0010008 endosome membrane "The lipid bilayer surrounding an endosome." [GOC:mah] cellular_component
+ENSG00000100365 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000100365 GO:0016176 superoxide-generating NADPH oxidase activator activity "Increases the activity of the enzyme superoxide-generating NADPH oxidase." [GOC:ai] molecular_function
+ENSG00000100365 GO:0032010 phagolysosome "A membrane-bounded intracellular vesicle formed by maturation of an early phagosome following the ingestion of particulate material by phagocytosis; during maturation, phagosomes acquire markers of late endosomes and lysosomes." [GOC:mah, PMID:12388753] cellular_component
+ENSG00000100365 GO:0032266 phosphatidylinositol-3-phosphate binding "Interacting selectively and non-covalently with phosphatidylinositol-3-phosphate, a derivative of phosphatidylinositol in which the inositol ring is phosphorylated at the 3' position." [GOC:bf, PMID:10209156, PMID:11395417, PMID:11557775] molecular_function
+ENSG00000100365 GO:0042590 antigen processing and presentation of exogenous peptide antigen via MHC class I "The process in which an antigen-presenting cell expresses a peptide antigen of exogenous origin on its cell surface in association with an MHC class I protein complex. The peptide antigen is typically, but not always, processed from a whole protein. Class I here refers to classical class I molecules." [GOC:add, ISBN:0781735149, PMID:15771591] biological_process
+ENSG00000100365 GO:0043020 NADPH oxidase complex "A enzyme complex of which the core is a heterodimer composed of a light (alpha) and heavy (beta) chain, and requires several other water-soluble proteins of cytosolic origin for activity. Functions in superoxide generation by the NADPH-dependent reduction of O2." [GOC:jl, PMID:11483596, PMID:12440767] cellular_component
+ENSG00000100365 GO:0043085 positive regulation of catalytic activity "Any process that activates or increases the activity of an enzyme." [GOC:jl, GOC:tb] biological_process
+ENSG00000100365 GO:0046983 protein dimerization activity "The formation of a protein dimer, a macromolecular structure consists of two noncovalently associated identical or nonidentical subunits." [ISBN:0198506732] molecular_function
+ENSG00000100365 GO:0051701 interaction with host "An interaction between two organisms living together in more or less intimate association. The term host is used for the larger (macro) of the two members of a symbiosis; the various forms of symbiosis include parasitism, commensalism and mutualism." [GOC:cc] biological_process
+ENSG00000100365 GO:0055114 oxidation-reduction process "A metabolic process that results in the removal or addition of one or more electrons to or from a substance, with or without the concomitant removal or addition of a proton or protons." [GOC:dhl, GOC:ecd, GOC:jh2, GOC:jid, GOC:mlg, GOC:rph] biological_process
+ENSG00000100365 GO:0090382 phagosome maturation "A process that is carried out at the cellular level which results in the arrangement of constituent parts of a phagosome within a cell. Phagosome maturation begins with endocytosis and formation of the early phagosome and ends with the formation of the hybrid organelle, the phagolysosome." [GOC:kmv, GOC:tb] biological_process
+ENSG00000100365 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000075234
+ENSG00000075234 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000075234 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000075234 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100079 GO:0030246 carbohydrate binding "Interacting selectively and non-covalently with any carbohydrate, which includes monosaccharides, oligosaccharides and polysaccharides as well as substances derived from monosaccharides by reduction of the carbonyl group (alditols), by oxidation of one or more hydroxy groups to afford the corresponding aldehydes, ketones, or carboxylic acids, or by replacement of one or more hydroxy group(s) by a hydrogen atom. Cyclitols are generally not regarded as carbohydrates." [CHEBI:16646, GOC:mah] molecular_function
+ENSG00000100079 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100079 GO:0016936 galactoside binding "Interacting selectively and non-covalently with any glycoside in which the sugar group is galactose." [CHEBI:24163, GOC:jl, ISBN:0198506732] molecular_function
+ENSG00000100079
+ENSG00000279560
+ENSG00000184983 GO:0005743 mitochondrial inner membrane "The inner, i.e. lumen-facing, lipid bilayer of the mitochondrial envelope. It is highly folded to form cristae." [GOC:ai] cellular_component
+ENSG00000184983 GO:0005747 mitochondrial respiratory chain complex I "A protein complex located in the mitochondrial inner membrane that forms part of the mitochondrial respiratory chain. It contains about 25 different polypeptide subunits, including NADH dehydrogenase (ubiquinone), flavin mononucleotide and several different iron-sulfur clusters containing non-heme iron. The iron undergoes oxidation-reduction between Fe(II) and Fe(III), and catalyzes proton translocation linked to the oxidation of NADH by ubiquinone." [GOC:mtg_sensu, ISBN:0198547684] cellular_component
+ENSG00000184983 GO:0006120 mitochondrial electron transport, NADH to ubiquinone "The transfer of electrons from NADH to ubiquinone that occurs during oxidative phosphorylation, mediated by the multisubunit enzyme known as complex I." [ISBN:0716731363] biological_process
+ENSG00000184983 GO:0006979 response to oxidative stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of oxidative stress, a state often resulting from exposure to high levels of reactive oxygen species, e.g. superoxide anions, hydrogen peroxide (H2O2), and hydroxyl radicals." [GOC:jl, PMID:12115731] biological_process
+ENSG00000184983 GO:0008137 NADH dehydrogenase (ubiquinone) activity "Catalysis of the reaction: NADH + H+ + ubiquinone = NAD+ + ubiquinol." [EC:1.6.5.3] molecular_function
+ENSG00000184983 GO:0022904 respiratory electron transport chain "A process in which a series of electron carriers operate together to transfer electrons from donors such as NADH and FADH2 to any of several different terminal electron acceptors to generate a transmembrane electrochemical gradient." [GOC:mtg_electron_transport, ISBN:0716720094] biological_process
+ENSG00000184983 GO:0031966 mitochondrial membrane "Either of the lipid bilayers that surround the mitochondrion and form the mitochondrial envelope." [GOC:mah, NIF_Subcellular:sao1045389829] cellular_component
+ENSG00000184983 GO:0044237 cellular metabolic process "The chemical reactions and pathways by which individual cells transform chemical substances." [GOC:go_curators] biological_process
+ENSG00000184983 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000184983 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000184983 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000184983 GO:0006091 generation of precursor metabolites and energy "The chemical reactions and pathways resulting in the formation of precursor metabolites, substances from which energy is derived, and any process involved in the liberation of energy from these substances." [GOC:jl] biological_process
+ENSG00000184983 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000184983 GO:0016491 oxidoreductase activity "Catalysis of an oxidation-reduction (redox) reaction, a reversible chemical reaction in which the oxidation state of an atom or atoms within a molecule is altered. One substrate acts as a hydrogen or electron donor and becomes oxidized, while the other acts as hydrogen or electron acceptor and becomes reduced." [GOC:go_curators] molecular_function
+ENSG00000184983 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000184983 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000184983 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000184983
+ENSG00000100227
+ENSG00000100227 GO:0000166 nucleotide binding "Interacting selectively and non-covalently with a nucleotide, any compound consisting of a nucleoside that is esterified with (ortho)phosphate or an oligophosphate at any hydroxyl group on the ribose or deoxyribose." [GOC:mah, ISBN:0198547684] molecular_function
+ENSG00000100227 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100227 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100227 GO:0016607 nuclear speck "A discrete extra-nucleolar subnuclear domain, 20-50 in number, in which splicing factors are seen to be localized by immunofluorescence microscopy." [http://www.cellnucleus.com/] cellular_component
+ENSG00000100227 GO:0016973 poly(A)+ mRNA export from nucleus "The directed movement of poly(A)+ mRNA out of the nucleus into the cytoplasm." [GOC:ai] biological_process
+ENSG00000100227 GO:0044822 poly(A) RNA binding "Interacting non-covalently with a poly(A) RNA, a RNA molecule which has a tail of adenine bases." [GOC:jl] molecular_function
+ENSG00000100227 GO:0045727 positive regulation of translation "Any process that activates or increases the frequency, rate or extent of the chemical reactions and pathways resulting in the formation of proteins by the translation of mRNA." [GOC:dph, GOC:go_curators, GOC:tb] biological_process
+ENSG00000100227 GO:0000346 transcription export complex "The transcription export (TREX) complex couples transcription elongation by RNA polymerase II to mRNA export. The complex associates with the polymerase and travels with it along the length of the transcribed gene. TREX is composed of the THO transcription elongation complex as well as other proteins that couple THO to mRNA export proteins. The TREX complex is known to be found in a wide range of eukaryotes, including S. cerevisiae and metazoans." [GOC:krc, PMID:11979277] cellular_component
+ENSG00000100227 GO:0035145 exon-exon junction complex "A multi-subunit complex deposited by the spliceosome upstream of messenger RNA exon-exon junctions. The exon-exon junction complex provides a binding platform for factors involved in mRNA export and nonsense-mediated mRNA decay." [PMID:11532962, PMID:11743026] cellular_component
+ENSG00000100227 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100227 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100227 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100227 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100227 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000100227 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100227 GO:0006913 nucleocytoplasmic transport "The directed movement of molecules between the nucleus and the cytoplasm." [GOC:go_curators] biological_process
+ENSG00000100227 GO:0003676 nucleic acid binding "Interacting selectively and non-covalently with any nucleic acid." [GOC:jl] molecular_function
+ENSG00000182541 GO:0005524 ATP binding "Interacting selectively and non-covalently with ATP, adenosine 5'-triphosphate, a universally important coenzyme and enzyme regulator." [ISBN:0198506732] molecular_function
+ENSG00000182541 GO:0008270 zinc ion binding "Interacting selectively and non-covalently with zinc (Zn) ions." [GOC:ai] molecular_function
+ENSG00000182541 GO:0042325 regulation of phosphorylation "Any process that modulates the frequency, rate or extent of addition of phosphate groups into a molecule." [GOC:jl] biological_process
+ENSG00000182541 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000182541 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000182541 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000182541 GO:0004672 protein kinase activity "Catalysis of the phosphorylation of an amino acid residue in a protein, usually according to the reaction: a protein + ATP = a phosphoprotein + ADP." [MetaCyc:PROTEIN-KINASE-RXN] molecular_function
+ENSG00000182541 GO:0016301 kinase activity "Catalysis of the transfer of a phosphate group, usually from ATP, to a substrate molecule." [ISBN:0198506732] molecular_function
+ENSG00000182541 GO:0006468 protein phosphorylation "The process of introducing a phosphate group on to a protein." [GOC:hb] biological_process
+ENSG00000182541 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000182541 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000182541 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000182541 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000182541 GO:0016772 transferase activity, transferring phosphorus-containing groups "Catalysis of the transfer of a phosphorus-containing group from one compound (donor) to another (acceptor)." [GOC:jl, ISBN:0198506732] molecular_function
+ENSG00000182541 GO:0004713 protein tyrosine kinase activity "Catalysis of the reaction: ATP + a protein tyrosine = ADP + protein tyrosine phosphate." [EC:2.7.10] molecular_function
+ENSG00000182541 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000182541 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000182541 GO:0004674 protein serine/threonine kinase activity "Catalysis of the reactions: ATP + protein serine = ADP + protein serine phosphate, and ATP + protein threonine = ADP + protein threonine phosphate." [GOC:bf] molecular_function
+ENSG00000182541 GO:0016310 phosphorylation "The process of introducing a phosphate group into a molecule, usually with the formation of a phosphoric ester, a phosphoric anhydride or a phosphoric amide." [ISBN:0198506732] biological_process
+ENSG00000182541 GO:0007283 spermatogenesis "The process of formation of spermatozoa, including spermatocytogenesis and spermiogenesis." [GOC:jid, ISBN:9780878933846] biological_process
+ENSG00000182541 GO:0005801 cis-Golgi network "The network of interconnected tubular and cisternal structures located at the convex side of the Golgi apparatus, which abuts the endoplasmic reticulum." [ISBN:0198506732, ISBN:0815316194] cellular_component
+ENSG00000182541 GO:0046982 protein heterodimerization activity "Interacting selectively and non-covalently with a nonidentical protein to form a heterodimer." [GOC:ai] molecular_function
+ENSG00000128268 GO:0003830 beta-1,4-mannosylglycoprotein 4-beta-N-acetylglucosaminyltransferase activity "Catalysis of the reaction: UDP-N-acetyl-D-glucosamine + beta-D-mannosyl-R = UDP + 4-(N-acetyl-beta-D-glucosaminyl)-beta-D-mannosyl-R." [EC:2.4.1.144] molecular_function
+ENSG00000128268 GO:0006487 protein N-linked glycosylation "A protein glycosylation process in which a carbohydrate or carbohydrate derivative unit is added to a protein via the N4 atom of peptidyl-asparagine, the omega-N of arginine, or the N1' atom peptidyl-tryptophan." [GOC:pr, RESID:AA0151, RESID:AA0156, RESID:AA0327] biological_process
+ENSG00000128268 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000128268 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000128268 GO:0005975 carbohydrate metabolic process "The chemical reactions and pathways involving carbohydrates, any of a group of organic compounds based of the general formula Cx(H2O)y. Includes the formation of carbohydrate derivatives by the addition of a carbohydrate residue to another molecule." [GOC:mah, ISBN:0198506732] biological_process
+ENSG00000128268 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000128268 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000128268 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000128268 GO:0016757 transferase activity, transferring glycosyl groups "Catalysis of the transfer of a glycosyl group from one compound (donor) to another (acceptor)." [GOC:jl, ISBN:0198506732] molecular_function
+ENSG00000128268 GO:0000139 Golgi membrane "The lipid bilayer surrounding any of the compartments of the Golgi apparatus." [GOC:mah] cellular_component
+ENSG00000128268 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000128268 GO:0018279 protein N-linked glycosylation via asparagine "The glycosylation of protein via the N4 atom of peptidyl-asparagine forming N4-glycosyl-L-asparagine; the most common form is N-acetylglucosaminyl asparagine; N-acetylgalactosaminyl asparagine and N4 glucosyl asparagine also occur. This modification typically occurs in extracellular peptides with an N-X-(ST) motif. Partial modification has been observed to occur with cysteine, rather than serine or threonine, in the third position; secondary structure features are important, and proline in the second or fourth positions inhibits modification." [GOC:jsg, RESID:AA0151, RESID:AA0420, RESID:AA0421] biological_process
+ENSG00000128268 GO:0043687 post-translational protein modification "The process of covalently altering one or more amino acids in a protein after the protein has been completely translated and released from the ribosome." [GOC:jsg] biological_process
+ENSG00000128268 GO:0044267 cellular protein metabolic process "The chemical reactions and pathways involving a specific protein, rather than of proteins in general, occurring at the level of an individual cell. Includes cellular protein modification." [GOC:jl] biological_process
+ENSG00000100288 GO:0016772 transferase activity, transferring phosphorus-containing groups "Catalysis of the transfer of a phosphorus-containing group from one compound (donor) to another (acceptor)." [GOC:jl, ISBN:0198506732] molecular_function
+ENSG00000100288 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100288 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100288 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000100288 GO:0006629 lipid metabolic process "The chemical reactions and pathways involving lipids, compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent. Includes fatty acids; neutral fats, other fatty-acid esters, and soaps; long-chain (fatty) alcohols and waxes; sphingoids and other long-chain bases; glycolipids, phospholipids and sphingolipids; and carotenes, polyprenols, sterols, terpenes and other isoprenoids." [GOC:ma] biological_process
+ENSG00000100288 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000100288 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100288 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100288 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000100288 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100288 GO:0016301 kinase activity "Catalysis of the transfer of a phosphate group, usually from ATP, to a substrate molecule." [ISBN:0198506732] molecular_function
+ENSG00000100288 GO:0004103 choline kinase activity "Catalysis of the reaction: ATP + choline = ADP + choline phosphate + 2 H(+)." [EC:2.7.1.32, RHEA:12840] molecular_function
+ENSG00000100288 GO:0004305 ethanolamine kinase activity "Catalysis of the reaction: ATP + ethanolamine = ADP + 2 H(+) + phosphoethanolamine." [EC:2.7.1.82, RHEA:13072] molecular_function
+ENSG00000100288 GO:0005524 ATP binding "Interacting selectively and non-covalently with ATP, adenosine 5'-triphosphate, a universally important coenzyme and enzyme regulator." [ISBN:0198506732] molecular_function
+ENSG00000100288 GO:0006644 phospholipid metabolic process "The chemical reactions and pathways involving phospholipids, any lipid containing phosphoric acid as a mono- or diester." [ISBN:0198506732] biological_process
+ENSG00000100288 GO:0006646 phosphatidylethanolamine biosynthetic process "The chemical reactions and pathways resulting in the formation of phosphatidylethanolamine, any of a class of glycerophospholipids in which a phosphatidyl group is esterified to the hydroxyl group of ethanolamine." [ISBN:0198506732] biological_process
+ENSG00000100288 GO:0006656 phosphatidylcholine biosynthetic process "The chemical reactions and pathways resulting in the formation of phosphatidylcholines, any of a class of glycerophospholipids in which the phosphatidyl group is esterified to the hydroxyl group of choline." [ISBN:0198506732] biological_process
+ENSG00000100288 GO:0006657 CDP-choline pathway "The phosphatidylcholine biosynthetic process that begins with the phosphorylation of choline and ends with the combination of CDP-choline with diacylglycerol to form phosphatidylcholine." [ISBN:0471331309, MetaCyc:PWY3O-450] biological_process
+ENSG00000100288 GO:0016310 phosphorylation "The process of introducing a phosphate group into a molecule, usually with the formation of a phosphoric ester, a phosphoric anhydride or a phosphoric amide." [ISBN:0198506732] biological_process
+ENSG00000100288 GO:0046474 glycerophospholipid biosynthetic process "The chemical reactions and pathways resulting in the formation of glycerophospholipids, any derivative of glycerophosphate that contains at least one O-acyl, O-alkyl, or O-alkenyl group attached to the glycerol residue." [ISBN:0198506732] biological_process
+ENSG00000025708 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000025708 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000025708 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000025708 GO:0034655 nucleobase-containing compound catabolic process "The chemical reactions and pathways resulting in the breakdown of nucleobases, nucleosides, nucleotides and nucleic acids." [GOC:mah] biological_process
+ENSG00000025708 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000025708 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000025708 GO:0030154 cell differentiation "The process in which relatively unspecialized cells, e.g. embryonic or regenerative cells, acquire specialized structural and/or functional features that characterize the cells, tissues, or organs of the mature organism or some other relatively stable phase of the organism's life history. Differentiation includes the processes involved in commitment of a cell to a specific fate and its subsequent development to the mature state." [ISBN:0198506732] biological_process
+ENSG00000025708 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000025708 GO:0016757 transferase activity, transferring glycosyl groups "Catalysis of the transfer of a glycosyl group from one compound (donor) to another (acceptor)." [GOC:jl, ISBN:0198506732] molecular_function
+ENSG00000025708 GO:0040011 locomotion "Self-propelled movement of a cell or organism from one location to another." [GOC:dgh] biological_process
+ENSG00000025708 GO:0006259 DNA metabolic process "Any cellular metabolic process involving deoxyribonucleic acid. This is one of the two main types of nucleic acid, consisting of a long, unbranched macromolecule formed from one, or more commonly, two, strands of linked deoxyribonucleotides." [ISBN:0198506732] biological_process
+ENSG00000025708 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000025708 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000025708 GO:0048646 anatomical structure formation involved in morphogenesis "The developmental process pertaining to the initial formation of an anatomical structure from unspecified parts. This process begins with the specific processes that contribute to the appearance of the discrete structure and ends when the structural rudiment is recognizable. An anatomical structure is any biological entity that occupies space and is distinguished from its surroundings. Anatomical structures can be macroscopic such as a carpel, or microscopic such as an acrosome." [GOC:dph, GOC:jid, GOC:tb] biological_process
+ENSG00000025708 GO:0007005 mitochondrion organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of a mitochondrion; includes mitochondrial morphogenesis and distribution, and replication of the mitochondrial genome as well as synthesis of new mitochondrial components." [GOC:dph, GOC:jl, GOC:mah, GOC:sgd_curators, PMID:9786946] biological_process
+ENSG00000025708 GO:0000002 mitochondrial genome maintenance "The maintenance of the structure and integrity of the mitochondrial genome; includes replication and segregation of the mitochondrial chromosome." [GOC:ai, GOC:vw] biological_process
+ENSG00000025708 GO:0001525 angiogenesis "Blood vessel formation when new vessels emerge from the proliferation of pre-existing blood vessels." [ISBN:0878932453] biological_process
+ENSG00000025708 GO:0004645 phosphorylase activity "Catalysis of the reaction: 1,4-alpha-D-glucosyl(n) + phosphate = 1,4-alpha-D-glucosyl(n-1) + alpha-D-glucose 1-phosphate. The name should be qualified in each instance by adding the name of the natural substrate, e.g. maltodextrin phosphorylase, starch phosphorylase, glycogen phosphorylase." [EC:2.4.1.1] molecular_function
+ENSG00000025708 GO:0005161 platelet-derived growth factor receptor binding "Interacting selectively and non-covalently with the platelet-derived growth factor receptor." [GOC:ai] molecular_function
+ENSG00000025708 GO:0006206 pyrimidine nucleobase metabolic process "The chemical reactions and pathways involving pyrimidine nucleobases, 1,3-diazine, organic nitrogenous bases." [CHEBI:26432, GOC:go_curators] biological_process
+ENSG00000025708 GO:0006220 pyrimidine nucleotide metabolic process "The chemical reactions and pathways involving a pyrimidine nucleotide, a compound consisting of nucleoside (a pyrimidine base linked to a deoxyribose or ribose sugar) esterified with a phosphate group at either the 3' or 5'-hydroxyl group of the sugar." [GOC:go_curators, ISBN:0198506732] biological_process
+ENSG00000025708 GO:0006260 DNA replication "The cellular metabolic process in which a cell duplicates one or more molecules of DNA. DNA replication begins when specific sequences, known as origins of replication, are recognized and bound by initiation proteins, and ends when the original DNA molecule has been completely duplicated and the copies topologically separated. The unit of replication usually corresponds to the genome of the cell, an organelle, or a virus. The template for replication can either be an existing DNA molecule or RNA." [GOC:mah] biological_process
+ENSG00000025708 GO:0006935 chemotaxis "The directed movement of a motile cell or organism, or the directed growth of a cell guided by a specific chemical concentration gradient. Movement may be towards a higher concentration (positive chemotaxis) or towards a lower concentration (negative chemotaxis)." [ISBN:0198506732] biological_process
+ENSG00000025708 GO:0008083 growth factor activity "The function that stimulates a cell to grow or proliferate. Most growth factors have other actions besides the induction of cell growth or proliferation." [ISBN:0815316194] molecular_function
+ENSG00000025708 GO:0009032 thymidine phosphorylase activity "Catalysis of the reaction: thymidine + phosphate = thymine + 2-deoxy-D-ribose 1-phosphate." [EC:2.4.2.4] molecular_function
+ENSG00000025708 GO:0016154 pyrimidine-nucleoside phosphorylase activity "Catalysis of the reaction: pyrimidine nucleoside + phosphate = pyrimidine + alpha-D-ribose 1-phosphate." [EC:2.4.2.2] molecular_function
+ENSG00000025708 GO:0016763 transferase activity, transferring pentosyl groups "Catalysis of the transfer of a pentosyl group from one compound (donor) to another (acceptor)." [GOC:jl] molecular_function
+ENSG00000025708 GO:0043097 pyrimidine nucleoside salvage "Any process that generates a pyrimidine nucleoside, one of a family of organic molecules consisting of a pyrimidine base covalently bonded to a sugar ribose, from derivatives of it, without de novo synthesis." [GOC:jl] biological_process
+ENSG00000025708 GO:0046135 pyrimidine nucleoside catabolic process "The chemical reactions and pathways resulting in the breakdown of one of a family of organic molecules consisting of a pyrimidine base covalently bonded to a sugar ribose (a ribonucleoside) or deoxyribose (a deoxyribonucleoside)." [GOC:ai] biological_process
+ENSG00000025708 GO:0055086 nucleobase-containing small molecule metabolic process "The cellular chemical reactions and pathways involving a nucleobase-containing small molecule: a nucleobase, a nucleoside, or a nucleotide." [GOC:vw] biological_process
+ENSG00000025708 GO:0008152 metabolic process "The chemical reactions and pathways, including anabolism and catabolism, by which living organisms transform chemical substances. Metabolic processes typically transform small molecules, but also include macromolecular processes such as DNA repair and replication, and protein synthesis and degradation." [GOC:go_curators, ISBN:0198547684] biological_process
+ENSG00000025708 GO:0006213 pyrimidine nucleoside metabolic process "The chemical reactions and pathways involving any pyrimidine nucleoside, one of a family of organic molecules consisting of a pyrimidine base covalently bonded to ribose (a ribonucleoside) or deoxyribose (a deoxyribonucleoside)." [GOC:jl, ISBN:0140512713] biological_process
+ENSG00000100139 GO:0008270 zinc ion binding "Interacting selectively and non-covalently with zinc (Zn) ions." [GOC:ai] molecular_function
+ENSG00000100139 GO:0032456 endocytic recycling "The directed movement of membrane-bounded vesicles from recycling endosomes back to the plasma membrane where they are recycled for further rounds of transport." [GOC:ecd, PMID:16473635] biological_process
+ENSG00000100139 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100139 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100139 GO:0016192 vesicle-mediated transport "A cellular transport process in which transported substances are moved in membrane-bounded vesicles; transported substances are enclosed in the vesicle lumen or located in the vesicle membrane. The process begins with a step that directs a substance to the forming vesicle, and includes vesicle budding and coating. Vesicles are then targeted to, and fuse with, an acceptor membrane." [GOC:ai, GOC:mah, ISBN:08789310662000] biological_process
+ENSG00000100139 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100139 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100139 GO:0007009 plasma membrane organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of the plasma membrane." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100139 GO:0061024 membrane organization "A process which results in the assembly, arrangement of constituent parts, or disassembly of a membrane. A membrane is a double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:dph, GOC:tb] biological_process
+ENSG00000100139 GO:0008289 lipid binding "Interacting selectively and non-covalently with a lipid." [GOC:ai] molecular_function
+ENSG00000100139 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100139 GO:0019899 enzyme binding "Interacting selectively and non-covalently with any enzyme." [GOC:jl] molecular_function
+ENSG00000100139 GO:0006605 protein targeting "The process of targeting specific proteins to particular membrane-bounded subcellular organelles. Usually requires an organelle specific protein sequence motif." [GOC:ma] biological_process
+ENSG00000100139 GO:0005768 endosome "A membrane-bounded organelle to which materials ingested by endocytosis are delivered." [ISBN:0198506732, PMID:19696797] cellular_component
+ENSG00000100139 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100139 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100139 GO:0005770 late endosome "A prelysosomal endocytic organelle differentiated from early endosomes by lower lumenal pH and different protein composition. Late endosomes are more spherical than early endosomes and are mostly juxtanuclear, being concentrated near the microtubule organizing center." [NIF_Subcellular:nlx_subcell_20090702, PMID:11964142, PMID:2557062] cellular_component
+ENSG00000100139 GO:0006612 protein targeting to membrane "The process of directing proteins towards a membrane, usually using signals contained within the protein." [GOC:curators] biological_process
+ENSG00000100139 GO:0006897 endocytosis "A vesicle-mediated transport process in which cells take up external materials or membrane constituents by the invagination of a small region of the plasma membrane to form a new membrane-bounded vesicle." [GOC:mah, ISBN:0198506732, ISBN:0716731363] biological_process
+ENSG00000100139 GO:0006898 receptor-mediated endocytosis "An endocytosis process in which cell surface receptors ensure specificity of transport. A specific receptor on the cell surface binds tightly to the extracellular macromolecule (the ligand) that it recognizes; the plasma-membrane region containing the receptor-ligand complex then undergoes endocytosis, forming a transport vesicle containing the receptor-ligand complex and excluding most other plasma-membrane proteins. Receptor-mediated endocytosis generally occurs via clathrin-coated pits and vesicles." [GOC:mah, ISBN:0716731363] biological_process
+ENSG00000100139 GO:0017137 Rab GTPase binding "Interacting selectively and non-covalently with Rab protein, any member of the Rab subfamily of the Ras superfamily of monomeric GTPases." [GOC:mah] molecular_function
+ENSG00000100139 GO:0019898 extrinsic component of membrane "The component of a membrane consisting of gene products and protein complexes that are loosely bound to one of its surfaces, but not integrated into the hydrophobic region." [GOC:dos, GOC:jl, GOC:mah] cellular_component
+ENSG00000100139 GO:0031175 neuron projection development "The process whose specific outcome is the progression of a neuron projection over time, from its formation to the mature structure. A neuron projection is any process extending from a neural cell, such as axons or dendrites (collectively called neurites)." [GOC:mah] biological_process
+ENSG00000100139 GO:0032458 slow endocytic recycling "The directed movement of membrane-bounded vesicles from deep (non-peripheral) compartments endocytic compartments back to the plasma membrane where they are recycled for further rounds of transport." [GOC:ecd, PMID:16473635] biological_process
+ENSG00000100139 GO:0036010 protein localization to endosome "A process in which a protein is transported to, or maintained in, a location within an endosome." [GOC:yaf] biological_process
+ENSG00000100139 GO:0042802 identical protein binding "Interacting selectively and non-covalently with an identical protein or proteins." [GOC:jl] molecular_function
+ENSG00000100139 GO:0055038 recycling endosome membrane "The lipid bilayer surrounding a recycling endosome." [GOC:jid, GOC:rph, PMID:10930469, PMID:15601896, PMID:16246101] cellular_component
+ENSG00000100139 GO:0070300 phosphatidic acid binding "Interacting selectively and non-covalently with phosphatidic acid, any of a class of glycerol phosphate in which both the remaining hydroxyl groups of the glycerol moiety are esterified with fatty acids." [CHEBI:16337, GOC:jp, ISBN:0198506732] molecular_function
+ENSG00000100139 GO:0097320 membrane tubulation "A modification in a plasma membrane resulting in formation of a tubular invagination." [GOC:BHF, PMID:15252009, PMID:20730103] biological_process
+ENSG00000100139 GO:1990126 retrograde transport, endosome to plasma membrane "The directed movement of membrane-bounded vesicles from endosomes back to the plasma membrane, a trafficking pathway that promotes the recycling of internalized transmembrane proteins." [PMID:23563491] biological_process
+ENSG00000100139 GO:0010008 endosome membrane "The lipid bilayer surrounding an endosome." [GOC:mah] cellular_component
+ENSG00000100139 GO:1990090 cellular response to nerve growth factor stimulus "A process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a nerve growth factor stimulus." [PMID:22399805, Wikipedia:Nerve_growth_factor] biological_process
+ENSG00000100139
+ENSG00000100351 GO:0002376 immune system process "Any process involved in the development or functioning of the immune system, an organismal system for calibrated responses to potential internal or invasive threats." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05] biological_process
+ENSG00000100351 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000100351 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100351 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000100351 GO:0007267 cell-cell signaling "Any process that mediates the transfer of information from one cell to another." [GOC:mah] biological_process
+ENSG00000100351 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100351 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000100351 GO:0005768 endosome "A membrane-bounded organelle to which materials ingested by endocytosis are delivered." [ISBN:0198506732, PMID:19696797] cellular_component
+ENSG00000100351 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100351 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100351 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100351 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100351 GO:0030674 protein binding, bridging "The binding activity of a molecule that brings together two or more protein molecules, or a protein and another macromolecule or complex, through a selective, non-covalent, often stoichiometric interaction, permitting those molecules to function in a coordinated way." [GOC:bf, GOC:mah, GOC:vw] molecular_function
+ENSG00000100351 GO:0005070 SH3/SH2 adaptor activity "Interacting selectively and non-covalently and simultaneously with one or more signal transduction molecules, usually acting as a scaffold to bring these molecules into close proximity either using their own SH2/SH3 domains (e.g. Grb2) or those of their target molecules (e.g. SAM68)." [GOC:mah, GOC:so] molecular_function
+ENSG00000100351 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100351 GO:0007265 Ras protein signal transduction "A series of molecular signals within the cell that are mediated by a member of the Ras superfamily of proteins switching to a GTP-bound active state." [GOC:bf] biological_process
+ENSG00000100351 GO:0009967 positive regulation of signal transduction "Any process that activates or increases the frequency, rate or extent of signal transduction." [GOC:sm] biological_process
+ENSG00000100351 GO:0031295 T cell costimulation "The process of providing, via surface-bound receptor-ligand pairs, a second, antigen-independent, signal in addition to that provided by the T cell receptor to augment T cell activation." [ISBN:0781735149] biological_process
+ENSG00000100351 GO:0038095 Fc-epsilon receptor signaling pathway "A series of molecular signals initiated by the binding of the Fc portion of immunoglobulin E (IgE) to an Fc-epsilon receptor on the surface of a signal-receiving cell, and ending with regulation of a downstream cellular process, e.g. transcription. The Fc portion of an immunoglobulin is its C-terminal constant region." [GOC:phg, PMID:12413516, PMID:15048725] biological_process
+ENSG00000100351 GO:0045087 innate immune response "Innate immune responses are defense responses mediated by germline encoded components that directly recognize components of potential pathogens." [GO_REF:0000022, GOC:add, GOC:ebc, GOC:mtg_15nov05, GOC:mtg_sensu] biological_process
+ENSG00000100351 GO:0050852 T cell receptor signaling pathway "A series of molecular signals initiated by the cross-linking of an antigen receptor on a T cell." [GOC:add] biological_process
+ENSG00000100196 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100196 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000100196 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000100196 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100196 GO:0016192 vesicle-mediated transport "A cellular transport process in which transported substances are moved in membrane-bounded vesicles; transported substances are enclosed in the vesicle lumen or located in the vesicle membrane. The process begins with a step that directs a substance to the forming vesicle, and includes vesicle budding and coating. Vesicles are then targeted to, and fuse with, an acceptor membrane." [GOC:ai, GOC:mah, ISBN:08789310662000] biological_process
+ENSG00000100196 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100196 GO:0005789 endoplasmic reticulum membrane "The lipid bilayer surrounding the endoplasmic reticulum." [GOC:mah] cellular_component
+ENSG00000100196 GO:0006621 protein retention in ER lumen "The retention in the endoplasmic reticulum (ER) lumen of soluble resident proteins. Sorting receptors retrieve proteins with ER localization signals, such as KDEL and HDEL sequences or some transmembrane domains, that have escaped to the cis-Golgi network and return them to the ER. Abnormally folded proteins and unassembled subunits are also selectively retained in the ER." [ISBN:0716731363, PMID:12972550] biological_process
+ENSG00000100196 GO:0006987 activation of signaling protein activity involved in unfolded protein response "The conversion of a specific protein, possessing protein kinase and endoribonuclease activities, to an active form as a result of signaling via the unfolded protein response." [GOC:dph, GOC:mah, GOC:tb, PMID:12042763] biological_process
+ENSG00000100196 GO:0015031 protein transport "The directed movement of proteins into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:ai] biological_process
+ENSG00000100196 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000100196 GO:0030968 endoplasmic reticulum unfolded protein response "The series of molecular signals generated as a consequence of the presence of unfolded proteins in the endoplasmic reticulum (ER) or other ER-related stress; results in changes in the regulation of transcription and translation." [GOC:mah, PMID:12042763] biological_process
+ENSG00000100196 GO:0044267 cellular protein metabolic process "The chemical reactions and pathways involving a specific protein, rather than of proteins in general, occurring at the level of an individual cell. Includes cellular protein modification." [GOC:jl] biological_process
+ENSG00000100196 GO:0046923 ER retention sequence binding "Interacting selectively and non-covalently with an endoplasmic reticulum (ER) retention sequence, a specific peptide sequence that ensures a protein is retained within the ER." [GOC:ai] molecular_function
+ENSG00000100196 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100196 GO:0022900 electron transport chain "A process in which a series of electron carriers operate together to transfer electrons from donors to any of several different terminal electron acceptors to generate a transmembrane electrochemical gradient." [GOC:mtg_electron_transport] biological_process
+ENSG00000100196 GO:0006091 generation of precursor metabolites and energy "The chemical reactions and pathways resulting in the formation of precursor metabolites, substances from which energy is derived, and any process involved in the liberation of energy from these substances." [GOC:jl] biological_process
+ENSG00000206069 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000206069 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100097 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100097 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100097 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100097 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000100097 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100097 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000100097 GO:0008219 cell death "Any biological process that results in permanent cessation of all vital functions of a cell. A cell should be considered dead when any one of the following molecular or morphological criteria is met: (1) the cell has lost the integrity of its plasma membrane; (2) the cell, including its nucleus, has undergone complete fragmentation into discrete bodies (frequently referred to as \"apoptotic bodies\"); and/or (3) its corpse (or its fragments) have been engulfed by an adjacent cell in vivo." [GOC:mah, GOC:mtg_apoptosis] biological_process
+ENSG00000100097 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100097 GO:0005622 intracellular "The living contents of a cell; the matter contained within (but not including) the plasma membrane, usually taken to exclude large vacuoles and masses of secretory or ingested material. In eukaryotes it includes the nucleus and cytoplasm." [ISBN:0198506732] cellular_component
+ENSG00000100097 GO:0005615 extracellular space "That part of a multicellular organism outside the cells proper, usually taken to be outside the plasma membranes, and occupied by fluid." [ISBN:0198547684] cellular_component
+ENSG00000100097 GO:0005578 proteinaceous extracellular matrix "A layer consisting mainly of proteins (especially collagen) and glycosaminoglycans (mostly as proteoglycans) that forms a sheet underlying or overlying cells such as endothelial and epithelial cells. The proteins are secreted by cells in the vicinity. An example of this component is found in Mus musculus." [GOC:mtg_sensu, ISBN:0198547684] cellular_component
+ENSG00000100097 GO:0004871 signal transducer activity "Conveys a signal across a cell to trigger a change in cell function or state. A signal is a physical entity or change in state that is used to transfer information in order to trigger a response." [GOC:go_curators] molecular_function
+ENSG00000100097 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100097 GO:0006915 apoptotic process "A programmed cell death process which begins when a cell receives an internal (e.g. DNA damage) or external signal (e.g. an extracellular death ligand), and proceeds through a series of biochemical events (signaling pathway phase) which trigger an execution phase. The execution phase is the last step of an apoptotic process, and is typically characterized by rounding-up of the cell, retraction of pseudopodes, reduction of cellular volume (pyknosis), chromatin condensation, nuclear fragmentation (karyorrhexis), plasma membrane blebbing and fragmentation of the cell into apoptotic bodies. When the execution phase is completed, the cell has died." [GOC:dhl, GOC:ecd, GOC:go_curators, GOC:mtg_apoptosis, GOC:tb, ISBN:0198506732, PMID:18846107, PMID:21494263] biological_process
+ENSG00000100097 GO:0016936 galactoside binding "Interacting selectively and non-covalently with any glycoside in which the sugar group is galactose." [CHEBI:24163, GOC:jl, ISBN:0198506732] molecular_function
+ENSG00000100097 GO:0042981 regulation of apoptotic process "Any process that modulates the occurrence or rate of cell death by apoptotic process." [GOC:jl, GOC:mtg_apoptosis] biological_process
+ENSG00000100097 GO:0043123 positive regulation of I-kappaB kinase/NF-kappaB signaling "Any process that activates or increases the frequency, rate or extent of I-kappaB kinase/NF-kappaB signaling." [GOC:jl] biological_process
+ENSG00000100097 GO:0044822 poly(A) RNA binding "Interacting non-covalently with a poly(A) RNA, a RNA molecule which has a tail of adenine bases." [GOC:jl] molecular_function
+ENSG00000100097 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000100097 GO:0031012 extracellular matrix "A structure lying external to one or more cells, which provides structural support for cells or tissues; may be completely external to the cell (as in animals and bacteria) or be part of the cell (as in plants)." [GOC:mah, NIF_Subcellular:nlx_subcell_20090513] cellular_component
+ENSG00000100097 GO:0030246 carbohydrate binding "Interacting selectively and non-covalently with any carbohydrate, which includes monosaccharides, oligosaccharides and polysaccharides as well as substances derived from monosaccharides by reduction of the carbonyl group (alditols), by oxidation of one or more hydroxy groups to afford the corresponding aldehydes, ketones, or carboxylic acids, or by replacement of one or more hydroxy group(s) by a hydrogen atom. Cyclitols are generally not regarded as carbohydrates." [CHEBI:16646, GOC:mah] molecular_function
+ENSG00000100097 GO:0002317 plasma cell differentiation "The process in which a B cell acquires the specialized features of a plasma cell. A plasma cell is a lymphocyte which develops from a B cell and produces high amounts of antibody." [GOC:jal] biological_process
+ENSG00000100097 GO:0031295 T cell costimulation "The process of providing, via surface-bound receptor-ligand pairs, a second, antigen-independent, signal in addition to that provided by the T cell receptor to augment T cell activation." [ISBN:0781735149] biological_process
+ENSG00000100097 GO:0045445 myoblast differentiation "The process in which a relatively unspecialized cell acquires specialized features of a myoblast. A myoblast is a mononucleate cell type that, by fusion with other myoblasts, gives rise to the myotubes that eventually develop into striated muscle fibers." [CL:0000056, GOC:go_curators, GOC:mtg_muscle] biological_process
+ENSG00000100097 GO:0042493 response to drug "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a drug stimulus. A drug is a substance used in the diagnosis, treatment or prevention of a disease." [GOC:jl] biological_process
+ENSG00000100097 GO:0010977 negative regulation of neuron projection development "Any process that decreases the rate, frequency or extent of neuron projection development. Neuron projection development is the process whose specific outcome is the progression of a neuron projection over time, from its formation to the mature structure. A neuron projection is any process extending from a neural cell, such as axons or dendrites (collectively called neurites)." [GOC:dph, GOC:tb] biological_process
+ENSG00000100097 GO:0048678 response to axon injury "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of an axon injury stimulus." [GOC:dgh, GOC:dph, GOC:jid, GOC:lm] biological_process
+ENSG00000100097 GO:0043236 laminin binding "Interacting selectively and non-covalently with laminins, glycoproteins that are major constituents of the basement membrane of cells." [GOC:ecd] molecular_function
+ENSG00000100097 GO:0071333 cellular response to glucose stimulus "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a glucose stimulus." [GOC:mah] biological_process
+ENSG00000100097 GO:0071407 cellular response to organic cyclic compound "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of an organic cyclic compound stimulus." [GOC:mah] biological_process
+ENSG00000100097 GO:0009986 cell surface "The external part of the cell wall and/or plasma membrane." [GOC:jl, GOC:mtg_sensu, GOC:sm] cellular_component
+ENSG00000100097 GO:0042803 protein homodimerization activity "Interacting selectively and non-covalently with an identical protein to form a homodimer." [GOC:jl] molecular_function
+ENSG00000100097 GO:0001948 glycoprotein binding "Interacting selectively and non-covalently with a glycoprotein, a protein that contains covalently bound glycose (monosaccharide) residues. These also include proteoglycans." [GOC:hjd, ISBN:0198506732] molecular_function
+ENSG00000100097 GO:0034120 positive regulation of erythrocyte aggregation "Any process that activates or increases the frequency, rate, or extent of erythrocyte aggregation." [GOC:add] biological_process
+ENSG00000100097 GO:0030395 lactose binding "Interacting selectively and non-covalently with lactose, a disaccharide of glucose and galactose, the carbohydrate of milk." [GOC:jl, ISBN:01928006X] molecular_function
+ENSG00000100097 GO:0010812 negative regulation of cell-substrate adhesion "Any process that decreases the frequency, rate or extent of cell-substrate adhesion. Cell-substrate adhesion is the attachment of a cell to the underlying substrate via adhesion molecules." [GOC:dph, GOC:pf, GOC:tb] biological_process
+ENSG00000100097 GO:0033555 multicellular organismal response to stress "Any process that results in a change in state or activity of a multicellular organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a stimulus indicating the organism is under stress. The stress is usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000100097 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100097
+ENSG00000133433 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000133433 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000133433 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000133433 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000133433 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000133433 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000133433 GO:0016765 transferase activity, transferring alkyl or aryl (other than methyl) groups "Catalysis of the transfer of an alkyl or aryl (but not methyl) group from one compound (donor) to another (acceptor)." [GOC:jl, ISBN:0198506732] molecular_function
+ENSG00000133433 GO:0004364 glutathione transferase activity "Catalysis of the reaction: R-X + glutathione = H-X + R-S-glutathione. R may be an aliphatic, aromatic or heterocyclic group; X may be a sulfate, nitrile or halide group." [EC:2.5.1.18] molecular_function
+ENSG00000133433 GO:0006805 xenobiotic metabolic process "The chemical reactions and pathways involving a xenobiotic compound, a compound foreign to living organisms. Used of chemical compounds, e.g. a xenobiotic chemical, such as a pesticide." [GOC:cab2] biological_process
+ENSG00000133433 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000133433 GO:1901687 glutathione derivative biosynthetic process "The chemical reactions and pathways resulting in the formation of glutathione derivative." [GOC:pr, GOC:TermGenie] biological_process
+ENSG00000133433 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000133433 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000133433 GO:0008152 metabolic process "The chemical reactions and pathways, including anabolism and catabolism, by which living organisms transform chemical substances. Metabolic processes typically transform small molecules, but also include macromolecular processes such as DNA repair and replication, and protein synthesis and degradation." [GOC:go_curators, ISBN:0198547684] biological_process
+ENSG00000133433 GO:0016740 transferase activity "Catalysis of the transfer of a group, e.g. a methyl group, glycosyl group, acyl group, phosphorus-containing, or other groups, from one compound (generally regarded as the donor) to another compound (generally regarded as the acceptor). Transferase is the systematic name for any enzyme of EC class 2." [ISBN:0198506732] molecular_function
+ENSG00000188636
+ENSG00000100201 GO:0003676 nucleic acid binding "Interacting selectively and non-covalently with any nucleic acid." [GOC:jl] molecular_function
+ENSG00000100201 GO:0005524 ATP binding "Interacting selectively and non-covalently with ATP, adenosine 5'-triphosphate, a universally important coenzyme and enzyme regulator." [ISBN:0198506732] molecular_function
+ENSG00000100201 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100201 GO:0006200 ATP catabolic process "The chemical reactions and pathways resulting in the breakdown of ATP, adenosine 5'-triphosphate, a universally important coenzyme and enzyme regulator." [GOC:ai] biological_process
+ENSG00000100201 GO:0008026 ATP-dependent helicase activity "Catalysis of the reaction: ATP + H2O = ADP + phosphate, to drive the unwinding of a DNA or RNA helix." [EC:3.6.1.3, GOC:jl] molecular_function
+ENSG00000100201 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100201 GO:0004386 helicase activity "Catalysis of the reaction: NTP + H2O = NDP + phosphate, to drive the unwinding of a DNA or RNA helix." [GOC:mah, ISBN:0198506732] molecular_function
+ENSG00000100201 GO:0016887 ATPase activity "Catalysis of the reaction: ATP + H2O = ADP + phosphate + 2 H+. May or may not be coupled to another reaction." [EC:3.6.1.3, GOC:jl] molecular_function
+ENSG00000100201 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100201 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000100201 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100201 GO:0034655 nucleobase-containing compound catabolic process "The chemical reactions and pathways resulting in the breakdown of nucleobases, nucleosides, nucleotides and nucleic acids." [GOC:mah] biological_process
+ENSG00000100201 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000100201 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100201 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100201 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100201 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100201 GO:0009791 post-embryonic development "The process whose specific outcome is the progression of the organism over time, from the completion of embryonic development to the mature structure. See embryonic development." [GOC:go_curators] biological_process
+ENSG00000100201 GO:0072358 cardiovascular system development "The process whose specific outcome is the progression of the cardiovascular system over time, from its formation to the mature structure. The cardiovascular system is the anatomical system that has as its parts the heart and blood vessels." [GOC:mah, UBERON:0004535] biological_process
+ENSG00000100201 GO:0003713 transcription coactivator activity "Interacting selectively and non-covalently with a activating transcription factor and also with the basal transcription machinery in order to increase the frequency, rate or extent of transcription. Cofactors generally do not bind the template nucleic acid, but rather mediate protein-protein interactions between activating transcription factors and the basal transcription machinery." [GOC:txnOH, PMID:10213677, PMID:16858867] molecular_function
+ENSG00000100201 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000100201 GO:0003724 RNA helicase activity "Catalysis of the reaction: NTP + H2O = NDP + phosphate, to drive the unwinding of a RNA helix." [GOC:jl, GOC:mah] molecular_function
+ENSG00000100201 GO:0006351 transcription, DNA-templated "The cellular synthesis of RNA on a template of DNA." [GOC:jl, GOC:txnOH] biological_process
+ENSG00000100201 GO:0006396 RNA processing "Any process involved in the conversion of one or more primary RNA transcripts into one or more mature RNA molecules." [GOC:mah] biological_process
+ENSG00000100201 GO:0008186 RNA-dependent ATPase activity "Catalysis of the reaction: ATP + H2O = ADP + phosphate; this reaction requires the presence of RNA, and it drives another reaction." [EC:3.6.1.3, GOC:jl] molecular_function
+ENSG00000100201 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000100201 GO:0030331 estrogen receptor binding "Interacting selectively and non-covalently with an estrogen receptor." [GOC:ai] molecular_function
+ENSG00000100201 GO:0033148 positive regulation of intracellular estrogen receptor signaling pathway "Any process that activates or increases the frequency, rate or extent of the activity of an intracellular estrogen receptor signaling pathway." [GOC:mah] biological_process
+ENSG00000100201 GO:0044822 poly(A) RNA binding "Interacting non-covalently with a poly(A) RNA, a RNA molecule which has a tail of adenine bases." [GOC:jl] molecular_function
+ENSG00000100201 GO:0045944 positive regulation of transcription from RNA polymerase II promoter "Any process that activates or increases the frequency, rate or extent of transcription from an RNA polymerase II promoter." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000100201 GO:2001014 regulation of skeletal muscle cell differentiation "Any process that modulates the frequency, rate or extent of skeletal muscle cell differentiation." [GOC:obol] biological_process
+ENSG00000100201 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000100201 GO:0000988 protein binding transcription factor activity "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules), in order to modulate transcription. A protein binding transcription factor may or may not also interact with the template nucleic acid (either DNA or RNA) as well." [GOC:txnOH] molecular_function
+ENSG00000211668 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000211668 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000099992 GO:0005097 Rab GTPase activator activity "Increases the rate of GTP hydrolysis by a GTPase of the Rab family." [GOC:mah] molecular_function
+ENSG00000099992 GO:0032851 positive regulation of Rab GTPase activity "Any process that activates or increases the activity of a GTPase of the Rab family." [GOC:mah] biological_process
+ENSG00000099992 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000099992 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000099992 GO:0030234 enzyme regulator activity "Binds to and modulates the activity of an enzyme." [GOC:mah] molecular_function
+ENSG00000099992 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000099992 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000099992 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000099992 GO:0016192 vesicle-mediated transport "A cellular transport process in which transported substances are moved in membrane-bounded vesicles; transported substances are enclosed in the vesicle lumen or located in the vesicle membrane. The process begins with a step that directs a substance to the forming vesicle, and includes vesicle budding and coating. Vesicles are then targeted to, and fuse with, an acceptor membrane." [GOC:ai, GOC:mah, ISBN:08789310662000] biological_process
+ENSG00000099992 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000099992 GO:0005085 guanyl-nucleotide exchange factor activity "Stimulates the exchange of guanyl nucleotides associated with a GTPase. Under normal cellular physiological conditions, the concentration of GTP is higher than that of GDP, favoring the replacement of GDP by GTP in association with the GTPase." [GOC:kd, GOC:mah] molecular_function
+ENSG00000099992 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000099992 GO:0005902 microvillus "Thin cylindrical membrane-covered projections on the surface of an animal cell containing a core bundle of actin filaments. Present in especially large numbers on the absorptive surface of intestinal cells." [ISBN:0813516194] cellular_component
+ENSG00000099992 GO:0030165 PDZ domain binding "Interacting selectively and non-covalently with a PDZ domain of a protein, a domain found in diverse signaling proteins." [GOC:go_curators, Pfam:PF00595] molecular_function
+ENSG00000099992 GO:0042147 retrograde transport, endosome to Golgi "The directed movement of membrane-bounded vesicles from endosomes back to the trans-Golgi network where they are recycled for further rounds of transport." [GOC:jl, PMID:10873832, PMID:16936697] biological_process
+ENSG00000099992 GO:0045862 positive regulation of proteolysis "Any process that activates or increases the frequency, rate or extent of the hydrolysis of a peptide bond or bonds within a protein." [GOC:go_curators] biological_process
+ENSG00000099992 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000099992 GO:0097202 activation of cysteine-type endopeptidase activity "Any process that initiates the activity of the inactive enzyme cysteine-type endopeptidase." [GOC:mtg_apoptosis, PMID:21726810] biological_process
+ENSG00000099992 GO:0051604 protein maturation "Any process leading to the attainment of the full functional capacity of a protein." [GOC:ai] biological_process
+ENSG00000099992 GO:0032313 regulation of Rab GTPase activity "Any process that modulates the activity of a GTPase of the Rab family." [GOC:mah] biological_process
+ENSG00000099992
+ENSG00000211680
+ENSG00000211681
+ENSG00000128394 GO:0006259 DNA metabolic process "Any cellular metabolic process involving deoxyribonucleic acid. This is one of the two main types of nucleic acid, consisting of a long, unbranched macromolecule formed from one, or more commonly, two, strands of linked deoxyribonucleotides." [ISBN:0198506732] biological_process
+ENSG00000128394 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000128394 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000128394 GO:0002376 immune system process "Any process involved in the development or functioning of the immune system, an organismal system for calibrated responses to potential internal or invasive threats." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05] biological_process
+ENSG00000128394 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000128394 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000128394 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000128394 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000128394 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000128394 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000128394 GO:0034655 nucleobase-containing compound catabolic process "The chemical reactions and pathways resulting in the breakdown of nucleobases, nucleosides, nucleotides and nucleic acids." [GOC:mah] biological_process
+ENSG00000128394 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000128394 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000128394 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000128394 GO:0016810 hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds "Catalysis of the hydrolysis of any carbon-nitrogen bond, C-N, with the exception of peptide bonds." [GOC:jl] molecular_function
+ENSG00000128394 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000128394 GO:0000932 cytoplasmic mRNA processing body "A focus in the cytoplasm where mRNAs may become inactivated by decapping or some other mechanism. mRNA processing and binding proteins are localized to these foci." [GOC:clt, PMID:12730603] cellular_component
+ENSG00000128394 GO:0002230 positive regulation of defense response to virus by host "Any host process that results in the promotion of antiviral immune response mechanisms, thereby limiting viral replication." [GOC:add, GOC:dph, GOC:tb, ISBN:0781735149] biological_process
+ENSG00000128394 GO:0004126 cytidine deaminase activity "Catalysis of the reaction: cytidine + H2O = uridine + NH3." [EC:3.5.4.5] molecular_function
+ENSG00000128394 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000128394 GO:0008270 zinc ion binding "Interacting selectively and non-covalently with zinc (Zn) ions." [GOC:ai] molecular_function
+ENSG00000128394 GO:0009972 cytidine deamination "The removal of amino group in the presence of water." [GOC:sm] biological_process
+ENSG00000128394 GO:0010529 negative regulation of transposition "Any process that decreases the frequency, rate or extent of transposition. Transposition results in the movement of discrete segments of DNA between nonhomologous sites." [GOC:dph, GOC:tb] biological_process
+ENSG00000128394 GO:0016553 base conversion or substitution editing "Any base modification or substitution events that result in alterations in the coding potential or structural properties of RNAs as a result of changes in the base-pairing properties of the modified ribonucleoside(s)." [PMID:11092837] biological_process
+ENSG00000128394 GO:0030529 ribonucleoprotein complex "A macromolecular complex containing both protein and RNA molecules." [GOC:krc] cellular_component
+ENSG00000128394 GO:0030895 apolipoprotein B mRNA editing enzyme complex "Protein complex that mediates editing of the mRNA encoding apolipoprotein B; catalyzes the deamination of C to U (residue 6666 in the human mRNA). Contains a catalytic subunit, APOBEC-1, and other proteins (e.g. human ASP; rat ASP and KSRP)." [PMID:10781591] cellular_component
+ENSG00000128394 GO:0044822 poly(A) RNA binding "Interacting non-covalently with a poly(A) RNA, a RNA molecule which has a tail of adenine bases." [GOC:jl] molecular_function
+ENSG00000128394 GO:0045071 negative regulation of viral genome replication "Any process that stops, prevents, or reduces the frequency, rate or extent of viral genome replication." [GOC:go_curators] biological_process
+ENSG00000128394 GO:0045087 innate immune response "Innate immune responses are defense responses mediated by germline encoded components that directly recognize components of potential pathogens." [GO_REF:0000022, GOC:add, GOC:ebc, GOC:mtg_15nov05, GOC:mtg_sensu] biological_process
+ENSG00000128394 GO:0045869 negative regulation of single stranded viral RNA replication via double stranded DNA intermediate "Any process that stops, prevents, or reduces the frequency, rate or extent of single stranded viral RNA replication via double stranded DNA intermediate." [GOC:go_curators] biological_process
+ENSG00000128394 GO:0048525 negative regulation of viral process "Any process that stops, prevents, or reduces the frequency, rate or extent of a multi-organism process in which a virus is a participant." [GOC:bf, GOC:jl] biological_process
+ENSG00000128394 GO:0051607 defense response to virus "Reactions triggered in response to the presence of a virus that act to protect the cell or organism." [GOC:ai] biological_process
+ENSG00000128394 GO:0070383 DNA cytosine deamination "The removal of an amino group from a cytosine residue in DNA, forming a uracil residue." [GOC:mah] biological_process
+ENSG00000128394 GO:0080111 DNA demethylation "The removal of a methyl group from one or more nucleotides within an DNA molecule." [PMID:17208187] biological_process
+ENSG00000128394 GO:0016814 hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds, in cyclic amidines "Catalysis of the hydrolysis of any non-peptide carbon-nitrogen bond in a cyclic amidine, a compound of the form R-C(=NH)-NH2." [ISBN:0198506732] molecular_function
+ENSG00000128394 GO:0003824 catalytic activity "Catalysis of a biochemical reaction at physiological temperatures. In biologically catalyzed reactions, the reactants are known as substrates, and the catalysts are naturally occurring macromolecular substances known as enzymes. Enzymes possess specific binding sites for substrates, and are usually composed wholly or largely of protein, but RNA that has catalytic activity (ribozyme) is often also regarded as enzymatic." [ISBN:0198506732] molecular_function
+ENSG00000128250 GO:0008270 zinc ion binding "Interacting selectively and non-covalently with zinc (Zn) ions." [GOC:ai] molecular_function
+ENSG00000128250 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000128250 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000128250 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000128266 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000128266 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000128266 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000128266 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000128266 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000128266 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000128266 GO:0034655 nucleobase-containing compound catabolic process "The chemical reactions and pathways resulting in the breakdown of nucleobases, nucleosides, nucleotides and nucleic acids." [GOC:mah] biological_process
+ENSG00000128266 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000128266 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000128266 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000128266 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000128266 GO:0005783 endoplasmic reticulum "The irregular network of unit membranes, visible only by electron microscopy, that occurs in the cytoplasm of many eukaryotic cells. The membranes form a complex meshwork of tubular channels, which are often expanded into slitlike cavities called cisternae. The ER takes two forms, rough (or granular), with ribosomes adhering to the outer surface, and smooth (with no ribosomes attached)." [ISBN:0198506732] cellular_component
+ENSG00000128266 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000128266 GO:0005635 nuclear envelope "The double lipid bilayer enclosing the nucleus and separating its contents from the rest of the cytoplasm; includes the intermembrane space, a gap of width 20-40 nm (also called the perinuclear space)." [ISBN:0198547684] cellular_component
+ENSG00000128266 GO:0004871 signal transducer activity "Conveys a signal across a cell to trigger a change in cell function or state. A signal is a physical entity or change in state that is used to transfer information in order to trigger a response." [GOC:go_curators] molecular_function
+ENSG00000128266 GO:0003924 GTPase activity "Catalysis of the reaction: GTP + H2O = GDP + phosphate." [ISBN:0198547684] molecular_function
+ENSG00000128266 GO:0005057 receptor signaling protein activity "Conveys a signal from an upstream receptor or intracellular signal transducer, converting the signal into a form where it can ultimately trigger a change in the state or activity of a cell." [GOC:bf] molecular_function
+ENSG00000128266 GO:0005525 GTP binding "Interacting selectively and non-covalently with GTP, guanosine triphosphate." [GOC:ai] molecular_function
+ENSG00000128266 GO:0005834 heterotrimeric G-protein complex "Any of a family of heterotrimeric GTP-binding and hydrolyzing proteins; they belong to a superfamily of GTPases that includes monomeric proteins such as EF-Tu and RAS. Heterotrimeric G-proteins consist of three subunits; the alpha subunit contains the guanine nucleotide binding site and possesses GTPase activity; the beta and gamma subunits are tightly associated and function as a beta-gamma heterodimer; extrinsic plasma membrane proteins (cytoplasmic face) that function as a complex to transduce signals from G-protein coupled receptors to an effector protein." [ISBN:0198547684] cellular_component
+ENSG00000128266 GO:0006184 GTP catabolic process "The chemical reactions and pathways resulting in the breakdown of GTP, guanosine triphosphate." [ISBN:0198506732] biological_process
+ENSG00000128266 GO:0007186 G-protein coupled receptor signaling pathway "A series of molecular signals that proceeds with an activated receptor promoting the exchange of GDP for GTP on the alpha-subunit of an associated heterotrimeric G-protein complex. The GTP-bound activated alpha-G-protein then dissociates from the beta- and gamma-subunits to further transmit the signal within the cell. The pathway begins with receptor-ligand interaction, or for basal GPCR signaling the pathway begins with the receptor activating its G protein in the absence of an agonist, and ends with regulation of a downstream cellular process, e.g. transcription." [GOC:bf, GOC:mah, Wikipedia:G_protein-coupled_receptor] biological_process
+ENSG00000128266 GO:0007188 adenylate cyclase-modulating G-protein coupled receptor signaling pathway "The series of molecular signals generated as a consequence of a G-protein coupled receptor binding to its physiological ligand, where the pathway proceeds through activation or inhibition of adenylyl cyclase activity and a subsequent change in the concentration of cyclic AMP (cAMP)." [GOC:mah, GOC:signaling, ISBN:0815316194] biological_process
+ENSG00000128266 GO:0031683 G-protein beta/gamma-subunit complex binding "Interacting selectively and non-covalently with a complex of G-protein beta/gamma subunits." [GOC:nln, GOC:vw] molecular_function
+ENSG00000128266 GO:0031821 G-protein coupled serotonin receptor binding "Interacting selectively and non-covalently with a metabotropic serotonin receptor." [GOC:mah, GOC:nln] molecular_function
+ENSG00000128266 GO:0035556 intracellular signal transduction "The process in which a signal is passed on to downstream components within the cell, which become activated themselves to further propagate the signal and finally trigger a change in the function or state of the cell." [GOC:bf, GOC:jl, GOC:signaling, ISBN:3527303782] biological_process
+ENSG00000128266 GO:0046872 metal ion binding "Interacting selectively and non-covalently with any metal ion." [GOC:ai] molecular_function
+ENSG00000128266 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000128266 GO:0019001 guanyl nucleotide binding "Interacting selectively and non-covalently with guanyl nucleotides, any compound consisting of guanosine esterified with (ortho)phosphate." [ISBN:0198506732] molecular_function
+ENSG00000128266 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000128266 GO:0007193 adenylate cyclase-inhibiting G-protein coupled receptor signaling pathway "The series of molecular signals generated as a consequence of a G-protein coupled receptor binding to its physiological ligand, where the pathway proceeds through inhibition of adenylyl cyclase activity and a subsequent decrease in the concentration of cyclic AMP (cAMP)." [GOC:dph, GOC:mah, GOC:signaling, GOC:tb, ISBN:0815316194] biological_process
+ENSG00000100197 GO:0005506 iron ion binding "Interacting selectively and non-covalently with iron (Fe) ions." [GOC:ai] molecular_function
+ENSG00000100197 GO:0016712 oxidoreductase activity, acting on paired donors, with incorporation or reduction of molecular oxygen, reduced flavin or flavoprotein as one donor, and incorporation of one atom of oxygen "Catalysis of an oxidation-reduction (redox) reaction in which hydrogen or electrons are transferred from reduced flavin or flavoprotein and one other donor, and one atom of oxygen is incorporated into one donor." [GOC:mah] molecular_function
+ENSG00000100197 GO:0020037 heme binding "Interacting selectively and non-covalently with heme, any compound of iron complexed in a porphyrin (tetrapyrrole) ring." [CHEBI:30413, GOC:ai] molecular_function
+ENSG00000100197 GO:0055114 oxidation-reduction process "A metabolic process that results in the removal or addition of one or more electrons to or from a substance, with or without the concomitant removal or addition of a proton or protons." [GOC:dhl, GOC:ecd, GOC:jh2, GOC:jid, GOC:mlg, GOC:rph] biological_process
+ENSG00000100197 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100197 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100197 GO:0016491 oxidoreductase activity "Catalysis of an oxidation-reduction (redox) reaction, a reversible chemical reaction in which the oxidation state of an atom or atoms within a molecule is altered. One substrate acts as a hydrogen or electron donor and becomes oxidized, while the other acts as hydrogen or electron acceptor and becomes reduced." [GOC:go_curators] molecular_function
+ENSG00000100197 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100197 GO:0016705 oxidoreductase activity, acting on paired donors, with incorporation or reduction of molecular oxygen "Catalysis of an oxidation-reduction (redox) reaction in which hydrogen or electrons are transferred from each of two donors, and molecular oxygen is reduced or incorporated into a donor." [GOC:mah] molecular_function
+ENSG00000100197 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000100197 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000100197 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100197 GO:0006629 lipid metabolic process "The chemical reactions and pathways involving lipids, compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent. Includes fatty acids; neutral fats, other fatty-acid esters, and soaps; long-chain (fatty) alcohols and waxes; sphingoids and other long-chain bases; glycolipids, phospholipids and sphingolipids; and carotenes, polyprenols, sterols, terpenes and other isoprenoids." [GOC:ma] biological_process
+ENSG00000100197 GO:0019748 secondary metabolic process "The chemical reactions and pathways resulting in many of the chemical changes of compounds that are not necessarily required for growth and maintenance of cells, and are often unique to a taxon. In multicellular organisms secondary metabolism is generally carried out in specific cell types, and may be useful for the organism as a whole. In unicellular organisms, secondary metabolism is often used for the production of antibiotics or for the utilization and acquisition of unusual nutrients." [GOC:go_curators] biological_process
+ENSG00000100197 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100197 GO:0005783 endoplasmic reticulum "The irregular network of unit membranes, visible only by electron microscopy, that occurs in the cytoplasm of many eukaryotic cells. The membranes form a complex meshwork of tubular channels, which are often expanded into slitlike cavities called cisternae. The ER takes two forms, rough (or granular), with ribosomes adhering to the outer surface, and smooth (with no ribosomes attached)." [ISBN:0198506732] cellular_component
+ENSG00000100197 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100197 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000100197 GO:0004497 monooxygenase activity "Catalysis of the incorporation of one atom from molecular oxygen into a compound and the reduction of the other atom of oxygen to water." [http://www.onelook.com/, ISBN:0198506732] molecular_function
+ENSG00000100197 GO:0005789 endoplasmic reticulum membrane "The lipid bilayer surrounding the endoplasmic reticulum." [GOC:mah] cellular_component
+ENSG00000100197 GO:0006805 xenobiotic metabolic process "The chemical reactions and pathways involving a xenobiotic compound, a compound foreign to living organisms. Used of chemical compounds, e.g. a xenobiotic chemical, such as a pesticide." [GOC:cab2] biological_process
+ENSG00000100197 GO:0008144 drug binding "Interacting selectively and non-covalently with a drug, any naturally occurring or synthetic substance, other than a nutrient, that, when administered or applied to an organism, affects the structure or functioning of the organism; in particular, any such substance used in the diagnosis, prevention, or treatment of disease." [GOC:jl, ISBN:0198506732] molecular_function
+ENSG00000100197 GO:0008202 steroid metabolic process "The chemical reactions and pathways involving steroids, compounds with a 1,2,cyclopentanoperhydrophenanthrene nucleus." [ISBN:0198547684] biological_process
+ENSG00000100197 GO:0009804 coumarin metabolic process "The chemical reactions and pathways involving coumarins, compounds derived from the phenylacrylic skeleton of cinnamic acids." [GOC:lr, GOC:yl] biological_process
+ENSG00000100197 GO:0009820 alkaloid metabolic process "The chemical reactions and pathways involving alkaloids, nitrogen containing natural products which are not otherwise classified as peptides, nonprotein amino acids, amines, cyanogenic glycosides, glucosinolates, cofactors, phytohormones or primary metabolites (such as purine or pyrimidine bases)." [GOC:lr, ISBN:0122146743] biological_process
+ENSG00000100197 GO:0009822 alkaloid catabolic process "The chemical reactions and pathways resulting in the breakdown of alkaloids, nitrogen containing natural products not otherwise classified as peptides, nonprotein amino acids, amines, cyanogenic glycosides, glucosinolates, cofactors, phytohormones or primary metabolites (such as purine or pyrimidine bases)." [GOC:lr, ISBN:01221146743] biological_process
+ENSG00000100197 GO:0016098 monoterpenoid metabolic process "The chemical reactions and pathways involving monoterpenoid compounds, terpenoids having a C10 skeleton." [CHEBI:25409, ISBN:0198547684] biological_process
+ENSG00000100197 GO:0017144 drug metabolic process "The chemical reactions and pathways involving a drug, a substance used in the diagnosis, treatment or prevention of a disease; as used here antibiotic substances (see antibiotic metabolism) are considered to be drugs, even if not used in medical or veterinary practice." [GOC:cab2] biological_process
+ENSG00000100197 GO:0033076 isoquinoline alkaloid metabolic process "The chemical reactions and pathways involving isoquinoline alkaloids, alkaloid compounds that contain bicyclic N-containing aromatic rings and are derived from a 3,4-dihydroxytyramine (dopamine) precursor that undergoes a Schiff base addition with aldehydes of different origin." [GOC:mah, http://www.life.uiuc.edu/ib/425/lecture32.html] biological_process
+ENSG00000100197 GO:0042737 drug catabolic process "The chemical reactions and pathways resulting in the breakdown of a drug, a substance used in the diagnosis, treatment or prevention of a disease." [GOC:go_curators] biological_process
+ENSG00000100197 GO:0046483 heterocycle metabolic process "The chemical reactions and pathways involving heterocyclic compounds, those with a cyclic molecular structure and at least two different atoms in the ring (or rings)." [CHEBI:5686, ISBN:0198506732] biological_process
+ENSG00000100197 GO:0051100 negative regulation of binding "Any process that stops or reduces the rate or extent of binding, the selective interaction of a molecule with one or more specific sites on another molecule." [GOC:ai] biological_process
+ENSG00000100197 GO:0070330 aromatase activity "Catalysis of the reduction of an aliphatic ring to yield an aromatic ring." [GOC:cb] molecular_function
+ENSG00000100197 GO:0070989 oxidative demethylation "The process of removing one or more methyl groups from a molecule, involving the oxidation (i.e. electron loss) of one or more atoms in the substrate." [GOC:BHF, GOC:mah, GOC:rl] biological_process
+ENSG00000100197 GO:0090350 negative regulation of cellular organofluorine metabolic process "Any process that decreases the rate, frequency or extent of the chemical reactions and pathways involving organofluorine compounds, as carried out by individual cells." [GOC:BHF] biological_process
+ENSG00000100218 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100218 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100218 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100218 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100218 GO:0005488 binding "The selective, non-covalent, often stoichiometric, interaction of a molecule with one or more specific sites on another molecule." [GOC:ceb, GOC:mah, ISBN:0198506732] molecular_function
+ENSG00000100218
+ENSG00000100056 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100056 GO:0048856 anatomical structure development "The biological process whose specific outcome is the progression of an anatomical structure from an initial condition to its mature state. This process begins with the formation of the structure and ends with the mature structure, whatever form that may be including its natural destruction. An anatomical structure is any biological entity that occupies space and is distinguished from its surroundings. Anatomical structures can be macroscopic such as a carpel, or microscopic such as an acrosome." [GO_REF:0000021, GOC:mtg_15jun06] biological_process
+ENSG00000100056 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100056 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100056 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100056 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100056 GO:0006397 mRNA processing "Any process involved in the conversion of a primary mRNA transcript into one or more mature mRNA(s) prior to translation into polypeptide." [GOC:mah] biological_process
+ENSG00000100056 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100056 GO:0000398 mRNA splicing, via spliceosome "The joining together of exons from one or more primary transcripts of messenger RNA (mRNA) and the excision of intron sequences, via a spliceosomal mechanism, so that mRNA consisting only of the joined exons is produced." [GOC:krc, ISBN:0198506732, ISBN:0879695897] biological_process
+ENSG00000100056 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100056 GO:0007399 nervous system development "The process whose specific outcome is the progression of nervous tissue over time, from its formation to its mature state." [GOC:dgh] biological_process
+ENSG00000100056 GO:0071013 catalytic step 2 spliceosome "A spliceosomal complex that contains three snRNPs, including U5, bound to a splicing intermediate in which the first catalytic cleavage of the 5' splice site has occurred. The precise subunit composition differs significantly from that of the catalytic step 1, or activated, spliceosome, and includes many proteins in addition to those found in the associated snRNPs." [GOC:ab, GOC:krc, GOC:mah, ISBN:0879695897, ISBN:0879697393, PMID:18322460, PMID:19239890] cellular_component
+ENSG00000100056
+ENSG00000211667 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000211667 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100003 GO:0005215 transporter activity "Enables the directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells." [GOC:ai, GOC:dgf] molecular_function
+ENSG00000100003 GO:0005622 intracellular "The living contents of a cell; the matter contained within (but not including) the plasma membrane, usually taken to exclude large vacuoles and masses of secretory or ingested material. In eukaryotes it includes the nucleus and cytoplasm." [ISBN:0198506732] cellular_component
+ENSG00000100003 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100003 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100003 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100003 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100003 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000100003 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100003 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100003 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100003 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100003 GO:0008289 lipid binding "Interacting selectively and non-covalently with a lipid." [GOC:ai] molecular_function
+ENSG00000100003 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100003 GO:0005543 phospholipid binding "Interacting selectively and non-covalently with phospholipids, a class of lipids containing phosphoric acid as a mono- or diester." [ISBN:0198506732] molecular_function
+ENSG00000100003 GO:0006351 transcription, DNA-templated "The cellular synthesis of RNA on a template of DNA." [GOC:jl, GOC:txnOH] biological_process
+ENSG00000100003 GO:0008431 vitamin E binding "Interacting selectively and non-covalently with vitamin E, tocopherol, which includes a series of eight structurally similar compounds. Alpha-tocopherol is the most active form in humans and is a powerful biological antioxidant." [CHEBI:33234, GOC:curators, ISBN:0721662544] molecular_function
+ENSG00000100003 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000100003 GO:0045540 regulation of cholesterol biosynthetic process "Any process that modulates the frequency, rate or extent of the chemical reactions and pathways resulting in the formation of cholesterol." [GOC:go_curators] biological_process
+ENSG00000100003 GO:0045893 positive regulation of transcription, DNA-templated "Any process that activates or increases the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000100003 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000100003
+ENSG00000100027 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100027 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100027 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100027
+ENSG00000075275 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000075275 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000075275 GO:0048856 anatomical structure development "The biological process whose specific outcome is the progression of an anatomical structure from an initial condition to its mature state. This process begins with the formation of the structure and ends with the mature structure, whatever form that may be including its natural destruction. An anatomical structure is any biological entity that occupies space and is distinguished from its surroundings. Anatomical structures can be macroscopic such as a carpel, or microscopic such as an acrosome." [GO_REF:0000021, GOC:mtg_15jun06] biological_process
+ENSG00000075275 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000075275 GO:0007155 cell adhesion "The attachment of a cell, either to another cell or to an underlying substrate such as the extracellular matrix, via cell adhesion molecules." [GOC:hb, GOC:pf] biological_process
+ENSG00000075275 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000075275 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000075275 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000075275 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000075275 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000075275 GO:0004871 signal transducer activity "Conveys a signal across a cell to trigger a change in cell function or state. A signal is a physical entity or change in state that is used to transfer information in order to trigger a response." [GOC:go_curators] molecular_function
+ENSG00000075275 GO:0048646 anatomical structure formation involved in morphogenesis "The developmental process pertaining to the initial formation of an anatomical structure from unspecified parts. This process begins with the specific processes that contribute to the appearance of the discrete structure and ends when the structural rudiment is recognizable. An anatomical structure is any biological entity that occupies space and is distinguished from its surroundings. Anatomical structures can be macroscopic such as a carpel, or microscopic such as an acrosome." [GOC:dph, GOC:jid, GOC:tb] biological_process
+ENSG00000075275 GO:0001736 establishment of planar polarity "Coordinated organization of groups of cells in the plane of an epithelium, such that they all orient to similar coordinates." [GOC:dph] biological_process
+ENSG00000075275 GO:0001843 neural tube closure "The last step in the formation of the neural tube, where the paired neural folds are brought together and fuse at the dorsal midline." [GOC:dph, ISBN:0878932437] biological_process
+ENSG00000075275 GO:0004888 transmembrane signaling receptor activity "Combining with an extracellular or intracellular signal and transmitting the signal from one side of the membrane to the other to initiate a change in cell activity." [GOC:go_curators, Wikipedia:Transmembrane_receptor] molecular_function
+ENSG00000075275 GO:0004930 G-protein coupled receptor activity "Combining with an extracellular signal and transmitting the signal across the membrane by activating an associated G-protein; promotes the exchange of GDP for GTP on the alpha subunit of a heterotrimeric G-protein complex." [GOC:bf, http://www.iuphar-db.org, Wikipedia:GPCR] molecular_function
+ENSG00000075275 GO:0005509 calcium ion binding "Interacting selectively and non-covalently with calcium ions (Ca2+)." [GOC:ai] molecular_function
+ENSG00000075275 GO:0005887 integral component of plasma membrane "The component of the plasma membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000075275 GO:0007156 homophilic cell adhesion via plasma membrane adhesion molecules "The attachment of a plasma membrane adhesion molecule in one cell to an identical molecule in an adjacent cell." [ISBN:0198506732] biological_process
+ENSG00000075275 GO:0007186 G-protein coupled receptor signaling pathway "A series of molecular signals that proceeds with an activated receptor promoting the exchange of GDP for GTP on the alpha-subunit of an associated heterotrimeric G-protein complex. The GTP-bound activated alpha-G-protein then dissociates from the beta- and gamma-subunits to further transmit the signal within the cell. The pathway begins with receptor-ligand interaction, or for basal GPCR signaling the pathway begins with the receptor activating its G protein in the absence of an agonist, and ends with regulation of a downstream cellular process, e.g. transcription." [GOC:bf, GOC:mah, Wikipedia:G_protein-coupled_receptor] biological_process
+ENSG00000075275 GO:0007218 neuropeptide signaling pathway "The series of molecular signals generated as a consequence of a peptide neurotransmitter binding to a cell surface receptor." [GOC:mah, ISBN:0815316194] biological_process
+ENSG00000075275 GO:0007417 central nervous system development "The process whose specific outcome is the progression of the central nervous system over time, from its formation to the mature structure. The central nervous system is the core nervous system that serves an integrating and coordinating function. In vertebrates it consists of the brain, spinal cord and spinal nerves. In those invertebrates with a central nervous system it typically consists of a brain, cerebral ganglia and a nerve cord." [GOC:bf, GOC:jid, ISBN:0582227089] biological_process
+ENSG00000075275 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000075275 GO:0046983 protein dimerization activity "The formation of a protein dimer, a macromolecular structure consists of two noncovalently associated identical or nonidentical subunits." [ISBN:0198506732] molecular_function
+ENSG00000075275 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000075275 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000075275 GO:0007166 cell surface receptor signaling pathway "A series of molecular signals initiated by activation of a receptor on the surface of a cell. The pathway begins with binding of an extracellular ligand to a cell surface receptor, or for receptors that signal in the absence of a ligand, by ligand-withdrawal or the activity of a constitutively active receptor. The pathway ends with regulation of a downstream cellular process, e.g. transcription." [GOC:bf, GOC:mah, GOC:pr, GOC:signaling] biological_process
+ENSG00000075275 GO:0007626 locomotory behavior "The specific movement from place to place of an organism in response to external or internal stimuli. Locomotion of a whole organism in a manner dependent upon some combination of that organism's internal state and external conditions." [GOC:dph] biological_process
+ENSG00000075275 GO:0042472 inner ear morphogenesis "The process in which the anatomical structures of the inner ear are generated and organized. The inner ear is the structure in vertebrates that contains the organs of balance and hearing. It consists of soft hollow sensory structures (the membranous labyrinth) containing fluid (endolymph) surrounded by fluid (perilymph) and encased in a bony cavity (the bony labyrinth). It consists of two chambers, the sacculus and utriculus, from which arise the cochlea and semicircular canals respectively." [GOC:jl, ISBN:0192801023] biological_process
+ENSG00000075275 GO:0001764 neuron migration "The characteristic movement of an immature neuron from germinal zones to specific positions where they will reside as they mature." [CL:0000540, GOC:go_curators] biological_process
+ENSG00000075275 GO:0009952 anterior/posterior pattern specification "The regionalization process in which specific areas of cell differentiation are determined along the anterior-posterior axis. The anterior-posterior axis is defined by a line that runs from the head or mouth of an organism to the tail or opposite end of the organism." [GOC:dph, GOC:go_curators, GOC:isa_complete, GOC:tb] biological_process
+ENSG00000075275 GO:0007266 Rho protein signal transduction "A series of molecular signals within the cell that are mediated by a member of the Rho family of proteins switching to a GTP-bound active state." [GOC:bf] biological_process
+ENSG00000075275 GO:0042060 wound healing "The series of events that restore integrity to a damaged tissue, following an injury." [GOC:bf, PMID:15269788] biological_process
+ENSG00000075275 GO:0045176 apical protein localization "Any process in which a protein is transported to, or maintained in, apical regions of the cell." [GOC:bf] biological_process
+ENSG00000075275 GO:0001942 hair follicle development "The process whose specific outcome is the progression of the hair follicle over time, from its formation to the mature structure. A hair follicle is a tube-like opening in the epidermis where the hair shaft develops and into which the sebaceous glands open." [GOC:dph, UBERON:0002073] biological_process
+ENSG00000075275 GO:0032956 regulation of actin cytoskeleton organization "Any process that modulates the frequency, rate or extent of the formation, arrangement of constituent parts, or disassembly of cytoskeletal structures comprising actin filaments and their associated proteins." [GOC:mah] biological_process
+ENSG00000075275 GO:0048105 establishment of body hair planar orientation "Orientation of body hairs, projections from the surface of an organism, such that the hairs all point in a uniform direction along the surface." [GOC:ascb_2009, GOC:dph, GOC:jid, GOC:tb] biological_process
+ENSG00000075275 GO:0060488 orthogonal dichotomous subdivision of terminal units involved in lung branching morphogenesis "The process in which a lung bud bifurcates perpendicular to the plane of the previous bud." [GOC:dph, GOC:mtg_lung] biological_process
+ENSG00000075275 GO:0060489 planar dichotomous subdivision of terminal units involved in lung branching morphogenesis "The process in which a lung bud bifurcates parallel to the plane of the previous bud." [GOC:dph, GOC:mtg_lung] biological_process
+ENSG00000075275 GO:0060490 lateral sprouting involved in lung morphogenesis "The process in which a branch forms along the side of the lung epithelial tube." [GOC:dph, GOC:mtg_lung] biological_process
+ENSG00000075275 GO:0090179 planar cell polarity pathway involved in neural tube closure "The series of molecular signals initiated by binding of a Wnt protein to a receptor on the surface of the target cell where activated receptors signal via downstream effectors that modulates the establishment of planar polarity contributing to neural tube closure." [GOC:ascb_2009, GOC:dph, GOC:tb] biological_process
+ENSG00000075275 GO:0042249 establishment of planar polarity of embryonic epithelium "Coordinated organization of groups of cells in the plane of an embryonic epithelium, such that they all orient to similar coordinates." [GOC:ascb_2009, GOC:dph, GOC:jl, GOC:tb] biological_process
+ENSG00000075275 GO:0090251 protein localization involved in establishment of planar polarity "Any process in which a protein is transported to, and/or maintained in, a specific location in a cell that contributes to the establishment of planar polarity." [GOC:ascb_2009, GOC:dph, GOC:tb] biological_process
+ENSG00000100095 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000100095 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100095 GO:0005783 endoplasmic reticulum "The irregular network of unit membranes, visible only by electron microscopy, that occurs in the cytoplasm of many eukaryotic cells. The membranes form a complex meshwork of tubular channels, which are often expanded into slitlike cavities called cisternae. The ER takes two forms, rough (or granular), with ribosomes adhering to the outer surface, and smooth (with no ribosomes attached)." [ISBN:0198506732] cellular_component
+ENSG00000100095 GO:0043025 neuronal cell body "The portion of a neuron that includes the nucleus, but excludes cell projections such as axons and dendrites." [GOC:go_curators] cellular_component
+ENSG00000100095 GO:0008344 adult locomotory behavior "Locomotory behavior in a fully developed and mature organism." [GOC:ai] biological_process
+ENSG00000100095 GO:0090036 regulation of protein kinase C signaling "Any process that modulates the frequency, rate, or extent of a series of reactions, mediated by the intracellular serine/threonine kinase protein kinase C, which occurs as a result of a single trigger reaction or compound." [GOC:dph, GOC:tb] biological_process
+ENSG00000100095 GO:0021680 cerebellar Purkinje cell layer development "The process whose specific outcome is the progression of the cerebellar Purkinje cell layer over time, from its formation to the mature structure. The Purkinje cell layer lies just underneath the molecular layer of the cerebellar cortex. It contains the neuronal cell bodies of the Purkinje cells that are arranged side by side in a single layer. Candelabrum interneurons are vertically oriented between the Purkinje cells. Purkinje neurons are inhibitory and provide the output of the cerebellar cortex through axons that project into the white matter. Extensive dendritic trees from the Purkinje cells extend upward in a single plane into the molecular layer where they synapse with parallel fibers of granule cells." [GO_REF:0000021, GOC:cls, GOC:dgh, GOC:dph, GOC:jid, GOC:mtg_15jun06, ISBN:0838580343] biological_process
+ENSG00000100095 GO:0060074 synapse maturation "The process that organizes a synapse so that it attains its fully functional state. Synaptic maturation plays a critical role in the establishment of effective synaptic connections in early development." [GOC:dph, GOC:ef] biological_process
+ENSG00000100095
+ENSG00000221890 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000221890 GO:0046872 metal ion binding "Interacting selectively and non-covalently with any metal ion." [GOC:ai] molecular_function
+ENSG00000221890 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000221890 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000221890 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000211664 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000211664 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000279973 GO:0005576 extracellular region "The space external to the outermost structure of a cell. For cells without external protective or external encapsulating structures this refers to space outside of the plasma membrane. This term covers the host cell environment outside an intracellular parasite." [GOC:go_curators] cellular_component
+ENSG00000279973 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100167 GO:0005525 GTP binding "Interacting selectively and non-covalently with GTP, guanosine triphosphate." [GOC:ai] molecular_function
+ENSG00000100167 GO:0007049 cell cycle "The progression of biochemical and morphological phases and events that occur in a cell during successive cell replication or nuclear replication events. Canonically, the cell cycle comprises the replication and segregation of genetic material followed by the division of the cell, but in endocycles or syncytial cells nuclear replication or nuclear division may not be followed by cell division." [GOC:go_curators, GOC:mtg_cell_cycle] biological_process
+ENSG00000100167 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100167 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100167 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100167 GO:0003924 GTPase activity "Catalysis of the reaction: GTP + H2O = GDP + phosphate." [ISBN:0198547684] molecular_function
+ENSG00000100167 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100167 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100167 GO:0030054 cell junction "A cellular component that forms a specialized region of connection between two cells or between a cell and the extracellular matrix. At a cell junction, anchoring proteins extend through the plasma membrane to link cytoskeletal proteins in one cell to cytoskeletal proteins in neighboring cells or to proteins in the extracellular matrix." [GOC:mah, http://www.vivo.colostate.edu/hbooks/cmb/cells/pmemb/junctions_a.html, ISBN:0198506732] cellular_component
+ENSG00000100167 GO:0031105 septin complex "A protein complex containing septins. Typically, these complexes contain multiple septins and are oligomeric." [GOC:mah, PMID:15385632] cellular_component
+ENSG00000100167 GO:0045202 synapse "The junction between a nerve fiber of one neuron and another neuron or muscle fiber or glial cell; the site of interneuronal communication. As the nerve fiber approaches the synapse it enlarges into a specialized structure, the presynaptic nerve ending, which contains mitochondria and synaptic vesicles. At the tip of the nerve ending is the presynaptic membrane; facing it, and separated from it by a minute cleft (the synaptic cleft) is a specialized area of membrane on the receiving cell, known as the postsynaptic membrane. In response to the arrival of nerve impulses, the presynaptic nerve ending secretes molecules of neurotransmitters into the synaptic cleft. These diffuse across the cleft and transmit the signal to the postsynaptic membrane." [ISBN:0198506732] cellular_component
+ENSG00000100167 GO:0051301 cell division "The process resulting in division and partitioning of components of a cell to form more cells; may or may not be accompanied by the physical separation of a cell into distinct, individually membrane-bounded daughter cells." [GOC:di, GOC:go_curators, GOC:pr] biological_process
+ENSG00000128203 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000128203 GO:0016491 oxidoreductase activity "Catalysis of an oxidation-reduction (redox) reaction, a reversible chemical reaction in which the oxidation state of an atom or atoms within a molecule is altered. One substrate acts as a hydrogen or electron donor and becomes oxidized, while the other acts as hydrogen or electron acceptor and becomes reduced." [GOC:go_curators] molecular_function
+ENSG00000128203 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000128203 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000128203 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000128203 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000128203 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000128203 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000128203 GO:0018193 peptidyl-amino acid modification "The alteration of an amino acid residue in a peptide." [GOC:mah] biological_process
+ENSG00000128203 GO:0046872 metal ion binding "Interacting selectively and non-covalently with any metal ion." [GOC:ai] molecular_function
+ENSG00000128203 GO:0051213 dioxygenase activity "Catalysis of an oxidation-reduction (redox) reaction in which both atoms of oxygen from one molecule of O2 are incorporated into the (reduced) product(s) of the reaction. The two atoms of oxygen may be distributed between two different products." [DOI:10.1016/S0040-4020(03)00944-X, GOC:bf, http://www.onelook.com/] molecular_function
+ENSG00000128203 GO:0055114 oxidation-reduction process "A metabolic process that results in the removal or addition of one or more electrons to or from a substance, with or without the concomitant removal or addition of a proton or protons." [GOC:dhl, GOC:ecd, GOC:jh2, GOC:jid, GOC:mlg, GOC:rph] biological_process
+ENSG00000248751 GO:0005097 Rab GTPase activator activity "Increases the rate of GTP hydrolysis by a GTPase of the Rab family." [GOC:mah] molecular_function
+ENSG00000248751 GO:0032851 positive regulation of Rab GTPase activity "Any process that activates or increases the activity of a GTPase of the Rab family." [GOC:mah] biological_process
+ENSG00000248751 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000248751 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000248751 GO:0030234 enzyme regulator activity "Binds to and modulates the activity of an enzyme." [GOC:mah] molecular_function
+ENSG00000248751 GO:0032313 regulation of Rab GTPase activity "Any process that modulates the activity of a GTPase of the Rab family." [GOC:mah] biological_process
+ENSG00000133460 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000133460 GO:0022891 substrate-specific transmembrane transporter activity "Enables the transfer of a specific substance or group of related substances from one side of a membrane to the other." [GOC:jid, GOC:mtg_transport, ISBN:0815340729] molecular_function
+ENSG00000133460 GO:0055085 transmembrane transport "The process in which a solute is transported from one side of a membrane to the other." [GOC:dph, GOC:jid] biological_process
+ENSG00000133460 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000133460 GO:0022857 transmembrane transporter activity "Enables the transfer of a substance from one side of a membrane to the other." [GOC:mtg_transport, ISBN:0815340729] molecular_function
+ENSG00000133460 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000133460 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000133460 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000133460 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000133460 GO:0008643 carbohydrate transport "The directed movement of carbohydrate into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore. Carbohydrates are any of a group of organic compounds based of the general formula Cx(H2O)y." [GOC:ai] biological_process
+ENSG00000133460 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000133460
+ENSG00000211647 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000211647 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100060 GO:0008152 metabolic process "The chemical reactions and pathways, including anabolism and catabolism, by which living organisms transform chemical substances. Metabolic processes typically transform small molecules, but also include macromolecular processes such as DNA repair and replication, and protein synthesis and degradation." [GOC:go_curators, ISBN:0198547684] biological_process
+ENSG00000100060 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000100060 GO:0016757 transferase activity, transferring glycosyl groups "Catalysis of the transfer of a glycosyl group from one compound (donor) to another (acceptor)." [GOC:jl, ISBN:0198506732] molecular_function
+ENSG00000100060 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100060 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100060 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100060 GO:0005615 extracellular space "That part of a multicellular organism outside the cells proper, usually taken to be outside the plasma membranes, and occupied by fluid." [ISBN:0198547684] cellular_component
+ENSG00000100060 GO:0007389 pattern specification process "Any developmental process that results in the creation of defined areas or spaces within an organism to which cells respond and eventually are instructed to differentiate." [GOC:go_curators, GOC:isa_complete, ISBN:0521436125] biological_process
+ENSG00000100060 GO:0030173 integral component of Golgi membrane "The component of the Golgi membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:go_curators] cellular_component
+ENSG00000100060 GO:0033829 O-fucosylpeptide 3-beta-N-acetylglucosaminyltransferase activity "Catalysis of the transfer of a beta-D-GlcNAc residue from UDP-D-GlcNAc to the fucose residue of a fucosylated protein acceptor." [EC:2.4.1.222] molecular_function
+ENSG00000100060 GO:0046872 metal ion binding "Interacting selectively and non-covalently with any metal ion." [GOC:ai] molecular_function
+ENSG00000100060 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100060 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100060 GO:0045747 positive regulation of Notch signaling pathway "Any process that activates or increases the frequency, rate or extent of the Notch signaling pathway." [GOC:go_curators] biological_process
+ENSG00000100060 GO:0032092 positive regulation of protein binding "Any process that activates or increases the frequency, rate or extent of protein binding." [GOC:mah] biological_process
+ENSG00000100060
+ENSG00000128313 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000128313 GO:0008289 lipid binding "Interacting selectively and non-covalently with a lipid." [GOC:ai] molecular_function
+ENSG00000128313 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000128313 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000128313 GO:0006629 lipid metabolic process "The chemical reactions and pathways involving lipids, compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent. Includes fatty acids; neutral fats, other fatty-acid esters, and soaps; long-chain (fatty) alcohols and waxes; sphingoids and other long-chain bases; glycolipids, phospholipids and sphingolipids; and carotenes, polyprenols, sterols, terpenes and other isoprenoids." [GOC:ma] biological_process
+ENSG00000128313 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000128313 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000128313 GO:0005576 extracellular region "The space external to the outermost structure of a cell. For cells without external protective or external encapsulating structures this refers to space outside of the plasma membrane. This term covers the host cell environment outside an intracellular parasite." [GOC:go_curators] cellular_component
+ENSG00000128313 GO:0006869 lipid transport "The directed movement of lipids into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore. Lipids are compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent." [ISBN:0198506732] biological_process
+ENSG00000128313 GO:0008035 high-density lipoprotein particle binding "Interacting selectively and non-covalently with high-density lipoprotein particle, a lipoprotein particle with a high density (typically 1.063-1.21 g/ml) and a diameter of 5-10 nm that contains APOAs and may contain APOCs and APOE." [GOC:mah] molecular_function
+ENSG00000128313 GO:0042157 lipoprotein metabolic process "The chemical reactions and pathways involving any conjugated, water-soluble protein in which the nonprotein group consists of a lipid or lipids." [ISBN:0198506732] biological_process
+ENSG00000182257
+ENSG00000211673 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000211673 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000211666 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000211666 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100266 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100266 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100266 GO:0030036 actin cytoskeleton organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of cytoskeletal structures comprising actin filaments and their associated proteins." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100266 GO:0070836 caveola assembly "The aggregation, arrangement and bonding together of a set of components to form a caveola. A caveola is a plasma membrane raft that forms a small pit, depression, or invagination that communicates with the outside of a cell and extends inward, indenting the cytoplasm and the cell membrane." [GOC:BHF, GOC:mah, GOC:vk, PMID:12633858] biological_process
+ENSG00000100266 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100266 GO:0022607 cellular component assembly "The aggregation, arrangement and bonding together of a cellular component." [GOC:isa_complete] biological_process
+ENSG00000100266 GO:0061024 membrane organization "A process which results in the assembly, arrangement of constituent parts, or disassembly of a membrane. A membrane is a double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:dph, GOC:tb] biological_process
+ENSG00000100266 GO:0007010 cytoskeleton organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of cytoskeletal structures." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100266 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100266 GO:0016192 vesicle-mediated transport "A cellular transport process in which transported substances are moved in membrane-bounded vesicles; transported substances are enclosed in the vesicle lumen or located in the vesicle membrane. The process begins with a step that directs a substance to the forming vesicle, and includes vesicle budding and coating. Vesicles are then targeted to, and fuse with, an acceptor membrane." [GOC:ai, GOC:mah, ISBN:08789310662000] biological_process
+ENSG00000100266 GO:0008289 lipid binding "Interacting selectively and non-covalently with a lipid." [GOC:ai] molecular_function
+ENSG00000100266 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100266 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100266 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100266 GO:0005856 cytoskeleton "Any of the various filamentous elements that form the internal framework of cells, and typically remain after treatment of the cells with mild detergent to remove membrane constituents and soluble components of the cytoplasm. The term embraces intermediate filaments, microfilaments, microtubules, the microtrabecular lattice, and other structures characterized by a polymeric filamentous nature and long-range order within the cell. The various elements of the cytoskeleton not only serve in the maintenance of cellular shape but also have roles in other cellular functions, including cellular movement, cell division, endocytosis, and movement of organelles." [GOC:mah, ISBN:0198547684, PMID:16959967] cellular_component
+ENSG00000100266 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100266 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100266 GO:0005215 transporter activity "Enables the directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells." [GOC:ai, GOC:dgf] molecular_function
+ENSG00000100266 GO:0005901 caveola "A membrane raft that forms small pit, depression, or invagination that communicates with the outside of a cell and extends inward, indenting the cytoplasm and the cell membrane. Examples include any of the minute pits or incuppings of the cell membrane formed during pinocytosis. Such caveolae may be pinched off to form free vesicles within the cytoplasm." [GOC:mah, ISBN:0721662544, PMID:16645198] cellular_component
+ENSG00000100266 GO:0005925 focal adhesion "Small region on the surface of a cell that anchors the cell to the extracellular matrix and that forms a point of termination of actin filaments." [ISBN:0124325653, ISBN:0815316208] cellular_component
+ENSG00000100266 GO:0019898 extrinsic component of membrane "The component of a membrane consisting of gene products and protein complexes that are loosely bound to one of its surfaces, but not integrated into the hydrophobic region." [GOC:dos, GOC:jl, GOC:mah] cellular_component
+ENSG00000100266 GO:0031410 cytoplasmic vesicle "A vesicle formed of membrane or protein, found in the cytoplasm of a cell." [GOC:mah] cellular_component
+ENSG00000100266 GO:0036010 protein localization to endosome "A process in which a protein is transported to, or maintained in, a location within an endosome." [GOC:yaf] biological_process
+ENSG00000100266 GO:0042802 identical protein binding "Interacting selectively and non-covalently with an identical protein or proteins." [GOC:jl] molecular_function
+ENSG00000100266 GO:0042995 cell projection "A prolongation or process extending from a cell, e.g. a flagellum or axon." [GOC:jl, http://www.cogsci.princeton.edu/~wn/] cellular_component
+ENSG00000100266 GO:0043231 intracellular membrane-bounded organelle "Organized structure of distinctive morphology and function, bounded by a single or double lipid bilayer membrane and occurring within the cell. Includes the nucleus, mitochondria, plastids, vacuoles, and vesicles. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100266 GO:0048858 cell projection morphogenesis "The process in which the anatomical structures of a cell projection are generated and organized." [GO_REF:0000021, GOC:mtg_15jun06] biological_process
+ENSG00000100266 GO:0055038 recycling endosome membrane "The lipid bilayer surrounding a recycling endosome." [GOC:jid, GOC:rph, PMID:10930469, PMID:15601896, PMID:16246101] cellular_component
+ENSG00000100266 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000100266 GO:0070300 phosphatidic acid binding "Interacting selectively and non-covalently with phosphatidic acid, any of a class of glycerol phosphate in which both the remaining hydroxyl groups of the glycerol moiety are esterified with fatty acids." [CHEBI:16337, GOC:jp, ISBN:0198506732] molecular_function
+ENSG00000100266 GO:0072584 caveolin-mediated endocytosis "An endocytosis process that begins when material is taken up into plasma membrane caveolae, which then pinch off to form endocytic caveolar carriers." [GOC:BHF, GOC:mah, PMID:17318224, PMID:18498251, PMID:8970738, PMID:9234965] biological_process
+ENSG00000100266 GO:0097320 membrane tubulation "A modification in a plasma membrane resulting in formation of a tubular invagination." [GOC:BHF, PMID:15252009, PMID:20730103] biological_process
+ENSG00000100266 GO:0007009 plasma membrane organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of the plasma membrane." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100266 GO:0008092 cytoskeletal protein binding "Interacting selectively and non-covalently with any protein component of any cytoskeleton (actin, microtubule, or intermediate filament cytoskeleton)." [GOC:mah] molecular_function
+ENSG00000100266 GO:0005911 cell-cell junction "A cell junction that forms a connection between two cells; excludes direct cytoplasmic junctions such as ring canals." [GOC:dgh, GOC:hb, GOC:mah] cellular_component
+ENSG00000100266 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000100266 GO:0045806 negative regulation of endocytosis "Any process that stops, prevents, or reduces the frequency, rate or extent of endocytosis." [GOC:go_curators] biological_process
+ENSG00000100266
+ENSG00000211674
+ENSG00000100422 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100422 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000100422 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100422 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000100422 GO:0006629 lipid metabolic process "The chemical reactions and pathways involving lipids, compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent. Includes fatty acids; neutral fats, other fatty-acid esters, and soaps; long-chain (fatty) alcohols and waxes; sphingoids and other long-chain bases; glycolipids, phospholipids and sphingolipids; and carotenes, polyprenols, sterols, terpenes and other isoprenoids." [GOC:ma] biological_process
+ENSG00000100422 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000100422 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100422 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100422 GO:0016301 kinase activity "Catalysis of the transfer of a phosphate group, usually from ATP, to a substrate molecule." [ISBN:0198506732] molecular_function
+ENSG00000100422 GO:0000287 magnesium ion binding "Interacting selectively and non-covalently with magnesium (Mg) ions." [GOC:ai] molecular_function
+ENSG00000100422 GO:0001729 ceramide kinase activity "Catalysis of the reaction: ATP + ceramide = ADP + ceramide-1-phosphate." [EC:2.7.1.138] molecular_function
+ENSG00000100422 GO:0003951 NAD+ kinase activity "Catalysis of the reaction: ATP + NAD(+) = ADP + 2 H(+) + NADP(+)." [EC:2.7.1.23, RHEA:18632] molecular_function
+ENSG00000100422 GO:0004143 diacylglycerol kinase activity "Catalysis of the reaction: NTP + 1,2-diacylglycerol = NDP + 1,2-diacylglycerol-3-phosphate." [EC:2.7.1.107, GOC:elh] molecular_function
+ENSG00000100422 GO:0005524 ATP binding "Interacting selectively and non-covalently with ATP, adenosine 5'-triphosphate, a universally important coenzyme and enzyme regulator." [ISBN:0198506732] molecular_function
+ENSG00000100422 GO:0006665 sphingolipid metabolic process "The chemical reactions and pathways involving sphingolipids, any of a class of lipids containing the long-chain amine diol sphingosine or a closely related base (a sphingoid)." [GOC:mah, ISBN:0198506732] biological_process
+ENSG00000100422 GO:0006672 ceramide metabolic process "The chemical reactions and pathways involving ceramides, any N-acylated sphingoid." [ISBN:0198547684] biological_process
+ENSG00000100422 GO:0006687 glycosphingolipid metabolic process "The chemical reactions and pathways involving glycosphingolipids, any compound with residues of sphingoid and at least one monosaccharide." [ISBN:0198547684] biological_process
+ENSG00000100422 GO:0007205 protein kinase C-activating G-protein coupled receptor signaling pathway "The series of molecular signals generated as a consequence of a G-protein coupled receptor binding to its physiological ligand, where the pathway proceeds with activation of protein kinase C (PKC). PKC is activated by second messengers including diacylglycerol (DAG)." [GOC:mah, GOC:signaling] biological_process
+ENSG00000100422 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000100422 GO:0046834 lipid phosphorylation "The process of introducing one or more phosphate groups into a lipid, any member of a group of substances soluble in lipid solvents but only sparingly soluble in aqueous solvents." [GOC:bf, ISBN:0198506732] biological_process
+ENSG00000100422 GO:0008152 metabolic process "The chemical reactions and pathways, including anabolism and catabolism, by which living organisms transform chemical substances. Metabolic processes typically transform small molecules, but also include macromolecular processes such as DNA repair and replication, and protein synthesis and degradation." [GOC:go_curators, ISBN:0198547684] biological_process
+ENSG00000100422 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000100422 GO:0016310 phosphorylation "The process of introducing a phosphate group into a molecule, usually with the formation of a phosphoric ester, a phosphoric anhydride or a phosphoric amide." [ISBN:0198506732] biological_process
+ENSG00000128242 GO:0005794 Golgi apparatus "A compound membranous cytoplasmic organelle of eukaryotic cells, consisting of flattened, ribosome-free vesicles arranged in a more or less regular stack. The Golgi apparatus differs from the endoplasmic reticulum in often having slightly thicker membranes, appearing in sections as a characteristic shallow semicircle so that the convex side (cis or entry face) abuts the endoplasmic reticulum, secretory vesicles emerging from the concave side (trans or exit face). In vertebrate cells there is usually one such organelle, while in invertebrates and plants, where they are known usually as dictyosomes, there may be several scattered in the cytoplasm. The Golgi apparatus processes proteins produced on the ribosomes of the rough endoplasmic reticulum; such processing includes modification of the core oligosaccharides of glycoproteins, and the sorting and packaging of proteins for transport to a variety of cellular locations. Three different regions of the Golgi are now recognized both in terms of structure and function: cis, in the vicinity of the cis face, trans, in the vicinity of the trans face, and medial, lying between the cis and trans regions." [ISBN:0198506732] cellular_component
+ENSG00000128242 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000128242 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000128242 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000128242 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000128242 GO:0006629 lipid metabolic process "The chemical reactions and pathways involving lipids, compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent. Includes fatty acids; neutral fats, other fatty-acid esters, and soaps; long-chain (fatty) alcohols and waxes; sphingoids and other long-chain bases; glycolipids, phospholipids and sphingolipids; and carotenes, polyprenols, sterols, terpenes and other isoprenoids." [GOC:ma] biological_process
+ENSG00000128242 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000128242 GO:0005975 carbohydrate metabolic process "The chemical reactions and pathways involving carbohydrates, any of a group of organic compounds based of the general formula Cx(H2O)y. Includes the formation of carbohydrate derivatives by the addition of a carbohydrate residue to another molecule." [GOC:mah, ISBN:0198506732] biological_process
+ENSG00000128242 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000128242 GO:0000139 Golgi membrane "The lipid bilayer surrounding any of the compartments of the Golgi apparatus." [GOC:mah] cellular_component
+ENSG00000128242 GO:0001733 galactosylceramide sulfotransferase activity "Catalysis of the reaction: 3'-phosphoadenosine 5'-phosphosulfate + a galactosylceramide = adenosine 3',5'-bisphosphate + a galactosylceramidesulfate." [EC:2.8.2.11, PMID:10727929] molecular_function
+ENSG00000128242 GO:0005887 integral component of plasma membrane "The component of the plasma membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000128242 GO:0006487 protein N-linked glycosylation "A protein glycosylation process in which a carbohydrate or carbohydrate derivative unit is added to a protein via the N4 atom of peptidyl-asparagine, the omega-N of arginine, or the N1' atom peptidyl-tryptophan." [GOC:pr, RESID:AA0151, RESID:AA0156, RESID:AA0327] biological_process
+ENSG00000128242 GO:0006665 sphingolipid metabolic process "The chemical reactions and pathways involving sphingolipids, any of a class of lipids containing the long-chain amine diol sphingosine or a closely related base (a sphingoid)." [GOC:mah, ISBN:0198506732] biological_process
+ENSG00000128242 GO:0006687 glycosphingolipid metabolic process "The chemical reactions and pathways involving glycosphingolipids, any compound with residues of sphingoid and at least one monosaccharide." [ISBN:0198547684] biological_process
+ENSG00000128242 GO:0008146 sulfotransferase activity "Catalysis of the transfer of a sulfate group from 3'-phosphoadenosine 5'-phosphosulfate to the hydroxyl group of an acceptor, producing the sulfated derivative and 3'-phosphoadenosine 5'-phosphate." [EC:2.8.2, GOC:curators] molecular_function
+ENSG00000128242 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000128242 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000128242 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000128242 GO:0007283 spermatogenesis "The process of formation of spermatozoa, including spermatocytogenesis and spermiogenesis." [GOC:jid, ISBN:9780878933846] biological_process
+ENSG00000128242 GO:0042552 myelination "The process in which myelin sheaths are formed and maintained around neurons. Oligodendrocytes in the brain and spinal cord and Schwann cells in the peripheral nervous system wrap axons with compact layers of their plasma membrane. Adjacent myelin segments are separated by a non-myelinated stretch of axon called a node of Ranvier." [GOC:dgh, GOC:mah] biological_process
+ENSG00000128242 GO:0006682 galactosylceramide biosynthetic process "The chemical reactions and pathways resulting in the formation of galactosylceramides, any compound formed by the replacement of the glycosidic hydroxyl group of a cyclic form of galactose by a ceramide group." [GOC:ai] biological_process
+ENSG00000211653 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000211653 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000184058 GO:0001525 angiogenesis "Blood vessel formation when new vessels emerge from the proliferation of pre-existing blood vessels." [ISBN:0878932453] biological_process
+ENSG00000184058 GO:0001568 blood vessel development "The process whose specific outcome is the progression of a blood vessel over time, from its formation to the mature structure. The blood vessel is the vasculature carrying blood." [GOC:hjd, UBERON:0001981] biological_process
+ENSG00000184058 GO:0001708 cell fate specification "The process involved in the specification of cell identity. Once specification has taken place, a cell will be committed to differentiate down a specific pathway if left in its normal environment." [GOC:go_curators] biological_process
+ENSG00000184058 GO:0001755 neural crest cell migration "The characteristic movement of cells from the dorsal ridge of the neural tube to a variety of locations in a vertebrate embryo." [GOC:ascb_2009, GOC:dph, GOC:tb, ISBN:0878932437] biological_process
+ENSG00000184058 GO:0001934 positive regulation of protein phosphorylation "Any process that activates or increases the frequency, rate or extent of addition of phosphate groups to amino acids within a protein." [GOC:hjd] biological_process
+ENSG00000184058 GO:0001945 lymph vessel development "The process whose specific outcome is the progression of a lymph vessel over time, from its formation to the mature structure." [GOC:dph, UBERON:0001473] biological_process
+ENSG00000184058 GO:0002053 positive regulation of mesenchymal cell proliferation "The process of activating or increasing the rate or extent of mesenchymal cell proliferation. Mesenchymal cells are loosely organized embryonic cells." [GOC:dph] biological_process
+ENSG00000184058 GO:0003007 heart morphogenesis "The developmental process in which the heart is generated and organized. The heart is a hollow, muscular organ, which, by contracting rhythmically, keeps up the circulation of the blood." [GOC:dph, GOC:isa_complete] biological_process
+ENSG00000184058 GO:0003148 outflow tract septum morphogenesis "The process in which the anatomical structures of the outflow tract septum are generated and organized. The outflow tract septum is a partition in the outflow tract." [GOC:mtg_heart] biological_process
+ENSG00000184058 GO:0003151 outflow tract morphogenesis "The process in which the anatomical structures of the outflow tract are generated and organized. The outflow tract is the portion of the heart through which blood flows into the arteries." [GOC:mtg_heart, UBERON:0004145] biological_process
+ENSG00000184058 GO:0003677 DNA binding "Any molecular function by which a gene product interacts selectively and non-covalently with DNA (deoxyribonucleic acid)." [GOC:dph, GOC:jl, GOC:tb, GOC:vw] molecular_function
+ENSG00000184058 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000184058 GO:0006351 transcription, DNA-templated "The cellular synthesis of RNA on a template of DNA." [GOC:jl, GOC:txnOH] biological_process
+ENSG00000184058 GO:0006357 regulation of transcription from RNA polymerase II promoter "Any process that modulates the frequency, rate or extent of transcription from an RNA polymerase II promoter." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000184058 GO:0007368 determination of left/right symmetry "The establishment of an organism's body plan or part of an organism with respect to the left and right halves. The pattern can either be symmetric, such that the halves are mirror images, or asymmetric where the pattern deviates from this symmetry." [GOC:dph, GOC:jid] biological_process
+ENSG00000184058 GO:0007389 pattern specification process "Any developmental process that results in the creation of defined areas or spaces within an organism to which cells respond and eventually are instructed to differentiate." [GOC:go_curators, GOC:isa_complete, ISBN:0521436125] biological_process
+ENSG00000184058 GO:0007498 mesoderm development "The process whose specific outcome is the progression of the mesoderm over time, from its formation to the mature structure. The mesoderm is the middle germ layer that develops into muscle, bone, cartilage, blood and connective tissue." [GOC:dph, GOC:tb] biological_process
+ENSG00000184058 GO:0007507 heart development "The process whose specific outcome is the progression of the heart over time, from its formation to the mature structure. The heart is a hollow, muscular organ, which, by contracting rhythmically, keeps up the circulation of the blood." [GOC:jid, UBERON:0000948] biological_process
+ENSG00000184058 GO:0007517 muscle organ development "The process whose specific outcome is the progression of the muscle over time, from its formation to the mature structure. The muscle is an organ consisting of a tissue made up of various elongated cells that are specialized to contract and thus to produce movement and mechanical work." [GOC:jid, ISBN:0198506732] biological_process
+ENSG00000184058 GO:0007605 sensory perception of sound "The series of events required for an organism to receive an auditory stimulus, convert it to a molecular signal, and recognize and characterize the signal. Sonic stimuli are detected in the form of vibrations and are processed to form a sound." [GOC:ai] biological_process
+ENSG00000184058 GO:0008283 cell proliferation "The multiplication or reproduction of cells, resulting in the expansion of a cell population." [GOC:mah, GOC:mb] biological_process
+ENSG00000184058 GO:0008284 positive regulation of cell proliferation "Any process that activates or increases the rate or extent of cell proliferation." [GOC:go_curators] biological_process
+ENSG00000184058 GO:0009952 anterior/posterior pattern specification "The regionalization process in which specific areas of cell differentiation are determined along the anterior-posterior axis. The anterior-posterior axis is defined by a line that runs from the head or mouth of an organism to the tail or opposite end of the organism." [GOC:dph, GOC:go_curators, GOC:isa_complete, GOC:tb] biological_process
+ENSG00000184058 GO:0021644 vagus nerve morphogenesis "The process in which the anatomical structure of the vagus nerve is generated and organized. This nerve is primarily sensory but also has visceromotor components. It originates in the brain stem and controls many autonomic functions of the heart, lungs, stomach, pharynx, larynx, trachea, esophagus and other gastrointestinal tract components. It controls some motor functions such as speech. The sensory branches mediate sensation from the pharynx, larynx, thorax and abdomen; it also innervates taste buds in the epiglottis." [GO_REF:0000021, GOC:cls, GOC:dgh, GOC:dph, GOC:jid, GOC:mtg_15jun06, ISBN:0838580343] biological_process
+ENSG00000184058 GO:0030855 epithelial cell differentiation "The process in which a relatively unspecialized cell acquires specialized features of an epithelial cell, any of the cells making up an epithelium." [GOC:ecd, PMID:11839751] biological_process
+ENSG00000184058 GO:0030878 thyroid gland development "The process whose specific outcome is the progression of the thyroid gland over time, from its formation to the mature structure. The thyroid gland is an endoderm-derived gland that produces thyroid hormone." [GOC:dgh] biological_process
+ENSG00000184058 GO:0035176 social behavior "Behavior directed towards society, or taking place between members of the same species. Occurs predominantly, or only, in individuals that are part of a group." [GOC:jh2, PMID:12848939, Wikipedia:Social_behavior] biological_process
+ENSG00000184058 GO:0035909 aorta morphogenesis "The process in which the anatomical structures of an aorta are generated and organized. An aorta is an artery that carries blood from the heart to other parts of the body." [GOC:bf, GOC:dgh, MA:0000062, UBERON:0000947, Wikipedia:Aorta] biological_process
+ENSG00000184058 GO:0042471 ear morphogenesis "The process in which the anatomical structures of the ear are generated and organized. The ear is the sense organ in vertebrates that is specialized for the detection of sound, and the maintenance of balance. Includes the outer ear and middle ear, which collect and transmit sound waves; and the inner ear, which contains the organs of balance and (except in fish) hearing. Also includes the pinna, the visible part of the outer ear, present in some mammals." [GOC:jl, ISBN:0192801023] biological_process
+ENSG00000184058 GO:0042472 inner ear morphogenesis "The process in which the anatomical structures of the inner ear are generated and organized. The inner ear is the structure in vertebrates that contains the organs of balance and hearing. It consists of soft hollow sensory structures (the membranous labyrinth) containing fluid (endolymph) surrounded by fluid (perilymph) and encased in a bony cavity (the bony labyrinth). It consists of two chambers, the sacculus and utriculus, from which arise the cochlea and semicircular canals respectively." [GOC:jl, ISBN:0192801023] biological_process
+ENSG00000184058 GO:0042473 outer ear morphogenesis "The process in which the anatomical structures of the outer ear are generated and organized. The outer ear is the part of the ear external to the tympanum (eardrum). It consists of a tube (the external auditory meatus) that directs sound waves on to the tympanum, and may also include the external pinna, which extends beyond the skull." [GOC:jl, ISBN:0192801023] biological_process
+ENSG00000184058 GO:0042474 middle ear morphogenesis "The process in which the anatomical structures of the middle ear are generated and organized. The middle ear is the air-filled cavity within the skull of vertebrates that lies between the outer ear and the inner ear. It is linked to the pharynx (and therefore to outside air) via the Eustachian tube and in mammals contains the three ear ossicles, which transmit auditory vibrations from the outer ear (via the tympanum) to the inner ear (via the oval window)." [GOC:jl, ISBN:0192801023] biological_process
+ENSG00000184058 GO:0042475 odontogenesis of dentin-containing tooth "The process whose specific outcome is the progression of a dentin-containing tooth over time, from its formation to the mature structure. A dentin-containing tooth is a hard, bony organ borne on the jaw or other bone of a vertebrate, and is composed mainly of dentin, a dense calcified substance, covered by a layer of enamel." [GOC:cjm, GOC:mah, GOC:mtg_sensu, PMID:10333884, PMID:15355794] biological_process
+ENSG00000184058 GO:0042693 muscle cell fate commitment "The process in which the cellular identity of muscle cells is acquired and determined." [CL:0000187, GOC:go_curators] biological_process
+ENSG00000184058 GO:0042803 protein homodimerization activity "Interacting selectively and non-covalently with an identical protein to form a homodimer." [GOC:jl] molecular_function
+ENSG00000184058 GO:0043410 positive regulation of MAPK cascade "Any process that activates or increases the frequency, rate or extent of signal transduction mediated by the MAPK cascade." [GOC:go_curators] biological_process
+ENSG00000184058 GO:0043565 sequence-specific DNA binding "Interacting selectively and non-covalently with DNA of a specific nucleotide composition, e.g. GC-rich DNA binding, or with a specific sequence motif or type of DNA e.g. promotor binding or rDNA binding." [GOC:jl] molecular_function
+ENSG00000184058 GO:0043587 tongue morphogenesis "The process in which the anatomical structures of the tongue are generated and organized. The tongue is the movable, muscular organ on the floor of the mouth of most vertebrates, in man other mammals is the principal organ of taste, aids in the prehension of food, in swallowing, and in modifying the voice as in speech." [GOC:jl, UBERON:0001723] biological_process
+ENSG00000184058 GO:0044344 cellular response to fibroblast growth factor stimulus "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of an fibroblast growth factor stimulus." [GOC:jl, GOC:yaf] biological_process
+ENSG00000184058 GO:0045596 negative regulation of cell differentiation "Any process that stops, prevents, or reduces the frequency, rate or extent of cell differentiation." [GOC:go_curators] biological_process
+ENSG00000184058 GO:0045893 positive regulation of transcription, DNA-templated "Any process that activates or increases the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000184058 GO:0045944 positive regulation of transcription from RNA polymerase II promoter "Any process that activates or increases the frequency, rate or extent of transcription from an RNA polymerase II promoter." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000184058 GO:0046983 protein dimerization activity "The formation of a protein dimer, a macromolecular structure consists of two noncovalently associated identical or nonidentical subunits." [ISBN:0198506732] molecular_function
+ENSG00000184058 GO:0048384 retinoic acid receptor signaling pathway "The series of molecular signals generated as a consequence of a retinoic acid receptor binding to one of its physiological ligands." [GOC:dgh] biological_process
+ENSG00000184058 GO:0048514 blood vessel morphogenesis "The process in which the anatomical structures of blood vessels are generated and organized. The blood vessel is the vasculature carrying blood." [GOC:jid] biological_process
+ENSG00000184058 GO:0048538 thymus development "The process whose specific outcome is the progression of the thymus over time, from its formation to the mature structure. The thymus is a symmetric bi-lobed organ involved primarily in the differentiation of immature to mature T cells, with unique vascular, nervous, epithelial, and lymphoid cell components." [GOC:add, ISBN:0781735149] biological_process
+ENSG00000184058 GO:0048644 muscle organ morphogenesis "The process in which the anatomical structures of muscle are generated and organized." [GOC:jid] biological_process
+ENSG00000184058 GO:0048701 embryonic cranial skeleton morphogenesis "The process in which the anatomical structures of the cranial skeleton are generated and organized during the embryonic phase." [GOC:dsf, GOC:jid, PMID:16049113] biological_process
+ENSG00000184058 GO:0048703 embryonic viscerocranium morphogenesis "The process in which the anatomical structures of the viscerocranium are generated and organized during the embryonic phase. The viscerocranium is the part of the skull comprising the facial bones." [GOC:dsf, GOC:jid, PMID:16049113] biological_process
+ENSG00000184058 GO:0048752 semicircular canal morphogenesis "The process in which the anatomical structures of the semicircular canals are generated and organized." [GOC:dgh, GOC:dph, GOC:jid] biological_process
+ENSG00000184058 GO:0048844 artery morphogenesis "The process in which the anatomical structures of arterial blood vessels are generated and organized. Arteries are blood vessels that transport blood from the heart to the body and its organs." [GOC:dsf, PMID:16740480] biological_process
+ENSG00000184058 GO:0050679 positive regulation of epithelial cell proliferation "Any process that activates or increases the rate or extent of epithelial cell proliferation." [GOC:ai] biological_process
+ENSG00000184058 GO:0060017 parathyroid gland development "The process whose specific outcome is the progression of the parathyroid gland over time, from its formation to the mature structure. The parathyroid gland is an organ specialised for secretion of parathyroid hormone." [GOC:dph, ISBN:0721662544] biological_process
+ENSG00000184058 GO:0060023 soft palate development "The biological process whose specific outcome is the progression of the soft palate from an initial condition to its mature state. This process begins with the formation of the structure and ends with the mature structure, whatever form that may be including its natural destruction. The soft palate is the posterior portion of the palate extending from the posterior edge of the hard palate." [GOC:dph, ISBN:0721662544] biological_process
+ENSG00000184058 GO:0060037 pharyngeal system development "The process whose specific outcome is the progression of the pharyngeal system over time, from its formation to the mature structure. The pharyngeal system is a transient embryonic complex that is specific to vertebrates. It comprises the pharyngeal arches, bulges of tissues of mesoderm and neural crest derivation through which pass nerves and pharyngeal arch arteries. The arches are separated internally by pharyngeal pouches, evaginations of foregut endoderm, and externally by pharyngeal clefts, invaginations of surface ectoderm. The development of the system ends when the stucture it contributes to are forming: the thymus, thyroid, parathyroids, maxilla, mandible, aortic arch, cardiac outflow tract, external and middle ear." [GOC:dph] biological_process
+ENSG00000184058 GO:0060325 face morphogenesis "The process in which the anatomical structures of the face are generated and organized. The face is the ventral division of the head." [GOC:dph] biological_process
+ENSG00000184058 GO:0060415 muscle tissue morphogenesis "The process in which the anatomical structures of muscle tissue are generated and organized. Muscle tissue consists of a set of cells that are part of an organ and carry out a contractive function." [GOC:dph] biological_process
+ENSG00000184058 GO:0060982 coronary artery morphogenesis "The process in which the anatomical structures of coronary arteries are generated and organized. Coronary arteries are blood vessels that transport blood to the heart muscle." [GOC:mtg_heart] biological_process
+ENSG00000184058 GO:0070166 enamel mineralization "The process in which calcium salts, mainly carbonated hydroxyapatite, are deposited in tooth enamel." [GOC:BHF, GOC:mah, GOC:sl, PMID:10206335, PMID:16931858, PMID:21196346] biological_process
+ENSG00000184058 GO:0071300 cellular response to retinoic acid "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a retinoic acid stimulus." [GOC:mah] biological_process
+ENSG00000184058 GO:0090103 cochlea morphogenesis "The process in which the cochlea is generated and organized." [GOC:dph, GOC:tb] biological_process
+ENSG00000184058 GO:0097152 mesenchymal cell apoptotic process "Any apoptotic process in a mesenchymal cell. A mesenchymal cell is a loosely associated cell that is part of the connective tissue in an organism. Mesenchymal cells give rise to more mature connective tissue cell types." [CL:0000134, GOC:mtg_apoptosis, GOC:yaf, PMID:18231833] biological_process
+ENSG00000184058 GO:2000027 regulation of organ morphogenesis "Any process that modulates the frequency, rate or extent of organ morphogenesis." [GOC:obol] biological_process
+ENSG00000184058 GO:2001037 positive regulation of tongue muscle cell differentiation "Any process that activates or increases the frequency, rate or extent of tongue muscle cell differentiation." [GOC:obol] biological_process
+ENSG00000184058 GO:2001054 negative regulation of mesenchymal cell apoptotic process "Any process that stops, prevents or reduces the frequency, rate or extent of mesenchymal cell apoptotic process." [GOC:mtg_apoptosis, GOC:obol] biological_process
+ENSG00000184058 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000184058 GO:0008219 cell death "Any biological process that results in permanent cessation of all vital functions of a cell. A cell should be considered dead when any one of the following molecular or morphological criteria is met: (1) the cell has lost the integrity of its plasma membrane; (2) the cell, including its nucleus, has undergone complete fragmentation into discrete bodies (frequently referred to as \"apoptotic bodies\"); and/or (3) its corpse (or its fragments) have been engulfed by an adjacent cell in vivo." [GOC:mah, GOC:mtg_apoptosis] biological_process
+ENSG00000184058 GO:0048856 anatomical structure development "The biological process whose specific outcome is the progression of an anatomical structure from an initial condition to its mature state. This process begins with the formation of the structure and ends with the mature structure, whatever form that may be including its natural destruction. An anatomical structure is any biological entity that occupies space and is distinguished from its surroundings. Anatomical structures can be macroscopic such as a carpel, or microscopic such as an acrosome." [GO_REF:0000021, GOC:mtg_15jun06] biological_process
+ENSG00000184058 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000184058 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000184058 GO:0030154 cell differentiation "The process in which relatively unspecialized cells, e.g. embryonic or regenerative cells, acquire specialized structural and/or functional features that characterize the cells, tissues, or organs of the mature organism or some other relatively stable phase of the organism's life history. Differentiation includes the processes involved in commitment of a cell to a specific fate and its subsequent development to the mature state." [ISBN:0198506732] biological_process
+ENSG00000184058 GO:0050877 neurological system process "A organ system process carried out by any of the organs or tissues of neurological system." [GOC:ai, GOC:mtg_cardio] biological_process
+ENSG00000184058 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000184058 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000184058 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000184058 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000184058 GO:0040011 locomotion "Self-propelled movement of a cell or organism from one location to another." [GOC:dgh] biological_process
+ENSG00000184058 GO:0048870 cell motility "Any process involved in the controlled self-propelled movement of a cell that results in translocation of the cell from one place to another." [GOC:dgh, GOC:dph, GOC:isa_complete, GOC:mlg] biological_process
+ENSG00000184058 GO:0048646 anatomical structure formation involved in morphogenesis "The developmental process pertaining to the initial formation of an anatomical structure from unspecified parts. This process begins with the specific processes that contribute to the appearance of the discrete structure and ends when the structural rudiment is recognizable. An anatomical structure is any biological entity that occupies space and is distinguished from its surroundings. Anatomical structures can be macroscopic such as a carpel, or microscopic such as an acrosome." [GOC:dph, GOC:jid, GOC:tb] biological_process
+ENSG00000184058 GO:0003700 sequence-specific DNA binding transcription factor activity "Interacting selectively and non-covalently with a specific DNA sequence in order to modulate transcription. The transcription factor may or may not also interact selectively with a protein or macromolecular complex." [GOC:curators, GOC:txnOH] molecular_function
+ENSG00000184058 GO:0001071 nucleic acid binding transcription factor activity "Interacting selectively and non-covalently with a DNA or RNA sequence in order to modulate transcription. The transcription factor may or may not also interact selectively with a protein or macromolecular complex." [GOC:txnOH] molecular_function
+ENSG00000184058 GO:0006355 regulation of transcription, DNA-templated "Any process that modulates the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000184058 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000184058 GO:0001974 blood vessel remodeling "The reorganization or renovation of existing blood vessels." [GOC:hjd] biological_process
+ENSG00000184058 GO:0071600 otic vesicle morphogenesis "The process in which the anatomical structures of the otic vesicle are generated and organized. The otic vesicle is a transient embryonic structure formed during development of the vertebrate inner ear." [GOC:mah] biological_process
+ENSG00000184058 GO:0072513 positive regulation of secondary heart field cardioblast proliferation "Any process that activates or increases the frequency, rate or extent of cardioblast proliferation in the second heart field. A cardioblast is a cardiac precursor cell. It is a cell that has been committed to a cardiac fate, but will undergo more cell division rather than terminally differentiating. The secondary heart field is the region of the heart that will form the majority of the mesodermal component of the right ventricle, the arterial pole (outflow tract) and the venous pole (inflow tract)." [GOC:mah, GOC:rl] biological_process
+ENSG00000025770 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000025770 GO:0005694 chromosome "A structure composed of a very long molecule of DNA and associated proteins (e.g. histones) that carries hereditary information." [ISBN:0198547684] cellular_component
+ENSG00000025770 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000025770 GO:0005654 nucleoplasm "That part of the nuclear content other than the chromosomes or the nucleolus." [GOC:ma, ISBN:0124325653] cellular_component
+ENSG00000025770 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000025770 GO:0007049 cell cycle "The progression of biochemical and morphological phases and events that occur in a cell during successive cell replication or nuclear replication events. Canonically, the cell cycle comprises the replication and segregation of genetic material followed by the division of the cell, but in endocycles or syncytial cells nuclear replication or nuclear division may not be followed by cell division." [GOC:go_curators, GOC:mtg_cell_cycle] biological_process
+ENSG00000025770 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000025770 GO:0000278 mitotic cell cycle "Progression through the phases of the mitotic cell cycle, the most common eukaryotic cell cycle, which canonically comprises four successive phases called G1, S, G2, and M and includes replication of the genome and the subsequent segregation of chromosomes into daughter cells. In some variant cell cycles nuclear replication or nuclear division may not be followed by cell division, or G1 and G2 phases may be absent." [GOC:mah, ISBN:0815316194, Reactome:69278] biological_process
+ENSG00000025770 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000025770 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000025770 GO:0030261 chromosome condensation "The progressive compaction of dispersed interphase chromatin into threadlike chromosomes prior to mitotic or meiotic nuclear division, or during apoptosis, in eukaryotic cells." [GOC:mah, ISBN:0815316194] biological_process
+ENSG00000025770 GO:0006259 DNA metabolic process "Any cellular metabolic process involving deoxyribonucleic acid. This is one of the two main types of nucleic acid, consisting of a long, unbranched macromolecule formed from one, or more commonly, two, strands of linked deoxyribonucleotides." [ISBN:0198506732] biological_process
+ENSG00000025770 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000025770 GO:0051276 chromosome organization "A process that is carried out at the cellular level that results in the assembly, arrangement of constituent parts, or disassembly of chromosomes, structures composed of a very long molecule of DNA and associated proteins that carries hereditary information. This term covers covalent modifications at the molecular level as well as spatial relationships among the major components of a chromosome." [GOC:ai, GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000025770
+ENSG00000278196 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000278196 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000211648 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000211648 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000159958 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000159958 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000159958 GO:0009897 external side of plasma membrane "The leaflet the plasma membrane that faces away from the cytoplasm and any proteins embedded or anchored in it or attached to its surface." [GOC:dos, GOC:tb] cellular_component
+ENSG00000159958 GO:0030890 positive regulation of B cell proliferation "Any process that activates or increases the rate or extent of B cell proliferation." [GOC:mah] biological_process
+ENSG00000159958 GO:0050776 regulation of immune response "Any process that modulates the frequency, rate or extent of the immune response, the immunological reaction of an organism to an immunogenic stimulus." [GOC:ai] biological_process
+ENSG00000159958 GO:0001782 B cell homeostasis "The process of regulating the proliferation and elimination of B cells such that the total number of B cells within a whole or part of an organism is stable over time in the absence of an outside stimulus." [GOC:add, ISBN:0781735149, PMID:12956429] biological_process
+ENSG00000159958 GO:0031295 T cell costimulation "The process of providing, via surface-bound receptor-ligand pairs, a second, antigen-independent, signal in addition to that provided by the T cell receptor to augment T cell activation." [ISBN:0781735149] biological_process
+ENSG00000159958 GO:0042102 positive regulation of T cell proliferation "Any process that activates or increases the rate or extent of T cell proliferation." [GOC:ai] biological_process
+ENSG00000159958 GO:0045078 positive regulation of interferon-gamma biosynthetic process "Any process that activates or increases the frequency, rate or extent of the chemical reactions and pathways resulting in the formation of interferon-gamma." [GOC:go_curators] biological_process
+ENSG00000159958 GO:0031296 B cell costimulation "The process of providing, via surface-bound receptor-ligand pairs, a second, antigen-independent, signal in addition to that provided by the B cell receptor to augment B cell activation." [ISBN:0781735149] biological_process
+ENSG00000159958 GO:0002636 positive regulation of germinal center formation "Any process that activates or increases the frequency, rate, or extent of germinal center formation." [GOC:add] biological_process
+ENSG00000243156 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000243156 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000243156 GO:0016491 oxidoreductase activity "Catalysis of an oxidation-reduction (redox) reaction, a reversible chemical reaction in which the oxidation state of an atom or atoms within a molecule is altered. One substrate acts as a hydrogen or electron donor and becomes oxidized, while the other acts as hydrogen or electron acceptor and becomes reduced." [GOC:go_curators] molecular_function
+ENSG00000243156 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000243156 GO:0007010 cytoskeleton organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of cytoskeletal structures." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000243156 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000243156 GO:0016192 vesicle-mediated transport "A cellular transport process in which transported substances are moved in membrane-bounded vesicles; transported substances are enclosed in the vesicle lumen or located in the vesicle membrane. The process begins with a step that directs a substance to the forming vesicle, and includes vesicle budding and coating. Vesicles are then targeted to, and fuse with, an acceptor membrane." [GOC:ai, GOC:mah, ISBN:08789310662000] biological_process
+ENSG00000243156 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000243156 GO:0005856 cytoskeleton "Any of the various filamentous elements that form the internal framework of cells, and typically remain after treatment of the cells with mild detergent to remove membrane constituents and soluble components of the cytoplasm. The term embraces intermediate filaments, microfilaments, microtubules, the microtrabecular lattice, and other structures characterized by a polymeric filamentous nature and long-range order within the cell. The various elements of the cytoskeleton not only serve in the maintenance of cellular shape but also have roles in other cellular functions, including cellular movement, cell division, endocytosis, and movement of organelles." [GOC:mah, ISBN:0198547684, PMID:16959967] cellular_component
+ENSG00000243156 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000243156 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000243156 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000243156 GO:0008092 cytoskeletal protein binding "Interacting selectively and non-covalently with any protein component of any cytoskeleton (actin, microtubule, or intermediate filament cytoskeleton)." [GOC:mah] molecular_function
+ENSG00000243156 GO:0003779 actin binding "Interacting selectively and non-covalently with monomeric or multimeric forms of actin, including actin filaments." [GOC:clt] molecular_function
+ENSG00000243156 GO:0006887 exocytosis "A process of secretion by a cell that results in the release of intracellular molecules (e.g. hormones, matrix proteins) contained within a membrane-bounded vesicle by fusion of the vesicle with the plasma membrane of a cell. This is the process in which most molecules are secreted from eukaryotic cells." [GOC:mah, ISBN:0716731363] biological_process
+ENSG00000243156 GO:0008270 zinc ion binding "Interacting selectively and non-covalently with zinc (Zn) ions." [GOC:ai] molecular_function
+ENSG00000243156 GO:0016709 oxidoreductase activity, acting on paired donors, with incorporation or reduction of molecular oxygen, NAD(P)H as one donor, and incorporation of one atom of oxygen "Catalysis of an oxidation-reduction (redox) reaction in which hydrogen or electrons are transferred from NADH or NADPH and one other donor, and one atom of oxygen is incorporated into one donor." [GOC:mah] molecular_function
+ENSG00000243156 GO:0030042 actin filament depolymerization "Disassembly of actin filaments by the removal of actin monomers from a filament." [GOC:mah] biological_process
+ENSG00000243156 GO:0055114 oxidation-reduction process "A metabolic process that results in the removal or addition of one or more electrons to or from a substance, with or without the concomitant removal or addition of a proton or protons." [GOC:dhl, GOC:ecd, GOC:jh2, GOC:jid, GOC:mlg, GOC:rph] biological_process
+ENSG00000243156 GO:0071949 FAD binding "Interacting selectively and non-covalently with the oxidized form, FAD, of flavin-adenine dinucleotide, the coenzyme or the prosthetic group of various flavoprotein oxidoreductase enzymes." [GOC:mah] molecular_function
+ENSG00000243156 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000243156
+ENSG00000239282
+ENSG00000279216
+ENSG00000128218 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000128218 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000128218 GO:0005783 endoplasmic reticulum "The irregular network of unit membranes, visible only by electron microscopy, that occurs in the cytoplasm of many eukaryotic cells. The membranes form a complex meshwork of tubular channels, which are often expanded into slitlike cavities called cisternae. The ER takes two forms, rough (or granular), with ribosomes adhering to the outer surface, and smooth (with no ribosomes attached)." [ISBN:0198506732] cellular_component
+ENSG00000128218 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000128218 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000211675 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000211675 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000211669 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000211669 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000198832 GO:0005783 endoplasmic reticulum "The irregular network of unit membranes, visible only by electron microscopy, that occurs in the cytoplasm of many eukaryotic cells. The membranes form a complex meshwork of tubular channels, which are often expanded into slitlike cavities called cisternae. The ER takes two forms, rough (or granular), with ribosomes adhering to the outer surface, and smooth (with no ribosomes attached)." [ISBN:0198506732] cellular_component
+ENSG00000198832 GO:0005794 Golgi apparatus "A compound membranous cytoplasmic organelle of eukaryotic cells, consisting of flattened, ribosome-free vesicles arranged in a more or less regular stack. The Golgi apparatus differs from the endoplasmic reticulum in often having slightly thicker membranes, appearing in sections as a characteristic shallow semicircle so that the convex side (cis or entry face) abuts the endoplasmic reticulum, secretory vesicles emerging from the concave side (trans or exit face). In vertebrate cells there is usually one such organelle, while in invertebrates and plants, where they are known usually as dictyosomes, there may be several scattered in the cytoplasm. The Golgi apparatus processes proteins produced on the ribosomes of the rough endoplasmic reticulum; such processing includes modification of the core oligosaccharides of glycoproteins, and the sorting and packaging of proteins for transport to a variety of cellular locations. Three different regions of the Golgi are now recognized both in terms of structure and function: cis, in the vicinity of the cis face, trans, in the vicinity of the trans face, and medial, lying between the cis and trans regions." [ISBN:0198506732] cellular_component
+ENSG00000198832 GO:0035264 multicellular organism growth "The increase in size or mass of an entire multicellular organism, as opposed to cell growth." [GOC:bf, GOC:curators, GOC:dph, GOC:tb] biological_process
+ENSG00000198832 GO:0060612 adipose tissue development "The process whose specific outcome is the progression of adipose tissue over time, from its formation to the mature structure. Adipose tissue is specialized tissue that is used to store fat." [GOC:dph] biological_process
+ENSG00000198832 GO:0042445 hormone metabolic process "The chemical reactions and pathways involving any hormone, naturally occurring substances secreted by specialized cells that affects the metabolism or behavior of other cells possessing functional receptors for the hormone." [CHEBI:24621, GOC:jl] biological_process
+ENSG00000198832 GO:0010269 response to selenium ion "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a stimulus from selenium ion." [GOC:mg] biological_process
+ENSG00000198832 GO:0035934 corticosterone secretion "The regulated release of corticosterone into the circulatory system. Corticosterone is a 21-carbon steroid hormone of the corticosteroid type produced in the cortex of the adrenal glands." [CHEBI:16827, GOC:sl] biological_process
+ENSG00000198832
+ENSG00000100368 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100368 GO:0004871 signal transducer activity "Conveys a signal across a cell to trigger a change in cell function or state. A signal is a physical entity or change in state that is used to transfer information in order to trigger a response." [GOC:go_curators] molecular_function
+ENSG00000100368 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000100368 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100368 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100368 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100368 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000100368 GO:0004872 receptor activity "Combining with an extracellular or intracellular messenger to initiate a change in cell activity." [GOC:ceb, ISBN:0198506732] molecular_function
+ENSG00000100368 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100368 GO:0005887 integral component of plasma membrane "The component of the plasma membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000100368 GO:0007585 respiratory gaseous exchange "The process of gaseous exchange between an organism and its environment. In plants, microorganisms, and many small animals, air or water makes direct contact with the organism's cells or tissue fluids, and the processes of diffusion supply the organism with dioxygen (O2) and remove carbon dioxide (CO2). In larger animals the efficiency of gaseous exchange is improved by specialized respiratory organs, such as lungs and gills, which are ventilated by breathing mechanisms." [ISBN:0198506732] biological_process
+ENSG00000100368 GO:0030526 granulocyte macrophage colony-stimulating factor receptor complex "The heterodimeric receptor for granulocyte macrophage colony-stimulating factor." [GOC:mah] cellular_component
+ENSG00000100368 GO:0036016 cellular response to interleukin-3 "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of an interleukin-3 stimulus." [GOC:yaf, PR:000001387] biological_process
+ENSG00000100368 GO:0038043 interleukin-5-mediated signaling pathway "A series of molecular signals initiated by the binding of interleukin-5 to a receptor on the surface of a cell, and ending with regulation of a downstream cellular process, e.g. transcription." [GOC:pg, GOC:signaling, PR:000001392] biological_process
+ENSG00000100368 GO:0038156 interleukin-3-mediated signaling pathway "A series of molecular signals initiated by the binding of interleukin-3 to a receptor on the surface of a cell, and ending with regulation of a downstream cellular process, e.g. transcription." [GOC:nhn, GOC:signaling] biological_process
+ENSG00000100368 GO:0004912 interleukin-3 receptor activity "Combining with interleukin-3 and transmitting the signal from one side of the membrane to the other to initiate a change in cell activity." [GOC:jl, GOC:signaling] molecular_function
+ENSG00000100368 GO:0004914 interleukin-5 receptor activity "Combining with interleukin-5 and transmitting the signal from one side of the membrane to the other to initiate a change in cell activity." [GOC:jl, GOC:signaling] molecular_function
+ENSG00000100368 GO:0004896 cytokine receptor activity "Combining with a cytokine and transmitting the signal from one side of the membrane to the other to initiate a change in cell activity." [GOC:add, GOC:mah] molecular_function
+ENSG00000100368 GO:0019221 cytokine-mediated signaling pathway "A series of molecular signals initiated by the binding of a cytokine to a receptor on the surface of a cell, and ending with regulation of a downstream cellular process, e.g. transcription." [GOC:mah, GOC:signaling, PMID:19295629] biological_process
+ENSG00000100368 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000100368 GO:0032496 response to lipopolysaccharide "Any process that results in a change in state or activity of an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a lipopolysaccharide stimulus; lipopolysaccharide is a major component of the cell wall of gram-negative bacteria." [GOC:add, ISBN:0721601464] biological_process
+ENSG00000128284 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000128284 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000128284 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000128284 GO:0008289 lipid binding "Interacting selectively and non-covalently with a lipid." [GOC:ai] molecular_function
+ENSG00000128284 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000128284 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000128284 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000128284 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000128284 GO:0005576 extracellular region "The space external to the outermost structure of a cell. For cells without external protective or external encapsulating structures this refers to space outside of the plasma membrane. This term covers the host cell environment outside an intracellular parasite." [GOC:go_curators] cellular_component
+ENSG00000128284 GO:0004871 signal transducer activity "Conveys a signal across a cell to trigger a change in cell function or state. A signal is a physical entity or change in state that is used to transfer information in order to trigger a response." [GOC:go_curators] molecular_function
+ENSG00000128284 GO:0005319 lipid transporter activity "Enables the directed movement of lipids into, out of or within a cell, or between cells." [GOC:ai] molecular_function
+ENSG00000128284 GO:0006869 lipid transport "The directed movement of lipids into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore. Lipids are compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent." [ISBN:0198506732] biological_process
+ENSG00000128284 GO:0006954 inflammatory response "The immediate defensive reaction (by vertebrate tissue) to infection or injury caused by chemical or physical agents. The process is characterized by local vasodilation, extravasation of plasma into intercellular spaces and accumulation of white blood cells and macrophages." [GO_REF:0000022, GOC:mtg_15nov05, ISBN:0198506732] biological_process
+ENSG00000128284 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000128284 GO:0042157 lipoprotein metabolic process "The chemical reactions and pathways involving any conjugated, water-soluble protein in which the nonprotein group consists of a lipid or lipids." [ISBN:0198506732] biological_process
+ENSG00000128284 GO:0043123 positive regulation of I-kappaB kinase/NF-kappaB signaling "Any process that activates or increases the frequency, rate or extent of I-kappaB kinase/NF-kappaB signaling." [GOC:jl] biological_process
+ENSG00000128284
+ENSG00000128159 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000128159 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000128159 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000128159 GO:0008092 cytoskeletal protein binding "Interacting selectively and non-covalently with any protein component of any cytoskeleton (actin, microtubule, or intermediate filament cytoskeleton)." [GOC:mah] molecular_function
+ENSG00000128159 GO:0007010 cytoskeleton organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of cytoskeletal structures." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000128159 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000128159 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000128159 GO:0005815 microtubule organizing center "An intracellular structure that can catalyze gamma-tubulin-dependent microtubule nucleation and that can anchor microtubules by interacting with their minus ends, plus ends or sides." [GOC:vw, http://en.wikipedia.org/wiki/Microtubule_organizing_center, ISBN:0815316194, PMID:17072892, PMID:17245416] cellular_component
+ENSG00000128159 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000128159 GO:0007049 cell cycle "The progression of biochemical and morphological phases and events that occur in a cell during successive cell replication or nuclear replication events. Canonically, the cell cycle comprises the replication and segregation of genetic material followed by the division of the cell, but in endocycles or syncytial cells nuclear replication or nuclear division may not be followed by cell division." [GOC:go_curators, GOC:mtg_cell_cycle] biological_process
+ENSG00000128159 GO:0000086 G2/M transition of mitotic cell cycle "The mitotic cell cycle transition by which a cell in G2 commits to M phase. The process begins when the kinase activity of M cyclin/CDK complex reaches a threshold high enough for the cell cycle to proceed. This is accomplished by activating a positive feedback loop that results in the accumulation of unphosphorylated and active M cyclin/CDK complex." [GOC:mtg_cell_cycle] biological_process
+ENSG00000128159 GO:0000278 mitotic cell cycle "Progression through the phases of the mitotic cell cycle, the most common eukaryotic cell cycle, which canonically comprises four successive phases called G1, S, G2, and M and includes replication of the genome and the subsequent segregation of chromosomes into daughter cells. In some variant cell cycles nuclear replication or nuclear division may not be followed by cell division, or G1 and G2 phases may be absent." [GOC:mah, ISBN:0815316194, Reactome:69278] biological_process
+ENSG00000128159 GO:0000922 spindle pole "Either of the ends of a spindle, where spindle microtubules are organized; usually contains a microtubule organizing center and accessory molecules, spindle microtubules and astral microtubules." [GOC:clt] cellular_component
+ENSG00000128159 GO:0005813 centrosome "A structure comprised of a core structure (in most organisms, a pair of centrioles) and peripheral material from which a microtubule-based structure, such as a spindle apparatus, is organized. Centrosomes occur close to the nucleus during interphase in many eukaryotic cells, though in animal cells it changes continually during the cell-division cycle." [GOC:mah, ISBN:0198547684] cellular_component
+ENSG00000128159 GO:0005874 microtubule "Any of the long, generally straight, hollow tubes of internal diameter 12-15 nm and external diameter 24 nm found in a wide variety of eukaryotic cells; each consists (usually) of 13 protofilaments of polymeric tubulin, staggered in such a manner that the tubulin monomers are arranged in a helical pattern on the microtubular surface, and with the alpha/beta axes of the tubulin subunits parallel to the long axis of the tubule; exist in equilibrium with pool of tubulin monomers and can be rapidly assembled or disassembled in response to physiological stimuli; concerned with force generation, e.g. in the spindle." [ISBN:0879693568] cellular_component
+ENSG00000128159 GO:0007020 microtubule nucleation "The process in which tubulin alpha-beta heterodimers begin aggregation to form an oligomeric tubulin structure (a microtubule seed). Microtubule nucleation is the initiating step in the formation of a microtubule in the absence of any existing microtubules ('de novo' microtubule formation)." [GOC:go_curators, ISBN:0815316194, PMID:12517712] biological_process
+ENSG00000128159 GO:0008017 microtubule binding "Interacting selectively and non-covalently with microtubules, filaments composed of tubulin monomers." [GOC:krc] molecular_function
+ENSG00000128159 GO:0008274 gamma-tubulin ring complex "A multiprotein complex composed of gamma-tubulin and other non-tubulin proteins that forms a flexible open ring structure thought to be the unit of nucleation at the minus end of a microtubule." [GOC:clt, PMID:12134075] cellular_component
+ENSG00000128159 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000128159 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000128159 GO:0000226 microtubule cytoskeleton organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of cytoskeletal structures comprising microtubules and their associated proteins." [GOC:mah] biological_process
+ENSG00000254413 GO:0016772 transferase activity, transferring phosphorus-containing groups "Catalysis of the transfer of a phosphorus-containing group from one compound (donor) to another (acceptor)." [GOC:jl, ISBN:0198506732] molecular_function
+ENSG00000254413 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000211670 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000211670 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000206203 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000206203 GO:0048856 anatomical structure development "The biological process whose specific outcome is the progression of an anatomical structure from an initial condition to its mature state. This process begins with the formation of the structure and ends with the mature structure, whatever form that may be including its natural destruction. An anatomical structure is any biological entity that occupies space and is distinguished from its surroundings. Anatomical structures can be macroscopic such as a carpel, or microscopic such as an acrosome." [GO_REF:0000021, GOC:mtg_15jun06] biological_process
+ENSG00000206203 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000206203 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000206203 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000206203 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000206203 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000206203 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000206203 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000206203 GO:0016301 kinase activity "Catalysis of the transfer of a phosphate group, usually from ATP, to a substrate molecule." [ISBN:0198506732] molecular_function
+ENSG00000206203 GO:0000287 magnesium ion binding "Interacting selectively and non-covalently with magnesium (Mg) ions." [GOC:ai] molecular_function
+ENSG00000206203 GO:0004674 protein serine/threonine kinase activity "Catalysis of the reactions: ATP + protein serine = ADP + protein serine phosphate, and ATP + protein threonine = ADP + protein threonine phosphate." [GOC:bf] molecular_function
+ENSG00000206203 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000206203 GO:0005524 ATP binding "Interacting selectively and non-covalently with ATP, adenosine 5'-triphosphate, a universally important coenzyme and enzyme regulator." [ISBN:0198506732] molecular_function
+ENSG00000206203 GO:0006468 protein phosphorylation "The process of introducing a phosphate group on to a protein." [GOC:hb] biological_process
+ENSG00000206203 GO:0007275 multicellular organismal development "The biological process whose specific outcome is the progression of a multicellular organism over time from an initial condition (e.g. a zygote or a young adult) to a later condition (e.g. a multicellular animal or an aged adult)." [GOC:dph, GOC:ems, GOC:isa_complete, GOC:tb] biological_process
+ENSG00000206203 GO:0007286 spermatid development "The process whose specific outcome is the progression of a spermatid over time, from its formation to the mature structure." [GOC:dph, GOC:go_curators] biological_process
+ENSG00000206203 GO:0046777 protein autophosphorylation "The phosphorylation by a protein of one or more of its own amino acid residues (cis-autophosphorylation), or residues on an identical protein (trans-autophosphorylation)." [ISBN:0198506732] biological_process
+ENSG00000206203 GO:0004672 protein kinase activity "Catalysis of the phosphorylation of an amino acid residue in a protein, usually according to the reaction: a protein + ATP = a phosphoprotein + ADP." [MetaCyc:PROTEIN-KINASE-RXN] molecular_function
+ENSG00000206203 GO:0016772 transferase activity, transferring phosphorus-containing groups "Catalysis of the transfer of a phosphorus-containing group from one compound (donor) to another (acceptor)." [GOC:jl, ISBN:0198506732] molecular_function
+ENSG00000206203 GO:0004713 protein tyrosine kinase activity "Catalysis of the reaction: ATP + a protein tyrosine = ADP + protein tyrosine phosphate." [EC:2.7.10] molecular_function
+ENSG00000206203 GO:0001669 acrosomal vesicle "A structure in the head of a spermatozoon that contains acid hydrolases, and is concerned with the breakdown of the outer membrane of the ovum during fertilization. It lies just beneath the plasma membrane and is derived from the lysosome." [ISBN:0124325653, ISBN:0198506732] cellular_component
+ENSG00000206203 GO:0005814 centriole "A cellular organelle, found close to the nucleus in many eukaryotic cells, consisting of a small cylinder with microtubular walls, 300-500 nm long and 150-250 nm in diameter. It contains nine short, parallel, peripheral microtubular fibrils, each fibril consisting of one complete microtubule fused to two incomplete microtubules. Cells usually have two centrioles, lying at right angles to each other. At division, each pair of centrioles generates another pair and the twin pairs form the pole of the mitotic spindle." [ISBN:0198547684] cellular_component
+ENSG00000100373 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100373 GO:0030154 cell differentiation "The process in which relatively unspecialized cells, e.g. embryonic or regenerative cells, acquire specialized structural and/or functional features that characterize the cells, tissues, or organs of the mature organism or some other relatively stable phase of the organism's life history. Differentiation includes the processes involved in commitment of a cell to a specific fate and its subsequent development to the mature state." [ISBN:0198506732] biological_process
+ENSG00000100373 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100373 GO:0005783 endoplasmic reticulum "The irregular network of unit membranes, visible only by electron microscopy, that occurs in the cytoplasm of many eukaryotic cells. The membranes form a complex meshwork of tubular channels, which are often expanded into slitlike cavities called cisternae. The ER takes two forms, rough (or granular), with ribosomes adhering to the outer surface, and smooth (with no ribosomes attached)." [ISBN:0198506732] cellular_component
+ENSG00000100373 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100373 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000100373 GO:0030855 epithelial cell differentiation "The process in which a relatively unspecialized cell acquires specialized features of an epithelial cell, any of the cells making up an epithelium." [GOC:ecd, PMID:11839751] biological_process
+ENSG00000100373 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000100373 GO:0000902 cell morphogenesis "The developmental process in which the size or shape of a cell is generated and organized." [GOC:clt, GOC:dph, GOC:go_curators, GOC:tb] biological_process
+ENSG00000100373 GO:0016324 apical plasma membrane "The region of the plasma membrane located at the apical end of the cell." [GOC:curators] cellular_component
+ENSG00000100373 GO:0001822 kidney development "The process whose specific outcome is the progression of the kidney over time, from its formation to the mature structure. The kidney is an organ that filters the blood and/or excretes the end products of body metabolism in the form of urine." [GOC:dph, GOC:mtg_kidney_jan10, ISBN:0124020607, ISBN:0721662544] biological_process
+ENSG00000100373 GO:0055078 sodium ion homeostasis "Any process involved in the maintenance of an internal steady state of sodium ions within an organism or cell." [GOC:ai, GOC:jid, GOC:mah] biological_process
+ENSG00000100373 GO:0006833 water transport "The directed movement of water (H2O) into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:ai] biological_process
+ENSG00000100373 GO:0060157 urinary bladder development "The process whose specific outcome is the progression of the urinary bladder over time, from its formation to the mature structure. The urinary bladder is an elastic, muscular sac situated in the anterior part of the pelvic cavity in which urine collects before excretion." [GOC:dph, GOC:ln, GOC:mr, http://en.wikipedia.org/wiki/Rhea_(bird), PMID:11768524, PMID:18276178, PMID:538956] biological_process
+ENSG00000100373 GO:0055075 potassium ion homeostasis "Any process involved in the maintenance of an internal steady state of potassium ions within an organism or cell." [GOC:jid, GOC:mah] biological_process
+ENSG00000100373 GO:0015840 urea transport "The directed movement of urea into, out of or within the cell. Urea is the water-soluble compound H2N-CO-NH2." [GOC:ai, ISBN:0198506732] biological_process
+ENSG00000254709 GO:0003823 antigen binding "Interacting selectively and non-covalently with an antigen, any substance which is capable of inducing a specific immune response and of reacting with the products of that response, the specific antibody or specifically sensitized T-lymphocytes, or both. Binding may counteract the biological activity of the antigen." [GOC:jl, ISBN:0198506732, ISBN:0721662544] molecular_function
+ENSG00000254709 GO:0005576 extracellular region "The space external to the outermost structure of a cell. For cells without external protective or external encapsulating structures this refers to space outside of the plasma membrane. This term covers the host cell environment outside an intracellular parasite." [GOC:go_curators] cellular_component
+ENSG00000254709 GO:0005615 extracellular space "That part of a multicellular organism outside the cells proper, usually taken to be outside the plasma membranes, and occupied by fluid." [ISBN:0198547684] cellular_component
+ENSG00000254709 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000254709 GO:0006955 immune response "Any immune system process that functions in the calibrated response of an organism to a potential internal or invasive threat." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05] biological_process
+ENSG00000254709 GO:0006956 complement activation "Any process involved in the activation of any of the steps of the complement cascade, which allows for the direct killing of microbes, the disposal of immune complexes, and the regulation of other immune processes; the initial steps of complement activation involve one of three pathways, the classical pathway, the alternative pathway, and the lectin pathway, all of which lead to the terminal complement pathway." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05, ISBN:0781735149] biological_process
+ENSG00000254709 GO:0006958 complement activation, classical pathway "Any process involved in the activation of any of the steps of the classical pathway of the complement cascade which allows for the direct killing of microbes, the disposal of immune complexes, and the regulation of other immune processes." [GOC:add, ISBN:0781735149] biological_process
+ENSG00000254709 GO:0038095 Fc-epsilon receptor signaling pathway "A series of molecular signals initiated by the binding of the Fc portion of immunoglobulin E (IgE) to an Fc-epsilon receptor on the surface of a signal-receiving cell, and ending with regulation of a downstream cellular process, e.g. transcription. The Fc portion of an immunoglobulin is its C-terminal constant region." [GOC:phg, PMID:12413516, PMID:15048725] biological_process
+ENSG00000254709 GO:0038096 Fc-gamma receptor signaling pathway involved in phagocytosis "An Fc-gamma receptor signaling pathway that contributes to the endocytic engulfment of external particulate material by phagocytes." [GOC:phg, PMID:12488490, PMID:15466916] biological_process
+ENSG00000254709 GO:0045087 innate immune response "Innate immune responses are defense responses mediated by germline encoded components that directly recognize components of potential pathogens." [GO_REF:0000022, GOC:add, GOC:ebc, GOC:mtg_15nov05, GOC:mtg_sensu] biological_process
+ENSG00000254709 GO:0050776 regulation of immune response "Any process that modulates the frequency, rate or extent of the immune response, the immunological reaction of an organism to an immunogenic stimulus." [GOC:ai] biological_process
+ENSG00000254709 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000254709 GO:0072562 blood microparticle "A phospholipid microvesicle that is derived from any of several cell types, such as platelets, blood cells, endothelial cells, or others, and contains membrane receptors as well as other proteins characteristic of the parental cell. Microparticles are heterogeneous in size, and are characterized as microvesicles free of nucleic acids." [GOC:BHF, GOC:mah, PMID:16373184] cellular_component
+ENSG00000254709 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000254709 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000254709 GO:0002376 immune system process "Any process involved in the development or functioning of the immune system, an organismal system for calibrated responses to potential internal or invasive threats." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05] biological_process
+ENSG00000254709 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000254709 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000254709 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000254709 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000254709 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000138892 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000138892 GO:0016874 ligase activity "Catalysis of the joining of two substances, or two groups within a single molecule, with the concomitant hydrolysis of the diphosphate bond in ATP or a similar triphosphate." [EC:6, GOC:mah] molecular_function
+ENSG00000138892 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000138892 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000138892 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000138892 GO:0005856 cytoskeleton "Any of the various filamentous elements that form the internal framework of cells, and typically remain after treatment of the cells with mild detergent to remove membrane constituents and soluble components of the cytoplasm. The term embraces intermediate filaments, microfilaments, microtubules, the microtrabecular lattice, and other structures characterized by a polymeric filamentous nature and long-range order within the cell. The various elements of the cytoskeleton not only serve in the maintenance of cellular shape but also have roles in other cellular functions, including cellular movement, cell division, endocytosis, and movement of organelles." [GOC:mah, ISBN:0198547684, PMID:16959967] cellular_component
+ENSG00000138892 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000138892 GO:0005929 cilium "A specialized eukaryotic organelle that consists of a filiform extrusion of the cell surface and of some cytoplasmic parts. Each cilium is largely bounded by an extrusion of the cytoplasmic (plasma) membrane, and contains a regular longitudinal array of microtubules, anchored to a basal body." [GOC:cilia, GOC:kmv, ISBN:0198547684] cellular_component
+ENSG00000138892 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000138892 GO:0005874 microtubule "Any of the long, generally straight, hollow tubes of internal diameter 12-15 nm and external diameter 24 nm found in a wide variety of eukaryotic cells; each consists (usually) of 13 protofilaments of polymeric tubulin, staggered in such a manner that the tubulin monomers are arranged in a helical pattern on the microtubular surface, and with the alpha/beta axes of the tubulin subunits parallel to the long axis of the tubule; exist in equilibrium with pool of tubulin monomers and can be rapidly assembled or disassembled in response to physiological stimuli; concerned with force generation, e.g. in the spindle." [ISBN:0879693568] cellular_component
+ENSG00000138892 GO:0005930 axoneme "The bundle of microtubules and associated proteins that forms the core of cilia (also called flagella) in eukaryotic cells and is responsible for their movements." [GOC:bf, GOC:cilia, ISBN:0198547684] cellular_component
+ENSG00000138892 GO:0015630 microtubule cytoskeleton "The part of the cytoskeleton (the internal framework of a cell) composed of microtubules and associated proteins." [GOC:jl, ISBN:0395825172] cellular_component
+ENSG00000138892 GO:0018094 protein polyglycylation "The addition of glycyl units covalently bound to the gamma carboxyl group peptidyl-glutamic acid." [RESID:AA0201] biological_process
+ENSG00000138892 GO:0070735 protein-glycine ligase activity "Catalysis of the posttranslational transfer of one or more glycine residues to a specific glutamate residue on a target protein." [GOC:mah, PMID:19524510] molecular_function
+ENSG00000138892 GO:0070736 protein-glycine ligase activity, initiating "Catalysis of the posttranslational transfer of a glycine residue to the gamma-carboxyl group(s) of one or more specific glutamate residues on a target protein." [GOC:mah, PMID:19524510] molecular_function
+ENSG00000138892 GO:0016989 sigma factor antagonist activity "The function of binding to a sigma factor and stopping, preventing or reducing the rate of its transcriptional activity." [GOC:jl, GOC:txnOH, Wikipedia:Anti-sigma_factors] molecular_function
+ENSG00000138892 GO:0000988 protein binding transcription factor activity "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules), in order to modulate transcription. A protein binding transcription factor may or may not also interact with the template nucleic acid (either DNA or RNA) as well." [GOC:txnOH] molecular_function
+ENSG00000138892 GO:0042384 cilium assembly "The assembly of a cilium, a specialized eukaryotic organelle that consists of a filiform extrusion of the cell surface. Each cilium is bounded by an extrusion of the cytoplasmic membrane, and contains a regular longitudinal array of microtubules, anchored basally in a centriole." [GOC:kmv, ISBN:0198506732] biological_process
+ENSG00000100211
+ENSG00000100211 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100211 GO:0030154 cell differentiation "The process in which relatively unspecialized cells, e.g. embryonic or regenerative cells, acquire specialized structural and/or functional features that characterize the cells, tissues, or organs of the mature organism or some other relatively stable phase of the organism's life history. Differentiation includes the processes involved in commitment of a cell to a specific fate and its subsequent development to the mature state." [ISBN:0198506732] biological_process
+ENSG00000100211 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100211 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100211 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100211 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100211 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100211 GO:0005802 trans-Golgi network "The network of interconnected tubular and cisternal structures located within the Golgi apparatus on the side distal to the endoplasmic reticulum, from which secretory vesicles emerge. The trans-Golgi network is important in the later stages of protein secretion where it is thought to play a key role in the sorting and targeting of secreted proteins to the correct destination." [GOC:vw, ISBN:0815316194] cellular_component
+ENSG00000100211 GO:0008013 beta-catenin binding "Interacting selectively and non-covalently with the beta subunit of the catenin complex." [GOC:bf] molecular_function
+ENSG00000100211 GO:0008104 protein localization "Any process in which a protein is transported to, or maintained in, a specific location." [GOC:ai] biological_process
+ENSG00000100211 GO:0030178 negative regulation of Wnt signaling pathway "Any process that stops, prevents, or reduces the frequency, rate or extent of the Wnt signaling pathway." [GOC:dph, GOC:go_curators, GOC:tb] biological_process
+ENSG00000100211 GO:0042802 identical protein binding "Interacting selectively and non-covalently with an identical protein or proteins." [GOC:jl] molecular_function
+ENSG00000100211 GO:0045444 fat cell differentiation "The process in which a relatively unspecialized cell acquires specialized features of an adipocyte, an animal connective tissue cell specialized for the synthesis and storage of fat." [CL:0000136, GOC:go_curators] biological_process
+ENSG00000100211 GO:0045892 negative regulation of transcription, DNA-templated "Any process that stops, prevents, or reduces the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000100211 GO:0055007 cardiac muscle cell differentiation "The process in which a cardiac muscle precursor cell acquires specialized features of a cardiac muscle cell. Cardiac muscle cells are striated muscle cells that are responsible for heart contraction." [GOC:devbiol, GOC:mtg_heart] biological_process
+ENSG00000100211 GO:0090090 negative regulation of canonical Wnt signaling pathway "Any process that decreases the rate, frequency, or extent of the Wnt signaling pathway through beta-catenin, the series of molecular signals initiated by binding of a Wnt protein to a frizzled family receptor on the surface of the target cell, followed by propagation of the signal via beta-catenin, and ending with a change in transcription of target genes." [GOC:dph, GOC:tb] biological_process
+ENSG00000100211 GO:0036064 ciliary basal body "A membrane-tethered, short cylindrical array of microtubules and associated proteins found at the base of a eukaryotic cilium (also called flagellum) that is similar in structure to a centriole and derives from it. The cilium basal body is the site of assembly and remodelling of the cilium and serves as a nucleation site for axoneme growth. As well as anchoring the cilium, it is thought to provide a selective gateway regulating the entry of ciliary proteins and vesicles by intraflagellar transport." [GOC:cilia, GOC:clt, PMID:21750193] cellular_component
+ENSG00000100211 GO:0042384 cilium assembly "The assembly of a cilium, a specialized eukaryotic organelle that consists of a filiform extrusion of the cell surface. Each cilium is bounded by an extrusion of the cytoplasmic membrane, and contains a regular longitudinal array of microtubules, anchored basally in a centriole." [GOC:kmv, ISBN:0198506732] biological_process
+ENSG00000100271 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100271 GO:0005874 microtubule "Any of the long, generally straight, hollow tubes of internal diameter 12-15 nm and external diameter 24 nm found in a wide variety of eukaryotic cells; each consists (usually) of 13 protofilaments of polymeric tubulin, staggered in such a manner that the tubulin monomers are arranged in a helical pattern on the microtubular surface, and with the alpha/beta axes of the tubulin subunits parallel to the long axis of the tubule; exist in equilibrium with pool of tubulin monomers and can be rapidly assembled or disassembled in response to physiological stimuli; concerned with force generation, e.g. in the spindle." [ISBN:0879693568] cellular_component
+ENSG00000100271 GO:0018095 protein polyglutamylation "The addition of one or more alpha-linked glutamyl units to the gamma carboxyl group of peptidyl-glutamic acid." [RESID:AA0202] biological_process
+ENSG00000100271 GO:0070740 tubulin-glutamic acid ligase activity "Catalysis of the posttranslational transfer of one or more glutamate residues to the gamma-carboxyl group(s) of one or more specific glutamate residues on a tubulin molecule." [GOC:mah, PMID:19524510] molecular_function
+ENSG00000100271 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100271 GO:0016874 ligase activity "Catalysis of the joining of two substances, or two groups within a single molecule, with the concomitant hydrolysis of the diphosphate bond in ATP or a similar triphosphate." [EC:6, GOC:mah] molecular_function
+ENSG00000100271 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000100271 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100271 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100271 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100271 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100271 GO:0060271 cilium morphogenesis "A process that is carried out at the cellular level and in which the structure of a cilium is organized." [GOC:BHF, GOC:dph] biological_process
+ENSG00000100271 GO:0035082 axoneme assembly "The assembly and organization of an axoneme, the bundle of microtubules and associated proteins that forms the core of cilia (also called flagella) in eukaryotic cells and is responsible for their movements." [GOC:bf, GOC:cilia, GOC:jl, ISBN:0815316194] biological_process
+ENSG00000100271 GO:0003351 epithelial cilium movement "The directed, self-propelled movement of a cilium of an epithelial cell. This movement is usually coordinated between many epithelial cells, and serves to move fluid." [GOC:dph] biological_process
+ENSG00000100271 GO:0044782 cilium organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of a cilium, a specialized eukaryotic organelle that consists of a filiform extrusion of the cell surface. Each cilium is bounded by an extrusion of the cytoplasmic membrane, and contains a regular longitudinal array of microtubules, anchored basally in a centriole." [GOC:jl] biological_process
+ENSG00000183741 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000183741 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000183741 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000183741 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000183741 GO:0005654 nucleoplasm "That part of the nuclear content other than the chromosomes or the nucleolus." [GOC:ma, ISBN:0124325653] cellular_component
+ENSG00000183741 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000183741 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000183741 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000183741 GO:0000122 negative regulation of transcription from RNA polymerase II promoter "Any process that stops, prevents, or reduces the frequency, rate or extent of transcription from an RNA polymerase II promoter." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000183741 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000183741 GO:0006351 transcription, DNA-templated "The cellular synthesis of RNA on a template of DNA." [GOC:jl, GOC:txnOH] biological_process
+ENSG00000183741 GO:0016568 chromatin modification "The alteration of DNA, protein, or sometimes RNA, in chromatin, which may result in changing the chromatin structure." [GOC:mah, PMID:20404130] biological_process
+ENSG00000183741 GO:0031519 PcG protein complex "A chromatin-associated multiprotein complex containing Polycomb Group proteins. In Drosophila, Polycomb group proteins are involved in the long-term maintenance of gene repression, and PcG protein complexes associate with Polycomb group response elements (PREs) in target genes to regulate higher-order chromatin structure." [PMID:9372908] cellular_component
+ENSG00000183741 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000183741 GO:0000792 heterochromatin "A compact and highly condensed form of chromatin." [GOC:elh] cellular_component
+ENSG00000183741 GO:0003727 single-stranded RNA binding "Interacting selectively and non-covalently with single-stranded RNA." [GOC:jl] molecular_function
+ENSG00000185838 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000185838 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000185838 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000185838 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000185838 GO:0007186 G-protein coupled receptor signaling pathway "A series of molecular signals that proceeds with an activated receptor promoting the exchange of GDP for GTP on the alpha-subunit of an associated heterotrimeric G-protein complex. The GTP-bound activated alpha-G-protein then dissociates from the beta- and gamma-subunits to further transmit the signal within the cell. The pathway begins with receptor-ligand interaction, or for basal GPCR signaling the pathway begins with the receptor activating its G protein in the absence of an agonist, and ends with regulation of a downstream cellular process, e.g. transcription." [GOC:bf, GOC:mah, Wikipedia:G_protein-coupled_receptor] biological_process
+ENSG00000185838 GO:0009898 cytoplasmic side of plasma membrane "The leaflet the plasma membrane that faces the cytoplasm and any proteins embedded or anchored in it or attached to its surface." [GOC:dos, GOC:tb] cellular_component
+ENSG00000185838 GO:0035556 intracellular signal transduction "The process in which a signal is passed on to downstream components within the cell, which become activated themselves to further propagate the signal and finally trigger a change in the function or state of the cell." [GOC:bf, GOC:jl, GOC:signaling, ISBN:3527303782] biological_process
+ENSG00000185838 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000185838 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000185838 GO:0035176 social behavior "Behavior directed towards society, or taking place between members of the same species. Occurs predominantly, or only, in individuals that are part of a group." [GOC:jh2, PMID:12848939, Wikipedia:Social_behavior] biological_process
+ENSG00000217442 GO:0006259 DNA metabolic process "Any cellular metabolic process involving deoxyribonucleic acid. This is one of the two main types of nucleic acid, consisting of a long, unbranched macromolecule formed from one, or more commonly, two, strands of linked deoxyribonucleotides." [ISBN:0198506732] biological_process
+ENSG00000217442 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000217442 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000217442 GO:0022607 cellular component assembly "The aggregation, arrangement and bonding together of a cellular component." [GOC:isa_complete] biological_process
+ENSG00000217442 GO:0051276 chromosome organization "A process that is carried out at the cellular level that results in the assembly, arrangement of constituent parts, or disassembly of chromosomes, structures composed of a very long molecule of DNA and associated proteins that carries hereditary information. This term covers covalent modifications at the molecular level as well as spatial relationships among the major components of a chromosome." [GOC:ai, GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000217442 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000217442 GO:0005694 chromosome "A structure composed of a very long molecule of DNA and associated proteins (e.g. histones) that carries hereditary information." [ISBN:0198547684] cellular_component
+ENSG00000217442 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000217442 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000217442 GO:0000801 central element "A structural unit of the synaptonemal complex found between the lateral elements." [GOC:elh] cellular_component
+ENSG00000217442 GO:0007130 synaptonemal complex assembly "The cell cycle process in which the synaptonemal complex is formed. This is a structure that holds paired chromosomes together during prophase I of meiosis and that promotes genetic recombination." [ISBN:0198506732] biological_process
+ENSG00000217442 GO:0007131 reciprocal meiotic recombination "The cell cycle process in which double strand breaks are formed and repaired through a double Holliday junction intermediate. This results in the equal exchange of genetic material between non-sister chromatids in a pair of homologous chromosomes. These reciprocal recombinant products ensure the proper segregation of homologous chromosomes during meiosis I and create genetic diversity." [PMID:2087779] biological_process
+ENSG00000217442 GO:0007283 spermatogenesis "The process of formation of spermatozoa, including spermatocytogenesis and spermiogenesis." [GOC:jid, ISBN:9780878933846] biological_process
+ENSG00000217442 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000217442 GO:0043065 positive regulation of apoptotic process "Any process that activates or increases the frequency, rate or extent of cell death by apoptotic process." [GOC:jl, GOC:mtg_apoptosis] biological_process
+ENSG00000128272 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000128272 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000128272 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000128272 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000128272 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000128272 GO:0003677 DNA binding "Any molecular function by which a gene product interacts selectively and non-covalently with DNA (deoxyribonucleic acid)." [GOC:dph, GOC:jl, GOC:tb, GOC:vw] molecular_function
+ENSG00000128272 GO:0006520 cellular amino acid metabolic process "The chemical reactions and pathways involving amino acids, carboxylic acids containing one or more amino groups, as carried out by individual cells." [CHEBI:33709, GOC:curators, ISBN:0198506732] biological_process
+ENSG00000128272 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000128272 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000128272 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000128272 GO:0005975 carbohydrate metabolic process "The chemical reactions and pathways involving carbohydrates, any of a group of organic compounds based of the general formula Cx(H2O)y. Includes the formation of carbohydrate derivatives by the addition of a carbohydrate residue to another molecule." [GOC:mah, ISBN:0198506732] biological_process
+ENSG00000128272 GO:0005856 cytoskeleton "Any of the various filamentous elements that form the internal framework of cells, and typically remain after treatment of the cells with mild detergent to remove membrane constituents and soluble components of the cytoplasm. The term embraces intermediate filaments, microfilaments, microtubules, the microtrabecular lattice, and other structures characterized by a polymeric filamentous nature and long-range order within the cell. The various elements of the cytoskeleton not only serve in the maintenance of cellular shape but also have roles in other cellular functions, including cellular movement, cell division, endocytosis, and movement of organelles." [GOC:mah, ISBN:0198547684, PMID:16959967] cellular_component
+ENSG00000128272 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000128272 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000128272 GO:0005654 nucleoplasm "That part of the nuclear content other than the chromosomes or the nucleolus." [GOC:ma, ISBN:0124325653] cellular_component
+ENSG00000128272 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000128272 GO:0001071 nucleic acid binding transcription factor activity "Interacting selectively and non-covalently with a DNA or RNA sequence in order to modulate transcription. The transcription factor may or may not also interact selectively with a protein or macromolecular complex." [GOC:txnOH] molecular_function
+ENSG00000128272 GO:0000981 sequence-specific DNA binding RNA polymerase II transcription factor activity "Interacting selectively and non-covalently with a specific DNA sequence in order to modulate transcription by RNA polymerase II. The transcription factor may or may not also interact selectively with a protein or macromolecular complex." [GOC:txnOH] molecular_function
+ENSG00000128272 GO:0001046 core promoter sequence-specific DNA binding "Interacting selectively and non-covalently with a sequence of DNA that is part of a core promoter region composed of the transcription start site and binding sites for the basal transcription machinery. The transcribed region might be described as a gene, cistron, or operon." [GOC:txnOH] molecular_function
+ENSG00000128272 GO:0003700 sequence-specific DNA binding transcription factor activity "Interacting selectively and non-covalently with a specific DNA sequence in order to modulate transcription. The transcription factor may or may not also interact selectively with a protein or macromolecular complex." [GOC:curators, GOC:txnOH] molecular_function
+ENSG00000128272 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000128272 GO:0006094 gluconeogenesis "The formation of glucose from noncarbohydrate precursors, such as pyruvate, amino acids and glycerol." [MetaCyc:GLUCONEO-PWY] biological_process
+ENSG00000128272 GO:0006355 regulation of transcription, DNA-templated "Any process that modulates the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000128272 GO:0006366 transcription from RNA polymerase II promoter "The synthesis of RNA from a DNA template by RNA polymerase II, originating at an RNA polymerase II promoter. Includes transcription of messenger RNA (mRNA) and certain small nuclear RNAs (snRNAs)." [GOC:jl, GOC:txnOH, ISBN:0321000382] biological_process
+ENSG00000128272 GO:0006987 activation of signaling protein activity involved in unfolded protein response "The conversion of a specific protein, possessing protein kinase and endoribonuclease activities, to an active form as a result of signaling via the unfolded protein response." [GOC:dph, GOC:mah, GOC:tb, PMID:12042763] biological_process
+ENSG00000128272 GO:0030968 endoplasmic reticulum unfolded protein response "The series of molecular signals generated as a consequence of the presence of unfolded proteins in the endoplasmic reticulum (ER) or other ER-related stress; results in changes in the regulation of transcription and translation." [GOC:mah, PMID:12042763] biological_process
+ENSG00000128272 GO:0032922 circadian regulation of gene expression "Any process that modulates the frequency, rate or extent of gene expression such that an expression pattern recurs with a regularity of approximately 24 hours." [GOC:mah] biological_process
+ENSG00000128272 GO:0034399 nuclear periphery "The portion of the nuclear lumen proximal to the inner nuclear membrane." [GOC:krc, GOC:mah] cellular_component
+ENSG00000128272 GO:0034976 response to endoplasmic reticulum stress "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a stress acting at the endoplasmic reticulum. ER stress usually results from the accumulation of unfolded or misfolded proteins in the ER lumen." [GOC:cjm, GOC:mah] biological_process
+ENSG00000128272 GO:0036091 positive regulation of transcription from RNA polymerase II promoter in response to oxidative stress "Any process that increases the frequency, rate or extent of transcription from an RNA polymerase II promoter as a result of a stimulus indicating the organism is under oxidative stress, a state often resulting from exposure to high levels of reactive oxygen species, e.g. superoxide anions, hydrogen peroxide (H2O2), and hydroxyl radicals." [GOC:rn, PMID:14978214, PMID:18439143] biological_process
+ENSG00000128272 GO:0043005 neuron projection "A prolongation or process extending from a nerve cell, e.g. an axon or dendrite." [GOC:jl, http://www.cogsci.princeton.edu/~wn/] cellular_component
+ENSG00000128272 GO:0043065 positive regulation of apoptotic process "Any process that activates or increases the frequency, rate or extent of cell death by apoptotic process." [GOC:jl, GOC:mtg_apoptosis] biological_process
+ENSG00000128272 GO:0043525 positive regulation of neuron apoptotic process "Any process that activates or increases the frequency, rate or extent of cell death of neurons by apoptotic process." [GOC:go_curators, GOC:mtg_apoptosis] biological_process
+ENSG00000128272 GO:0044212 transcription regulatory region DNA binding "Interacting selectively and non-covalently with a DNA region that regulates the transcription of a region of DNA, which may be a gene, cistron, or operon. Binding may occur as a sequence specific interaction or as an interaction observed only once a factor has been recruited to the DNA by other factors." [GOC:jl, GOC:txnOH, SO:0005836] molecular_function
+ENSG00000128272 GO:0044267 cellular protein metabolic process "The chemical reactions and pathways involving a specific protein, rather than of proteins in general, occurring at the level of an individual cell. Includes cellular protein modification." [GOC:jl] biological_process
+ENSG00000128272 GO:0045893 positive regulation of transcription, DNA-templated "Any process that activates or increases the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000128272 GO:0045944 positive regulation of transcription from RNA polymerase II promoter "Any process that activates or increases the frequency, rate or extent of transcription from an RNA polymerase II promoter." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000128272 GO:0070059 intrinsic apoptotic signaling pathway in response to endoplasmic reticulum stress "A series of molecular signals in which an intracellular signal is conveyed to trigger the apoptotic death of a cell. The pathway is induced in response to a stimulus indicating endoplasmic reticulum (ER) stress, and ends when the execution phase of apoptosis is triggered. ER stress usually results from the accumulation of unfolded or misfolded proteins in the ER lumen." [GOC:mah, GOC:mtg_apoptosis, PMID:18701708] biological_process
+ENSG00000128272 GO:1903204 negative regulation of oxidative stress-induced neuron death "Any process that stops, prevents or reduces the frequency, rate or extent of oxidative stress-induced neuron death." [GO_REF:0000058, GOC:bf, GOC:PARL, GOC:TermGenie, PMID:24252804] biological_process
+ENSG00000128272 GO:1990037 Lewy body core "The center portion of a Lewy body. In Parkinson's disease, it contains a matted meshwork of filaments." [NIF_Subcellular:sao6587439252] cellular_component
+ENSG00000128272 GO:1990440 positive regulation of transcription from RNA polymerase II promoter in response to endoplasmic reticulum stress "Any process that activates or increases the frequency, rate or extent of transcription from an RNA polymerase II promoter as a result of an endoplasmic reticulum stress." [GOC:bf, GOC:PARL, PMID:21113145] biological_process
+ENSG00000128272 GO:0043565 sequence-specific DNA binding "Interacting selectively and non-covalently with DNA of a specific nucleotide composition, e.g. GC-rich DNA binding, or with a specific sequence motif or type of DNA e.g. promotor binding or rDNA binding." [GOC:jl] molecular_function
+ENSG00000128272 GO:0006357 regulation of transcription from RNA polymerase II promoter "Any process that modulates the frequency, rate or extent of transcription from an RNA polymerase II promoter." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000128272 GO:0005667 transcription factor complex "A protein complex that is capable of associating with DNA by direct binding, or via other DNA-binding proteins or complexes, and regulating transcription." [GOC:jl] cellular_component
+ENSG00000128272 GO:0007623 circadian rhythm "Any biological process in an organism that recurs with a regularity of approximately 24 hours." [GOC:bf, GOC:go_curators] biological_process
+ENSG00000128272 GO:0000978 RNA polymerase II core promoter proximal region sequence-specific DNA binding "Interacting selectively and non-covalently with a sequence of DNA that is in cis with and relatively close to a core promoter for RNA polymerase II." [GOC:txnOH] molecular_function
+ENSG00000128272 GO:0001077 RNA polymerase II core promoter proximal region sequence-specific DNA binding transcription factor activity involved in positive regulation of transcription "Interacting selectively and non-covalently with a sequence of DNA that is in cis with and relatively close to a core promoter for RNA polymerase II (RNAP II) in order to activate or increase the frequency, rate or extent of transcription from the RNAP II promoter." [GOC:txnOH] molecular_function
+ENSG00000128272 GO:0007214 gamma-aminobutyric acid signaling pathway "The series of molecular signals generated by the binding of gamma-aminobutyric acid (GABA, 4-aminobutyrate), an amino acid which acts as a neurotransmitter in some organisms, to a cell surface receptor." [GOC:mah] biological_process
+ENSG00000128272 GO:0008022 protein C-terminus binding "Interacting selectively and non-covalently with a protein C-terminus, the end of any peptide chain at which the 1-carboxy function of a constituent amino acid is not attached in peptide linkage to another amino-acid residue." [ISBN:0198506732] molecular_function
+ENSG00000128272 GO:0032590 dendrite membrane "The portion of the plasma membrane surrounding a dendrite." [GOC:mah] cellular_component
+ENSG00000128272 GO:0043267 negative regulation of potassium ion transport "Any process that stops, prevents, or reduces the frequency, rate or extent of the directed movement of potassium ions (K+) into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:jl] biological_process
+ENSG00000242247 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000242247 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000242247 GO:0016192 vesicle-mediated transport "A cellular transport process in which transported substances are moved in membrane-bounded vesicles; transported substances are enclosed in the vesicle lumen or located in the vesicle membrane. The process begins with a step that directs a substance to the forming vesicle, and includes vesicle budding and coating. Vesicles are then targeted to, and fuse with, an acceptor membrane." [GOC:ai, GOC:mah, ISBN:08789310662000] biological_process
+ENSG00000242247 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000242247 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000242247 GO:0008565 protein transporter activity "Enables the directed movement of proteins into, out of or within a cell, or between cells." [ISBN:0198506732] molecular_function
+ENSG00000242247 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000242247 GO:0030234 enzyme regulator activity "Binds to and modulates the activity of an enzyme." [GOC:mah] molecular_function
+ENSG00000242247 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000242247 GO:0005794 Golgi apparatus "A compound membranous cytoplasmic organelle of eukaryotic cells, consisting of flattened, ribosome-free vesicles arranged in a more or less regular stack. The Golgi apparatus differs from the endoplasmic reticulum in often having slightly thicker membranes, appearing in sections as a characteristic shallow semicircle so that the convex side (cis or entry face) abuts the endoplasmic reticulum, secretory vesicles emerging from the concave side (trans or exit face). In vertebrate cells there is usually one such organelle, while in invertebrates and plants, where they are known usually as dictyosomes, there may be several scattered in the cytoplasm. The Golgi apparatus processes proteins produced on the ribosomes of the rough endoplasmic reticulum; such processing includes modification of the core oligosaccharides of glycoproteins, and the sorting and packaging of proteins for transport to a variety of cellular locations. Three different regions of the Golgi are now recognized both in terms of structure and function: cis, in the vicinity of the cis face, trans, in the vicinity of the trans face, and medial, lying between the cis and trans regions." [ISBN:0198506732] cellular_component
+ENSG00000242247 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000242247 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000242247 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000242247 GO:0006886 intracellular protein transport "The directed movement of proteins in a cell, including the movement of proteins between specific compartments or structures within a cell, such as organelles of a eukaryotic cell." [GOC:mah] biological_process
+ENSG00000242247 GO:0008060 ARF GTPase activator activity "Increases the rate of GTP hydrolysis by the GTPase ARF." [GOC:mah] molecular_function
+ENSG00000242247 GO:0008270 zinc ion binding "Interacting selectively and non-covalently with zinc (Zn) ions." [GOC:ai] molecular_function
+ENSG00000242247 GO:0009306 protein secretion "The controlled release of proteins from a cell." [GOC:ai] biological_process
+ENSG00000242247 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000242247 GO:0032312 regulation of ARF GTPase activity "Any process that modulates the activity of the GTPase ARF." [GOC:mah] biological_process
+ENSG00000242247 GO:0043547 positive regulation of GTPase activity "Any process that activates or increases the activity of a GTPase." [GOC:jl] biological_process
+ENSG00000242247
+ENSG00000205560 GO:0008152 metabolic process "The chemical reactions and pathways, including anabolism and catabolism, by which living organisms transform chemical substances. Metabolic processes typically transform small molecules, but also include macromolecular processes such as DNA repair and replication, and protein synthesis and degradation." [GOC:go_curators, ISBN:0198547684] biological_process
+ENSG00000205560 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000205560 GO:0006629 lipid metabolic process "The chemical reactions and pathways involving lipids, compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent. Includes fatty acids; neutral fats, other fatty-acid esters, and soaps; long-chain (fatty) alcohols and waxes; sphingoids and other long-chain bases; glycolipids, phospholipids and sphingolipids; and carotenes, polyprenols, sterols, terpenes and other isoprenoids." [GOC:ma] biological_process
+ENSG00000205560 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000205560 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000205560 GO:0055085 transmembrane transport "The process in which a solute is transported from one side of a membrane to the other." [GOC:dph, GOC:jid] biological_process
+ENSG00000205560 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000205560 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000205560 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000205560 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000205560 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000205560 GO:0016746 transferase activity, transferring acyl groups "Catalysis of the transfer of an acyl group from one compound (donor) to another (acceptor)." [GOC:jl, ISBN:0198506732] molecular_function
+ENSG00000205560 GO:0004095 carnitine O-palmitoyltransferase activity "Catalysis of the reaction: palmitoyl-CoA + L-carnitine = CoA + L-palmitoylcarnitine." [EC:2.3.1.21] molecular_function
+ENSG00000205560 GO:0005741 mitochondrial outer membrane "The outer, i.e. cytoplasm-facing, lipid bilayer of the mitochondrial envelope." [GOC:ai] cellular_component
+ENSG00000205560 GO:0006635 fatty acid beta-oxidation "A fatty acid oxidation process that results in the complete oxidation of a long-chain fatty acid. Fatty acid beta-oxidation begins with the addition of coenzyme A to a fatty acid, and occurs by successive cycles of reactions during each of which the fatty acid is shortened by a two-carbon fragment removed as acetyl coenzyme A; the cycle continues until only two or three carbons remain (as acetyl-CoA or propionyl-CoA respectively)." [GOC:mah, ISBN:0198506732, MetaCyc:FAO-PWY] biological_process
+ENSG00000205560 GO:0006853 carnitine shuttle "The transfer of acyl groups to and from acyl-CoA molecules to form O-acylcarnitine, which can exchange across the mitochondrial inner membrane with unacylated carnitine." [ISBN:0198547684] biological_process
+ENSG00000205560 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000205560 GO:0044255 cellular lipid metabolic process "The chemical reactions and pathways involving lipids, as carried out by individual cells." [GOC:jl] biological_process
+ENSG00000205560 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000205560 GO:0043231 intracellular membrane-bounded organelle "Organized structure of distinctive morphology and function, bounded by a single or double lipid bilayer membrane and occurring within the cell. Includes the nucleus, mitochondria, plastids, vacuoles, and vesicles. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000205560 GO:0015909 long-chain fatty acid transport "The directed movement of long-chain fatty acids into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore. A long-chain fatty acid is a fatty acid with a chain length between C13 and C22." [CHEBI:15904, GOC:ai] biological_process
+ENSG00000100065 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100065 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100065 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100065 GO:0005198 structural molecule activity "The action of a molecule that contributes to the structural integrity of a complex or assembly within or outside a cell." [GOC:mah] molecular_function
+ENSG00000100065 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100065 GO:0006461 protein complex assembly "The aggregation, arrangement and bonding together of a set of components to form a protein complex." [GOC:ai] biological_process
+ENSG00000100065 GO:0022607 cellular component assembly "The aggregation, arrangement and bonding together of a cellular component." [GOC:isa_complete] biological_process
+ENSG00000100065 GO:0065003 macromolecular complex assembly "The aggregation, arrangement and bonding together of a set of macromolecules to form a complex." [GOC:jl] biological_process
+ENSG00000100065 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100065 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100065 GO:0007250 activation of NF-kappaB-inducing kinase activity "The stimulation of the activity of NF-kappaB-inducing kinase through phosphorylation at specific residues." [GOC:jl, PMID:12773372] biological_process
+ENSG00000100065 GO:0030159 receptor signaling complex scaffold activity "Functions to provide a physical support for the assembly of a multiprotein receptor signaling complex." [GOC:mah] molecular_function
+ENSG00000100065 GO:0032449 CBM complex "A protein complex comprising Carma1, Bcl10 and MALT1; plays a role in signal transduction during NF-kappaB activation." [PMID:12909454] cellular_component
+ENSG00000100065 GO:0042981 regulation of apoptotic process "Any process that modulates the occurrence or rate of cell death by apoptotic process." [GOC:jl, GOC:mtg_apoptosis] biological_process
+ENSG00000100065
+ENSG00000100335
+ENSG00000100335 GO:0000266 mitochondrial fission "The division of a mitochondrion within a cell to form two or more separate mitochondrial compartments." [PMID:11038192] biological_process
+ENSG00000100335 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100335 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000100335 GO:0005741 mitochondrial outer membrane "The outer, i.e. cytoplasm-facing, lipid bilayer of the mitochondrial envelope." [GOC:ai] cellular_component
+ENSG00000100335 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000100335 GO:0019003 GDP binding "Interacting selectively and non-covalently with GDP, guanosine 5'-diphosphate." [GOC:ai] molecular_function
+ENSG00000100335 GO:0042802 identical protein binding "Interacting selectively and non-covalently with an identical protein or proteins." [GOC:jl] molecular_function
+ENSG00000100335 GO:0043531 ADP binding "Interacting selectively and non-covalently with ADP, adenosine 5'-diphosphate." [GOC:jl] molecular_function
+ENSG00000100335 GO:0090141 positive regulation of mitochondrial fission "Any process that increases the rate, frequency or extent of mitochondrial fission. Mitochondrial fission is the division of a mitochondrion within a cell to form two or more separate mitochondrial compartments." [GOC:ascb_2009, GOC:dph, GOC:tb] biological_process
+ENSG00000100335 GO:0090314 positive regulation of protein targeting to membrane "Any process that increases the frequency, rate or extent of the process of directing proteins towards a membrane, usually using signals contained within the protein." [GOC:tb] biological_process
+ENSG00000100335 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100335 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100335 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100335 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100335 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100335 GO:0007005 mitochondrion organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of a mitochondrion; includes mitochondrial morphogenesis and distribution, and replication of the mitochondrial genome as well as synthesis of new mitochondrial components." [GOC:dph, GOC:jl, GOC:mah, GOC:sgd_curators, PMID:9786946] biological_process
+ENSG00000211656 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000211656 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100412 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100412 GO:0008219 cell death "Any biological process that results in permanent cessation of all vital functions of a cell. A cell should be considered dead when any one of the following molecular or morphological criteria is met: (1) the cell has lost the integrity of its plasma membrane; (2) the cell, including its nucleus, has undergone complete fragmentation into discrete bodies (frequently referred to as \"apoptotic bodies\"); and/or (3) its corpse (or its fragments) have been engulfed by an adjacent cell in vivo." [GOC:mah, GOC:mtg_apoptosis] biological_process
+ENSG00000100412 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000100412 GO:0006091 generation of precursor metabolites and energy "The chemical reactions and pathways resulting in the formation of precursor metabolites, substances from which energy is derived, and any process involved in the liberation of energy from these substances." [GOC:jl] biological_process
+ENSG00000100412 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100412 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000100412 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100412 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100412 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100412 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100412 GO:0016829 lyase activity "Catalysis of the cleavage of C-C, C-O, C-N and other bonds by other means than by hydrolysis or oxidation, or conversely adding a group to a double bond. They differ from other enzymes in that two substrates are involved in one reaction direction, but only one in the other direction. When acting on the single substrate, a molecule is eliminated and this generates either a new double bond or a new ring." [EC:4.-.-.-, ISBN:0198547684] molecular_function
+ENSG00000100412 GO:0003994 aconitate hydratase activity "Catalysis of the reaction: citrate = isocitrate. The reaction occurs in two steps: (1) citrate = cis-aconitate + H2O, (2) cis-aconitate + H2O = isocitrate. This reaction is the interconversion of citrate and isocitrate via the labile, enzyme-bound intermediate cis-aconitate. Water is removed from one part of the citrate molecule and added back to a different atom to form isocitrate." [EC:4.2.1.3, GOC:pde, GOC:vw] molecular_function
+ENSG00000100412 GO:0005506 iron ion binding "Interacting selectively and non-covalently with iron (Fe) ions." [GOC:ai] molecular_function
+ENSG00000100412 GO:0005759 mitochondrial matrix "The gel-like material, with considerable fine structure, that lies in the matrix space, or lumen, of a mitochondrion. It contains the enzymes of the tricarboxylic acid cycle and, in some organisms, the enzymes concerned with fatty acid oxidation." [GOC:as, ISBN:0198506732] cellular_component
+ENSG00000100412 GO:0006099 tricarboxylic acid cycle "A nearly universal metabolic pathway in which the acetyl group of acetyl coenzyme A is effectively oxidized to two CO2 and four pairs of electrons are transferred to coenzymes. The acetyl group combines with oxaloacetate to form citrate, which undergoes successive transformations to isocitrate, 2-oxoglutarate, succinyl-CoA, succinate, fumarate, malate, and oxaloacetate again, thus completing the cycle. In eukaryotes the tricarboxylic acid is confined to the mitochondria. See also glyoxylate cycle." [ISBN:0198506732] biological_process
+ENSG00000100412 GO:0006101 citrate metabolic process "The chemical reactions and pathways involving citrate, 2-hydroxy-1,2,3-propanetricarboyxlate. Citrate is widely distributed in nature and is an important intermediate in the TCA cycle and the glyoxylate cycle." [ISBN:0198506732] biological_process
+ENSG00000100412 GO:0044237 cellular metabolic process "The chemical reactions and pathways by which individual cells transform chemical substances." [GOC:go_curators] biological_process
+ENSG00000100412 GO:0008152 metabolic process "The chemical reactions and pathways, including anabolism and catabolism, by which living organisms transform chemical substances. Metabolic processes typically transform small molecules, but also include macromolecular processes such as DNA repair and replication, and protein synthesis and degradation." [GOC:go_curators, ISBN:0198547684] biological_process
+ENSG00000100412 GO:0051539 4 iron, 4 sulfur cluster binding "Interacting selectively and non-covalently with a 4 iron, 4 sulfur (4Fe-4S) cluster; this cluster consists of four iron atoms, with the inorganic sulfur atoms found between the irons and acting as bridging ligands." [GOC:ai, PMID:15952888, Wikipedia:Iron-sulfur_cluster] molecular_function
+ENSG00000100412 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100412 GO:0006102 isocitrate metabolic process "The chemical reactions and pathways involving isocitrate, the anion of isocitric acid, 1-hydroxy-1,2,3-propanetricarboxylic acid. Isocitrate is an important intermediate in the TCA cycle and the glycoxylate cycle." [ISBN:0198506732] biological_process
+ENSG00000100412 GO:0051538 3 iron, 4 sulfur cluster binding "Interacting selectively and non-covalently with a 3 iron, 4 sulfur (3Fe-4S) cluster; this cluster consists of three iron atoms, with the inorganic sulfur atoms found between the irons and acting as bridging ligands. It is essentially a 4Fe-4S cluster with one iron missing." [GOC:ai, PMID:15952888, Wikipedia:Iron-sulfur_cluster] molecular_function
+ENSG00000244509 GO:0002376 immune system process "Any process involved in the development or functioning of the immune system, an organismal system for calibrated responses to potential internal or invasive threats." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05] biological_process
+ENSG00000244509 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000244509 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000244509 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000244509 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000244509 GO:0016810 hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds "Catalysis of the hydrolysis of any carbon-nitrogen bond, C-N, with the exception of peptide bonds." [GOC:jl] molecular_function
+ENSG00000244509 GO:0044403 symbiosis, encompassing mutualism through parasitism "An interaction between two organisms living together in more or less intimate association. The term host is usually used for the larger (macro) of the two members of a symbiosis. The smaller (micro) member is called the symbiont organism. Microscopic symbionts are often referred to as endosymbionts. The various forms of symbiosis include parasitism, in which the association is disadvantageous or destructive to one of the organisms; mutualism, in which the association is advantageous, or often necessary to one or both and not harmful to either; and commensalism, in which one member of the association benefits while the other is not affected. However, mutualism, parasitism, and commensalism are often not discrete categories of interactions and should rather be perceived as a continuum of interaction ranging from parasitism to mutualism. In fact, the direction of a symbiotic interaction can change during the lifetime of the symbionts due to developmental changes as well as changes in the biotic/abiotic environment in which the interaction occurs." [GOC:cc, http://www.free-definition.com] biological_process
+ENSG00000244509 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000244509 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000244509 GO:0034655 nucleobase-containing compound catabolic process "The chemical reactions and pathways resulting in the breakdown of nucleobases, nucleosides, nucleotides and nucleic acids." [GOC:mah] biological_process
+ENSG00000244509 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000244509 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000244509 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000244509 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000244509 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000244509 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000244509 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000244509 GO:0008270 zinc ion binding "Interacting selectively and non-covalently with zinc (Zn) ions." [GOC:ai] molecular_function
+ENSG00000244509 GO:0009972 cytidine deamination "The removal of amino group in the presence of water." [GOC:sm] biological_process
+ENSG00000244509 GO:0010529 negative regulation of transposition "Any process that decreases the frequency, rate or extent of transposition. Transposition results in the movement of discrete segments of DNA between nonhomologous sites." [GOC:dph, GOC:tb] biological_process
+ENSG00000244509 GO:0016032 viral process "A multi-organism process in which a virus is a participant. The other participant is the host. Includes infection of a host cell, replication of the viral genome, and assembly of progeny virus particles. In some cases the viral genetic material may integrate into the host genome and only subsequently, under particular circumstances, 'complete' its life cycle." [GOC:bf, GOC:jl, GOC:mah] biological_process
+ENSG00000244509 GO:0016814 hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds, in cyclic amidines "Catalysis of the hydrolysis of any non-peptide carbon-nitrogen bond in a cyclic amidine, a compound of the form R-C(=NH)-NH2." [ISBN:0198506732] molecular_function
+ENSG00000244509 GO:0044822 poly(A) RNA binding "Interacting non-covalently with a poly(A) RNA, a RNA molecule which has a tail of adenine bases." [GOC:jl] molecular_function
+ENSG00000244509 GO:0045071 negative regulation of viral genome replication "Any process that stops, prevents, or reduces the frequency, rate or extent of viral genome replication." [GOC:go_curators] biological_process
+ENSG00000244509 GO:0045087 innate immune response "Innate immune responses are defense responses mediated by germline encoded components that directly recognize components of potential pathogens." [GO_REF:0000022, GOC:add, GOC:ebc, GOC:mtg_15nov05, GOC:mtg_sensu] biological_process
+ENSG00000244509 GO:0051607 defense response to virus "Reactions triggered in response to the presence of a virus that act to protect the cell or organism." [GOC:ai] biological_process
+ENSG00000244509 GO:0080111 DNA demethylation "The removal of a methyl group from one or more nucleotides within an DNA molecule." [PMID:17208187] biological_process
+ENSG00000244509 GO:0006259 DNA metabolic process "Any cellular metabolic process involving deoxyribonucleic acid. This is one of the two main types of nucleic acid, consisting of a long, unbranched macromolecule formed from one, or more commonly, two, strands of linked deoxyribonucleotides." [ISBN:0198506732] biological_process
+ENSG00000244509 GO:0003824 catalytic activity "Catalysis of a biochemical reaction at physiological temperatures. In biologically catalyzed reactions, the reactants are known as substrates, and the catalysts are naturally occurring macromolecular substances known as enzymes. Enzymes possess specific binding sites for substrates, and are usually composed wholly or largely of protein, but RNA that has catalytic activity (ribozyme) is often also regarded as enzymatic." [ISBN:0198506732] molecular_function
+ENSG00000244509
+ENSG00000243811 GO:0002376 immune system process "Any process involved in the development or functioning of the immune system, an organismal system for calibrated responses to potential internal or invasive threats." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05] biological_process
+ENSG00000243811 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000243811 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000243811 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000243811 GO:0016810 hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds "Catalysis of the hydrolysis of any carbon-nitrogen bond, C-N, with the exception of peptide bonds." [GOC:jl] molecular_function
+ENSG00000243811 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000243811 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000243811 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000243811 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000243811 GO:0000932 cytoplasmic mRNA processing body "A focus in the cytoplasm where mRNAs may become inactivated by decapping or some other mechanism. mRNA processing and binding proteins are localized to these foci." [GOC:clt, PMID:12730603] cellular_component
+ENSG00000243811 GO:0008270 zinc ion binding "Interacting selectively and non-covalently with zinc (Zn) ions." [GOC:ai] molecular_function
+ENSG00000243811 GO:0010529 negative regulation of transposition "Any process that decreases the frequency, rate or extent of transposition. Transposition results in the movement of discrete segments of DNA between nonhomologous sites." [GOC:dph, GOC:tb] biological_process
+ENSG00000243811 GO:0016814 hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds, in cyclic amidines "Catalysis of the hydrolysis of any non-peptide carbon-nitrogen bond in a cyclic amidine, a compound of the form R-C(=NH)-NH2." [ISBN:0198506732] molecular_function
+ENSG00000243811 GO:0045087 innate immune response "Innate immune responses are defense responses mediated by germline encoded components that directly recognize components of potential pathogens." [GO_REF:0000022, GOC:add, GOC:ebc, GOC:mtg_15nov05, GOC:mtg_sensu] biological_process
+ENSG00000243811 GO:0045869 negative regulation of single stranded viral RNA replication via double stranded DNA intermediate "Any process that stops, prevents, or reduces the frequency, rate or extent of single stranded viral RNA replication via double stranded DNA intermediate." [GOC:go_curators] biological_process
+ENSG00000243811 GO:0051607 defense response to virus "Reactions triggered in response to the presence of a virus that act to protect the cell or organism." [GOC:ai] biological_process
+ENSG00000243811 GO:0070383 DNA cytosine deamination "The removal of an amino group from a cytosine residue in DNA, forming a uracil residue." [GOC:mah] biological_process
+ENSG00000243811 GO:0006259 DNA metabolic process "Any cellular metabolic process involving deoxyribonucleic acid. This is one of the two main types of nucleic acid, consisting of a long, unbranched macromolecule formed from one, or more commonly, two, strands of linked deoxyribonucleotides." [ISBN:0198506732] biological_process
+ENSG00000243811 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000243811 GO:0003824 catalytic activity "Catalysis of a biochemical reaction at physiological temperatures. In biologically catalyzed reactions, the reactants are known as substrates, and the catalysts are naturally occurring macromolecular substances known as enzymes. Enzymes possess specific binding sites for substrates, and are usually composed wholly or largely of protein, but RNA that has catalytic activity (ribozyme) is often also regarded as enzymatic." [ISBN:0198506732] molecular_function
+ENSG00000243811 GO:0008152 metabolic process "The chemical reactions and pathways, including anabolism and catabolism, by which living organisms transform chemical substances. Metabolic processes typically transform small molecules, but also include macromolecular processes such as DNA repair and replication, and protein synthesis and degradation." [GOC:go_curators, ISBN:0198547684] biological_process
+ENSG00000100083 GO:0006886 intracellular protein transport "The directed movement of proteins in a cell, including the movement of proteins between specific compartments or structures within a cell, such as organelles of a eukaryotic cell." [GOC:mah] biological_process
+ENSG00000100083 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100083 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100083 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100083 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100083 GO:0016192 vesicle-mediated transport "A cellular transport process in which transported substances are moved in membrane-bounded vesicles; transported substances are enclosed in the vesicle lumen or located in the vesicle membrane. The process begins with a step that directs a substance to the forming vesicle, and includes vesicle budding and coating. Vesicles are then targeted to, and fuse with, an acceptor membrane." [GOC:ai, GOC:mah, ISBN:08789310662000] biological_process
+ENSG00000100083 GO:0005794 Golgi apparatus "A compound membranous cytoplasmic organelle of eukaryotic cells, consisting of flattened, ribosome-free vesicles arranged in a more or less regular stack. The Golgi apparatus differs from the endoplasmic reticulum in often having slightly thicker membranes, appearing in sections as a characteristic shallow semicircle so that the convex side (cis or entry face) abuts the endoplasmic reticulum, secretory vesicles emerging from the concave side (trans or exit face). In vertebrate cells there is usually one such organelle, while in invertebrates and plants, where they are known usually as dictyosomes, there may be several scattered in the cytoplasm. The Golgi apparatus processes proteins produced on the ribosomes of the rough endoplasmic reticulum; such processing includes modification of the core oligosaccharides of glycoproteins, and the sorting and packaging of proteins for transport to a variety of cellular locations. Three different regions of the Golgi are now recognized both in terms of structure and function: cis, in the vicinity of the cis face, trans, in the vicinity of the trans face, and medial, lying between the cis and trans regions." [ISBN:0198506732] cellular_component
+ENSG00000100083 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100083 GO:0005768 endosome "A membrane-bounded organelle to which materials ingested by endocytosis are delivered." [ISBN:0198506732, PMID:19696797] cellular_component
+ENSG00000100083 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100083 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100083 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100083 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000100083 GO:0030131 clathrin adaptor complex "A membrane coat adaptor complex that links clathrin to a membrane." [GOC:mah] cellular_component
+ENSG00000100083 GO:0043231 intracellular membrane-bounded organelle "Organized structure of distinctive morphology and function, bounded by a single or double lipid bilayer membrane and occurring within the cell. Includes the nucleus, mitochondria, plastids, vacuoles, and vesicles. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100083 GO:0005622 intracellular "The living contents of a cell; the matter contained within (but not including) the plasma membrane, usually taken to exclude large vacuoles and masses of secretory or ingested material. In eukaryotes it includes the nucleus and cytoplasm." [ISBN:0198506732] cellular_component
+ENSG00000100083 GO:0045732 positive regulation of protein catabolic process "Any process that activates or increases the frequency, rate or extent of the chemical reactions and pathways resulting in the breakdown of a protein by the destruction of the native, active configuration, with or without the hydrolysis of peptide bonds." [GOC:go_curators] biological_process
+ENSG00000196419 GO:0000723 telomere maintenance "Any process that contributes to the maintenance of proper telomeric length and structure by affecting and monitoring the activity of telomeric proteins and the length of telomeric DNA. These processes includes those that shorten and lengthen the telomeric DNA sequences." [GOC:elh, PMID:11092831] biological_process
+ENSG00000196419 GO:0003684 damaged DNA binding "Interacting selectively and non-covalently with damaged DNA." [GOC:jl] molecular_function
+ENSG00000196419 GO:0004003 ATP-dependent DNA helicase activity "Catalysis of the reaction: ATP + H2O = ADP + phosphate; this reaction drives the unwinding of the DNA helix." [EC:3.6.1.3, GOC:jl] molecular_function
+ENSG00000196419 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000196419 GO:0032508 DNA duplex unwinding "The process in which interchain hydrogen bonds between two strands of DNA are broken or 'melted', generating a region of unpaired single strands." [GOC:isa_complete, GOC:mah] biological_process
+ENSG00000196419 GO:0042162 telomeric DNA binding "Interacting selectively and non-covalently with a telomere, a specific structure at the end of a linear chromosome required for the integrity and maintenance of the end." [GOC:jl, SO:0000624] molecular_function
+ENSG00000196419 GO:0043564 Ku70:Ku80 complex "Heterodimeric protein complex composed of a 70 kDa and a 80 kDa subunit, binds DNA through a channel formed by the heterodimer. Functions in DNA double stranded break repair, chromosome maintenance, transcription regulation, V(D)J recombination, and activation of DNA-PK." [PMID:12518983] cellular_component
+ENSG00000196419 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000196419 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000196419 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000196419 GO:0003677 DNA binding "Any molecular function by which a gene product interacts selectively and non-covalently with DNA (deoxyribonucleic acid)." [GOC:dph, GOC:jl, GOC:tb, GOC:vw] molecular_function
+ENSG00000196419 GO:0006259 DNA metabolic process "Any cellular metabolic process involving deoxyribonucleic acid. This is one of the two main types of nucleic acid, consisting of a long, unbranched macromolecule formed from one, or more commonly, two, strands of linked deoxyribonucleotides." [ISBN:0198506732] biological_process
+ENSG00000196419 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000196419 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000196419 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000196419 GO:0004386 helicase activity "Catalysis of the reaction: NTP + H2O = NDP + phosphate, to drive the unwinding of a DNA or RNA helix." [GOC:mah, ISBN:0198506732] molecular_function
+ENSG00000196419 GO:0016887 ATPase activity "Catalysis of the reaction: ATP + H2O = ADP + phosphate + 2 H+. May or may not be coupled to another reaction." [EC:3.6.1.3, GOC:jl] molecular_function
+ENSG00000196419 GO:0042592 homeostatic process "Any biological process involved in the maintenance of an internal steady state." [GOC:jl, ISBN:0395825172] biological_process
+ENSG00000196419 GO:0051276 chromosome organization "A process that is carried out at the cellular level that results in the assembly, arrangement of constituent parts, or disassembly of chromosomes, structures composed of a very long molecule of DNA and associated proteins that carries hereditary information. This term covers covalent modifications at the molecular level as well as spatial relationships among the major components of a chromosome." [GOC:ai, GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000196419 GO:0044403 symbiosis, encompassing mutualism through parasitism "An interaction between two organisms living together in more or less intimate association. The term host is usually used for the larger (macro) of the two members of a symbiosis. The smaller (micro) member is called the symbiont organism. Microscopic symbionts are often referred to as endosymbionts. The various forms of symbiosis include parasitism, in which the association is disadvantageous or destructive to one of the organisms; mutualism, in which the association is advantageous, or often necessary to one or both and not harmful to either; and commensalism, in which one member of the association benefits while the other is not affected. However, mutualism, parasitism, and commensalism are often not discrete categories of interactions and should rather be perceived as a continuum of interaction ranging from parasitism to mutualism. In fact, the direction of a symbiotic interaction can change during the lifetime of the symbionts due to developmental changes as well as changes in the biotic/abiotic environment in which the interaction occurs." [GOC:cc, http://www.free-definition.com] biological_process
+ENSG00000196419 GO:0016829 lyase activity "Catalysis of the cleavage of C-C, C-O, C-N and other bonds by other means than by hydrolysis or oxidation, or conversely adding a group to a double bond. They differ from other enzymes in that two substrates are involved in one reaction direction, but only one in the other direction. When acting on the single substrate, a molecule is eliminated and this generates either a new double bond or a new ring." [EC:4.-.-.-, ISBN:0198547684] molecular_function
+ENSG00000196419 GO:0002376 immune system process "Any process involved in the development or functioning of the immune system, an organismal system for calibrated responses to potential internal or invasive threats." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05] biological_process
+ENSG00000196419 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000196419 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000196419 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000196419 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000196419 GO:0005654 nucleoplasm "That part of the nuclear content other than the chromosomes or the nucleolus." [GOC:ma, ISBN:0124325653] cellular_component
+ENSG00000196419 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000196419 GO:0000783 nuclear telomere cap complex "A complex of DNA and protein located at the end of a linear chromosome in the nucleus that protects and stabilizes a linear chromosome." [GOC:elh] cellular_component
+ENSG00000196419 GO:0003690 double-stranded DNA binding "Interacting selectively and non-covalently with double-stranded DNA." [GOC:elh] molecular_function
+ENSG00000196419 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000196419 GO:0005524 ATP binding "Interacting selectively and non-covalently with ATP, adenosine 5'-triphosphate, a universally important coenzyme and enzyme regulator." [ISBN:0198506732] molecular_function
+ENSG00000196419 GO:0005667 transcription factor complex "A protein complex that is capable of associating with DNA by direct binding, or via other DNA-binding proteins or complexes, and regulating transcription." [GOC:jl] cellular_component
+ENSG00000196419 GO:0006266 DNA ligation "The re-formation of a broken phosphodiester bond in the DNA backbone, carried out by DNA ligase." [ISBN:0815316194] biological_process
+ENSG00000196419 GO:0006281 DNA repair "The process of restoring DNA after damage. Genomes are subject to damage by chemical and physical agents in the environment (e.g. UV and ionizing radiations, chemical mutagens, fungal and bacterial toxins, etc.) and by free radicals or alkylating agents endogenously generated in metabolism. DNA is also damaged because of errors during its replication. A variety of different DNA repair pathways have been reported that include direct reversal, base excision repair, nucleotide excision repair, photoreactivation, bypass, double-strand break repair pathway, and mismatch repair pathway." [PMID:11563486] biological_process
+ENSG00000196419 GO:0006302 double-strand break repair "The repair of double-strand breaks in DNA via homologous and nonhomologous mechanisms to reform a continuous DNA helix." [GOC:elh] biological_process
+ENSG00000196419 GO:0006303 double-strand break repair via nonhomologous end joining "The repair of a double-strand break in DNA in which the two broken ends are rejoined with little or no sequence complementarity. Information at the DNA ends may be lost due to the modification of broken DNA ends." [PMID:10827453] biological_process
+ENSG00000196419 GO:0006351 transcription, DNA-templated "The cellular synthesis of RNA on a template of DNA." [GOC:jl, GOC:txnOH] biological_process
+ENSG00000196419 GO:0008022 protein C-terminus binding "Interacting selectively and non-covalently with a protein C-terminus, the end of any peptide chain at which the 1-carboxy function of a constituent amino acid is not attached in peptide linkage to another amino-acid residue." [ISBN:0198506732] molecular_function
+ENSG00000196419 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000196419 GO:0016032 viral process "A multi-organism process in which a virus is a participant. The other participant is the host. Includes infection of a host cell, replication of the viral genome, and assembly of progeny virus particles. In some cases the viral genetic material may integrate into the host genome and only subsequently, under particular circumstances, 'complete' its life cycle." [GOC:bf, GOC:jl, GOC:mah] biological_process
+ENSG00000196419 GO:0032481 positive regulation of type I interferon production "Any process that activates or increases the frequency, rate, or extent of type I interferon production. Type I interferons include the interferon-alpha, beta, delta, episilon, zeta, kappa, tau, and omega gene families." [GOC:add, GOC:mah] biological_process
+ENSG00000196419 GO:0044212 transcription regulatory region DNA binding "Interacting selectively and non-covalently with a DNA region that regulates the transcription of a region of DNA, which may be a gene, cistron, or operon. Binding may occur as a sequence specific interaction or as an interaction observed only once a factor has been recruited to the DNA by other factors." [GOC:jl, GOC:txnOH, SO:0005836] molecular_function
+ENSG00000196419 GO:0044822 poly(A) RNA binding "Interacting non-covalently with a poly(A) RNA, a RNA molecule which has a tail of adenine bases." [GOC:jl] molecular_function
+ENSG00000196419 GO:0045087 innate immune response "Innate immune responses are defense responses mediated by germline encoded components that directly recognize components of potential pathogens." [GO_REF:0000022, GOC:add, GOC:ebc, GOC:mtg_15nov05, GOC:mtg_sensu] biological_process
+ENSG00000196419 GO:0045892 negative regulation of transcription, DNA-templated "Any process that stops, prevents, or reduces the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000196419 GO:0045893 positive regulation of transcription, DNA-templated "Any process that activates or increases the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000196419 GO:0045944 positive regulation of transcription from RNA polymerase II promoter "Any process that activates or increases the frequency, rate or extent of transcription from an RNA polymerase II promoter." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000196419 GO:0051575 5'-deoxyribose-5-phosphate lyase activity "Catalysis of the beta-elimination of the 5' deoxyribose-5-phosphate at an abasic site in DNA where a DNA-(apurinic or apyrimidinic site) lyase has already cleaved the C-O-P bond 3' to the apurinic or apyrimidinic site." [PMID:11251121, PMID:16120966] molecular_function
+ENSG00000196419 GO:0070419 nonhomologous end joining complex "A protein complex that plays a role in DNA double-strand break repair via nonhomologous end joining. Such complexes typically contain a specialized DNA ligase (e.g. Lig4 in eukaryotes) and one or more proteins that bind to DNA ends." [GOC:mah, PMID:17072889, PMID:17938628] cellular_component
+ENSG00000196419 GO:0075713 establishment of integrated proviral latency "A process by which the virus integrates into the host genome and establishes as a stable provirus or prophage." [GOC:jl] biological_process
+ENSG00000196419 GO:0003691 double-stranded telomeric DNA binding "Interacting selectively and non-covalently with double-stranded telomere-associated DNA." [GOC:jl, ISBN:0321000382] molecular_function
+ENSG00000196419 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000196419 GO:0006974 cellular response to DNA damage stimulus "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a stimulus indicating damage to its DNA from environmental insults or errors during metabolism." [GOC:go_curators] biological_process
+ENSG00000196419 GO:0010212 response to ionizing radiation "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a ionizing radiation stimulus. Ionizing radiation is radiation with sufficient energy to remove electrons from atoms and may arise from spontaneous decay of unstable isotopes, resulting in alpha and beta particles and gamma rays. Ionizing radiation also includes X-rays." [PMID:12509526] biological_process
+ENSG00000196419 GO:0050769 positive regulation of neurogenesis "Any process that activates or increases the frequency, rate or extent of neurogenesis, the origin and formation of neurons." [GOC:ai] biological_process
+ENSG00000196419 GO:0033151 V(D)J recombination "The process in which immune receptor V, D, and J, or V and J gene segments, depending on the specific receptor, are recombined within a single locus utilizing the conserved heptamer and nonomer recombination signal sequences (RSS)." [GOC:add, ISBN:0781700221, ISBN:0781735149] biological_process
+ENSG00000196419 GO:0007420 brain development "The process whose specific outcome is the progression of the brain over time, from its formation to the mature structure. Brain development begins with patterning events in the neural tube and ends with the mature structure that is the center of thought and emotion. The brain is responsible for the coordination and control of bodily activities and the interpretation of information from the senses (sight, hearing, smell, etc.)." [GOC:dph, GOC:jid, GOC:tb, UBERON:0000955] biological_process
+ENSG00000196419 GO:0071481 cellular response to X-ray "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of X-ray radiation. An X-ray is a form of electromagnetic radiation with a wavelength in the range of 10 nanometers to 100 picometers (corresponding to frequencies in the range 30 PHz to 3 EHz)." [GOC:mah] biological_process
+ENSG00000196419 GO:0071475 cellular hyperosmotic salinity response "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of detection of, or exposure to, an increase in the concentration of salt (particularly but not exclusively sodium and chloride ions) in the environment." [GOC:mah] biological_process
+ENSG00000196419 GO:0005730 nucleolus "A small, dense body one or more of which are present in the nucleus of eukaryotic cells. It is rich in RNA and protein, is not bounded by a limiting membrane, and is not seen during mitosis. Its prime function is the transcription of the nucleolar DNA into 45S ribosomal-precursor RNA, the processing of this RNA into 5.8S, 18S, and 28S components of ribosomal RNA, and the association of these components with 5S RNA and proteins synthesized outside the nucleolus. This association results in the formation of ribonucleoprotein precursors; these pass into the cytoplasm and mature into the 40S and 60S subunits of the ribosome." [ISBN:0198506732] cellular_component
+ENSG00000211657 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000211657 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000073150 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000073150 GO:0005921 gap junction "A cell-cell junction that is composed of an array of small channels that permit small molecules to pass from one cell to another. At gap junctions, the membranes of two adjacent cells are separated by a uniform narrow gap of about 2-4 nm that is spanned by channel-forming proteins called connexins, which form hexagonal tubes called connexons." [GOC:mah, GOC:mtg_muscle, http://www.vivo.colostate.edu/hbooks/cmb/cells/pmemb/junctions_g.html, ISBN:0815332181] cellular_component
+ENSG00000073150 GO:0006811 ion transport "The directed movement of charged atoms or small charged molecules into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:ai] biological_process
+ENSG00000073150 GO:0007268 synaptic transmission "The process of communication from a neuron to a target (neuron, muscle, or secretory cell) across a synapse." [GOC:jl, MeSH:D009435] biological_process
+ENSG00000073150 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000073150 GO:0034214 protein hexamerization "The formation of a protein hexamer, a macromolecular structure consisting of six noncovalently associated identical or nonidentical subunits." [GOC:ecd] biological_process
+ENSG00000073150 GO:0055077 gap junction hemi-channel activity "A wide pore channel activity that enables the transport of a solute across a membrane via a gap junction hemi-channel. Two gap junction hemi-channels coupled together form a complete gap junction." [GOC:dgh] molecular_function
+ENSG00000073150 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000073150 GO:0022857 transmembrane transporter activity "Enables the transfer of a substance from one side of a membrane to the other." [GOC:mtg_transport, ISBN:0815340729] molecular_function
+ENSG00000073150 GO:0006461 protein complex assembly "The aggregation, arrangement and bonding together of a set of components to form a protein complex." [GOC:ai] biological_process
+ENSG00000073150 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000073150 GO:0022607 cellular component assembly "The aggregation, arrangement and bonding together of a cellular component." [GOC:isa_complete] biological_process
+ENSG00000073150 GO:0065003 macromolecular complex assembly "The aggregation, arrangement and bonding together of a set of macromolecules to form a complex." [GOC:jl] biological_process
+ENSG00000073150 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000073150 GO:0007267 cell-cell signaling "Any process that mediates the transfer of information from one cell to another." [GOC:mah] biological_process
+ENSG00000073150 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000073150 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000073150 GO:0055085 transmembrane transport "The process in which a solute is transported from one side of a membrane to the other." [GOC:dph, GOC:jid] biological_process
+ENSG00000073150 GO:0002931 response to ischemia "Any process that results in a change in state or activity of an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a inadequate blood supply." [GOC:hjd] biological_process
+ENSG00000073150 GO:0015267 channel activity "Catalysis of energy-independent facilitated diffusion, mediated by passage of a solute through a transmembrane aqueous pore or channel. Stereospecificity is not exhibited but this transport may be specific for a particular molecular species or class of molecules." [GOC:mtg_transport, ISBN:0815340729, TC:1.-.-.-.-] molecular_function
+ENSG00000073150
+ENSG00000184708 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000184708 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000184708 GO:0008565 protein transporter activity "Enables the directed movement of proteins into, out of or within a cell, or between cells." [ISBN:0198506732] molecular_function
+ENSG00000184708 GO:0015031 protein transport "The directed movement of proteins into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:ai] biological_process
+ENSG00000184708 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000184708 GO:0043231 intracellular membrane-bounded organelle "Organized structure of distinctive morphology and function, bounded by a single or double lipid bilayer membrane and occurring within the cell. Includes the nucleus, mitochondria, plastids, vacuoles, and vesicles. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000184708 GO:0044822 poly(A) RNA binding "Interacting non-covalently with a poly(A) RNA, a RNA molecule which has a tail of adenine bases." [GOC:jl] molecular_function
+ENSG00000184708 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000184708 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000184708 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000184708 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000184708 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000184708 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000184708 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000184708 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000184708
+ENSG00000196588 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000196588 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000196588 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000196588 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000196588 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000196588 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000196588 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000196588 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000196588 GO:0008092 cytoskeletal protein binding "Interacting selectively and non-covalently with any protein component of any cytoskeleton (actin, microtubule, or intermediate filament cytoskeleton)." [GOC:mah] molecular_function
+ENSG00000196588 GO:0000988 protein binding transcription factor activity "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules), in order to modulate transcription. A protein binding transcription factor may or may not also interact with the template nucleic acid (either DNA or RNA) as well." [GOC:txnOH] molecular_function
+ENSG00000196588 GO:0003713 transcription coactivator activity "Interacting selectively and non-covalently with a activating transcription factor and also with the basal transcription machinery in order to increase the frequency, rate or extent of transcription. Cofactors generally do not bind the template nucleic acid, but rather mediate protein-protein interactions between activating transcription factors and the basal transcription machinery." [GOC:txnOH, PMID:10213677, PMID:16858867] molecular_function
+ENSG00000196588 GO:0003779 actin binding "Interacting selectively and non-covalently with monomeric or multimeric forms of actin, including actin filaments." [GOC:clt] molecular_function
+ENSG00000196588 GO:0003785 actin monomer binding "Interacting selectively and non-covalently with monomeric actin, also known as G-actin." [GOC:ai] molecular_function
+ENSG00000196588 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000196588 GO:0006351 transcription, DNA-templated "The cellular synthesis of RNA on a template of DNA." [GOC:jl, GOC:txnOH] biological_process
+ENSG00000196588 GO:0010735 positive regulation of transcription via serum response element binding "Any process that increases the frequency, rate or extent of the specifically regulated synthesis of RNA from DNA encoding a specific set of genes as a result of a transcription factor interacting with a serum response element (SRE). A serum response element is a short sequence with dyad symmetry found in the promoters of some of the cellular immediate-early genes, regulated by serum." [GOC:BHF, GOC:dph, GOC:rl, GOC:tb] biological_process
+ENSG00000196588 GO:0043522 leucine zipper domain binding "Interacting selectively and non-covalently with a leucine zipper domain, a protein secondary structure exhibiting a periodic repetition of leucine residues at every seventh position over a distance covering eight helical turns." [GOC:jl, InterPro:IPR002158] molecular_function
+ENSG00000196588 GO:0045944 positive regulation of transcription from RNA polymerase II promoter "Any process that activates or increases the frequency, rate or extent of transcription from an RNA polymerase II promoter." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000196588 GO:0051145 smooth muscle cell differentiation "The process in which a relatively unspecialized cell acquires specialized features of a smooth muscle cell; smooth muscle lacks transverse striations in its constituent fibers and are almost always involuntary." [CL:0000192, GOC:ai] biological_process
+ENSG00000196588 GO:0030154 cell differentiation "The process in which relatively unspecialized cells, e.g. embryonic or regenerative cells, acquire specialized structural and/or functional features that characterize the cells, tissues, or organs of the mature organism or some other relatively stable phase of the organism's life history. Differentiation includes the processes involved in commitment of a cell to a specific fate and its subsequent development to the mature state." [ISBN:0198506732] biological_process
+ENSG00000196588 GO:0003700 sequence-specific DNA binding transcription factor activity "Interacting selectively and non-covalently with a specific DNA sequence in order to modulate transcription. The transcription factor may or may not also interact selectively with a protein or macromolecular complex." [GOC:curators, GOC:txnOH] molecular_function
+ENSG00000196588 GO:0045893 positive regulation of transcription, DNA-templated "Any process that activates or increases the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000196588 GO:2001234 negative regulation of apoptotic signaling pathway "Any process that stops, prevents or reduces the frequency, rate or extent of apoptotic signaling pathway." [GOC:mtg_apoptosis] biological_process
+ENSG00000196588 GO:0000976 transcription regulatory region sequence-specific DNA binding "Interacting selectively and non-covalently with a specific sequence of DNA that is part of a regulatory region that controls transcription of that section of the DNA. The transcribed region might be described as a gene, cistron, or operon." [GOC:txnOH] molecular_function
+ENSG00000196588 GO:0043154 negative regulation of cysteine-type endopeptidase activity involved in apoptotic process "Any process that stops, prevents, or reduces the frequency, rate or extent of a cysteine-type endopeptidase activity involved in the apoptotic process." [GOC:jl, GOC:mtg_apoptosis] biological_process
+ENSG00000196588 GO:0006357 regulation of transcription from RNA polymerase II promoter "Any process that modulates the frequency, rate or extent of transcription from an RNA polymerase II promoter." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000196588 GO:0003676 nucleic acid binding "Interacting selectively and non-covalently with any nucleic acid." [GOC:jl] molecular_function
+ENSG00000196588
+ENSG00000172967 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000172967 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000172967 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000242114 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000242114 GO:0008219 cell death "Any biological process that results in permanent cessation of all vital functions of a cell. A cell should be considered dead when any one of the following molecular or morphological criteria is met: (1) the cell has lost the integrity of its plasma membrane; (2) the cell, including its nucleus, has undergone complete fragmentation into discrete bodies (frequently referred to as \"apoptotic bodies\"); and/or (3) its corpse (or its fragments) have been engulfed by an adjacent cell in vivo." [GOC:mah, GOC:mtg_apoptosis] biological_process
+ENSG00000242114 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000242114 GO:0007005 mitochondrion organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of a mitochondrion; includes mitochondrial morphogenesis and distribution, and replication of the mitochondrial genome as well as synthesis of new mitochondrial components." [GOC:dph, GOC:jl, GOC:mah, GOC:sgd_curators, PMID:9786946] biological_process
+ENSG00000242114 GO:0000266 mitochondrial fission "The division of a mitochondrion within a cell to form two or more separate mitochondrial compartments." [PMID:11038192] biological_process
+ENSG00000242114 GO:0005743 mitochondrial inner membrane "The inner, i.e. lumen-facing, lipid bilayer of the mitochondrial envelope. It is highly folded to form cristae." [GOC:ai] cellular_component
+ENSG00000242114 GO:0006915 apoptotic process "A programmed cell death process which begins when a cell receives an internal (e.g. DNA damage) or external signal (e.g. an extracellular death ligand), and proceeds through a series of biochemical events (signaling pathway phase) which trigger an execution phase. The execution phase is the last step of an apoptotic process, and is typically characterized by rounding-up of the cell, retraction of pseudopodes, reduction of cellular volume (pyknosis), chromatin condensation, nuclear fragmentation (karyorrhexis), plasma membrane blebbing and fragmentation of the cell into apoptotic bodies. When the execution phase is completed, the cell has died." [GOC:dhl, GOC:ecd, GOC:go_curators, GOC:mtg_apoptosis, GOC:tb, ISBN:0198506732, PMID:18846107, PMID:21494263] biological_process
+ENSG00000242114 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000242114
+ENSG00000215012 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000215012 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000215012 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000215012 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000215012 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000215012 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000215012 GO:0051881 regulation of mitochondrial membrane potential "Any process that modulates the establishment or extent of the mitochondrial membrane potential, the electric potential existing across the mitochondrial membrane arising from charges in the membrane itself and from the charges present in the media on either side of the membrane." [GOC:ai] biological_process
+ENSG00000215012 GO:0097345 mitochondrial outer membrane permeabilization "The process by which the mitochondrial outer membrane becomes permeable to the passing of proteins and other molecules from the intermembrane space to the cytosol as part of the apoptotic signaling pathway." [GOC:BHF, GOC:mtg_apoptosis, GOC:pg, PMID:21041309] biological_process
+ENSG00000215012 GO:0007005 mitochondrion organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of a mitochondrion; includes mitochondrial morphogenesis and distribution, and replication of the mitochondrial genome as well as synthesis of new mitochondrial components." [GOC:dph, GOC:jl, GOC:mah, GOC:sgd_curators, PMID:9786946] biological_process
+ENSG00000215012 GO:0061024 membrane organization "A process which results in the assembly, arrangement of constituent parts, or disassembly of a membrane. A membrane is a double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:dph, GOC:tb] biological_process
+ENSG00000215012
+ENSG00000213923 GO:0005524 ATP binding "Interacting selectively and non-covalently with ATP, adenosine 5'-triphosphate, a universally important coenzyme and enzyme regulator." [ISBN:0198506732] molecular_function
+ENSG00000213923 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000213923 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000213923 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000213923 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000213923 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000213923 GO:0006259 DNA metabolic process "Any cellular metabolic process involving deoxyribonucleic acid. This is one of the two main types of nucleic acid, consisting of a long, unbranched macromolecule formed from one, or more commonly, two, strands of linked deoxyribonucleotides." [ISBN:0198506732] biological_process
+ENSG00000213923 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000213923 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000213923 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000213923 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000213923 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000213923 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000213923 GO:0016301 kinase activity "Catalysis of the transfer of a phosphate group, usually from ATP, to a substrate molecule." [ISBN:0198506732] molecular_function
+ENSG00000213923 GO:0007049 cell cycle "The progression of biochemical and morphological phases and events that occur in a cell during successive cell replication or nuclear replication events. Canonically, the cell cycle comprises the replication and segregation of genetic material followed by the division of the cell, but in endocycles or syncytial cells nuclear replication or nuclear division may not be followed by cell division." [GOC:go_curators, GOC:mtg_cell_cycle] biological_process
+ENSG00000213923 GO:0000086 G2/M transition of mitotic cell cycle "The mitotic cell cycle transition by which a cell in G2 commits to M phase. The process begins when the kinase activity of M cyclin/CDK complex reaches a threshold high enough for the cell cycle to proceed. This is accomplished by activating a positive feedback loop that results in the accumulation of unphosphorylated and active M cyclin/CDK complex." [GOC:mtg_cell_cycle] biological_process
+ENSG00000213923 GO:0000278 mitotic cell cycle "Progression through the phases of the mitotic cell cycle, the most common eukaryotic cell cycle, which canonically comprises four successive phases called G1, S, G2, and M and includes replication of the genome and the subsequent segregation of chromosomes into daughter cells. In some variant cell cycles nuclear replication or nuclear division may not be followed by cell division, or G1 and G2 phases may be absent." [GOC:mah, ISBN:0815316194, Reactome:69278] biological_process
+ENSG00000213923 GO:0004672 protein kinase activity "Catalysis of the phosphorylation of an amino acid residue in a protein, usually according to the reaction: a protein + ATP = a phosphoprotein + ADP." [MetaCyc:PROTEIN-KINASE-RXN] molecular_function
+ENSG00000213923 GO:0004674 protein serine/threonine kinase activity "Catalysis of the reactions: ATP + protein serine = ADP + protein serine phosphate, and ATP + protein threonine = ADP + protein threonine phosphate." [GOC:bf] molecular_function
+ENSG00000213923 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000213923 GO:0006281 DNA repair "The process of restoring DNA after damage. Genomes are subject to damage by chemical and physical agents in the environment (e.g. UV and ionizing radiations, chemical mutagens, fungal and bacterial toxins, etc.) and by free radicals or alkylating agents endogenously generated in metabolism. DNA is also damaged because of errors during its replication. A variety of different DNA repair pathways have been reported that include direct reversal, base excision repair, nucleotide excision repair, photoreactivation, bypass, double-strand break repair pathway, and mismatch repair pathway." [PMID:11563486] biological_process
+ENSG00000213923 GO:0006468 protein phosphorylation "The process of introducing a phosphate group on to a protein." [GOC:hb] biological_process
+ENSG00000213923 GO:0032436 positive regulation of proteasomal ubiquitin-dependent protein catabolic process "Any process that activates or increases the frequency, rate or extent of the breakdown of a protein or peptide by hydrolysis of its peptide bonds, initiated by the covalent attachment of ubiquitin, and mediated by the proteasome." [GOC:mah] biological_process
+ENSG00000213923 GO:0032922 circadian regulation of gene expression "Any process that modulates the frequency, rate or extent of gene expression such that an expression pattern recurs with a regularity of approximately 24 hours." [GOC:mah] biological_process
+ENSG00000213923 GO:0042752 regulation of circadian rhythm "Any process that modulates the frequency, rate or extent of a circadian rhythm. A circadian rhythm is a biological process in an organism that recurs with a regularity of approximately 24 hours." [GOC:dph, GOC:jl, GOC:tb] biological_process
+ENSG00000213923 GO:0044822 poly(A) RNA binding "Interacting non-covalently with a poly(A) RNA, a RNA molecule which has a tail of adenine bases." [GOC:jl] molecular_function
+ENSG00000213923 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000213923 GO:0016772 transferase activity, transferring phosphorus-containing groups "Catalysis of the transfer of a phosphorus-containing group from one compound (donor) to another (acceptor)." [GOC:jl, ISBN:0198506732] molecular_function
+ENSG00000213923 GO:0004713 protein tyrosine kinase activity "Catalysis of the reaction: ATP + a protein tyrosine = ADP + protein tyrosine phosphate." [EC:2.7.10] molecular_function
+ENSG00000213923
+ENSG00000213923 GO:0030529 ribonucleoprotein complex "A macromolecular complex containing both protein and RNA molecules." [GOC:krc] cellular_component
+ENSG00000213923 GO:0034613 cellular protein localization "Any process in which a protein is transported to, and/or maintained in, a specific location at the level of a cell. Localization at the cellular level encompasses movement within the cell, from within the cell to the cell surface, or from one location to another at the surface of a cell." [GOC:mah] biological_process
+ENSG00000213923 GO:0090263 positive regulation of canonical Wnt signaling pathway "Any process that increases the rate, frequency, or extent of the Wnt signaling pathway through beta-catenin, the series of molecular signals initiated by binding of a Wnt protein to a frizzled family receptor on the surface of the target cell, followed by propagation of the signal via beta-catenin, and ending with a change in transcription of target genes." [GOC:tb] biological_process
+ENSG00000213923 GO:0030178 negative regulation of Wnt signaling pathway "Any process that stops, prevents, or reduces the frequency, rate or extent of the Wnt signaling pathway." [GOC:dph, GOC:go_curators, GOC:tb] biological_process
+ENSG00000213923 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000077942 GO:0005576 extracellular region "The space external to the outermost structure of a cell. For cells without external protective or external encapsulating structures this refers to space outside of the plasma membrane. This term covers the host cell environment outside an intracellular parasite." [GOC:go_curators] cellular_component
+ENSG00000077942 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000077942
+ENSG00000077942 GO:0005509 calcium ion binding "Interacting selectively and non-covalently with calcium ions (Ca2+)." [GOC:ai] molecular_function
+ENSG00000077942 GO:0005578 proteinaceous extracellular matrix "A layer consisting mainly of proteins (especially collagen) and glycosaminoglycans (mostly as proteoglycans) that forms a sheet underlying or overlying cells such as endothelial and epithelial cells. The proteins are secreted by cells in the vicinity. An example of this component is found in Mus musculus." [GOC:mtg_sensu, ISBN:0198547684] cellular_component
+ENSG00000077942 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000077942 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000077942 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000077942 GO:0005201 extracellular matrix structural constituent "The action of a molecule that contributes to the structural integrity of the extracellular matrix." [GOC:mah] molecular_function
+ENSG00000077942 GO:0005615 extracellular space "That part of a multicellular organism outside the cells proper, usually taken to be outside the plasma membranes, and occupied by fluid." [ISBN:0198547684] cellular_component
+ENSG00000077942 GO:0016032 viral process "A multi-organism process in which a virus is a participant. The other participant is the host. Includes infection of a host cell, replication of the viral genome, and assembly of progeny virus particles. In some cases the viral genetic material may integrate into the host genome and only subsequently, under particular circumstances, 'complete' its life cycle." [GOC:bf, GOC:jl, GOC:mah] biological_process
+ENSG00000077942 GO:0030198 extracellular matrix organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of an extracellular matrix." [GOC:mah] biological_process
+ENSG00000077942 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000077942 GO:0031012 extracellular matrix "A structure lying external to one or more cells, which provides structural support for cells or tissues; may be completely external to the cell (as in animals and bacteria) or be part of the cell (as in plants)." [GOC:mah, NIF_Subcellular:nlx_subcell_20090513] cellular_component
+ENSG00000077942 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000077942 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000077942 GO:0044403 symbiosis, encompassing mutualism through parasitism "An interaction between two organisms living together in more or less intimate association. The term host is usually used for the larger (macro) of the two members of a symbiosis. The smaller (micro) member is called the symbiont organism. Microscopic symbionts are often referred to as endosymbionts. The various forms of symbiosis include parasitism, in which the association is disadvantageous or destructive to one of the organisms; mutualism, in which the association is advantageous, or often necessary to one or both and not harmful to either; and commensalism, in which one member of the association benefits while the other is not affected. However, mutualism, parasitism, and commensalism are often not discrete categories of interactions and should rather be perceived as a continuum of interaction ranging from parasitism to mutualism. In fact, the direction of a symbiotic interaction can change during the lifetime of the symbionts due to developmental changes as well as changes in the biotic/abiotic environment in which the interaction occurs." [GOC:cc, http://www.free-definition.com] biological_process
+ENSG00000077942 GO:0005198 structural molecule activity "The action of a molecule that contributes to the structural integrity of a complex or assembly within or outside a cell." [GOC:mah] molecular_function
+ENSG00000077942 GO:0005604 basement membrane "A thin layer of dense material found in various animal tissues interposed between the cells and the adjacent connective tissue. It consists of the basal lamina plus an associated layer of reticulin fibers." [ISBN:0198547684] cellular_component
+ENSG00000077942 GO:0010952 positive regulation of peptidase activity "Any process that increases the frequency, rate or extent of peptidase activity, the hydrolysis of peptide bonds within proteins." [GOC:dph, GOC:tb] biological_process
+ENSG00000077942 GO:0016504 peptidase activator activity "Increases the activity of a peptidase, any enzyme that catalyzes the hydrolysis peptide bonds." [GOC:ai] molecular_function
+ENSG00000077942 GO:0007566 embryo implantation "Attachment of the blastocyst to the uterine lining." [GOC:isa_complete, http://www.medterms.com] biological_process
+ENSG00000211659 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000211659 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000278195 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000278195 GO:0005929 cilium "A specialized eukaryotic organelle that consists of a filiform extrusion of the cell surface and of some cytoplasmic parts. Each cilium is largely bounded by an extrusion of the cytoplasmic (plasma) membrane, and contains a regular longitudinal array of microtubules, anchored to a basal body." [GOC:cilia, GOC:kmv, ISBN:0198547684] cellular_component
+ENSG00000278195 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000278195 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000278195 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000278195 GO:0007267 cell-cell signaling "Any process that mediates the transfer of information from one cell to another." [GOC:mah] biological_process
+ENSG00000278195 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000278195 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000278195 GO:0004871 signal transducer activity "Conveys a signal across a cell to trigger a change in cell function or state. A signal is a physical entity or change in state that is used to transfer information in order to trigger a response." [GOC:go_curators] molecular_function
+ENSG00000278195 GO:0004994 somatostatin receptor activity "Combining with somatostatin to initiate a change in cell activity. Somatostatin is a peptide hormone that regulates the endocrine system by signaling via G-protein coupled somatostatin receptors. Somatostatin has two active forms produced by proteolytic cleavage: a 14 amino acid peptide (SST-14) and a 28 amino acid peptide (SST-28)." [GOC:ai, GOC:bf, Wikipedia:Somatostatin] molecular_function
+ENSG00000278195 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000278195 GO:0005887 integral component of plasma membrane "The component of the plasma membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000278195 GO:0007187 G-protein coupled receptor signaling pathway, coupled to cyclic nucleotide second messenger "The series of molecular signals generated as a consequence of a G-protein coupled receptor binding to its physiological ligand, where the pathway proceeds with activation or inhibition of a nucleotide cyclase activity and a subsequent change in the concentration of a cyclic nucleotide." [GOC:mah, GOC:signaling, ISBN:0815316194] biological_process
+ENSG00000278195 GO:0008285 negative regulation of cell proliferation "Any process that stops, prevents or reduces the rate or extent of cell proliferation." [GOC:go_curators] biological_process
+ENSG00000278195 GO:0008628 hormone-mediated apoptotic signaling pathway "A series of molecular signals mediated by the detection of a hormone, and which triggers the apoptotic signaling pathway in a cell. The pathway starts with reception of a hormone signal, and ends when the execution phase of apoptosis is triggered." [GOC:bf, GOC:mtg_apoptosis] biological_process
+ENSG00000278195 GO:0031513 nonmotile primary cilium "A primary cilium which contains a variable array of axonemal microtubules but does not contain molecular motors. Nonmotile primary cilia are found on many different cell types and function as sensory organelles that concentrate and organize sensory signaling molecules." [GOC:dgh, GOC:kmv, PMID:17009929, PMID:20144998] cellular_component
+ENSG00000278195 GO:0038170 somatostatin signaling pathway "The series of molecular signals generated as a consequence of the peptide somatostatin (SST) binding to a somatostatin receptor (SSTR). The pathway proceeds with the receptor transmitting the signal to a heterotrimeric G-protein complex and ends with regulation of a downstream cellular process, e.g. transcription." [GOC:bf, GOC:nhn, GOC:signaling, PMID:18006219, Wikipedia:Somatostatin] biological_process
+ENSG00000278195 GO:0004930 G-protein coupled receptor activity "Combining with an extracellular signal and transmitting the signal across the membrane by activating an associated G-protein; promotes the exchange of GDP for GTP on the alpha subunit of a heterotrimeric G-protein complex." [GOC:bf, http://www.iuphar-db.org, Wikipedia:GPCR] molecular_function
+ENSG00000278195 GO:0007186 G-protein coupled receptor signaling pathway "A series of molecular signals that proceeds with an activated receptor promoting the exchange of GDP for GTP on the alpha-subunit of an associated heterotrimeric G-protein complex. The GTP-bound activated alpha-G-protein then dissociates from the beta- and gamma-subunits to further transmit the signal within the cell. The pathway begins with receptor-ligand interaction, or for basal GPCR signaling the pathway begins with the receptor activating its G protein in the absence of an agonist, and ends with regulation of a downstream cellular process, e.g. transcription." [GOC:bf, GOC:mah, Wikipedia:G_protein-coupled_receptor] biological_process
+ENSG00000278195 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000278195 GO:0060170 ciliary membrane "The portion of the plasma membrane surrounding a cilium." [GOC:cilia, GOC:dph, GOC:rph] cellular_component
+ENSG00000278195 GO:0042594 response to starvation "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a starvation stimulus, deprivation of nourishment." [GOC:go_curators] biological_process
+ENSG00000278195 GO:0007283 spermatogenesis "The process of formation of spermatozoa, including spermatocytogenesis and spermiogenesis." [GOC:jid, ISBN:9780878933846] biological_process
+ENSG00000278195 GO:0030900 forebrain development "The process whose specific outcome is the progression of the forebrain over time, from its formation to the mature structure. The forebrain is the anterior of the three primary divisions of the developing chordate brain or the corresponding part of the adult brain (in vertebrates, includes especially the cerebral hemispheres, the thalamus, and the hypothalamus and especially in higher vertebrates is the main control center for sensory and associative information processing, visceral functions, and voluntary motor functions)." [http://www2.merriam-webster.com/cgi-bin/mwmednlm?book=Medical&va=forebrain] biological_process
+ENSG00000278195 GO:0021549 cerebellum development "The process whose specific outcome is the progression of the cerebellum over time, from its formation to the mature structure. The cerebellum is the portion of the brain in the back of the head between the cerebrum and the pons. In mice, the cerebellum controls balance for walking and standing, modulates the force and range of movement and is involved in the learning of motor skills." [GO_REF:0000021, GOC:cls, GOC:dgh, GOC:dph, GOC:jid, GOC:mtg_15jun06, ISBN:0838580343] biological_process
+ENSG00000278195 GO:0071385 cellular response to glucocorticoid stimulus "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a glucocorticoid stimulus. Glucocorticoids are hormonal C21 corticosteroids synthesized from cholesterol with the ability to bind with the cortisol receptor and trigger similar effects. Glucocorticoids act primarily on carbohydrate and protein metabolism, and have anti-inflammatory effects." [GOC:mah] biological_process
+ENSG00000278195 GO:0071392 cellular response to estradiol stimulus "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of stimulus by estradiol, a C18 steroid hormone hydroxylated at C3 and C17 that acts as a potent estrogen." [GOC:mah] biological_process
+ENSG00000278195 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000099968 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000099968 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000099968 GO:0006915 apoptotic process "A programmed cell death process which begins when a cell receives an internal (e.g. DNA damage) or external signal (e.g. an extracellular death ligand), and proceeds through a series of biochemical events (signaling pathway phase) which trigger an execution phase. The execution phase is the last step of an apoptotic process, and is typically characterized by rounding-up of the cell, retraction of pseudopodes, reduction of cellular volume (pyknosis), chromatin condensation, nuclear fragmentation (karyorrhexis), plasma membrane blebbing and fragmentation of the cell into apoptotic bodies. When the execution phase is completed, the cell has died." [GOC:dhl, GOC:ecd, GOC:go_curators, GOC:mtg_apoptosis, GOC:tb, ISBN:0198506732, PMID:18846107, PMID:21494263] biological_process
+ENSG00000099968 GO:0006919 activation of cysteine-type endopeptidase activity involved in apoptotic process "Any process that initiates the activity of the inactive enzyme cysteine-type endopeptidase in the context of an apoptotic process." [GOC:al, GOC:dph, GOC:jl, GOC:mtg_apoptosis, GOC:tb, PMID:14744432, PMID:18328827, Wikipedia:Caspase] biological_process
+ENSG00000099968 GO:0008656 cysteine-type endopeptidase activator activity involved in apoptotic process "Increases the rate of proteolysis catalyzed by a cysteine-type endopeptidase involved in the apoptotic process." [GOC:mah, GOC:mtg_apoptosis] molecular_function
+ENSG00000099968 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000099968 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000099968 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000099968 GO:0030234 enzyme regulator activity "Binds to and modulates the activity of an enzyme." [GOC:mah] molecular_function
+ENSG00000099968 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000099968 GO:0051604 protein maturation "Any process leading to the attainment of the full functional capacity of a protein." [GOC:ai] biological_process
+ENSG00000099968 GO:0008219 cell death "Any biological process that results in permanent cessation of all vital functions of a cell. A cell should be considered dead when any one of the following molecular or morphological criteria is met: (1) the cell has lost the integrity of its plasma membrane; (2) the cell, including its nucleus, has undergone complete fragmentation into discrete bodies (frequently referred to as \"apoptotic bodies\"); and/or (3) its corpse (or its fragments) have been engulfed by an adjacent cell in vivo." [GOC:mah, GOC:mtg_apoptosis] biological_process
+ENSG00000099968 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000099968 GO:0042981 regulation of apoptotic process "Any process that modulates the occurrence or rate of cell death by apoptotic process." [GOC:jl, GOC:mtg_apoptosis] biological_process
+ENSG00000099968
+ENSG00000244752 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000244752 GO:0005198 structural molecule activity "The action of a molecule that contributes to the structural integrity of a complex or assembly within or outside a cell." [GOC:mah] molecular_function
+ENSG00000244752 GO:0042802 identical protein binding "Interacting selectively and non-covalently with an identical protein or proteins." [GOC:jl] molecular_function
+ENSG00000244752 GO:0050896 response to stimulus "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a stimulus. The process begins with detection of the stimulus and ends with a change in state or activity or the cell or organism." [GOC:ai, GOC:bf] biological_process
+ENSG00000244752 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000244752 GO:0043010 camera-type eye development "The process whose specific outcome is the progression of the camera-type eye over time, from its formation to the mature structure. The camera-type eye is an organ of sight that receives light through an aperture and focuses it through a lens, projecting it on a photoreceptor field." [GOC:go_curators, GOC:mtg_sensu] biological_process
+ENSG00000244752 GO:0042803 protein homodimerization activity "Interacting selectively and non-covalently with an identical protein to form a homodimer." [GOC:jl] molecular_function
+ENSG00000244752 GO:0007601 visual perception "The series of events required for an organism to receive a visual stimulus, convert it to a molecular signal, and recognize and characterize the signal. Visual stimuli are detected in the form of photons and are processed to form an image." [GOC:ai] biological_process
+ENSG00000244752 GO:0005212 structural constituent of eye lens "The action of a molecule that contributes to the structural integrity of the lens of an eye." [GOC:mah] molecular_function
+ENSG00000100304 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000100304 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100280 GO:0006886 intracellular protein transport "The directed movement of proteins in a cell, including the movement of proteins between specific compartments or structures within a cell, such as organelles of a eukaryotic cell." [GOC:mah] biological_process
+ENSG00000100280 GO:0008565 protein transporter activity "Enables the directed movement of proteins into, out of or within a cell, or between cells." [ISBN:0198506732] molecular_function
+ENSG00000100280 GO:0016192 vesicle-mediated transport "A cellular transport process in which transported substances are moved in membrane-bounded vesicles; transported substances are enclosed in the vesicle lumen or located in the vesicle membrane. The process begins with a step that directs a substance to the forming vesicle, and includes vesicle budding and coating. Vesicles are then targeted to, and fuse with, an acceptor membrane." [GOC:ai, GOC:mah, ISBN:08789310662000] biological_process
+ENSG00000100280 GO:0030117 membrane coat "Any of several different proteinaceous coats that can associate with membranes. Membrane coats include those formed by clathrin plus an adaptor complex, the COPI and COPII complexes, and possibly others. They are found associated with membranes on many vesicles as well as other membrane features such as pits and perhaps tubules." [GOC:mah] cellular_component
+ENSG00000100280 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100280 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100280 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100280 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100280 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100280 GO:0000139 Golgi membrane "The lipid bilayer surrounding any of the compartments of the Golgi apparatus." [GOC:mah] cellular_component
+ENSG00000100280 GO:0005215 transporter activity "Enables the directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells." [GOC:ai, GOC:dgf] molecular_function
+ENSG00000100280 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100280 GO:0005765 lysosomal membrane "The lipid bilayer surrounding the lysosome and separating its contents from the cell cytoplasm." [GOC:ai] cellular_component
+ENSG00000100280 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000100280 GO:0006892 post-Golgi vesicle-mediated transport "The directed movement of substances from the Golgi to other parts of the cell, including organelles and the plasma membrane, mediated by small transport vesicles." [GOC:ai, GOC:mah] biological_process
+ENSG00000100280 GO:0016032 viral process "A multi-organism process in which a virus is a participant. The other participant is the host. Includes infection of a host cell, replication of the viral genome, and assembly of progeny virus particles. In some cases the viral genetic material may integrate into the host genome and only subsequently, under particular circumstances, 'complete' its life cycle." [GOC:bf, GOC:jl, GOC:mah] biological_process
+ENSG00000100280 GO:0019886 antigen processing and presentation of exogenous peptide antigen via MHC class II "The process in which an antigen-presenting cell expresses a peptide antigen of exogenous origin on its cell surface in association with an MHC class II protein complex. The peptide antigen is typically, but not always, processed from a whole protein." [GOC:add, ISBN:0781735149, PMID:15771591] biological_process
+ENSG00000100280 GO:0019901 protein kinase binding "Interacting selectively and non-covalently with a protein kinase, any enzyme that catalyzes the transfer of a phosphate group, usually from ATP, to a protein substrate." [GOC:jl] molecular_function
+ENSG00000100280 GO:0030131 clathrin adaptor complex "A membrane coat adaptor complex that links clathrin to a membrane." [GOC:mah] cellular_component
+ENSG00000100280 GO:0030659 cytoplasmic vesicle membrane "The lipid bilayer surrounding a cytoplasmic vesicle." [GOC:mah] cellular_component
+ENSG00000100280 GO:0032588 trans-Golgi network membrane "The lipid bilayer surrounding any of the compartments that make up the trans-Golgi network." [GOC:mah] cellular_component
+ENSG00000100280 GO:0050690 regulation of defense response to virus by virus "Any viral process that modulates the frequency, rate, or extent of the antiviral response of the host cell or organism." [GOC:ai, GOC:dph] biological_process
+ENSG00000100280 GO:0061024 membrane organization "A process which results in the assembly, arrangement of constituent parts, or disassembly of a membrane. A membrane is a double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:dph, GOC:tb] biological_process
+ENSG00000100280 GO:0019899 enzyme binding "Interacting selectively and non-covalently with any enzyme." [GOC:jl] molecular_function
+ENSG00000100280 GO:0002376 immune system process "Any process involved in the development or functioning of the immune system, an organismal system for calibrated responses to potential internal or invasive threats." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05] biological_process
+ENSG00000100280 GO:0044403 symbiosis, encompassing mutualism through parasitism "An interaction between two organisms living together in more or less intimate association. The term host is usually used for the larger (macro) of the two members of a symbiosis. The smaller (micro) member is called the symbiont organism. Microscopic symbionts are often referred to as endosymbionts. The various forms of symbiosis include parasitism, in which the association is disadvantageous or destructive to one of the organisms; mutualism, in which the association is advantageous, or often necessary to one or both and not harmful to either; and commensalism, in which one member of the association benefits while the other is not affected. However, mutualism, parasitism, and commensalism are often not discrete categories of interactions and should rather be perceived as a continuum of interaction ranging from parasitism to mutualism. In fact, the direction of a symbiotic interaction can change during the lifetime of the symbionts due to developmental changes as well as changes in the biotic/abiotic environment in which the interaction occurs." [GOC:cc, http://www.free-definition.com] biological_process
+ENSG00000100280 GO:0005488 binding "The selective, non-covalent, often stoichiometric, interaction of a molecule with one or more specific sites on another molecule." [GOC:ceb, GOC:mah, ISBN:0198506732] molecular_function
+ENSG00000100280 GO:0015031 protein transport "The directed movement of proteins into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:ai] biological_process
+ENSG00000189060 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000189060 GO:0003677 DNA binding "Any molecular function by which a gene product interacts selectively and non-covalently with DNA (deoxyribonucleic acid)." [GOC:dph, GOC:jl, GOC:tb, GOC:vw] molecular_function
+ENSG00000189060 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000189060 GO:0005856 cytoskeleton "Any of the various filamentous elements that form the internal framework of cells, and typically remain after treatment of the cells with mild detergent to remove membrane constituents and soluble components of the cytoplasm. The term embraces intermediate filaments, microfilaments, microtubules, the microtrabecular lattice, and other structures characterized by a polymeric filamentous nature and long-range order within the cell. The various elements of the cytoskeleton not only serve in the maintenance of cellular shape but also have roles in other cellular functions, including cellular movement, cell division, endocytosis, and movement of organelles." [GOC:mah, ISBN:0198547684, PMID:16959967] cellular_component
+ENSG00000189060 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000189060 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000189060 GO:0008219 cell death "Any biological process that results in permanent cessation of all vital functions of a cell. A cell should be considered dead when any one of the following molecular or morphological criteria is met: (1) the cell has lost the integrity of its plasma membrane; (2) the cell, including its nucleus, has undergone complete fragmentation into discrete bodies (frequently referred to as \"apoptotic bodies\"); and/or (3) its corpse (or its fragments) have been engulfed by an adjacent cell in vivo." [GOC:mah, GOC:mtg_apoptosis] biological_process
+ENSG00000189060 GO:0006461 protein complex assembly "The aggregation, arrangement and bonding together of a set of components to form a protein complex." [GOC:ai] biological_process
+ENSG00000189060 GO:0022607 cellular component assembly "The aggregation, arrangement and bonding together of a cellular component." [GOC:isa_complete] biological_process
+ENSG00000189060 GO:0065003 macromolecular complex assembly "The aggregation, arrangement and bonding together of a set of macromolecules to form a complex." [GOC:jl] biological_process
+ENSG00000189060 GO:0006259 DNA metabolic process "Any cellular metabolic process involving deoxyribonucleic acid. This is one of the two main types of nucleic acid, consisting of a long, unbranched macromolecule formed from one, or more commonly, two, strands of linked deoxyribonucleotides." [ISBN:0198506732] biological_process
+ENSG00000189060 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000189060 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000189060 GO:0034655 nucleobase-containing compound catabolic process "The chemical reactions and pathways resulting in the breakdown of nucleobases, nucleosides, nucleotides and nucleic acids." [GOC:mah] biological_process
+ENSG00000189060 GO:0005794 Golgi apparatus "A compound membranous cytoplasmic organelle of eukaryotic cells, consisting of flattened, ribosome-free vesicles arranged in a more or less regular stack. The Golgi apparatus differs from the endoplasmic reticulum in often having slightly thicker membranes, appearing in sections as a characteristic shallow semicircle so that the convex side (cis or entry face) abuts the endoplasmic reticulum, secretory vesicles emerging from the concave side (trans or exit face). In vertebrate cells there is usually one such organelle, while in invertebrates and plants, where they are known usually as dictyosomes, there may be several scattered in the cytoplasm. The Golgi apparatus processes proteins produced on the ribosomes of the rough endoplasmic reticulum; such processing includes modification of the core oligosaccharides of glycoproteins, and the sorting and packaging of proteins for transport to a variety of cellular locations. Three different regions of the Golgi are now recognized both in terms of structure and function: cis, in the vicinity of the cis face, trans, in the vicinity of the trans face, and medial, lying between the cis and trans regions." [ISBN:0198506732] cellular_component
+ENSG00000189060 GO:0005654 nucleoplasm "That part of the nuclear content other than the chromosomes or the nucleolus." [GOC:ma, ISBN:0124325653] cellular_component
+ENSG00000189060 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000189060 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000189060 GO:0000786 nucleosome "A complex comprised of DNA wound around a multisubunit core and associated proteins, which forms the primary packing unit of DNA into higher order structures." [GOC:elh] cellular_component
+ENSG00000189060 GO:0000790 nuclear chromatin "The ordered and organized complex of DNA, protein, and sometimes RNA, that forms the chromosome in the nucleus." [GOC:elh, PMID:20404130] cellular_component
+ENSG00000189060 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000189060 GO:0005719 nuclear euchromatin "The dispersed less dense form of chromatin in the interphase nucleus. It exists in at least two forms, a some being in the form of transcriptionally active chromatin which is the least condensed, while the rest is inactive euchromatin which is more condensed than active chromatin but less condensed than heterochromatin." [ISBN:0198506732] cellular_component
+ENSG00000189060 GO:0006309 apoptotic DNA fragmentation "The cleavage of DNA during apoptosis, which usually occurs in two stages: cleavage into fragments of about 50 kbp followed by cleavage between nucleosomes to yield 200 bp fragments." [GOC:dph, GOC:mah, GOC:mtg_apoptosis, GOC:tb, ISBN:0721639976, PMID:15723341, PMID:23379520] biological_process
+ENSG00000189060 GO:0006334 nucleosome assembly "The aggregation, arrangement and bonding together of a nucleosome, the beadlike structural units of eukaryotic chromatin composed of histones and DNA." [GOC:mah] biological_process
+ENSG00000189060 GO:0006915 apoptotic process "A programmed cell death process which begins when a cell receives an internal (e.g. DNA damage) or external signal (e.g. an extracellular death ligand), and proceeds through a series of biochemical events (signaling pathway phase) which trigger an execution phase. The execution phase is the last step of an apoptotic process, and is typically characterized by rounding-up of the cell, retraction of pseudopodes, reduction of cellular volume (pyknosis), chromatin condensation, nuclear fragmentation (karyorrhexis), plasma membrane blebbing and fragmentation of the cell into apoptotic bodies. When the execution phase is completed, the cell has died." [GOC:dhl, GOC:ecd, GOC:go_curators, GOC:mtg_apoptosis, GOC:tb, ISBN:0198506732, PMID:18846107, PMID:21494263] biological_process
+ENSG00000189060 GO:0006921 cellular component disassembly involved in execution phase of apoptosis "The breakdown of structures such as organelles, proteins, or other macromolecular structures during apoptosis." [GOC:dph, GOC:mah, GOC:mtg_apoptosis, GOC:tb] biological_process
+ENSG00000189060 GO:0015629 actin cytoskeleton "The part of the cytoskeleton (the internal framework of a cell) composed of actin and associated proteins. Includes actin cytoskeleton-associated complexes." [GOC:jl, ISBN:0395825172, ISBN:0815316194] cellular_component
+ENSG00000189060 GO:0031490 chromatin DNA binding "Interacting selectively and non-covalently with DNA that is assembled into chromatin." [GOC:mah] molecular_function
+ENSG00000189060 GO:0044822 poly(A) RNA binding "Interacting non-covalently with a poly(A) RNA, a RNA molecule which has a tail of adenine bases." [GOC:jl] molecular_function
+ENSG00000189060 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000100055 GO:0005086 ARF guanyl-nucleotide exchange factor activity "Stimulates the exchange of guanyl nucleotides associated with the GTPase ARF. Under normal cellular physiological conditions, the concentration of GTP is higher than that of GDP, favoring the replacement of GDP by GTP in association with the GTPase." [GOC:mah] molecular_function
+ENSG00000100055 GO:0032012 regulation of ARF protein signal transduction "Any process that modulates the frequency, rate or extent of ARF protein signal transduction." [GOC:mah] biological_process
+ENSG00000100055 GO:0043547 positive regulation of GTPase activity "Any process that activates or increases the activity of a GTPase." [GOC:jl] biological_process
+ENSG00000100055 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100055 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100055 GO:0008289 lipid binding "Interacting selectively and non-covalently with a lipid." [GOC:ai] molecular_function
+ENSG00000100055 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100055 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000100055 GO:0030155 regulation of cell adhesion "Any process that modulates the frequency, rate or extent of attachment of a cell to another cell or to the extracellular matrix." [GOC:mah] biological_process
+ENSG00000100055
+ENSG00000100300 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100300 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000100300 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000100300 GO:0031965 nuclear membrane "Either of the lipid bilayers that surround the nucleus and form the nuclear envelope; excludes the intermembrane space." [GOC:mah, GOC:pz] cellular_component
+ENSG00000100300 GO:0043231 intracellular membrane-bounded organelle "Organized structure of distinctive morphology and function, bounded by a single or double lipid bilayer membrane and occurring within the cell. Includes the nucleus, mitochondria, plastids, vacuoles, and vesicles. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100300 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100300 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100300 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100300 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100300 GO:0008289 lipid binding "Interacting selectively and non-covalently with a lipid." [GOC:ai] molecular_function
+ENSG00000100300 GO:0004871 signal transducer activity "Conveys a signal across a cell to trigger a change in cell function or state. A signal is a physical entity or change in state that is used to transfer information in order to trigger a response." [GOC:go_curators] molecular_function
+ENSG00000100300 GO:0008283 cell proliferation "The multiplication or reproduction of cells, resulting in the expansion of a cell population." [GOC:mah, GOC:mb] biological_process
+ENSG00000100300 GO:0006629 lipid metabolic process "The chemical reactions and pathways involving lipids, compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent. Includes fatty acids; neutral fats, other fatty-acid esters, and soaps; long-chain (fatty) alcohols and waxes; sphingoids and other long-chain bases; glycolipids, phospholipids and sphingolipids; and carotenes, polyprenols, sterols, terpenes and other isoprenoids." [GOC:ma] biological_process
+ENSG00000100300 GO:0007267 cell-cell signaling "Any process that mediates the transfer of information from one cell to another." [GOC:mah] biological_process
+ENSG00000100300 GO:0008219 cell death "Any biological process that results in permanent cessation of all vital functions of a cell. A cell should be considered dead when any one of the following molecular or morphological criteria is met: (1) the cell has lost the integrity of its plasma membrane; (2) the cell, including its nucleus, has undergone complete fragmentation into discrete bodies (frequently referred to as \"apoptotic bodies\"); and/or (3) its corpse (or its fragments) have been engulfed by an adjacent cell in vivo." [GOC:mah, GOC:mtg_apoptosis] biological_process
+ENSG00000100300 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100300 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000100300 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100300 GO:0051186 cofactor metabolic process "The chemical reactions and pathways involving a cofactor, a substance that is required for the activity of an enzyme or other protein. Cofactors may be inorganic, such as the metal atoms zinc, iron, and copper in certain forms, or organic, in which case they are referred to as coenzymes. Cofactors may either be bound tightly to active sites or bind loosely with the substrate." [GOC:ai] biological_process
+ENSG00000100300 GO:0006605 protein targeting "The process of targeting specific proteins to particular membrane-bounded subcellular organelles. Usually requires an organelle specific protein sequence motif." [GOC:ma] biological_process
+ENSG00000100300 GO:0005741 mitochondrial outer membrane "The outer, i.e. cytoplasm-facing, lipid bilayer of the mitochondrial envelope." [GOC:ai] cellular_component
+ENSG00000100300 GO:0006626 protein targeting to mitochondrion "The process of directing proteins towards and into the mitochondrion, usually mediated by mitochondrial proteins that recognize signals contained within the imported protein." [GOC:mcc, ISBN:0716731363] biological_process
+ENSG00000100300 GO:0006783 heme biosynthetic process "The chemical reactions and pathways resulting in the formation of heme, any compound of iron complexed in a porphyrin (tetrapyrrole) ring, from less complex precursors." [GOC:jl] biological_process
+ENSG00000100300 GO:0006820 anion transport "The directed movement of anions, atoms or small molecules with a net negative charge, into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:ai] biological_process
+ENSG00000100300 GO:0006869 lipid transport "The directed movement of lipids into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore. Lipids are compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent." [ISBN:0198506732] biological_process
+ENSG00000100300 GO:0006915 apoptotic process "A programmed cell death process which begins when a cell receives an internal (e.g. DNA damage) or external signal (e.g. an extracellular death ligand), and proceeds through a series of biochemical events (signaling pathway phase) which trigger an execution phase. The execution phase is the last step of an apoptotic process, and is typically characterized by rounding-up of the cell, retraction of pseudopodes, reduction of cellular volume (pyknosis), chromatin condensation, nuclear fragmentation (karyorrhexis), plasma membrane blebbing and fragmentation of the cell into apoptotic bodies. When the execution phase is completed, the cell has died." [GOC:dhl, GOC:ecd, GOC:go_curators, GOC:mtg_apoptosis, GOC:tb, ISBN:0198506732, PMID:18846107, PMID:21494263] biological_process
+ENSG00000100300 GO:0007268 synaptic transmission "The process of communication from a neuron to a target (neuron, muscle, or secretory cell) across a synapse." [GOC:jl, MeSH:D009435] biological_process
+ENSG00000100300 GO:0008202 steroid metabolic process "The chemical reactions and pathways involving steroids, compounds with a 1,2,cyclopentanoperhydrophenanthrene nucleus." [ISBN:0198547684] biological_process
+ENSG00000100300 GO:0008503 benzodiazepine receptor activity "Combining with benzodiazepines, a class of drugs with hypnotic, anxiolytic, anticonvulsive, amnestic and myorelaxant properties, to initiate a change in cell activity." [GOC:jl] molecular_function
+ENSG00000100300 GO:0015485 cholesterol binding "Interacting selectively and non-covalently with cholesterol (cholest-5-en-3-beta-ol); the principal sterol of vertebrates and the precursor of many steroids, including bile acids and steroid hormones." [GOC:jl, ISBN:0198506732] molecular_function
+ENSG00000100300 GO:0032374 regulation of cholesterol transport "Any process that modulates the frequency, rate or extent of the directed movement of cholesterol into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:mah] biological_process
+ENSG00000100300 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000100300 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100300 GO:0042493 response to drug "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a drug stimulus. A drug is a substance used in the diagnosis, treatment or prevention of a disease." [GOC:jl] biological_process
+ENSG00000100300 GO:0032570 response to progesterone "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a progesterone stimulus." [GOC:sl] biological_process
+ENSG00000100300 GO:0007568 aging "A developmental process that is a deterioration and loss of function over time. Aging includes loss of functions such as resistance to disease, homeostasis, and fertility, as well as wear and tear. Aging includes cellular senescence, but is more inclusive. May precede death (GO:0016265) and may succeed developmental maturation (GO:0021700)." [GOC:PO_curators] biological_process
+ENSG00000100300 GO:0043065 positive regulation of apoptotic process "Any process that activates or increases the frequency, rate or extent of cell death by apoptotic process." [GOC:jl, GOC:mtg_apoptosis] biological_process
+ENSG00000100300 GO:0048678 response to axon injury "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of an axon injury stimulus." [GOC:dgh, GOC:dph, GOC:jid, GOC:lm] biological_process
+ENSG00000100300 GO:0051928 positive regulation of calcium ion transport "Any process that activates or increases the frequency, rate or extent of the directed movement of calcium ions into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:ai] biological_process
+ENSG00000100300 GO:0071222 cellular response to lipopolysaccharide "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a lipopolysaccharide stimulus; lipopolysaccharide is a major component of the cell wall of gram-negative bacteria." [GOC:mah] biological_process
+ENSG00000100300 GO:0033574 response to testosterone "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a testosterone stimulus." [GOC:sl] biological_process
+ENSG00000100300 GO:0045019 negative regulation of nitric oxide biosynthetic process "Any process that stops, prevents, or reduces the frequency, rate or extent of the chemical reactions and pathways resulting in the formation of nitric oxide." [GOC:go_curators] biological_process
+ENSG00000100300 GO:0032720 negative regulation of tumor necrosis factor production "Any process that stops, prevents, or reduces the frequency, rate, or extent of tumor necrosis factor production." [GOC:mah] biological_process
+ENSG00000100300 GO:0050810 regulation of steroid biosynthetic process "Any process that modulates the frequency, rate or extent of the chemical reactions and pathways resulting in the formation of steroids, compounds with a 1,2,cyclopentanoperhydrophenanthrene nucleus." [GOC:ai] biological_process
+ENSG00000100300 GO:0051901 positive regulation of mitochondrial depolarization "Any process that activates, maintains or increases the frequency, rate or extent of the change in the membrane potential of the mitochondria from negative to positive." [GOC:ai] biological_process
+ENSG00000100300 GO:0060252 positive regulation of glial cell proliferation "Any process that activates or increases the rate or extent of glial cell proliferation." [GOC:dph, GOC:sl, GOC:tb] biological_process
+ENSG00000100300 GO:0006821 chloride transport "The directed movement of chloride into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:krc] biological_process
+ENSG00000100300 GO:0008347 glial cell migration "The orderly movement of a glial cell, non-neuronal cells that provide support and nutrition, maintain homeostasis, form myelin, and participate in signal transmission in the nervous system." [GOC:jl, GOC:mtg_sensu] biological_process
+ENSG00000100300 GO:0006811 ion transport "The directed movement of charged atoms or small charged molecules into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:ai] biological_process
+ENSG00000100300 GO:0006694 steroid biosynthetic process "The chemical reactions and pathways resulting in the formation of steroids, compounds with a 1,2,cyclopentanoperhydrophenanthrene nucleus; includes de novo formation and steroid interconversion by modification." [GOC:go_curators] biological_process
+ENSG00000100300 GO:2000379 positive regulation of reactive oxygen species metabolic process "Any process that activates or increases the frequency, rate or extent of reactive oxygen species metabolic process." [GOC:mah] biological_process
+ENSG00000100300 GO:0071476 cellular hypotonic response "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of detection of, or exposure to, a hypotonic environment, i.e. an environment with a lower concentration of solutes than the organism or cell." [GOC:mah] biological_process
+ENSG00000100300 GO:0060242 contact inhibition "The cellular process in which cells stop growing or dividing in response to increased cell density." [GOC:dph, PMID:17376520] biological_process
+ENSG00000100300 GO:0010940 positive regulation of necrotic cell death "Any process that increases the rate, frequency or extent of necrotic cell death. Necrotic cell death is a cell death process that is morphologically characterized by a gain in cell volume (oncosis), swelling of organelles, plasma membrane rupture and subsequent loss of intracellular contents." [PMID:16507998] biological_process
+ENSG00000100300 GO:0005497 androgen binding "Interacting selectively and non-covalently with any androgen, male sex hormones." [CHEBI:50113, GOC:jl] molecular_function
+ENSG00000100300 GO:0048266 behavioral response to pain "Any process that results in a change in the behavior of an organism as a result of a pain stimulus. Pain stimuli cause activation of nociceptors, peripheral receptors for pain, include receptors which are sensitive to painful mechanical stimuli, extreme heat or cold, and chemical stimuli." [GOC:jid] biological_process
+ENSG00000100300 GO:0030325 adrenal gland development "The process whose specific outcome is the progression of the adrenal gland over time, from its formation to the mature structure. This gland can either be a discrete structure located bilaterally above each kidney, or a cluster of cells in the head kidney that perform the functions of the adrenal gland. In either case, this organ consists of two cells types, aminergic chromaffin cells and steroidogenic cortical cells." [GOC:dgh] biological_process
+ENSG00000100300 GO:0060253 negative regulation of glial cell proliferation "Any process that stops or decreases the rate or extent of glial cell proliferation." [GOC:dph, GOC:sl, GOC:tb] biological_process
+ENSG00000100300 GO:0010266 response to vitamin B1 "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a vitamin B1 stimulus." [GOC:pz] biological_process
+ENSG00000100300 GO:0048265 response to pain "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a pain stimulus. Pain stimuli cause activation of nociceptors, peripheral receptors for pain, include receptors which are sensitive to painful mechanical stimuli, extreme heat or cold, and chemical stimuli." [GOC:jid, PMID:10203867, PMID:12723742, PMID:12843304, Wikipedia:Pain] biological_process
+ENSG00000100300 GO:0071294 cellular response to zinc ion "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a zinc ion stimulus." [GOC:mah] biological_process
+ENSG00000100300 GO:0010042 response to manganese ion "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a manganese ion stimulus." [GOC:sm] biological_process
+ENSG00000100300 GO:0014012 peripheral nervous system axon regeneration "The regrowth of axons outside the central nervous system (outside the brain and spinal cord) following an axonal injury." [GOC:ef] biological_process
+ENSG00000234409
+ENSG00000196576 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000196576 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000196576 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000196576 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000196576 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000196576 GO:0004871 signal transducer activity "Conveys a signal across a cell to trigger a change in cell function or state. A signal is a physical entity or change in state that is used to transfer information in order to trigger a response." [GOC:go_curators] molecular_function
+ENSG00000196576 GO:0048856 anatomical structure development "The biological process whose specific outcome is the progression of an anatomical structure from an initial condition to its mature state. This process begins with the formation of the structure and ends with the mature structure, whatever form that may be including its natural destruction. An anatomical structure is any biological entity that occupies space and is distinguished from its surroundings. Anatomical structures can be macroscopic such as a carpel, or microscopic such as an acrosome." [GO_REF:0000021, GOC:mtg_15jun06] biological_process
+ENSG00000196576 GO:0008283 cell proliferation "The multiplication or reproduction of cells, resulting in the expansion of a cell population." [GOC:mah, GOC:mb] biological_process
+ENSG00000196576 GO:0005622 intracellular "The living contents of a cell; the matter contained within (but not including) the plasma membrane, usually taken to exclude large vacuoles and masses of secretory or ingested material. In eukaryotes it includes the nucleus and cytoplasm." [ISBN:0198506732] cellular_component
+ENSG00000196576 GO:0030234 enzyme regulator activity "Binds to and modulates the activity of an enzyme." [GOC:mah] molecular_function
+ENSG00000196576 GO:0048646 anatomical structure formation involved in morphogenesis "The developmental process pertaining to the initial formation of an anatomical structure from unspecified parts. This process begins with the specific processes that contribute to the appearance of the discrete structure and ends when the structural rudiment is recognizable. An anatomical structure is any biological entity that occupies space and is distinguished from its surroundings. Anatomical structures can be macroscopic such as a carpel, or microscopic such as an acrosome." [GOC:dph, GOC:jid, GOC:tb] biological_process
+ENSG00000196576 GO:0001843 neural tube closure "The last step in the formation of the neural tube, where the paired neural folds are brought together and fuse at the dorsal midline." [GOC:dph, ISBN:0878932437] biological_process
+ENSG00000196576 GO:0001932 regulation of protein phosphorylation "Any process that modulates the frequency, rate or extent of addition of phosphate groups into an amino acid in a protein." [GOC:hjd] biological_process
+ENSG00000196576 GO:0005096 GTPase activator activity "Increases the activity of a GTPase, an enzyme that catalyzes the hydrolysis of GTP." [GOC:mah] molecular_function
+ENSG00000196576 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000196576 GO:0005887 integral component of plasma membrane "The component of the plasma membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000196576 GO:0007405 neuroblast proliferation "The expansion of a neuroblast population by cell division. A neuroblast is any cell that will divide and give rise to a neuron." [GOC:ai, GOC:mtg_sensu, GOC:sart] biological_process
+ENSG00000196576 GO:0007420 brain development "The process whose specific outcome is the progression of the brain over time, from its formation to the mature structure. Brain development begins with patterning events in the neural tube and ends with the mature structure that is the center of thought and emotion. The brain is responsible for the coordination and control of bodily activities and the interpretation of information from the senses (sight, hearing, smell, etc.)." [GOC:dph, GOC:jid, GOC:tb, UBERON:0000955] biological_process
+ENSG00000196576 GO:0008360 regulation of cell shape "Any process that modulates the surface configuration of a cell." [GOC:dph, GOC:go_curators, GOC:tb] biological_process
+ENSG00000196576 GO:0009986 cell surface "The external part of the cell wall and/or plasma membrane." [GOC:jl, GOC:mtg_sensu, GOC:sm] cellular_component
+ENSG00000196576 GO:0017154 semaphorin receptor activity "Combining with a semaphorin, and transmitting the signal from one side of the membrane to the other to initiate a change in cell activity." [GOC:mah, GOC:signaling, PMID:15239958] molecular_function
+ENSG00000196576 GO:0032319 regulation of Rho GTPase activity "Any process that modulates the activity of a GTPase of the Rho family." [GOC:mah] biological_process
+ENSG00000196576 GO:0043547 positive regulation of GTPase activity "Any process that activates or increases the activity of a GTPase." [GOC:jl] biological_process
+ENSG00000196576 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000196576 GO:0071526 semaphorin-plexin signaling pathway "A series of molecular signals generated as a consequence of a semaphorin receptor (composed of a plexin and a neurophilin) binding to a semaphorin ligand." [GOC:BHF, GOC:mah, GOC:vk, PMID:15239959] biological_process
+ENSG00000196576 GO:2001222 regulation of neuron migration "Any process that modulates the frequency, rate or extent of neuron migration." [GOC:obol] biological_process
+ENSG00000196576 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000196576 GO:0004872 receptor activity "Combining with an extracellular or intracellular messenger to initiate a change in cell activity." [GOC:ceb, ISBN:0198506732] molecular_function
+ENSG00000196576 GO:0007275 multicellular organismal development "The biological process whose specific outcome is the progression of a multicellular organism over time from an initial condition (e.g. a zygote or a young adult) to a later condition (e.g. a multicellular animal or an aged adult)." [GOC:dph, GOC:ems, GOC:isa_complete, GOC:tb] biological_process
+ENSG00000196576 GO:0050772 positive regulation of axonogenesis "Any process that activates or increases the frequency, rate or extent of axonogenesis." [GOC:ai] biological_process
+ENSG00000196576
+ENSG00000169575 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000169575 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100294 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100294 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100294 GO:0006629 lipid metabolic process "The chemical reactions and pathways involving lipids, compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent. Includes fatty acids; neutral fats, other fatty-acid esters, and soaps; long-chain (fatty) alcohols and waxes; sphingoids and other long-chain bases; glycolipids, phospholipids and sphingolipids; and carotenes, polyprenols, sterols, terpenes and other isoprenoids." [GOC:ma] biological_process
+ENSG00000100294 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000100294 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000100294 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100294 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000100294 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100294 GO:0016746 transferase activity, transferring acyl groups "Catalysis of the transfer of an acyl group from one compound (donor) to another (acceptor)." [GOC:jl, ISBN:0198506732] molecular_function
+ENSG00000100294 GO:0004314 [acyl-carrier-protein] S-malonyltransferase activity "Catalysis of the reaction: malonyl-CoA + [acyl-carrier protein] = CoA + malonyl-[acyl-carrier protein]." [EC:2.3.1.39] molecular_function
+ENSG00000100294 GO:0006633 fatty acid biosynthetic process "The chemical reactions and pathways resulting in the formation of a fatty acid, any of the aliphatic monocarboxylic acids that can be liberated by hydrolysis from naturally occurring fats and oils. Fatty acids are predominantly straight-chain acids of 4 to 24 carbon atoms, which may be saturated or unsaturated; branched fatty acids and hydroxy fatty acids also occur, and very long chain acids of over 30 carbons are found in waxes." [GOC:mah, ISBN:0198506732] biological_process
+ENSG00000100294 GO:0008152 metabolic process "The chemical reactions and pathways, including anabolism and catabolism, by which living organisms transform chemical substances. Metabolic processes typically transform small molecules, but also include macromolecular processes such as DNA repair and replication, and protein synthesis and degradation." [GOC:go_curators, ISBN:0198547684] biological_process
+ENSG00000100294 GO:0016740 transferase activity "Catalysis of the transfer of a group, e.g. a methyl group, glycosyl group, acyl group, phosphorus-containing, or other groups, from one compound (generally regarded as the donor) to another compound (generally regarded as the acceptor). Transferase is the systematic name for any enzyme of EC class 2." [ISBN:0198506732] molecular_function
+ENSG00000100294 GO:0044822 poly(A) RNA binding "Interacting non-covalently with a poly(A) RNA, a RNA molecule which has a tail of adenine bases." [GOC:jl] molecular_function
+ENSG00000100294 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000100294 GO:0003824 catalytic activity "Catalysis of a biochemical reaction at physiological temperatures. In biologically catalyzed reactions, the reactants are known as substrates, and the catalysts are naturally occurring macromolecular substances known as enzymes. Enzymes possess specific binding sites for substrates, and are usually composed wholly or largely of protein, but RNA that has catalytic activity (ribozyme) is often also regarded as enzymatic." [ISBN:0198506732] molecular_function
+ENSG00000100353 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100353 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000100353 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100353 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100353 GO:0006412 translation "The cellular metabolic process in which a protein is formed, using the sequence of a mature mRNA molecule to specify the sequence of amino acids in a polypeptide chain. Translation is mediated by the ribosome, and begins with the formation of a ternary complex between aminoacylated initiator methionine tRNA, GTP, and initiation factor 2, which subsequently associates with the small subunit of the ribosome and an mRNA. Translation ends with the release of a polypeptide chain from the ribosome." [GOC:go_curators] biological_process
+ENSG00000100353 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000100353 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100353 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000100353 GO:0022607 cellular component assembly "The aggregation, arrangement and bonding together of a cellular component." [GOC:isa_complete] biological_process
+ENSG00000100353 GO:0022618 ribonucleoprotein complex assembly "The aggregation, arrangement and bonding together of proteins and RNA molecules to form a ribonucleoprotein complex." [GOC:jl] biological_process
+ENSG00000100353 GO:0065003 macromolecular complex assembly "The aggregation, arrangement and bonding together of a set of macromolecules to form a complex." [GOC:jl] biological_process
+ENSG00000100353 GO:0001731 formation of translation preinitiation complex "The joining of the small ribosomal subunit, ternary complex, and mRNA." [GOC:hjd] biological_process
+ENSG00000100353 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100353 GO:0005852 eukaryotic translation initiation factor 3 complex "A complex of several polypeptides that plays at least two important roles in protein synthesis: First, eIF3 binds to the 40S ribosome and facilitates loading of the Met-tRNA/eIF2.GTP ternary complex to form the 43S preinitiation complex. Subsequently, eIF3 apparently assists eIF4 in recruiting mRNAs to the 43S complex. The eIF3 complex contains five conserved core subunits, and may contain several additional proteins; the non-core subunits are thought to mediate association of the complex with specific sets of mRNAs." [PMID:15904532] cellular_component
+ENSG00000100353 GO:0006413 translational initiation "The process preceding formation of the peptide bond between the first two amino acids of a protein. This includes the formation of a complex of the ribosome, mRNA, and an initiation complex that contains the first aminoacyl-tRNA." [ISBN:019879276X] biological_process
+ENSG00000100353 GO:0006446 regulation of translational initiation "Any process that modulates the frequency, rate or extent of translational initiation." [GOC:go_curators] biological_process
+ENSG00000100353 GO:0010467 gene expression "The process in which a gene's sequence is converted into a mature gene product or products (proteins or RNA). This includes the production of an RNA transcript as well as any processing to produce a mature RNA product or an mRNA (for protein-coding genes) and the translation of that mRNA into protein. Some protein processing events may be included when they are required to form an active form of a product from an inactive precursor form." [GOC:dph, GOC:tb] biological_process
+ENSG00000100353 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000100353 GO:0016282 eukaryotic 43S preinitiation complex "A protein complex composed of the 40S ribosomal subunit plus eIF1A, eIF3, and eIF2-GTP-bound methionyl-initiator methionine tRNA." [GOC:hjd, PMID:15145049] cellular_component
+ENSG00000100353 GO:0033290 eukaryotic 48S preinitiation complex "A protein complex composed of the small ribosomal subunit, eIF3, eIF1A, methionyl-initiatior methionine and a capped mRNA. The complex is initially positioned at the 5'-end of the capped mRNA." [GOC:hjd, PMID:15145049] cellular_component
+ENSG00000100353 GO:0044267 cellular protein metabolic process "The chemical reactions and pathways involving a specific protein, rather than of proteins in general, occurring at the level of an individual cell. Includes cellular protein modification." [GOC:jl] biological_process
+ENSG00000100353 GO:0044822 poly(A) RNA binding "Interacting non-covalently with a poly(A) RNA, a RNA molecule which has a tail of adenine bases." [GOC:jl] molecular_function
+ENSG00000100353 GO:0003743 translation initiation factor activity "Functions in the initiation of ribosome-mediated translation of mRNA into a polypeptide." [ISBN:0198506732] molecular_function
+ENSG00000100353 GO:0008135 translation factor activity, nucleic acid binding "Functions during translation by interacting selectively and non-covalently with nucleic acids during polypeptide synthesis at the ribosome." [GOC:ai, GOC:vw] molecular_function
+ENSG00000100353 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100353 GO:0071541 eukaryotic translation initiation factor 3 complex, eIF3m "An eukaryotic translation initiation factor 3 complex that contains the PCI-domain protein eIF3m." [PMID:15904532, PMID:19061185] cellular_component
+ENSG00000100353 GO:0001732 formation of cytoplasmic translation initiation complex "Joining of the large subunit, with release of IF2/eIF2 and IF3/eIF3. This leaves the functional ribosome at the AUG, with the methionyl/formyl-methionyl-tRNA positioned at the P site." [GOC:hjd] biological_process
+ENSG00000176177 GO:0006886 intracellular protein transport "The directed movement of proteins in a cell, including the movement of proteins between specific compartments or structures within a cell, such as organelles of a eukaryotic cell." [GOC:mah] biological_process
+ENSG00000176177 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000176177 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000279624
+ENSG00000100418 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100418 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100418 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100418 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100418 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100418 GO:0006508 proteolysis "The hydrolysis of proteins into smaller polypeptides and/or amino acids by cleavage of their peptide bonds." [GOC:bf, GOC:mah] biological_process
+ENSG00000100418 GO:0008233 peptidase activity "Catalysis of the hydrolysis of a peptide bond. A peptide bond is a covalent bond formed when the carbon atom from the carboxyl group of one amino acid shares electrons with the nitrogen atom from the amino group of a second amino acid." [GOC:jl, ISBN:0815332181] molecular_function
+ENSG00000100418 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100418 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100418 GO:0042802 identical protein binding "Interacting selectively and non-covalently with an identical protein or proteins." [GOC:jl] molecular_function
+ENSG00000099995 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000099995 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000099995 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000099995 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000099995 GO:0006397 mRNA processing "Any process involved in the conversion of a primary mRNA transcript into one or more mature mRNA(s) prior to translation into polypeptide." [GOC:mah] biological_process
+ENSG00000099995 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000099995 GO:0005654 nucleoplasm "That part of the nuclear content other than the chromosomes or the nucleolus." [GOC:ma, ISBN:0124325653] cellular_component
+ENSG00000099995 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000099995 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000099995 GO:0022607 cellular component assembly "The aggregation, arrangement and bonding together of a cellular component." [GOC:isa_complete] biological_process
+ENSG00000099995 GO:0022618 ribonucleoprotein complex assembly "The aggregation, arrangement and bonding together of proteins and RNA molecules to form a ribonucleoprotein complex." [GOC:jl] biological_process
+ENSG00000099995 GO:0065003 macromolecular complex assembly "The aggregation, arrangement and bonding together of a set of macromolecules to form a complex." [GOC:jl] biological_process
+ENSG00000099995 GO:0000389 mRNA 3'-splice site recognition "Recognition of the intron 3'-splice site by components of the assembling U2- or U12-type spliceosome." [GOC:krc, ISBN:0879695897] biological_process
+ENSG00000099995 GO:0000398 mRNA splicing, via spliceosome "The joining together of exons from one or more primary transcripts of messenger RNA (mRNA) and the excision of intron sequences, via a spliceosomal mechanism, so that mRNA consisting only of the joined exons is produced." [GOC:krc, ISBN:0198506732, ISBN:0879695897] biological_process
+ENSG00000099995 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000099995 GO:0005681 spliceosomal complex "Any of a series of ribonucleoprotein complexes that contain RNA and small nuclear ribonucleoproteins (snRNPs), and are formed sequentially during the splicing of a messenger RNA primary transcript to excise an intron." [GOC:mah, ISBN:0198547684, PMID:19239890] cellular_component
+ENSG00000099995 GO:0005684 U2-type spliceosomal complex "Any spliceosomal complex that forms during the splicing of a messenger RNA primary transcript to excise an intron that has canonical consensus sequences near the 5' and 3' ends." [GOC:krc, GOC:mah, PMID:11343900] cellular_component
+ENSG00000099995 GO:0008380 RNA splicing "The process of removing sections of the primary RNA transcript to remove sequences not present in the mature form of the RNA and joining the remaining sections to form the mature form of the RNA." [GOC:krc, GOC:mah] biological_process
+ENSG00000099995 GO:0010467 gene expression "The process in which a gene's sequence is converted into a mature gene product or products (proteins or RNA). This includes the production of an RNA transcript as well as any processing to produce a mature RNA product or an mRNA (for protein-coding genes) and the translation of that mRNA into protein. Some protein processing events may be included when they are required to form an active form of a product from an inactive precursor form." [GOC:dph, GOC:tb] biological_process
+ENSG00000099995 GO:0044822 poly(A) RNA binding "Interacting non-covalently with a poly(A) RNA, a RNA molecule which has a tail of adenine bases." [GOC:jl] molecular_function
+ENSG00000099995 GO:0071013 catalytic step 2 spliceosome "A spliceosomal complex that contains three snRNPs, including U5, bound to a splicing intermediate in which the first catalytic cleavage of the 5' splice site has occurred. The precise subunit composition differs significantly from that of the catalytic step 1, or activated, spliceosome, and includes many proteins in addition to those found in the associated snRNPs." [GOC:ab, GOC:krc, GOC:mah, ISBN:0879695897, ISBN:0879697393, PMID:18322460, PMID:19239890] cellular_component
+ENSG00000099995 GO:0006396 RNA processing "Any process involved in the conversion of one or more primary RNA transcripts into one or more mature RNA molecules." [GOC:mah] biological_process
+ENSG00000099995
+ENSG00000100299 GO:0003824 catalytic activity "Catalysis of a biochemical reaction at physiological temperatures. In biologically catalyzed reactions, the reactants are known as substrates, and the catalysts are naturally occurring macromolecular substances known as enzymes. Enzymes possess specific binding sites for substrates, and are usually composed wholly or largely of protein, but RNA that has catalytic activity (ribozyme) is often also regarded as enzymatic." [ISBN:0198506732] molecular_function
+ENSG00000100299 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100299 GO:0008152 metabolic process "The chemical reactions and pathways, including anabolism and catabolism, by which living organisms transform chemical substances. Metabolic processes typically transform small molecules, but also include macromolecular processes such as DNA repair and replication, and protein synthesis and degradation." [GOC:go_curators, ISBN:0198547684] biological_process
+ENSG00000100299 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100299 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000100299 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000100299 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100299 GO:0006629 lipid metabolic process "The chemical reactions and pathways involving lipids, compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent. Includes fatty acids; neutral fats, other fatty-acid esters, and soaps; long-chain (fatty) alcohols and waxes; sphingoids and other long-chain bases; glycolipids, phospholipids and sphingolipids; and carotenes, polyprenols, sterols, terpenes and other isoprenoids." [GOC:ma] biological_process
+ENSG00000100299 GO:0005764 lysosome "A small lytic vacuole that has cell cycle-independent morphology and is found in most animal cells and that contains a variety of hydrolases, most of which have their maximal activities in the pH range 5-6. The contained enzymes display latency if properly isolated. About 40 different lysosomal hydrolases are known and lysosomes have a great variety of morphologies and functions." [GOC:mah, ISBN:0198506732] cellular_component
+ENSG00000100299 GO:0005773 vacuole "A closed structure, found only in eukaryotic cells, that is completely surrounded by unit membrane and contains liquid material. Cells contain one or several vacuoles, that may have different functions from each other. Vacuoles have a diverse array of functions. They can act as a storage organelle for nutrients or waste products, as a degradative compartment, as a cost-effective way of increasing cell size, and as a homeostatic regulator controlling both turgor pressure and pH of the cytosol." [GOC:mtg_sensu, ISBN:0198506732] cellular_component
+ENSG00000100299 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100299 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100299 GO:0004098 cerebroside-sulfatase activity "Catalysis of the reaction: a cerebroside 3-sulfate + H2O = a cerebroside + sulfate." [EC:3.1.6.8] molecular_function
+ENSG00000100299 GO:0005509 calcium ion binding "Interacting selectively and non-covalently with calcium ions (Ca2+)." [GOC:ai] molecular_function
+ENSG00000100299 GO:0005788 endoplasmic reticulum lumen "The volume enclosed by the membranes of the endoplasmic reticulum." [ISBN:0198547684] cellular_component
+ENSG00000100299 GO:0006665 sphingolipid metabolic process "The chemical reactions and pathways involving sphingolipids, any of a class of lipids containing the long-chain amine diol sphingosine or a closely related base (a sphingoid)." [GOC:mah, ISBN:0198506732] biological_process
+ENSG00000100299 GO:0006687 glycosphingolipid metabolic process "The chemical reactions and pathways involving glycosphingolipids, any compound with residues of sphingoid and at least one monosaccharide." [ISBN:0198547684] biological_process
+ENSG00000100299 GO:0008484 sulfuric ester hydrolase activity "Catalysis of the reaction: RSO-R' + H2O = RSOOH + R'H. This reaction is the hydrolysis of any sulfuric ester bond, any ester formed from sulfuric acid, O=SO(OH)2." [GOC:ai] molecular_function
+ENSG00000100299 GO:0043202 lysosomal lumen "The volume enclosed within the lysosomal membrane." [GOC:jl, PMID:15213228] cellular_component
+ENSG00000100299 GO:0043687 post-translational protein modification "The process of covalently altering one or more amino acids in a protein after the protein has been completely translated and released from the ribosome." [GOC:jsg] biological_process
+ENSG00000100299 GO:0044267 cellular protein metabolic process "The chemical reactions and pathways involving a specific protein, rather than of proteins in general, occurring at the level of an individual cell. Includes cellular protein modification." [GOC:jl] biological_process
+ENSG00000100299 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000100299 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000100299 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000100299 GO:0007339 binding of sperm to zona pellucida "The process in which the sperm binds to the zona pellucida glycoprotein layer of the egg. The process begins with the attachment of the sperm plasma membrane to the zona pellucida and includes attachment of the acrosome inner membrane to the zona pellucida after the acrosomal reaction takes place." [GOC:dph, ISBN:0878932437] biological_process
+ENSG00000100299 GO:0005615 extracellular space "That part of a multicellular organism outside the cells proper, usually taken to be outside the plasma membranes, and occupied by fluid." [ISBN:0198547684] cellular_component
+ENSG00000100299 GO:0001669 acrosomal vesicle "A structure in the head of a spermatozoon that contains acid hydrolases, and is concerned with the breakdown of the outer membrane of the ovum during fertilization. It lies just beneath the plasma membrane and is derived from the lysosome." [ISBN:0124325653, ISBN:0198506732] cellular_component
+ENSG00000100299 GO:0005768 endosome "A membrane-bounded organelle to which materials ingested by endocytosis are delivered." [ISBN:0198506732, PMID:19696797] cellular_component
+ENSG00000100299 GO:0051597 response to methylmercury "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a methylmercury stimulus." [GOC:ai] biological_process
+ENSG00000100299 GO:0043627 response to estrogen "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of stimulus by an estrogen, C18 steroid hormones that can stimulate the development of female sexual characteristics." [GOC:jl, ISBN:0198506732] biological_process
+ENSG00000100299 GO:0009268 response to pH "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a pH stimulus. pH is a measure of the acidity or basicity of an aqueous solution." [GOC:jl, http://en.wikipedia.org/wiki/PH] biological_process
+ENSG00000100299 GO:0045471 response to ethanol "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of an ethanol stimulus." [GOC:go_curators] biological_process
+ENSG00000100299 GO:0007584 response to nutrient "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a nutrient stimulus." [GOC:go_curators] biological_process
+ENSG00000100299 GO:0006914 autophagy "The process in which cells digest parts of their own cytoplasm; allows for both recycling of macromolecular constituents under conditions of cellular stress and remodeling the intracellular structure for cell differentiation." [ISBN:0198547684, PMID:11099404, PMID:9412464] biological_process
+ENSG00000100299 GO:0004065 arylsulfatase activity "Catalysis of the reaction: a phenol sulfate + H2O = a phenol + sulfate." [EC:3.1.6.1] molecular_function
+ENSG00000100299 GO:0031232 extrinsic component of external side of plasma membrane "The component of a plasma membrane consisting of gene products and protein complexes that are loosely bound to its external surface, but not integrated into the hydrophobic region." [GOC:dos, GOC:mah] cellular_component
+ENSG00000100299 GO:0007417 central nervous system development "The process whose specific outcome is the progression of the central nervous system over time, from its formation to the mature structure. The central nervous system is the core nervous system that serves an integrating and coordinating function. In vertebrates it consists of the brain, spinal cord and spinal nerves. In those invertebrates with a central nervous system it typically consists of a brain, cerebral ganglia and a nerve cord." [GOC:bf, GOC:jid, ISBN:0582227089] biological_process
+ENSG00000100299 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000188064 GO:0005578 proteinaceous extracellular matrix "A layer consisting mainly of proteins (especially collagen) and glycosaminoglycans (mostly as proteoglycans) that forms a sheet underlying or overlying cells such as endothelial and epithelial cells. The proteins are secreted by cells in the vicinity. An example of this component is found in Mus musculus." [GOC:mtg_sensu, ISBN:0198547684] cellular_component
+ENSG00000188064 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000188064 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000188064 GO:0048856 anatomical structure development "The biological process whose specific outcome is the progression of an anatomical structure from an initial condition to its mature state. This process begins with the formation of the structure and ends with the mature structure, whatever form that may be including its natural destruction. An anatomical structure is any biological entity that occupies space and is distinguished from its surroundings. Anatomical structures can be macroscopic such as a carpel, or microscopic such as an acrosome." [GO_REF:0000021, GOC:mtg_15jun06] biological_process
+ENSG00000188064 GO:0008283 cell proliferation "The multiplication or reproduction of cells, resulting in the expansion of a cell population." [GOC:mah, GOC:mb] biological_process
+ENSG00000188064 GO:0007155 cell adhesion "The attachment of a cell, either to another cell or to an underlying substrate such as the extracellular matrix, via cell adhesion molecules." [GOC:hb, GOC:pf] biological_process
+ENSG00000188064 GO:0040007 growth "The increase in size or mass of an entire organism, a part of an organism or a cell." [GOC:bf, GOC:ma] biological_process
+ENSG00000188064 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000188064 GO:0042592 homeostatic process "Any biological process involved in the maintenance of an internal steady state." [GOC:jl, ISBN:0395825172] biological_process
+ENSG00000188064 GO:0030154 cell differentiation "The process in which relatively unspecialized cells, e.g. embryonic or regenerative cells, acquire specialized structural and/or functional features that characterize the cells, tissues, or organs of the mature organism or some other relatively stable phase of the organism's life history. Differentiation includes the processes involved in commitment of a cell to a specific fate and its subsequent development to the mature state." [ISBN:0198506732] biological_process
+ENSG00000188064 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000188064 GO:0005615 extracellular space "That part of a multicellular organism outside the cells proper, usually taken to be outside the plasma membranes, and occupied by fluid." [ISBN:0198547684] cellular_component
+ENSG00000188064 GO:0005576 extracellular region "The space external to the outermost structure of a cell. For cells without external protective or external encapsulating structures this refers to space outside of the plasma membrane. This term covers the host cell environment outside an intracellular parasite." [GOC:go_curators] cellular_component
+ENSG00000188064 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000188064 GO:0009790 embryo development "The process whose specific outcome is the progression of an embryo from its formation until the end of its embryonic life stage. The end of the embryonic stage is organism-specific. For example, for mammals, the process would begin with zygote formation and end with birth. For insects, the process would begin at zygote formation and end with larval hatching. For plant zygotic embryos, this would be from zygote formation to the end of seed dormancy. For plant vegetative embryos, this would be from the initial determination of the cell or group of cells to form an embryo until the point when the embryo becomes independent of the parent plant." [GOC:go_curators, GOC:isa_complete, GOC:mtg_sensu] biological_process
+ENSG00000188064 GO:0001701 in utero embryonic development "The process whose specific outcome is the progression of the embryo in the uterus over time, from formation of the zygote in the oviduct, to birth. An example of this process is found in Mus musculus." [GOC:go_curators, GOC:mtg_sensu] biological_process
+ENSG00000188064 GO:0003338 metanephros morphogenesis "The process in which the anatomical structures of the metanephros are generated and organized." [GOC:dph, GOC:yaf] biological_process
+ENSG00000188064 GO:0005109 frizzled binding "Interacting selectively and non-covalently with the frizzled (fz) receptor." [GOC:ceb, PR:000001315] molecular_function
+ENSG00000188064 GO:0005788 endoplasmic reticulum lumen "The volume enclosed by the membranes of the endoplasmic reticulum." [ISBN:0198547684] cellular_component
+ENSG00000188064 GO:0005796 Golgi lumen "The volume enclosed by the membranes of any cisterna or subcompartment of the Golgi apparatus, including the cis- and trans-Golgi networks." [GOC:mah] cellular_component
+ENSG00000188064 GO:0016055 Wnt signaling pathway "The series of molecular signals initiated by binding of a Wnt protein to a frizzled family receptor on the surface of the target cell and ending with a change in cell state." [GOC:dph, GOC:go_curators, PMID:11532397] biological_process
+ENSG00000188064 GO:0016332 establishment or maintenance of polarity of embryonic epithelium "Any cellular process that results in the specification, formation or maintenance of anisotropic intracellular organization of epithelial cells in an embryo." [GOC:isa_complete, GOC:mah] biological_process
+ENSG00000188064 GO:0021871 forebrain regionalization "The regionalization process resulting in the creation of areas within the forebrain that will direct the behavior of cell migration in differentiation as the forebrain develops." [GO_REF:0000021, GOC:cls, GOC:dgh, GOC:dph, GOC:isa_complete, GOC:jid, GOC:mtg_15jun06, PMID:16226447] biological_process
+ENSG00000188064 GO:0022009 central nervous system vasculogenesis "The differentiation of endothelial cells from progenitor cells during blood vessel development, and the de novo formation of blood vessels and tubes in the central nervous system. The capillary endothelial cells in the brain are specialized to form the blood-brain barrier." [GO_REF:0000021, GOC:cls, GOC:dgh, GOC:dph, GOC:jid, GOC:mtg_15jun06] biological_process
+ENSG00000188064 GO:0030182 neuron differentiation "The process in which a relatively unspecialized cell acquires specialized features of a neuron." [GOC:mah] biological_process
+ENSG00000188064 GO:0030324 lung development "The process whose specific outcome is the progression of the lung over time, from its formation to the mature structure. In all air-breathing vertebrates the lungs are developed from the ventral wall of the oesophagus as a pouch which divides into two sacs. In amphibians and many reptiles the lungs retain very nearly this primitive sac-like character, but in the higher forms the connection with the esophagus becomes elongated into the windpipe and the inner walls of the sacs become more and more divided, until, in the mammals, the air spaces become minutely divided into tubes ending in small air cells, in the walls of which the blood circulates in a fine network of capillaries. In mammals the lungs are more or less divided into lobes, and each lung occupies a separate cavity in the thorax." [GOC:jid, UBERON:0002048] biological_process
+ENSG00000188064 GO:0032364 oxygen homeostasis "A homeostatic process involved in the maintenance of an internal steady state of oxygen within an organism or cell." [GOC:rph] biological_process
+ENSG00000188064 GO:0044237 cellular metabolic process "The chemical reactions and pathways by which individual cells transform chemical substances." [GOC:go_curators] biological_process
+ENSG00000188064 GO:0045165 cell fate commitment "The commitment of cells to specific cell fates and their capacity to differentiate into particular kinds of cells. Positional information is established through protein signals that emanate from a localized source within a cell (the initial one-cell zygote) or within a developmental field." [ISBN:0716731185] biological_process
+ENSG00000188064 GO:0045669 positive regulation of osteoblast differentiation "Any process that activates or increases the frequency, rate or extent of osteoblast differentiation." [GOC:go_curators] biological_process
+ENSG00000188064 GO:0046330 positive regulation of JNK cascade "Any process that activates or increases the frequency, rate or extent of signal transduction mediated by the JNK cascade." [GOC:bf] biological_process
+ENSG00000188064 GO:0048144 fibroblast proliferation "The multiplication or reproduction of fibroblast cells, resulting in the expansion of the fibroblast population." [GOC:jid] biological_process
+ENSG00000188064 GO:0048568 embryonic organ development "Development, taking place during the embryonic phase, of a tissue or tissues that work together to perform a specific function or functions. Development pertains to the process whose specific outcome is the progression of a structure over time, from its formation to the mature structure. Organs are commonly observed as visibly distinct structures, but may also exist as loosely associated clusters of cells that work together to perform a specific function or functions." [GOC:jid] biological_process
+ENSG00000188064 GO:0050808 synapse organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of a synapse, the junction between a neuron and a target (neuron, muscle, or secretory cell)." [GOC:ai, GOC:pr] biological_process
+ENSG00000188064 GO:0060070 canonical Wnt signaling pathway "The series of molecular signals initiated by binding of a Wnt protein to a frizzled family receptor on the surface of the target cell, followed by propagation of the signal via beta-catenin, and ending with a change in transcription of target genes. In this pathway, the activated receptor signals via downstream effectors that result in the inhibition of beta-catenin phosphorylation, thereby preventing degradation of beta-catenin. Stabilized beta-catenin can then accumulate and travel to the nucleus to trigger changes in transcription of target genes." [GOC:bf, GOC:dph, PMID:11532397, PMID:19619488] biological_process
+ENSG00000188064 GO:0060425 lung morphogenesis "The process in which the anatomical structures of the lung are generated and organized." [GOC:dph] biological_process
+ENSG00000188064 GO:0060428 lung epithelium development "The biological process whose specific outcome is the progression of the lung epithelium from an initial condition to its mature state. This process begins with the formation of lung epithelium and ends with the mature structure. The lung epithelium is the specialized epithelium that lines the inside of the lung." [GOC:dph, GOC:mtg_lung] biological_process
+ENSG00000188064 GO:0060482 lobar bronchus development "The biological process whose specific outcome is the progression of a lobar bronchus from an initial condition to its mature state. This process begins with the formation of the lobar bronchus and ends with the mature structure. The lobar bronchus is the major airway within the respiratory tree that starts by division of the principal bronchi on both sides and ends at the point of its own subdivision into tertiary or segmental bronchi." [GOC:dph, GOC:mtg_lung] biological_process
+ENSG00000188064 GO:0060535 trachea cartilage morphogenesis "The process in which the anatomical structures of cartilage in the trachea are generated and organized." [GOC:dph] biological_process
+ENSG00000188064 GO:0060560 developmental growth involved in morphogenesis "The increase in size or mass of an anatomical structure that contributes to the structure attaining its shape." [GOC:dph] biological_process
+ENSG00000188064 GO:0060669 embryonic placenta morphogenesis "The process in which the embryonic placenta is generated and organized." [GOC:dph] biological_process
+ENSG00000188064 GO:0060710 chorio-allantoic fusion "The cell-cell adhesion process in which the cells of the chorion fuse to the cells of the allantois." [GOC:dph] biological_process
+ENSG00000188064 GO:0061180 mammary gland epithelium development "The process whose specific outcome is the progression of the mammary gland epithelium over time, from its formation to the mature structure. The mammary gland is a large compound sebaceous gland that in female mammals is modified to secrete milk." [GOC:dph, GOC:yaf] biological_process
+ENSG00000188064 GO:0070307 lens fiber cell development "The process whose specific outcome is the progression of a lens fiber cell over time, from its formation to the mature structure. Cell development does not include the steps involved in committing a cell to a lens fiber cell fate. A lens fiber cell is any of the elongated, tightly packed cells that make up the bulk of the mature lens in a camera-type eye." [GOC:mah, PMID:7693735] biological_process
+ENSG00000188064 GO:0071300 cellular response to retinoic acid "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a retinoic acid stimulus." [GOC:mah] biological_process
+ENSG00000188064 GO:0072053 renal inner medulla development "The process whose specific outcome is the progression of the renal inner medulla over time, from its formation to the mature structure. The renal inner medulla is unique to mammalian kidneys and is the innermost region of the mammalian kidney." [GOC:mtg_kidney_jan10] biological_process
+ENSG00000188064 GO:0072054 renal outer medulla development "The process whose specific outcome is the progression of the renal outer medulla over time, from its formation to the mature structure. The renal outer medulla is the region of the kidney that lies between the renal cortex and the renal inner medulla." [GOC:mtg_kidney_jan10] biological_process
+ENSG00000188064 GO:0072060 outer medullary collecting duct development "The process whose specific outcome is the progression of the outer medullary collecting duct over time, from its formation to the mature structure. The outer medullary collecting duct is the portion of the collecting duct that lies in the renal outer medulla." [GOC:mtg_kidney_jan10] biological_process
+ENSG00000188064 GO:0072061 inner medullary collecting duct development "The process whose specific outcome is the progression of the inner medullary collecting duct over time, from its formation to the mature structure. The inner medullary collecting duct is the portion of the collecting duct that lies in the renal inner medulla." [GOC:mtg_kidney_jan10] biological_process
+ENSG00000188064 GO:0072089 stem cell proliferation "The multiplication or reproduction of stem cells, resulting in the expansion of a stem cell population. A stem cell is a cell that retains the ability to divide and proliferate throughout life to provide progenitor cells that can differentiate into specialized cells." [GOC:mtg_kidney_jan10] biological_process
+ENSG00000188064 GO:0072205 metanephric collecting duct development "The process whose specific outcome is the progression of a collecting duct in the metanephros over time, from its formation to the mature structure. The collecting duct responds to vasopressin and aldosterone to regulate water, electrolyte and acid-base balance. The collecting duct is the final common path through which urine flows before entering the ureter and then emptying into the bladder." [GOC:mtg_kidney_jan10] biological_process
+ENSG00000188064 GO:0072207 metanephric epithelium development "The process whose specific outcome is the progression of an epithelium in the metanephros over time, from its formation to the mature structure. An epithelium is a tissue that covers the internal or external surfaces of an anatomical structure." [GOC:mtg_kidney_jan10] biological_process
+ENSG00000188064 GO:0072236 metanephric loop of Henle development "The process whose specific outcome is the progression of the metanephric loop of Henle over time, from its formation to the mature structure. The metanephric loop of Henle is a metanephric nephron tubule that connects the proximal convoluted tubule to the distal convoluted tubule in the metanephros." [GOC:mtg_kidney_jan10] biological_process
+ENSG00000188064 GO:0005102 receptor binding "Interacting selectively and non-covalently with one or more specific sites on a receptor molecule, a macromolecule that undergoes combination with a hormone, neurotransmitter, drug or intracellular messenger to initiate a change in cell function." [GOC:bf, GOC:ceb, ISBN:0198506732] molecular_function
+ENSG00000188064 GO:0007275 multicellular organismal development "The biological process whose specific outcome is the progression of a multicellular organism over time from an initial condition (e.g. a zygote or a young adult) to a later condition (e.g. a multicellular animal or an aged adult)." [GOC:dph, GOC:ems, GOC:isa_complete, GOC:tb] biological_process
+ENSG00000188064 GO:0008284 positive regulation of cell proliferation "Any process that activates or increases the rate or extent of cell proliferation." [GOC:go_curators] biological_process
+ENSG00000188064 GO:0001525 angiogenesis "Blood vessel formation when new vessels emerge from the proliferation of pre-existing blood vessels." [ISBN:0878932453] biological_process
+ENSG00000188064 GO:0010628 positive regulation of gene expression "Any process that increases the frequency, rate or extent of gene expression. Gene expression is the process in which a gene's coding sequence is converted into a mature gene product or products (proteins or RNA). This includes the production of an RNA transcript as well as any processing to produce a mature RNA product or an mRNA (for protein-coding genes) and the translation of that mRNA into protein. Some protein processing events may be included when they are required to form an active form of a product from an inactive precursor form." [GOC:dph, GOC:tb] biological_process
+ENSG00000188064 GO:0042475 odontogenesis of dentin-containing tooth "The process whose specific outcome is the progression of a dentin-containing tooth over time, from its formation to the mature structure. A dentin-containing tooth is a hard, bony organ borne on the jaw or other bone of a vertebrate, and is composed mainly of dentin, a dense calcified substance, covered by a layer of enamel." [GOC:cjm, GOC:mah, GOC:mtg_sensu, PMID:10333884, PMID:15355794] biological_process
+ENSG00000188064 GO:0045879 negative regulation of smoothened signaling pathway "Any process that stops, prevents, or reduces the frequency, rate or extent of smoothened signaling." [GOC:go_curators] biological_process
+ENSG00000188064 GO:0051145 smooth muscle cell differentiation "The process in which a relatively unspecialized cell acquires specialized features of a smooth muscle cell; smooth muscle lacks transverse striations in its constituent fibers and are almost always involuntary." [CL:0000192, GOC:ai] biological_process
+ENSG00000188064 GO:0001944 vasculature development "The process whose specific outcome is the progression of the vasculature over time, from its formation to the mature structure. The vasculature is an interconnected tubular multi-tissue structure that contains fluid that is actively transported around the organism." [GOC:dph, UBERON:0002409] biological_process
+ENSG00000188064 GO:0060484 lung-associated mesenchyme development "The biological process whose specific outcome is the progression of a lung-associated mesenchyme from an initial condition to its mature state. This process begins with the formation of lung-associated mesenchyme and ends with the mature structure. Lung-associated mesenchyme is the tissue made up of loosely connected mesenchymal cells in the lung." [GOC:dph, GOC:mtg_lung] biological_process
+ENSG00000188064 GO:0060033 anatomical structure regression "The developmental process in which an anatomical stucture is destroyed as a part of its normal progression." [GOC:dph] biological_process
+ENSG00000188064 GO:1902262 apoptotic process involved in patterning of blood vessels "Any apoptotic process that is involved in patterning of blood vessels." [GOC:dph, GOC:mtg_apoptosis, GOC:TermGenie, PMID:16163358] biological_process
+ENSG00000188064 GO:0048812 neuron projection morphogenesis "The process in which the anatomical structures of a neuron projection are generated and organized. A neuron projection is any process extending from a neural cell, such as axons or dendrites." [GOC:mah] biological_process
+ENSG00000188064 GO:0051384 response to glucocorticoid "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a glucocorticoid stimulus. Glucocorticoids are hormonal C21 corticosteroids synthesized from cholesterol with the ability to bind with the cortisol receptor and trigger similar effects. Glucocorticoids act primarily on carbohydrate and protein metabolism, and have anti-inflammatory effects." [CHEBI:24261, GOC:ai, PMID:9884123] biological_process
+ENSG00000188064 GO:0031175 neuron projection development "The process whose specific outcome is the progression of a neuron projection over time, from its formation to the mature structure. A neuron projection is any process extending from a neural cell, such as axons or dendrites (collectively called neurites)." [GOC:mah] biological_process
+ENSG00000188064 GO:0032536 regulation of cell projection size "A process that modulates the size of a cell projection." [GOC:mah] biological_process
+ENSG00000188064 GO:0007257 activation of JUN kinase activity "The initiation of the activity of the inactive enzyme JUN kinase (JNK)." [GOC:bf] biological_process
+ENSG00000188064 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000184076 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000184076 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000184076 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000184076 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000184076 GO:0006091 generation of precursor metabolites and energy "The chemical reactions and pathways resulting in the formation of precursor metabolites, substances from which energy is derived, and any process involved in the liberation of energy from these substances." [GOC:jl] biological_process
+ENSG00000184076 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000184076 GO:0016491 oxidoreductase activity "Catalysis of an oxidation-reduction (redox) reaction, a reversible chemical reaction in which the oxidation state of an atom or atoms within a molecule is altered. One substrate acts as a hydrogen or electron donor and becomes oxidized, while the other acts as hydrogen or electron acceptor and becomes reduced." [GOC:go_curators] molecular_function
+ENSG00000184076 GO:0022857 transmembrane transporter activity "Enables the transfer of a substance from one side of a membrane to the other." [GOC:mtg_transport, ISBN:0815340729] molecular_function
+ENSG00000184076 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000184076 GO:0005743 mitochondrial inner membrane "The inner, i.e. lumen-facing, lipid bilayer of the mitochondrial envelope. It is highly folded to form cristae." [GOC:ai] cellular_component
+ENSG00000184076 GO:0005750 mitochondrial respiratory chain complex III "A protein complex located in the mitochondrial inner membrane that forms part of the mitochondrial respiratory chain. Contains about 10 polypeptide subunits including four redox centers: cytochrome b/b6, cytochrome c1 and an 2Fe-2S cluster. Catalyzes the oxidation of ubiquinol by oxidized cytochrome c1." [GOC:mtg_sensu, ISBN:0198547684] cellular_component
+ENSG00000184076 GO:0006122 mitochondrial electron transport, ubiquinol to cytochrome c "The transfer of electrons from ubiquinol to cytochrome c that occurs during oxidative phosphorylation, mediated by the multisubunit enzyme known as complex III." [ISBN:0716731363] biological_process
+ENSG00000184076 GO:0008121 ubiquinol-cytochrome-c reductase activity "Catalysis of the transfer of a solute or solutes from one side of a membrane to the other according to the reaction: CoQH2 + 2 ferricytochrome c = CoQ + 2 ferrocytochrome c + 2 H+." [EC:1.10.2.2, ISBN:0198547684] molecular_function
+ENSG00000184076 GO:0022904 respiratory electron transport chain "A process in which a series of electron carriers operate together to transfer electrons from donors such as NADH and FADH2 to any of several different terminal electron acceptors to generate a transmembrane electrochemical gradient." [GOC:mtg_electron_transport, ISBN:0716720094] biological_process
+ENSG00000184076 GO:0044237 cellular metabolic process "The chemical reactions and pathways by which individual cells transform chemical substances." [GOC:go_curators] biological_process
+ENSG00000184076 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000184076 GO:1902600 hydrogen ion transmembrane transport "The directed movement of hydrogen ion (proton) across a membrane." [GO_REF:0000069, GOC:pr, GOC:TermGenie] biological_process
+ENSG00000184076 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000184076 GO:0055085 transmembrane transport "The process in which a solute is transported from one side of a membrane to the other." [GOC:dph, GOC:jid] biological_process
+ENSG00000184076 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000184076
+ENSG00000240972 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000240972 GO:0006461 protein complex assembly "The aggregation, arrangement and bonding together of a set of components to form a protein complex." [GOC:ai] biological_process
+ENSG00000240972 GO:0022607 cellular component assembly "The aggregation, arrangement and bonding together of a cellular component." [GOC:isa_complete] biological_process
+ENSG00000240972 GO:0065003 macromolecular complex assembly "The aggregation, arrangement and bonding together of a set of macromolecules to form a complex." [GOC:jl] biological_process
+ENSG00000240972 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000240972 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000240972 GO:0040011 locomotion "Self-propelled movement of a cell or organism from one location to another." [GOC:dgh] biological_process
+ENSG00000240972 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000240972 GO:0016853 isomerase activity "Catalysis of the geometric or structural changes within one molecule. Isomerase is the systematic name for any enzyme of EC class 5." [ISBN:0198506732] molecular_function
+ENSG00000240972 GO:0002376 immune system process "Any process involved in the development or functioning of the immune system, an organismal system for calibrated responses to potential internal or invasive threats." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05] biological_process
+ENSG00000240972 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000240972 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000240972 GO:0008283 cell proliferation "The multiplication or reproduction of cells, resulting in the expansion of a cell population." [GOC:mah, GOC:mb] biological_process
+ENSG00000240972 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000240972 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000240972 GO:0005576 extracellular region "The space external to the outermost structure of a cell. For cells without external protective or external encapsulating structures this refers to space outside of the plasma membrane. This term covers the host cell environment outside an intracellular parasite." [GOC:go_curators] cellular_component
+ENSG00000240972 GO:0006629 lipid metabolic process "The chemical reactions and pathways involving lipids, compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent. Includes fatty acids; neutral fats, other fatty-acid esters, and soaps; long-chain (fatty) alcohols and waxes; sphingoids and other long-chain bases; glycolipids, phospholipids and sphingolipids; and carotenes, polyprenols, sterols, terpenes and other isoprenoids." [GOC:ma] biological_process
+ENSG00000240972 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000240972 GO:0001516 prostaglandin biosynthetic process "The chemical reactions and pathways resulting in the formation of prostaglandins, any of a group of biologically active metabolites which contain a cyclopentane ring." [GOC:ai] biological_process
+ENSG00000240972 GO:0004167 dopachrome isomerase activity "Catalysis of the reaction: L-dopachrome = 5,6-dihydroxyindole-2-carboxylate." [EC:5.3.3.12, RHEA:13044] molecular_function
+ENSG00000240972 GO:0005102 receptor binding "Interacting selectively and non-covalently with one or more specific sites on a receptor molecule, a macromolecule that undergoes combination with a hormone, neurotransmitter, drug or intracellular messenger to initiate a change in cell function." [GOC:bf, GOC:ceb, ISBN:0198506732] molecular_function
+ENSG00000240972 GO:0005125 cytokine activity "Functions to control the survival, growth, differentiation and effector function of tissues and cells." [ISBN:0198599471] molecular_function
+ENSG00000240972 GO:0005126 cytokine receptor binding "Interacting selectively and non-covalently with a cytokine receptor." [GOC:mah, GOC:vw] molecular_function
+ENSG00000240972 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000240972 GO:0006954 inflammatory response "The immediate defensive reaction (by vertebrate tissue) to infection or injury caused by chemical or physical agents. The process is characterized by local vasodilation, extravasation of plasma into intercellular spaces and accumulation of white blood cells and macrophages." [GO_REF:0000022, GOC:mtg_15nov05, ISBN:0198506732] biological_process
+ENSG00000240972 GO:0007166 cell surface receptor signaling pathway "A series of molecular signals initiated by activation of a receptor on the surface of a cell. The pathway begins with binding of an extracellular ligand to a cell surface receptor, or for receptors that signal in the absence of a ligand, by ligand-withdrawal or the activity of a constitutively active receptor. The pathway ends with regulation of a downstream cellular process, e.g. transcription." [GOC:bf, GOC:mah, GOC:pr, GOC:signaling] biological_process
+ENSG00000240972 GO:0009986 cell surface "The external part of the cell wall and/or plasma membrane." [GOC:jl, GOC:mtg_sensu, GOC:sm] cellular_component
+ENSG00000240972 GO:0010629 negative regulation of gene expression "Any process that decreases the frequency, rate or extent of gene expression. Gene expression is the process in which a gene's coding sequence is converted into a mature gene product or products (proteins or RNA). This includes the production of an RNA transcript as well as any processing to produce a mature RNA product or an mRNA (for protein-coding genes) and the translation of that mRNA into protein. Some protein processing events may be included when they are required to form an active form of a product from an inactive precursor form." [GOC:dph, GOC:tb] biological_process
+ENSG00000240972 GO:0010739 positive regulation of protein kinase A signaling "Any process that increases the rate, frequency, or extent of protein kinase A signaling. PKA signaling is the series of reactions, mediated by the intracellular serine/threonine kinase protein kinase A, which occurs as a result of a single trigger reaction or compound." [GOC:BHF, GOC:dph, GOC:tb] biological_process
+ENSG00000240972 GO:0019752 carboxylic acid metabolic process "The chemical reactions and pathways involving carboxylic acids, any organic acid containing one or more carboxyl (COOH) groups or anions (COO-)." [ISBN:0198506732] biological_process
+ENSG00000240972 GO:0030890 positive regulation of B cell proliferation "Any process that activates or increases the rate or extent of B cell proliferation." [GOC:mah] biological_process
+ENSG00000240972 GO:0031982 vesicle "Any small, fluid-filled, spherical organelle enclosed by membrane or protein." [GOC:mah, GOC:pz] cellular_component
+ENSG00000240972 GO:0033138 positive regulation of peptidyl-serine phosphorylation "Any process that activates or increases the frequency, rate or extent of the phosphorylation of peptidyl-serine." [GOC:mah] biological_process
+ENSG00000240972 GO:0042056 chemoattractant activity "Providing the environmental signal that initiates the directed movement of a motile cell or organism towards a higher concentration of that signal." [GOC:go_curators, ISBN:0198506732] molecular_function
+ENSG00000240972 GO:0042327 positive regulation of phosphorylation "Any process that activates or increases the frequency, rate or extent of addition of phosphate groups to a molecule." [GOC:jl] biological_process
+ENSG00000240972 GO:0043030 regulation of macrophage activation "Any process that modulates the frequency or rate of macrophage activation." [GOC:jl] biological_process
+ENSG00000240972 GO:0043066 negative regulation of apoptotic process "Any process that stops, prevents, or reduces the frequency, rate or extent of cell death by apoptotic process." [GOC:jl, GOC:mtg_apoptosis] biological_process
+ENSG00000240972 GO:0043518 negative regulation of DNA damage response, signal transduction by p53 class mediator "Any process that stops, prevents, or reduces the frequency, rate or extent of the cascade of processes induced by the cell cycle regulator phosphoprotein p53, or an equivalent protein, in response to the detection of DNA damage." [GOC:jl] biological_process
+ENSG00000240972 GO:0045087 innate immune response "Innate immune responses are defense responses mediated by germline encoded components that directly recognize components of potential pathogens." [GO_REF:0000022, GOC:add, GOC:ebc, GOC:mtg_15nov05, GOC:mtg_sensu] biological_process
+ENSG00000240972 GO:0048146 positive regulation of fibroblast proliferation "Any process that activates or increases the frequency, rate or extent of multiplication or reproduction of fibroblast cells." [GOC:jid] biological_process
+ENSG00000240972 GO:0050178 phenylpyruvate tautomerase activity "Catalysis of the reaction: keto-phenylpyruvate = enol-phenylpyruvate." [EC:5.3.2.1, MetaCyc:PHENYLPYRUVATE-TAUTOMERASE-RXN] molecular_function
+ENSG00000240972 GO:0050715 positive regulation of cytokine secretion "Any process that activates or increases the frequency, rate or extent of the regulated release of cytokines from a cell." [GOC:ai] biological_process
+ENSG00000240972 GO:0050731 positive regulation of peptidyl-tyrosine phosphorylation "Any process that activates or increases the frequency, rate or extent of the phosphorylation of peptidyl-tyrosine." [GOC:ai] biological_process
+ENSG00000240972 GO:0050918 positive chemotaxis "The directed movement of a motile cell or organism towards a higher concentration of a chemical." [GOC:ai, GOC:bf, GOC:isa_complete] biological_process
+ENSG00000240972 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000240972 GO:0070207 protein homotrimerization "The formation of a protein homotrimer, a macromolecular structure consisting of three noncovalently associated identical subunits." [GOC:hjd] biological_process
+ENSG00000240972 GO:0070374 positive regulation of ERK1 and ERK2 cascade "Any process that activates or increases the frequency, rate or extent of signal transduction mediated by the ERK1 and ERK2 cascade." [GOC:mah] biological_process
+ENSG00000240972 GO:0071157 negative regulation of cell cycle arrest "Any process that decreases the rate, frequency, or extent of cell cycle arrest, the process in which the cell cycle is halted during one of the normal phases." [GOC:mah] biological_process
+ENSG00000240972 GO:0090344 negative regulation of cell aging "Any process that decreases the rate, frequency, or extent of cell aging. Cell aging is the progression of the cell from its inception to the end of its lifespan." [GOC:BHF, GOC:dph, GOC:tb] biological_process
+ENSG00000240972 GO:1902166 negative regulation of intrinsic apoptotic signaling pathway in response to DNA damage by p53 class mediator "Any process that stops, prevents or reduces the frequency, rate or extent of intrinsic apoptotic signaling pathway in response to DNA damage by p53 class mediator." [GOC:TermGenie, PMID:17719541] biological_process
+ENSG00000240972 GO:0005615 extracellular space "That part of a multicellular organism outside the cells proper, usually taken to be outside the plasma membranes, and occupied by fluid." [ISBN:0198547684] cellular_component
+ENSG00000240972 GO:0042127 regulation of cell proliferation "Any process that modulates the frequency, rate or extent of cell proliferation." [GOC:jl] biological_process
+ENSG00000240972 GO:0001934 positive regulation of protein phosphorylation "Any process that activates or increases the frequency, rate or extent of addition of phosphate groups to amino acids within a protein." [GOC:hjd] biological_process
+ENSG00000240972 GO:0033033 negative regulation of myeloid cell apoptotic process "Any process that stops, prevents, or reduces the frequency, rate, or extent of a myeloid cell apoptotic process." [GOC:add, GOC:mtg_apoptosis] biological_process
+ENSG00000240972 GO:0007569 cell aging "An aging process that has as participant a cell after a cell has stopped dividing. Cell aging may occur when a cell has temporarily stopped dividing through cell cycle arrest (GO:0007050) or when a cell has permanently stopped dividing, in which case it is undergoing cellular senescence (GO:0090398). May precede cell death (GO:0008219) and succeed cell maturation (GO:0048469)." [GOC:PO_curators] biological_process
+ENSG00000240972 GO:0031666 positive regulation of lipopolysaccharide-mediated signaling pathway "Any process that activates or increases the frequency, rate or extent of signaling in response to detection of lipopolysaccharide." [GOC:mah] biological_process
+ENSG00000240972 GO:0043406 positive regulation of MAP kinase activity "Any process that activates or increases the frequency, rate or extent of MAP kinase activity." [GOC:dph, GOC:go_curators] biological_process
+ENSG00000240972 GO:0030330 DNA damage response, signal transduction by p53 class mediator "A cascade of processes induced by the cell cycle regulator phosphoprotein p53, or an equivalent protein, in response to the detection of DNA damage." [GOC:go_curators] biological_process
+ENSG00000240972 GO:0002906 negative regulation of mature B cell apoptotic process "Any process that stops, prevents, or reduces the frequency, rate, or extent of mature B cell apoptotic process." [GOC:add, GOC:mtg_apoptosis] biological_process
+ENSG00000240972 GO:2000343 positive regulation of chemokine (C-X-C motif) ligand 2 production "Any process that activates or increases the frequency, rate or extent of chemokine (C-X-C motif) ligand 2 production." [GOC:BHF, GOC:mah] biological_process
+ENSG00000240972 GO:0032269 negative regulation of cellular protein metabolic process "Any process that stops, prevents, or reduces the frequency, rate or extent of the chemical reactions and pathways involving a protein, occurring at the level of an individual cell." [GOC:mah] biological_process
+ENSG00000240972 GO:0061078 positive regulation of prostaglandin secretion involved in immune response "Any process that activates or increases the frequency, rate or extent of the regulated release of a prostaglandin from a cell and contributes to the immune response." [GOC:BHF, GOC:dph] biological_process
+ENSG00000240972 GO:0061081 positive regulation of myeloid leukocyte cytokine production involved in immune response "Any process that modulates the rate, frequency, or extent of the production of a cytokine that contributes to the immune response." [GOC:BHF, GOC:dph] biological_process
+ENSG00000240972 GO:0090238 positive regulation of arachidonic acid secretion "Any process that increases the rate, frequency, or extent of arachidonic acid secretion, the controlled release of arachidonic acid from a cell or a tissue." [GOC:BHF, GOC:dph, GOC:tb] biological_process
+ENSG00000040608 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000040608 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000040608 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000040608 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000040608 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000040608 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000040608 GO:0005783 endoplasmic reticulum "The irregular network of unit membranes, visible only by electron microscopy, that occurs in the cytoplasm of many eukaryotic cells. The membranes form a complex meshwork of tubular channels, which are often expanded into slitlike cavities called cisternae. The ER takes two forms, rough (or granular), with ribosomes adhering to the outer surface, and smooth (with no ribosomes attached)." [ISBN:0198506732] cellular_component
+ENSG00000040608 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000040608 GO:0004872 receptor activity "Combining with an extracellular or intracellular messenger to initiate a change in cell activity." [GOC:ceb, ISBN:0198506732] molecular_function
+ENSG00000040608 GO:0007409 axonogenesis "De novo generation of a long process of a neuron, that carries efferent (outgoing) action potentials from the cell body towards target cells. Refers to the morphogenesis or creation of shape or form of the developing axon." [GOC:dph, GOC:jid, GOC:pg, GOC:pr, ISBN:0198506732] biological_process
+ENSG00000040608 GO:0009986 cell surface "The external part of the cell wall and/or plasma membrane." [GOC:jl, GOC:mtg_sensu, GOC:sm] cellular_component
+ENSG00000040608 GO:0031225 anchored component of membrane "The component of a membrane consisting of the gene products that are tethered to the membrane only by a covalently attached anchor, such as a lipid group that is embedded in the membrane. Gene products with peptide sequences that are embedded in the membrane are excluded from this grouping." [GOC:dos, GOC:mah] cellular_component
+ENSG00000040608 GO:0048011 neurotrophin TRK receptor signaling pathway "A series of molecular signals initiated by the binding of a neurotrophin to a receptor on the surface of the target cell where the receptor possesses tyrosine kinase activity, and ending with regulation of a downstream cellular process, e.g. transcription." [GOC:bf, GOC:ceb, GOC:jc, GOC:signaling, PMID:12065629, Wikipedia:Trk_receptor] biological_process
+ENSG00000040608 GO:0050770 regulation of axonogenesis "Any process that modulates the frequency, rate or extent of axonogenesis, the generation of an axon, the long process of a neuron." [GOC:ai] biological_process
+ENSG00000040608 GO:0050771 negative regulation of axonogenesis "Any process that stops, prevents, or reduces the frequency, rate or extent of axonogenesis." [GOC:ai] biological_process
+ENSG00000040608 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000040608 GO:0030426 growth cone "The migrating motile tip of a growing nerve cell axon or dendrite." [ISBN:0815316194] cellular_component
+ENSG00000183628
+ENSG00000183628 GO:0007155 cell adhesion "The attachment of a cell, either to another cell or to an underlying substrate such as the extracellular matrix, via cell adhesion molecules." [GOC:hb, GOC:pf] biological_process
+ENSG00000183628 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000183628 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000183628 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000183628 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000183628 GO:0005578 proteinaceous extracellular matrix "A layer consisting mainly of proteins (especially collagen) and glycosaminoglycans (mostly as proteoglycans) that forms a sheet underlying or overlying cells such as endothelial and epithelial cells. The proteins are secreted by cells in the vicinity. An example of this component is found in Mus musculus." [GOC:mtg_sensu, ISBN:0198547684] cellular_component
+ENSG00000183628 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000183628 GO:0009887 organ morphogenesis "Morphogenesis of an organ. An organ is defined as a tissue or set of tissues that work together to perform a specific function or functions. Morphogenesis is the process in which anatomical structures are generated and organized. Organs are commonly observed as visibly distinct structures, but may also exist as loosely associated clusters of cells that work together to perform a specific function or functions." [GOC:dgh, GOC:go_curators, ISBN:0471245208, ISBN:0721662544] biological_process
+ENSG00000211661 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000211661 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100075 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100075 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000100075 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100075 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100075 GO:0055085 transmembrane transport "The process in which a solute is transported from one side of a membrane to the other." [GOC:dph, GOC:jid] biological_process
+ENSG00000100075 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000100075 GO:0006629 lipid metabolic process "The chemical reactions and pathways involving lipids, compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent. Includes fatty acids; neutral fats, other fatty-acid esters, and soaps; long-chain (fatty) alcohols and waxes; sphingoids and other long-chain bases; glycolipids, phospholipids and sphingolipids; and carotenes, polyprenols, sterols, terpenes and other isoprenoids." [GOC:ma] biological_process
+ENSG00000100075 GO:0006790 sulfur compound metabolic process "The chemical reactions and pathways involving the nonmetallic element sulfur or compounds that contain sulfur, such as the amino acids methionine and cysteine or the tripeptide glutathione." [GOC:ai] biological_process
+ENSG00000100075 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000100075 GO:0051186 cofactor metabolic process "The chemical reactions and pathways involving a cofactor, a substance that is required for the activity of an enzyme or other protein. Cofactors may be inorganic, such as the metal atoms zinc, iron, and copper in certain forms, or organic, in which case they are referred to as coenzymes. Cofactors may either be bound tightly to active sites or bind loosely with the substrate." [GOC:ai] biological_process
+ENSG00000100075 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100075 GO:0022857 transmembrane transporter activity "Enables the transfer of a substance from one side of a membrane to the other." [GOC:mtg_transport, ISBN:0815340729] molecular_function
+ENSG00000100075 GO:0005975 carbohydrate metabolic process "The chemical reactions and pathways involving carbohydrates, any of a group of organic compounds based of the general formula Cx(H2O)y. Includes the formation of carbohydrate derivatives by the addition of a carbohydrate residue to another molecule." [GOC:mah, ISBN:0198506732] biological_process
+ENSG00000100075 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100075 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100075 GO:0005743 mitochondrial inner membrane "The inner, i.e. lumen-facing, lipid bilayer of the mitochondrial envelope. It is highly folded to form cristae." [GOC:ai] cellular_component
+ENSG00000100075 GO:0006006 glucose metabolic process "The chemical reactions and pathways involving glucose, the aldohexose gluco-hexose. D-glucose is dextrorotatory and is sometimes known as dextrose; it is an important source of energy for living organisms and is found free as well as combined in homo- and hetero-oligosaccharides and polysaccharides." [ISBN:0198506732] biological_process
+ENSG00000100075 GO:0006094 gluconeogenesis "The formation of glucose from noncarbohydrate precursors, such as pyruvate, amino acids and glycerol." [MetaCyc:GLUCONEO-PWY] biological_process
+ENSG00000100075 GO:0015137 citrate transmembrane transporter activity "Catalysis of the transfer of citrate, 2-hydroxy-1,2,3-propanetricarboyxlate, from one side of the membrane to the other." [GOC:ai] molecular_function
+ENSG00000100075 GO:0015746 citrate transport "The directed movement of citrate, 2-hydroxy-1,2,3-propanetricarboyxlate, into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:krc] biological_process
+ENSG00000100075 GO:0019432 triglyceride biosynthetic process "The chemical reactions and pathways resulting in the formation of a triglyceride, any triester of glycerol." [ISBN:0198506732] biological_process
+ENSG00000100075 GO:0035338 long-chain fatty-acyl-CoA biosynthetic process "The chemical reactions and pathways resulting in the formation of a long-chain fatty-acyl-CoA any derivative of coenzyme A in which the sulfhydryl group is in a thioester linkage with a long-chain fatty-acyl group. Long-chain fatty-acyl-CoAs have chain lengths of C13 or more." [CHEBI:33184, ISBN:0198506732] biological_process
+ENSG00000100075 GO:0044255 cellular lipid metabolic process "The chemical reactions and pathways involving lipids, as carried out by individual cells." [GOC:jl] biological_process
+ENSG00000100075 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000100075 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000133422 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000133422 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000133422 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000133422 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000133422 GO:0008270 zinc ion binding "Interacting selectively and non-covalently with zinc (Zn) ions." [GOC:ai] molecular_function
+ENSG00000133422 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000133422 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000133422 GO:0030246 carbohydrate binding "Interacting selectively and non-covalently with any carbohydrate, which includes monosaccharides, oligosaccharides and polysaccharides as well as substances derived from monosaccharides by reduction of the carbonyl group (alditols), by oxidation of one or more hydroxy groups to afford the corresponding aldehydes, ketones, or carboxylic acids, or by replacement of one or more hydroxy group(s) by a hydrogen atom. Cyclitols are generally not regarded as carbohydrates." [CHEBI:16646, GOC:mah] molecular_function
+ENSG00000133422
+ENSG00000211649 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000211649 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000274600 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000274600 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000161133 GO:0036459 ubiquitinyl hydrolase activity "Catalysis of the thiol-dependent hydrolysis of an ester, thioester, amide, peptide or isopeptide bond formed by the C-terminal glycine of ubiquitin." [EC:3.4.19.12, GOC:bf, GOC:ka] molecular_function
+ENSG00000161133 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000161133 GO:0006511 ubiquitin-dependent protein catabolic process "The chemical reactions and pathways resulting in the breakdown of a protein or peptide by hydrolysis of its peptide bonds, initiated by the covalent attachment of a ubiquitin group, or multiple ubiquitin groups, to the protein." [GOC:go_curators] biological_process
+ENSG00000161133 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000161133 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000161133 GO:0008234 cysteine-type peptidase activity "Catalysis of the hydrolysis of peptide bonds in a polypeptide chain by a mechanism in which the sulfhydryl group of a cysteine residue at the active center acts as a nucleophile." [GOC:mah, http://merops.sanger.ac.uk/about/glossary.htm#CATTYPE] molecular_function
+ENSG00000161133 GO:0008233 peptidase activity "Catalysis of the hydrolysis of a peptide bond. A peptide bond is a covalent bond formed when the carbon atom from the carboxyl group of one amino acid shares electrons with the nitrogen atom from the amino group of a second amino acid." [GOC:jl, ISBN:0815332181] molecular_function
+ENSG00000239900 GO:0003824 catalytic activity "Catalysis of a biochemical reaction at physiological temperatures. In biologically catalyzed reactions, the reactants are known as substrates, and the catalysts are naturally occurring macromolecular substances known as enzymes. Enzymes possess specific binding sites for substrates, and are usually composed wholly or largely of protein, but RNA that has catalytic activity (ribozyme) is often also regarded as enzymatic." [ISBN:0198506732] molecular_function
+ENSG00000239900 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000239900 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000239900 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000239900 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000239900 GO:0006461 protein complex assembly "The aggregation, arrangement and bonding together of a set of components to form a protein complex." [GOC:ai] biological_process
+ENSG00000239900 GO:0022607 cellular component assembly "The aggregation, arrangement and bonding together of a cellular component." [GOC:isa_complete] biological_process
+ENSG00000239900 GO:0065003 macromolecular complex assembly "The aggregation, arrangement and bonding together of a set of macromolecules to form a complex." [GOC:jl] biological_process
+ENSG00000239900 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000239900 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000239900 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000239900 GO:0016829 lyase activity "Catalysis of the cleavage of C-C, C-O, C-N and other bonds by other means than by hydrolysis or oxidation, or conversely adding a group to a double bond. They differ from other enzymes in that two substrates are involved in one reaction direction, but only one in the other direction. When acting on the single substrate, a molecule is eliminated and this generates either a new double bond or a new ring." [EC:4.-.-.-, ISBN:0198547684] molecular_function
+ENSG00000239900 GO:0004018 N6-(1,2-dicarboxyethyl)AMP AMP-lyase (fumarate-forming) activity "Catalysis of the reaction: N6-(1,2-dicarboxyethyl)AMP = fumarate + AMP." [EC:4.3.2.2] molecular_function
+ENSG00000239900 GO:0006144 purine nucleobase metabolic process "The chemical reactions and pathways involving purine nucleobases, one of the two classes of nitrogen-containing ring compounds found in DNA and RNA, which include adenine and guanine." [CHEBI:26386, GOC:go_curators] biological_process
+ENSG00000239900 GO:0006164 purine nucleotide biosynthetic process "The chemical reactions and pathways resulting in the formation of a purine nucleotide, a compound consisting of nucleoside (a purine base linked to a deoxyribose or ribose sugar) esterified with a phosphate group at either the 3' or 5'-hydroxyl group of the sugar." [GOC:go_curators, ISBN:0198506732] biological_process
+ENSG00000239900 GO:0006167 AMP biosynthetic process "The chemical reactions and pathways resulting in the formation of AMP, adenosine monophosphate." [GOC:go_curators, ISBN:0198506732] biological_process
+ENSG00000239900 GO:0006189 'de novo' IMP biosynthetic process "The chemical reactions and pathways resulting in the formation of IMP, inosine monophosphate, by the stepwise assembly of a purine ring on ribose 5-phosphate." [GOC:mah, ISBN:0716720094] biological_process
+ENSG00000239900 GO:0008152 metabolic process "The chemical reactions and pathways, including anabolism and catabolism, by which living organisms transform chemical substances. Metabolic processes typically transform small molecules, but also include macromolecular processes such as DNA repair and replication, and protein synthesis and degradation." [GOC:go_curators, ISBN:0198547684] biological_process
+ENSG00000239900 GO:0009168 purine ribonucleoside monophosphate biosynthetic process "The chemical reactions and pathways resulting in the formation of purine ribonucleoside monophosphate, a compound consisting of a purine base linked to a ribose sugar esterified with phosphate on the sugar." [GOC:go_curators, ISBN:0198506732] biological_process
+ENSG00000239900 GO:0044208 'de novo' AMP biosynthetic process "The chemical reactions and pathways resulting in the formation of adenosine monophosphate (AMP) from inosine 5'-monophosphate (IMP)." [GOC:ecd, PMID:10888601] biological_process
+ENSG00000239900 GO:0051262 protein tetramerization "The formation of a protein tetramer, a macromolecular structure consisting of four noncovalently associated identical or nonidentical subunits." [GOC:ecd] biological_process
+ENSG00000239900 GO:0055086 nucleobase-containing small molecule metabolic process "The cellular chemical reactions and pathways involving a nucleobase-containing small molecule: a nucleobase, a nucleoside, or a nucleotide." [GOC:vw] biological_process
+ENSG00000239900 GO:0070626 (S)-2-(5-amino-1-(5-phospho-D-ribosyl)imidazole-4-carboxamido)succinate AMP-lyase (fumarate-forming) activity "Catalysis of the reaction: (S)-2-(5-amino-1-(5-phospho-D-ribosyl)imidazole-4-carboxamido)succinate = fumarate + 5-amino-1-(5-phospho-D-ribosyl)imidazole-4-carboxamide." [GOC:mah, GOC:pde] molecular_function
+ENSG00000239900 GO:0009152 purine ribonucleotide biosynthetic process "The chemical reactions and pathways resulting in the formation of a purine ribonucleotide, a compound consisting of ribonucleoside (a purine base linked to a ribose sugar) esterified with a phosphate group at either the 3' or 5'-hydroxyl group of the sugar." [GOC:go_curators, ISBN:0198506732] biological_process
+ENSG00000239900 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000239900 GO:0042594 response to starvation "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a starvation stimulus, deprivation of nourishment." [GOC:go_curators] biological_process
+ENSG00000239900 GO:0001666 response to hypoxia "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a stimulus indicating lowered oxygen tension. Hypoxia, defined as a decline in O2 levels below normoxic levels of 20.8 - 20.95%, results in metabolic adaptation at both the cellular and organismal level." [GOC:hjd] biological_process
+ENSG00000239900 GO:0007584 response to nutrient "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a nutrient stimulus." [GOC:go_curators] biological_process
+ENSG00000239900 GO:0014850 response to muscle activity "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a muscle activity stimulus." [GOC:mtg_muscle] biological_process
+ENSG00000239900 GO:0009156 ribonucleoside monophosphate biosynthetic process "The chemical reactions and pathways resulting in the formation of a ribonucleoside monophosphate, a compound consisting of a nucleobase linked to a ribose sugar esterified with phosphate on the sugar." [GOC:go_curators, ISBN:0198506732] biological_process
+ENSG00000239900 GO:0006163 purine nucleotide metabolic process "The chemical reactions and pathways involving a purine nucleotide, a compound consisting of nucleoside (a purine base linked to a deoxyribose or ribose sugar) esterified with a phosphate group at either the 3' or 5'-hydroxyl group of the sugar." [GOC:go_curators, ISBN:0198506732] biological_process
+ENSG00000239900 GO:0009060 aerobic respiration "The enzymatic release of energy from inorganic and organic compounds (especially carbohydrates and fats) which requires oxygen as the terminal electron acceptor." [GOC:das, GOC:jl, ISBN:0140513590] biological_process
+ENSG00000239900
+ENSG00000100319 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100319 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100319 GO:0006397 mRNA processing "Any process involved in the conversion of a primary mRNA transcript into one or more mature mRNA(s) prior to translation into polypeptide." [GOC:mah] biological_process
+ENSG00000100319 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100319 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100319 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100319 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100319 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100319 GO:0005689 U12-type spliceosomal complex "Any spliceosomal complex that forms during the splicing of a messenger RNA primary transcript to excise an intron; the series of U12-type spliceosomal complexes is involved in the splicing of the majority of introns that contain atypical AT-AC terminal dinucleotides, as well as other non-canonical introns. The entire splice site signal, not just the terminal dinucleotides, is involved in determining which spliceosome utilizes the site." [GOC:krc, GOC:mah, PMID:11574683, PMID:11971955] cellular_component
+ENSG00000100319 GO:0008270 zinc ion binding "Interacting selectively and non-covalently with zinc (Zn) ions." [GOC:ai] molecular_function
+ENSG00000100319 GO:0008380 RNA splicing "The process of removing sections of the primary RNA transcript to remove sequences not present in the mature form of the RNA and joining the remaining sections to form the mature form of the RNA." [GOC:krc, GOC:mah] biological_process
+ENSG00000100319 GO:0046872 metal ion binding "Interacting selectively and non-covalently with any metal ion." [GOC:ai] molecular_function
+ENSG00000186998 GO:0005581 collagen trimer "A protein complex consisting of three collagen chains assembled into a left-handed triple helix. These trimers typically assemble into higher order structures." [GOC:dos, GOC:mah, ISBN:0721639976, PMID:19693541, PMID:21421911] cellular_component
+ENSG00000186998 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000186998 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000186998 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000186998 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000186998 GO:0005783 endoplasmic reticulum "The irregular network of unit membranes, visible only by electron microscopy, that occurs in the cytoplasm of many eukaryotic cells. The membranes form a complex meshwork of tubular channels, which are often expanded into slitlike cavities called cisternae. The ER takes two forms, rough (or granular), with ribosomes adhering to the outer surface, and smooth (with no ribosomes attached)." [ISBN:0198506732] cellular_component
+ENSG00000186998 GO:0005794 Golgi apparatus "A compound membranous cytoplasmic organelle of eukaryotic cells, consisting of flattened, ribosome-free vesicles arranged in a more or less regular stack. The Golgi apparatus differs from the endoplasmic reticulum in often having slightly thicker membranes, appearing in sections as a characteristic shallow semicircle so that the convex side (cis or entry face) abuts the endoplasmic reticulum, secretory vesicles emerging from the concave side (trans or exit face). In vertebrate cells there is usually one such organelle, while in invertebrates and plants, where they are known usually as dictyosomes, there may be several scattered in the cytoplasm. The Golgi apparatus processes proteins produced on the ribosomes of the rough endoplasmic reticulum; such processing includes modification of the core oligosaccharides of glycoproteins, and the sorting and packaging of proteins for transport to a variety of cellular locations. Three different regions of the Golgi are now recognized both in terms of structure and function: cis, in the vicinity of the cis face, trans, in the vicinity of the trans face, and medial, lying between the cis and trans regions." [ISBN:0198506732] cellular_component
+ENSG00000186998 GO:0005578 proteinaceous extracellular matrix "A layer consisting mainly of proteins (especially collagen) and glycosaminoglycans (mostly as proteoglycans) that forms a sheet underlying or overlying cells such as endothelial and epithelial cells. The proteins are secreted by cells in the vicinity. An example of this component is found in Mus musculus." [GOC:mtg_sensu, ISBN:0198547684] cellular_component
+ENSG00000186998
+ENSG00000100354
+ENSG00000100354 GO:0000166 nucleotide binding "Interacting selectively and non-covalently with a nucleotide, any compound consisting of a nucleoside that is esterified with (ortho)phosphate or an oligophosphate at any hydroxyl group on the ribose or deoxyribose." [GOC:mah, ISBN:0198547684] molecular_function
+ENSG00000100354 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100354 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000100354 GO:0006417 regulation of translation "Any process that modulates the frequency, rate or extent of the chemical reactions and pathways resulting in the formation of proteins by the translation of mRNA." [GOC:isa_complete] biological_process
+ENSG00000100354 GO:0007173 epidermal growth factor receptor signaling pathway "A series of molecular signals initiated by binding of a ligand to the tyrosine kinase receptor EGFR (ERBB1) on the surface of a cell. The pathway ends with regulation of a downstream cellular process, e.g. transcription." [GOC:ceb, PR:000006933] biological_process
+ENSG00000100354 GO:0007219 Notch signaling pathway "A series of molecular signals initiated by the binding of an extracellular ligand to the receptor Notch on the surface of a target cell, and ending with regulation of a downstream cellular process, e.g. transcription." [GOC:go_curators, GOC:signaling] biological_process
+ENSG00000100354 GO:0008543 fibroblast growth factor receptor signaling pathway "The series of molecular signals generated as a consequence of a fibroblast growth factor receptor binding to one of its physiological ligands." [GOC:ceb] biological_process
+ENSG00000100354 GO:0010467 gene expression "The process in which a gene's sequence is converted into a mature gene product or products (proteins or RNA). This includes the production of an RNA transcript as well as any processing to produce a mature RNA product or an mRNA (for protein-coding genes) and the translation of that mRNA into protein. Some protein processing events may be included when they are required to form an active form of a product from an inactive precursor form." [GOC:dph, GOC:tb] biological_process
+ENSG00000100354 GO:0031047 gene silencing by RNA "Any process in which RNA molecules inactivate expression of target genes." [GOC:dph, GOC:mah, GOC:tb, PMID:15020054] biological_process
+ENSG00000100354 GO:0038095 Fc-epsilon receptor signaling pathway "A series of molecular signals initiated by the binding of the Fc portion of immunoglobulin E (IgE) to an Fc-epsilon receptor on the surface of a signal-receiving cell, and ending with regulation of a downstream cellular process, e.g. transcription. The Fc portion of an immunoglobulin is its C-terminal constant region." [GOC:phg, PMID:12413516, PMID:15048725] biological_process
+ENSG00000100354 GO:0044822 poly(A) RNA binding "Interacting non-covalently with a poly(A) RNA, a RNA molecule which has a tail of adenine bases." [GOC:jl] molecular_function
+ENSG00000100354 GO:0045087 innate immune response "Innate immune responses are defense responses mediated by germline encoded components that directly recognize components of potential pathogens." [GO_REF:0000022, GOC:add, GOC:ebc, GOC:mtg_15nov05, GOC:mtg_sensu] biological_process
+ENSG00000100354 GO:0048011 neurotrophin TRK receptor signaling pathway "A series of molecular signals initiated by the binding of a neurotrophin to a receptor on the surface of the target cell where the receptor possesses tyrosine kinase activity, and ending with regulation of a downstream cellular process, e.g. transcription." [GOC:bf, GOC:ceb, GOC:jc, GOC:signaling, PMID:12065629, Wikipedia:Trk_receptor] biological_process
+ENSG00000100354 GO:0048015 phosphatidylinositol-mediated signaling "A series of molecular signals in which a cell uses a phosphatidylinositol-mediated signaling to convert a signal into a response. Phosphatidylinositols include phosphatidylinositol (PtdIns) and its phosphorylated derivatives." [GOC:bf, GOC:ceb, ISBN:0198506732] biological_process
+ENSG00000100354 GO:0060213 positive regulation of nuclear-transcribed mRNA poly(A) tail shortening "Any process that increases the frequency, rate or extent of poly(A) tail shortening of a nuclear-transcribed mRNA. Poly(A) tail shortening is the decrease in length of the poly(A) tail of an mRNA from full length to an oligo(A) length." [GOC:dph, GOC:tb] biological_process
+ENSG00000100354 GO:1900153 positive regulation of nuclear-transcribed mRNA catabolic process, deadenylation-dependent decay "Any process that activates or increases the frequency, rate or extent of nuclear-transcribed mRNA catabolic process, deadenylation-dependent decay." [GOC:mcc, GOC:TermGenie] biological_process
+ENSG00000100354 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100354 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000100354 GO:0002376 immune system process "Any process involved in the development or functioning of the immune system, an organismal system for calibrated responses to potential internal or invasive threats." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05] biological_process
+ENSG00000100354 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000100354 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100354 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000100354 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000177096 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000177096 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000177096 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000177096 GO:0016192 vesicle-mediated transport "A cellular transport process in which transported substances are moved in membrane-bounded vesicles; transported substances are enclosed in the vesicle lumen or located in the vesicle membrane. The process begins with a step that directs a substance to the forming vesicle, and includes vesicle budding and coating. Vesicles are then targeted to, and fuse with, an acceptor membrane." [GOC:ai, GOC:mah, ISBN:08789310662000] biological_process
+ENSG00000177096 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000177096 GO:0016023 cytoplasmic membrane-bounded vesicle "A membrane-bounded vesicle found in the cytoplasm of the cell." [GOC:ai, GOC:mah] cellular_component
+ENSG00000177096 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000177096 GO:0005768 endosome "A membrane-bounded organelle to which materials ingested by endocytosis are delivered." [ISBN:0198506732, PMID:19696797] cellular_component
+ENSG00000177096 GO:0001881 receptor recycling "The process that results in the return of receptor molecules to an active state and an active cellular location after they have been stimulated by a ligand. An active state is when the receptor is ready to receive a signal." [GOC:dph] biological_process
+ENSG00000177096 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000177096 GO:0005769 early endosome "A membrane-bounded organelle that receives incoming material from primary endocytic vesicles that have been generated by clathrin-dependent and clathrin-independent endocytosis; vesicles fuse with the early endosome to deliver cargo for sorting into recycling or degradation pathways." [GOC:mah, NIF_Subcellular:nlx_subcell_20090701, PMID:19696797] cellular_component
+ENSG00000177096 GO:0005802 trans-Golgi network "The network of interconnected tubular and cisternal structures located within the Golgi apparatus on the side distal to the endoplasmic reticulum, from which secretory vesicles emerge. The trans-Golgi network is important in the later stages of protein secretion where it is thought to play a key role in the sorting and targeting of secreted proteins to the correct destination." [GOC:vw, ISBN:0815316194] cellular_component
+ENSG00000177096 GO:0007032 endosome organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of endosomes." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000177096 GO:0030136 clathrin-coated vesicle "A vesicle with a coat formed of clathrin connected to the membrane via one of the clathrin adaptor complexes." [GOC:mah, PMID:11252894] cellular_component
+ENSG00000177096 GO:0042147 retrograde transport, endosome to Golgi "The directed movement of membrane-bounded vesicles from endosomes back to the trans-Golgi network where they are recycled for further rounds of transport." [GOC:jl, PMID:10873832, PMID:16936697] biological_process
+ENSG00000177096 GO:0042803 protein homodimerization activity "Interacting selectively and non-covalently with an identical protein to form a homodimer." [GOC:jl] molecular_function
+ENSG00000177096 GO:0055037 recycling endosome "Organelle consisting of networks of 60nm tubules organized around the microtubule organizing centre in some cell types. They transport molecules (e.g., receptors, transporters, lipids) derived from endosomes, the Golgi apparatus, or the cytoplasm to the plasma membrane. Transported molecules may be recycled for reuse, or may be newly synthesized." [GOC:dph, GOC:jid, GOC:kmv, GOC:rph, PMID:10930469, PMID:15601896, PMID:16246101, PMID:21556374, PMID:21562044] cellular_component
+ENSG00000177096
+ENSG00000100226 GO:0005525 GTP binding "Interacting selectively and non-covalently with GTP, guanosine triphosphate." [GOC:ai] molecular_function
+ENSG00000100226 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100226 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100226 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000100226 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100226 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000100226 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100226 GO:0002376 immune system process "Any process involved in the development or functioning of the immune system, an organismal system for calibrated responses to potential internal or invasive threats." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05] biological_process
+ENSG00000100226 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000100226 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100226 GO:0034655 nucleobase-containing compound catabolic process "The chemical reactions and pathways resulting in the breakdown of nucleobases, nucleosides, nucleotides and nucleic acids." [GOC:mah] biological_process
+ENSG00000100226 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000100226 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000100226 GO:0003924 GTPase activity "Catalysis of the reaction: GTP + H2O = GDP + phosphate." [ISBN:0198547684] molecular_function
+ENSG00000100226 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100226 GO:0000177 cytoplasmic exosome (RNase complex) "Complex of 3'-5' exoribonucleases found in the cytoplasm." [PMID:10465791] cellular_component
+ENSG00000100226 GO:0006184 GTP catabolic process "The chemical reactions and pathways resulting in the breakdown of GTP, guanosine triphosphate." [ISBN:0198506732] biological_process
+ENSG00000100226 GO:0006955 immune response "Any immune system process that functions in the calibrated response of an organism to a potential internal or invasive threat." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05] biological_process
+ENSG00000100226 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000100226 GO:0044822 poly(A) RNA binding "Interacting non-covalently with a poly(A) RNA, a RNA molecule which has a tail of adenine bases." [GOC:jl] molecular_function
+ENSG00000100226 GO:0061014 positive regulation of mRNA catabolic process "Any process that increases the rate, frequency, or extent of a mRNA catabolic process, the chemical reactions and pathways resulting in the breakdown of RNA, ribonucleic acid, one of the two main type of nucleic acid, consisting of a long, unbranched macromolecule formed from ribonucleotides joined in 3',5'-phosphodiester linkage." [GOC:ascb_2009, GOC:dph, GOC:tb] biological_process
+ENSG00000100226
+ENSG00000128253 GO:0008270 zinc ion binding "Interacting selectively and non-covalently with zinc (Zn) ions." [GOC:ai] molecular_function
+ENSG00000128253 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000128253 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000128253 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100314 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100314 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000100314 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100314 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100314 GO:0005509 calcium ion binding "Interacting selectively and non-covalently with calcium ions (Ca2+)." [GOC:ai] molecular_function
+ENSG00000100314 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000100314 GO:0032588 trans-Golgi network membrane "The lipid bilayer surrounding any of the compartments that make up the trans-Golgi network." [GOC:mah] cellular_component
+ENSG00000100413 GO:0002376 immune system process "Any process involved in the development or functioning of the immune system, an organismal system for calibrated responses to potential internal or invasive threats." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05] biological_process
+ENSG00000100413 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000100413 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100413 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100413 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100413 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000100413 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100413 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000100413 GO:0005815 microtubule organizing center "An intracellular structure that can catalyze gamma-tubulin-dependent microtubule nucleation and that can anchor microtubules by interacting with their minus ends, plus ends or sides." [GOC:vw, http://en.wikipedia.org/wiki/Microtubule_organizing_center, ISBN:0815316194, PMID:17072892, PMID:17245416] cellular_component
+ENSG00000100413 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100413 GO:0005654 nucleoplasm "That part of the nuclear content other than the chromosomes or the nucleolus." [GOC:ma, ISBN:0124325653] cellular_component
+ENSG00000100413 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100413 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100413 GO:0016779 nucleotidyltransferase activity "Catalysis of the transfer of a nucleotidyl group to a reactant." [ISBN:0198506732] molecular_function
+ENSG00000100413 GO:0003677 DNA binding "Any molecular function by which a gene product interacts selectively and non-covalently with DNA (deoxyribonucleic acid)." [GOC:dph, GOC:jl, GOC:tb, GOC:vw] molecular_function
+ENSG00000100413 GO:0003899 DNA-directed RNA polymerase activity "Catalysis of the reaction: nucleoside triphosphate + RNA(n) = diphosphate + RNA(n+1). Utilizes a DNA template, i.e. the catalysis of DNA-template-directed extension of the 3'-end of an RNA strand by one nucleotide at a time. Can initiate a chain 'de novo'." [EC:2.7.7.6] molecular_function
+ENSG00000100413 GO:0005666 DNA-directed RNA polymerase III complex "RNA polymerase III, one of three nuclear DNA-directed RNA polymerases found in all eukaryotes, is a multisubunit complex; typically it produces 5S rRNA, tRNAs and some of the small nuclear RNAs. Two large subunits comprise the most conserved portion including the catalytic site and share similarity with other eukaryotic and bacterial multisubunit RNA polymerases. The remainder of the complex is composed of smaller subunits (generally ten or more), some of which are also found in RNA polymerase I and others of which are also found in RNA polymerases I and II. Although the core is competent to mediate ribonucleic acid synthesis, it requires additional factors to select the appropriate template." [GOC:krc, GOC:mtg_sensu] cellular_component
+ENSG00000100413 GO:0005813 centrosome "A structure comprised of a core structure (in most organisms, a pair of centrioles) and peripheral material from which a microtubule-based structure, such as a spindle apparatus, is organized. Centrosomes occur close to the nucleus during interphase in many eukaryotic cells, though in animal cells it changes continually during the cell-division cycle." [GOC:mah, ISBN:0198547684] cellular_component
+ENSG00000100413 GO:0006139 nucleobase-containing compound metabolic process "Any cellular metabolic process involving nucleobases, nucleosides, nucleotides and nucleic acids." [GOC:ai] biological_process
+ENSG00000100413 GO:0006383 transcription from RNA polymerase III promoter "The synthesis of RNA from a DNA template by RNA polymerase III, originating at an RNAP III promoter." [GOC:jl, GOC:txnOH] biological_process
+ENSG00000100413 GO:0006384 transcription initiation from RNA polymerase III promoter "Any process involved in the assembly of the RNA polymerase III preinitiation complex (PIC) at an RNA polymerase III promoter region of a DNA template, resulting in the subsequent synthesis of RNA from that promoter. The initiation phase includes PIC assembly and the formation of the first few bonds in the RNA chain, including abortive initiation, which occurs when the first few nucleotides are repeatedly synthesized and then released. Promoter clearance, or release, is the transition between the initiation and elongation phases of transcription." [GOC:mah, GOC:txnOH] biological_process
+ENSG00000100413 GO:0006385 transcription elongation from RNA polymerase III promoter "The extension of an RNA molecule after transcription initiation and promoter clearance at an RNA polymerase III promoter by the addition of ribonucleotides catalyzed by RNA polymerase III." [GOC:mah, GOC:txnOH] biological_process
+ENSG00000100413 GO:0006386 termination of RNA polymerase III transcription "The process in which transcription by RNA polymerase III is terminated; Pol III has an intrinsic ability to terminate transcription upon incorporation of 4 to 6 contiguous U residues." [GOC:mah, PMID:12944462] biological_process
+ENSG00000100413 GO:0010467 gene expression "The process in which a gene's sequence is converted into a mature gene product or products (proteins or RNA). This includes the production of an RNA transcript as well as any processing to produce a mature RNA product or an mRNA (for protein-coding genes) and the translation of that mRNA into protein. Some protein processing events may be included when they are required to form an active form of a product from an inactive precursor form." [GOC:dph, GOC:tb] biological_process
+ENSG00000100413 GO:0032481 positive regulation of type I interferon production "Any process that activates or increases the frequency, rate, or extent of type I interferon production. Type I interferons include the interferon-alpha, beta, delta, episilon, zeta, kappa, tau, and omega gene families." [GOC:add, GOC:mah] biological_process
+ENSG00000100413 GO:0043231 intracellular membrane-bounded organelle "Organized structure of distinctive morphology and function, bounded by a single or double lipid bilayer membrane and occurring within the cell. Includes the nucleus, mitochondria, plastids, vacuoles, and vesicles. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100413 GO:0045087 innate immune response "Innate immune responses are defense responses mediated by germline encoded components that directly recognize components of potential pathogens." [GO_REF:0000022, GOC:add, GOC:ebc, GOC:mtg_15nov05, GOC:mtg_sensu] biological_process
+ENSG00000100413 GO:0051607 defense response to virus "Reactions triggered in response to the presence of a virus that act to protect the cell or organism." [GOC:ai] biological_process
+ENSG00000100413 GO:0001056 RNA polymerase III activity "Catalysis of the reaction: nucleoside triphosphate + RNA(n) = diphosphate + RNA(n+1). Utilizes a DNA template that contains an RNA polymerase III specific promoter to direct initiation and catalyses DNA-template-directed extension of the 3'-end of an RNA strand by one nucleotide at a time. Can initiate a chain 'de novo'." [GOC:txnOH] molecular_function
+ENSG00000100413 GO:0006351 transcription, DNA-templated "The cellular synthesis of RNA on a template of DNA." [GOC:jl, GOC:txnOH] biological_process
+ENSG00000100399 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100399 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100399 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100399 GO:0005578 proteinaceous extracellular matrix "A layer consisting mainly of proteins (especially collagen) and glycosaminoglycans (mostly as proteoglycans) that forms a sheet underlying or overlying cells such as endothelial and epithelial cells. The proteins are secreted by cells in the vicinity. An example of this component is found in Mus musculus." [GOC:mtg_sensu, ISBN:0198547684] cellular_component
+ENSG00000100399 GO:0031012 extracellular matrix "A structure lying external to one or more cells, which provides structural support for cells or tissues; may be completely external to the cell (as in animals and bacteria) or be part of the cell (as in plants)." [GOC:mah, NIF_Subcellular:nlx_subcell_20090513] cellular_component
+ENSG00000099994 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000099994 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000099994 GO:0002376 immune system process "Any process involved in the development or functioning of the immune system, an organismal system for calibrated responses to potential internal or invasive threats." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05] biological_process
+ENSG00000099994 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000099994 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000099994 GO:0016192 vesicle-mediated transport "A cellular transport process in which transported substances are moved in membrane-bounded vesicles; transported substances are enclosed in the vesicle lumen or located in the vesicle membrane. The process begins with a step that directs a substance to the forming vesicle, and includes vesicle budding and coating. Vesicles are then targeted to, and fuse with, an acceptor membrane." [GOC:ai, GOC:mah, ISBN:08789310662000] biological_process
+ENSG00000099994 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000099994 GO:0005044 scavenger receptor activity "Combining with any modified low-density lipoprotein (LDL) or other polyanionic ligand and delivering the ligand into the cell via endocytosis. Ligands include acetylated and oxidized LDL, Gram-positive and Gram-negative bacteria, apoptotic cells, beta-amyloid fibrils, and advanced glycation end products (AGEs)." [GOC:bf, PMID:11790542, PMID:12379907, PMID:12621157, PMID:20981357] molecular_function
+ENSG00000099994 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000099994 GO:0006898 receptor-mediated endocytosis "An endocytosis process in which cell surface receptors ensure specificity of transport. A specific receptor on the cell surface binds tightly to the extracellular macromolecule (the ligand) that it recognizes; the plasma-membrane region containing the receptor-ligand complex then undergoes endocytosis, forming a transport vesicle containing the receptor-ligand complex and excluding most other plasma-membrane proteins. Receptor-mediated endocytosis generally occurs via clathrin-coated pits and vesicles." [GOC:mah, ISBN:0716731363] biological_process
+ENSG00000099994 GO:0006955 immune response "Any immune system process that functions in the calibrated response of an organism to a potential internal or invasive threat." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05] biological_process
+ENSG00000099994 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000099994 GO:0030247 polysaccharide binding "Interacting selectively and non-covalently with any polysaccharide, a polymer of many (typically more than 10) monosaccharide residues linked glycosidically." [CHEBI:18154, GOC:mah] molecular_function
+ENSG00000099994 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000099994 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000251357 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000251357 GO:0022857 transmembrane transporter activity "Enables the transfer of a substance from one side of a membrane to the other." [GOC:mtg_transport, ISBN:0815340729] molecular_function
+ENSG00000251357 GO:0055085 transmembrane transport "The process in which a solute is transported from one side of a membrane to the other." [GOC:dph, GOC:jid] biological_process
+ENSG00000251357 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000251357 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000251357 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000251357 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100276 GO:0003924 GTPase activity "Catalysis of the reaction: GTP + H2O = GDP + phosphate." [ISBN:0198547684] molecular_function
+ENSG00000100276 GO:0005525 GTP binding "Interacting selectively and non-covalently with GTP, guanosine triphosphate." [GOC:ai] molecular_function
+ENSG00000100276 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100276 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000100276 GO:0006184 GTP catabolic process "The chemical reactions and pathways resulting in the breakdown of GTP, guanosine triphosphate." [ISBN:0198506732] biological_process
+ENSG00000100276 GO:0007264 small GTPase mediated signal transduction "Any series of molecular signals in which a small monomeric GTPase relays one or more of the signals." [GOC:mah] biological_process
+ENSG00000100276 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000100276 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100276 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000100276 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100276 GO:0034655 nucleobase-containing compound catabolic process "The chemical reactions and pathways resulting in the breakdown of nucleobases, nucleosides, nucleotides and nucleic acids." [GOC:mah] biological_process
+ENSG00000100276 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000100276 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100276 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100276 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100276 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100276 GO:0005622 intracellular "The living contents of a cell; the matter contained within (but not including) the plasma membrane, usually taken to exclude large vacuoles and masses of secretory or ingested material. In eukaryotes it includes the nucleus and cytoplasm." [ISBN:0198506732] cellular_component
+ENSG00000100276 GO:0015031 protein transport "The directed movement of proteins into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:ai] biological_process
+ENSG00000100276 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100276 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000185721 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000185721 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000185721 GO:0008134 transcription factor binding "Interacting selectively and non-covalently with a transcription factor, any protein required to initiate or regulate transcription." [ISBN:0198506732] molecular_function
+ENSG00000185721 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000185721 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000185721 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000185721 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000185721 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000185721 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000185721 GO:0005525 GTP binding "Interacting selectively and non-covalently with GTP, guanosine triphosphate." [GOC:ai] molecular_function
+ENSG00000185721 GO:0005844 polysome "A multiribosomal structure representing a linear array of ribosomes held together by messenger RNA. They represent the active complexes in cellular protein synthesis and are able to incorporate amino acids into polypeptides both in vivo and in vitro." [ISBN:0198506732, NIF_Subcellular:sao1038025871] cellular_component
+ENSG00000185721 GO:0006351 transcription, DNA-templated "The cellular synthesis of RNA on a template of DNA." [GOC:jl, GOC:txnOH] biological_process
+ENSG00000185721 GO:0007275 multicellular organismal development "The biological process whose specific outcome is the progression of a multicellular organism over time from an initial condition (e.g. a zygote or a young adult) to a later condition (e.g. a multicellular animal or an aged adult)." [GOC:dph, GOC:ems, GOC:isa_complete, GOC:tb] biological_process
+ENSG00000185721 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000185721 GO:0042802 identical protein binding "Interacting selectively and non-covalently with an identical protein or proteins." [GOC:jl] molecular_function
+ENSG00000185721 GO:0015093 ferrous iron transmembrane transporter activity "Catalysis of the transfer of ferrous iron (Fe(II) or Fe2+) ions from one side of a membrane to the other." [ISBN:0198506732] molecular_function
+ENSG00000185721 GO:0022857 transmembrane transporter activity "Enables the transfer of a substance from one side of a membrane to the other." [GOC:mtg_transport, ISBN:0815340729] molecular_function
+ENSG00000185721 GO:0015684 ferrous iron transport "The directed movement of ferrous iron (Fe(II) or Fe2+) ions into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:ai] biological_process
+ENSG00000185721 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000185721 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000185721
+ENSG00000128311 GO:0004792 thiosulfate sulfurtransferase activity "Catalysis of the reaction: hydrogen cyanide + thiosulfate = H(+) + sulfite + thiocyanate." [EC:2.8.1.1, RHEA:16884] molecular_function
+ENSG00000128311 GO:0008152 metabolic process "The chemical reactions and pathways, including anabolism and catabolism, by which living organisms transform chemical substances. Metabolic processes typically transform small molecules, but also include macromolecular processes such as DNA repair and replication, and protein synthesis and degradation." [GOC:go_curators, ISBN:0198547684] biological_process
+ENSG00000128311 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000128311 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000128311 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000128311 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000128311 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000128311 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000128311 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000128311 GO:0030154 cell differentiation "The process in which relatively unspecialized cells, e.g. embryonic or regenerative cells, acquire specialized structural and/or functional features that characterize the cells, tissues, or organs of the mature organism or some other relatively stable phase of the organism's life history. Differentiation includes the processes involved in commitment of a cell to a specific fate and its subsequent development to the mature state." [ISBN:0198506732] biological_process
+ENSG00000128311 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000128311 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000128311 GO:0019843 rRNA binding "Interacting selectively and non-covalently with ribosomal RNA." [GOC:jl] molecular_function
+ENSG00000128311 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000128311 GO:0005615 extracellular space "That part of a multicellular organism outside the cells proper, usually taken to be outside the plasma membranes, and occupied by fluid." [ISBN:0198547684] cellular_component
+ENSG00000128311 GO:0006520 cellular amino acid metabolic process "The chemical reactions and pathways involving amino acids, carboxylic acids containing one or more amino groups, as carried out by individual cells." [CHEBI:33709, GOC:curators, ISBN:0198506732] biological_process
+ENSG00000128311 GO:0006790 sulfur compound metabolic process "The chemical reactions and pathways involving the nonmetallic element sulfur or compounds that contain sulfur, such as the amino acids methionine and cysteine or the tripeptide glutathione." [GOC:ai] biological_process
+ENSG00000128311 GO:0000096 sulfur amino acid metabolic process "The chemical reactions and pathways involving amino acids containing sulfur, comprising cysteine, homocysteine, methionine and selenocysteine." [GOC:ai] biological_process
+ENSG00000128311 GO:0000098 sulfur amino acid catabolic process "The chemical reactions and pathways resulting in the breakdown of amino acids containing sulfur, comprising cysteine, methionine and selenocysteine." [GOC:ai] biological_process
+ENSG00000128311 GO:0005759 mitochondrial matrix "The gel-like material, with considerable fine structure, that lies in the matrix space, or lumen, of a mitochondrion. It contains the enzymes of the tricarboxylic acid cycle and, in some organisms, the enzymes concerned with fatty acid oxidation." [GOC:as, ISBN:0198506732] cellular_component
+ENSG00000128311 GO:0008097 5S rRNA binding "Interacting selectively and non-covalently with 5S ribosomal RNA, the smallest RNA constituent of a ribosome." [GOC:jl, ISBN:0321000382] molecular_function
+ENSG00000128311 GO:0009440 cyanate catabolic process "The chemical reactions and pathways resulting in the breakdown of cyanate, NCO-, the anion of cyanic acid." [ISBN:0198506732] biological_process
+ENSG00000128311 GO:0030855 epithelial cell differentiation "The process in which a relatively unspecialized cell acquires specialized features of an epithelial cell, any of the cells making up an epithelium." [GOC:ecd, PMID:11839751] biological_process
+ENSG00000128311 GO:0035928 rRNA import into mitochondrion "The directed movement of rRNA, ribosomal ribonucleic acid, from the cytoplasm into a mitochondrion." [GOC:ans, PMID:20691904] biological_process
+ENSG00000128311 GO:0051029 rRNA transport "The directed movement of rRNA, ribosomal ribonucleic acid, into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:ai] biological_process
+ENSG00000128311 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000128311 GO:0070221 sulfide oxidation, using sulfide:quinone oxidoreductase "A sulfide oxidation process that proceeds via the reaction catalyzed by sulfide:quinone oxidoreductase." [MetaCyc:P222-PWY] biological_process
+ENSG00000128311 GO:0005743 mitochondrial inner membrane "The inner, i.e. lumen-facing, lipid bilayer of the mitochondrial envelope. It is highly folded to form cristae." [GOC:ai] cellular_component
+ENSG00000128309 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000128309 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000128309 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000128309 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000128309 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000128309 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000128309 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000128309 GO:0004792 thiosulfate sulfurtransferase activity "Catalysis of the reaction: hydrogen cyanide + thiosulfate = H(+) + sulfite + thiocyanate." [EC:2.8.1.1, RHEA:16884] molecular_function
+ENSG00000128309 GO:0009440 cyanate catabolic process "The chemical reactions and pathways resulting in the breakdown of cyanate, NCO-, the anion of cyanic acid." [ISBN:0198506732] biological_process
+ENSG00000128309 GO:0009636 response to toxic substance "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a toxic stimulus." [GOC:lr] biological_process
+ENSG00000128309 GO:0016784 3-mercaptopyruvate sulfurtransferase activity "Catalysis of the reaction: 3-mercaptopyruvate + cyanide = pyruvate + thiocyanate." [EC:2.8.1.2] molecular_function
+ENSG00000128309 GO:0030054 cell junction "A cellular component that forms a specialized region of connection between two cells or between a cell and the extracellular matrix. At a cell junction, anchoring proteins extend through the plasma membrane to link cytoskeletal proteins in one cell to cytoskeletal proteins in neighboring cells or to proteins in the extracellular matrix." [GOC:mah, http://www.vivo.colostate.edu/hbooks/cmb/cells/pmemb/junctions_a.html, ISBN:0198506732] cellular_component
+ENSG00000128309 GO:0043005 neuron projection "A prolongation or process extending from a nerve cell, e.g. an axon or dendrite." [GOC:jl, http://www.cogsci.princeton.edu/~wn/] cellular_component
+ENSG00000128309 GO:0045202 synapse "The junction between a nerve fiber of one neuron and another neuron or muscle fiber or glial cell; the site of interneuronal communication. As the nerve fiber approaches the synapse it enlarges into a specialized structure, the presynaptic nerve ending, which contains mitochondria and synaptic vesicles. At the tip of the nerve ending is the presynaptic membrane; facing it, and separated from it by a minute cleft (the synaptic cleft) is a specialized area of membrane on the receiving cell, known as the postsynaptic membrane. In response to the arrival of nerve impulses, the presynaptic nerve ending secretes molecules of neurotransmitters into the synaptic cleft. These diffuse across the cleft and transmit the signal to the postsynaptic membrane." [ISBN:0198506732] cellular_component
+ENSG00000128309 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000128309 GO:0070814 hydrogen sulfide biosynthetic process "The chemical reactions and pathways resulting in the formation of hydrogen sulfide, H2S." [CHEBI:16136, GOC:mah] biological_process
+ENSG00000128309 GO:0006790 sulfur compound metabolic process "The chemical reactions and pathways involving the nonmetallic element sulfur or compounds that contain sulfur, such as the amino acids methionine and cysteine or the tripeptide glutathione." [GOC:ai] biological_process
+ENSG00000128309 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000128309 GO:0008152 metabolic process "The chemical reactions and pathways, including anabolism and catabolism, by which living organisms transform chemical substances. Metabolic processes typically transform small molecules, but also include macromolecular processes such as DNA repair and replication, and protein synthesis and degradation." [GOC:go_curators, ISBN:0198547684] biological_process
+ENSG00000128309 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000128309 GO:0005743 mitochondrial inner membrane "The inner, i.e. lumen-facing, lipid bilayer of the mitochondrial envelope. It is highly folded to form cristae." [GOC:ai] cellular_component
+ENSG00000128309 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100395 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100395 GO:0042393 histone binding "Interacting selectively and non-covalently with a histone, any of a group of water-soluble proteins found in association with the DNA of plant and animal chromosomes. They are involved in the condensation and coiling of chromosomes during cell division and have also been implicated in nonspecific suppression of gene activity." [GOC:jl] molecular_function
+ENSG00000100395 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100395 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100395 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000100395 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100395 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100395 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100395 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100395 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100395 GO:0006351 transcription, DNA-templated "The cellular synthesis of RNA on a template of DNA." [GOC:jl, GOC:txnOH] biological_process
+ENSG00000100395 GO:0006355 regulation of transcription, DNA-templated "Any process that modulates the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000100395 GO:0008270 zinc ion binding "Interacting selectively and non-covalently with zinc (Zn) ions." [GOC:ai] molecular_function
+ENSG00000100395 GO:0016568 chromatin modification "The alteration of DNA, protein, or sometimes RNA, in chromatin, which may result in changing the chromatin structure." [GOC:mah, PMID:20404130] biological_process
+ENSG00000100395 GO:0035064 methylated histone binding "Interacting selectively and non-covalently with a histone protein in which a residue has been modified by methylation. Histones are any of a group of water-soluble proteins found in association with the DNA of plant and animal chromosomes." [GOC:bf, PMID:14585615] molecular_function
+ENSG00000100395
+ENSG00000169184 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000169184 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000169184 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000169184 GO:0001957 intramembranous ossification "Direct ossification that occurs within mesenchyme or an accumulation of relatively unspecialized cells." [ISBN:0878932437] biological_process
+ENSG00000169184
+ENSG00000275793 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000275793 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000211672 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000211672 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000180957 GO:0005622 intracellular "The living contents of a cell; the matter contained within (but not including) the plasma membrane, usually taken to exclude large vacuoles and masses of secretory or ingested material. In eukaryotes it includes the nucleus and cytoplasm." [ISBN:0198506732] cellular_component
+ENSG00000180957 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000180957 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000180957 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000180957 GO:0006629 lipid metabolic process "The chemical reactions and pathways involving lipids, compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent. Includes fatty acids; neutral fats, other fatty-acid esters, and soaps; long-chain (fatty) alcohols and waxes; sphingoids and other long-chain bases; glycolipids, phospholipids and sphingolipids; and carotenes, polyprenols, sterols, terpenes and other isoprenoids." [GOC:ma] biological_process
+ENSG00000180957 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000180957 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000180957 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000180957 GO:0008289 lipid binding "Interacting selectively and non-covalently with a lipid." [GOC:ai] molecular_function
+ENSG00000180957 GO:0000139 Golgi membrane "The lipid bilayer surrounding any of the compartments of the Golgi apparatus." [GOC:mah] cellular_component
+ENSG00000180957 GO:0005789 endoplasmic reticulum membrane "The lipid bilayer surrounding the endoplasmic reticulum." [GOC:mah] cellular_component
+ENSG00000180957 GO:0006644 phospholipid metabolic process "The chemical reactions and pathways involving phospholipids, any lipid containing phosphoric acid as a mono- or diester." [ISBN:0198506732] biological_process
+ENSG00000180957 GO:0015914 phospholipid transport "The directed movement of phospholipids into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore. Phospholipids are any lipids containing phosphoric acid as a mono- or diester." [GOC:ai] biological_process
+ENSG00000180957 GO:0046474 glycerophospholipid biosynthetic process "The chemical reactions and pathways resulting in the formation of glycerophospholipids, any derivative of glycerophosphate that contains at least one O-acyl, O-alkyl, or O-alkenyl group attached to the glycerol residue." [ISBN:0198506732] biological_process
+ENSG00000180957 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000180957 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000180957 GO:0001701 in utero embryonic development "The process whose specific outcome is the progression of the embryo in the uterus over time, from formation of the zygote in the oviduct, to birth. An example of this process is found in Mus musculus." [GOC:go_curators, GOC:mtg_sensu] biological_process
+ENSG00000211665 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000211665 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000184113 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000184113 GO:0007155 cell adhesion "The attachment of a cell, either to another cell or to an underlying substrate such as the extracellular matrix, via cell adhesion molecules." [GOC:hb, GOC:pf] biological_process
+ENSG00000184113 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000184113 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000184113 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000184113 GO:0005198 structural molecule activity "The action of a molecule that contributes to the structural integrity of a complex or assembly within or outside a cell." [GOC:mah] molecular_function
+ENSG00000184113 GO:0005911 cell-cell junction "A cell junction that forms a connection between two cells; excludes direct cytoplasmic junctions such as ring canals." [GOC:dgh, GOC:hb, GOC:mah] cellular_component
+ENSG00000184113 GO:0005923 tight junction "An occluding cell-cell junction that is composed of a branching network of sealing strands that completely encircles the apical end of each cell in an epithelial sheet; the outer leaflets of the two interacting plasma membranes are seen to be tightly apposed where sealing strands are present. Each sealing strand is composed of a long row of transmembrane adhesion proteins embedded in each of the two interacting plasma membranes." [GOC:mah, ISBN:0815332181] cellular_component
+ENSG00000184113 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000184113 GO:0016338 calcium-independent cell-cell adhesion via plasma membrane cell-adhesion molecules "The attachment of one cell to another cell via adhesion molecules that do not require the presence of calcium for the interaction." [GOC:hb] biological_process
+ENSG00000184113 GO:0042802 identical protein binding "Interacting selectively and non-covalently with an identical protein or proteins." [GOC:jl] molecular_function
+ENSG00000184113 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000184113 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000184113 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000184113 GO:0043220 Schmidt-Lanterman incisure "Regions within compact myelin in which the cytoplasmic faces of the enveloping myelin sheath are not tightly juxtaposed, and include cytoplasm from the cell responsible for making the myelin. Schmidt-Lanterman incisures occur in the compact myelin internode, while lateral loops are analogous structures found in the paranodal region adjacent to the nodes of Ranvier." [GOC:dgh] cellular_component
+ENSG00000184113 GO:0033270 paranode region of axon "An axon part that is located adjacent to the nodes of Ranvier and surrounded by lateral loop portions of myelin sheath." [GOC:mah, GOC:mh, NIF_Subcellular:sao936144858] cellular_component
+ENSG00000184113 GO:0042552 myelination "The process in which myelin sheaths are formed and maintained around neurons. Oligodendrocytes in the brain and spinal cord and Schwann cells in the peripheral nervous system wrap axons with compact layers of their plasma membrane. Adjacent myelin segments are separated by a non-myelinated stretch of axon called a node of Ranvier." [GOC:dgh, GOC:mah] biological_process
+ENSG00000100347 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100347 GO:0007005 mitochondrion organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of a mitochondrion; includes mitochondrial morphogenesis and distribution, and replication of the mitochondrial genome as well as synthesis of new mitochondrial components." [GOC:dph, GOC:jl, GOC:mah, GOC:sgd_curators, PMID:9786946] biological_process
+ENSG00000100347 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100347 GO:0055085 transmembrane transport "The process in which a solute is transported from one side of a membrane to the other." [GOC:dph, GOC:jid] biological_process
+ENSG00000100347 GO:0061024 membrane organization "A process which results in the assembly, arrangement of constituent parts, or disassembly of a membrane. A membrane is a double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:dph, GOC:tb] biological_process
+ENSG00000100347 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100347 GO:0006605 protein targeting "The process of targeting specific proteins to particular membrane-bounded subcellular organelles. Usually requires an organelle specific protein sequence motif." [GOC:ma] biological_process
+ENSG00000100347 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000100347 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100347 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100347 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100347 GO:0001401 mitochondrial sorting and assembly machinery complex "A large complex of the mitochondrial outer membrane that mediates sorting of some imported proteins to the outer membrane and their assembly in the membrane; functions after import of incoming proteins by the mitochondrial outer membrane translocase complex." [PMID:12891361] cellular_component
+ENSG00000100347 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100347 GO:0005741 mitochondrial outer membrane "The outer, i.e. cytoplasm-facing, lipid bilayer of the mitochondrial envelope." [GOC:ai] cellular_component
+ENSG00000100347 GO:0006626 protein targeting to mitochondrion "The process of directing proteins towards and into the mitochondrion, usually mediated by mitochondrial proteins that recognize signals contained within the imported protein." [GOC:mcc, ISBN:0716731363] biological_process
+ENSG00000100347 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000100347 GO:0044267 cellular protein metabolic process "The chemical reactions and pathways involving a specific protein, rather than of proteins in general, occurring at the level of an individual cell. Includes cellular protein modification." [GOC:jl] biological_process
+ENSG00000100347 GO:0045040 protein import into mitochondrial outer membrane "The process comprising the insertion of proteins from outside the organelle into the mitochondrial outer membrane, mediated by large outer membrane translocase complexes." [GOC:mcc, GOC:vw, PMID:18672008] biological_process
+ENSG00000100347 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000100347 GO:0019867 outer membrane "The external membrane of Gram-negative bacteria or certain organelles such as mitochondria and chloroplasts; freely permeable to most ions and metabolites." [GOC:go_curators] cellular_component
+ENSG00000100347 GO:0005743 mitochondrial inner membrane "The inner, i.e. lumen-facing, lipid bilayer of the mitochondrial envelope. It is highly folded to form cristae." [GOC:ai] cellular_component
+ENSG00000100416 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100416 GO:0008033 tRNA processing "The process in which a pre-tRNA molecule is converted to a mature tRNA, ready for addition of an aminoacyl group." [GOC:jl, PMID:12533506] biological_process
+ENSG00000100416 GO:0008168 methyltransferase activity "Catalysis of the transfer of a methyl group to an acceptor molecule." [ISBN:0198506732] molecular_function
+ENSG00000100416 GO:0032259 methylation "The process in which a methyl group is covalently attached to a molecule." [GOC:mah] biological_process
+ENSG00000100416 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100416 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100416 GO:0006399 tRNA metabolic process "The chemical reactions and pathways involving tRNA, transfer RNA, a class of relatively small RNA molecules responsible for mediating the insertion of amino acids into the sequence of nascent polypeptide chains during protein synthesis. Transfer RNA is characterized by the presence of many unusual minor bases, the function of which has not been completely established." [ISBN:0198506732] biological_process
+ENSG00000100416 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100416 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100416 GO:0004066 asparagine synthase (glutamine-hydrolyzing) activity "Catalysis of the reaction: ATP + L-aspartate + L-glutamine = AMP + diphosphate + L-asparagine + L-glutamate." [EC:6.3.5.4] molecular_function
+ENSG00000100416 GO:0016874 ligase activity "Catalysis of the joining of two substances, or two groups within a single molecule, with the concomitant hydrolysis of the diphosphate bond in ATP or a similar triphosphate." [EC:6, GOC:mah] molecular_function
+ENSG00000100416 GO:0006529 asparagine biosynthetic process "The chemical reactions and pathways resulting in the formation of asparagine, 2-amino-3-carbamoylpropanoic acid." [GOC:go_curators] biological_process
+ENSG00000100416 GO:0006520 cellular amino acid metabolic process "The chemical reactions and pathways involving amino acids, carboxylic acids containing one or more amino groups, as carried out by individual cells." [CHEBI:33709, GOC:curators, ISBN:0198506732] biological_process
+ENSG00000100416 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000100416 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000100416 GO:0016740 transferase activity "Catalysis of the transfer of a group, e.g. a methyl group, glycosyl group, acyl group, phosphorus-containing, or other groups, from one compound (generally regarded as the donor) to another compound (generally regarded as the acceptor). Transferase is the systematic name for any enzyme of EC class 2." [ISBN:0198506732] molecular_function
+ENSG00000100416 GO:0004810 tRNA adenylyltransferase activity "Catalysis of the reaction: ATP + tRNA(n) = diphosphate + tRNA(n+1)." [EC:2.7.7.25] molecular_function
+ENSG00000100416 GO:0016779 nucleotidyltransferase activity "Catalysis of the transfer of a nucleotidyl group to a reactant." [ISBN:0198506732] molecular_function
+ENSG00000100416 GO:0000049 tRNA binding "Interacting selectively and non-covalently with transfer RNA." [GOC:ai] molecular_function
+ENSG00000100416 GO:0005524 ATP binding "Interacting selectively and non-covalently with ATP, adenosine 5'-triphosphate, a universally important coenzyme and enzyme regulator." [ISBN:0198506732] molecular_function
+ENSG00000100416 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000100416 GO:0016783 sulfurtransferase activity "Catalysis of the transfer of sulfur atoms from one compound (donor) to another (acceptor)." [GOC:ai, ISBN:0721662544] molecular_function
+ENSG00000100416 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100416 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100416 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000100241 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100241 GO:0006470 protein dephosphorylation "The process of removing one or more phosphoric residues from a protein." [GOC:hb] biological_process
+ENSG00000100241 GO:0008138 protein tyrosine/serine/threonine phosphatase activity "Catalysis of the reactions: protein serine + H2O = protein serine + phosphate; protein threonine phosphate + H2O = protein threonine + phosphate; and protein tyrosine phosphate + H2O = protein tyrosine + phosphate." [GOC:mah] molecular_function
+ENSG00000100241 GO:0008219 cell death "Any biological process that results in permanent cessation of all vital functions of a cell. A cell should be considered dead when any one of the following molecular or morphological criteria is met: (1) the cell has lost the integrity of its plasma membrane; (2) the cell, including its nucleus, has undergone complete fragmentation into discrete bodies (frequently referred to as \"apoptotic bodies\"); and/or (3) its corpse (or its fragments) have been engulfed by an adjacent cell in vivo." [GOC:mah, GOC:mtg_apoptosis] biological_process
+ENSG00000100241 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000100241 GO:0017112 Rab guanyl-nucleotide exchange factor activity "Stimulates the exchange of guanyl nucleotides associated with a GTPase of the Rab family. Under normal cellular physiological conditions, the concentration of GTP is higher than that of GDP, favoring the replacement of GDP by GTP in association with the GTPase." [GOC:mah] molecular_function
+ENSG00000100241 GO:0032851 positive regulation of Rab GTPase activity "Any process that activates or increases the activity of a GTPase of the Rab family." [GOC:mah] biological_process
+ENSG00000100241 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100241 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100241 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100241 GO:0016791 phosphatase activity "Catalysis of the hydrolysis of phosphoric monoesters, releasing inorganic phosphate." [EC:3.1.3, EC:3.1.3.41, GOC:curators] molecular_function
+ENSG00000100241 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000100241 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100241 GO:0016311 dephosphorylation "The process of removing one or more phosphoric (ester or anhydride) residues from a molecule." [ISBN:0198506732] biological_process
+ENSG00000100241 GO:0007283 spermatogenesis "The process of formation of spermatozoa, including spermatocytogenesis and spermiogenesis." [GOC:jid, ISBN:9780878933846] biological_process
+ENSG00000100241
+ENSG00000196431 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000196431 GO:0050877 neurological system process "A organ system process carried out by any of the organs or tissues of neurological system." [GOC:ai, GOC:mtg_cardio] biological_process
+ENSG00000196431 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000196431 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000196431 GO:0007601 visual perception "The series of events required for an organism to receive a visual stimulus, convert it to a molecular signal, and recognize and characterize the signal. Visual stimuli are detected in the form of photons and are processed to form an image." [GOC:ai] biological_process
+ENSG00000196431 GO:0043010 camera-type eye development "The process whose specific outcome is the progression of the camera-type eye over time, from its formation to the mature structure. The camera-type eye is an organ of sight that receives light through an aperture and focuses it through a lens, projecting it on a photoreceptor field." [GOC:go_curators, GOC:mtg_sensu] biological_process
+ENSG00000196431 GO:0048856 anatomical structure development "The biological process whose specific outcome is the progression of an anatomical structure from an initial condition to its mature state. This process begins with the formation of the structure and ends with the mature structure, whatever form that may be including its natural destruction. An anatomical structure is any biological entity that occupies space and is distinguished from its surroundings. Anatomical structures can be macroscopic such as a carpel, or microscopic such as an acrosome." [GO_REF:0000021, GOC:mtg_15jun06] biological_process
+ENSG00000196431 GO:0005212 structural constituent of eye lens "The action of a molecule that contributes to the structural integrity of the lens of an eye." [GOC:mah] molecular_function
+ENSG00000100216 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100216 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100216 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100216 GO:0008565 protein transporter activity "Enables the directed movement of proteins into, out of or within a cell, or between cells." [ISBN:0198506732] molecular_function
+ENSG00000100216 GO:0022857 transmembrane transporter activity "Enables the transfer of a substance from one side of a membrane to the other." [GOC:mtg_transport, ISBN:0815340729] molecular_function
+ENSG00000100216 GO:0006605 protein targeting "The process of targeting specific proteins to particular membrane-bounded subcellular organelles. Usually requires an organelle specific protein sequence motif." [GOC:ma] biological_process
+ENSG00000100216 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100216 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100216 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000100216 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100216 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100216 GO:0005742 mitochondrial outer membrane translocase complex "A large complex of the mitochondrial outer membrane that mediates transport of proteins into all mitochondrial compartments." [PMID:12581629] cellular_component
+ENSG00000100216 GO:0006626 protein targeting to mitochondrion "The process of directing proteins towards and into the mitochondrion, usually mediated by mitochondrial proteins that recognize signals contained within the imported protein." [GOC:mcc, ISBN:0716731363] biological_process
+ENSG00000100216 GO:0008320 protein transmembrane transporter activity "Catalysis of the transfer of a protein from one side of a membrane to the other." [GOC:jl] molecular_function
+ENSG00000100216 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000100216 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000100216 GO:0044267 cellular protein metabolic process "The chemical reactions and pathways involving a specific protein, rather than of proteins in general, occurring at the level of an individual cell. Includes cellular protein modification." [GOC:jl] biological_process
+ENSG00000100216 GO:0045040 protein import into mitochondrial outer membrane "The process comprising the insertion of proteins from outside the organelle into the mitochondrial outer membrane, mediated by large outer membrane translocase complexes." [GOC:mcc, GOC:vw, PMID:18672008] biological_process
+ENSG00000100216 GO:0007005 mitochondrion organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of a mitochondrion; includes mitochondrial morphogenesis and distribution, and replication of the mitochondrial genome as well as synthesis of new mitochondrial components." [GOC:dph, GOC:jl, GOC:mah, GOC:sgd_curators, PMID:9786946] biological_process
+ENSG00000100216 GO:0055085 transmembrane transport "The process in which a solute is transported from one side of a membrane to the other." [GOC:dph, GOC:jid] biological_process
+ENSG00000100216 GO:0061024 membrane organization "A process which results in the assembly, arrangement of constituent parts, or disassembly of a membrane. A membrane is a double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:dph, GOC:tb] biological_process
+ENSG00000100216 GO:0006886 intracellular protein transport "The directed movement of proteins in a cell, including the movement of proteins between specific compartments or structures within a cell, such as organelles of a eukaryotic cell." [GOC:mah] biological_process
+ENSG00000100216 GO:0005741 mitochondrial outer membrane "The outer, i.e. cytoplasm-facing, lipid bilayer of the mitochondrial envelope." [GOC:ai] cellular_component
+ENSG00000100216 GO:0005743 mitochondrial inner membrane "The inner, i.e. lumen-facing, lipid bilayer of the mitochondrial envelope. It is highly folded to form cristae." [GOC:ai] cellular_component
+ENSG00000184470 GO:0016491 oxidoreductase activity "Catalysis of an oxidation-reduction (redox) reaction, a reversible chemical reaction in which the oxidation state of an atom or atoms within a molecule is altered. One substrate acts as a hydrogen or electron donor and becomes oxidized, while the other acts as hydrogen or electron acceptor and becomes reduced." [GOC:go_curators] molecular_function
+ENSG00000184470 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000184470 GO:0050660 flavin adenine dinucleotide binding "Interacting selectively and non-covalently with FAD, flavin-adenine dinucleotide, the coenzyme or the prosthetic group of various flavoprotein oxidoreductase enzymes, in either the oxidized form, FAD, or the reduced form, FADH2." [CHEBI:24040, GOC:ai, GOC:imk, ISBN:0198506732] molecular_function
+ENSG00000184470 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000184470 GO:0055114 oxidation-reduction process "A metabolic process that results in the removal or addition of one or more electrons to or from a substance, with or without the concomitant removal or addition of a proton or protons." [GOC:dhl, GOC:ecd, GOC:jh2, GOC:jid, GOC:mlg, GOC:rph] biological_process
+ENSG00000184470 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000184470 GO:0008033 tRNA processing "The process in which a pre-tRNA molecule is converted to a mature tRNA, ready for addition of an aminoacyl group." [GOC:jl, PMID:12533506] biological_process
+ENSG00000184470 GO:0006399 tRNA metabolic process "The chemical reactions and pathways involving tRNA, transfer RNA, a class of relatively small RNA molecules responsible for mediating the insertion of amino acids into the sequence of nascent polypeptide chains during protein synthesis. Transfer RNA is characterized by the presence of many unusual minor bases, the function of which has not been completely established." [ISBN:0198506732] biological_process
+ENSG00000184470 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000184470 GO:0045454 cell redox homeostasis "Any process that maintains the redox environment of a cell or compartment within a cell." [GOC:ai, GOC:dph, GOC:tb] biological_process
+ENSG00000184470 GO:0042592 homeostatic process "Any biological process involved in the maintenance of an internal steady state." [GOC:jl, ISBN:0395825172] biological_process
+ENSG00000184470 GO:0004791 thioredoxin-disulfide reductase activity "Catalysis of the reaction: NADP(+) + thioredoxin = H(+) + NADPH + thioredoxin disulfide." [EC:1.8.1.9, RHEA:20348] molecular_function
+ENSG00000184470 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000184470 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000184470 GO:0030097 hemopoiesis "The process whose specific outcome is the progression of the myeloid and lymphoid derived organ/tissue systems of the blood and other parts of the body over time, from formation to the mature structure. The site of hemopoiesis is variable during development, but occurs primarily in bone marrow or kidney in many adult vertebrates." [GOC:dgh, ISBN:0198506732] biological_process
+ENSG00000184470 GO:0007507 heart development "The process whose specific outcome is the progression of the heart over time, from its formation to the mature structure. The heart is a hollow, muscular organ, which, by contracting rhythmically, keeps up the circulation of the blood." [GOC:jid, UBERON:0000948] biological_process
+ENSG00000184470 GO:0050661 NADP binding "Interacting selectively and non-covalently with nicotinamide-adenine dinucleotide phosphate, a coenzyme involved in many redox and biosynthetic reactions; binding may be to either the oxidized form, NADP+, or the reduced form, NADPH." [GOC:ai] molecular_function
+ENSG00000184470 GO:0016668 oxidoreductase activity, acting on a sulfur group of donors, NAD(P) as acceptor "Catalysis of an oxidation-reduction (redox) reaction in which a sulfur-containing group acts as a hydrogen or electron donor and reduces NAD or NADP." [GOC:jl] molecular_function
+ENSG00000100246 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100246 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100246 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100246 GO:0005929 cilium "A specialized eukaryotic organelle that consists of a filiform extrusion of the cell surface and of some cytoplasmic parts. Each cilium is largely bounded by an extrusion of the cytoplasmic (plasma) membrane, and contains a regular longitudinal array of microtubules, anchored to a basal body." [GOC:cilia, GOC:kmv, ISBN:0198547684] cellular_component
+ENSG00000100246 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100246 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000100246 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100246 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100246 GO:0003774 motor activity "Catalysis of movement along a polymeric molecule such as a microfilament or microtubule, coupled to the hydrolysis of a nucleoside triphosphate." [GOC:mah, ISBN:0815316194] molecular_function
+ENSG00000100246 GO:0005874 microtubule "Any of the long, generally straight, hollow tubes of internal diameter 12-15 nm and external diameter 24 nm found in a wide variety of eukaryotic cells; each consists (usually) of 13 protofilaments of polymeric tubulin, staggered in such a manner that the tubulin monomers are arranged in a helical pattern on the microtubular surface, and with the alpha/beta axes of the tubulin subunits parallel to the long axis of the tubule; exist in equilibrium with pool of tubulin monomers and can be rapidly assembled or disassembled in response to physiological stimuli; concerned with force generation, e.g. in the spindle." [ISBN:0879693568] cellular_component
+ENSG00000100246 GO:0007017 microtubule-based process "Any cellular process that depends upon or alters the microtubule cytoskeleton, that part of the cytoskeleton comprising microtubules and their associated proteins." [GOC:mah] biological_process
+ENSG00000100246 GO:0008152 metabolic process "The chemical reactions and pathways, including anabolism and catabolism, by which living organisms transform chemical substances. Metabolic processes typically transform small molecules, but also include macromolecular processes such as DNA repair and replication, and protein synthesis and degradation." [GOC:go_curators, ISBN:0198547684] biological_process
+ENSG00000100246 GO:0030286 dynein complex "Any of several large complexes that contain two or three dynein heavy chains and several light chains, and have microtubule motor activity." [ISBN:0815316194] cellular_component
+ENSG00000100246 GO:0048011 neurotrophin TRK receptor signaling pathway "A series of molecular signals initiated by the binding of a neurotrophin to a receptor on the surface of the target cell where the receptor possesses tyrosine kinase activity, and ending with regulation of a downstream cellular process, e.g. transcription." [GOC:bf, GOC:ceb, GOC:jc, GOC:signaling, PMID:12065629, Wikipedia:Trk_receptor] biological_process
+ENSG00000100246 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000100246 GO:0005875 microtubule associated complex "Any multimeric complex connected to a microtubule." [GOC:jl] cellular_component
+ENSG00000100360 GO:0005525 GTP binding "Interacting selectively and non-covalently with GTP, guanosine triphosphate." [GOC:ai] molecular_function
+ENSG00000100360 GO:0007264 small GTPase mediated signal transduction "Any series of molecular signals in which a small monomeric GTPase relays one or more of the signals." [GOC:mah] biological_process
+ENSG00000100360 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000100360 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100360 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100360 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100360 GO:0030992 intraciliary transport particle B "The larger subcomplex of the intraciliary transport particle; characterized complexes have molecular weights around 550 kDa." [GOC:cilia, GOC:kmv, PMID:14570576, PMID:19253336] cellular_component
+ENSG00000100360 GO:0031514 motile cilium "A cilium which has a variable arrangement of axonemal microtubules, contains molecular motors, and beats with a characteristic whip-like pattern that promotes cell motility or transport of fluids and other cells across a cell surface. Motile cilia are typically found in multiple copies on epithelial cells that line the lumenal ducts of various tissues. Motile cilia may also function as sensory organelles." [GOC:dgh, GOC:kmv, PMID:17009929, PMID:20144998] cellular_component
+ENSG00000100360 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100360 GO:0005929 cilium "A specialized eukaryotic organelle that consists of a filiform extrusion of the cell surface and of some cytoplasmic parts. Each cilium is largely bounded by an extrusion of the cytoplasmic (plasma) membrane, and contains a regular longitudinal array of microtubules, anchored to a basal body." [GOC:cilia, GOC:kmv, ISBN:0198547684] cellular_component
+ENSG00000100360 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100360 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100360 GO:0005622 intracellular "The living contents of a cell; the matter contained within (but not including) the plasma membrane, usually taken to exclude large vacuoles and masses of secretory or ingested material. In eukaryotes it includes the nucleus and cytoplasm." [ISBN:0198506732] cellular_component
+ENSG00000100360 GO:0015031 protein transport "The directed movement of proteins into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:ai] biological_process
+ENSG00000100360 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100360 GO:0006184 GTP catabolic process "The chemical reactions and pathways resulting in the breakdown of GTP, guanosine triphosphate." [ISBN:0198506732] biological_process
+ENSG00000100360 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000100360 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100360 GO:0034655 nucleobase-containing compound catabolic process "The chemical reactions and pathways resulting in the breakdown of nucleobases, nucleosides, nucleotides and nucleic acids." [GOC:mah] biological_process
+ENSG00000100360 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000100360 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000100360 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100360 GO:0005813 centrosome "A structure comprised of a core structure (in most organisms, a pair of centrioles) and peripheral material from which a microtubule-based structure, such as a spindle apparatus, is organized. Centrosomes occur close to the nucleus during interphase in many eukaryotic cells, though in animal cells it changes continually during the cell-division cycle." [GOC:mah, ISBN:0198547684] cellular_component
+ENSG00000100360 GO:0097228 sperm principal piece "The segment of the sperm flagellum where the mitochondrial sheath ends, and the outer dense fibers (ODFs) associated with outer axonemal doublets 3 and 8 are replaced by the 2 longitudinal columns of the fibrous sheath (FS) which run the length of the principal piece and are stabilized by circumferential ribs. The principal piece makes up ~2/3 of the length of the sperm flagellum and is defined by the presence of the FS and of only 7 (rather than 9) ODFs which taper and then terminate near the distal end of the principal piece." [GOC:cjm, MP:0009836] cellular_component
+ENSG00000100360 GO:0097225 sperm midpiece "The highly organized segment of the sperm flagellum which begins at the connecting piece and is characterized by the presence of 9 outer dense fibers (ODFs) that lie outside each of the 9 outer axonemal microtubule doublets and by a sheath of mitochondria that encloses the ODFs and the axoneme; the midpiece terminates about one-fourth of the way down the sperm flagellum at the annulus, which marks the beginning of the principal piece." [GOC:cjm, MP:0009831] cellular_component
+ENSG00000073146 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000073146 GO:0048856 anatomical structure development "The biological process whose specific outcome is the progression of an anatomical structure from an initial condition to its mature state. This process begins with the formation of the structure and ends with the mature structure, whatever form that may be including its natural destruction. An anatomical structure is any biological entity that occupies space and is distinguished from its surroundings. Anatomical structures can be macroscopic such as a carpel, or microscopic such as an acrosome." [GO_REF:0000021, GOC:mtg_15jun06] biological_process
+ENSG00000073146 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000073146 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000073146 GO:0034655 nucleobase-containing compound catabolic process "The chemical reactions and pathways resulting in the breakdown of nucleobases, nucleosides, nucleotides and nucleic acids." [GOC:mah] biological_process
+ENSG00000073146 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000073146 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000073146 GO:0005622 intracellular "The living contents of a cell; the matter contained within (but not including) the plasma membrane, usually taken to exclude large vacuoles and masses of secretory or ingested material. In eukaryotes it includes the nucleus and cytoplasm." [ISBN:0198506732] cellular_component
+ENSG00000073146 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000073146 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000073146 GO:0004386 helicase activity "Catalysis of the reaction: NTP + H2O = NDP + phosphate, to drive the unwinding of a DNA or RNA helix." [GOC:mah, ISBN:0198506732] molecular_function
+ENSG00000073146 GO:0016887 ATPase activity "Catalysis of the reaction: ATP + H2O = ADP + phosphate + 2 H+. May or may not be coupled to another reaction." [EC:3.6.1.3, GOC:jl] molecular_function
+ENSG00000073146 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000073146 GO:0000287 magnesium ion binding "Interacting selectively and non-covalently with magnesium (Mg) ions." [GOC:ai] molecular_function
+ENSG00000073146 GO:0004004 ATP-dependent RNA helicase activity "Catalysis of the reaction: ATP + H2O = ADP + phosphate; this reaction drives the unwinding of an RNA helix." [EC:3.6.1.3, GOC:jl] molecular_function
+ENSG00000073146 GO:0005524 ATP binding "Interacting selectively and non-covalently with ATP, adenosine 5'-triphosphate, a universally important coenzyme and enzyme regulator." [ISBN:0198506732] molecular_function
+ENSG00000073146 GO:0006200 ATP catabolic process "The chemical reactions and pathways resulting in the breakdown of ATP, adenosine 5'-triphosphate, a universally important coenzyme and enzyme regulator." [GOC:ai] biological_process
+ENSG00000073146 GO:0007275 multicellular organismal development "The biological process whose specific outcome is the progression of a multicellular organism over time from an initial condition (e.g. a zygote or a young adult) to a later condition (e.g. a multicellular animal or an aged adult)." [GOC:dph, GOC:ems, GOC:isa_complete, GOC:tb] biological_process
+ENSG00000073146 GO:0007281 germ cell development "The process whose specific outcome is the progression of an immature germ cell over time, from its formation to the mature structure (gamete). A germ cell is any reproductive cell in a multicellular organism." [GOC:go_curators] biological_process
+ENSG00000073146 GO:0007283 spermatogenesis "The process of formation of spermatozoa, including spermatocytogenesis and spermiogenesis." [GOC:jid, ISBN:9780878933846] biological_process
+ENSG00000073146
+ENSG00000100033 GO:0004657 proline dehydrogenase activity "Catalysis of the reaction: L-proline + acceptor = (S)-1-pyrroline-5-carboxylate + reduced acceptor." [EC:1.5.99.8] molecular_function
+ENSG00000100033 GO:0006562 proline catabolic process "The chemical reactions and pathways resulting in the breakdown of proline (pyrrolidine-2-carboxylic acid), a chiral, cyclic, nonessential alpha-amino acid found in peptide linkage in proteins." [GOC:jl, ISBN:0198506732] biological_process
+ENSG00000100033 GO:0055114 oxidation-reduction process "A metabolic process that results in the removal or addition of one or more electrons to or from a substance, with or without the concomitant removal or addition of a proton or protons." [GOC:dhl, GOC:ecd, GOC:jh2, GOC:jid, GOC:mlg, GOC:rph] biological_process
+ENSG00000100033 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100033 GO:0006520 cellular amino acid metabolic process "The chemical reactions and pathways involving amino acids, carboxylic acids containing one or more amino groups, as carried out by individual cells." [CHEBI:33709, GOC:curators, ISBN:0198506732] biological_process
+ENSG00000100033 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000100033 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000100033 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100033 GO:0016491 oxidoreductase activity "Catalysis of an oxidation-reduction (redox) reaction, a reversible chemical reaction in which the oxidation state of an atom or atoms within a molecule is altered. One substrate acts as a hydrogen or electron donor and becomes oxidized, while the other acts as hydrogen or electron acceptor and becomes reduced." [GOC:go_curators] molecular_function
+ENSG00000100033 GO:0005743 mitochondrial inner membrane "The inner, i.e. lumen-facing, lipid bilayer of the mitochondrial envelope. It is highly folded to form cristae." [GOC:ai] cellular_component
+ENSG00000100033 GO:0006560 proline metabolic process "The chemical reactions and pathways involving proline (pyrrolidine-2-carboxylic acid), a chiral, cyclic, nonessential alpha-amino acid found in peptide linkage in proteins." [GOC:jl, ISBN:0198506732] biological_process
+ENSG00000100033 GO:0008631 intrinsic apoptotic signaling pathway in response to oxidative stress "A series of molecular signals in which an intracellular signal is conveyed to trigger the apoptotic death of a cell. The pathway is induced in response to oxidative stress, a state often resulting from exposure to high levels of reactive oxygen species, and ends when the execution phase of apoptosis is triggered." [GOC:ai, GOC:mtg_apoptosis] biological_process
+ENSG00000100033 GO:0010133 proline catabolic process to glutamate "The chemical reactions and pathways resulting in the breakdown of proline into other compounds, including glutamate." [GOC:pz] biological_process
+ENSG00000100033 GO:0019470 4-hydroxyproline catabolic process "The chemical reactions and pathways resulting in the breakdown of 4-hydroxyproline, C5H9NO3, a derivative of the amino acid proline." [GOC:ai] biological_process
+ENSG00000100033 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100033 GO:0071949 FAD binding "Interacting selectively and non-covalently with the oxidized form, FAD, of flavin-adenine dinucleotide, the coenzyme or the prosthetic group of various flavoprotein oxidoreductase enzymes." [GOC:mah] molecular_function
+ENSG00000100033 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100033 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000100033 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000100033 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100033
+ENSG00000211682
+ENSG00000100307 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100307 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100307 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100307 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000100307 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100307 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100307 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100307 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100307 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100307 GO:0000122 negative regulation of transcription from RNA polymerase II promoter "Any process that stops, prevents, or reduces the frequency, rate or extent of transcription from an RNA polymerase II promoter." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000100307 GO:0000790 nuclear chromatin "The ordered and organized complex of DNA, protein, and sometimes RNA, that forms the chromosome in the nucleus." [GOC:elh, PMID:20404130] cellular_component
+ENSG00000100307 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100307 GO:0006351 transcription, DNA-templated "The cellular synthesis of RNA on a template of DNA." [GOC:jl, GOC:txnOH] biological_process
+ENSG00000100307 GO:0016568 chromatin modification "The alteration of DNA, protein, or sometimes RNA, in chromatin, which may result in changing the chromatin structure." [GOC:mah, PMID:20404130] biological_process
+ENSG00000100307 GO:0031519 PcG protein complex "A chromatin-associated multiprotein complex containing Polycomb Group proteins. In Drosophila, Polycomb group proteins are involved in the long-term maintenance of gene repression, and PcG protein complexes associate with Polycomb group response elements (PREs) in target genes to regulate higher-order chromatin structure." [PMID:9372908] cellular_component
+ENSG00000100307 GO:0035102 PRC1 complex "A multiprotein complex that mediates monoubiquitination of lysine residues of histone H2A (lysine-118 in Drosophila or lysine-119 in mammals). The complex is required for stable long-term maintenance of transcriptionally repressed states and is involved in chromatin remodeling." [GOC:bf, PMID:10412979] cellular_component
+ENSG00000100307 GO:0003682 chromatin binding "Interacting selectively and non-covalently with chromatin, the network of fibers of DNA, protein, and sometimes RNA, that make up the chromosomes of the eukaryotic nucleus during interphase." [GOC:jl, ISBN:0198506732, PMID:20404130] molecular_function
+ENSG00000100307 GO:0035064 methylated histone binding "Interacting selectively and non-covalently with a histone protein in which a residue has been modified by methylation. Histones are any of a group of water-soluble proteins found in association with the DNA of plant and animal chromosomes." [GOC:bf, PMID:14585615] molecular_function
+ENSG00000100307 GO:0000792 heterochromatin "A compact and highly condensed form of chromatin." [GOC:elh] cellular_component
+ENSG00000100307 GO:0048733 sebaceous gland development "The process whose specific outcome is the progression of the sebaceous gland over time, from its formation to the mature structure." [GOC:jid] biological_process
+ENSG00000100307 GO:0003727 single-stranded RNA binding "Interacting selectively and non-covalently with single-stranded RNA." [GOC:jl] molecular_function
+ENSG00000100242 GO:0007010 cytoskeleton organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of cytoskeletal structures." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100242 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100242 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100242 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100242 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100242 GO:0008092 cytoskeletal protein binding "Interacting selectively and non-covalently with any protein component of any cytoskeleton (actin, microtubule, or intermediate filament cytoskeleton)." [GOC:mah] molecular_function
+ENSG00000100242 GO:0061024 membrane organization "A process which results in the assembly, arrangement of constituent parts, or disassembly of a membrane. A membrane is a double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:dph, GOC:tb] biological_process
+ENSG00000100242 GO:0005768 endosome "A membrane-bounded organelle to which materials ingested by endocytosis are delivered." [ISBN:0198506732, PMID:19696797] cellular_component
+ENSG00000100242 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100242 GO:0005635 nuclear envelope "The double lipid bilayer enclosing the nucleus and separating its contents from the rest of the cytoplasm; includes the intermembrane space, a gap of width 20-40 nm (also called the perinuclear space)." [ISBN:0198547684] cellular_component
+ENSG00000100242 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100242 GO:0005521 lamin binding "Interacting selectively and non-covalently with lamin; any of a group of intermediate-filament proteins that form the fibrous matrix on the inner surface of the nuclear envelope." [GOC:jl, ISBN:0198506732] molecular_function
+ENSG00000100242 GO:0006998 nuclear envelope organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of the nuclear envelope." [GOC:dph, GOC:ems, GOC:jl, GOC:mah] biological_process
+ENSG00000100242 GO:0007052 mitotic spindle organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of the microtubule spindle during a mitotic cell cycle." [GOC:mah] biological_process
+ENSG00000100242 GO:0007097 nuclear migration "The directed movement of the nucleus." [GOC:ai] biological_process
+ENSG00000100242 GO:0008017 microtubule binding "Interacting selectively and non-covalently with microtubules, filaments composed of tubulin monomers." [GOC:krc] molecular_function
+ENSG00000100242 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000100242 GO:0030335 positive regulation of cell migration "Any process that activates or increases the frequency, rate or extent of cell migration." [GOC:go_curators] biological_process
+ENSG00000100242 GO:0031022 nuclear migration along microfilament "The directed movement of the nucleus along microfilaments within the cell, mediated by motor proteins." [GOC:mah] biological_process
+ENSG00000100242 GO:0031965 nuclear membrane "Either of the lipid bilayers that surround the nucleus and form the nuclear envelope; excludes the intermembrane space." [GOC:mah, GOC:pz] cellular_component
+ENSG00000100242 GO:0034993 SUN-KASH complex "A protein complex that spans the nuclear outer and inner membranes, thereby linking the major cytoplasmic cytoskeleton elements to the nuclear lumen; the complex is conserved in eukaryotes and contains proteins with SUN and KASH domains." [GOC:mah, PMID:18692466] cellular_component
+ENSG00000100242 GO:0042802 identical protein binding "Interacting selectively and non-covalently with an identical protein or proteins." [GOC:jl] molecular_function
+ENSG00000100242 GO:0051642 centrosome localization "Any process in which a centrosome is transported to, and/or maintained in, a specific location within the cell." [GOC:ai] biological_process
+ENSG00000100242 GO:0090286 cytoskeletal anchoring at nuclear membrane "The process in which cytoskeletal filaments are directly or indirectly linked to the nuclear membrane." [GOC:tb] biological_process
+ENSG00000100242 GO:0090292 nuclear matrix anchoring at nuclear membrane "The process in which the nuclear matrix, the dense fibrillar network lying on the inner side of the nuclear membrane, is directly or indirectly linked to the nuclear membrane." [GOC:tb] biological_process
+ENSG00000100242 GO:0000794 condensed nuclear chromosome "A highly compacted molecule of DNA and associated proteins resulting in a cytologically distinct structure that remains in the nucleus." [GOC:elh] cellular_component
+ENSG00000100242 GO:0000784 nuclear chromosome, telomeric region "The terminal region of a linear chromosome in the nucleus that includes the telomeric DNA repeats and associated proteins." [GOC:elh] cellular_component
+ENSG00000100242 GO:0005637 nuclear inner membrane "The inner, i.e. lumen-facing, lipid bilayer of the nuclear envelope." [GOC:ma] cellular_component
+ENSG00000100242
+ENSG00000211662 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000211662 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000185340 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000185340 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000185340 GO:0007050 cell cycle arrest "A regulatory process that halts progression through the cell cycle during one of the normal phases (G1, S, G2, M)." [GOC:dph, GOC:mah, GOC:tb] biological_process
+ENSG00000185340 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000185340 GO:0001578 microtubule bundle formation "A process that results in a parallel arrangement of microtubules." [GOC:dph] biological_process
+ENSG00000185340 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000185340 GO:0005856 cytoskeleton "Any of the various filamentous elements that form the internal framework of cells, and typically remain after treatment of the cells with mild detergent to remove membrane constituents and soluble components of the cytoplasm. The term embraces intermediate filaments, microfilaments, microtubules, the microtrabecular lattice, and other structures characterized by a polymeric filamentous nature and long-range order within the cell. The various elements of the cytoskeleton not only serve in the maintenance of cellular shape but also have roles in other cellular functions, including cellular movement, cell division, endocytosis, and movement of organelles." [GOC:mah, ISBN:0198547684, PMID:16959967] cellular_component
+ENSG00000185340 GO:0007026 negative regulation of microtubule depolymerization "Any process that stops, prevents, or reduces the frequency, rate or extent of microtubule depolymerization; prevention of depolymerization of a microtubule can result from binding by 'capping' at the plus end (e.g. by interaction with another cellular protein of structure) or by exposing microtubules to a stabilizing drug such as taxol." [GOC:mah, ISBN:0815316194] biological_process
+ENSG00000185340 GO:0008017 microtubule binding "Interacting selectively and non-covalently with microtubules, filaments composed of tubulin monomers." [GOC:krc] molecular_function
+ENSG00000185340 GO:0008093 cytoskeletal adaptor activity "The binding activity of a molecule that brings together a cytoskeletal protein and one or more other molecules, permitting them to function in a coordinated way." [GOC:mtg_MIT_16mar07] molecular_function
+ENSG00000185340 GO:0009267 cellular response to starvation "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of deprivation of nourishment." [GOC:jl] biological_process
+ENSG00000185340 GO:0010629 negative regulation of gene expression "Any process that decreases the frequency, rate or extent of gene expression. Gene expression is the process in which a gene's coding sequence is converted into a mature gene product or products (proteins or RNA). This includes the production of an RNA transcript as well as any processing to produce a mature RNA product or an mRNA (for protein-coding genes) and the translation of that mRNA into protein. Some protein processing events may be included when they are required to form an active form of a product from an inactive precursor form." [GOC:dph, GOC:tb] biological_process
+ENSG00000185340 GO:0030308 negative regulation of cell growth "Any process that stops, prevents, or reduces the frequency, rate, extent or direction of cell growth." [GOC:go_curators] biological_process
+ENSG00000185340 GO:0045647 negative regulation of erythrocyte differentiation "Any process that stops, prevents, or reduces the frequency, rate or extent of erythrocyte differentiation." [GOC:go_curators] biological_process
+ENSG00000185340 GO:0046966 thyroid hormone receptor binding "Interacting selectively and non-covalently with a thyroid hormone receptor." [GOC:ai] molecular_function
+ENSG00000185340 GO:0051726 regulation of cell cycle "Any process that modulates the rate or extent of progression through the cell cycle." [GOC:ai, GOC:dph, GOC:tb] biological_process
+ENSG00000185340 GO:0097067 cellular response to thyroid hormone stimulus "A change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a thyroid hormone stimulus." [GOC:sjw, PMID:9916872] biological_process
+ENSG00000185340 GO:0001725 stress fiber "A contractile actin filament bundle that consists of short actin filaments with alternating polarity, cross-linked by alpha-actinin and possibly other actin bundling proteins, and with myosin present in a periodic distribution along the fiber." [PMID:16651381] cellular_component
+ENSG00000185340 GO:0005874 microtubule "Any of the long, generally straight, hollow tubes of internal diameter 12-15 nm and external diameter 24 nm found in a wide variety of eukaryotic cells; each consists (usually) of 13 protofilaments of polymeric tubulin, staggered in such a manner that the tubulin monomers are arranged in a helical pattern on the microtubular surface, and with the alpha/beta axes of the tubulin subunits parallel to the long axis of the tubule; exist in equilibrium with pool of tubulin monomers and can be rapidly assembled or disassembled in response to physiological stimuli; concerned with force generation, e.g. in the spindle." [ISBN:0879693568] cellular_component
+ENSG00000185340 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000185340 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000185340 GO:0008134 transcription factor binding "Interacting selectively and non-covalently with a transcription factor, any protein required to initiate or regulate transcription." [ISBN:0198506732] molecular_function
+ENSG00000185340 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000185340 GO:0008092 cytoskeletal protein binding "Interacting selectively and non-covalently with any protein component of any cytoskeleton (actin, microtubule, or intermediate filament cytoskeleton)." [GOC:mah] molecular_function
+ENSG00000185340 GO:0030674 protein binding, bridging "The binding activity of a molecule that brings together two or more protein molecules, or a protein and another macromolecule or complex, through a selective, non-covalent, often stoichiometric interaction, permitting those molecules to function in a coordinated way." [GOC:bf, GOC:mah, GOC:vw] molecular_function
+ENSG00000185340 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000185340 GO:0007010 cytoskeleton organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of cytoskeletal structures." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000187051 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000187051 GO:0019899 enzyme binding "Interacting selectively and non-covalently with any enzyme." [GOC:jl] molecular_function
+ENSG00000187051 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000187051 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000187051 GO:0005730 nucleolus "A small, dense body one or more of which are present in the nucleus of eukaryotic cells. It is rich in RNA and protein, is not bounded by a limiting membrane, and is not seen during mitosis. Its prime function is the transcription of the nucleolar DNA into 45S ribosomal-precursor RNA, the processing of this RNA into 5.8S, 18S, and 28S components of ribosomal RNA, and the association of these components with 5S RNA and proteins synthesized outside the nucleolus. This association results in the formation of ribonucleoprotein precursors; these pass into the cytoplasm and mature into the 40S and 60S subunits of the ribosome." [ISBN:0198506732] cellular_component
+ENSG00000187051 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000187051 GO:0005654 nucleoplasm "That part of the nuclear content other than the chromosomes or the nucleolus." [GOC:ma, ISBN:0124325653] cellular_component
+ENSG00000187051 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000187051 GO:0044822 poly(A) RNA binding "Interacting non-covalently with a poly(A) RNA, a RNA molecule which has a tail of adenine bases." [GOC:jl] molecular_function
+ENSG00000187051 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000187051
+ENSG00000099949 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000099949 GO:0001071 nucleic acid binding transcription factor activity "Interacting selectively and non-covalently with a DNA or RNA sequence in order to modulate transcription. The transcription factor may or may not also interact selectively with a protein or macromolecular complex." [GOC:txnOH] molecular_function
+ENSG00000099949 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000099949 GO:0003700 sequence-specific DNA binding transcription factor activity "Interacting selectively and non-covalently with a specific DNA sequence in order to modulate transcription. The transcription factor may or may not also interact selectively with a protein or macromolecular complex." [GOC:curators, GOC:txnOH] molecular_function
+ENSG00000099949 GO:0006355 regulation of transcription, DNA-templated "Any process that modulates the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000099949 GO:0009653 anatomical structure morphogenesis "The process in which anatomical structures are generated and organized. Morphogenesis pertains to the creation of form." [GOC:go_curators, ISBN:0521436125] biological_process
+ENSG00000099949 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000099949
+ENSG00000100220 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100220 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100220 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000100220 GO:0008092 cytoskeletal protein binding "Interacting selectively and non-covalently with any protein component of any cytoskeleton (actin, microtubule, or intermediate filament cytoskeleton)." [GOC:mah] molecular_function
+ENSG00000100220 GO:0006399 tRNA metabolic process "The chemical reactions and pathways involving tRNA, transfer RNA, a class of relatively small RNA molecules responsible for mediating the insertion of amino acids into the sequence of nascent polypeptide chains during protein synthesis. Transfer RNA is characterized by the presence of many unusual minor bases, the function of which has not been completely established." [ISBN:0198506732] biological_process
+ENSG00000100220 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100220 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100220 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100220 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100220 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100220 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100220 GO:0016874 ligase activity "Catalysis of the joining of two substances, or two groups within a single molecule, with the concomitant hydrolysis of the diphosphate bond in ATP or a similar triphosphate." [EC:6, GOC:mah] molecular_function
+ENSG00000100220 GO:0003972 RNA ligase (ATP) activity "Catalysis of the reaction: ATP + ribonucleotide(n) + ribonucleotide(m) = AMP + diphosphate + ribonucleotide(n+m)." [EC:6.5.1.3] molecular_function
+ENSG00000100220 GO:0005524 ATP binding "Interacting selectively and non-covalently with ATP, adenosine 5'-triphosphate, a universally important coenzyme and enzyme regulator." [ISBN:0198506732] molecular_function
+ENSG00000100220 GO:0006388 tRNA splicing, via endonucleolytic cleavage and ligation "Splicing of tRNA substrates via recognition of the folded RNA structure that brings the 5' and 3' splice sites into proximity and cleavage of the RNA at both the 3' and 5' splice sites by an endonucleolytic mechanism, followed by ligation of the exons." [GOC:krc, ISBN:0879695897, PMID:9582290] biological_process
+ENSG00000100220 GO:0017166 vinculin binding "Interacting selectively and non-covalently with vinculin, a protein found in muscle, fibroblasts, and epithelial cells that binds actin and appears to mediate attachment of actin filaments to integral proteins of the plasma membrane." [ISBN:0721662544] molecular_function
+ENSG00000100220 GO:0044822 poly(A) RNA binding "Interacting non-covalently with a poly(A) RNA, a RNA molecule which has a tail of adenine bases." [GOC:jl] molecular_function
+ENSG00000100220 GO:0046872 metal ion binding "Interacting selectively and non-covalently with any metal ion." [GOC:ai] molecular_function
+ENSG00000100220 GO:0072669 tRNA-splicing ligase complex "A protein complex that catalyzes the ligation of cleaved pre-tRNAs by directly joining spliced tRNA halves to mature-sized tRNAs by incorporating the precursor-derived splice junction phosphate into the mature tRNA as a canonical 3',5'-phosphodiester." [GOC:sp, PMID:21311021] cellular_component
+ENSG00000100220 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100220 GO:0008452 RNA ligase activity "Catalysis of the formation of a phosphodiester bond between a hydroxyl group at the end of one RNA chain and the 5'-phosphate group at the end of another." [GOC:mah] molecular_function
+ENSG00000100220 GO:0006396 RNA processing "Any process involved in the conversion of one or more primary RNA transcripts into one or more mature RNA molecules." [GOC:mah] biological_process
+ENSG00000100220 GO:0001701 in utero embryonic development "The process whose specific outcome is the progression of the embryo in the uterus over time, from formation of the zygote in the oviduct, to birth. An example of this process is found in Mus musculus." [GOC:go_curators, GOC:mtg_sensu] biological_process
+ENSG00000100220 GO:0001890 placenta development "The process whose specific outcome is the progression of the placenta over time, from its formation to the mature structure. The placenta is an organ of metabolic interchange between fetus and mother, partly of embryonic origin and partly of maternal origin." [GOC:add, ISBN:068340007X] biological_process
+ENSG00000100372 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100372 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000100372 GO:0043231 intracellular membrane-bounded organelle "Organized structure of distinctive morphology and function, bounded by a single or double lipid bilayer membrane and occurring within the cell. Includes the nucleus, mitochondria, plastids, vacuoles, and vesicles. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100372 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100372 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100372 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100372 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100372 GO:0022857 transmembrane transporter activity "Enables the transfer of a substance from one side of a membrane to the other." [GOC:mtg_transport, ISBN:0815340729] molecular_function
+ENSG00000100372 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000100372 GO:0006629 lipid metabolic process "The chemical reactions and pathways involving lipids, compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent. Includes fatty acids; neutral fats, other fatty-acid esters, and soaps; long-chain (fatty) alcohols and waxes; sphingoids and other long-chain bases; glycolipids, phospholipids and sphingolipids; and carotenes, polyprenols, sterols, terpenes and other isoprenoids." [GOC:ma] biological_process
+ENSG00000100372 GO:0055085 transmembrane transport "The process in which a solute is transported from one side of a membrane to the other." [GOC:dph, GOC:jid] biological_process
+ENSG00000100372 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000100372 GO:0005777 peroxisome "A small organelle enclosed by a single membrane, and found in most eukaryotic cells. Contains peroxidases and other enzymes involved in a variety of metabolic processes including free radical detoxification, lipid catabolism and biosynthesis, and hydrogen peroxide metabolism." [GOC:pm, PMID:9302272, UniProtKB-KW:KW-0576] cellular_component
+ENSG00000100372 GO:0001561 fatty acid alpha-oxidation "A metabolic pathway by which 3-methyl branched fatty acids are degraded. These compounds are not degraded by the normal peroxisomal beta-oxidation pathway, because the 3-methyl blocks the dehydrogenation of the hydroxyl group by hydroxyacyl-CoA dehydrogenase. The 3-methyl branched fatty acid is converted in several steps to pristenic acid, which can then feed into the beta-oxidative pathway." [http://www.peroxisome.org/Scientist/Biochemistry/alpha-oxidation.html] biological_process
+ENSG00000100372 GO:0005347 ATP transmembrane transporter activity "Catalysis of the transfer of ATP, adenosine triphosphate, from one side of a membrane to the other." [GOC:ai] molecular_function
+ENSG00000100372 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100372 GO:0005778 peroxisomal membrane "The lipid bilayer surrounding a peroxisome." [GOC:mah] cellular_component
+ENSG00000100372 GO:0005779 integral component of peroxisomal membrane "The component of the peroxisomal membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:mah] cellular_component
+ENSG00000100372 GO:0006635 fatty acid beta-oxidation "A fatty acid oxidation process that results in the complete oxidation of a long-chain fatty acid. Fatty acid beta-oxidation begins with the addition of coenzyme A to a fatty acid, and occurs by successive cycles of reactions during each of which the fatty acid is shortened by a two-carbon fragment removed as acetyl coenzyme A; the cycle continues until only two or three carbons remain (as acetyl-CoA or propionyl-CoA respectively)." [GOC:mah, ISBN:0198506732, MetaCyc:FAO-PWY] biological_process
+ENSG00000100372 GO:0015217 ADP transmembrane transporter activity "Catalysis of the transfer of ADP, adenosine diphosphate, from one side of a membrane to the other." [GOC:ai] molecular_function
+ENSG00000100372 GO:0015228 coenzyme A transmembrane transporter activity "Enables the directed movement of coenzyme A across a membrane into, out of or within a cell, or between cells. Coenzyme A, 3'-phosphoadenosine-(5')diphospho(4')pantatheine, is an acyl carrier in many acylation and acyl-transfer reactions in which the intermediate is a thiol ester." [GOC:ai] molecular_function
+ENSG00000100372 GO:0015230 FAD transmembrane transporter activity "Enables the directed movement of flavin-adenine dinucleotide (FAD) across a membrane into, out of or within a cell, or between cells. FAD forms the coenzyme of the prosthetic group of various flavoprotein oxidoreductase enzymes, in which it functions as an electron acceptor by being reversibly converted to its reduced form." [ISBN:0198506732] molecular_function
+ENSG00000100372 GO:0015866 ADP transport "The directed movement of ADP, adenosine diphosphate, into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:ai] biological_process
+ENSG00000100372 GO:0015867 ATP transport "The directed movement of ATP, adenosine triphosphate, into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:ai] biological_process
+ENSG00000100372 GO:0015908 fatty acid transport "The directed movement of fatty acids into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore. Fatty acids are aliphatic monocarboxylic acids liberated from naturally occurring fats and oils by hydrolysis." [GOC:ai] biological_process
+ENSG00000100372 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000100372 GO:0035349 coenzyme A transmembrane transport "The process in which coenzyme A is transported from one side of a membrane to the other by means of some agent such as a transporter or pore. Coenzyme A, 3'-phosphoadenosine-(5')diphospho(4')pantatheine, is an acyl carrier in many acylation and acyl-transfer reactions in which the intermediate is a thiol ester." [GOC:bf] biological_process
+ENSG00000100372 GO:0035350 FAD transmembrane transport "The process in which flavin-adenine dinucleotide (FAD) is transported from one side of a membrane to the other by means of some agent such as a transporter or pore. FAD forms the coenzyme of the prosthetic group of various flavoprotein oxidoreductase enzymes, in which it functions as an electron acceptor by being reversibly converted to its reduced form." [GOC:bf, ISBN:0198506732] biological_process
+ENSG00000100372 GO:0043132 NAD transport "The directed movement of nicotinamide adenine dinucleotide into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore; transport may be of either the oxidized form, NAD, or the reduced form, NADH." [GOC:jl] biological_process
+ENSG00000100372 GO:0044255 cellular lipid metabolic process "The chemical reactions and pathways involving lipids, as carried out by individual cells." [GOC:jl] biological_process
+ENSG00000100372 GO:0044610 FMN transmembrane transporter activity "Enables the directed movement of flavine mononucleotide (FMN) into, out of or within a cell, or between cells." [GOC:ans, PMID:22185573] molecular_function
+ENSG00000100372 GO:0051087 chaperone binding "Interacting selectively and non-covalently with a chaperone protein, a class of proteins that bind to nascent or unfolded polypeptides and ensure correct folding or transport." [http://www.onelook.com] molecular_function
+ENSG00000100372 GO:0051724 NAD transporter activity "Enables the directed movement of nicotinamide adenine dinucleotide into, out of or within a cell, or between cells; transport may be of either the oxidized form, NAD, or the reduced form, NADH." [GOC:ai] molecular_function
+ENSG00000100372 GO:0080121 AMP transport "The directed movement of AMP, adenosine monophosphate, into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [PMID:18923018] biological_process
+ENSG00000100372 GO:0080122 AMP transmembrane transporter activity "Catalysis of the transfer of AMP, adenosine monophosphate, from one side of a membrane to the other." [PMID:18923018] molecular_function
+ENSG00000100372 GO:1901679 nucleotide transmembrane transport "The directed movement of nucleotide across a membrane." [GOC:pr, GOC:TermGenie] biological_process
+ENSG00000100372 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000100372 GO:0005215 transporter activity "Enables the directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells." [GOC:ai, GOC:dgf] molecular_function
+ENSG00000100372 GO:0005743 mitochondrial inner membrane "The inner, i.e. lumen-facing, lipid bilayer of the mitochondrial envelope. It is highly folded to form cristae." [GOC:ai] cellular_component
+ENSG00000100372
+ENSG00000130943 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000130943 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000130943 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000130943 GO:0016192 vesicle-mediated transport "A cellular transport process in which transported substances are moved in membrane-bounded vesicles; transported substances are enclosed in the vesicle lumen or located in the vesicle membrane. The process begins with a step that directs a substance to the forming vesicle, and includes vesicle budding and coating. Vesicles are then targeted to, and fuse with, an acceptor membrane." [GOC:ai, GOC:mah, ISBN:08789310662000] biological_process
+ENSG00000130943 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000130943 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000130943 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000130943 GO:0022857 transmembrane transporter activity "Enables the transfer of a substance from one side of a membrane to the other." [GOC:mtg_transport, ISBN:0815340729] molecular_function
+ENSG00000130943 GO:0005262 calcium channel activity "Catalysis of facilitated diffusion of a calcium ion (by an energy-independent process) involving passage through a transmembrane aqueous pore or channel without evidence for a carrier-mediated mechanism." [GOC:mtg_transport, GOC:pr, ISBN:0815340729] molecular_function
+ENSG00000130943 GO:0005509 calcium ion binding "Interacting selectively and non-covalently with calcium ions (Ca2+)." [GOC:ai] molecular_function
+ENSG00000130943 GO:0007218 neuropeptide signaling pathway "The series of molecular signals generated as a consequence of a peptide neurotransmitter binding to a cell surface receptor." [GOC:mah, ISBN:0815316194] biological_process
+ENSG00000130943 GO:0007340 acrosome reaction "The discharge, by sperm, of a single, anterior secretory granule following the sperm's attachment to the zona pellucida surrounding the oocyte. The process begins with the fusion of the outer acrosomal membrane with the sperm plasma membrane and ends with the exocytosis of the acrosomal contents into the egg." [GOC:dph, PMID:3886029] biological_process
+ENSG00000130943 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000130943 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000130943 GO:0050982 detection of mechanical stimulus "The series of events by which a mechanical stimulus is received and converted into a molecular signal." [GOC:ai, GOC:dos] biological_process
+ENSG00000130943 GO:0070588 calcium ion transmembrane transport "A process in which a calcium ion is transported from one side of a membrane to the other by means of some agent such as a transporter or pore." [GOC:mah] biological_process
+ENSG00000130943 GO:0055085 transmembrane transport "The process in which a solute is transported from one side of a membrane to the other." [GOC:dph, GOC:jid] biological_process
+ENSG00000130943 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000130943 GO:0005216 ion channel activity "Catalysis of facilitated diffusion of an ion (by an energy-independent process) by passage through a transmembrane aqueous pore or channel without evidence for a carrier-mediated mechanism." [GOC:cy, GOC:mtg_transport, ISBN:0815340729] molecular_function
+ENSG00000130943 GO:0006811 ion transport "The directed movement of charged atoms or small charged molecules into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:ai] biological_process
+ENSG00000130943 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000130943 GO:0060046 regulation of acrosome reaction "Any process that modulates the frequency, rate or extent of the acrosome reaction." [GOC:dph] biological_process
+ENSG00000186976 GO:0005509 calcium ion binding "Interacting selectively and non-covalently with calcium ions (Ca2+)." [GOC:ai] molecular_function
+ENSG00000186976 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000186976 GO:0006351 transcription, DNA-templated "The cellular synthesis of RNA on a template of DNA." [GOC:jl, GOC:txnOH] biological_process
+ENSG00000186976 GO:0006355 regulation of transcription, DNA-templated "Any process that modulates the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000186976 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000186976 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000186976 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000186976 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000186976 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000186976 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000186976 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000241878 GO:0004609 phosphatidylserine decarboxylase activity "Catalysis of the reaction: H(+) + phosphatidyl-L-serine = CO(2) + phosphatidylethanolamine." [EC:4.1.1.65, RHEA:20831] molecular_function
+ENSG00000241878 GO:0008654 phospholipid biosynthetic process "The chemical reactions and pathways resulting in the formation of phospholipids, any lipid containing phosphoric acid as a mono- or diester." [ISBN:0198506732] biological_process
+ENSG00000241878 GO:0006629 lipid metabolic process "The chemical reactions and pathways involving lipids, compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent. Includes fatty acids; neutral fats, other fatty-acid esters, and soaps; long-chain (fatty) alcohols and waxes; sphingoids and other long-chain bases; glycolipids, phospholipids and sphingolipids; and carotenes, polyprenols, sterols, terpenes and other isoprenoids." [GOC:ma] biological_process
+ENSG00000241878 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000241878 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000241878 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000241878 GO:0016829 lyase activity "Catalysis of the cleavage of C-C, C-O, C-N and other bonds by other means than by hydrolysis or oxidation, or conversely adding a group to a double bond. They differ from other enzymes in that two substrates are involved in one reaction direction, but only one in the other direction. When acting on the single substrate, a molecule is eliminated and this generates either a new double bond or a new ring." [EC:4.-.-.-, ISBN:0198547684] molecular_function
+ENSG00000241878 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000241878 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000241878 GO:0006644 phospholipid metabolic process "The chemical reactions and pathways involving phospholipids, any lipid containing phosphoric acid as a mono- or diester." [ISBN:0198506732] biological_process
+ENSG00000241878 GO:0006646 phosphatidylethanolamine biosynthetic process "The chemical reactions and pathways resulting in the formation of phosphatidylethanolamine, any of a class of glycerophospholipids in which a phosphatidyl group is esterified to the hydroxyl group of ethanolamine." [ISBN:0198506732] biological_process
+ENSG00000241878 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000241878 GO:0046474 glycerophospholipid biosynthetic process "The chemical reactions and pathways resulting in the formation of glycerophospholipids, any derivative of glycerophosphate that contains at least one O-acyl, O-alkyl, or O-alkenyl group attached to the glycerol residue." [ISBN:0198506732] biological_process
+ENSG00000241878 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000241878 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000241878
+ENSG00000185386 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000185386 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000185386 GO:0002376 immune system process "Any process involved in the development or functioning of the immune system, an organismal system for calibrated responses to potential internal or invasive threats." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05] biological_process
+ENSG00000185386 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000185386 GO:0030154 cell differentiation "The process in which relatively unspecialized cells, e.g. embryonic or regenerative cells, acquire specialized structural and/or functional features that characterize the cells, tissues, or organs of the mature organism or some other relatively stable phase of the organism's life history. Differentiation includes the processes involved in commitment of a cell to a specific fate and its subsequent development to the mature state." [ISBN:0198506732] biological_process
+ENSG00000185386 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000185386 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000185386 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000185386 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000185386 GO:0005654 nucleoplasm "That part of the nuclear content other than the chromosomes or the nucleolus." [GOC:ma, ISBN:0124325653] cellular_component
+ENSG00000185386 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000185386 GO:0004871 signal transducer activity "Conveys a signal across a cell to trigger a change in cell function or state. A signal is a physical entity or change in state that is used to transfer information in order to trigger a response." [GOC:go_curators] molecular_function
+ENSG00000185386 GO:0016301 kinase activity "Catalysis of the transfer of a phosphate group, usually from ATP, to a substrate molecule." [ISBN:0198506732] molecular_function
+ENSG00000185386 GO:0000187 activation of MAPK activity "The initiation of the activity of the inactive enzyme MAP kinase (MAPK)." [PMID:9561267] biological_process
+ENSG00000185386 GO:0002224 toll-like receptor signaling pathway "Any series of molecular signals generated as a consequence of binding to a toll-like receptor. Toll-like receptors directly bind pattern motifs from a variety of microbial sources to initiate innate immune response." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05, ISBN:0781735149, PMID:12467241, PMID:12524386, PMID:12855817, PMID:15585605, PMID:15728447] biological_process
+ENSG00000185386 GO:0002755 MyD88-dependent toll-like receptor signaling pathway "Any series of molecular signals generated as a consequence of binding to a toll-like receptor where the MyD88 adaptor molecule mediates transduction of the signal. Toll-like receptors directly bind pattern motifs from a variety of microbial sources to initiate innate immune response." [GOC:add, ISBN:0781735149, PMID:12467241, PMID:12524386, PMID:12855817, PMID:15585605, PMID:15728447] biological_process
+ENSG00000185386 GO:0002756 MyD88-independent toll-like receptor signaling pathway "Any series of molecular signals generated as a consequence of binding to a toll-like receptor not relying on the MyD88 adaptor molecule. Toll-like receptors directly bind pattern motifs from a variety of microbial sources to initiate innate immune response." [GOC:add, ISBN:0781735149, PMID:12467241, PMID:12524386, PMID:12855817, PMID:15585605, PMID:15728447] biological_process
+ENSG00000185386 GO:0004674 protein serine/threonine kinase activity "Catalysis of the reactions: ATP + protein serine = ADP + protein serine phosphate, and ATP + protein threonine = ADP + protein threonine phosphate." [GOC:bf] molecular_function
+ENSG00000185386 GO:0004707 MAP kinase activity "Catalysis of the reaction: protein + ATP = protein phosphate + ADP. This reaction is the phosphorylation of proteins. Mitogen-activated protein kinase; a family of protein kinases that perform a crucial step in relaying signals from the plasma membrane to the nucleus. They are activated by a wide range of proliferation- or differentiation-inducing signals; activation is strong with agonists such as polypeptide growth factors and tumor-promoting phorbol esters, but weak (in most cell backgrounds) by stress stimuli." [GOC:ma, ISBN:0198547684] molecular_function
+ENSG00000185386 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000185386 GO:0006351 transcription, DNA-templated "The cellular synthesis of RNA on a template of DNA." [GOC:jl, GOC:txnOH] biological_process
+ENSG00000185386 GO:0007265 Ras protein signal transduction "A series of molecular signals within the cell that are mediated by a member of the Ras superfamily of proteins switching to a GTP-bound active state." [GOC:bf] biological_process
+ENSG00000185386 GO:0010467 gene expression "The process in which a gene's sequence is converted into a mature gene product or products (proteins or RNA). This includes the production of an RNA transcript as well as any processing to produce a mature RNA product or an mRNA (for protein-coding genes) and the translation of that mRNA into protein. Some protein processing events may be included when they are required to form an active form of a product from an inactive precursor form." [GOC:dph, GOC:tb] biological_process
+ENSG00000185386 GO:0016070 RNA metabolic process "The cellular chemical reactions and pathways involving RNA, ribonucleic acid, one of the two main type of nucleic acid, consisting of a long, unbranched macromolecule formed from ribonucleotides joined in 3',5'-phosphodiester linkage." [ISBN:0198506732] biological_process
+ENSG00000185386 GO:0016071 mRNA metabolic process "The chemical reactions and pathways involving mRNA, messenger RNA, which is responsible for carrying the coded genetic 'message', transcribed from DNA, to sites of protein assembly at the ribosomes." [ISBN:0198506732] biological_process
+ENSG00000185386 GO:0034134 toll-like receptor 2 signaling pathway "Any series of molecular signals generated as a consequence of binding to toll-like receptor 2." [GOC:add, PMID:16551253, PMID:17328678] biological_process
+ENSG00000185386 GO:0034138 toll-like receptor 3 signaling pathway "Any series of molecular signals generated as a consequence of binding to toll-like receptor 3." [GOC:add, PMID:16551253, PMID:17328678] biological_process
+ENSG00000185386 GO:0034142 toll-like receptor 4 signaling pathway "Any series of molecular signals generated as a consequence of binding to toll-like receptor 4." [GOC:add, PMID:16551253, PMID:17328678] biological_process
+ENSG00000185386 GO:0034146 toll-like receptor 5 signaling pathway "Any series of molecular signals generated as a consequence of binding to toll-like receptor 5." [GOC:add, PMID:16551253, PMID:17328678] biological_process
+ENSG00000185386 GO:0034162 toll-like receptor 9 signaling pathway "Any series of molecular signals generated as a consequence of binding to toll-like receptor 9." [GOC:add, PMID:16551253, PMID:17328678] biological_process
+ENSG00000185386 GO:0034166 toll-like receptor 10 signaling pathway "Any series of molecular signals generated as a consequence of binding to toll-like receptor 10." [GOC:add, PMID:16551253, PMID:17328678] biological_process
+ENSG00000185386 GO:0035556 intracellular signal transduction "The process in which a signal is passed on to downstream components within the cell, which become activated themselves to further propagate the signal and finally trigger a change in the function or state of the cell." [GOC:bf, GOC:jl, GOC:signaling, ISBN:3527303782] biological_process
+ENSG00000185386 GO:0035666 TRIF-dependent toll-like receptor signaling pathway "Any series of molecular signals generated as a consequence of binding to a toll-like receptor where the TRIF adaptor mediates transduction of the signal. Toll-like receptors directly bind pattern motifs from a variety of microbial sources to initiate innate immune response." [GOC:BHF, PMID:12855817] biological_process
+ENSG00000185386 GO:0038123 toll-like receptor TLR1:TLR2 signaling pathway "A series of molecular signals initiated by the binding of a heterodimeric TLR1:TLR2 complex to one of it's physiological ligands, followed by transmission of the signal by the activated receptor, and ending with regulation of a downstream cellular process, e.g. transcription." [GOC:nhn, GOC:signaling, PMID:17318230] biological_process
+ENSG00000185386 GO:0038124 toll-like receptor TLR6:TLR2 signaling pathway "A series of molecular signals initiated by the binding of a heterodimeric TLR6:TLR2 complex to one of it's physiological ligands, followed by transmission of the signal by the activated receptor, and ending with regulation of a downstream cellular process, e.g. transcription." [GOC:nhn, GOC:signaling, PMID:17318230] biological_process
+ENSG00000185386 GO:0042692 muscle cell differentiation "The process in which a relatively unspecialized cell acquires specialized features of a muscle cell." [CL:0000187, GOC:go_curators] biological_process
+ENSG00000185386 GO:0045087 innate immune response "Innate immune responses are defense responses mediated by germline encoded components that directly recognize components of potential pathogens." [GO_REF:0000022, GOC:add, GOC:ebc, GOC:mtg_15nov05, GOC:mtg_sensu] biological_process
+ENSG00000185386 GO:0048011 neurotrophin TRK receptor signaling pathway "A series of molecular signals initiated by the binding of a neurotrophin to a receptor on the surface of the target cell where the receptor possesses tyrosine kinase activity, and ending with regulation of a downstream cellular process, e.g. transcription." [GOC:bf, GOC:ceb, GOC:jc, GOC:signaling, PMID:12065629, Wikipedia:Trk_receptor] biological_process
+ENSG00000185386 GO:0051090 regulation of sequence-specific DNA binding transcription factor activity "Any process that modulates the frequency, rate or extent of the activity of a transcription factor, any factor involved in the initiation or regulation of transcription." [GOC:ai] biological_process
+ENSG00000185386 GO:0051149 positive regulation of muscle cell differentiation "Any process that activates or increases the frequency, rate or extent of muscle cell differentiation." [CL:0000187, GOC:ai] biological_process
+ENSG00000185386 GO:0051403 stress-activated MAPK cascade "A series of molecular signals in which a stress-activated MAP kinase cascade relays one or more of the signals; MAP kinase cascades involve at least three protein kinase activities and culminate in the phosphorylation and activation of a MAP kinase." [GOC:ai, PMID:15936270] biological_process
+ENSG00000185386 GO:0004672 protein kinase activity "Catalysis of the phosphorylation of an amino acid residue in a protein, usually according to the reaction: a protein + ATP = a phosphoprotein + ADP." [MetaCyc:PROTEIN-KINASE-RXN] molecular_function
+ENSG00000185386 GO:0005524 ATP binding "Interacting selectively and non-covalently with ATP, adenosine 5'-triphosphate, a universally important coenzyme and enzyme regulator." [ISBN:0198506732] molecular_function
+ENSG00000185386 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000185386 GO:0006468 protein phosphorylation "The process of introducing a phosphate group on to a protein." [GOC:hb] biological_process
+ENSG00000185386 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000185386 GO:0016773 phosphotransferase activity, alcohol group as acceptor "Catalysis of the transfer of a phosphorus-containing group from one compound (donor) to an alcohol group (acceptor)." [GOC:jl] molecular_function
+ENSG00000185386 GO:0009103 lipopolysaccharide biosynthetic process "The chemical reactions and pathways resulting in the formation of lipopolysaccharides, any of a group of related, structurally complex components of the outer membrane of Gram-negative bacteria." [GOC:ai, GOC:mr] biological_process
+ENSG00000185386 GO:0005975 carbohydrate metabolic process "The chemical reactions and pathways involving carbohydrates, any of a group of organic compounds based of the general formula Cx(H2O)y. Includes the formation of carbohydrate derivatives by the addition of a carbohydrate residue to another molecule." [GOC:mah, ISBN:0198506732] biological_process
+ENSG00000185386 GO:0006629 lipid metabolic process "The chemical reactions and pathways involving lipids, compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent. Includes fatty acids; neutral fats, other fatty-acid esters, and soaps; long-chain (fatty) alcohols and waxes; sphingoids and other long-chain bases; glycolipids, phospholipids and sphingolipids; and carotenes, polyprenols, sterols, terpenes and other isoprenoids." [GOC:ma] biological_process
+ENSG00000185386 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000185386 GO:0016772 transferase activity, transferring phosphorus-containing groups "Catalysis of the transfer of a phosphorus-containing group from one compound (donor) to another (acceptor)." [GOC:jl, ISBN:0198506732] molecular_function
+ENSG00000185386 GO:0004713 protein tyrosine kinase activity "Catalysis of the reaction: ATP + a protein tyrosine = ADP + protein tyrosine phosphate." [EC:2.7.10] molecular_function
+ENSG00000133424 GO:0008152 metabolic process "The chemical reactions and pathways, including anabolism and catabolism, by which living organisms transform chemical substances. Metabolic processes typically transform small molecules, but also include macromolecular processes such as DNA repair and replication, and protein synthesis and degradation." [GOC:go_curators, ISBN:0198547684] biological_process
+ENSG00000133424 GO:0016740 transferase activity "Catalysis of the transfer of a group, e.g. a methyl group, glycosyl group, acyl group, phosphorus-containing, or other groups, from one compound (generally regarded as the donor) to another compound (generally regarded as the acceptor). Transferase is the systematic name for any enzyme of EC class 2." [ISBN:0198506732] molecular_function
+ENSG00000133424 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000133424 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000133424 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000133424 GO:0016757 transferase activity, transferring glycosyl groups "Catalysis of the transfer of a glycosyl group from one compound (donor) to another (acceptor)." [GOC:jl, ISBN:0198506732] molecular_function
+ENSG00000133424 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000133424 GO:0006629 lipid metabolic process "The chemical reactions and pathways involving lipids, compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent. Includes fatty acids; neutral fats, other fatty-acid esters, and soaps; long-chain (fatty) alcohols and waxes; sphingoids and other long-chain bases; glycolipids, phospholipids and sphingolipids; and carotenes, polyprenols, sterols, terpenes and other isoprenoids." [GOC:ma] biological_process
+ENSG00000133424 GO:0005975 carbohydrate metabolic process "The chemical reactions and pathways involving carbohydrates, any of a group of organic compounds based of the general formula Cx(H2O)y. Includes the formation of carbohydrate derivatives by the addition of a carbohydrate residue to another molecule." [GOC:mah, ISBN:0198506732] biological_process
+ENSG00000133424 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000133424 GO:0006044 N-acetylglucosamine metabolic process "The chemical reactions and pathways involving N-acetylglucosamine. The D isomer is a common structural unit of glycoproteins in plants, bacteria and animals; it is often the terminal sugar of an oligosaccharide group of a glycoprotein." [ISBN:0198506732] biological_process
+ENSG00000133424 GO:0006486 protein glycosylation "A protein modification process that results in the addition of a carbohydrate or carbohydrate derivative unit to a protein amino acid, e.g. the addition of glycan chains to proteins." [GOC:curators, GOC:pr] biological_process
+ENSG00000133424 GO:0006688 glycosphingolipid biosynthetic process "The chemical reactions and pathways resulting in the formation of glycosphingolipid, a compound with residues of sphingoid and at least one monosaccharide." [GOC:go_curators] biological_process
+ENSG00000133424 GO:0008375 acetylglucosaminyltransferase activity "Catalysis of the transfer of an N-acetylglucosaminyl residue from UDP-N-acetyl-glucosamine to a sugar." [ISBN:0198506732] molecular_function
+ENSG00000133424 GO:0009101 glycoprotein biosynthetic process "The chemical reactions and pathways resulting in the formation of glycoproteins, any protein that contains covalently bound glycose (i.e. monosaccharide) residues; the glycose occurs most commonly as oligosaccharide or fairly small polysaccharide but occasionally as monosaccharide." [GOC:go_curators, ISBN:0198506732] biological_process
+ENSG00000133424 GO:0030173 integral component of Golgi membrane "The component of the Golgi membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:go_curators] cellular_component
+ENSG00000133424 GO:0046716 muscle cell cellular homeostasis "The cellular homeostatic process that preserves a muscle cell in a stable functional or structural state." [GOC:mah, PMID:3091429, PMID:7781901] biological_process
+ENSG00000133424 GO:0042592 homeostatic process "Any biological process involved in the maintenance of an internal steady state." [GOC:jl, ISBN:0395825172] biological_process
+ENSG00000133424 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000133424
+ENSG00000169635 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000169635 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000169635 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000169635 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000169635 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000169635 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000169635 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000169635 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000169635 GO:0003677 DNA binding "Any molecular function by which a gene product interacts selectively and non-covalently with DNA (deoxyribonucleic acid)." [GOC:dph, GOC:jl, GOC:tb, GOC:vw] molecular_function
+ENSG00000169635 GO:0006351 transcription, DNA-templated "The cellular synthesis of RNA on a template of DNA." [GOC:jl, GOC:txnOH] biological_process
+ENSG00000169635 GO:0008022 protein C-terminus binding "Interacting selectively and non-covalently with a protein C-terminus, the end of any peptide chain at which the 1-carboxy function of a constituent amino acid is not attached in peptide linkage to another amino-acid residue." [ISBN:0198506732] molecular_function
+ENSG00000169635 GO:0045892 negative regulation of transcription, DNA-templated "Any process that stops, prevents, or reduces the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000169635 GO:0046872 metal ion binding "Interacting selectively and non-covalently with any metal ion." [GOC:ai] molecular_function
+ENSG00000169635 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000169635 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100150
+ENSG00000100150 GO:0005096 GTPase activator activity "Increases the activity of a GTPase, an enzyme that catalyzes the hydrolysis of GTP." [GOC:mah] molecular_function
+ENSG00000100150 GO:0005765 lysosomal membrane "The lipid bilayer surrounding the lysosome and separating its contents from the cell cytoplasm." [GOC:ai] cellular_component
+ENSG00000100150 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000100150 GO:0035556 intracellular signal transduction "The process in which a signal is passed on to downstream components within the cell, which become activated themselves to further propagate the signal and finally trigger a change in the function or state of the cell." [GOC:bf, GOC:jl, GOC:signaling, ISBN:3527303782] biological_process
+ENSG00000100150 GO:0043547 positive regulation of GTPase activity "Any process that activates or increases the activity of a GTPase." [GOC:jl] biological_process
+ENSG00000100150 GO:0048471 perinuclear region of cytoplasm "Cytoplasm situated near, or occurring around, the nucleus." [GOC:jid] cellular_component
+ENSG00000100150 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100150 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100150 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000100150 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100150 GO:0030234 enzyme regulator activity "Binds to and modulates the activity of an enzyme." [GOC:mah] molecular_function
+ENSG00000100034 GO:0004722 protein serine/threonine phosphatase activity "Catalysis of the reaction: protein serine phosphate + H2O = protein serine + phosphate, and protein threonine phosphate + H2O = protein threonine + phosphate." [GOC:bf] molecular_function
+ENSG00000100034 GO:0046872 metal ion binding "Interacting selectively and non-covalently with any metal ion." [GOC:ai] molecular_function
+ENSG00000100034 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100034 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100034 GO:0016791 phosphatase activity "Catalysis of the hydrolysis of phosphoric monoesters, releasing inorganic phosphate." [EC:3.1.3, EC:3.1.3.41, GOC:curators] molecular_function
+ENSG00000100034 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100034 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100034 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100034 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000100034 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000100034 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100034 GO:0006469 negative regulation of protein kinase activity "Any process that stops, prevents, or reduces the frequency, rate or extent of protein kinase activity." [GOC:go_curators] biological_process
+ENSG00000100034 GO:0010628 positive regulation of gene expression "Any process that increases the frequency, rate or extent of gene expression. Gene expression is the process in which a gene's coding sequence is converted into a mature gene product or products (proteins or RNA). This includes the production of an RNA transcript as well as any processing to produce a mature RNA product or an mRNA (for protein-coding genes) and the translation of that mRNA into protein. Some protein processing events may be included when they are required to form an active form of a product from an inactive precursor form." [GOC:dph, GOC:tb] biological_process
+ENSG00000100034 GO:0010634 positive regulation of epithelial cell migration "Any process that activates or increases the frequency, rate or extent of epithelial cell migration." [GOC:BHF, GOC:dph, GOC:tb] biological_process
+ENSG00000100034 GO:0010811 positive regulation of cell-substrate adhesion "Any process that increases the frequency, rate or extent of cell-substrate adhesion. Cell-substrate adhesion is the attachment of a cell to the underlying substrate via adhesion molecules." [GOC:dph, GOC:pf, GOC:tb] biological_process
+ENSG00000100034 GO:0033137 negative regulation of peptidyl-serine phosphorylation "Any process that stops, prevents, or reduces the frequency, rate or extent of the phosphorylation of peptidyl-serine." [GOC:mah] biological_process
+ENSG00000100034 GO:0033192 calmodulin-dependent protein phosphatase activity "Catalysis of the reaction: protein serine/threonine phosphate + H2O = protein serine/threonine + phosphate, dependent on the presence of calcium-bound calmodulin." [GOC:mah, PMID:15359118] molecular_function
+ENSG00000100034 GO:0035690 cellular response to drug "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a drug stimulus. A drug is a substance used in the diagnosis, treatment or prevention of a disease." [GOC:sl] biological_process
+ENSG00000100034 GO:0035970 peptidyl-threonine dephosphorylation "The removal of phosphoric residues from peptidyl-O-phospho-L-threonine to form peptidyl-threonine." [GOC:bf] biological_process
+ENSG00000100034 GO:0043280 positive regulation of cysteine-type endopeptidase activity involved in apoptotic process "Any process that activates or increases the activity of a cysteine-type endopeptidase involved in the apoptotic process." [GOC:jl, GOC:mtg_apoptosis] biological_process
+ENSG00000100034 GO:0044387 negative regulation of protein kinase activity by regulation of protein phosphorylation "The stopping, prevention, or reduction in frequency, rate or extent of protein kinase activity as a result of regulating the phosphorylation status of that protein kinase." [GOC:jl] biological_process
+ENSG00000100034 GO:0045892 negative regulation of transcription, DNA-templated "Any process that stops, prevents, or reduces the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000100034 GO:0045927 positive regulation of growth "Any process that activates or increases the rate or extent of growth, the increase in size or mass of all or part of an organism." [GOC:go_curators] biological_process
+ENSG00000100034 GO:0050921 positive regulation of chemotaxis "Any process that activates or increases the frequency, rate or extent of the directed movement of a motile cell or organism in response to a specific chemical concentration gradient." [GOC:ai] biological_process
+ENSG00000100034 GO:0051496 positive regulation of stress fiber assembly "Any process that activates or increases the frequency, rate or extent of the assembly of a stress fiber, a bundle of microfilaments and other proteins found in fibroblasts." [GOC:ai] biological_process
+ENSG00000100034 GO:0051894 positive regulation of focal adhesion assembly "Any process that activates or increases the frequency, rate or extent of focal adhesion assembly, the establishment and maturation of focal adhesions." [GOC:ai] biological_process
+ENSG00000100034 GO:0097193 intrinsic apoptotic signaling pathway "A series of molecular signals in which an intracellular signal is conveyed to trigger the apoptotic death of a cell. The pathway starts with reception of an intracellular signal (e.g. DNA damage, endoplasmic reticulum stress, oxidative stress etc.), and ends when the execution phase of apoptosis is triggered. The intrinsic apoptotic signaling pathway is crucially regulated by permeabilization of the mitochondrial outer membrane (MOMP)." [GOC:mtg_apoptosis, GOC:yaf, PMID:11919192, PMID:17340152, PMID:18852119] biological_process
+ENSG00000100034 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000100034 GO:0003824 catalytic activity "Catalysis of a biochemical reaction at physiological temperatures. In biologically catalyzed reactions, the reactants are known as substrates, and the catalysts are naturally occurring macromolecular substances known as enzymes. Enzymes possess specific binding sites for substrates, and are usually composed wholly or largely of protein, but RNA that has catalytic activity (ribozyme) is often also regarded as enzymatic." [ISBN:0198506732] molecular_function
+ENSG00000100034 GO:0048471 perinuclear region of cytoplasm "Cytoplasm situated near, or occurring around, the nucleus." [GOC:jid] cellular_component
+ENSG00000100034 GO:0016576 histone dephosphorylation "The modification of histones by removal of phosphate groups." [GOC:ai] biological_process
+ENSG00000100034 GO:0006470 protein dephosphorylation "The process of removing one or more phosphoric residues from a protein." [GOC:hb] biological_process
+ENSG00000100034
+ENSG00000100344 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100344 GO:0016746 transferase activity, transferring acyl groups "Catalysis of the transfer of an acyl group from one compound (donor) to another (acceptor)." [GOC:jl, ISBN:0198506732] molecular_function
+ENSG00000100344 GO:0006629 lipid metabolic process "The chemical reactions and pathways involving lipids, compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent. Includes fatty acids; neutral fats, other fatty-acid esters, and soaps; long-chain (fatty) alcohols and waxes; sphingoids and other long-chain bases; glycolipids, phospholipids and sphingolipids; and carotenes, polyprenols, sterols, terpenes and other isoprenoids." [GOC:ma] biological_process
+ENSG00000100344 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100344 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000100344 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000100344 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000100344 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100344 GO:0004623 phospholipase A2 activity "Catalysis of the reaction: phosphatidylcholine + H2O = 1-acylglycerophosphocholine + a carboxylate." [EC:3.1.1.4] molecular_function
+ENSG00000100344 GO:0004806 triglyceride lipase activity "Catalysis of the reaction: triacylglycerol + H2O = diacylglycerol + a carboxylate." [EC:3.1.1.3] molecular_function
+ENSG00000100344 GO:0005789 endoplasmic reticulum membrane "The lipid bilayer surrounding the endoplasmic reticulum." [GOC:mah] cellular_component
+ENSG00000100344 GO:0006644 phospholipid metabolic process "The chemical reactions and pathways involving phospholipids, any lipid containing phosphoric acid as a mono- or diester." [ISBN:0198506732] biological_process
+ENSG00000100344 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000100344 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000100344 GO:0019432 triglyceride biosynthetic process "The chemical reactions and pathways resulting in the formation of a triglyceride, any triester of glycerol." [ISBN:0198506732] biological_process
+ENSG00000100344 GO:0019433 triglyceride catabolic process "The chemical reactions and pathways resulting in the breakdown of a triglyceride, any triester of glycerol." [ISBN:0198506732] biological_process
+ENSG00000100344 GO:0036155 acylglycerol acyl-chain remodeling "Remodeling the acyl chains of an acylglycerol, through sequential deacylation and re-acylation reactions, to generate an acylglycerol containing different types of fatty acid acyl chains." [CHEBI:47778, GOC:mw, PMID:15364929] biological_process
+ENSG00000100344 GO:0046474 glycerophospholipid biosynthetic process "The chemical reactions and pathways resulting in the formation of glycerophospholipids, any derivative of glycerophosphate that contains at least one O-acyl, O-alkyl, or O-alkenyl group attached to the glycerol residue." [ISBN:0198506732] biological_process
+ENSG00000100344 GO:0051264 mono-olein transacylation activity "Catalysis of the reaction: mono-olein + mono-olein = diolein + glycerol. Mono-olein, also known as mono-oleoylglycerol, is the monoglyceride formed from oleic acid, 9-octodecenoic acid; diolein is also known as dioleoylglycerol." [GOC:ai, PMID:15364929] molecular_function
+ENSG00000100344 GO:0051265 diolein transacylation activity "Catalysis of the reaction: diolein + mono-olein = triolein + glycerol. Mono-olein, also known as mono-oleoylglycerol, is the monoglyceride formed from oleic acid, 9-octodecenoic acid; diolein is also known as dioleoylglycerol, and triolein as trioleoylglycerol and olein." [GOC:ai, PMID:15364929] molecular_function
+ENSG00000100344 GO:0008152 metabolic process "The chemical reactions and pathways, including anabolism and catabolism, by which living organisms transform chemical substances. Metabolic processes typically transform small molecules, but also include macromolecular processes such as DNA repair and replication, and protein synthesis and degradation." [GOC:go_curators, ISBN:0198547684] biological_process
+ENSG00000183773 GO:0016491 oxidoreductase activity "Catalysis of an oxidation-reduction (redox) reaction, a reversible chemical reaction in which the oxidation state of an atom or atoms within a molecule is altered. One substrate acts as a hydrogen or electron donor and becomes oxidized, while the other acts as hydrogen or electron acceptor and becomes reduced." [GOC:go_curators] molecular_function
+ENSG00000183773 GO:0046872 metal ion binding "Interacting selectively and non-covalently with any metal ion." [GOC:ai] molecular_function
+ENSG00000183773 GO:0051537 2 iron, 2 sulfur cluster binding "Interacting selectively and non-covalently with a 2 iron, 2 sulfur (2Fe-2S) cluster; this cluster consists of two iron atoms, with two inorganic sulfur atoms found between the irons and acting as bridging ligands." [GOC:ai, PMID:15952888, Wikipedia:Iron-sulfur_cluster] molecular_function
+ENSG00000183773 GO:0055114 oxidation-reduction process "A metabolic process that results in the removal or addition of one or more electrons to or from a substance, with or without the concomitant removal or addition of a proton or protons." [GOC:dhl, GOC:ecd, GOC:jh2, GOC:jid, GOC:mlg, GOC:rph] biological_process
+ENSG00000183773 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000183773 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000183773 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000183773 GO:0042592 homeostatic process "Any biological process involved in the maintenance of an internal steady state." [GOC:jl, ISBN:0395825172] biological_process
+ENSG00000183773 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000183773 GO:0005783 endoplasmic reticulum "The irregular network of unit membranes, visible only by electron microscopy, that occurs in the cytoplasm of many eukaryotic cells. The membranes form a complex meshwork of tubular channels, which are often expanded into slitlike cavities called cisternae. The ER takes two forms, rough (or granular), with ribosomes adhering to the outer surface, and smooth (with no ribosomes attached)." [ISBN:0198506732] cellular_component
+ENSG00000183773 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000183773 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000183773 GO:0005743 mitochondrial inner membrane "The inner, i.e. lumen-facing, lipid bilayer of the mitochondrial envelope. It is highly folded to form cristae." [GOC:ai] cellular_component
+ENSG00000183773 GO:0045454 cell redox homeostasis "Any process that maintains the redox environment of a cell or compartment within a cell." [GOC:ai, GOC:dph, GOC:tb] biological_process
+ENSG00000183773 GO:0050660 flavin adenine dinucleotide binding "Interacting selectively and non-covalently with FAD, flavin-adenine dinucleotide, the coenzyme or the prosthetic group of various flavoprotein oxidoreductase enzymes, in either the oxidized form, FAD, or the reduced form, FADH2." [CHEBI:24040, GOC:ai, GOC:imk, ISBN:0198506732] molecular_function
+ENSG00000183773 GO:0097194 execution phase of apoptosis "A stage of the apoptotic process that starts with the controlled breakdown of the cell through the action of effector caspases or other effector molecules (e.g. cathepsins, calpains etc.). Key steps of the execution phase are rounding-up of the cell, retraction of pseudopodes, reduction of cellular volume (pyknosis), chromatin condensation, nuclear fragmentation (karyorrhexis), plasma membrane blebbing and fragmentation of the cell into apoptotic bodies. When the execution phase is completed, the cell has died." [GOC:mtg_apoptosis, PMID:21760595] biological_process
+ENSG00000183773
+ENSG00000248405 GO:0005622 intracellular "The living contents of a cell; the matter contained within (but not including) the plasma membrane, usually taken to exclude large vacuoles and masses of secretory or ingested material. In eukaryotes it includes the nucleus and cytoplasm." [ISBN:0198506732] cellular_component
+ENSG00000248405 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000248405 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000248405 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000248405 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000248405 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000248405 GO:0030234 enzyme regulator activity "Binds to and modulates the activity of an enzyme." [GOC:mah] molecular_function
+ENSG00000248405 GO:0005100 Rho GTPase activator activity "Increases the rate of GTP hydrolysis by a GTPase of the Rho family." [GOC:mah] molecular_function
+ENSG00000248405 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000248405 GO:0007264 small GTPase mediated signal transduction "Any series of molecular signals in which a small monomeric GTPase relays one or more of the signals." [GOC:mah] biological_process
+ENSG00000248405 GO:0032321 positive regulation of Rho GTPase activity "Any process that activates or increases the activity of a GTPase of the Rho family." [GOC:mah] biological_process
+ENSG00000248405 GO:0051056 regulation of small GTPase mediated signal transduction "Any process that modulates the frequency, rate or extent of small GTPase mediated signal transduction." [GOC:go_curators] biological_process
+ENSG00000248405 GO:0070374 positive regulation of ERK1 and ERK2 cascade "Any process that activates or increases the frequency, rate or extent of signal transduction mediated by the ERK1 and ERK2 cascade." [GOC:mah] biological_process
+ENSG00000100380 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100380 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100380 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100380 GO:0030674 protein binding, bridging "The binding activity of a molecule that brings together two or more protein molecules, or a protein and another macromolecule or complex, through a selective, non-covalent, often stoichiometric interaction, permitting those molecules to function in a coordinated way." [GOC:bf, GOC:mah, GOC:vw] molecular_function
+ENSG00000100380 GO:0006457 protein folding "The process of assisting in the covalent and noncovalent assembly of single chain polypeptides or multisubunit complexes into the correct tertiary structure." [GOC:go_curators, GOC:rb] biological_process
+ENSG00000100380 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100380 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100380 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000100380 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100380 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000100380 GO:0051260 protein homooligomerization "The process of creating protein oligomers, compounds composed of a small number, usually between three and ten, of identical component monomers. Oligomers may be formed by the polymerization of a number of monomers or the depolymerization of a large protein polymer." [GOC:ai] biological_process
+ENSG00000100380 GO:0051082 unfolded protein binding "Interacting selectively and non-covalently with an unfolded protein." [GOC:ai] molecular_function
+ENSG00000100380 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100380 GO:0032403 protein complex binding "Interacting selectively and non-covalently with any protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:mah] molecular_function
+ENSG00000100380 GO:0019904 protein domain specific binding "Interacting selectively and non-covalently with a specific domain of a protein." [GOC:go_curators] molecular_function
+ENSG00000100380 GO:0042802 identical protein binding "Interacting selectively and non-covalently with an identical protein or proteins." [GOC:jl] molecular_function
+ENSG00000100380 GO:0051087 chaperone binding "Interacting selectively and non-covalently with a chaperone protein, a class of proteins that bind to nascent or unfolded polypeptides and ensure correct folding or transport." [http://www.onelook.com] molecular_function
+ENSG00000100380 GO:0030544 Hsp70 protein binding "Interacting selectively and non-covalently with Hsp70 proteins, any of a group of heat shock proteins around 70kDa in size." [ISBN:0198506732] molecular_function
+ENSG00000100380 GO:0070389 chaperone cofactor-dependent protein refolding "The process of assisting in the restoration of the biological activity of an unfolded or misfolded protein, which is dependent on additional protein cofactors. This process occurs over one or several cycles of nucleotide hydrolysis-dependent binding and release." [GOC:mah, GOC:rb] biological_process
+ENSG00000100380 GO:0061084 negative regulation of protein refolding "Any process that decreases the rate, frequency, or extent of protein refolding. Protein refolding is the process carried out by a cell that restores the biological activity of an unfolded or misfolded protein, using helper proteins such as chaperones." [GOC:BHF, GOC:dph, GOC:tb] biological_process
+ENSG00000100380 GO:0032564 dATP binding "Interacting selectively and non-covalently with dATP, deoxyadenosine triphosphate." [GOC:mah] molecular_function
+ENSG00000100380
+ENSG00000100038 GO:0003677 DNA binding "Any molecular function by which a gene product interacts selectively and non-covalently with DNA (deoxyribonucleic acid)." [GOC:dph, GOC:jl, GOC:tb, GOC:vw] molecular_function
+ENSG00000100038 GO:0003917 DNA topoisomerase type I activity "Catalysis of a DNA topological transformation by transiently cleaving one DNA strand at a time to allow passage of another strand; changes the linking number by +1 per catalytic cycle." [PMID:8811192] molecular_function
+ENSG00000100038 GO:0006265 DNA topological change "The process in which a transformation is induced in the topological structure of a double-stranded DNA helix, resulting in a change in linking number." [ISBN:071673706X, ISBN:0935702490] biological_process
+ENSG00000100038 GO:0006259 DNA metabolic process "Any cellular metabolic process involving deoxyribonucleic acid. This is one of the two main types of nucleic acid, consisting of a long, unbranched macromolecule formed from one, or more commonly, two, strands of linked deoxyribonucleotides." [ISBN:0198506732] biological_process
+ENSG00000100038 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100038 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100038 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100038 GO:0016853 isomerase activity "Catalysis of the geometric or structural changes within one molecule. Isomerase is the systematic name for any enzyme of EC class 5." [ISBN:0198506732] molecular_function
+ENSG00000100038 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100038 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100038 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100038 GO:0003916 DNA topoisomerase activity "Catalysis of the transient cleavage and passage of individual DNA strands or double helices through one another, resulting a topological transformation in double-stranded DNA." [GOC:mah, PMID:8811192] molecular_function
+ENSG00000100038 GO:0044822 poly(A) RNA binding "Interacting non-covalently with a poly(A) RNA, a RNA molecule which has a tail of adenine bases." [GOC:jl] molecular_function
+ENSG00000100038 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000100038 GO:0000793 condensed chromosome "A highly compacted molecule of DNA and associated proteins resulting in a cytologically distinct structure." [GOC:elh] cellular_component
+ENSG00000100038 GO:0007059 chromosome segregation "The process in which genetic material, in the form of chromosomes, is organized into specific structures and then physically separated and apportioned to two or more sets. In eukaryotes, chromosome segregation begins with the condensation of chromosomes, includes chromosome separation, and ends when chromosomes have completed movement to the spindle poles." [GOC:jl, GOC:mah, GOC:mtg_cell_cycle, GOC:vw] biological_process
+ENSG00000100038
+ENSG00000100298 GO:0000932 cytoplasmic mRNA processing body "A focus in the cytoplasm where mRNAs may become inactivated by decapping or some other mechanism. mRNA processing and binding proteins are localized to these foci." [GOC:clt, PMID:12730603] cellular_component
+ENSG00000100298 GO:0004126 cytidine deaminase activity "Catalysis of the reaction: cytidine + H2O = uridine + NH3." [EC:3.5.4.5] molecular_function
+ENSG00000100298 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100298 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100298 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100298 GO:0008270 zinc ion binding "Interacting selectively and non-covalently with zinc (Zn) ions." [GOC:ai] molecular_function
+ENSG00000100298 GO:0009972 cytidine deamination "The removal of amino group in the presence of water." [GOC:sm] biological_process
+ENSG00000100298 GO:0010529 negative regulation of transposition "Any process that decreases the frequency, rate or extent of transposition. Transposition results in the movement of discrete segments of DNA between nonhomologous sites." [GOC:dph, GOC:tb] biological_process
+ENSG00000100298 GO:0045087 innate immune response "Innate immune responses are defense responses mediated by germline encoded components that directly recognize components of potential pathogens." [GO_REF:0000022, GOC:add, GOC:ebc, GOC:mtg_15nov05, GOC:mtg_sensu] biological_process
+ENSG00000100298 GO:0045869 negative regulation of single stranded viral RNA replication via double stranded DNA intermediate "Any process that stops, prevents, or reduces the frequency, rate or extent of single stranded viral RNA replication via double stranded DNA intermediate." [GOC:go_curators] biological_process
+ENSG00000100298 GO:0048525 negative regulation of viral process "Any process that stops, prevents, or reduces the frequency, rate or extent of a multi-organism process in which a virus is a participant." [GOC:bf, GOC:jl] biological_process
+ENSG00000100298 GO:0051607 defense response to virus "Reactions triggered in response to the presence of a virus that act to protect the cell or organism." [GOC:ai] biological_process
+ENSG00000100298 GO:0070383 DNA cytosine deamination "The removal of an amino group from a cytosine residue in DNA, forming a uracil residue." [GOC:mah] biological_process
+ENSG00000100298 GO:0006259 DNA metabolic process "Any cellular metabolic process involving deoxyribonucleic acid. This is one of the two main types of nucleic acid, consisting of a long, unbranched macromolecule formed from one, or more commonly, two, strands of linked deoxyribonucleotides." [ISBN:0198506732] biological_process
+ENSG00000100298 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100298 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100298 GO:0002376 immune system process "Any process involved in the development or functioning of the immune system, an organismal system for calibrated responses to potential internal or invasive threats." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05] biological_process
+ENSG00000100298 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000100298 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000100298 GO:0034655 nucleobase-containing compound catabolic process "The chemical reactions and pathways resulting in the breakdown of nucleobases, nucleosides, nucleotides and nucleic acids." [GOC:mah] biological_process
+ENSG00000100298 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000100298 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100298 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100298 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100298 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100298 GO:0016810 hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds "Catalysis of the hydrolysis of any carbon-nitrogen bond, C-N, with the exception of peptide bonds." [GOC:jl] molecular_function
+ENSG00000100298 GO:0016787 hydrolase activity "Catalysis of the hydrolysis of various bonds, e.g. C-O, C-N, C-C, phosphoric anhydride bonds, etc. Hydrolase is the systematic name for any enzyme of EC class 3." [ISBN:0198506732] molecular_function
+ENSG00000100298 GO:0016814 hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds, in cyclic amidines "Catalysis of the hydrolysis of any non-peptide carbon-nitrogen bond in a cyclic amidine, a compound of the form R-C(=NH)-NH2." [ISBN:0198506732] molecular_function
+ENSG00000100298 GO:0003824 catalytic activity "Catalysis of a biochemical reaction at physiological temperatures. In biologically catalyzed reactions, the reactants are known as substrates, and the catalysts are naturally occurring macromolecular substances known as enzymes. Enzymes possess specific binding sites for substrates, and are usually composed wholly or largely of protein, but RNA that has catalytic activity (ribozyme) is often also regarded as enzymatic." [ISBN:0198506732] molecular_function
+ENSG00000100376 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000100376 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100376 GO:0003676 nucleic acid binding "Interacting selectively and non-covalently with any nucleic acid." [GOC:jl] molecular_function
+ENSG00000100376 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100376
+ENSG00000184792
+ENSG00000184792 GO:0006869 lipid transport "The directed movement of lipids into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore. Lipids are compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent." [ISBN:0198506732] biological_process
+ENSG00000184792 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000184792 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000184792 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000184792 GO:0008289 lipid binding "Interacting selectively and non-covalently with a lipid." [GOC:ai] molecular_function
+ENSG00000184792 GO:0015485 cholesterol binding "Interacting selectively and non-covalently with cholesterol (cholest-5-en-3-beta-ol); the principal sterol of vertebrates and the precursor of many steroids, including bile acids and steroid hormones." [GOC:jl, ISBN:0198506732] molecular_function
+ENSG00000184792 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000184792 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100154 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100154 GO:0007067 mitotic nuclear division "A cell cycle process comprising the steps by which the nucleus of a eukaryotic cell divides; the process involves condensation of chromosomal DNA into a highly compacted form. Canonically, mitosis produces two daughter nuclei whose chromosome complement is identical to that of the mother cell." [GOC:dph, GOC:ma, GOC:mah, ISBN:0198547684] biological_process
+ENSG00000100154 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100154 GO:0005815 microtubule organizing center "An intracellular structure that can catalyze gamma-tubulin-dependent microtubule nucleation and that can anchor microtubules by interacting with their minus ends, plus ends or sides." [GOC:vw, http://en.wikipedia.org/wiki/Microtubule_organizing_center, ISBN:0815316194, PMID:17072892, PMID:17245416] cellular_component
+ENSG00000100154 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100154 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100154 GO:0005813 centrosome "A structure comprised of a core structure (in most organisms, a pair of centrioles) and peripheral material from which a microtubule-based structure, such as a spindle apparatus, is organized. Centrosomes occur close to the nucleus during interphase in many eukaryotic cells, though in animal cells it changes continually during the cell-division cycle." [GOC:mah, ISBN:0198547684] cellular_component
+ENSG00000100154 GO:0007346 regulation of mitotic cell cycle "Any process that modulates the rate or extent of progress through the mitotic cell cycle." [GOC:dph, GOC:go_curators, GOC:tb] biological_process
+ENSG00000100154 GO:0030496 midbody "A thin cytoplasmic bridge formed between daughter cells at the end of cytokinesis. The midbody forms where the contractile ring constricts, and may persist for some time before finally breaking to complete cytokinesis." [ISBN:0815316194] cellular_component
+ENSG00000100154 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100154 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100154
+ENSG00000093009 GO:0006270 DNA replication initiation "The process in which DNA-dependent DNA replication is started; this involves the separation of a stretch of the DNA double helix, the recruitment of DNA polymerases and the initiation of polymerase action." [ISBN:071673706X, ISBN:0815316194] biological_process
+ENSG00000093009 GO:0006259 DNA metabolic process "Any cellular metabolic process involving deoxyribonucleic acid. This is one of the two main types of nucleic acid, consisting of a long, unbranched macromolecule formed from one, or more commonly, two, strands of linked deoxyribonucleotides." [ISBN:0198506732] biological_process
+ENSG00000093009 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000093009 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000093009 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000093009 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000093009 GO:0005815 microtubule organizing center "An intracellular structure that can catalyze gamma-tubulin-dependent microtubule nucleation and that can anchor microtubules by interacting with their minus ends, plus ends or sides." [GOC:vw, http://en.wikipedia.org/wiki/Microtubule_organizing_center, ISBN:0815316194, PMID:17072892, PMID:17245416] cellular_component
+ENSG00000093009 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000093009 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000093009 GO:0005654 nucleoplasm "That part of the nuclear content other than the chromosomes or the nucleolus." [GOC:ma, ISBN:0124325653] cellular_component
+ENSG00000093009 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000093009 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000093009 GO:0007049 cell cycle "The progression of biochemical and morphological phases and events that occur in a cell during successive cell replication or nuclear replication events. Canonically, the cell cycle comprises the replication and segregation of genetic material followed by the division of the cell, but in endocycles or syncytial cells nuclear replication or nuclear division may not be followed by cell division." [GOC:go_curators, GOC:mtg_cell_cycle] biological_process
+ENSG00000093009 GO:0000076 DNA replication checkpoint "A cell cycle checkpoint that prevents the initiation of nuclear division until DNA replication is complete, thereby ensuring that progeny inherit a full complement of the genome." [GOC:curators, GOC:rn, PMID:11728327, PMID:12537518] biological_process
+ENSG00000093009 GO:0000082 G1/S transition of mitotic cell cycle "The mitotic cell cycle transition by which a cell in G1 commits to S phase. The process begins with the build up of G1 cyclin-dependent kinase (G1 CDK), resulting in the activation of transcription of G1 cyclins. The process ends with the positive feedback of the G1 cyclins on the G1 CDK which commits the cell to S phase, in which DNA replication is initiated." [GOC:mtg_cell_cycle] biological_process
+ENSG00000093009 GO:0000083 regulation of transcription involved in G1/S transition of mitotic cell cycle "Any process that regulates transcription such that the target genes are involved in the transition between G1 and S phase of the mitotic cell cycle." [GOC:mtg_cell_cycle] biological_process
+ENSG00000093009 GO:0000278 mitotic cell cycle "Progression through the phases of the mitotic cell cycle, the most common eukaryotic cell cycle, which canonically comprises four successive phases called G1, S, G2, and M and includes replication of the genome and the subsequent segregation of chromosomes into daughter cells. In some variant cell cycles nuclear replication or nuclear division may not be followed by cell division, or G1 and G2 phases may be absent." [GOC:mah, ISBN:0815316194, Reactome:69278] biological_process
+ENSG00000093009 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000093009 GO:0005813 centrosome "A structure comprised of a core structure (in most organisms, a pair of centrioles) and peripheral material from which a microtubule-based structure, such as a spindle apparatus, is organized. Centrosomes occur close to the nucleus during interphase in many eukaryotic cells, though in animal cells it changes continually during the cell-division cycle." [GOC:mah, ISBN:0198547684] cellular_component
+ENSG00000093009 GO:0006260 DNA replication "The cellular metabolic process in which a cell duplicates one or more molecules of DNA. DNA replication begins when specific sequences, known as origins of replication, are recognized and bound by initiation proteins, and ends when the original DNA molecule has been completely duplicated and the copies topologically separated. The unit of replication usually corresponds to the genome of the cell, an organelle, or a virus. The template for replication can either be an existing DNA molecule or RNA." [GOC:mah] biological_process
+ENSG00000093009 GO:0006271 DNA strand elongation involved in DNA replication "The process in which a DNA strand is synthesized from template DNA during replication by the action of polymerases, which add nucleotides to the 3' end of the nascent DNA strand." [ISBN:071673706X, ISBN:0815316194] biological_process
+ENSG00000100225 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100225 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000100225 GO:0008219 cell death "Any biological process that results in permanent cessation of all vital functions of a cell. A cell should be considered dead when any one of the following molecular or morphological criteria is met: (1) the cell has lost the integrity of its plasma membrane; (2) the cell, including its nucleus, has undergone complete fragmentation into discrete bodies (frequently referred to as \"apoptotic bodies\"); and/or (3) its corpse (or its fragments) have been engulfed by an adjacent cell in vivo." [GOC:mah, GOC:mtg_apoptosis] biological_process
+ENSG00000100225 GO:0006605 protein targeting "The process of targeting specific proteins to particular membrane-bounded subcellular organelles. Usually requires an organelle specific protein sequence motif." [GOC:ma] biological_process
+ENSG00000100225 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100225 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000100225 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100225 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000100225 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000100225 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100225 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100225 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100225 GO:0006914 autophagy "The process in which cells digest parts of their own cytoplasm; allows for both recycling of macromolecular constituents under conditions of cellular stress and remodeling the intracellular structure for cell differentiation." [ISBN:0198547684, PMID:11099404, PMID:9412464] biological_process
+ENSG00000100225 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100225 GO:0000151 ubiquitin ligase complex "A protein complex that includes a ubiquitin-protein ligase and other proteins that may confer substrate specificity on the complex." [GOC:jh2, PMID:9529603] cellular_component
+ENSG00000100225 GO:0000422 mitochondrion degradation "The autophagic process in which mitochondria are delivered to the vacuole and degraded in response to changing cellular conditions." [PMID:15798367] biological_process
+ENSG00000100225 GO:0004842 ubiquitin-protein transferase activity "Catalysis of the transfer of ubiquitin from one protein to another via the reaction X-Ub + Y --> Y-Ub + X, where both X-Ub and Y-Ub are covalent linkages." [GOC:BioGRID, GOC:jh2, PMID:9635407] molecular_function
+ENSG00000100225 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100225 GO:0006511 ubiquitin-dependent protein catabolic process "The chemical reactions and pathways resulting in the breakdown of a protein or peptide by hydrolysis of its peptide bonds, initiated by the covalent attachment of a ubiquitin group, or multiple ubiquitin groups, to the protein." [GOC:go_curators] biological_process
+ENSG00000100225 GO:0006626 protein targeting to mitochondrion "The process of directing proteins towards and into the mitochondrion, usually mediated by mitochondrial proteins that recognize signals contained within the imported protein." [GOC:mcc, ISBN:0716731363] biological_process
+ENSG00000100225 GO:0016567 protein ubiquitination "The process in which one or more ubiquitin groups are added to a protein." [GOC:ai] biological_process
+ENSG00000100225 GO:0031647 regulation of protein stability "Any process that affects the structure and integrity of a protein by altering the likelihood of its degradation or aggregation." [GOC:dph, GOC:mah, GOC:tb] biological_process
+ENSG00000100225 GO:0019901 protein kinase binding "Interacting selectively and non-covalently with a protein kinase, any enzyme that catalyzes the transfer of a phosphate group, usually from ATP, to a protein substrate." [GOC:jl] molecular_function
+ENSG00000100225 GO:2000134 negative regulation of G1/S transition of mitotic cell cycle "Any cell cycle regulatory process that prevents the commitment of a cell from G1 to S phase of the mitotic cell cycle." [GOC:mtg_cell_cycle] biological_process
+ENSG00000100225 GO:0045620 negative regulation of lymphocyte differentiation "Any process that stops, prevents, or reduces the frequency, rate or extent of lymphocyte differentiation." [GOC:go_curators] biological_process
+ENSG00000100225 GO:0045736 negative regulation of cyclin-dependent protein serine/threonine kinase activity "Any process that stops, prevents, or reduces the frequency, rate or extent of cyclin-dependent protein serine/threonine kinase activity." [GOC:go_curators, GOC:pr] biological_process
+ENSG00000100225
+ENSG00000100146
+ENSG00000100146 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100146 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100146 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100146 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100146 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100146 GO:0000988 protein binding transcription factor activity "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules), in order to modulate transcription. A protein binding transcription factor may or may not also interact with the template nucleic acid (either DNA or RNA) as well." [GOC:txnOH] molecular_function
+ENSG00000100146 GO:0003713 transcription coactivator activity "Interacting selectively and non-covalently with a activating transcription factor and also with the basal transcription machinery in order to increase the frequency, rate or extent of transcription. Cofactors generally do not bind the template nucleic acid, but rather mediate protein-protein interactions between activating transcription factors and the basal transcription machinery." [GOC:txnOH, PMID:10213677, PMID:16858867] molecular_function
+ENSG00000100146 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100146 GO:0006357 regulation of transcription from RNA polymerase II promoter "Any process that modulates the frequency, rate or extent of transcription from an RNA polymerase II promoter." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000100146 GO:0009653 anatomical structure morphogenesis "The process in which anatomical structures are generated and organized. Morphogenesis pertains to the creation of form." [GOC:go_curators, ISBN:0521436125] biological_process
+ENSG00000100146 GO:0042802 identical protein binding "Interacting selectively and non-covalently with an identical protein or proteins." [GOC:jl] molecular_function
+ENSG00000100146 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100146 GO:0003677 DNA binding "Any molecular function by which a gene product interacts selectively and non-covalently with DNA (deoxyribonucleic acid)." [GOC:dph, GOC:jl, GOC:tb, GOC:vw] molecular_function
+ENSG00000100146 GO:0003682 chromatin binding "Interacting selectively and non-covalently with chromatin, the network of fibers of DNA, protein, and sometimes RNA, that make up the chromosomes of the eukaryotic nucleus during interphase." [GOC:jl, ISBN:0198506732, PMID:20404130] molecular_function
+ENSG00000100146 GO:0003700 sequence-specific DNA binding transcription factor activity "Interacting selectively and non-covalently with a specific DNA sequence in order to modulate transcription. The transcription factor may or may not also interact selectively with a protein or macromolecular complex." [GOC:curators, GOC:txnOH] molecular_function
+ENSG00000100146 GO:0044212 transcription regulatory region DNA binding "Interacting selectively and non-covalently with a DNA region that regulates the transcription of a region of DNA, which may be a gene, cistron, or operon. Binding may occur as a sequence specific interaction or as an interaction observed only once a factor has been recruited to the DNA by other factors." [GOC:jl, GOC:txnOH, SO:0005836] molecular_function
+ENSG00000100146 GO:0045944 positive regulation of transcription from RNA polymerase II promoter "Any process that activates or increases the frequency, rate or extent of transcription from an RNA polymerase II promoter." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000100146 GO:0002052 positive regulation of neuroblast proliferation "Any process that activates or increases the rate of neuroblast proliferation." [GOC:dph] biological_process
+ENSG00000100146 GO:0030154 cell differentiation "The process in which relatively unspecialized cells, e.g. embryonic or regenerative cells, acquire specialized structural and/or functional features that characterize the cells, tissues, or organs of the mature organism or some other relatively stable phase of the organism's life history. Differentiation includes the processes involved in commitment of a cell to a specific fate and its subsequent development to the mature state." [ISBN:0198506732] biological_process
+ENSG00000100146 GO:0048589 developmental growth "The increase in size or mass of an entire organism, a part of an organism or a cell, where the increase in size or mass has the specific outcome of the progression of the organism over time from one condition to another." [GOC:go_curators] biological_process
+ENSG00000100146 GO:0001701 in utero embryonic development "The process whose specific outcome is the progression of the embryo in the uterus over time, from formation of the zygote in the oviduct, to birth. An example of this process is found in Mus musculus." [GOC:go_curators, GOC:mtg_sensu] biological_process
+ENSG00000100146 GO:0000981 sequence-specific DNA binding RNA polymerase II transcription factor activity "Interacting selectively and non-covalently with a specific DNA sequence in order to modulate transcription by RNA polymerase II. The transcription factor may or may not also interact selectively with a protein or macromolecular complex." [GOC:txnOH] molecular_function
+ENSG00000100146 GO:0006366 transcription from RNA polymerase II promoter "The synthesis of RNA from a DNA template by RNA polymerase II, originating at an RNA polymerase II promoter. Includes transcription of messenger RNA (mRNA) and certain small nuclear RNAs (snRNAs)." [GOC:jl, GOC:txnOH, ISBN:0321000382] biological_process
+ENSG00000100146 GO:0045893 positive regulation of transcription, DNA-templated "Any process that activates or increases the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000100146 GO:0043066 negative regulation of apoptotic process "Any process that stops, prevents, or reduces the frequency, rate or extent of cell death by apoptotic process." [GOC:jl, GOC:mtg_apoptosis] biological_process
+ENSG00000100146 GO:0003705 RNA polymerase II distal enhancer sequence-specific DNA binding transcription factor activity "Interacting selectively and non-covalently with a sequence of DNA that is in a distal enhancer region for RNA polymerase II (RNAP II) in order to modulate transcription by RNAP II." [GOC:jl, GOC:txnOH] molecular_function
+ENSG00000100146 GO:0090090 negative regulation of canonical Wnt signaling pathway "Any process that decreases the rate, frequency, or extent of the Wnt signaling pathway through beta-catenin, the series of molecular signals initiated by binding of a Wnt protein to a frizzled family receptor on the surface of the target cell, followed by propagation of the signal via beta-catenin, and ending with a change in transcription of target genes." [GOC:dph, GOC:tb] biological_process
+ENSG00000100146 GO:0001190 RNA polymerase II transcription factor binding transcription factor activity involved in positive regulation of transcription "Interacting selectively and non-covalently with an RNA polymerase II transcription factor, which may be a single protein or a complex, in order to increase the frequency, rate or extent of transcription from an RNA polymerase II promoter. A protein binding transcription factor may or may not also interact with the template nucleic acid (either DNA or RNA) as well." [GOC:txnOH] molecular_function
+ENSG00000100146 GO:0000980 RNA polymerase II distal enhancer sequence-specific DNA binding "Interacting selectively and non-covalently with a RNA polymerase II (Pol II) distal enhancer. In mammalian cells, enhancers are distal sequences that increase the utilization of some promoters, and can function in either orientation and in any location (upstream or downstream) relative to the core promoter." [GOC:txnOH] molecular_function
+ENSG00000100146 GO:0030318 melanocyte differentiation "The process in which a relatively unspecialized cell acquires specialized features of a melanocyte." [GOC:mah] biological_process
+ENSG00000100146 GO:0048709 oligodendrocyte differentiation "The process in which a relatively unspecialized cell acquires the specialized features of an oligodendrocyte. An oligodendrocyte is a type of glial cell involved in myelinating the axons of neurons in the central nervous system." [GOC:vp, PMID:15139015] biological_process
+ENSG00000100146 GO:0048469 cell maturation "A developmental process, independent of morphogenetic (shape) change, that is required for a cell to attain its fully functional state." [GOC:go_curators] biological_process
+ENSG00000100146 GO:0000978 RNA polymerase II core promoter proximal region sequence-specific DNA binding "Interacting selectively and non-covalently with a sequence of DNA that is in cis with and relatively close to a core promoter for RNA polymerase II." [GOC:txnOH] molecular_function
+ENSG00000100146 GO:0007422 peripheral nervous system development "The process whose specific outcome is the progression of the peripheral nervous system over time, from its formation to the mature structure. The peripheral nervous system is one of the two major divisions of the nervous system. Nerves in the PNS connect the central nervous system (CNS) with sensory organs, other organs, muscles, blood vessels and glands." [GOC:go_curators, UBERON:0000010] biological_process
+ENSG00000100146 GO:0001755 neural crest cell migration "The characteristic movement of cells from the dorsal ridge of the neural tube to a variety of locations in a vertebrate embryo." [GOC:ascb_2009, GOC:dph, GOC:tb, ISBN:0878932437] biological_process
+ENSG00000100146 GO:0048546 digestive tract morphogenesis "The process in which the anatomical structures of the digestive tract are generated and organized. The digestive tract is the anatomical structure through which food passes and is processed." [GOC:dph, GOC:go_curators, PMID:12618131] biological_process
+ENSG00000100146 GO:0048484 enteric nervous system development "The process whose specific outcome is the progression of the enteric nervous system over time, from its formation to the mature structure. The enteric nervous system is composed of two ganglionated neural plexuses in the gut wall which form one of the three major divisions of the autonomic nervous system. The enteric nervous system innervates the gastrointestinal tract, the pancreas, and the gall bladder. It contains sensory neurons, interneurons, and motor neurons. Thus the circuitry can autonomously sense the tension and the chemical environment in the gut and regulate blood vessel tone, motility, secretions, and fluid transport. The system is itself governed by the central nervous system and receives both parasympathetic and sympathetic innervation." [FMA:66070, GOC:jid, GOC:sr] biological_process
+ENSG00000100146 GO:0014015 positive regulation of gliogenesis "Any process that activates or increases the frequency, rate or extent of gliogenesis, the formation of mature glia." [GOC:ef] biological_process
+ENSG00000100146 GO:0031315 extrinsic component of mitochondrial outer membrane "The component of a mitochondrial outer membrane consisting of gene products and protein complexes that are loosely bound to one of its surfaces, but not integrated into the hydrophobic region." [GOC:dos, GOC:mah] cellular_component
+ENSG00000100146 GO:0008134 transcription factor binding "Interacting selectively and non-covalently with a transcription factor, any protein required to initiate or regulate transcription." [ISBN:0198506732] molecular_function
+ENSG00000100146 GO:0045892 negative regulation of transcription, DNA-templated "Any process that stops, prevents, or reduces the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000100146 GO:0048863 stem cell differentiation "The process in which a relatively unspecialized cell acquires specialized features of a stem cell. A stem cell is a cell that retains the ability to divide and proliferate throughout life to provide progenitor cells that can differentiate into specialized cells." [CL:0000034, GOC:isa_complete] biological_process
+ENSG00000100146 GO:0006355 regulation of transcription, DNA-templated "Any process that modulates the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000100151
+ENSG00000100151 GO:0019904 protein domain specific binding "Interacting selectively and non-covalently with a specific domain of a protein." [GOC:go_curators] molecular_function
+ENSG00000100151 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100151 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100151 GO:0008092 cytoskeletal protein binding "Interacting selectively and non-covalently with any protein component of any cytoskeleton (actin, microtubule, or intermediate filament cytoskeleton)." [GOC:mah] molecular_function
+ENSG00000100151 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100151 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100151 GO:0061024 membrane organization "A process which results in the assembly, arrangement of constituent parts, or disassembly of a membrane. A membrane is a double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:dph, GOC:tb] biological_process
+ENSG00000100151 GO:0006259 DNA metabolic process "Any cellular metabolic process involving deoxyribonucleic acid. This is one of the two main types of nucleic acid, consisting of a long, unbranched macromolecule formed from one, or more commonly, two, strands of linked deoxyribonucleotides." [ISBN:0198506732] biological_process
+ENSG00000100151 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100151 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000100151 GO:0048856 anatomical structure development "The biological process whose specific outcome is the progression of an anatomical structure from an initial condition to its mature state. This process begins with the formation of the structure and ends with the mature structure, whatever form that may be including its natural destruction. An anatomical structure is any biological entity that occupies space and is distinguished from its surroundings. Anatomical structures can be macroscopic such as a carpel, or microscopic such as an acrosome." [GO_REF:0000021, GOC:mtg_15jun06] biological_process
+ENSG00000100151 GO:0019899 enzyme binding "Interacting selectively and non-covalently with any enzyme." [GOC:jl] molecular_function
+ENSG00000100151 GO:0016887 ATPase activity "Catalysis of the reaction: ATP + H2O = ADP + phosphate + 2 H+. May or may not be coupled to another reaction." [EC:3.6.1.3, GOC:jl] molecular_function
+ENSG00000100151 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100151 GO:0007267 cell-cell signaling "Any process that mediates the transfer of information from one cell to another." [GOC:mah] biological_process
+ENSG00000100151 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000100151 GO:0016192 vesicle-mediated transport "A cellular transport process in which transported substances are moved in membrane-bounded vesicles; transported substances are enclosed in the vesicle lumen or located in the vesicle membrane. The process begins with a step that directs a substance to the forming vesicle, and includes vesicle budding and coating. Vesicles are then targeted to, and fuse with, an acceptor membrane." [GOC:ai, GOC:mah, ISBN:08789310662000] biological_process
+ENSG00000100151 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000100151 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000100151 GO:0034655 nucleobase-containing compound catabolic process "The chemical reactions and pathways resulting in the breakdown of nucleobases, nucleosides, nucleotides and nucleic acids." [GOC:mah] biological_process
+ENSG00000100151 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000100151 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000100151 GO:0005794 Golgi apparatus "A compound membranous cytoplasmic organelle of eukaryotic cells, consisting of flattened, ribosome-free vesicles arranged in a more or less regular stack. The Golgi apparatus differs from the endoplasmic reticulum in often having slightly thicker membranes, appearing in sections as a characteristic shallow semicircle so that the convex side (cis or entry face) abuts the endoplasmic reticulum, secretory vesicles emerging from the concave side (trans or exit face). In vertebrate cells there is usually one such organelle, while in invertebrates and plants, where they are known usually as dictyosomes, there may be several scattered in the cytoplasm. The Golgi apparatus processes proteins produced on the ribosomes of the rough endoplasmic reticulum; such processing includes modification of the core oligosaccharides of glycoproteins, and the sorting and packaging of proteins for transport to a variety of cellular locations. Three different regions of the Golgi are now recognized both in terms of structure and function: cis, in the vicinity of the cis face, trans, in the vicinity of the trans face, and medial, lying between the cis and trans regions." [ISBN:0198506732] cellular_component
+ENSG00000100151 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100151 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100151 GO:0001664 G-protein coupled receptor binding "Interacting selectively and non-covalently with a G-protein coupled receptor." [GOC:ceb, GOC:dph] molecular_function
+ENSG00000100151 GO:0002092 positive regulation of receptor internalization "Any process that activates or increases the frequency, rate or extent of receptor internalization." [GOC:hjd] biological_process
+ENSG00000100151 GO:0005080 protein kinase C binding "Interacting selectively and non-covalently with protein kinase C." [GOC:jl] molecular_function
+ENSG00000100151 GO:0005102 receptor binding "Interacting selectively and non-covalently with one or more specific sites on a receptor molecule, a macromolecule that undergoes combination with a hormone, neurotransmitter, drug or intracellular messenger to initiate a change in cell function." [GOC:bf, GOC:ceb, ISBN:0198506732] molecular_function
+ENSG00000100151 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100151 GO:0006200 ATP catabolic process "The chemical reactions and pathways resulting in the breakdown of ATP, adenosine 5'-triphosphate, a universally important coenzyme and enzyme regulator." [GOC:ai] biological_process
+ENSG00000100151 GO:0006468 protein phosphorylation "The process of introducing a phosphate group on to a protein." [GOC:hb] biological_process
+ENSG00000100151 GO:0006890 retrograde vesicle-mediated transport, Golgi to ER "The directed movement of substances from the Golgi back to the endoplasmic reticulum, mediated by vesicles bearing specific protein coats such as COPI or COG." [ISBN:0716731363, PMID:16510524] biological_process
+ENSG00000100151 GO:0007205 protein kinase C-activating G-protein coupled receptor signaling pathway "The series of molecular signals generated as a consequence of a G-protein coupled receptor binding to its physiological ligand, where the pathway proceeds with activation of protein kinase C (PKC). PKC is activated by second messengers including diacylglycerol (DAG)." [GOC:mah, GOC:signaling] biological_process
+ENSG00000100151 GO:0007268 synaptic transmission "The process of communication from a neuron to a target (neuron, muscle, or secretory cell) across a synapse." [GOC:jl, MeSH:D009435] biological_process
+ENSG00000100151 GO:0008022 protein C-terminus binding "Interacting selectively and non-covalently with a protein C-terminus, the end of any peptide chain at which the 1-carboxy function of a constituent amino acid is not attached in peptide linkage to another amino-acid residue." [ISBN:0198506732] molecular_function
+ENSG00000100151 GO:0015844 monoamine transport "The directed movement of monoamines, organic compounds that contain one amino group that is connected to an aromatic ring by an ethylene group (-CH2-CH2-), into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [CHEBI:25375, GOC:mah] biological_process
+ENSG00000100151 GO:0021782 glial cell development "The process aimed at the progression of a glial cell over time, from initial commitment of the cell to a specific fate, to the fully functional differentiated cell." [GO_REF:0000021, GOC:cls, GOC:dgh, GOC:dph, GOC:jid, GOC:mtg_15jun06] biological_process
+ENSG00000100151 GO:0030054 cell junction "A cellular component that forms a specialized region of connection between two cells or between a cell and the extracellular matrix. At a cell junction, anchoring proteins extend through the plasma membrane to link cytoskeletal proteins in one cell to cytoskeletal proteins in neighboring cells or to proteins in the extracellular matrix." [GOC:mah, http://www.vivo.colostate.edu/hbooks/cmb/cells/pmemb/junctions_a.html, ISBN:0198506732] cellular_component
+ENSG00000100151 GO:0030666 endocytic vesicle membrane "The lipid bilayer surrounding an endocytic vesicle." [GOC:mah] cellular_component
+ENSG00000100151 GO:0034316 negative regulation of Arp2/3 complex-mediated actin nucleation "Any process that stops, prevents, or reduces the frequency, rate or extent of actin nucleation mediated by the Arp2/3 complex and interacting proteins." [GOC:mah, PMID:16959963] biological_process
+ENSG00000100151 GO:0036294 cellular response to decreased oxygen levels "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a stimulus reflecting a decline in the level of oxygen." [GOC:al] biological_process
+ENSG00000100151 GO:0042149 cellular response to glucose starvation "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of deprivation of glucose." [GOC:jl] biological_process
+ENSG00000100151 GO:0042734 presynaptic membrane "A specialized area of membrane of the axon terminal that faces the plasma membrane of the neuron or muscle fiber with which the axon terminal establishes a synaptic junction; many synaptic junctions exhibit structural presynaptic characteristics, such as conical, electron-dense internal protrusions, that distinguish it from the remainder of the axon plasma membrane." [GOC:jl, ISBN:0815316194] cellular_component
+ENSG00000100151 GO:0042802 identical protein binding "Interacting selectively and non-covalently with an identical protein or proteins." [GOC:jl] molecular_function
+ENSG00000100151 GO:0043005 neuron projection "A prolongation or process extending from a nerve cell, e.g. an axon or dendrite." [GOC:jl, http://www.cogsci.princeton.edu/~wn/] cellular_component
+ENSG00000100151 GO:0043045 DNA methylation involved in embryo development "The covalent transfer of a methyl group to C-5 of cytosine that contributes to the epigenetic regulation of embryonic gene expression." [GOC:go_curators, PMID:12138111] biological_process
+ENSG00000100151 GO:0043046 DNA methylation involved in gamete generation "The covalent transfer of a methyl group to C-5 of cytosine that contributes to the establishment of DNA methylation patterns in the gamete." [GOC:go_curators, PMID:12138111] biological_process
+ENSG00000100151 GO:0043113 receptor clustering "The receptor metabolic process that results in grouping of a set of receptors at a cellular location, often to amplify the sensitivity of a signaling response." [GOC:bf, GOC:jl, GOC:pr, PMID:19747931, PMID:21453460] biological_process
+ENSG00000100151 GO:0045161 neuronal ion channel clustering "The process in which voltage-gated ion channels become localized to distinct subcellular domains in the neuron. Specific targeting, clustering, and maintenance of these channels in their respective domains are essential to achieve high conduction velocities of action potential propagation." [PMID:11456440] biological_process
+ENSG00000100151 GO:0045202 synapse "The junction between a nerve fiber of one neuron and another neuron or muscle fiber or glial cell; the site of interneuronal communication. As the nerve fiber approaches the synapse it enlarges into a specialized structure, the presynaptic nerve ending, which contains mitochondria and synaptic vesicles. At the tip of the nerve ending is the presynaptic membrane; facing it, and separated from it by a minute cleft (the synaptic cleft) is a specialized area of membrane on the receiving cell, known as the postsynaptic membrane. In response to the arrival of nerve impulses, the presynaptic nerve ending secretes molecules of neurotransmitters into the synaptic cleft. These diffuse across the cleft and transmit the signal to the postsynaptic membrane." [ISBN:0198506732] cellular_component
+ENSG00000100151 GO:0045211 postsynaptic membrane "A specialized area of membrane facing the presynaptic membrane on the tip of the nerve ending and separated from it by a minute cleft (the synaptic cleft). Neurotransmitters across the synaptic cleft and transmit the signal to the postsynaptic membrane." [ISBN:0198506732] cellular_component
+ENSG00000100151 GO:0046872 metal ion binding "Interacting selectively and non-covalently with any metal ion." [GOC:ai] molecular_function
+ENSG00000100151 GO:0048471 perinuclear region of cytoplasm "Cytoplasm situated near, or occurring around, the nucleus." [GOC:jid] cellular_component
+ENSG00000100151 GO:0051015 actin filament binding "Interacting selectively and non-covalently with an actin filament, also known as F-actin, a helical filamentous polymer of globular G-actin subunits." [ISBN:0198506732] molecular_function
+ENSG00000100151 GO:0060292 long term synaptic depression "A process that modulates synaptic plasticity such that synapses are changed resulting in the decrease in the rate, or frequency of synaptic transmission at the synapse." [GOC:dgh, GOC:dph] biological_process
+ENSG00000100151 GO:0071933 Arp2/3 complex binding "Interacting selectively and non-covalently with an Arp2/3 complex, a protein complex that contains two actin-related proteins, Arp2 and Arp3, and five novel proteins (ARPC1-5)." [GOC:mah] molecular_function
+ENSG00000100151 GO:0097061 dendritic spine organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of a dendritic spine. A dendritic spine is a specialized protrusion from a neuronal dendrite and is involved in synaptic transmission." [GOC:BHF, PMID:20410104] biological_process
+ENSG00000100151 GO:0097062 dendritic spine maintenance "The organization process that preserves a dendritic spine in a stable functional or structural state. A dendritic spine is a specialized protrusion from a neuronal dendrite and is involved in synaptic transmission." [GOC:BHF, PMID:20410104] biological_process
+ENSG00000100151 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000100151 GO:0006605 protein targeting "The process of targeting specific proteins to particular membrane-bounded subcellular organelles. Usually requires an organelle specific protein sequence motif." [GOC:ma] biological_process
+ENSG00000100151 GO:0097481 neuronal postsynaptic density "A postsynaptic density that is part of a neuron." [GOC:BHF, GOC:pr, GOC:rl] cellular_component
+ENSG00000183569
+ENSG00000183569 GO:0008152 metabolic process "The chemical reactions and pathways, including anabolism and catabolism, by which living organisms transform chemical substances. Metabolic processes typically transform small molecules, but also include macromolecular processes such as DNA repair and replication, and protein synthesis and degradation." [GOC:go_curators, ISBN:0198547684] biological_process
+ENSG00000183569 GO:0016787 hydrolase activity "Catalysis of the hydrolysis of various bonds, e.g. C-O, C-N, C-C, phosphoric anhydride bonds, etc. Hydrolase is the systematic name for any enzyme of EC class 3." [ISBN:0198506732] molecular_function
+ENSG00000183569 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000183569 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000183569 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000183569 GO:0005777 peroxisome "A small organelle enclosed by a single membrane, and found in most eukaryotic cells. Contains peroxidases and other enzymes involved in a variety of metabolic processes including free radical detoxification, lipid catabolism and biosynthesis, and hydrogen peroxide metabolism." [GOC:pm, PMID:9302272, UniProtKB-KW:KW-0576] cellular_component
+ENSG00000183569 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000183569 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000183569 GO:0016023 cytoplasmic membrane-bounded vesicle "A membrane-bounded vesicle found in the cytoplasm of the cell." [GOC:ai, GOC:mah] cellular_component
+ENSG00000198062 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000198062 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000188677 GO:0003779 actin binding "Interacting selectively and non-covalently with monomeric or multimeric forms of actin, including actin filaments." [GOC:clt] molecular_function
+ENSG00000188677 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000188677 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000188677 GO:0005856 cytoskeleton "Any of the various filamentous elements that form the internal framework of cells, and typically remain after treatment of the cells with mild detergent to remove membrane constituents and soluble components of the cytoplasm. The term embraces intermediate filaments, microfilaments, microtubules, the microtrabecular lattice, and other structures characterized by a polymeric filamentous nature and long-range order within the cell. The various elements of the cytoskeleton not only serve in the maintenance of cellular shape but also have roles in other cellular functions, including cellular movement, cell division, endocytosis, and movement of organelles." [GOC:mah, ISBN:0198547684, PMID:16959967] cellular_component
+ENSG00000188677 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000188677 GO:0005925 focal adhesion "Small region on the surface of a cell that anchors the cell to the extracellular matrix and that forms a point of termination of actin filaments." [ISBN:0124325653, ISBN:0815316208] cellular_component
+ENSG00000188677 GO:0007155 cell adhesion "The attachment of a cell, either to another cell or to an underlying substrate such as the extracellular matrix, via cell adhesion molecules." [GOC:hb, GOC:pf] biological_process
+ENSG00000188677 GO:0030027 lamellipodium "A thin sheetlike process extended by the leading edge of a crawling fibroblast; contains a dense meshwork of actin filaments." [ISBN:0815316194] cellular_component
+ENSG00000188677 GO:0030031 cell projection assembly "Formation of a prolongation or process extending from a cell, e.g. a flagellum or axon." [GOC:jl, GOC:mah, http://www.cogsci.princeton.edu/~wn/] biological_process
+ENSG00000188677 GO:0030032 lamellipodium assembly "Formation of a lamellipodium, a thin sheetlike extension of the surface of a migrating cell." [GOC:mah, ISBN:0815316194] biological_process
+ENSG00000188677 GO:0031532 actin cytoskeleton reorganization "A process that is carried out at the cellular level which results in dynamic structural changes to the arrangement of constituent parts of cytoskeletal structures comprising actin filaments and their associated proteins." [GOC:ecd, GOC:mah] biological_process
+ENSG00000188677 GO:0034329 cell junction assembly "A cellular process that results in the aggregation, arrangement and bonding together of a set of components to form a cell junction." [GOC:mah] biological_process
+ENSG00000188677 GO:0071963 establishment or maintenance of cell polarity regulating cell shape "Any cellular process that results in the specification, formation or maintenance of a polarized intracellular organization or cell growth patterns that regulate the shape of a cell." [GOC:mah] biological_process
+ENSG00000188677 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000188677 GO:0022607 cellular component assembly "The aggregation, arrangement and bonding together of a cellular component." [GOC:isa_complete] biological_process
+ENSG00000188677 GO:0034330 cell junction organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of a cell junction. A cell junction is a specialized region of connection between two cells or between a cell and the extracellular matrix." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000188677 GO:0007010 cytoskeleton organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of cytoskeletal structures." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000188677 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000188677 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000188677 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000188677 GO:0008092 cytoskeletal protein binding "Interacting selectively and non-covalently with any protein component of any cytoskeleton (actin, microtubule, or intermediate filament cytoskeleton)." [GOC:mah] molecular_function
+ENSG00000130538 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000130538 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000130538 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000130538 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000130538 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000130538 GO:0004871 signal transducer activity "Conveys a signal across a cell to trigger a change in cell function or state. A signal is a physical entity or change in state that is used to transfer information in order to trigger a response." [GOC:go_curators] molecular_function
+ENSG00000130538 GO:0004930 G-protein coupled receptor activity "Combining with an extracellular signal and transmitting the signal across the membrane by activating an associated G-protein; promotes the exchange of GDP for GTP on the alpha subunit of a heterotrimeric G-protein complex." [GOC:bf, http://www.iuphar-db.org, Wikipedia:GPCR] molecular_function
+ENSG00000130538 GO:0004984 olfactory receptor activity "Combining with an odorant and transmitting the signal from one side of the membrane to the other to initiate a change in cell activity in response to detection of smell." [GOC:bf, GOC:dph, GOC:sart, PMID:19135896, PMID:21041441] molecular_function
+ENSG00000130538 GO:0007186 G-protein coupled receptor signaling pathway "A series of molecular signals that proceeds with an activated receptor promoting the exchange of GDP for GTP on the alpha-subunit of an associated heterotrimeric G-protein complex. The GTP-bound activated alpha-G-protein then dissociates from the beta- and gamma-subunits to further transmit the signal within the cell. The pathway begins with receptor-ligand interaction, or for basal GPCR signaling the pathway begins with the receptor activating its G protein in the absence of an agonist, and ends with regulation of a downstream cellular process, e.g. transcription." [GOC:bf, GOC:mah, Wikipedia:G_protein-coupled_receptor] biological_process
+ENSG00000130538 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000130538 GO:0050911 detection of chemical stimulus involved in sensory perception of smell "The series of events involved in the perception of smell in which an olfactory chemical stimulus is received and converted into a molecular signal." [GOC:ai] biological_process
+ENSG00000075218 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000075218 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000075218 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000075218 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000075218 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000075218 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000075218 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000075218 GO:0005881 cytoplasmic microtubule "Any microtubule in the cytoplasm of a cell." [GOC:mah] cellular_component
+ENSG00000075218 GO:0006977 DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest "A cascade of processes induced by the cell cycle regulator phosphoprotein p53, or an equivalent protein, in response to the detection of DNA damage and resulting in the stopping or reduction in rate of the cell cycle." [GOC:go_curators] biological_process
+ENSG00000075218 GO:0007017 microtubule-based process "Any cellular process that depends upon or alters the microtubule cytoskeleton, that part of the cytoskeleton comprising microtubules and their associated proteins." [GOC:mah] biological_process
+ENSG00000075218 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000183785 GO:0005525 GTP binding "Interacting selectively and non-covalently with GTP, guanosine triphosphate." [GOC:ai] molecular_function
+ENSG00000183785 GO:0005874 microtubule "Any of the long, generally straight, hollow tubes of internal diameter 12-15 nm and external diameter 24 nm found in a wide variety of eukaryotic cells; each consists (usually) of 13 protofilaments of polymeric tubulin, staggered in such a manner that the tubulin monomers are arranged in a helical pattern on the microtubular surface, and with the alpha/beta axes of the tubulin subunits parallel to the long axis of the tubule; exist in equilibrium with pool of tubulin monomers and can be rapidly assembled or disassembled in response to physiological stimuli; concerned with force generation, e.g. in the spindle." [ISBN:0879693568] cellular_component
+ENSG00000183785 GO:0007017 microtubule-based process "Any cellular process that depends upon or alters the microtubule cytoskeleton, that part of the cytoskeleton comprising microtubules and their associated proteins." [GOC:mah] biological_process
+ENSG00000183785 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000183785 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000183785 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000183785 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000183785 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000183785 GO:0003924 GTPase activity "Catalysis of the reaction: GTP + H2O = GDP + phosphate." [ISBN:0198547684] molecular_function
+ENSG00000183785 GO:0005200 structural constituent of cytoskeleton "The action of a molecule that contributes to the structural integrity of a cytoskeletal structure." [GOC:mah] molecular_function
+ENSG00000183785 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000183785 GO:0006184 GTP catabolic process "The chemical reactions and pathways resulting in the breakdown of GTP, guanosine triphosphate." [ISBN:0198506732] biological_process
+ENSG00000183785 GO:0051258 protein polymerization "The process of creating protein polymers, compounds composed of a large number of component monomers; polymeric proteins may be made up of different or identical monomers. Polymerization occurs by the addition of extra monomers to an existing poly- or oligomeric protein." [GOC:ai] biological_process
+ENSG00000183785 GO:0006461 protein complex assembly "The aggregation, arrangement and bonding together of a set of components to form a protein complex." [GOC:ai] biological_process
+ENSG00000183785 GO:0022607 cellular component assembly "The aggregation, arrangement and bonding together of a cellular component." [GOC:isa_complete] biological_process
+ENSG00000183785 GO:0065003 macromolecular complex assembly "The aggregation, arrangement and bonding together of a set of macromolecules to form a complex." [GOC:jl] biological_process
+ENSG00000183785 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000183785 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000183785 GO:0034655 nucleobase-containing compound catabolic process "The chemical reactions and pathways resulting in the breakdown of nucleobases, nucleosides, nucleotides and nucleic acids." [GOC:mah] biological_process
+ENSG00000183785 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000183785 GO:0005198 structural molecule activity "The action of a molecule that contributes to the structural integrity of a complex or assembly within or outside a cell." [GOC:mah] molecular_function
+ENSG00000100239 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100239 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100239 GO:0043231 intracellular membrane-bounded organelle "Organized structure of distinctive morphology and function, bounded by a single or double lipid bilayer membrane and occurring within the cell. Includes the nucleus, mitochondria, plastids, vacuoles, and vesicles. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100239 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100239 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100239 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100239 GO:0005488 binding "The selective, non-covalent, often stoichiometric, interaction of a molecule with one or more specific sites on another molecule." [GOC:ceb, GOC:mah, ISBN:0198506732] molecular_function
+ENSG00000100239
+ENSG00000100138 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100138 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000100138 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100138 GO:0042254 ribosome biogenesis "A cellular process that results in the biosynthesis of constituent macromolecules, assembly, and arrangement of constituent parts of ribosome subunits; includes transport to the sites of protein synthesis." [GOC:ma] biological_process
+ENSG00000100138 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100138 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100138 GO:0005730 nucleolus "A small, dense body one or more of which are present in the nucleus of eukaryotic cells. It is rich in RNA and protein, is not bounded by a limiting membrane, and is not seen during mitosis. Its prime function is the transcription of the nucleolar DNA into 45S ribosomal-precursor RNA, the processing of this RNA into 5.8S, 18S, and 28S components of ribosomal RNA, and the association of these components with 5S RNA and proteins synthesized outside the nucleolus. This association results in the formation of ribonucleoprotein precursors; these pass into the cytoplasm and mature into the 40S and 60S subunits of the ribosome." [ISBN:0198506732] cellular_component
+ENSG00000100138 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100138 GO:0005654 nucleoplasm "That part of the nuclear content other than the chromosomes or the nucleolus." [GOC:ma, ISBN:0124325653] cellular_component
+ENSG00000100138 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100138 GO:0006397 mRNA processing "Any process involved in the conversion of a primary mRNA transcript into one or more mature mRNA(s) prior to translation into polypeptide." [GOC:mah] biological_process
+ENSG00000100138 GO:0000398 mRNA splicing, via spliceosome "The joining together of exons from one or more primary transcripts of messenger RNA (mRNA) and the excision of intron sequences, via a spliceosomal mechanism, so that mRNA consisting only of the joined exons is produced." [GOC:krc, ISBN:0198506732, ISBN:0879695897] biological_process
+ENSG00000100138 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100138 GO:0005681 spliceosomal complex "Any of a series of ribonucleoprotein complexes that contain RNA and small nuclear ribonucleoproteins (snRNPs), and are formed sequentially during the splicing of a messenger RNA primary transcript to excise an intron." [GOC:mah, ISBN:0198547684, PMID:19239890] cellular_component
+ENSG00000100138 GO:0008380 RNA splicing "The process of removing sections of the primary RNA transcript to remove sequences not present in the mature form of the RNA and joining the remaining sections to form the mature form of the RNA." [GOC:krc, GOC:mah] biological_process
+ENSG00000100138 GO:0010467 gene expression "The process in which a gene's sequence is converted into a mature gene product or products (proteins or RNA). This includes the production of an RNA transcript as well as any processing to produce a mature RNA product or an mRNA (for protein-coding genes) and the translation of that mRNA into protein. Some protein processing events may be included when they are required to form an active form of a product from an inactive precursor form." [GOC:dph, GOC:tb] biological_process
+ENSG00000100138 GO:0031428 box C/D snoRNP complex "A ribonucleoprotein complex containing small nucleolar RNA of the box C/D type that can carry out ribose-2'-O-methylation of target RNAs." [ISBN:0879695897, PMID:17284456] cellular_component
+ENSG00000100138 GO:0044822 poly(A) RNA binding "Interacting non-covalently with a poly(A) RNA, a RNA molecule which has a tail of adenine bases." [GOC:jl] molecular_function
+ENSG00000100138 GO:0030515 snoRNA binding "Interacting selectively and non-covalently with small nucleolar RNA." [GOC:mah] molecular_function
+ENSG00000100138 GO:0030529 ribonucleoprotein complex "A macromolecular complex containing both protein and RNA molecules." [GOC:krc] cellular_component
+ENSG00000100350 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100350 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100350 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100350 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000100350 GO:0016491 oxidoreductase activity "Catalysis of an oxidation-reduction (redox) reaction, a reversible chemical reaction in which the oxidation state of an atom or atoms within a molecule is altered. One substrate acts as a hydrogen or electron donor and becomes oxidized, while the other acts as hydrogen or electron acceptor and becomes reduced." [GOC:go_curators] molecular_function
+ENSG00000100350 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100350 GO:0001948 glycoprotein binding "Interacting selectively and non-covalently with a glycoprotein, a protein that contains covalently bound glycose (monosaccharide) residues. These also include proteoglycans." [GOC:hjd, ISBN:0198506732] molecular_function
+ENSG00000100350 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100350 GO:0005788 endoplasmic reticulum lumen "The volume enclosed by the membranes of the endoplasmic reticulum." [ISBN:0198547684] cellular_component
+ENSG00000100350 GO:0030433 ER-associated ubiquitin-dependent protein catabolic process "The chemical reactions and pathways resulting in the breakdown of proteins transported from the endoplasmic reticulum and targeted to cytoplasmic proteasomes for degradation. This process acts on misfolded proteins as well as in the regulated degradation of correctly folded proteins." [GOC:mah, GOC:rb, PMID:14607247, PMID:19520858] biological_process
+ENSG00000100350 GO:0050660 flavin adenine dinucleotide binding "Interacting selectively and non-covalently with FAD, flavin-adenine dinucleotide, the coenzyme or the prosthetic group of various flavoprotein oxidoreductase enzymes, in either the oxidized form, FAD, or the reduced form, FADH2." [CHEBI:24040, GOC:ai, GOC:imk, ISBN:0198506732] molecular_function
+ENSG00000100350 GO:0055114 oxidation-reduction process "A metabolic process that results in the removal or addition of one or more electrons to or from a substance, with or without the concomitant removal or addition of a proton or protons." [GOC:dhl, GOC:ecd, GOC:jh2, GOC:jid, GOC:mlg, GOC:rph] biological_process
+ENSG00000100350
+ENSG00000161180
+ENSG00000186654
+ENSG00000186654 GO:0007049 cell cycle "The progression of biochemical and morphological phases and events that occur in a cell during successive cell replication or nuclear replication events. Canonically, the cell cycle comprises the replication and segregation of genetic material followed by the division of the cell, but in endocycles or syncytial cells nuclear replication or nuclear division may not be followed by cell division." [GOC:go_curators, GOC:mtg_cell_cycle] biological_process
+ENSG00000186654 GO:0031932 TORC2 complex "A protein complex that contains at least TOR (target of rapamycin) and Rictor (rapamycin-insensitive companion of TOR), or orthologs of, in complex with other signaling components. Mediates the phosphorylation and activation of PKB (also called AKT). In Saccharomyces, the complex contains Avo1p, Avo2p, Tsc11p, Lst8p, Bit61p, Slm1p, Slm2p, and Tor2p." [GOC:bf, GOC:jh, PMID:14736892, PMID:15780592, PMID:16469695, PMID:21548787] cellular_component
+ENSG00000186654 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000186654 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000186654 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000186654 GO:0014068 positive regulation of phosphatidylinositol 3-kinase signaling "Any process that activates or increases the frequency, rate or extent of signal transduction mediated by the phosphatidylinositol 3-kinase cascade." [GOC:ef] biological_process
+ENSG00000186654 GO:0001934 positive regulation of protein phosphorylation "Any process that activates or increases the frequency, rate or extent of addition of phosphate groups to amino acids within a protein." [GOC:hjd] biological_process
+ENSG00000177663 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000177663 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000177663 GO:0004871 signal transducer activity "Conveys a signal across a cell to trigger a change in cell function or state. A signal is a physical entity or change in state that is used to transfer information in order to trigger a response." [GOC:go_curators] molecular_function
+ENSG00000177663 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000177663 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000177663 GO:0005576 extracellular region "The space external to the outermost structure of a cell. For cells without external protective or external encapsulating structures this refers to space outside of the plasma membrane. This term covers the host cell environment outside an intracellular parasite." [GOC:go_curators] cellular_component
+ENSG00000177663 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000177663 GO:0005887 integral component of plasma membrane "The component of the plasma membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000177663 GO:0007166 cell surface receptor signaling pathway "A series of molecular signals initiated by activation of a receptor on the surface of a cell. The pathway begins with binding of an extracellular ligand to a cell surface receptor, or for receptors that signal in the absence of a ligand, by ligand-withdrawal or the activity of a constitutively active receptor. The pathway ends with regulation of a downstream cellular process, e.g. transcription." [GOC:bf, GOC:mah, GOC:pr, GOC:signaling] biological_process
+ENSG00000177663 GO:0019221 cytokine-mediated signaling pathway "A series of molecular signals initiated by the binding of a cytokine to a receptor on the surface of a cell, and ending with regulation of a downstream cellular process, e.g. transcription." [GOC:mah, GOC:signaling, PMID:19295629] biological_process
+ENSG00000177663 GO:0030368 interleukin-17 receptor activity "Combining with any member of the interleukin-17 family of cytokines and transmitting the signal from one side of the membrane to the other to initiate a change in cell activity." [GOC:add, GOC:jl, GOC:signaling] molecular_function
+ENSG00000177663 GO:0032747 positive regulation of interleukin-23 production "Any process that activates or increases the frequency, rate, or extent of interleukin-23 production." [GOC:mah] biological_process
+ENSG00000177663 GO:0072537 fibroblast activation "A change in the morphology or behavior of a fibroblast resulting from exposure to an activating factor such as a cellular or soluble ligand." [CL:0000057, GOC:BHF, GOC:mah] biological_process
+ENSG00000177663 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000177663 GO:1900017 positive regulation of cytokine production involved in inflammatory response "Any process that activates or increases the frequency, rate or extent of cytokine production involved in inflammatory response." [GOC:TermGenie] biological_process
+ENSG00000177663 GO:0071345 cellular response to cytokine stimulus "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a cytokine stimulus." [GOC:mah] biological_process
+ENSG00000170638
+ENSG00000167065
+ENSG00000167065 GO:0000188 inactivation of MAPK activity "Any process that terminates the activity of the active enzyme MAP kinase." [PMID:9561267] biological_process
+ENSG00000167065 GO:0004725 protein tyrosine phosphatase activity "Catalysis of the reaction: protein tyrosine phosphate + H2O = protein tyrosine + phosphate." [EC:3.1.3.48] molecular_function
+ENSG00000167065 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000167065 GO:0017017 MAP kinase tyrosine/serine/threonine phosphatase activity "Catalysis of the reaction: MAP kinase serine/threonine/tyrosine phosphate + H2O = MAP kinase serine/threonine/tyrosine + phosphate." [GOC:mah, PMID:12184814] molecular_function
+ENSG00000167065 GO:0035335 peptidyl-tyrosine dephosphorylation "The removal of phosphoric residues from peptidyl-O-phospho-tyrosine to form peptidyl-tyrosine." [GOC:bf] biological_process
+ENSG00000167065 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000167065 GO:0016791 phosphatase activity "Catalysis of the hydrolysis of phosphoric monoesters, releasing inorganic phosphate." [EC:3.1.3, EC:3.1.3.41, GOC:curators] molecular_function
+ENSG00000167065 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000167065 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000167065 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000167065 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000167065 GO:0008138 protein tyrosine/serine/threonine phosphatase activity "Catalysis of the reactions: protein serine + H2O = protein serine + phosphate; protein threonine phosphate + H2O = protein threonine + phosphate; and protein tyrosine phosphate + H2O = protein tyrosine + phosphate." [GOC:mah] molecular_function
+ENSG00000167065 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000167065 GO:0006470 protein dephosphorylation "The process of removing one or more phosphoric residues from a protein." [GOC:hb] biological_process
+ENSG00000167065 GO:0016311 dephosphorylation "The process of removing one or more phosphoric (ester or anhydride) residues from a molecule." [ISBN:0198506732] biological_process
+ENSG00000100379 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100379 GO:0042802 identical protein binding "Interacting selectively and non-covalently with an identical protein or proteins." [GOC:jl] molecular_function
+ENSG00000100379 GO:0051260 protein homooligomerization "The process of creating protein oligomers, compounds composed of a small number, usually between three and ten, of identical component monomers. Oligomers may be formed by the polymerization of a number of monomers or the depolymerization of a large protein polymer." [GOC:ai] biological_process
+ENSG00000100379 GO:0006461 protein complex assembly "The aggregation, arrangement and bonding together of a set of components to form a protein complex." [GOC:ai] biological_process
+ENSG00000100379 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100379 GO:0022607 cellular component assembly "The aggregation, arrangement and bonding together of a cellular component." [GOC:isa_complete] biological_process
+ENSG00000100379 GO:0065003 macromolecular complex assembly "The aggregation, arrangement and bonding together of a set of macromolecules to form a complex." [GOC:jl] biological_process
+ENSG00000100379 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000215193 GO:0006605 protein targeting "The process of targeting specific proteins to particular membrane-bounded subcellular organelles. Usually requires an organelle specific protein sequence motif." [GOC:ma] biological_process
+ENSG00000215193 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000215193 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000215193 GO:0055085 transmembrane transport "The process in which a solute is transported from one side of a membrane to the other." [GOC:dph, GOC:jid] biological_process
+ENSG00000215193 GO:0061024 membrane organization "A process which results in the assembly, arrangement of constituent parts, or disassembly of a membrane. A membrane is a double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:dph, GOC:tb] biological_process
+ENSG00000215193 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000215193 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000215193 GO:0005777 peroxisome "A small organelle enclosed by a single membrane, and found in most eukaryotic cells. Contains peroxidases and other enzymes involved in a variety of metabolic processes including free radical detoxification, lipid catabolism and biosynthesis, and hydrogen peroxide metabolism." [GOC:pm, PMID:9302272, UniProtKB-KW:KW-0576] cellular_component
+ENSG00000215193 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000215193 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000215193 GO:0005779 integral component of peroxisomal membrane "The component of the peroxisomal membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:mah] cellular_component
+ENSG00000215193 GO:0008022 protein C-terminus binding "Interacting selectively and non-covalently with a protein C-terminus, the end of any peptide chain at which the 1-carboxy function of a constituent amino acid is not attached in peptide linkage to another amino-acid residue." [ISBN:0198506732] molecular_function
+ENSG00000215193 GO:0016558 protein import into peroxisome matrix "The import of proteins into the peroxisomal matrix. A peroxisome targeting signal (PTS) binds to a soluble receptor protein in the cytosol, and the resulting complex then binds to a receptor protein in the peroxisome membrane and is imported. The cargo protein is then released into the peroxisome matrix." [ISBN:0716731363, PMID:11687502, PMID:11988772] biological_process
+ENSG00000215193 GO:0032403 protein complex binding "Interacting selectively and non-covalently with any protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:mah] molecular_function
+ENSG00000215193 GO:0045046 protein import into peroxisome membrane "The targeting of proteins into the peroxisomal membrane. The process is not well understood, but both signals and mechanism differ from those involved in peroxisomal matrix protein import." [ISBN:0716731363, PMID:11687502] biological_process
+ENSG00000215193 GO:0051117 ATPase binding "Interacting selectively and non-covalently with an ATPase, any enzyme that catalyzes the hydrolysis of ATP." [GOC:ai] molecular_function
+ENSG00000215193 GO:0019899 enzyme binding "Interacting selectively and non-covalently with any enzyme." [GOC:jl] molecular_function
+ENSG00000211654 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000211654 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000198089 GO:0000086 G2/M transition of mitotic cell cycle "The mitotic cell cycle transition by which a cell in G2 commits to M phase. The process begins when the kinase activity of M cyclin/CDK complex reaches a threshold high enough for the cell cycle to proceed. This is accomplished by activating a positive feedback loop that results in the accumulation of unphosphorylated and active M cyclin/CDK complex." [GOC:mtg_cell_cycle] biological_process
+ENSG00000198089 GO:0000278 mitotic cell cycle "Progression through the phases of the mitotic cell cycle, the most common eukaryotic cell cycle, which canonically comprises four successive phases called G1, S, G2, and M and includes replication of the genome and the subsequent segregation of chromosomes into daughter cells. In some variant cell cycles nuclear replication or nuclear division may not be followed by cell division, or G1 and G2 phases may be absent." [GOC:mah, ISBN:0815316194, Reactome:69278] biological_process
+ENSG00000198089 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000198089 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000198089 GO:0005856 cytoskeleton "Any of the various filamentous elements that form the internal framework of cells, and typically remain after treatment of the cells with mild detergent to remove membrane constituents and soluble components of the cytoplasm. The term embraces intermediate filaments, microfilaments, microtubules, the microtrabecular lattice, and other structures characterized by a polymeric filamentous nature and long-range order within the cell. The various elements of the cytoskeleton not only serve in the maintenance of cellular shape but also have roles in other cellular functions, including cellular movement, cell division, endocytosis, and movement of organelles." [GOC:mah, ISBN:0198547684, PMID:16959967] cellular_component
+ENSG00000198089 GO:0010923 negative regulation of phosphatase activity "Any process that decreases the rate or frequency of phosphatase activity. Phosphatases catalyze the hydrolysis of phosphoric monoesters, releasing inorganic phosphate." [GOC:BHF, GOC:dph, GOC:tb] biological_process
+ENSG00000198089 GO:0019902 phosphatase binding "Interacting selectively and non-covalently with any phosphatase." [GOC:jl] molecular_function
+ENSG00000198089 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000198089 GO:0019899 enzyme binding "Interacting selectively and non-covalently with any enzyme." [GOC:jl] molecular_function
+ENSG00000198089 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000198089 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000198089 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000198089 GO:0007049 cell cycle "The progression of biochemical and morphological phases and events that occur in a cell during successive cell replication or nuclear replication events. Canonically, the cell cycle comprises the replication and segregation of genetic material followed by the division of the cell, but in endocycles or syncytial cells nuclear replication or nuclear division may not be followed by cell division." [GOC:go_curators, GOC:mtg_cell_cycle] biological_process
+ENSG00000198089
+ENSG00000100348 GO:0006662 glycerol ether metabolic process "The chemical reactions and pathways involving glycerol ethers, any anhydride formed between two organic hydroxy compounds, one of which is glycerol." [GOC:ai, ISBN:0198506732] biological_process
+ENSG00000100348 GO:0015035 protein disulfide oxidoreductase activity "Catalysis of the reaction: a protein with reduced sulfide groups = a protein with oxidized disulfide bonds." [MetaCyc:DISULFOXRED-RXN] molecular_function
+ENSG00000100348 GO:0045454 cell redox homeostasis "Any process that maintains the redox environment of a cell or compartment within a cell." [GOC:ai, GOC:dph, GOC:tb] biological_process
+ENSG00000100348 GO:0055114 oxidation-reduction process "A metabolic process that results in the removal or addition of one or more electrons to or from a substance, with or without the concomitant removal or addition of a proton or protons." [GOC:dhl, GOC:ecd, GOC:jh2, GOC:jid, GOC:mlg, GOC:rph] biological_process
+ENSG00000100348 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100348 GO:0042592 homeostatic process "Any biological process involved in the maintenance of an internal steady state." [GOC:jl, ISBN:0395825172] biological_process
+ENSG00000100348 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100348 GO:0016491 oxidoreductase activity "Catalysis of an oxidation-reduction (redox) reaction, a reversible chemical reaction in which the oxidation state of an atom or atoms within a molecule is altered. One substrate acts as a hydrogen or electron donor and becomes oxidized, while the other acts as hydrogen or electron acceptor and becomes reduced." [GOC:go_curators] molecular_function
+ENSG00000100348 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000100348 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100348 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000100348 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100348 GO:0005730 nucleolus "A small, dense body one or more of which are present in the nucleus of eukaryotic cells. It is rich in RNA and protein, is not bounded by a limiting membrane, and is not seen during mitosis. Its prime function is the transcription of the nucleolar DNA into 45S ribosomal-precursor RNA, the processing of this RNA into 5.8S, 18S, and 28S components of ribosomal RNA, and the association of these components with 5S RNA and proteins synthesized outside the nucleolus. This association results in the formation of ribonucleoprotein precursors; these pass into the cytoplasm and mature into the 40S and 60S subunits of the ribosome." [ISBN:0198506732] cellular_component
+ENSG00000100348 GO:0005759 mitochondrial matrix "The gel-like material, with considerable fine structure, that lies in the matrix space, or lumen, of a mitochondrion. It contains the enzymes of the tricarboxylic acid cycle and, in some organisms, the enzymes concerned with fatty acid oxidation." [GOC:as, ISBN:0198506732] cellular_component
+ENSG00000100348 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100348 GO:0042493 response to drug "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a drug stimulus. A drug is a substance used in the diagnosis, treatment or prevention of a disease." [GOC:jl] biological_process
+ENSG00000100348 GO:0014070 response to organic cyclic compound "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of an organic cyclic compound stimulus." [CHEBI:33832, GOC:ef] biological_process
+ENSG00000100348 GO:0043025 neuronal cell body "The portion of a neuron that includes the nucleus, but excludes cell projections such as axons and dendrites." [GOC:go_curators] cellular_component
+ENSG00000100348 GO:0030425 dendrite "A neuron projection that has a short, tapering, often branched, morphology, receives and integrates signals from other neurons or from sensory stimuli, and conducts a nerve impulse towards the axon or the cell body. In most neurons, the impulse is conveyed from dendrites to axon via the cell body, but in some types of unipolar neuron, the impulse does not travel via the cell body." [GOC:dos, GOC:mah, GOC:nln, ISBN:0198506732] cellular_component
+ENSG00000100348 GO:0032403 protein complex binding "Interacting selectively and non-covalently with any protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:mah] molecular_function
+ENSG00000100348 GO:0031669 cellular response to nutrient levels "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a stimulus reflecting the presence, absence, or concentration of nutrients." [GOC:mah] biological_process
+ENSG00000100348 GO:0009749 response to glucose "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a glucose stimulus." [GOC:jl] biological_process
+ENSG00000100348 GO:0048678 response to axon injury "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of an axon injury stimulus." [GOC:dgh, GOC:dph, GOC:jid, GOC:lm] biological_process
+ENSG00000100348 GO:0001666 response to hypoxia "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a stimulus indicating lowered oxygen tension. Hypoxia, defined as a decline in O2 levels below normoxic levels of 20.8 - 20.95%, results in metabolic adaptation at both the cellular and organismal level." [GOC:hjd] biological_process
+ENSG00000100348 GO:0009725 response to hormone "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a hormone stimulus." [GOC:jl] biological_process
+ENSG00000100348 GO:0007584 response to nutrient "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a nutrient stimulus." [GOC:go_curators] biological_process
+ENSG00000100348 GO:0006979 response to oxidative stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of oxidative stress, a state often resulting from exposure to high levels of reactive oxygen species, e.g. superoxide anions, hydrogen peroxide (H2O2), and hydroxyl radicals." [GOC:jl, PMID:12115731] biological_process
+ENSG00000100348 GO:0008113 peptide-methionine (S)-S-oxide reductase activity "Catalysis of the reactions: peptide-L-methionine + thioredoxin disulfide + H2O = peptide-L-methionine (S)-S-oxide + thioredoxin, and L-methionine + thioredoxin disulfide + H2O = L-methionine (S)-S-oxide + thioredoxin. Can act on oxidized methionine in peptide linkage with specificity for the S enantiomer. Thioredoxin disulfide is the oxidized form of thioredoxin." [EC:1.8.4.11, GOC:mah, GOC:vw, PMID:11169920] molecular_function
+ENSG00000100348 GO:0033743 peptide-methionine (R)-S-oxide reductase activity "Catalysis of the reaction: peptide-L-methionine + H(2)O + thioredoxin disulfide = peptide-L-methionine (R)-S-oxide + thioredoxin. Can act on oxidized methionine in peptide linkage with specificity for the R enantiomer. Thioredoxin disulfide is the oxidized form of thioredoxin." [EC:1.8.4.12, GOC:mah, GOC:vw, RHEA:24167] molecular_function
+ENSG00000184208 GO:0005576 extracellular region "The space external to the outermost structure of a cell. For cells without external protective or external encapsulating structures this refers to space outside of the plasma membrane. This term covers the host cell environment outside an intracellular parasite." [GOC:go_curators] cellular_component
+ENSG00000184208 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000211663 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000211663 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000070413 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000070413 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000070413 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000070413 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000070413 GO:0007155 cell adhesion "The attachment of a cell, either to another cell or to an underlying substrate such as the extracellular matrix, via cell adhesion molecules." [GOC:hb, GOC:pf] biological_process
+ENSG00000070413 GO:0009887 organ morphogenesis "Morphogenesis of an organ. An organ is defined as a tissue or set of tissues that work together to perform a specific function or functions. Morphogenesis is the process in which anatomical structures are generated and organized. Organs are commonly observed as visibly distinct structures, but may also exist as loosely associated clusters of cells that work together to perform a specific function or functions." [GOC:dgh, GOC:go_curators, ISBN:0471245208, ISBN:0721662544] biological_process
+ENSG00000070413 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000070413 GO:0030246 carbohydrate binding "Interacting selectively and non-covalently with any carbohydrate, which includes monosaccharides, oligosaccharides and polysaccharides as well as substances derived from monosaccharides by reduction of the carbonyl group (alditols), by oxidation of one or more hydroxy groups to afford the corresponding aldehydes, ketones, or carboxylic acids, or by replacement of one or more hydroxy group(s) by a hydrogen atom. Cyclitols are generally not regarded as carbohydrates." [CHEBI:16646, GOC:mah] molecular_function
+ENSG00000070413 GO:0050890 cognition "The operation of the mind by which an organism becomes aware of objects of thought or perception; it includes the mental activities associated with thinking, learning, and memory." [http://www.onelook.com/, ISBN:0721619908] biological_process
+ENSG00000070413 GO:0050877 neurological system process "A organ system process carried out by any of the organs or tissues of neurological system." [GOC:ai, GOC:mtg_cardio] biological_process
+ENSG00000099889 GO:0007155 cell adhesion "The attachment of a cell, either to another cell or to an underlying substrate such as the extracellular matrix, via cell adhesion molecules." [GOC:hb, GOC:pf] biological_process
+ENSG00000099889 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000099889 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000099889 GO:0005622 intracellular "The living contents of a cell; the matter contained within (but not including) the plasma membrane, usually taken to exclude large vacuoles and masses of secretory or ingested material. In eukaryotes it includes the nucleus and cytoplasm." [ISBN:0198506732] cellular_component
+ENSG00000099889 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000099889 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000099889 GO:0007275 multicellular organismal development "The biological process whose specific outcome is the progression of a multicellular organism over time from an initial condition (e.g. a zygote or a young adult) to a later condition (e.g. a multicellular animal or an aged adult)." [GOC:dph, GOC:ems, GOC:isa_complete, GOC:tb] biological_process
+ENSG00000099889 GO:0016337 single organismal cell-cell adhesion "The attachment of one cell to another cell via adhesion molecules, where both cells are part of the same organism." [GOC:hb] biological_process
+ENSG00000099889 GO:0016339 calcium-dependent cell-cell adhesion via plasma membrane cell adhesion molecules "The attachment of one cell to another cell via adhesion molecules that require the presence of calcium for the interaction." [GOC:hb] biological_process
+ENSG00000099889 GO:0005488 binding "The selective, non-covalent, often stoichiometric, interaction of a molecule with one or more specific sites on another molecule." [GOC:ceb, GOC:mah, ISBN:0198506732] molecular_function
+ENSG00000099889 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000099889 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000099889 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000099889 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000128165 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000128165 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000128165 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000128165 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000128165 GO:0005576 extracellular region "The space external to the outermost structure of a cell. For cells without external protective or external encapsulating structures this refers to space outside of the plasma membrane. This term covers the host cell environment outside an intracellular parasite." [GOC:go_curators] cellular_component
+ENSG00000128165 GO:0048646 anatomical structure formation involved in morphogenesis "The developmental process pertaining to the initial formation of an anatomical structure from unspecified parts. This process begins with the specific processes that contribute to the appearance of the discrete structure and ends when the structural rudiment is recognizable. An anatomical structure is any biological entity that occupies space and is distinguished from its surroundings. Anatomical structures can be macroscopic such as a carpel, or microscopic such as an acrosome." [GOC:dph, GOC:jid, GOC:tb] biological_process
+ENSG00000128165 GO:0001525 angiogenesis "Blood vessel formation when new vessels emerge from the proliferation of pre-existing blood vessels." [ISBN:0878932453] biological_process
+ENSG00000128165 GO:0005179 hormone activity "The action characteristic of a hormone, any substance formed in very small amounts in one specialized organ or group of cells and carried (sometimes in the bloodstream) to another organ or group of cells in the same organism, upon which it has a specific regulatory action. The term was originally applied to agents with a stimulatory physiological action in vertebrate animals (as opposed to a chalone, which has a depressant action). Usage is now extended to regulatory compounds in lower animals and plants, and to synthetic substances having comparable effects; all bind receptors and trigger some biological process." [GOC:dph, GOC:mah, ISBN:0198506732] molecular_function
+ENSG00000128165 GO:0006468 protein phosphorylation "The process of introducing a phosphate group on to a protein." [GOC:hb] biological_process
+ENSG00000128165 GO:0010628 positive regulation of gene expression "Any process that increases the frequency, rate or extent of gene expression. Gene expression is the process in which a gene's coding sequence is converted into a mature gene product or products (proteins or RNA). This includes the production of an RNA transcript as well as any processing to produce a mature RNA product or an mRNA (for protein-coding genes) and the translation of that mRNA into protein. Some protein processing events may be included when they are required to form an active form of a product from an inactive precursor form." [GOC:dph, GOC:tb] biological_process
+ENSG00000128165 GO:0032403 protein complex binding "Interacting selectively and non-covalently with any protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:mah] molecular_function
+ENSG00000128165 GO:0045766 positive regulation of angiogenesis "Any process that activates or increases angiogenesis." [GOC:go_curators] biological_process
+ENSG00000128165 GO:0007586 digestion "The whole of the physical, chemical, and biochemical processes carried out by multicellular organisms to break down ingested nutrients into components that may be easily absorbed and directed into metabolism." [GOC:isa_complete, ISBN:0198506732] biological_process
+ENSG00000128165 GO:0045776 negative regulation of blood pressure "Any process in which the force of blood traveling through the circulatory system is decreased." [GOC:go_curators, GOC:mtg_cardio] biological_process
+ENSG00000128165 GO:0007631 feeding behavior "Behavior associated with the intake of food." [GOC:mah] biological_process
+ENSG00000128165 GO:0007189 adenylate cyclase-activating G-protein coupled receptor signaling pathway "The series of molecular signals generated as a consequence of a G-protein coupled receptor binding to its physiological ligand, where the pathway proceeds through activation of adenylyl cyclase activity and a subsequent increase in the concentration of cyclic AMP (cAMP)." [GOC:dph, GOC:mah, GOC:signaling, GOC:tb, ISBN:0815316194] biological_process
+ENSG00000138867
+ENSG00000100142 GO:0003677 DNA binding "Any molecular function by which a gene product interacts selectively and non-covalently with DNA (deoxyribonucleic acid)." [GOC:dph, GOC:jl, GOC:tb, GOC:vw] molecular_function
+ENSG00000100142 GO:0003899 DNA-directed RNA polymerase activity "Catalysis of the reaction: nucleoside triphosphate + RNA(n) = diphosphate + RNA(n+1). Utilizes a DNA template, i.e. the catalysis of DNA-template-directed extension of the 3'-end of an RNA strand by one nucleotide at a time. Can initiate a chain 'de novo'." [EC:2.7.7.6] molecular_function
+ENSG00000100142 GO:0006351 transcription, DNA-templated "The cellular synthesis of RNA on a template of DNA." [GOC:jl, GOC:txnOH] biological_process
+ENSG00000100142 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100142 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000100142 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100142 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100142 GO:0016779 nucleotidyltransferase activity "Catalysis of the transfer of a nucleotidyl group to a reactant." [ISBN:0198506732] molecular_function
+ENSG00000100142 GO:0002376 immune system process "Any process involved in the development or functioning of the immune system, an organismal system for calibrated responses to potential internal or invasive threats." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05] biological_process
+ENSG00000100142 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000100142 GO:0044403 symbiosis, encompassing mutualism through parasitism "An interaction between two organisms living together in more or less intimate association. The term host is usually used for the larger (macro) of the two members of a symbiosis. The smaller (micro) member is called the symbiont organism. Microscopic symbionts are often referred to as endosymbionts. The various forms of symbiosis include parasitism, in which the association is disadvantageous or destructive to one of the organisms; mutualism, in which the association is advantageous, or often necessary to one or both and not harmful to either; and commensalism, in which one member of the association benefits while the other is not affected. However, mutualism, parasitism, and commensalism are often not discrete categories of interactions and should rather be perceived as a continuum of interaction ranging from parasitism to mutualism. In fact, the direction of a symbiotic interaction can change during the lifetime of the symbionts due to developmental changes as well as changes in the biotic/abiotic environment in which the interaction occurs." [GOC:cc, http://www.free-definition.com] biological_process
+ENSG00000100142 GO:0006397 mRNA processing "Any process involved in the conversion of a primary mRNA transcript into one or more mature mRNA(s) prior to translation into polypeptide." [GOC:mah] biological_process
+ENSG00000100142 GO:0006259 DNA metabolic process "Any cellular metabolic process involving deoxyribonucleic acid. This is one of the two main types of nucleic acid, consisting of a long, unbranched macromolecule formed from one, or more commonly, two, strands of linked deoxyribonucleotides." [ISBN:0198506732] biological_process
+ENSG00000100142 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100142 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000100142 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100142 GO:0005730 nucleolus "A small, dense body one or more of which are present in the nucleus of eukaryotic cells. It is rich in RNA and protein, is not bounded by a limiting membrane, and is not seen during mitosis. Its prime function is the transcription of the nucleolar DNA into 45S ribosomal-precursor RNA, the processing of this RNA into 5.8S, 18S, and 28S components of ribosomal RNA, and the association of these components with 5S RNA and proteins synthesized outside the nucleolus. This association results in the formation of ribonucleoprotein precursors; these pass into the cytoplasm and mature into the 40S and 60S subunits of the ribosome." [ISBN:0198506732] cellular_component
+ENSG00000100142 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100142 GO:0005654 nucleoplasm "That part of the nuclear content other than the chromosomes or the nucleolus." [GOC:ma, ISBN:0124325653] cellular_component
+ENSG00000100142 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100142 GO:0000398 mRNA splicing, via spliceosome "The joining together of exons from one or more primary transcripts of messenger RNA (mRNA) and the excision of intron sequences, via a spliceosomal mechanism, so that mRNA consisting only of the joined exons is produced." [GOC:krc, ISBN:0198506732, ISBN:0879695897] biological_process
+ENSG00000100142 GO:0005665 DNA-directed RNA polymerase II, core complex "RNA polymerase II, one of three nuclear DNA-directed RNA polymerases found in all eukaryotes, is a multisubunit complex; typically it produces mRNAs, snoRNAs, and some of the snRNAs. Two large subunits comprise the most conserved portion including the catalytic site and share similarity with other eukaryotic and bacterial multisubunit RNA polymerases. The largest subunit of RNA polymerase II contains an essential carboxyl-terminal domain (CTD) composed of a variable number of heptapeptide repeats (YSPTSPS). The remainder of the complex is composed of smaller subunits (generally ten or more), some of which are also found in RNA polymerases I and III. Although the core is competent to mediate ribonucleic acid synthesis, it requires additional factors to select the appropriate template." [GOC:krc, GOC:mtg_sensu] cellular_component
+ENSG00000100142 GO:0005666 DNA-directed RNA polymerase III complex "RNA polymerase III, one of three nuclear DNA-directed RNA polymerases found in all eukaryotes, is a multisubunit complex; typically it produces 5S rRNA, tRNAs and some of the small nuclear RNAs. Two large subunits comprise the most conserved portion including the catalytic site and share similarity with other eukaryotic and bacterial multisubunit RNA polymerases. The remainder of the complex is composed of smaller subunits (generally ten or more), some of which are also found in RNA polymerase I and others of which are also found in RNA polymerases I and II. Although the core is competent to mediate ribonucleic acid synthesis, it requires additional factors to select the appropriate template." [GOC:krc, GOC:mtg_sensu] cellular_component
+ENSG00000100142 GO:0005736 DNA-directed RNA polymerase I complex "RNA polymerase I, one of three nuclear DNA-directed RNA polymerases found in all eukaryotes, is a multisubunit complex; typically it produces rRNAs. Two large subunits comprise the most conserved portion including the catalytic site and share similarity with other eukaryotic and bacterial multisubunit RNA polymerases. The remainder of the complex is composed of smaller subunits (generally ten or more), some of which are also found in RNA polymerase III and others of which are also found in RNA polymerases II and III. Although the core is competent to mediate ribonucleic acid synthesis, it requires additional factors to select the appropriate template." [GOC:krc, GOC:mtg_sensu] cellular_component
+ENSG00000100142 GO:0006281 DNA repair "The process of restoring DNA after damage. Genomes are subject to damage by chemical and physical agents in the environment (e.g. UV and ionizing radiations, chemical mutagens, fungal and bacterial toxins, etc.) and by free radicals or alkylating agents endogenously generated in metabolism. DNA is also damaged because of errors during its replication. A variety of different DNA repair pathways have been reported that include direct reversal, base excision repair, nucleotide excision repair, photoreactivation, bypass, double-strand break repair pathway, and mismatch repair pathway." [PMID:11563486] biological_process
+ENSG00000100142 GO:0006283 transcription-coupled nucleotide-excision repair "The nucleotide-excision repair process that carries out preferential repair of DNA lesions on the actively transcribed strand of the DNA duplex. In addition, the transcription-coupled nucleotide-excision repair pathway is required for the recognition and repair of a small subset of lesions that are not recognized by the global genome nucleotide excision repair pathway." [PMID:10197977, PMID:11900249] biological_process
+ENSG00000100142 GO:0006289 nucleotide-excision repair "A DNA repair process in which a small region of the strand surrounding the damage is removed from the DNA helix as an oligonucleotide. The small gap left in the DNA helix is filled in by the sequential action of DNA polymerase and DNA ligase. Nucleotide excision repair recognizes a wide range of substrates, including damage caused by UV irradiation (pyrimidine dimers and 6-4 photoproducts) and chemicals (intrastrand cross-links and bulky adducts)." [PMID:10197977] biological_process
+ENSG00000100142 GO:0006360 transcription from RNA polymerase I promoter "The synthesis of RNA from a DNA template by RNA polymerase I (RNAP I), originating at an RNAP I promoter." [GOC:jl, GOC:txnOH] biological_process
+ENSG00000100142 GO:0006366 transcription from RNA polymerase II promoter "The synthesis of RNA from a DNA template by RNA polymerase II, originating at an RNA polymerase II promoter. Includes transcription of messenger RNA (mRNA) and certain small nuclear RNAs (snRNAs)." [GOC:jl, GOC:txnOH, ISBN:0321000382] biological_process
+ENSG00000100142 GO:0006367 transcription initiation from RNA polymerase II promoter "Any process involved in the assembly of the RNA polymerase II preinitiation complex (PIC) at an RNA polymerase II promoter region of a DNA template, resulting in the subsequent synthesis of RNA from that promoter. The initiation phase includes PIC assembly and the formation of the first few bonds in the RNA chain, including abortive initiation, which occurs when the first few nucleotides are repeatedly synthesized and then released. Promoter clearance, or release, is the transition between the initiation and elongation phases of transcription." [GOC:mah, GOC:txnOH] biological_process
+ENSG00000100142 GO:0006368 transcription elongation from RNA polymerase II promoter "The extension of an RNA molecule after transcription initiation and promoter clearance at an RNA polymerase II promoter by the addition of ribonucleotides catalyzed by RNA polymerase II." [GOC:mah, GOC:txnOH] biological_process
+ENSG00000100142 GO:0006370 7-methylguanosine mRNA capping "Addition of the 7-methylguanosine cap to the 5' end of a nascent messenger RNA transcript." [GOC:mah, PMID:9266685] biological_process
+ENSG00000100142 GO:0006383 transcription from RNA polymerase III promoter "The synthesis of RNA from a DNA template by RNA polymerase III, originating at an RNAP III promoter." [GOC:jl, GOC:txnOH] biological_process
+ENSG00000100142 GO:0006385 transcription elongation from RNA polymerase III promoter "The extension of an RNA molecule after transcription initiation and promoter clearance at an RNA polymerase III promoter by the addition of ribonucleotides catalyzed by RNA polymerase III." [GOC:mah, GOC:txnOH] biological_process
+ENSG00000100142 GO:0006386 termination of RNA polymerase III transcription "The process in which transcription by RNA polymerase III is terminated; Pol III has an intrinsic ability to terminate transcription upon incorporation of 4 to 6 contiguous U residues." [GOC:mah, PMID:12944462] biological_process
+ENSG00000100142 GO:0008380 RNA splicing "The process of removing sections of the primary RNA transcript to remove sequences not present in the mature form of the RNA and joining the remaining sections to form the mature form of the RNA." [GOC:krc, GOC:mah] biological_process
+ENSG00000100142 GO:0010467 gene expression "The process in which a gene's sequence is converted into a mature gene product or products (proteins or RNA). This includes the production of an RNA transcript as well as any processing to produce a mature RNA product or an mRNA (for protein-coding genes) and the translation of that mRNA into protein. Some protein processing events may be included when they are required to form an active form of a product from an inactive precursor form." [GOC:dph, GOC:tb] biological_process
+ENSG00000100142 GO:0016032 viral process "A multi-organism process in which a virus is a participant. The other participant is the host. Includes infection of a host cell, replication of the viral genome, and assembly of progeny virus particles. In some cases the viral genetic material may integrate into the host genome and only subsequently, under particular circumstances, 'complete' its life cycle." [GOC:bf, GOC:jl, GOC:mah] biological_process
+ENSG00000100142 GO:0032481 positive regulation of type I interferon production "Any process that activates or increases the frequency, rate, or extent of type I interferon production. Type I interferons include the interferon-alpha, beta, delta, episilon, zeta, kappa, tau, and omega gene families." [GOC:add, GOC:mah] biological_process
+ENSG00000100142 GO:0045087 innate immune response "Innate immune responses are defense responses mediated by germline encoded components that directly recognize components of potential pathogens." [GO_REF:0000022, GOC:add, GOC:ebc, GOC:mtg_15nov05, GOC:mtg_sensu] biological_process
+ENSG00000100142 GO:0050434 positive regulation of viral transcription "Any process that activates or increases the frequency, rate or extent of viral transcription." [GOC:ai] biological_process
+ENSG00000100142 GO:0001054 RNA polymerase I activity "Catalysis of the reaction: nucleoside triphosphate + RNA(n) = diphosphate + RNA(n+1). Utilizes a DNA template that contains an RNA polymerase I specific promoter to direct initiation and catalyzes DNA-template-directed extension of the 3'-end of an RNA strand by one nucleotide at a time. Can initiate a chain 'de novo'." [GOC:txnOH] molecular_function
+ENSG00000100142 GO:0001055 RNA polymerase II activity "Catalysis of the reaction: nucleoside triphosphate + RNA(n) = diphosphate + RNA(n+1). Utilizes a DNA template that contains an RNA polymerase II specific promoter to direct initiation and catalyses DNA-template-directed extension of the 3'-end of an RNA strand by one nucleotide at a time. Can initiate a chain 'de novo'." [GOC:txnOH] molecular_function
+ENSG00000100142 GO:0001056 RNA polymerase III activity "Catalysis of the reaction: nucleoside triphosphate + RNA(n) = diphosphate + RNA(n+1). Utilizes a DNA template that contains an RNA polymerase III specific promoter to direct initiation and catalyses DNA-template-directed extension of the 3'-end of an RNA strand by one nucleotide at a time. Can initiate a chain 'de novo'." [GOC:txnOH] molecular_function
+ENSG00000100142
+ENSG00000100156 GO:0002376 immune system process "Any process involved in the development or functioning of the immune system, an organismal system for calibrated responses to potential internal or invasive threats." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05] biological_process
+ENSG00000100156 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100156 GO:0040011 locomotion "Self-propelled movement of a cell or organism from one location to another." [GOC:dgh] biological_process
+ENSG00000100156 GO:0048870 cell motility "Any process involved in the controlled self-propelled movement of a cell that results in translocation of the cell from one place to another." [GOC:dgh, GOC:dph, GOC:isa_complete, GOC:mlg] biological_process
+ENSG00000100156 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000100156 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100156 GO:0055085 transmembrane transport "The process in which a solute is transported from one side of a membrane to the other." [GOC:dph, GOC:jid] biological_process
+ENSG00000100156 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100156 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100156 GO:0022857 transmembrane transporter activity "Enables the transfer of a substance from one side of a membrane to the other." [GOC:mtg_transport, ISBN:0815340729] molecular_function
+ENSG00000100156 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000100156 GO:0005887 integral component of plasma membrane "The component of the plasma membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000100156 GO:0006090 pyruvate metabolic process "The chemical reactions and pathways involving pyruvate, 2-oxopropanoate." [GOC:go_curators] biological_process
+ENSG00000100156 GO:0007596 blood coagulation "The sequential process in which the multiple coagulation factors of the blood interact, ultimately resulting in the formation of an insoluble fibrin clot; it may be divided into three stages: stage 1, the formation of intrinsic and extrinsic prothrombin converting principle; stage 2, the formation of thrombin; stage 3, the formation of stable fibrin polymers." [http://www.graylab.ac.uk/omd/, ISBN:0198506732] biological_process
+ENSG00000100156 GO:0015129 lactate transmembrane transporter activity "Catalysis of the transfer of lactate from one side of the membrane to the other. Lactate is 2-hydroxypropanoate, CH3-CHOH-COOH; L(+)-lactate is formed by anaerobic glycolysis in animal tissues, and DL-lactate is found in sour milk, molasses and certain fruit juices." [GOC:ai, ISBN:0198506732] molecular_function
+ENSG00000100156 GO:0015293 symporter activity "Enables the active transport of a solute across a membrane by a mechanism whereby two or more species are transported together in the same direction in a tightly coupled process not directly linked to a form of energy other than chemiosmotic energy." [GOC:mtg_transport, ISBN:0815340729, PMID:10839820] molecular_function
+ENSG00000100156 GO:0015355 secondary active monocarboxylate transmembrane transporter activity "Catalysis of the movement of a monocarboxylate, any compound containing a single carboxyl group (COOH or COO-), by uniport, symport or antiport across a membrane by a carrier-mediated mechanism." [GOC:bf, GOC:jl] molecular_function
+ENSG00000100156 GO:0015727 lactate transport "The directed movement of lactate into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore. Lactate is 2-hydroxypropanoate, CH3-CHOH-COOH; L(+)-lactate is formed by anaerobic glycolysis in animal tissues, and DL-lactate is found in sour milk, molasses and certain fruit juices." [GOC:ai, ISBN:0198506732] biological_process
+ENSG00000100156 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000100156 GO:0035873 lactate transmembrane transport "The directed movement of lactate across a membrane by means of some agent such as a transporter or pore. Lactate is 2-hydroxypropanoate, CH3-CHOH-COOH; L(+)-lactate is formed by anaerobic glycolysis in animal tissues, and DL-lactate is found in sour milk, molasses and certain fruit juices." [GOC:mcc, ISBN:0198506732] biological_process
+ENSG00000100156 GO:0044237 cellular metabolic process "The chemical reactions and pathways by which individual cells transform chemical substances." [GOC:go_curators] biological_process
+ENSG00000100156 GO:0050900 leukocyte migration "The movement of a leukocyte within or between different tissues and organs of the body." [GOC:add, ISBN:0781735149, PMID:14680625, PMID:14708592, PMID:7507411, PMID:8600538] biological_process
+ENSG00000100156 GO:0015711 organic anion transport "The directed movement of organic anions into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore. Organic anions are atoms or small molecules with a negative charge which contain carbon in covalent linkage." [GOC:ai, GOC:krc] biological_process
+ENSG00000100156 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000100156
+ENSG00000100036 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000100036 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100036 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000100036 GO:0055085 transmembrane transport "The process in which a solute is transported from one side of a membrane to the other." [GOC:dph, GOC:jid] biological_process
+ENSG00000100036 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100036 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000099937 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000099937 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000099937 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000099937 GO:0040011 locomotion "Self-propelled movement of a cell or organism from one location to another." [GOC:dgh] biological_process
+ENSG00000099937 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000099937 GO:0005615 extracellular space "That part of a multicellular organism outside the cells proper, usually taken to be outside the plasma membranes, and occupied by fluid." [ISBN:0198547684] cellular_component
+ENSG00000099937 GO:0005576 extracellular region "The space external to the outermost structure of a cell. For cells without external protective or external encapsulating structures this refers to space outside of the plasma membrane. This term covers the host cell environment outside an intracellular parasite." [GOC:go_curators] cellular_component
+ENSG00000099937 GO:0030234 enzyme regulator activity "Binds to and modulates the activity of an enzyme." [GOC:mah] molecular_function
+ENSG00000099937 GO:0004866 endopeptidase inhibitor activity "Stops, prevents or reduces the activity of an endopeptidase, any enzyme that hydrolyzes nonterminal peptide bonds in polypeptides." [GOC:jl] molecular_function
+ENSG00000099937 GO:0004867 serine-type endopeptidase inhibitor activity "Stops, prevents or reduces the activity of serine-type endopeptidases, enzymes that catalyze the hydrolysis of nonterminal peptide bonds in a polypeptide chain; a serine residue (and a histidine residue) are at the active center of the enzyme." [GOC:ai] molecular_function
+ENSG00000099937 GO:0006935 chemotaxis "The directed movement of a motile cell or organism, or the directed growth of a cell guided by a specific chemical concentration gradient. Movement may be towards a higher concentration (positive chemotaxis) or towards a lower concentration (negative chemotaxis)." [ISBN:0198506732] biological_process
+ENSG00000099937 GO:0007596 blood coagulation "The sequential process in which the multiple coagulation factors of the blood interact, ultimately resulting in the formation of an insoluble fibrin clot; it may be divided into three stages: stage 1, the formation of intrinsic and extrinsic prothrombin converting principle; stage 2, the formation of thrombin; stage 3, the formation of stable fibrin polymers." [http://www.graylab.ac.uk/omd/, ISBN:0198506732] biological_process
+ENSG00000099937 GO:0008201 heparin binding "Interacting selectively and non-covalently with heparin, any member of a group of glycosaminoglycans found mainly as an intracellular component of mast cells and which consist predominantly of alternating alpha-(1->4)-linked D-galactose and N-acetyl-D-glucosamine-6-sulfate residues." [GOC:jl, ISBN:0198506732] molecular_function
+ENSG00000099937 GO:0010951 negative regulation of endopeptidase activity "Any process that decreases the frequency, rate or extent of endopeptidase activity, the endohydrolysis of peptide bonds within proteins." [GOC:dph, GOC:tb] biological_process
+ENSG00000099937 GO:0030162 regulation of proteolysis "Any process that modulates the frequency, rate or extent of the hydrolysis of a peptide bond or bonds within a protein." [GOC:mah] biological_process
+ENSG00000099937 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000099937 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000183307
+ENSG00000099953 GO:0004222 metalloendopeptidase activity "Catalysis of the hydrolysis of internal, alpha-peptide bonds in a polypeptide chain by a mechanism in which water acts as a nucleophile, one or two metal ions hold the water molecule in place, and charged amino acid side chains are ligands for the metal ions." [GOC:mah, http://merops.sanger.ac.uk/about/glossary.htm#CATTYPE, http://merops.sanger.ac.uk/about/glossary.htm#ENDOPEPTIDASE] molecular_function
+ENSG00000099953 GO:0006508 proteolysis "The hydrolysis of proteins into smaller polypeptides and/or amino acids by cleavage of their peptide bonds." [GOC:bf, GOC:mah] biological_process
+ENSG00000099953 GO:0007275 multicellular organismal development "The biological process whose specific outcome is the progression of a multicellular organism over time from an initial condition (e.g. a zygote or a young adult) to a later condition (e.g. a multicellular animal or an aged adult)." [GOC:dph, GOC:ems, GOC:isa_complete, GOC:tb] biological_process
+ENSG00000099953 GO:0008270 zinc ion binding "Interacting selectively and non-covalently with zinc (Zn) ions." [GOC:ai] molecular_function
+ENSG00000099953 GO:0031012 extracellular matrix "A structure lying external to one or more cells, which provides structural support for cells or tissues; may be completely external to the cell (as in animals and bacteria) or be part of the cell (as in plants)." [GOC:mah, NIF_Subcellular:nlx_subcell_20090513] cellular_component
+ENSG00000099953 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000099953 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000099953 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000099953 GO:0008233 peptidase activity "Catalysis of the hydrolysis of a peptide bond. A peptide bond is a covalent bond formed when the carbon atom from the carboxyl group of one amino acid shares electrons with the nitrogen atom from the amino group of a second amino acid." [GOC:jl, ISBN:0815332181] molecular_function
+ENSG00000099953 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000099953 GO:0030198 extracellular matrix organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of an extracellular matrix." [GOC:mah] biological_process
+ENSG00000099953 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000099953 GO:0005578 proteinaceous extracellular matrix "A layer consisting mainly of proteins (especially collagen) and glycosaminoglycans (mostly as proteoglycans) that forms a sheet underlying or overlying cells such as endothelial and epithelial cells. The proteins are secreted by cells in the vicinity. An example of this component is found in Mus musculus." [GOC:mtg_sensu, ISBN:0198547684] cellular_component
+ENSG00000099953 GO:0005576 extracellular region "The space external to the outermost structure of a cell. For cells without external protective or external encapsulating structures this refers to space outside of the plasma membrane. This term covers the host cell environment outside an intracellular parasite." [GOC:go_curators] cellular_component
+ENSG00000099953 GO:0005509 calcium ion binding "Interacting selectively and non-covalently with calcium ions (Ca2+)." [GOC:ai] molecular_function
+ENSG00000099953 GO:0005796 Golgi lumen "The volume enclosed by the membranes of any cisterna or subcompartment of the Golgi apparatus, including the cis- and trans-Golgi networks." [GOC:mah] cellular_component
+ENSG00000099953 GO:0022617 extracellular matrix disassembly "A process that results in the breakdown of the extracellular matrix." [GOC:jid] biological_process
+ENSG00000099953 GO:0030574 collagen catabolic process "The proteolytic chemical reactions and pathways resulting in the breakdown of collagen in the extracellular matrix, usually carried out by proteases secreted by nearby cells." [GOC:mah, ISBN:0815316194] biological_process
+ENSG00000099953 GO:0008237 metallopeptidase activity "Catalysis of the hydrolysis of peptide bonds by a mechanism in which water acts as a nucleophile, one or two metal ions hold the water molecule in place, and charged amino acid side chains are ligands for the metal ions." [GOC:mah, http://merops.sanger.ac.uk/about/glossary.htm#CATTYPE] molecular_function
+ENSG00000099953 GO:0030199 collagen fibril organization "Any process that determines the size and arrangement of collagen fibrils within an extracellular matrix." [GOC:mah, ISBN:0815316194] biological_process
+ENSG00000099953 GO:0045599 negative regulation of fat cell differentiation "Any process that stops, prevents, or reduces the frequency, rate or extent of adipocyte differentiation." [GOC:go_curators] biological_process
+ENSG00000099953 GO:0071711 basement membrane organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of the basement membrane." [GOC:mah] biological_process
+ENSG00000198125 GO:0005344 oxygen transporter activity "Enables the directed movement of oxygen into, out of or within a cell, or between cells." [GOC:ai] molecular_function
+ENSG00000198125 GO:0005506 iron ion binding "Interacting selectively and non-covalently with iron (Fe) ions." [GOC:ai] molecular_function
+ENSG00000198125 GO:0020037 heme binding "Interacting selectively and non-covalently with heme, any compound of iron complexed in a porphyrin (tetrapyrrole) ring." [CHEBI:30413, GOC:ai] molecular_function
+ENSG00000198125 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000198125 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000198125 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000198125 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000198125 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000198125 GO:0050873 brown fat cell differentiation "The process in which a relatively unspecialized cell acquires specialized features of a brown adipocyte, an animal connective tissue cell involved in adaptive thermogenesis. Brown adipocytes contain multiple small droplets of triglycerides and a high number of mitochondria." [PMID:12588810] biological_process
+ENSG00000198125 GO:0007507 heart development "The process whose specific outcome is the progression of the heart over time, from its formation to the mature structure. The heart is a hollow, muscular organ, which, by contracting rhythmically, keeps up the circulation of the blood." [GOC:jid, UBERON:0000948] biological_process
+ENSG00000198125 GO:0001666 response to hypoxia "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a stimulus indicating lowered oxygen tension. Hypoxia, defined as a decline in O2 levels below normoxic levels of 20.8 - 20.95%, results in metabolic adaptation at both the cellular and organismal level." [GOC:hjd] biological_process
+ENSG00000198125 GO:0043353 enucleate erythrocyte differentiation "The process in which a myeloid precursor cell acquires specialized features of an erythrocyte without a nucleus. An example of this process is found in Mus musculus." [GOC:go_curators] biological_process
+ENSG00000198125 GO:0009725 response to hormone "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a hormone stimulus." [GOC:jl] biological_process
+ENSG00000198125 GO:0042542 response to hydrogen peroxide "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a hydrogen peroxide (H2O2) stimulus." [GOC:jl] biological_process
+ENSG00000198125 GO:0019825 oxygen binding "Interacting selectively and non-covalently with oxygen (O2)." [GOC:jl] molecular_function
+ENSG00000198125 GO:0015671 oxygen transport "The directed movement of oxygen (O2) into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:ai] biological_process
+ENSG00000198125 GO:0031444 slow-twitch skeletal muscle fiber contraction "A process in which force is generated within slow-twitch skeletal muscle tissue, resulting in a change in muscle geometry. Force generation involves a chemo-mechanical energy conversion step that is carried out by the actin/myosin complex activity, which generates force through ATP hydrolysis. The slow-twitch skeletal muscle is characterized by slow time parameters, low force development and resistance to fatigue." [GOC:ef, GOC:mah, GOC:mtg_muscle] biological_process
+ENSG00000099917 GO:0001104 RNA polymerase II transcription cofactor activity "Interacting selectively and non-covalently with an RNA polymerase II (RNAP II) regulatory transcription factor and also with the RNAP II basal transcription machinery in order to modulate transcription. Cofactors generally do not bind DNA, but rather mediate protein-protein interactions between regulatory transcription factors and the basal RNAP II transcription machinery." [GOC:txnOH, PMID:10213677, PMID:16858867] molecular_function
+ENSG00000099917 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000099917 GO:0006357 regulation of transcription from RNA polymerase II promoter "Any process that modulates the frequency, rate or extent of transcription from an RNA polymerase II promoter." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000099917 GO:0006366 transcription from RNA polymerase II promoter "The synthesis of RNA from a DNA template by RNA polymerase II, originating at an RNA polymerase II promoter. Includes transcription of messenger RNA (mRNA) and certain small nuclear RNAs (snRNAs)." [GOC:jl, GOC:txnOH, ISBN:0321000382] biological_process
+ENSG00000099917 GO:0016592 mediator complex "A protein complex that interacts with the carboxy-terminal domain of the largest subunit of RNA polymerase II and plays an active role in transducing the signal from a transcription factor to the transcriptional machinery. The mediator complex is required for activation of transcription of most protein-coding genes, but can also act as a transcriptional corepressor. The Saccharomyces complex contains several identifiable subcomplexes: a head domain comprising Srb2, -4, and -5, Med6, -8, and -11, and Rox3 proteins; a middle domain comprising Med1, -4, and -7, Nut1 and -2, Cse2, Rgr1, Soh1, and Srb7 proteins; a tail consisting of Gal11p, Med2p, Pgd1p, and Sin4p; and a regulatory subcomplex comprising Ssn2, -3, and -8, and Srb8 proteins. Metazoan mediator complexes have similar modular structures and include homologs of yeast Srb and Med proteins." [PMID:11454195, PMID:16168358, PMID:17870225] cellular_component
+ENSG00000099917 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000099917 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000099917 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000099917 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000099917 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000099917 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000099917 GO:0000988 protein binding transcription factor activity "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules), in order to modulate transcription. A protein binding transcription factor may or may not also interact with the template nucleic acid (either DNA or RNA) as well." [GOC:txnOH] molecular_function
+ENSG00000099917 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000099917 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000099917 GO:0005654 nucleoplasm "That part of the nuclear content other than the chromosomes or the nucleolus." [GOC:ma, ISBN:0124325653] cellular_component
+ENSG00000099917 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000099917 GO:0006367 transcription initiation from RNA polymerase II promoter "Any process involved in the assembly of the RNA polymerase II preinitiation complex (PIC) at an RNA polymerase II promoter region of a DNA template, resulting in the subsequent synthesis of RNA from that promoter. The initiation phase includes PIC assembly and the formation of the first few bonds in the RNA chain, including abortive initiation, which occurs when the first few nucleotides are repeatedly synthesized and then released. Promoter clearance, or release, is the transition between the initiation and elongation phases of transcription." [GOC:mah, GOC:txnOH] biological_process
+ENSG00000099917 GO:0010467 gene expression "The process in which a gene's sequence is converted into a mature gene product or products (proteins or RNA). This includes the production of an RNA transcript as well as any processing to produce a mature RNA product or an mRNA (for protein-coding genes) and the translation of that mRNA into protein. Some protein processing events may be included when they are required to form an active form of a product from an inactive precursor form." [GOC:dph, GOC:tb] biological_process
+ENSG00000099917 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000099917 GO:0019827 stem cell maintenance "The process by which an organism or tissue maintains a population of stem cells of a single type. This can be achieved by a number of mechanisms: stem cell asymmetric division maintains stem cell numbers; stem cell symmetric division increases them; maintenance of a stem cell niche maintains the conditions for commitment to the stem cell fate for some types of stem cell; stem cells may arise de novo from other cell types. " [GOC:mah, ISBN:0878932437] biological_process
+ENSG00000099917
+ENSG00000168135 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000168135 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000168135 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000168135 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000168135 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000168135 GO:0007267 cell-cell signaling "Any process that mediates the transfer of information from one cell to another." [GOC:mah] biological_process
+ENSG00000168135 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000168135 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000168135 GO:0022857 transmembrane transporter activity "Enables the transfer of a substance from one side of a membrane to the other." [GOC:mtg_transport, ISBN:0815340729] molecular_function
+ENSG00000168135 GO:0005242 inward rectifier potassium channel activity "Catalysis of the transmembrane transfer of a potassium ion by an inwardly-rectifying voltage-gated channel. An inwardly rectifying current-voltage relation is one where at any given driving force the inward flow of K+ ions exceeds the outward flow for the opposite driving force. The inward-rectification is due to a voltage-dependent block of the channel pore by a specific ligand or ligands, and as a result the macroscopic conductance depends on the difference between membrane voltage and the K+ equilibrium potential rather than on membrane voltage itself." [GOC:cb, GOC:mah, PMID:14977398] molecular_function
+ENSG00000168135 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000168135 GO:0006813 potassium ion transport "The directed movement of potassium ions (K+) into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:ai] biological_process
+ENSG00000168135 GO:0007268 synaptic transmission "The process of communication from a neuron to a target (neuron, muscle, or secretory cell) across a synapse." [GOC:jl, MeSH:D009435] biological_process
+ENSG00000168135 GO:0008076 voltage-gated potassium channel complex "A protein complex that forms a transmembrane channel through which potassium ions may cross a cell membrane in response to changes in membrane potential." [GOC:mah] cellular_component
+ENSG00000168135 GO:0016323 basolateral plasma membrane "The region of the plasma membrane that includes the basal end and sides of the cell. Often used in reference to animal polarized epithelial membranes, where the basal membrane is the part attached to the extracellular matrix, or in plant cells, where the basal membrane is defined with respect to the zygotic axis." [GOC:go_curators] cellular_component
+ENSG00000168135 GO:0030054 cell junction "A cellular component that forms a specialized region of connection between two cells or between a cell and the extracellular matrix. At a cell junction, anchoring proteins extend through the plasma membrane to link cytoskeletal proteins in one cell to cytoskeletal proteins in neighboring cells or to proteins in the extracellular matrix." [GOC:mah, http://www.vivo.colostate.edu/hbooks/cmb/cells/pmemb/junctions_a.html, ISBN:0198506732] cellular_component
+ENSG00000168135 GO:0030165 PDZ domain binding "Interacting selectively and non-covalently with a PDZ domain of a protein, a domain found in diverse signaling proteins." [GOC:go_curators, Pfam:PF00595] molecular_function
+ENSG00000168135 GO:0031410 cytoplasmic vesicle "A vesicle formed of membrane or protein, found in the cytoplasm of a cell." [GOC:mah] cellular_component
+ENSG00000168135 GO:0034765 regulation of ion transmembrane transport "Any process that modulates the frequency, rate or extent of the directed movement of ions from one side of a membrane to the other." [GOC:mah] biological_process
+ENSG00000168135 GO:0045211 postsynaptic membrane "A specialized area of membrane facing the presynaptic membrane on the tip of the nerve ending and separated from it by a minute cleft (the synaptic cleft). Neurotransmitters across the synaptic cleft and transmit the signal to the postsynaptic membrane." [ISBN:0198506732] cellular_component
+ENSG00000168135 GO:0071805 potassium ion transmembrane transport "A process in which a potassium ion is transported from one side of a membrane to the other." [GOC:mah] biological_process
+ENSG00000168135 GO:0055085 transmembrane transport "The process in which a solute is transported from one side of a membrane to the other." [GOC:dph, GOC:jid] biological_process
+ENSG00000168135 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000241973 GO:0046854 phosphatidylinositol phosphorylation "The process of introducing one or more phosphate groups into a phosphatidylinositol, any glycerophosphoinositol having one phosphatidyl group esterified to one of the hydroxy groups of inositol." [ISBN:0198506732] biological_process
+ENSG00000241973 GO:0048015 phosphatidylinositol-mediated signaling "A series of molecular signals in which a cell uses a phosphatidylinositol-mediated signaling to convert a signal into a response. Phosphatidylinositols include phosphatidylinositol (PtdIns) and its phosphorylated derivatives." [GOC:bf, GOC:ceb, ISBN:0198506732] biological_process
+ENSG00000241973 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000241973 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000241973 GO:0006629 lipid metabolic process "The chemical reactions and pathways involving lipids, compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent. Includes fatty acids; neutral fats, other fatty-acid esters, and soaps; long-chain (fatty) alcohols and waxes; sphingoids and other long-chain bases; glycolipids, phospholipids and sphingolipids; and carotenes, polyprenols, sterols, terpenes and other isoprenoids." [GOC:ma] biological_process
+ENSG00000241973 GO:0004430 1-phosphatidylinositol 4-kinase activity "Catalysis of the reaction: 1-phosphatidyl-1D-myo-inositol + ATP = 1-phosphatidyl-1D-myo-inositol 4-phosphate + ADP + 2 H(+)." [EC:2.7.1.67, RHEA:19880] molecular_function
+ENSG00000241973 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000241973 GO:0005524 ATP binding "Interacting selectively and non-covalently with ATP, adenosine 5'-triphosphate, a universally important coenzyme and enzyme regulator." [ISBN:0198506732] molecular_function
+ENSG00000241973 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000241973 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000241973 GO:0005925 focal adhesion "Small region on the surface of a cell that anchors the cell to the extracellular matrix and that forms a point of termination of actin filaments." [ISBN:0124325653, ISBN:0815316208] cellular_component
+ENSG00000241973 GO:0006644 phospholipid metabolic process "The chemical reactions and pathways involving phospholipids, any lipid containing phosphoric acid as a mono- or diester." [ISBN:0198506732] biological_process
+ENSG00000241973 GO:0006661 phosphatidylinositol biosynthetic process "The chemical reactions and pathways resulting in the formation of phosphatidylinositol, any glycophospholipid in which the sn-glycerol 3-phosphate residue is esterified to the 1-hydroxyl group of 1D-myo-inositol." [CHEBI:28874, ISBN:0198506732] biological_process
+ENSG00000241973 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000241973 GO:0030660 Golgi-associated vesicle membrane "The lipid bilayer surrounding a vesicle associated with the Golgi apparatus." [GOC:mah] cellular_component
+ENSG00000241973 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000241973 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000241973 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000241973 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000241973 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000241973 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000241973 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000241973 GO:0016301 kinase activity "Catalysis of the transfer of a phosphate group, usually from ATP, to a substrate molecule." [ISBN:0198506732] molecular_function
+ENSG00000241973 GO:0016773 phosphotransferase activity, alcohol group as acceptor "Catalysis of the transfer of a phosphorus-containing group from one compound (donor) to an alcohol group (acceptor)." [GOC:jl] molecular_function
+ENSG00000241973 GO:0016772 transferase activity, transferring phosphorus-containing groups "Catalysis of the transfer of a phosphorus-containing group from one compound (donor) to another (acceptor)." [GOC:jl, ISBN:0198506732] molecular_function
+ENSG00000241973 GO:0005488 binding "The selective, non-covalent, often stoichiometric, interaction of a molecule with one or more specific sites on another molecule." [GOC:ceb, GOC:mah, ISBN:0198506732] molecular_function
+ENSG00000241973
+ENSG00000100324 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000100324 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000100324 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100324 GO:0002376 immune system process "Any process involved in the development or functioning of the immune system, an organismal system for calibrated responses to potential internal or invasive threats." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05] biological_process
+ENSG00000100324 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100324 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100324 GO:0030234 enzyme regulator activity "Binds to and modulates the activity of an enzyme." [GOC:mah] molecular_function
+ENSG00000100324 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000100324 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100324 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100324 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100324 GO:0000187 activation of MAPK activity "The initiation of the activity of the inactive enzyme MAP kinase (MAPK)." [PMID:9561267] biological_process
+ENSG00000100324 GO:0002224 toll-like receptor signaling pathway "Any series of molecular signals generated as a consequence of binding to a toll-like receptor. Toll-like receptors directly bind pattern motifs from a variety of microbial sources to initiate innate immune response." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05, ISBN:0781735149, PMID:12467241, PMID:12524386, PMID:12855817, PMID:15585605, PMID:15728447] biological_process
+ENSG00000100324 GO:0002755 MyD88-dependent toll-like receptor signaling pathway "Any series of molecular signals generated as a consequence of binding to a toll-like receptor where the MyD88 adaptor molecule mediates transduction of the signal. Toll-like receptors directly bind pattern motifs from a variety of microbial sources to initiate innate immune response." [GOC:add, ISBN:0781735149, PMID:12467241, PMID:12524386, PMID:12855817, PMID:15585605, PMID:15728447] biological_process
+ENSG00000100324 GO:0002756 MyD88-independent toll-like receptor signaling pathway "Any series of molecular signals generated as a consequence of binding to a toll-like receptor not relying on the MyD88 adaptor molecule. Toll-like receptors directly bind pattern motifs from a variety of microbial sources to initiate innate immune response." [GOC:add, ISBN:0781735149, PMID:12467241, PMID:12524386, PMID:12855817, PMID:15585605, PMID:15728447] biological_process
+ENSG00000100324 GO:0003824 catalytic activity "Catalysis of a biochemical reaction at physiological temperatures. In biologically catalyzed reactions, the reactants are known as substrates, and the catalysts are naturally occurring macromolecular substances known as enzymes. Enzymes possess specific binding sites for substrates, and are usually composed wholly or largely of protein, but RNA that has catalytic activity (ribozyme) is often also regarded as enzymatic." [ISBN:0198506732] molecular_function
+ENSG00000100324 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100324 GO:0007249 I-kappaB kinase/NF-kappaB signaling "The process in which a signal is passed on to downstream components within the cell through the I-kappaB-kinase (IKK)-dependent activation of NF-kappaB. The cascade begins with activation of a trimeric IKK complex (consisting of catalytic kinase subunits IKKalpha and/or IKKbeta, and the regulatory scaffold protein NEMO) and ends with the regulation of transcription of target genes by NF-kappaB. In a resting state, NF-kappaB dimers are bound to I-kappaB proteins, sequestering NF-kappaB in the cytoplasm. Phosphorylation of I-kappaB targets I-kappaB for ubiquitination and proteasomal degradation, thus releasing the NF-kappaB dimers, which can translocate to the nucleus to bind DNA and regulate transcription." [GOC:bf, GOC:jl, PMID:12773372, Reactome:REACT_13696.1] biological_process
+ENSG00000100324 GO:0007254 JNK cascade "An intracellular protein kinase cascade containing at least a JNK (a MAPK), a JNKK (a MAPKK) and a JUN3K (a MAP3K). The cascade can also contain two additional tiers: the upstream MAP4K and the downstream MAP Kinase-activated kinase (MAPKAPK). The kinases in each tier phosphorylate and activate the kinases in the downstream tier to transmit a signal within a cell." [GOC:bf, GOC:signaling, PMID:11790549, PMID:20811974] biological_process
+ENSG00000100324 GO:0008047 enzyme activator activity "Increases the activity of an enzyme." [GOC:mah] molecular_function
+ENSG00000100324 GO:0010008 endosome membrane "The lipid bilayer surrounding an endosome." [GOC:mah] cellular_component
+ENSG00000100324 GO:0034134 toll-like receptor 2 signaling pathway "Any series of molecular signals generated as a consequence of binding to toll-like receptor 2." [GOC:add, PMID:16551253, PMID:17328678] biological_process
+ENSG00000100324 GO:0034138 toll-like receptor 3 signaling pathway "Any series of molecular signals generated as a consequence of binding to toll-like receptor 3." [GOC:add, PMID:16551253, PMID:17328678] biological_process
+ENSG00000100324 GO:0034142 toll-like receptor 4 signaling pathway "Any series of molecular signals generated as a consequence of binding to toll-like receptor 4." [GOC:add, PMID:16551253, PMID:17328678] biological_process
+ENSG00000100324 GO:0034146 toll-like receptor 5 signaling pathway "Any series of molecular signals generated as a consequence of binding to toll-like receptor 5." [GOC:add, PMID:16551253, PMID:17328678] biological_process
+ENSG00000100324 GO:0034162 toll-like receptor 9 signaling pathway "Any series of molecular signals generated as a consequence of binding to toll-like receptor 9." [GOC:add, PMID:16551253, PMID:17328678] biological_process
+ENSG00000100324 GO:0034166 toll-like receptor 10 signaling pathway "Any series of molecular signals generated as a consequence of binding to toll-like receptor 10." [GOC:add, PMID:16551253, PMID:17328678] biological_process
+ENSG00000100324 GO:0035666 TRIF-dependent toll-like receptor signaling pathway "Any series of molecular signals generated as a consequence of binding to a toll-like receptor where the TRIF adaptor mediates transduction of the signal. Toll-like receptors directly bind pattern motifs from a variety of microbial sources to initiate innate immune response." [GOC:BHF, PMID:12855817] biological_process
+ENSG00000100324 GO:0035872 nucleotide-binding domain, leucine rich repeat containing receptor signaling pathway "A series of molecular signals generated as a consequence of a nucleotide-binding domain, leucine rich repeat containing receptor (NLR) binding to one of its physiological ligands. NLRs are cytoplasmic receptors defined by their tripartite domain architecture that contains: a variable C-terminus, a middle nucleotide-binding domain, and a LRR domain that is variable in the repeats composition and number. The NLR signaling pathway begins with binding of a ligand to a NLR receptor and ends with regulation of a downstream cellular process." [GOC:sj, PMID:18280719, Reactome:168643] biological_process
+ENSG00000100324 GO:0038095 Fc-epsilon receptor signaling pathway "A series of molecular signals initiated by the binding of the Fc portion of immunoglobulin E (IgE) to an Fc-epsilon receptor on the surface of a signal-receiving cell, and ending with regulation of a downstream cellular process, e.g. transcription. The Fc portion of an immunoglobulin is its C-terminal constant region." [GOC:phg, PMID:12413516, PMID:15048725] biological_process
+ENSG00000100324 GO:0038123 toll-like receptor TLR1:TLR2 signaling pathway "A series of molecular signals initiated by the binding of a heterodimeric TLR1:TLR2 complex to one of it's physiological ligands, followed by transmission of the signal by the activated receptor, and ending with regulation of a downstream cellular process, e.g. transcription." [GOC:nhn, GOC:signaling, PMID:17318230] biological_process
+ENSG00000100324 GO:0038124 toll-like receptor TLR6:TLR2 signaling pathway "A series of molecular signals initiated by the binding of a heterodimeric TLR6:TLR2 complex to one of it's physiological ligands, followed by transmission of the signal by the activated receptor, and ending with regulation of a downstream cellular process, e.g. transcription." [GOC:nhn, GOC:signaling, PMID:17318230] biological_process
+ENSG00000100324 GO:0045087 innate immune response "Innate immune responses are defense responses mediated by germline encoded components that directly recognize components of potential pathogens." [GO_REF:0000022, GOC:add, GOC:ebc, GOC:mtg_15nov05, GOC:mtg_sensu] biological_process
+ENSG00000100324 GO:0051092 positive regulation of NF-kappaB transcription factor activity "Any process that activates or increases the frequency, rate or extent of activity of the transcription factor NF-kappaB." [GOC:dph, GOC:tb, PMID:15087454, PMID:15170030] biological_process
+ENSG00000100324 GO:0051403 stress-activated MAPK cascade "A series of molecular signals in which a stress-activated MAP kinase cascade relays one or more of the signals; MAP kinase cascades involve at least three protein kinase activities and culminate in the phosphorylation and activation of a MAP kinase." [GOC:ai, PMID:15936270] biological_process
+ENSG00000100324 GO:0070423 nucleotide-binding oligomerization domain containing signaling pathway "Any series of molecular signals generated as a consequence of binding to a nucleotide-binding oligomerization domain containing (NOD) protein." [GOC:add, PMID:17944960, PMID:18585455] biological_process
+ENSG00000100324 GO:0001701 in utero embryonic development "The process whose specific outcome is the progression of the embryo in the uterus over time, from formation of the zygote in the oviduct, to birth. An example of this process is found in Mus musculus." [GOC:go_curators, GOC:mtg_sensu] biological_process
+ENSG00000100324 GO:0007179 transforming growth factor beta receptor signaling pathway "A series of molecular signals initiated by the binding of an extracellular ligand to a transforming growth factor beta receptor on the surface of a target cell, and ending with regulation of a downstream cellular process, e.g. transcription." [GOC:BHF, GOC:mah, GOC:signaling] biological_process
+ENSG00000100324 GO:0030324 lung development "The process whose specific outcome is the progression of the lung over time, from its formation to the mature structure. In all air-breathing vertebrates the lungs are developed from the ventral wall of the oesophagus as a pouch which divides into two sacs. In amphibians and many reptiles the lungs retain very nearly this primitive sac-like character, but in the higher forms the connection with the esophagus becomes elongated into the windpipe and the inner walls of the sacs become more and more divided, until, in the mammals, the air spaces become minutely divided into tubes ending in small air cells, in the walls of which the blood circulates in a fine network of capillaries. In mammals the lungs are more or less divided into lobes, and each lung occupies a separate cavity in the thorax." [GOC:jid, UBERON:0002048] biological_process
+ENSG00000100324 GO:0003007 heart morphogenesis "The developmental process in which the heart is generated and organized. The heart is a hollow, muscular organ, which, by contracting rhythmically, keeps up the circulation of the blood." [GOC:dph, GOC:isa_complete] biological_process
+ENSG00000100324 GO:0019209 kinase activator activity "Increases the activity of a kinase, an enzyme which catalyzes of the transfer of a phosphate group, usually from ATP, to a substrate molecule." [GOC:ai] molecular_function
+ENSG00000100324 GO:0000185 activation of MAPKKK activity "Any process that initiates the activity of the inactive enzyme MAP kinase kinase kinase (MAPKKK)." [PMID:9561267] biological_process
+ENSG00000100324 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100324 GO:0032403 protein complex binding "Interacting selectively and non-covalently with any protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:mah] molecular_function
+ENSG00000100324 GO:0043406 positive regulation of MAP kinase activity "Any process that activates or increases the frequency, rate or extent of MAP kinase activity." [GOC:dph, GOC:go_curators] biological_process
+ENSG00000100324 GO:0048273 mitogen-activated protein kinase p38 binding "Interacting selectively and non-covalently with mitogen-activated protein kinase p38, an enzyme that catalyzes the transfer of phosphate from ATP to hydroxyl side chains on proteins in response to mitogen activation." [GOC:curators, PMID:17827184] molecular_function
+ENSG00000100219 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100219 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000100219 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000100219 GO:0002376 immune system process "Any process involved in the development or functioning of the immune system, an organismal system for calibrated responses to potential internal or invasive threats." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05] biological_process
+ENSG00000100219 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100219 GO:0005654 nucleoplasm "That part of the nuclear content other than the chromosomes or the nucleolus." [GOC:ma, ISBN:0124325653] cellular_component
+ENSG00000100219 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100219 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100219 GO:0001071 nucleic acid binding transcription factor activity "Interacting selectively and non-covalently with a DNA or RNA sequence in order to modulate transcription. The transcription factor may or may not also interact selectively with a protein or macromolecular complex." [GOC:txnOH] molecular_function
+ENSG00000100219 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100219 GO:0003677 DNA binding "Any molecular function by which a gene product interacts selectively and non-covalently with DNA (deoxyribonucleic acid)." [GOC:dph, GOC:jl, GOC:tb, GOC:vw] molecular_function
+ENSG00000100219 GO:0003700 sequence-specific DNA binding transcription factor activity "Interacting selectively and non-covalently with a specific DNA sequence in order to modulate transcription. The transcription factor may or may not also interact selectively with a protein or macromolecular complex." [GOC:curators, GOC:txnOH] molecular_function
+ENSG00000100219 GO:0006955 immune response "Any immune system process that functions in the calibrated response of an organism to a potential internal or invasive threat." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05] biological_process
+ENSG00000100219 GO:0006987 activation of signaling protein activity involved in unfolded protein response "The conversion of a specific protein, possessing protein kinase and endoribonuclease activities, to an active form as a result of signaling via the unfolded protein response." [GOC:dph, GOC:mah, GOC:tb, PMID:12042763] biological_process
+ENSG00000100219 GO:0030968 endoplasmic reticulum unfolded protein response "The series of molecular signals generated as a consequence of the presence of unfolded proteins in the endoplasmic reticulum (ER) or other ER-related stress; results in changes in the regulation of transcription and translation." [GOC:mah, PMID:12042763] biological_process
+ENSG00000100219 GO:0044267 cellular protein metabolic process "The chemical reactions and pathways involving a specific protein, rather than of proteins in general, occurring at the level of an individual cell. Includes cellular protein modification." [GOC:jl] biological_process
+ENSG00000100219 GO:1900103 positive regulation of endoplasmic reticulum unfolded protein response "Any process that activates or increases the frequency, rate or extent of endoplasmic reticulum unfolded protein response." [GOC:TermGenie] biological_process
+ENSG00000100219 GO:0043565 sequence-specific DNA binding "Interacting selectively and non-covalently with DNA of a specific nucleotide composition, e.g. GC-rich DNA binding, or with a specific sequence motif or type of DNA e.g. promotor binding or rDNA binding." [GOC:jl] molecular_function
+ENSG00000100219 GO:0006355 regulation of transcription, DNA-templated "Any process that modulates the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000100219 GO:0045944 positive regulation of transcription from RNA polymerase II promoter "Any process that activates or increases the frequency, rate or extent of transcription from an RNA polymerase II promoter." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000100219 GO:0000981 sequence-specific DNA binding RNA polymerase II transcription factor activity "Interacting selectively and non-covalently with a specific DNA sequence in order to modulate transcription by RNA polymerase II. The transcription factor may or may not also interact selectively with a protein or macromolecular complex." [GOC:txnOH] molecular_function
+ENSG00000100219 GO:0042593 glucose homeostasis "Any process involved in the maintenance of an internal steady state of glucose within an organism or cell." [GOC:go_curators] biological_process
+ENSG00000100219 GO:0002070 epithelial cell maturation "The developmental process, independent of morphogenetic (shape) change, that is required for an epithelial cell to attain its fully functional state. An epithelial cell is a cell usually found in a two-dimensional sheet with a free surface." [GOC:dph] biological_process
+ENSG00000100219 GO:0001085 RNA polymerase II transcription factor binding "Interacting selectively and non-covalently with an RNA polymerase II transcription factor, any protein required to initiate or regulate transcription by RNA polymerase II." [GOC:txnOH] molecular_function
+ENSG00000100219 GO:0031017 exocrine pancreas development "The process whose specific outcome is the progression of the exocrine pancreas over time, from its formation to the mature structure. The exocrine pancreas produces and store zymogens of digestive enzymes, such as chymotrypsinogen and trypsinogen in the acinar cells." [GOC:cvs] biological_process
+ENSG00000100219 GO:0060691 epithelial cell maturation involved in salivary gland development "The developmental process, independent of morphogenetic (shape) change, that is required for an epithelial cell of the salivary gland to attain its fully functional state." [GOC:dph] biological_process
+ENSG00000100219 GO:1901800 positive regulation of proteasomal protein catabolic process "Any process that activates or increases the frequency, rate or extent of proteasomal protein catabolic process." [GOC:BHF, GOC:rl, GOC:TermGenie, PMID:21669198] biological_process
+ENSG00000100219 GO:0042493 response to drug "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a drug stimulus. A drug is a substance used in the diagnosis, treatment or prevention of a disease." [GOC:jl] biological_process
+ENSG00000100219 GO:0071236 cellular response to antibiotic "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of an antibiotic stimulus. An antibiotic is a chemical substance produced by a microorganism which has the capacity to inhibit the growth of or to kill other microorganisms." [GOC:mah] biological_process
+ENSG00000100219 GO:0051602 response to electrical stimulus "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of an electrical stimulus." [GOC:ai] biological_process
+ENSG00000100219 GO:0060096 serotonin secretion, neurotransmission "The regulated release of serotonin by a cell, in which released serotonin acts as a neurotransmitter." [GOC:dph] biological_process
+ENSG00000100219 GO:0006366 transcription from RNA polymerase II promoter "The synthesis of RNA from a DNA template by RNA polymerase II, originating at an RNA polymerase II promoter. Includes transcription of messenger RNA (mRNA) and certain small nuclear RNAs (snRNAs)." [GOC:jl, GOC:txnOH, ISBN:0321000382] biological_process
+ENSG00000250479
+ENSG00000250479 GO:0007005 mitochondrion organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of a mitochondrion; includes mitochondrial morphogenesis and distribution, and replication of the mitochondrial genome as well as synthesis of new mitochondrial components." [GOC:dph, GOC:jl, GOC:mah, GOC:sgd_curators, PMID:9786946] biological_process
+ENSG00000250479 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000250479 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000250479 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000250479 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000250479 GO:0006091 generation of precursor metabolites and energy "The chemical reactions and pathways resulting in the formation of precursor metabolites, substances from which energy is derived, and any process involved in the liberation of energy from these substances." [GOC:jl] biological_process
+ENSG00000250479 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000250479 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000250479 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000250479 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000250479 GO:0005758 mitochondrial intermembrane space "The region between the inner and outer lipid bilayers of the mitochondrial envelope." [GOC:mah] cellular_component
+ENSG00000250479 GO:0006119 oxidative phosphorylation "The phosphorylation of ADP to ATP that accompanies the oxidation of a metabolite through the operation of the respiratory chain. Oxidation of compounds establishes a proton gradient across the membrane, providing the energy for ATP synthesis." [ISBN:0198506732, ISBN:0471331309] biological_process
+ENSG00000250479 GO:0006754 ATP biosynthetic process "The chemical reactions and pathways resulting in the formation of ATP, adenosine 5'-triphosphate, a universally important coenzyme and enzyme regulator." [GOC:go_curators, ISBN:0198506732] biological_process
+ENSG00000250479 GO:2000984 negative regulation of ATP citrate synthase activity "Any process that stops, prevents or reduces the frequency, rate or extent of ATP citrate synthase activity." [GOC:BHF] biological_process
+ENSG00000100162 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100162 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100162 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100162 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100162 GO:0007049 cell cycle "The progression of biochemical and morphological phases and events that occur in a cell during successive cell replication or nuclear replication events. Canonically, the cell cycle comprises the replication and segregation of genetic material followed by the division of the cell, but in endocycles or syncytial cells nuclear replication or nuclear division may not be followed by cell division." [GOC:go_curators, GOC:mtg_cell_cycle] biological_process
+ENSG00000100162 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100162 GO:0000278 mitotic cell cycle "Progression through the phases of the mitotic cell cycle, the most common eukaryotic cell cycle, which canonically comprises four successive phases called G1, S, G2, and M and includes replication of the genome and the subsequent segregation of chromosomes into daughter cells. In some variant cell cycles nuclear replication or nuclear division may not be followed by cell division, or G1 and G2 phases may be absent." [GOC:mah, ISBN:0815316194, Reactome:69278] biological_process
+ENSG00000100162 GO:0000776 kinetochore "A multisubunit complex that is located at the centromeric region of DNA and provides an attachment point for the spindle microtubules." [GOC:elh] cellular_component
+ENSG00000100162 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000100162
+ENSG00000242259
+ENSG00000183597
+ENSG00000183597 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000099904 GO:0008152 metabolic process "The chemical reactions and pathways, including anabolism and catabolism, by which living organisms transform chemical substances. Metabolic processes typically transform small molecules, but also include macromolecular processes such as DNA repair and replication, and protein synthesis and degradation." [GOC:go_curators, ISBN:0198547684] biological_process
+ENSG00000099904 GO:0008270 zinc ion binding "Interacting selectively and non-covalently with zinc (Zn) ions." [GOC:ai] molecular_function
+ENSG00000099904 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000099904 GO:0019706 protein-cysteine S-palmitoyltransferase activity "Catalysis of the transfer of a palmitoyl group to a sulfur atom on the cysteine of a protein molecule." [EC:2.3.1.225, GOC:ai, GOC:pr] molecular_function
+ENSG00000099904 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000099904 GO:0016746 transferase activity, transferring acyl groups "Catalysis of the transfer of an acyl group from one compound (donor) to another (acceptor)." [GOC:jl, ISBN:0198506732] molecular_function
+ENSG00000099904 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000099904 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000099904 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000099904 GO:0004872 receptor activity "Combining with an extracellular or intracellular messenger to initiate a change in cell activity." [GOC:ceb, ISBN:0198506732] molecular_function
+ENSG00000099904 GO:0007275 multicellular organismal development "The biological process whose specific outcome is the progression of a multicellular organism over time from an initial condition (e.g. a zygote or a young adult) to a later condition (e.g. a multicellular animal or an aged adult)." [GOC:dph, GOC:ems, GOC:isa_complete, GOC:tb] biological_process
+ENSG00000099904 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000099904 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000099904 GO:0005794 Golgi apparatus "A compound membranous cytoplasmic organelle of eukaryotic cells, consisting of flattened, ribosome-free vesicles arranged in a more or less regular stack. The Golgi apparatus differs from the endoplasmic reticulum in often having slightly thicker membranes, appearing in sections as a characteristic shallow semicircle so that the convex side (cis or entry face) abuts the endoplasmic reticulum, secretory vesicles emerging from the concave side (trans or exit face). In vertebrate cells there is usually one such organelle, while in invertebrates and plants, where they are known usually as dictyosomes, there may be several scattered in the cytoplasm. The Golgi apparatus processes proteins produced on the ribosomes of the rough endoplasmic reticulum; such processing includes modification of the core oligosaccharides of glycoproteins, and the sorting and packaging of proteins for transport to a variety of cellular locations. Three different regions of the Golgi are now recognized both in terms of structure and function: cis, in the vicinity of the cis face, trans, in the vicinity of the trans face, and medial, lying between the cis and trans regions." [ISBN:0198506732] cellular_component
+ENSG00000099904 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000099904 GO:0016409 palmitoyltransferase activity "Catalysis of the transfer of a palmitoyl (CH3-[CH2]14-CO-) group to an acceptor molecule." [GOC:ai] molecular_function
+ENSG00000099904 GO:0018345 protein palmitoylation "The covalent attachment of a palmitoyl group to a protein." [GOC:jl, PMID:15520806] biological_process
+ENSG00000099904 GO:0031410 cytoplasmic vesicle "A vesicle formed of membrane or protein, found in the cytoplasm of a cell." [GOC:mah] cellular_component
+ENSG00000099904 GO:0007626 locomotory behavior "The specific movement from place to place of an organism in response to external or internal stimuli. Locomotion of a whole organism in a manner dependent upon some combination of that organism's internal state and external conditions." [GOC:dph] biological_process
+ENSG00000099904 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000099904 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000128346 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000128346 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000128346 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000128346
+ENSG00000093010 GO:0000287 magnesium ion binding "Interacting selectively and non-covalently with magnesium (Mg) ions." [GOC:ai] molecular_function
+ENSG00000093010 GO:0032259 methylation "The process in which a methyl group is covalently attached to a molecule." [GOC:mah] biological_process
+ENSG00000093010 GO:0042135 neurotransmitter catabolic process "The chemical reactions and pathways resulting in the breakdown of any of a group of substances that are released on excitation from the axon terminal of a presynaptic neuron of the central or peripheral nervous system and travel across the synaptic cleft to either excite or inhibit the target cell." [CHEBI:25512, GOC:jl] biological_process
+ENSG00000093010 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000093010 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000093010 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000093010 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000093010 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000093010 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000093010 GO:0008168 methyltransferase activity "Catalysis of the transfer of a methyl group to an acceptor molecule." [ISBN:0198506732] molecular_function
+ENSG00000093010 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000093010 GO:0007267 cell-cell signaling "Any process that mediates the transfer of information from one cell to another." [GOC:mah] biological_process
+ENSG00000093010 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000093010 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000093010 GO:0006805 xenobiotic metabolic process "The chemical reactions and pathways involving a xenobiotic compound, a compound foreign to living organisms. Used of chemical compounds, e.g. a xenobiotic chemical, such as a pesticide." [GOC:cab2] biological_process
+ENSG00000093010 GO:0007268 synaptic transmission "The process of communication from a neuron to a target (neuron, muscle, or secretory cell) across a synapse." [GOC:jl, MeSH:D009435] biological_process
+ENSG00000093010 GO:0008171 O-methyltransferase activity "Catalysis of the transfer of a methyl group to the oxygen atom of an acceptor molecule." [GOC:ai] molecular_function
+ENSG00000093010 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000093010 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000093010 GO:0016206 catechol O-methyltransferase activity "Catalysis of the reaction: S-adenosyl-L-methionine + a catechol = S-adenosyl-L-homocysteine + a guaiacol." [EC:2.1.1.6] molecular_function
+ENSG00000093010 GO:0042136 neurotransmitter biosynthetic process "The chemical reactions and pathways resulting in the formation of any of a group of substances that are released on excitation from the axon terminal of a presynaptic neuron of the central or peripheral nervous system and travel across the synaptic cleft to either excite or inhibit the target cell." [CHEBI:25512, GOC:jl] biological_process
+ENSG00000093010 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000093010 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000093010 GO:0006584 catecholamine metabolic process "The chemical reactions and pathways involving any of a group of physiologically important biogenic amines that possess a catechol (3,4-dihydroxyphenyl) nucleus and are derivatives of 3,4-dihydroxyphenylethylamine." [GOC:jl, ISBN:0198506732] biological_process
+ENSG00000093010 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000093010 GO:0042417 dopamine metabolic process "The chemical reactions and pathways involving dopamine, a catecholamine neurotransmitter and a metabolic precursor of noradrenaline and adrenaline." [GOC:jl, ISBN:0198506732] biological_process
+ENSG00000093010 GO:0009712 catechol-containing compound metabolic process "The chemical reactions and pathways involving a compound containing a pyrocatechol (1,2-benzenediol) nucleus or substituent." [GOC:sm, ISBN:0198547684] biological_process
+ENSG00000093010 GO:0016036 cellular response to phosphate starvation "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of deprivation of phosphate." [GOC:jl] biological_process
+ENSG00000093010 GO:0042420 dopamine catabolic process "The chemical reactions and pathways resulting in the breakdown of dopamine, a catecholamine neurotransmitter and a metabolic precursor of noradrenaline and adrenaline." [GOC:jl, ISBN:0198506732] biological_process
+ENSG00000093010 GO:0030424 axon "The long process of a neuron that conducts nerve impulses, usually away from the cell body to the terminals and varicosities, which are sites of storage and release of neurotransmitter." [GOC:nln, ISBN:0198506732] cellular_component
+ENSG00000093010 GO:0042493 response to drug "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a drug stimulus. A drug is a substance used in the diagnosis, treatment or prevention of a disease." [GOC:jl] biological_process
+ENSG00000093010 GO:0032496 response to lipopolysaccharide "Any process that results in a change in state or activity of an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a lipopolysaccharide stimulus; lipopolysaccharide is a major component of the cell wall of gram-negative bacteria." [GOC:add, ISBN:0721601464] biological_process
+ENSG00000093010 GO:0014070 response to organic cyclic compound "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of an organic cyclic compound stimulus." [CHEBI:33832, GOC:ef] biological_process
+ENSG00000093010 GO:0030425 dendrite "A neuron projection that has a short, tapering, often branched, morphology, receives and integrates signals from other neurons or from sensory stimuli, and conducts a nerve impulse towards the axon or the cell body. In most neurons, the impulse is conveyed from dendrites to axon via the cell body, but in some types of unipolar neuron, the impulse does not travel via the cell body." [GOC:dos, GOC:mah, GOC:nln, ISBN:0198506732] cellular_component
+ENSG00000093010 GO:0044297 cell body "The portion of a cell bearing surface projections such as axons, dendrites, cilia, or flagella that includes the nucleus, but excludes all cell projections." [GOC:go_curators] cellular_component
+ENSG00000093010 GO:0043197 dendritic spine "Protrusion from a dendrite. Spines are specialised subcellular compartments involved in the synaptic transmission. They are linked to the dendritic shaft by a restriction. Because of their bulb shape, they function as a biochemical and an electrical compartment. Spine remodeling is though to be involved in synaptic plasticity." [GOC:nln] cellular_component
+ENSG00000093010 GO:0007565 female pregnancy "The set of physiological processes that allow an embryo or foetus to develop within the body of a female animal. It covers the time from fertilization of a female ovum by a male spermatozoon until birth." [ISBN:0192800825] biological_process
+ENSG00000093010 GO:0045211 postsynaptic membrane "A specialized area of membrane facing the presynaptic membrane on the tip of the nerve ending and separated from it by a minute cleft (the synaptic cleft). Neurotransmitters across the synaptic cleft and transmit the signal to the postsynaptic membrane." [ISBN:0198506732] cellular_component
+ENSG00000093010 GO:0051930 regulation of sensory perception of pain "Any process that modulates the frequency, rate or extent of the sensory perception of pain, the series of events required for an organism to receive a painful stimulus, convert it to a molecular signal, and recognize and characterize the signal." [GOC:ai] biological_process
+ENSG00000093010 GO:0035814 negative regulation of renal sodium excretion "Any process that decreases the amount of sodium excreted in urine over a unit of time." [GOC:mtg_25march11, GOC:yaf] biological_process
+ENSG00000093010 GO:0048662 negative regulation of smooth muscle cell proliferation "Any process that stops, prevents or reduces the rate or extent of smooth muscle cell proliferation." [CL:0000192, GOC:ebc] biological_process
+ENSG00000093010 GO:0050668 positive regulation of homocysteine metabolic process "Any process that activates or increases the frequency, rate or extent of the chemical reactions and pathways involving homocysteine." [GOC:ai] biological_process
+ENSG00000093010 GO:0048609 multicellular organismal reproductive process "The process, occurring above the cellular level, that is pertinent to the reproductive function of a multicellular organism. This includes the integrated processes at the level of tissues and organs." [GOC:dph, GOC:jid, GOC:tb] biological_process
+ENSG00000093010 GO:0008210 estrogen metabolic process "The chemical reactions and pathways involving estrogens, C18 steroid hormones that can stimulate the development of female sexual characteristics. Also found in plants." [ISBN:0198506732] biological_process
+ENSG00000093010 GO:0045963 negative regulation of dopamine metabolic process "Any process that stops, prevents, or reduces the frequency, rate or extent of the chemical reactions and pathways involving dopamine." [GOC:go_curators] biological_process
+ENSG00000093010 GO:0048265 response to pain "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a pain stimulus. Pain stimuli cause activation of nociceptors, peripheral receptors for pain, include receptors which are sensitive to painful mechanical stimuli, extreme heat or cold, and chemical stimuli." [GOC:jid, PMID:10203867, PMID:12723742, PMID:12843304, Wikipedia:Pain] biological_process
+ENSG00000093010 GO:0032502 developmental process "A biological process whose specific outcome is the progression of an integrated living unit: an anatomical structure (which may be a subcellular structure, cell, tissue, or organ), or organism over time from an initial condition to a later condition." [GOC:isa_complete] biological_process
+ENSG00000093010 GO:0007614 short-term memory "The memory process that deals with the storage, retrieval and modification of information received a short time (up to about 30 minutes) ago. This type of memory is typically dependent on direct, transient effects of second messenger activation." [http://hebb.mit.edu/courses/9.03/lecture4.html, ISBN:0582227089] biological_process
+ENSG00000093010 GO:0007612 learning "Any process in an organism in which a relatively long-lasting adaptive behavioral change occurs as the result of experience." [ISBN:0582227089, ISBN:0721662544] biological_process
+ENSG00000099991 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000099991 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000099991 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000099991 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000099991 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000099991 GO:0006461 protein complex assembly "The aggregation, arrangement and bonding together of a set of components to form a protein complex." [GOC:ai] biological_process
+ENSG00000099991 GO:0022607 cellular component assembly "The aggregation, arrangement and bonding together of a cellular component." [GOC:isa_complete] biological_process
+ENSG00000099991 GO:0065003 macromolecular complex assembly "The aggregation, arrangement and bonding together of a set of macromolecules to form a complex." [GOC:jl] biological_process
+ENSG00000099991 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000099991 GO:0005654 nucleoplasm "That part of the nuclear content other than the chromosomes or the nucleolus." [GOC:ma, ISBN:0124325653] cellular_component
+ENSG00000099991 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000099991 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000099991 GO:0030234 enzyme regulator activity "Binds to and modulates the activity of an enzyme." [GOC:mah] molecular_function
+ENSG00000099991 GO:0004864 protein phosphatase inhibitor activity "Stops, prevents or reduces the activity of a protein phosphatase, an enzyme that hydrolyzes phosphate groups from phosphorylated proteins." [GOC:ai] molecular_function
+ENSG00000099991 GO:0006336 DNA replication-independent nucleosome assembly "The formation of nucleosomes outside the context of DNA replication." [GOC:mah] biological_process
+ENSG00000099991 GO:0007166 cell surface receptor signaling pathway "A series of molecular signals initiated by activation of a receptor on the surface of a cell. The pathway begins with binding of an extracellular ligand to a cell surface receptor, or for receptors that signal in the absence of a ligand, by ligand-withdrawal or the activity of a constitutively active receptor. The pathway ends with regulation of a downstream cellular process, e.g. transcription." [GOC:bf, GOC:mah, GOC:pr, GOC:signaling] biological_process
+ENSG00000099991 GO:0016235 aggresome "An inclusion body formed by dynein-dependent retrograde transport of an aggregated protein on microtubules." [PMID:11121744] cellular_component
+ENSG00000099991 GO:0016568 chromatin modification "The alteration of DNA, protein, or sometimes RNA, in chromatin, which may result in changing the chromatin structure." [GOC:mah, PMID:20404130] biological_process
+ENSG00000099991 GO:0043086 negative regulation of catalytic activity "Any process that stops or reduces the activity of an enzyme." [GOC:jl, GOC:tb] biological_process
+ENSG00000099991
+ENSG00000100426 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100426 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100426 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100426 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100426 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100426 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100426 GO:0003677 DNA binding "Any molecular function by which a gene product interacts selectively and non-covalently with DNA (deoxyribonucleic acid)." [GOC:dph, GOC:jl, GOC:tb, GOC:vw] molecular_function
+ENSG00000100426 GO:0046872 metal ion binding "Interacting selectively and non-covalently with any metal ion." [GOC:ai] molecular_function
+ENSG00000100426 GO:0046983 protein dimerization activity "The formation of a protein dimer, a macromolecular structure consists of two noncovalently associated identical or nonidentical subunits." [ISBN:0198506732] molecular_function
+ENSG00000100426 GO:0003676 nucleic acid binding "Interacting selectively and non-covalently with any nucleic acid." [GOC:jl] molecular_function
+ENSG00000169314
+ENSG00000100121 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100121 GO:0006629 lipid metabolic process "The chemical reactions and pathways involving lipids, compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent. Includes fatty acids; neutral fats, other fatty-acid esters, and soaps; long-chain (fatty) alcohols and waxes; sphingoids and other long-chain bases; glycolipids, phospholipids and sphingolipids; and carotenes, polyprenols, sterols, terpenes and other isoprenoids." [GOC:ma] biological_process
+ENSG00000100121 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100121 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000100121 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000100121 GO:0006520 cellular amino acid metabolic process "The chemical reactions and pathways involving amino acids, carboxylic acids containing one or more amino groups, as carried out by individual cells." [CHEBI:33709, GOC:curators, ISBN:0198506732] biological_process
+ENSG00000100121 GO:0006790 sulfur compound metabolic process "The chemical reactions and pathways involving the nonmetallic element sulfur or compounds that contain sulfur, such as the amino acids methionine and cysteine or the tripeptide glutathione." [GOC:ai] biological_process
+ENSG00000100121 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100121 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100121 GO:0016746 transferase activity, transferring acyl groups "Catalysis of the transfer of an acyl group from one compound (donor) to another (acceptor)." [GOC:jl, ISBN:0198506732] molecular_function
+ENSG00000100121 GO:0003840 gamma-glutamyltransferase activity "Catalysis of the reaction: (5-L-glutamyl)-peptide + an amino acid = peptide + 5-L-glutamyl-amino acid." [EC:2.3.2.2] molecular_function
+ENSG00000100121 GO:0006749 glutathione metabolic process "The chemical reactions and pathways involving glutathione, the tripeptide glutamylcysteinylglycine, which acts as a coenzyme for some enzymes and as an antioxidant in the protection of sulfhydryl groups in enzymes and other proteins; it has a specific role in the reduction of hydrogen peroxide (H2O2) and oxidized ascorbate, and it participates in the gamma-glutamyl cycle." [CHEBI:16856, ISBN:0198506732] biological_process
+ENSG00000100121 GO:0019370 leukotriene biosynthetic process "The chemical reactions and pathways resulting in the formation of leukotriene, a pharmacologically active substance derived from a polyunsaturated fatty acid, such as arachidonic acid." [GOC:go_curators] biological_process
+ENSG00000100121 GO:0031362 anchored component of external side of plasma membrane "The component of the plasma membrane consisting of the gene products that are tethered to the membrane only by a covalently attached anchor, such as a lipid group embedded in the membrane. Gene products with peptide sequences that are embedded in the membrane are excluded from this grouping." [GOC:dos, GOC:mah] cellular_component
+ENSG00000100121 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000100121 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000241484 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000241484 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000241484 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000241484 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000241484 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000241484 GO:0030234 enzyme regulator activity "Binds to and modulates the activity of an enzyme." [GOC:mah] molecular_function
+ENSG00000241484 GO:0005100 Rho GTPase activator activity "Increases the rate of GTP hydrolysis by a GTPase of the Rho family." [GOC:mah] molecular_function
+ENSG00000241484 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000241484 GO:0007264 small GTPase mediated signal transduction "Any series of molecular signals in which a small monomeric GTPase relays one or more of the signals." [GOC:mah] biological_process
+ENSG00000241484 GO:0032321 positive regulation of Rho GTPase activity "Any process that activates or increases the activity of a GTPase of the Rho family." [GOC:mah] biological_process
+ENSG00000241484 GO:0051056 regulation of small GTPase mediated signal transduction "Any process that modulates the frequency, rate or extent of small GTPase mediated signal transduction." [GOC:go_curators] biological_process
+ENSG00000241484 GO:0070374 positive regulation of ERK1 and ERK2 cascade "Any process that activates or increases the frequency, rate or extent of signal transduction mediated by the ERK1 and ERK2 cascade." [GOC:mah] biological_process
+ENSG00000241484 GO:0005622 intracellular "The living contents of a cell; the matter contained within (but not including) the plasma membrane, usually taken to exclude large vacuoles and masses of secretory or ingested material. In eukaryotes it includes the nucleus and cytoplasm." [ISBN:0198506732] cellular_component
+ENSG00000241484
+ENSG00000100028 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100028 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100028 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100028 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000100028 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100028 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100028 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100028 GO:0006397 mRNA processing "Any process involved in the conversion of a primary mRNA transcript into one or more mature mRNA(s) prior to translation into polypeptide." [GOC:mah] biological_process
+ENSG00000100028 GO:0019899 enzyme binding "Interacting selectively and non-covalently with any enzyme." [GOC:jl] molecular_function
+ENSG00000100028 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000100028 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000100028 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100028 GO:0005654 nucleoplasm "That part of the nuclear content other than the chromosomes or the nucleolus." [GOC:ma, ISBN:0124325653] cellular_component
+ENSG00000100028 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100028 GO:0022607 cellular component assembly "The aggregation, arrangement and bonding together of a cellular component." [GOC:isa_complete] biological_process
+ENSG00000100028 GO:0022618 ribonucleoprotein complex assembly "The aggregation, arrangement and bonding together of proteins and RNA molecules to form a ribonucleoprotein complex." [GOC:jl] biological_process
+ENSG00000100028 GO:0065003 macromolecular complex assembly "The aggregation, arrangement and bonding together of a set of macromolecules to form a complex." [GOC:jl] biological_process
+ENSG00000100028 GO:0000387 spliceosomal snRNP assembly "The aggregation, arrangement and bonding together of one or more snRNA and multiple protein components to form a ribonucleoprotein complex that is involved in formation of the spliceosome." [GOC:krc, GOC:mah, ISBN:0879695897] biological_process
+ENSG00000100028 GO:0000398 mRNA splicing, via spliceosome "The joining together of exons from one or more primary transcripts of messenger RNA (mRNA) and the excision of intron sequences, via a spliceosomal mechanism, so that mRNA consisting only of the joined exons is produced." [GOC:krc, ISBN:0198506732, ISBN:0879695897] biological_process
+ENSG00000100028 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100028 GO:0005681 spliceosomal complex "Any of a series of ribonucleoprotein complexes that contain RNA and small nuclear ribonucleoproteins (snRNPs), and are formed sequentially during the splicing of a messenger RNA primary transcript to excise an intron." [GOC:mah, ISBN:0198547684, PMID:19239890] cellular_component
+ENSG00000100028 GO:0005683 U7 snRNP "A ribonucleoprotein complex that contains the U7 snRNA and is required for the 3'-end processing of replication-dependent histone pre-mRNAs." [PMID:12872004] cellular_component
+ENSG00000100028 GO:0005685 U1 snRNP "A ribonucleoprotein complex that contains small nuclear RNA U1." [GOC:krc, GOC:mah] cellular_component
+ENSG00000100028 GO:0005687 U4 snRNP "A ribonucleoprotein complex that contains small nuclear RNA U4." [GOC:krc, GOC:mah] cellular_component
+ENSG00000100028 GO:0005689 U12-type spliceosomal complex "Any spliceosomal complex that forms during the splicing of a messenger RNA primary transcript to excise an intron; the series of U12-type spliceosomal complexes is involved in the splicing of the majority of introns that contain atypical AT-AC terminal dinucleotides, as well as other non-canonical introns. The entire splice site signal, not just the terminal dinucleotides, is involved in determining which spliceosome utilizes the site." [GOC:krc, GOC:mah, PMID:11574683, PMID:11971955] cellular_component
+ENSG00000100028 GO:0006366 transcription from RNA polymerase II promoter "The synthesis of RNA from a DNA template by RNA polymerase II, originating at an RNA polymerase II promoter. Includes transcription of messenger RNA (mRNA) and certain small nuclear RNAs (snRNAs)." [GOC:jl, GOC:txnOH, ISBN:0321000382] biological_process
+ENSG00000100028 GO:0006369 termination of RNA polymerase II transcription "The process in which the synthesis of an RNA molecule by RNA polymerase II using a DNA template is completed." [GOC:mah, GOC:txnOH] biological_process
+ENSG00000100028 GO:0008334 histone mRNA metabolic process "The chemical reactions and pathways involving an mRNA encoding a histone." [GOC:krc, GOC:mah, PMID:17855393] biological_process
+ENSG00000100028 GO:0008380 RNA splicing "The process of removing sections of the primary RNA transcript to remove sequences not present in the mature form of the RNA and joining the remaining sections to form the mature form of the RNA." [GOC:krc, GOC:mah] biological_process
+ENSG00000100028 GO:0010467 gene expression "The process in which a gene's sequence is converted into a mature gene product or products (proteins or RNA). This includes the production of an RNA transcript as well as any processing to produce a mature RNA product or an mRNA (for protein-coding genes) and the translation of that mRNA into protein. Some protein processing events may be included when they are required to form an active form of a product from an inactive precursor form." [GOC:dph, GOC:tb] biological_process
+ENSG00000100028 GO:0016070 RNA metabolic process "The cellular chemical reactions and pathways involving RNA, ribonucleic acid, one of the two main type of nucleic acid, consisting of a long, unbranched macromolecule formed from ribonucleotides joined in 3',5'-phosphodiester linkage." [ISBN:0198506732] biological_process
+ENSG00000100028 GO:0030532 small nuclear ribonucleoprotein complex "A complex composed of RNA of the small nuclear RNA (snRNA) class and protein, found in the nucleus of a eukaryotic cell. These are typically named after the snRNA(s) they contain, e.g. U1 snRNP or U4/U6 snRNP. Many, but not all, of these complexes are involved in splicing of nuclear mRNAs." [GOC:krc, GOC:mah, ISBN:0879695897] cellular_component
+ENSG00000100028 GO:0031124 mRNA 3'-end processing "Any process involved in forming the mature 3' end of an mRNA molecule." [GOC:mah] biological_process
+ENSG00000100028 GO:0034660 ncRNA metabolic process "The chemical reactions and pathways involving non-coding RNA transcripts (ncRNAs)." [GOC:mah] biological_process
+ENSG00000100028 GO:0034709 methylosome "A large (20 S) protein complex that possesses protein arginine methyltransferase activity and modifies specific arginines to dimethylarginines in the arginine- and glycine-rich domains of several spliceosomal Sm proteins, thereby targeting these proteins to the survival of motor neurons (SMN) complex for assembly into small nuclear ribonucleoprotein (snRNP) core particles. Proteins found in the methylosome include the methyltransferase JBP1 (PRMT5), pICln (CLNS1A), MEP50 (WDR77), and unmethylated forms of SM proteins that have RG domains." [PMID:11713266, PMID:11756452] cellular_component
+ENSG00000100028 GO:0034715 pICln-Sm protein complex "A protein complex that contains pICln (CLNS1A) and several Sm proteins, including SmD1, SmD2, SmE, SmF, and SmG." [GOC:mah, PMID:11713266] cellular_component
+ENSG00000100028 GO:0034719 SMN-Sm protein complex "A protein complex formed by the association of several methylated Sm proteins with the SMN complex; the latter contains the survival motor neuron (SMN) protein and at least eight additional integral components, including the Gemin2-8 and unrip proteins; additional proteins, including galectin-1 and galectin-3, are also found in the SMN-SM complex. The SMN-Sm complex is involved in spliceosomal snRNP assembly in the cytoplasm." [PMID:11522829, PMID:17401408] cellular_component
+ENSG00000100028 GO:0044822 poly(A) RNA binding "Interacting non-covalently with a poly(A) RNA, a RNA molecule which has a tail of adenine bases." [GOC:jl] molecular_function
+ENSG00000100028 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000100028 GO:0071013 catalytic step 2 spliceosome "A spliceosomal complex that contains three snRNPs, including U5, bound to a splicing intermediate in which the first catalytic cleavage of the 5' splice site has occurred. The precise subunit composition differs significantly from that of the catalytic step 1, or activated, spliceosome, and includes many proteins in addition to those found in the associated snRNPs." [GOC:ab, GOC:krc, GOC:mah, ISBN:0879695897, ISBN:0879697393, PMID:18322460, PMID:19239890] cellular_component
+ENSG00000100028 GO:0071208 histone pre-mRNA DCP binding "Interacting selectively and non-covalently with the downstream cleavage product (DCP) generated by histone pre-mRNA 3'-end processing." [PMID:19470752] molecular_function
+ENSG00000100028
+ENSG00000198355 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000198355 GO:0007049 cell cycle "The progression of biochemical and morphological phases and events that occur in a cell during successive cell replication or nuclear replication events. Canonically, the cell cycle comprises the replication and segregation of genetic material followed by the division of the cell, but in endocycles or syncytial cells nuclear replication or nuclear division may not be followed by cell division." [GOC:go_curators, GOC:mtg_cell_cycle] biological_process
+ENSG00000198355 GO:0008219 cell death "Any biological process that results in permanent cessation of all vital functions of a cell. A cell should be considered dead when any one of the following molecular or morphological criteria is met: (1) the cell has lost the integrity of its plasma membrane; (2) the cell, including its nucleus, has undergone complete fragmentation into discrete bodies (frequently referred to as \"apoptotic bodies\"); and/or (3) its corpse (or its fragments) have been engulfed by an adjacent cell in vivo." [GOC:mah, GOC:mtg_apoptosis] biological_process
+ENSG00000198355 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000198355 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000198355 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000198355 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000198355 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000198355 GO:0016301 kinase activity "Catalysis of the transfer of a phosphate group, usually from ATP, to a substrate molecule." [ISBN:0198506732] molecular_function
+ENSG00000198355 GO:0004674 protein serine/threonine kinase activity "Catalysis of the reactions: ATP + protein serine = ADP + protein serine phosphate, and ATP + protein threonine = ADP + protein threonine phosphate." [GOC:bf] molecular_function
+ENSG00000198355 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000198355 GO:0005524 ATP binding "Interacting selectively and non-covalently with ATP, adenosine 5'-triphosphate, a universally important coenzyme and enzyme regulator." [ISBN:0198506732] molecular_function
+ENSG00000198355 GO:0006468 protein phosphorylation "The process of introducing a phosphate group on to a protein." [GOC:hb] biological_process
+ENSG00000198355 GO:0006915 apoptotic process "A programmed cell death process which begins when a cell receives an internal (e.g. DNA damage) or external signal (e.g. an extracellular death ligand), and proceeds through a series of biochemical events (signaling pathway phase) which trigger an execution phase. The execution phase is the last step of an apoptotic process, and is typically characterized by rounding-up of the cell, retraction of pseudopodes, reduction of cellular volume (pyknosis), chromatin condensation, nuclear fragmentation (karyorrhexis), plasma membrane blebbing and fragmentation of the cell into apoptotic bodies. When the execution phase is completed, the cell has died." [GOC:dhl, GOC:ecd, GOC:go_curators, GOC:mtg_apoptosis, GOC:tb, ISBN:0198506732, PMID:18846107, PMID:21494263] biological_process
+ENSG00000198355 GO:0007346 regulation of mitotic cell cycle "Any process that modulates the rate or extent of progress through the mitotic cell cycle." [GOC:dph, GOC:go_curators, GOC:tb] biological_process
+ENSG00000198355 GO:0043066 negative regulation of apoptotic process "Any process that stops, prevents, or reduces the frequency, rate or extent of cell death by apoptotic process." [GOC:jl, GOC:mtg_apoptosis] biological_process
+ENSG00000198355 GO:0061179 negative regulation of insulin secretion involved in cellular response to glucose stimulus "Any process that decreases the frequency, rate or extent of the regulated release of insulin that contributes to the response of a cell to glucose." [GOC:BHF, GOC:dph] biological_process
+ENSG00000198355 GO:0004672 protein kinase activity "Catalysis of the phosphorylation of an amino acid residue in a protein, usually according to the reaction: a protein + ATP = a phosphoprotein + ADP." [MetaCyc:PROTEIN-KINASE-RXN] molecular_function
+ENSG00000198355 GO:0016773 phosphotransferase activity, alcohol group as acceptor "Catalysis of the transfer of a phosphorus-containing group from one compound (donor) to an alcohol group (acceptor)." [GOC:jl] molecular_function
+ENSG00000198355 GO:0009103 lipopolysaccharide biosynthetic process "The chemical reactions and pathways resulting in the formation of lipopolysaccharides, any of a group of related, structurally complex components of the outer membrane of Gram-negative bacteria." [GOC:ai, GOC:mr] biological_process
+ENSG00000198355 GO:0005975 carbohydrate metabolic process "The chemical reactions and pathways involving carbohydrates, any of a group of organic compounds based of the general formula Cx(H2O)y. Includes the formation of carbohydrate derivatives by the addition of a carbohydrate residue to another molecule." [GOC:mah, ISBN:0198506732] biological_process
+ENSG00000198355 GO:0006629 lipid metabolic process "The chemical reactions and pathways involving lipids, compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent. Includes fatty acids; neutral fats, other fatty-acid esters, and soaps; long-chain (fatty) alcohols and waxes; sphingoids and other long-chain bases; glycolipids, phospholipids and sphingolipids; and carotenes, polyprenols, sterols, terpenes and other isoprenoids." [GOC:ma] biological_process
+ENSG00000198355 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000198355 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000198355 GO:0016772 transferase activity, transferring phosphorus-containing groups "Catalysis of the transfer of a phosphorus-containing group from one compound (donor) to another (acceptor)." [GOC:jl, ISBN:0198506732] molecular_function
+ENSG00000198355 GO:0004713 protein tyrosine kinase activity "Catalysis of the reaction: ATP + a protein tyrosine = ADP + protein tyrosine phosphate." [EC:2.7.10] molecular_function
+ENSG00000198355 GO:0046777 protein autophosphorylation "The phosphorylation by a protein of one or more of its own amino acid residues (cis-autophosphorylation), or residues on an identical protein (trans-autophosphorylation)." [ISBN:0198506732] biological_process
+ENSG00000198355 GO:0016572 histone phosphorylation "The modification of histones by addition of phosphate groups." [GOC:ai] biological_process
+ENSG00000178026
+ENSG00000189269
+ENSG00000100336 GO:0005576 extracellular region "The space external to the outermost structure of a cell. For cells without external protective or external encapsulating structures this refers to space outside of the plasma membrane. This term covers the host cell environment outside an intracellular parasite." [GOC:go_curators] cellular_component
+ENSG00000100336 GO:0006869 lipid transport "The directed movement of lipids into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore. Lipids are compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent." [ISBN:0198506732] biological_process
+ENSG00000100336 GO:0008289 lipid binding "Interacting selectively and non-covalently with a lipid." [GOC:ai] molecular_function
+ENSG00000100336 GO:0042157 lipoprotein metabolic process "The chemical reactions and pathways involving any conjugated, water-soluble protein in which the nonprotein group consists of a lipid or lipids." [ISBN:0198506732] biological_process
+ENSG00000100336 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100336 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100336 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100336 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100336 GO:0006629 lipid metabolic process "The chemical reactions and pathways involving lipids, compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent. Includes fatty acids; neutral fats, other fatty-acid esters, and soaps; long-chain (fatty) alcohols and waxes; sphingoids and other long-chain bases; glycolipids, phospholipids and sphingolipids; and carotenes, polyprenols, sterols, terpenes and other isoprenoids." [GOC:ma] biological_process
+ENSG00000100336 GO:0005615 extracellular space "That part of a multicellular organism outside the cells proper, usually taken to be outside the plasma membranes, and occupied by fluid." [ISBN:0198547684] cellular_component
+ENSG00000100336
+ENSG00000183765 GO:0004672 protein kinase activity "Catalysis of the phosphorylation of an amino acid residue in a protein, usually according to the reaction: a protein + ATP = a phosphoprotein + ADP." [MetaCyc:PROTEIN-KINASE-RXN] molecular_function
+ENSG00000183765 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000183765 GO:0016301 kinase activity "Catalysis of the transfer of a phosphate group, usually from ATP, to a substrate molecule." [ISBN:0198506732] molecular_function
+ENSG00000183765 GO:0005524 ATP binding "Interacting selectively and non-covalently with ATP, adenosine 5'-triphosphate, a universally important coenzyme and enzyme regulator." [ISBN:0198506732] molecular_function
+ENSG00000183765 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000183765 GO:0006468 protein phosphorylation "The process of introducing a phosphate group on to a protein." [GOC:hb] biological_process
+ENSG00000183765 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000183765 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000183765 GO:0016772 transferase activity, transferring phosphorus-containing groups "Catalysis of the transfer of a phosphorus-containing group from one compound (donor) to another (acceptor)." [GOC:jl, ISBN:0198506732] molecular_function
+ENSG00000183765 GO:0004713 protein tyrosine kinase activity "Catalysis of the reaction: ATP + a protein tyrosine = ADP + protein tyrosine phosphate." [EC:2.7.10] molecular_function
+ENSG00000183765 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000183765 GO:0007568 aging "A developmental process that is a deterioration and loss of function over time. Aging includes loss of functions such as resistance to disease, homeostasis, and fertility, as well as wear and tear. Aging includes cellular senescence, but is more inclusive. May precede death (GO:0016265) and may succeed developmental maturation (GO:0021700)." [GOC:PO_curators] biological_process
+ENSG00000183765 GO:0006461 protein complex assembly "The aggregation, arrangement and bonding together of a set of components to form a protein complex." [GOC:ai] biological_process
+ENSG00000183765 GO:0007010 cytoskeleton organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of cytoskeletal structures." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000183765 GO:0022607 cellular component assembly "The aggregation, arrangement and bonding together of a cellular component." [GOC:isa_complete] biological_process
+ENSG00000183765 GO:0065003 macromolecular complex assembly "The aggregation, arrangement and bonding together of a set of macromolecules to form a complex." [GOC:jl] biological_process
+ENSG00000183765 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000183765 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000183765 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000183765 GO:0019899 enzyme binding "Interacting selectively and non-covalently with any enzyme." [GOC:jl] molecular_function
+ENSG00000183765 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000183765 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000183765 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000183765 GO:0006259 DNA metabolic process "Any cellular metabolic process involving deoxyribonucleic acid. This is one of the two main types of nucleic acid, consisting of a long, unbranched macromolecule formed from one, or more commonly, two, strands of linked deoxyribonucleotides." [ISBN:0198506732] biological_process
+ENSG00000183765 GO:0005654 nucleoplasm "That part of the nuclear content other than the chromosomes or the nucleolus." [GOC:ma, ISBN:0124325653] cellular_component
+ENSG00000183765 GO:0000077 DNA damage checkpoint "A cell cycle checkpoint that regulates progression through the cell cycle in response to DNA damage. A DNA damage checkpoint may blocks cell cycle progression (in G1, G2 or metaphase) or slow the rate at which S phase proceeds." [GOC:mtg_cell_cycle] biological_process
+ENSG00000183765 GO:0000086 G2/M transition of mitotic cell cycle "The mitotic cell cycle transition by which a cell in G2 commits to M phase. The process begins when the kinase activity of M cyclin/CDK complex reaches a threshold high enough for the cell cycle to proceed. This is accomplished by activating a positive feedback loop that results in the accumulation of unphosphorylated and active M cyclin/CDK complex." [GOC:mtg_cell_cycle] biological_process
+ENSG00000183765 GO:0004674 protein serine/threonine kinase activity "Catalysis of the reactions: ATP + protein serine = ADP + protein serine phosphate, and ATP + protein threonine = ADP + protein threonine phosphate." [GOC:bf] molecular_function
+ENSG00000183765 GO:0006302 double-strand break repair "The repair of double-strand breaks in DNA via homologous and nonhomologous mechanisms to reform a continuous DNA helix." [GOC:elh] biological_process
+ENSG00000183765 GO:0006351 transcription, DNA-templated "The cellular synthesis of RNA on a template of DNA." [GOC:jl, GOC:txnOH] biological_process
+ENSG00000183765 GO:0006355 regulation of transcription, DNA-templated "Any process that modulates the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000183765 GO:0006974 cellular response to DNA damage stimulus "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a stimulus indicating damage to its DNA from environmental insults or errors during metabolism." [GOC:go_curators] biological_process
+ENSG00000183765 GO:0006975 DNA damage induced protein phosphorylation "The widespread phosphorylation of various molecules, triggering many downstream processes, that occurs in response to the detection of DNA damage." [GOC:go_curators] biological_process
+ENSG00000183765 GO:0008630 intrinsic apoptotic signaling pathway in response to DNA damage "A series of molecular signals in which an intracellular signal is conveyed to trigger the apoptotic death of a cell. The pathway is induced by the detection of DNA damage, and ends when the execution phase of apoptosis is triggered." [GOC:go_curators, GOC:mtg_apoptosis] biological_process
+ENSG00000183765 GO:0016605 PML body "A class of nuclear body; they react against SP100 auto-antibodies (PML, promyelocytic leukemia); cells typically contain 10-30 PML bodies per nucleus; alterations in the localization of PML bodies occurs after viral infection." [GOC:ma, PMID:10944585] cellular_component
+ENSG00000183765 GO:0019901 protein kinase binding "Interacting selectively and non-covalently with a protein kinase, any enzyme that catalyzes the transfer of a phosphate group, usually from ATP, to a protein substrate." [GOC:jl] molecular_function
+ENSG00000183765 GO:0031625 ubiquitin protein ligase binding "Interacting selectively and non-covalently with a ubiquitin protein ligase enzyme, any of the E3 proteins." [GOC:vp] molecular_function
+ENSG00000183765 GO:0042176 regulation of protein catabolic process "Any process that modulates the frequency, rate or extent of the chemical reactions and pathways resulting in the breakdown of a protein by the destruction of the native, active configuration, with or without the hydrolysis of peptide bonds." [GOC:go_curators, GOC:jl] biological_process
+ENSG00000183765 GO:0042770 signal transduction in response to DNA damage "A cascade of processes induced by the detection of DNA damage within a cell." [GOC:go_curators] biological_process
+ENSG00000183765 GO:0042802 identical protein binding "Interacting selectively and non-covalently with an identical protein or proteins." [GOC:jl] molecular_function
+ENSG00000183765 GO:0042803 protein homodimerization activity "Interacting selectively and non-covalently with an identical protein to form a homodimer." [GOC:jl] molecular_function
+ENSG00000183765 GO:0044257 cellular protein catabolic process "The chemical reactions and pathways resulting in the breakdown of a protein by individual cells." [GOC:jl] biological_process
+ENSG00000183765 GO:0045893 positive regulation of transcription, DNA-templated "Any process that activates or increases the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000183765 GO:0046777 protein autophosphorylation "The phosphorylation by a protein of one or more of its own amino acid residues (cis-autophosphorylation), or residues on an identical protein (trans-autophosphorylation)." [ISBN:0198506732] biological_process
+ENSG00000183765 GO:0046872 metal ion binding "Interacting selectively and non-covalently with any metal ion." [GOC:ai] molecular_function
+ENSG00000183765 GO:0050821 protein stabilization "Any process involved in maintaining the structure and integrity of a protein and preventing it from degradation or aggregation." [GOC:ai] biological_process
+ENSG00000183765 GO:0072428 signal transduction involved in intra-S DNA damage checkpoint "A signal transduction process that contributes to an intra-S DNA damage checkpoint." [GOC:mah] biological_process
+ENSG00000183765 GO:0090307 spindle assembly involved in mitosis "The aggregation, arrangement and bonding together of a set of components to form the spindle that contributes to the process of mitosis." [GOC:tb, GOC:vw] biological_process
+ENSG00000183765 GO:0090399 replicative senescence "A cell aging process associated with the dismantling of a cell as a response to telomere shortening and/or cellular aging." [GOC:BHF] biological_process
+ENSG00000183765 GO:0000781 chromosome, telomeric region "The terminal region of a linear chromosome that includes the telomeric DNA repeats and associated proteins." [GOC:elh] cellular_component
+ENSG00000183765 GO:0006915 apoptotic process "A programmed cell death process which begins when a cell receives an internal (e.g. DNA damage) or external signal (e.g. an extracellular death ligand), and proceeds through a series of biochemical events (signaling pathway phase) which trigger an execution phase. The execution phase is the last step of an apoptotic process, and is typically characterized by rounding-up of the cell, retraction of pseudopodes, reduction of cellular volume (pyknosis), chromatin condensation, nuclear fragmentation (karyorrhexis), plasma membrane blebbing and fragmentation of the cell into apoptotic bodies. When the execution phase is completed, the cell has died." [GOC:dhl, GOC:ecd, GOC:go_curators, GOC:mtg_apoptosis, GOC:tb, ISBN:0198506732, PMID:18846107, PMID:21494263] biological_process
+ENSG00000183765 GO:0010332 response to gamma radiation "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a gamma radiation stimulus. Gamma radiation is a form of electromagnetic radiation (EMR) or light emission of a specific frequency produced from sub-atomic particle interaction, such as electron-positron annihilation and radioactive decay. Gamma rays are generally characterized as EMR having the highest frequency and energy, and also the shortest wavelength, within the electromagnetic radiation spectrum." [GOC:tair_curators, Wikipedia:Gamma_ray] biological_process
+ENSG00000183765
+ENSG00000185686 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000185686 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000185686 GO:0008134 transcription factor binding "Interacting selectively and non-covalently with a transcription factor, any protein required to initiate or regulate transcription." [ISBN:0198506732] molecular_function
+ENSG00000185686 GO:0030154 cell differentiation "The process in which relatively unspecialized cells, e.g. embryonic or regenerative cells, acquire specialized structural and/or functional features that characterize the cells, tissues, or organs of the mature organism or some other relatively stable phase of the organism's life history. Differentiation includes the processes involved in commitment of a cell to a specific fate and its subsequent development to the mature state." [ISBN:0198506732] biological_process
+ENSG00000185686 GO:0008219 cell death "Any biological process that results in permanent cessation of all vital functions of a cell. A cell should be considered dead when any one of the following molecular or morphological criteria is met: (1) the cell has lost the integrity of its plasma membrane; (2) the cell, including its nucleus, has undergone complete fragmentation into discrete bodies (frequently referred to as \"apoptotic bodies\"); and/or (3) its corpse (or its fragments) have been engulfed by an adjacent cell in vivo." [GOC:mah, GOC:mtg_apoptosis] biological_process
+ENSG00000185686 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000185686 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000185686 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000185686 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000185686 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000185686 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000185686 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000185686 GO:0006351 transcription, DNA-templated "The cellular synthesis of RNA on a template of DNA." [GOC:jl, GOC:txnOH] biological_process
+ENSG00000185686 GO:0006915 apoptotic process "A programmed cell death process which begins when a cell receives an internal (e.g. DNA damage) or external signal (e.g. an extracellular death ligand), and proceeds through a series of biochemical events (signaling pathway phase) which trigger an execution phase. The execution phase is the last step of an apoptotic process, and is typically characterized by rounding-up of the cell, retraction of pseudopodes, reduction of cellular volume (pyknosis), chromatin condensation, nuclear fragmentation (karyorrhexis), plasma membrane blebbing and fragmentation of the cell into apoptotic bodies. When the execution phase is completed, the cell has died." [GOC:dhl, GOC:ecd, GOC:go_curators, GOC:mtg_apoptosis, GOC:tb, ISBN:0198506732, PMID:18846107, PMID:21494263] biological_process
+ENSG00000185686 GO:0008284 positive regulation of cell proliferation "Any process that activates or increases the rate or extent of cell proliferation." [GOC:go_curators] biological_process
+ENSG00000185686 GO:0040008 regulation of growth "Any process that modulates the frequency, rate or extent of the growth of all or part of an organism so that it occurs at its proper speed, either globally or in a specific part of the organism's development." [GOC:ems, GOC:mah] biological_process
+ENSG00000185686 GO:0042974 retinoic acid receptor binding "Interacting selectively and non-covalently with the retinoic acid receptor, a ligand-regulated transcription factor belonging to the nuclear receptor superfamily." [GOC:jl, PMID:12476796] molecular_function
+ENSG00000185686 GO:0043066 negative regulation of apoptotic process "Any process that stops, prevents, or reduces the frequency, rate or extent of cell death by apoptotic process." [GOC:jl, GOC:mtg_apoptosis] biological_process
+ENSG00000185686 GO:0045596 negative regulation of cell differentiation "Any process that stops, prevents, or reduces the frequency, rate or extent of cell differentiation." [GOC:go_curators] biological_process
+ENSG00000185686 GO:0045892 negative regulation of transcription, DNA-templated "Any process that stops, prevents, or reduces the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000185686 GO:0048387 negative regulation of retinoic acid receptor signaling pathway "Any process that stops, prevents, or reduces the frequency, rate or extent of retinoic acid receptor signaling pathway activity." [GOC:dgh] biological_process
+ENSG00000185686
+ENSG00000100147
+ENSG00000100147 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100147 GO:0005576 extracellular region "The space external to the outermost structure of a cell. For cells without external protective or external encapsulating structures this refers to space outside of the plasma membrane. This term covers the host cell environment outside an intracellular parasite." [GOC:go_curators] cellular_component
+ENSG00000100147 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000185608 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000185608 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000185608 GO:0005840 ribosome "An intracellular organelle, about 200 A in diameter, consisting of RNA and protein. It is the site of protein biosynthesis resulting from translation of messenger RNA (mRNA). It consists of two subunits, one large and one small, each containing only protein and RNA. Both the ribosome and its subunits are characterized by their sedimentation coefficients, expressed in Svedberg units (symbol: S). Hence, the prokaryotic ribosome (70S) comprises a large (50S) subunit and a small (30S) subunit, while the eukaryotic ribosome (80S) comprises a large (60S) subunit and a small (40S) subunit. Two sites on the ribosomal large subunit are involved in translation, namely the aminoacyl site (A site) and peptidyl site (P site). Ribosomes from prokaryotes, eukaryotes, mitochondria, and chloroplasts have characteristically distinct ribosomal proteins." [ISBN:0198506732] cellular_component
+ENSG00000185608 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000185608 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000185608 GO:0005730 nucleolus "A small, dense body one or more of which are present in the nucleus of eukaryotic cells. It is rich in RNA and protein, is not bounded by a limiting membrane, and is not seen during mitosis. Its prime function is the transcription of the nucleolar DNA into 45S ribosomal-precursor RNA, the processing of this RNA into 5.8S, 18S, and 28S components of ribosomal RNA, and the association of these components with 5S RNA and proteins synthesized outside the nucleolus. This association results in the formation of ribonucleoprotein precursors; these pass into the cytoplasm and mature into the 40S and 60S subunits of the ribosome." [ISBN:0198506732] cellular_component
+ENSG00000185608 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000185608 GO:0005761 mitochondrial ribosome "A ribosome found in the mitochondrion of a eukaryotic cell; contains a characteristic set of proteins distinct from those of cytosolic ribosomes." [GOC:mah, ISBN:0198506732] cellular_component
+ENSG00000185608 GO:0009653 anatomical structure morphogenesis "The process in which anatomical structures are generated and organized. Morphogenesis pertains to the creation of form." [GOC:go_curators, ISBN:0521436125] biological_process
+ENSG00000185608 GO:0044822 poly(A) RNA binding "Interacting non-covalently with a poly(A) RNA, a RNA molecule which has a tail of adenine bases." [GOC:jl] molecular_function
+ENSG00000185608 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000185608 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000203618 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000203618 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000203618 GO:0007155 cell adhesion "The attachment of a cell, either to another cell or to an underlying substrate such as the extracellular matrix, via cell adhesion molecules." [GOC:hb, GOC:pf] biological_process
+ENSG00000203618 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000203618 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000203618 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000203618 GO:0004871 signal transducer activity "Conveys a signal across a cell to trigger a change in cell function or state. A signal is a physical entity or change in state that is used to transfer information in order to trigger a response." [GOC:go_curators] molecular_function
+ENSG00000203618 GO:0004888 transmembrane signaling receptor activity "Combining with an extracellular or intracellular signal and transmitting the signal from one side of the membrane to the other to initiate a change in cell activity." [GOC:go_curators, Wikipedia:Transmembrane_receptor] molecular_function
+ENSG00000203618 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000203618 GO:0005887 integral component of plasma membrane "The component of the plasma membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000203618 GO:0007166 cell surface receptor signaling pathway "A series of molecular signals initiated by activation of a receptor on the surface of a cell. The pathway begins with binding of an extracellular ligand to a cell surface receptor, or for receptors that signal in the absence of a ligand, by ligand-withdrawal or the activity of a constitutively active receptor. The pathway ends with regulation of a downstream cellular process, e.g. transcription." [GOC:bf, GOC:mah, GOC:pr, GOC:signaling] biological_process
+ENSG00000203618 GO:0007596 blood coagulation "The sequential process in which the multiple coagulation factors of the blood interact, ultimately resulting in the formation of an insoluble fibrin clot; it may be divided into three stages: stage 1, the formation of intrinsic and extrinsic prothrombin converting principle; stage 2, the formation of thrombin; stage 3, the formation of stable fibrin polymers." [http://www.graylab.ac.uk/omd/, ISBN:0198506732] biological_process
+ENSG00000203618 GO:0007597 blood coagulation, intrinsic pathway "A protein activation cascade that contributes to blood coagulation and consists of the interactions among high molecular weight kininogen, prekallikrein, and factor XII that lead to the activation of clotting factor X." [GOC:add, GOC:mah, GOC:pde] biological_process
+ENSG00000203618 GO:0030168 platelet activation "A series of progressive, overlapping events triggered by exposure of the platelets to subendothelial tissue. These events include shape change, adhesiveness, aggregation, and release reactions. When carried through to completion, these events lead to the formation of a stable hemostatic plug." [http://www.graylab.ac.uk/omd/] biological_process
+ENSG00000159496 GO:0005085 guanyl-nucleotide exchange factor activity "Stimulates the exchange of guanyl nucleotides associated with a GTPase. Under normal cellular physiological conditions, the concentration of GTP is higher than that of GDP, favoring the replacement of GDP by GTP in association with the GTPase." [GOC:kd, GOC:mah] molecular_function
+ENSG00000159496 GO:0007264 small GTPase mediated signal transduction "Any series of molecular signals in which a small monomeric GTPase relays one or more of the signals." [GOC:mah] biological_process
+ENSG00000159496 GO:0043547 positive regulation of GTPase activity "Any process that activates or increases the activity of a GTPase." [GOC:jl] biological_process
+ENSG00000159496 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000159496 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000159496 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000159496 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000159496 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000159496 GO:0031410 cytoplasmic vesicle "A vesicle formed of membrane or protein, found in the cytoplasm of a cell." [GOC:mah] cellular_component
+ENSG00000159496
+ENSG00000184436 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000184436 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000184436 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000184436 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000184436 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000184436 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000184436 GO:0005694 chromosome "A structure composed of a very long molecule of DNA and associated proteins (e.g. histones) that carries hereditary information." [ISBN:0198547684] cellular_component
+ENSG00000184436 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000184436 GO:0003677 DNA binding "Any molecular function by which a gene product interacts selectively and non-covalently with DNA (deoxyribonucleic acid)." [GOC:dph, GOC:jl, GOC:tb, GOC:vw] molecular_function
+ENSG00000184436 GO:0006351 transcription, DNA-templated "The cellular synthesis of RNA on a template of DNA." [GOC:jl, GOC:txnOH] biological_process
+ENSG00000184436 GO:0016607 nuclear speck "A discrete extra-nucleolar subnuclear domain, 20-50 in number, in which splicing factors are seen to be localized by immunofluorescence microscopy." [http://www.cellnucleus.com/] cellular_component
+ENSG00000184436 GO:0045892 negative regulation of transcription, DNA-templated "Any process that stops, prevents, or reduces the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000184436 GO:0046872 metal ion binding "Interacting selectively and non-covalently with any metal ion." [GOC:ai] molecular_function
+ENSG00000184436 GO:0047485 protein N-terminus binding "Interacting selectively and non-covalently with a protein N-terminus, the end of any peptide chain at which the 2-amino (or 2-imino) function of a constituent amino acid is not attached in peptide linkage to another amino-acid residue." [ISBN:0198506732] molecular_function
+ENSG00000184436 GO:0070742 C2H2 zinc finger domain binding "Interacting selectively and non-covalently with a C2H2-type zinc finger domain of a protein. The C2H2 zinc finger is the classical zinc finger domain, in which two conserved cysteines and histidines co-ordinate a zinc ion." [GOC:BHF, GOC:mah, Pfam:PF00096] molecular_function
+ENSG00000184436 GO:0003676 nucleic acid binding "Interacting selectively and non-covalently with any nucleic acid." [GOC:jl] molecular_function
+ENSG00000100084 GO:0000790 nuclear chromatin "The ordered and organized complex of DNA, protein, and sometimes RNA, that forms the chromosome in the nucleus." [GOC:elh, PMID:20404130] cellular_component
+ENSG00000100084 GO:0003682 chromatin binding "Interacting selectively and non-covalently with chromatin, the network of fibers of DNA, protein, and sometimes RNA, that make up the chromosomes of the eukaryotic nucleus during interphase." [GOC:jl, ISBN:0198506732, PMID:20404130] molecular_function
+ENSG00000100084 GO:0003700 sequence-specific DNA binding transcription factor activity "Interacting selectively and non-covalently with a specific DNA sequence in order to modulate transcription. The transcription factor may or may not also interact selectively with a protein or macromolecular complex." [GOC:curators, GOC:txnOH] molecular_function
+ENSG00000100084 GO:0003714 transcription corepressor activity "Interacting selectively and non-covalently with a repressing transcription factor and also with the basal transcription machinery in order to stop, prevent, or reduce the frequency, rate or extent of transcription. Cofactors generally do not bind the template nucleic acid, but rather mediate protein-protein interactions between repressive transcription factors and the basal transcription machinery." [GOC:txnOH, PMID:10213677, PMID:16858867] molecular_function
+ENSG00000100084 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100084 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100084 GO:0005654 nucleoplasm "That part of the nuclear content other than the chromosomes or the nucleolus." [GOC:ma, ISBN:0124325653] cellular_component
+ENSG00000100084 GO:0006336 DNA replication-independent nucleosome assembly "The formation of nucleosomes outside the context of DNA replication." [GOC:mah] biological_process
+ENSG00000100084 GO:0006351 transcription, DNA-templated "The cellular synthesis of RNA on a template of DNA." [GOC:jl, GOC:txnOH] biological_process
+ENSG00000100084 GO:0006357 regulation of transcription from RNA polymerase II promoter "Any process that modulates the frequency, rate or extent of transcription from an RNA polymerase II promoter." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000100084 GO:0009653 anatomical structure morphogenesis "The process in which anatomical structures are generated and organized. Morphogenesis pertains to the creation of form." [GOC:go_curators, ISBN:0521436125] biological_process
+ENSG00000100084 GO:0016568 chromatin modification "The alteration of DNA, protein, or sometimes RNA, in chromatin, which may result in changing the chromatin structure." [GOC:mah, PMID:20404130] biological_process
+ENSG00000100084 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100084 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100084 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100084 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000100084 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100084 GO:0006461 protein complex assembly "The aggregation, arrangement and bonding together of a set of components to form a protein complex." [GOC:ai] biological_process
+ENSG00000100084 GO:0022607 cellular component assembly "The aggregation, arrangement and bonding together of a cellular component." [GOC:isa_complete] biological_process
+ENSG00000100084 GO:0065003 macromolecular complex assembly "The aggregation, arrangement and bonding together of a set of macromolecules to form a complex." [GOC:jl] biological_process
+ENSG00000100084 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100084 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100084 GO:0000988 protein binding transcription factor activity "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules), in order to modulate transcription. A protein binding transcription factor may or may not also interact with the template nucleic acid (either DNA or RNA) as well." [GOC:txnOH] molecular_function
+ENSG00000100084 GO:0001071 nucleic acid binding transcription factor activity "Interacting selectively and non-covalently with a DNA or RNA sequence in order to modulate transcription. The transcription factor may or may not also interact selectively with a protein or macromolecular complex." [GOC:txnOH] molecular_function
+ENSG00000100084 GO:0006355 regulation of transcription, DNA-templated "Any process that modulates the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000100084 GO:0001649 osteoblast differentiation "The process whereby a relatively unspecialized cell acquires the specialized features of an osteoblast, a mesodermal or neural crest cell that gives rise to bone." [CL:0000062, GO_REF:0000034, GOC:jid] biological_process
+ENSG00000100084 GO:0007369 gastrulation "A complex and coordinated series of cellular movements that occurs at the end of cleavage during embryonic development of most animals. The details of gastrulation vary from species to species, but usually result in the formation of the three primary germ layers, ectoderm, mesoderm and endoderm." [GOC:curators, ISBN:9780878933846] biological_process
+ENSG00000100084 GO:0042692 muscle cell differentiation "The process in which a relatively unspecialized cell acquires specialized features of a muscle cell." [CL:0000187, GOC:go_curators] biological_process
+ENSG00000100084
+ENSG00000128254 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000128254 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100429 GO:0008152 metabolic process "The chemical reactions and pathways, including anabolism and catabolism, by which living organisms transform chemical substances. Metabolic processes typically transform small molecules, but also include macromolecular processes such as DNA repair and replication, and protein synthesis and degradation." [GOC:go_curators, ISBN:0198547684] biological_process
+ENSG00000100429 GO:0016787 hydrolase activity "Catalysis of the hydrolysis of various bonds, e.g. C-O, C-N, C-C, phosphoric anhydride bonds, etc. Hydrolase is the systematic name for any enzyme of EC class 3." [ISBN:0198506732] molecular_function
+ENSG00000100429 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100429 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100429 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000100429 GO:0016810 hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds "Catalysis of the hydrolysis of any carbon-nitrogen bond, C-N, with the exception of peptide bonds." [GOC:jl] molecular_function
+ENSG00000100429 GO:0019899 enzyme binding "Interacting selectively and non-covalently with any enzyme." [GOC:jl] molecular_function
+ENSG00000100429 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000100429 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000100429 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100429 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100429 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100429 GO:0005654 nucleoplasm "That part of the nuclear content other than the chromosomes or the nucleolus." [GOC:ma, ISBN:0124325653] cellular_component
+ENSG00000100429 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100429 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100429 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100429 GO:0000118 histone deacetylase complex "A protein complex that possesses histone deacetylase activity." [GOC:mah] cellular_component
+ENSG00000100429 GO:0000122 negative regulation of transcription from RNA polymerase II promoter "Any process that stops, prevents, or reduces the frequency, rate or extent of transcription from an RNA polymerase II promoter." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000100429 GO:0004407 histone deacetylase activity "Catalysis of the reaction: histone N6-acetyl-L-lysine + H2O = histone L-lysine + acetate. This reaction represents the removal of an acetyl group from a histone, a class of proteins complexed to DNA in chromatin and chromosomes." [EC:3.5.1.-, PMID:9893272] molecular_function
+ENSG00000100429 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100429 GO:0006351 transcription, DNA-templated "The cellular synthesis of RNA on a template of DNA." [GOC:jl, GOC:txnOH] biological_process
+ENSG00000100429 GO:0006355 regulation of transcription, DNA-templated "Any process that modulates the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000100429 GO:0006476 protein deacetylation "The removal of an acetyl group from a protein amino acid. An acetyl group is CH3CO-, derived from acetic [ethanoic] acid." [GOC:ai] biological_process
+ENSG00000100429 GO:0007219 Notch signaling pathway "A series of molecular signals initiated by the binding of an extracellular ligand to the receptor Notch on the surface of a target cell, and ending with regulation of a downstream cellular process, e.g. transcription." [GOC:go_curators, GOC:signaling] biological_process
+ENSG00000100429 GO:0016568 chromatin modification "The alteration of DNA, protein, or sometimes RNA, in chromatin, which may result in changing the chromatin structure." [GOC:mah, PMID:20404130] biological_process
+ENSG00000100429 GO:0016575 histone deacetylation "The modification of histones by removal of acetyl groups." [GOC:ai] biological_process
+ENSG00000100429 GO:0032041 NAD-dependent histone deacetylase activity (H3-K14 specific) "Catalysis of the reaction: histone H3 N6-acetyl-L-lysine (position 14) + H2O = histone H3 L-lysine (position 14) + acetate. This reaction requires the presence of NAD, and represents the removal of an acetyl group from lysine at position 14 of the histone H3 protein." [EC:3.5.1.17, EC:3.5.1.98, RHEA:24551] molecular_function
+ENSG00000100429 GO:0033558 protein deacetylase activity "Catalysis of the hydrolysis of an acetyl group or groups from a protein substrate." [GOC:mah] molecular_function
+ENSG00000100429 GO:0042826 histone deacetylase binding "Interacting selectively and non-covalently with the enzyme histone deacetylase." [GOC:jl] molecular_function
+ENSG00000100429 GO:0045892 negative regulation of transcription, DNA-templated "Any process that stops, prevents, or reduces the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000100429 GO:0046969 NAD-dependent histone deacetylase activity (H3-K9 specific) "Catalysis of the reaction: histone H3 N6-acetyl-L-lysine (position 9) + H2O = histone H3 L-lysine (position 9) + acetate. This reaction requires the presence of NAD, and represents the removal of an acetyl group from lysine at position 9 of the histone H3 protein." [EC:3.5.1.17, EC:3.5.1.98, RHEA:24551] molecular_function
+ENSG00000100429 GO:0046970 NAD-dependent histone deacetylase activity (H4-K16 specific) "Catalysis of the reaction: histone H4 N6-acetyl-L-lysine (position 16) + H2O = histone H4 L-lysine (position 16) + acetate. This reaction requires the presence of NAD, and represents the removal of an acetyl group from lysine at position 16 of the histone H4 protein." [EC:3.5.1.17, EC:3.5.1.98, GOC:vw, RHEA:24551] molecular_function
+ENSG00000100429 GO:0070932 histone H3 deacetylation "The modification of histone H3 by the removal of one or more acetyl groups." [GOC:BHF, GOC:rl] biological_process
+ENSG00000100429 GO:0070933 histone H4 deacetylation "The modification of histone H4 by the removal of one or more acetyl groups." [GOC:BHF, GOC:rl] biological_process
+ENSG00000100429 GO:0097372 NAD-dependent histone deacetylase activity (H3-K18 specific) "Catalysis of the reaction: histone H3 N6-acetyl-L-lysine (position 18) + H2O = histone H3 L-lysine (position 18) + acetate. This reaction requires the presence of NAD, and represents the removal of an acetyl group from lysine at position 18 of the histone H3 protein." [EC:3.5.1.17, EC:3.5.1.98, GOC:sp, PMID:22722849, RHEA:24551] molecular_function
+ENSG00000100429 GO:0014003 oligodendrocyte development "The process aimed at the progression of an oligodendrocyte over time, from initial commitment of the cell to a specific fate, to the fully functional differentiated cell. An oligodendrocyte is a type of glial cell involved in myelinating the axons in the central nervous system." [GOC:dgh, GOC:ef] biological_process
+ENSG00000100170 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100170 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100170 GO:0055085 transmembrane transport "The process in which a solute is transported from one side of a membrane to the other." [GOC:dph, GOC:jid] biological_process
+ENSG00000100170 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000100170 GO:0005975 carbohydrate metabolic process "The chemical reactions and pathways involving carbohydrates, any of a group of organic compounds based of the general formula Cx(H2O)y. Includes the formation of carbohydrate derivatives by the addition of a carbohydrate residue to another molecule." [GOC:mah, ISBN:0198506732] biological_process
+ENSG00000100170 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100170 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000100170 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100170 GO:0022857 transmembrane transporter activity "Enables the transfer of a substance from one side of a membrane to the other." [GOC:mtg_transport, ISBN:0815340729] molecular_function
+ENSG00000100170 GO:0005412 glucose:sodium symporter activity "Catalysis of the transfer of a solute or solutes from one side of a membrane to the other according to the reaction: glucose(out) + Na+(out) = glucose(in) + Na+(in)." [TC:2.A.21.3.-] molecular_function
+ENSG00000100170 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100170 GO:0005887 integral component of plasma membrane "The component of the plasma membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000100170 GO:0006814 sodium ion transport "The directed movement of sodium ions (Na+) into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:ai] biological_process
+ENSG00000100170 GO:0008645 hexose transport "The directed movement of hexose into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore. Hexoses are any aldoses with a chain of six carbon atoms in the molecule." [GOC:ai] biological_process
+ENSG00000100170 GO:0015758 glucose transport "The directed movement of the hexose monosaccharide glucose into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:ai] biological_process
+ENSG00000100170 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000100170 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100170 GO:0005215 transporter activity "Enables the directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells." [GOC:ai, GOC:dgf] molecular_function
+ENSG00000100170 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000100170 GO:0001656 metanephros development "The process whose specific outcome is the progression of the metanephros over time, from its formation to the mature structure. In mammals, the metanephros is the excretory organ of the fetus, which develops into the mature kidney and is formed from the rear portion of the nephrogenic cord. The metanephros is an endocrine and metabolic organ that filters the blood and excretes the end products of body metabolism in the form of urine." [GOC:bf, ISBN:0192800752] biological_process
+ENSG00000100170 GO:0005903 brush border "Dense covering of microvilli on the apical surface of epithelial cells in tissues such as the intestine, kidney, and choroid plexus; the microvilli aid absorption by increasing the surface area of the cell." [GOC:sl, ISBN:0815316194] cellular_component
+ENSG00000100170 GO:0016324 apical plasma membrane "The region of the plasma membrane located at the apical end of the cell." [GOC:curators] cellular_component
+ENSG00000100170 GO:0005355 glucose transmembrane transporter activity "Catalysis of the transfer of the hexose monosaccharide glucose from one side of the membrane to the other." [GOC:ai, GOC:mtg_transport, ISBN:0815340729] molecular_function
+ENSG00000100170 GO:0050892 intestinal absorption "Any process in which nutrients are taken up from the contents of the intestine." [GOC:ai, GOC:dph] biological_process
+ENSG00000100170 GO:0031526 brush border membrane "The portion of the plasma membrane surrounding the brush border." [GOC:mah] cellular_component
+ENSG00000100170 GO:0005911 cell-cell junction "A cell junction that forms a connection between two cells; excludes direct cytoplasmic junctions such as ring canals." [GOC:dgh, GOC:hb, GOC:mah] cellular_component
+ENSG00000211650 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000211650 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000239511
+ENSG00000184702 GO:0005525 GTP binding "Interacting selectively and non-covalently with GTP, guanosine triphosphate." [GOC:ai] molecular_function
+ENSG00000184702 GO:0007049 cell cycle "The progression of biochemical and morphological phases and events that occur in a cell during successive cell replication or nuclear replication events. Canonically, the cell cycle comprises the replication and segregation of genetic material followed by the division of the cell, but in endocycles or syncytial cells nuclear replication or nuclear division may not be followed by cell division." [GOC:go_curators, GOC:mtg_cell_cycle] biological_process
+ENSG00000184702 GO:0031105 septin complex "A protein complex containing septins. Typically, these complexes contain multiple septins and are oligomeric." [GOC:mah, PMID:15385632] cellular_component
+ENSG00000184702 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000184702 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000184702 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000184702 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000184702 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000184702 GO:0016023 cytoplasmic membrane-bounded vesicle "A membrane-bounded vesicle found in the cytoplasm of the cell." [GOC:ai, GOC:mah] cellular_component
+ENSG00000184702 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000184702 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000184702 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000184702 GO:0034655 nucleobase-containing compound catabolic process "The chemical reactions and pathways resulting in the breakdown of nucleobases, nucleosides, nucleotides and nucleic acids." [GOC:mah] biological_process
+ENSG00000184702 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000184702 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000184702 GO:0005198 structural molecule activity "The action of a molecule that contributes to the structural integrity of a complex or assembly within or outside a cell." [GOC:mah] molecular_function
+ENSG00000184702 GO:0003924 GTPase activity "Catalysis of the reaction: GTP + H2O = GDP + phosphate." [ISBN:0198547684] molecular_function
+ENSG00000184702 GO:0000910 cytokinesis "The division of the cytoplasm and the plasma membrane of a cell and its separation into two daughter cells." [GOC:mtg_cell_cycle] biological_process
+ENSG00000184702 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000184702 GO:0006184 GTP catabolic process "The chemical reactions and pathways resulting in the breakdown of GTP, guanosine triphosphate." [ISBN:0198506732] biological_process
+ENSG00000184702 GO:0008021 synaptic vesicle "A secretory organelle, some 50 nm in diameter, of presynaptic nerve terminals; accumulates in high concentrations of neurotransmitters and secretes these into the synaptic cleft by fusion with the 'active zone' of the presynaptic plasma membrane." [PMID:10099709] cellular_component
+ENSG00000184702 GO:0016080 synaptic vesicle targeting "The process in which synaptic vesicles are directed to specific destination membranes, mediated by molecules at the vesicle membrane and target membrane surfaces." [GOC:mah] biological_process
+ENSG00000184702 GO:0017157 regulation of exocytosis "Any process that modulates the frequency, rate or extent of exocytosis." [GOC:go_curators] biological_process
+ENSG00000184702 GO:2000300 regulation of synaptic vesicle exocytosis "Any process that modulates the frequency, rate or extent of synaptic vesicle exocytosis." [GOC:obol] biological_process
+ENSG00000184702 GO:0043679 axon terminus "Terminal inflated portion of the axon, containing the specialized apparatus necessary to release neurotransmitters. The axon terminus is considered to be the whole region of thickening and the terminal button is a specialized region of it." [GOC:dph, GOC:jl] cellular_component
+ENSG00000184702 GO:0045202 synapse "The junction between a nerve fiber of one neuron and another neuron or muscle fiber or glial cell; the site of interneuronal communication. As the nerve fiber approaches the synapse it enlarges into a specialized structure, the presynaptic nerve ending, which contains mitochondria and synaptic vesicles. At the tip of the nerve ending is the presynaptic membrane; facing it, and separated from it by a minute cleft (the synaptic cleft) is a specialized area of membrane on the receiving cell, known as the postsynaptic membrane. In response to the arrival of nerve impulses, the presynaptic nerve ending secretes molecules of neurotransmitters into the synaptic cleft. These diffuse across the cleft and transmit the signal to the postsynaptic membrane." [ISBN:0198506732] cellular_component
+ENSG00000184702 GO:0005938 cell cortex "The region of a cell that lies just beneath the plasma membrane and often, but not always, contains a network of actin filaments and associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000184702 GO:0043195 terminal bouton "Terminal inflated portion of the axon, containing the specialized apparatus necessary to release neurotransmitters. The axon terminus is considered to be the whole region of thickening and the terminal bouton is a specialized region of it." [GOC:dph, GOC:mc, GOC:nln, PMID:10218156, PMID:8409967] cellular_component
+ENSG00000184702
+ENSG00000099985 GO:0005576 extracellular region "The space external to the outermost structure of a cell. For cells without external protective or external encapsulating structures this refers to space outside of the plasma membrane. This term covers the host cell environment outside an intracellular parasite." [GOC:go_curators] cellular_component
+ENSG00000099985 GO:0006955 immune response "Any immune system process that functions in the calibrated response of an organism to a potential internal or invasive threat." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05] biological_process
+ENSG00000099985 GO:0002376 immune system process "Any process involved in the development or functioning of the immune system, an organismal system for calibrated responses to potential internal or invasive threats." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05] biological_process
+ENSG00000099985 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000099985 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000099985 GO:0008283 cell proliferation "The multiplication or reproduction of cells, resulting in the expansion of a cell population." [GOC:mah, GOC:mb] biological_process
+ENSG00000099985 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000099985 GO:0005615 extracellular space "That part of a multicellular organism outside the cells proper, usually taken to be outside the plasma membranes, and occupied by fluid." [ISBN:0198547684] cellular_component
+ENSG00000099985 GO:0002675 positive regulation of acute inflammatory response "Any process that activates or increases the frequency, rate, or extent of an acute inflammatory response." [GOC:add] biological_process
+ENSG00000099985 GO:0005125 cytokine activity "Functions to control the survival, growth, differentiation and effector function of tissues and cells." [ISBN:0198599471] molecular_function
+ENSG00000099985 GO:0005147 oncostatin-M receptor binding "Interacting selectively and non-covalently with the oncostatin-M receptor." [GOC:ai] molecular_function
+ENSG00000099985 GO:0007275 multicellular organismal development "The biological process whose specific outcome is the progression of a multicellular organism over time from an initial condition (e.g. a zygote or a young adult) to a later condition (e.g. a multicellular animal or an aged adult)." [GOC:dph, GOC:ems, GOC:isa_complete, GOC:tb] biological_process
+ENSG00000099985 GO:0008083 growth factor activity "The function that stimulates a cell to grow or proliferate. Most growth factors have other actions besides the induction of cell growth or proliferation." [ISBN:0815316194] molecular_function
+ENSG00000099985 GO:0008284 positive regulation of cell proliferation "Any process that activates or increases the rate or extent of cell proliferation." [GOC:go_curators] biological_process
+ENSG00000099985 GO:0008285 negative regulation of cell proliferation "Any process that stops, prevents or reduces the rate or extent of cell proliferation." [GOC:go_curators] biological_process
+ENSG00000099985 GO:0033138 positive regulation of peptidyl-serine phosphorylation "Any process that activates or increases the frequency, rate or extent of the phosphorylation of peptidyl-serine." [GOC:mah] biological_process
+ENSG00000099985 GO:0040008 regulation of growth "Any process that modulates the frequency, rate or extent of the growth of all or part of an organism so that it occurs at its proper speed, either globally or in a specific part of the organism's development." [GOC:ems, GOC:mah] biological_process
+ENSG00000099985 GO:0043410 positive regulation of MAPK cascade "Any process that activates or increases the frequency, rate or extent of signal transduction mediated by the MAPK cascade." [GOC:go_curators] biological_process
+ENSG00000099985 GO:0045944 positive regulation of transcription from RNA polymerase II promoter "Any process that activates or increases the frequency, rate or extent of transcription from an RNA polymerase II promoter." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000099985 GO:0046888 negative regulation of hormone secretion "Any process that stops, prevents, or reduces the frequency, rate or extent of the regulated release of a hormone from a cell." [GOC:ai] biological_process
+ENSG00000099985 GO:0050731 positive regulation of peptidyl-tyrosine phosphorylation "Any process that activates or increases the frequency, rate or extent of the phosphorylation of peptidyl-tyrosine." [GOC:ai] biological_process
+ENSG00000099985 GO:0051781 positive regulation of cell division "Any process that activates or increases the frequency, rate or extent of cell division." [GOC:ai] biological_process
+ENSG00000099985 GO:0048266 behavioral response to pain "Any process that results in a change in the behavior of an organism as a result of a pain stimulus. Pain stimuli cause activation of nociceptors, peripheral receptors for pain, include receptors which are sensitive to painful mechanical stimuli, extreme heat or cold, and chemical stimuli." [GOC:jid] biological_process
+ENSG00000099985 GO:2001235 positive regulation of apoptotic signaling pathway "Any process that activates or increases the frequency, rate or extent of apoptotic signaling pathway." [GOC:mtg_apoptosis] biological_process
+ENSG00000099985 GO:0009408 response to heat "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a heat stimulus, a temperature stimulus above the optimal temperature for that organism." [GOC:lr] biological_process
+ENSG00000099985 GO:0007422 peripheral nervous system development "The process whose specific outcome is the progression of the peripheral nervous system over time, from its formation to the mature structure. The peripheral nervous system is one of the two major divisions of the nervous system. Nerves in the PNS connect the central nervous system (CNS) with sensory organs, other organs, muscles, blood vessels and glands." [GOC:go_curators, UBERON:0000010] biological_process
+ENSG00000099985 GO:0042503 tyrosine phosphorylation of Stat3 protein "The process of introducing a phosphate group to a tyrosine residue of a Stat3 protein." [GOC:jl, PMID:11426647] biological_process
+ENSG00000099985 GO:0045835 negative regulation of meiosis "Any process that stops, prevents, or reduces the frequency, rate or extent of meiosis." [GOC:go_curators] biological_process
+ENSG00000099985 GO:0042506 tyrosine phosphorylation of Stat5 protein "The process of introducing a phosphate group to a tyrosine residue of a Stat5 protein." [GOC:jl, PMID:11426647] biological_process
+ENSG00000099985 GO:0042508 tyrosine phosphorylation of Stat1 protein "The process of introducing a phosphate group to a tyrosine residue of a Stat1 protein." [GOC:jl, PMID:10918594] biological_process
+ENSG00000099985
+ENSG00000184117 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000184117 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000184117 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000184117 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000184117 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000184117 GO:0005743 mitochondrial inner membrane "The inner, i.e. lumen-facing, lipid bilayer of the mitochondrial envelope. It is highly folded to form cristae." [GOC:ai] cellular_component
+ENSG00000184117 GO:0019233 sensory perception of pain "The series of events required for an organism to receive a painful stimulus, convert it to a molecular signal, and recognize and characterize the signal. Pain is medically defined as the physical sensation of discomfort or distress caused by injury or illness, so can hence be described as a harmful stimulus which signals current (or impending) tissue damage. Pain may come from extremes of temperature, mechanical damage, electricity or from noxious chemical substances. This is a neurological process." [http://www.onelook.com/] biological_process
+ENSG00000184117 GO:0097060 synaptic membrane "A specialized area of membrane on either the presynaptic or the postsynaptic side of a synapse, the junction between a nerve fiber of one neuron and another neuron or muscle fiber or glial cell." [GOC:BHF, PMID:20410104] cellular_component
+ENSG00000184117 GO:0042165 neurotransmitter binding "Interacting selectively and non-covalently with a neurotransmitter, any chemical substance that is capable of transmitting (or inhibiting the transmission of) a nerve impulse from a neuron to another cell." [ISBN:0198506732] molecular_function
+ENSG00000184117
+ENSG00000159873 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000159873 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000159873
+ENSG00000128245 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000128245 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000128245 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000128245 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000128245 GO:0061024 membrane organization "A process which results in the assembly, arrangement of constituent parts, or disassembly of a membrane. A membrane is a double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:dph, GOC:tb] biological_process
+ENSG00000128245 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000128245 GO:0048856 anatomical structure development "The biological process whose specific outcome is the progression of an anatomical structure from an initial condition to its mature state. This process begins with the formation of the structure and ends with the mature structure, whatever form that may be including its natural destruction. An anatomical structure is any biological entity that occupies space and is distinguished from its surroundings. Anatomical structures can be macroscopic such as a carpel, or microscopic such as an acrosome." [GO_REF:0000021, GOC:mtg_15jun06] biological_process
+ENSG00000128245 GO:0019899 enzyme binding "Interacting selectively and non-covalently with any enzyme." [GOC:jl] molecular_function
+ENSG00000128245 GO:0008219 cell death "Any biological process that results in permanent cessation of all vital functions of a cell. A cell should be considered dead when any one of the following molecular or morphological criteria is met: (1) the cell has lost the integrity of its plasma membrane; (2) the cell, including its nucleus, has undergone complete fragmentation into discrete bodies (frequently referred to as \"apoptotic bodies\"); and/or (3) its corpse (or its fragments) have been engulfed by an adjacent cell in vivo." [GOC:mah, GOC:mtg_apoptosis] biological_process
+ENSG00000128245 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000128245 GO:0006629 lipid metabolic process "The chemical reactions and pathways involving lipids, compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent. Includes fatty acids; neutral fats, other fatty-acid esters, and soaps; long-chain (fatty) alcohols and waxes; sphingoids and other long-chain bases; glycolipids, phospholipids and sphingolipids; and carotenes, polyprenols, sterols, terpenes and other isoprenoids." [GOC:ma] biological_process
+ENSG00000128245 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000128245 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000128245 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000128245 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000128245 GO:0002028 regulation of sodium ion transport "Any process that modulates the frequency, rate or extent of the directed movement of sodium ions (Na+) into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:dph] biological_process
+ENSG00000128245 GO:0005159 insulin-like growth factor receptor binding "Interacting selectively and non-covalently with the insulin-like growth factor receptor." [GOC:jl] molecular_function
+ENSG00000128245 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000128245 GO:0006713 glucocorticoid catabolic process "The chemical reactions and pathways resulting in the breakdown of glucocorticoids, hormonal C21 corticosteroids synthesized from cholesterol." [ISBN:0198506732] biological_process
+ENSG00000128245 GO:0006886 intracellular protein transport "The directed movement of proteins in a cell, including the movement of proteins between specific compartments or structures within a cell, such as organelles of a eukaryotic cell." [GOC:mah] biological_process
+ENSG00000128245 GO:0006915 apoptotic process "A programmed cell death process which begins when a cell receives an internal (e.g. DNA damage) or external signal (e.g. an extracellular death ligand), and proceeds through a series of biochemical events (signaling pathway phase) which trigger an execution phase. The execution phase is the last step of an apoptotic process, and is typically characterized by rounding-up of the cell, retraction of pseudopodes, reduction of cellular volume (pyknosis), chromatin condensation, nuclear fragmentation (karyorrhexis), plasma membrane blebbing and fragmentation of the cell into apoptotic bodies. When the execution phase is completed, the cell has died." [GOC:dhl, GOC:ecd, GOC:go_curators, GOC:mtg_apoptosis, GOC:tb, ISBN:0198506732, PMID:18846107, PMID:21494263] biological_process
+ENSG00000128245 GO:0014704 intercalated disc "A complex cell-cell junction at which myofibrils terminate in cardiomyocytes; mediates mechanical and electrochemical integration between individual cardiomyocytes. The intercalated disc contains regions of tight mechanical attachment (fasciae adherentes and desmosomes) and electrical coupling (gap junctions) between adjacent cells." [GOC:mtg_muscle, PMID:11732910] cellular_component
+ENSG00000128245 GO:0017080 sodium channel regulator activity "Modulates the activity of a sodium channel." [GOC:mah] molecular_function
+ENSG00000128245 GO:0019904 protein domain specific binding "Interacting selectively and non-covalently with a specific domain of a protein." [GOC:go_curators] molecular_function
+ENSG00000128245 GO:0021762 substantia nigra development "The progression of the substantia nigra over time from its initial formation until its mature state. The substantia nigra is the layer of gray substance that separates the posterior parts of the cerebral peduncles (tegmentum mesencephali) from the anterior parts; it normally includes a posterior compact part with many pigmented cells (pars compacta) and an anterior reticular part whose cells contain little pigment (pars reticularis)." [GO_REF:0000021, GOC:cls, GOC:dgh, GOC:dph, GOC:jid, GOC:mtg_15jun06, ISBN:0838580343, ISBN:0878937420] biological_process
+ENSG00000128245 GO:0030659 cytoplasmic vesicle membrane "The lipid bilayer surrounding a cytoplasmic vesicle." [GOC:mah] cellular_component
+ENSG00000128245 GO:0035259 glucocorticoid receptor binding "Interacting selectively and non-covalently with a glucocorticoid receptor." [GOC:bf] molecular_function
+ENSG00000128245 GO:0042921 glucocorticoid receptor signaling pathway "Any series of molecular signals generated as a consequence of a glucocorticoid binding to its receptor." [GOC:mah] biological_process
+ENSG00000128245 GO:0044325 ion channel binding "Interacting selectively and non-covalently with one or more specific sites on an ion channel, a protein complex that spans a membrane and forms a water-filled channel across the phospholipid bilayer allowing selective ion transport down its electrochemical gradient." [GOC:BHF, GOC:jl] molecular_function
+ENSG00000128245 GO:0045664 regulation of neuron differentiation "Any process that modulates the frequency, rate or extent of neuron differentiation." [GOC:go_curators] biological_process
+ENSG00000128245 GO:0045893 positive regulation of transcription, DNA-templated "Any process that activates or increases the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000128245 GO:0046982 protein heterodimerization activity "Interacting selectively and non-covalently with a nonidentical protein to form a heterodimer." [GOC:ai] molecular_function
+ENSG00000128245 GO:0048167 regulation of synaptic plasticity "A process that modulates synaptic plasticity, the ability of synapses to change as circumstances require. They may alter function, such as increasing or decreasing their sensitivity, or they may increase or decrease in actual numbers." [GOC:dph, GOC:jid, GOC:tb, http://www.mercksource.com, PMID:11891290] biological_process
+ENSG00000128245 GO:0050774 negative regulation of dendrite morphogenesis "Any process that stops, prevents, or reduces the frequency, rate or extent of dendrite morphogenesis." [GOC:ai] biological_process
+ENSG00000128245 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000128245 GO:0086010 membrane depolarization during action potential "The process in which membrane potential changes in the depolarizing direction from the negative resting potential towards the positive membrane potential that will be the peak of the action potential." [GOC:BHF, GOC:mtg_cardiac_conduct_nov11] biological_process
+ENSG00000128245 GO:0097193 intrinsic apoptotic signaling pathway "A series of molecular signals in which an intracellular signal is conveyed to trigger the apoptotic death of a cell. The pathway starts with reception of an intracellular signal (e.g. DNA damage, endoplasmic reticulum stress, oxidative stress etc.), and ends when the execution phase of apoptosis is triggered. The intrinsic apoptotic signaling pathway is crucially regulated by permeabilization of the mitochondrial outer membrane (MOMP)." [GOC:mtg_apoptosis, GOC:yaf, PMID:11919192, PMID:17340152, PMID:18852119] biological_process
+ENSG00000128245 GO:1900740 positive regulation of protein insertion into mitochondrial membrane involved in apoptotic signaling pathway "Any process that activates or increases the frequency, rate or extent of protein insertion into mitochondrial membrane involved in apoptotic signaling pathway." [GOC:mtg_apoptosis, GOC:TermGenie] biological_process
+ENSG00000128245 GO:2000649 regulation of sodium ion transmembrane transporter activity "Any process that modulates the frequency, rate or extent of sodium ion transmembrane transporter activity." [GOC:obol] biological_process
+ENSG00000128245 GO:0003779 actin binding "Interacting selectively and non-covalently with monomeric or multimeric forms of actin, including actin filaments." [GOC:clt] molecular_function
+ENSG00000183864 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000183864 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000183864 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000183864 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000183864 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000183864 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000183864 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000183864 GO:0007292 female gamete generation "Generation of the female gamete; specialised haploid cells produced by meiosis and along with a male gamete takes part in sexual reproduction." [GOC:dph, ISBN:0198506732] biological_process
+ENSG00000183864 GO:0008285 negative regulation of cell proliferation "Any process that stops, prevents or reduces the rate or extent of cell proliferation." [GOC:go_curators] biological_process
+ENSG00000183864 GO:0010468 regulation of gene expression "Any process that modulates the frequency, rate or extent of gene expression. Gene expression is the process in which a gene's coding sequence is converted into a mature gene product or products (proteins or RNA). This includes the production of an RNA transcript as well as any processing to produce a mature RNA product or an mRNA (for protein-coding genes) and the translation of that mRNA into protein. Some protein processing events may be included when they are required to form an active form of a product from an inactive precursor form." [GOC:dph, GOC:tb] biological_process
+ENSG00000183864 GO:0045671 negative regulation of osteoclast differentiation "Any process that stops, prevents, or reduces the frequency, rate or extent of osteoclast differentiation." [GOC:go_curators] biological_process
+ENSG00000183864 GO:0045778 positive regulation of ossification "Any process that activates or increases the frequency, rate or extent of bone formation." [GOC:go_curators] biological_process
+ENSG00000183864 GO:0042809 vitamin D receptor binding "Interacting selectively and non-covalently with the vitamin D receptor, a nuclear receptor that mediates the action of vitamin D by binding DNA and controlling the transcription of hormone-sensitive genes." [GOC:jl, PMID:12637589] molecular_function
+ENSG00000183864
+ENSG00000172404 GO:0051087 chaperone binding "Interacting selectively and non-covalently with a chaperone protein, a class of proteins that bind to nascent or unfolded polypeptides and ensure correct folding or transport." [http://www.onelook.com] molecular_function
+ENSG00000172404 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000128285 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000128285 GO:0004871 signal transducer activity "Conveys a signal across a cell to trigger a change in cell function or state. A signal is a physical entity or change in state that is used to transfer information in order to trigger a response." [GOC:go_curators] molecular_function
+ENSG00000128285 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000128285 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000128285 GO:0006091 generation of precursor metabolites and energy "The chemical reactions and pathways resulting in the formation of precursor metabolites, substances from which energy is derived, and any process involved in the liberation of energy from these substances." [GOC:jl] biological_process
+ENSG00000128285 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000128285 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000128285 GO:0004930 G-protein coupled receptor activity "Combining with an extracellular signal and transmitting the signal across the membrane by activating an associated G-protein; promotes the exchange of GDP for GTP on the alpha subunit of a heterotrimeric G-protein complex." [GOC:bf, http://www.iuphar-db.org, Wikipedia:GPCR] molecular_function
+ENSG00000128285 GO:0005887 integral component of plasma membrane "The component of the plasma membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000128285 GO:0007186 G-protein coupled receptor signaling pathway "A series of molecular signals that proceeds with an activated receptor promoting the exchange of GDP for GTP on the alpha-subunit of an associated heterotrimeric G-protein complex. The GTP-bound activated alpha-G-protein then dissociates from the beta- and gamma-subunits to further transmit the signal within the cell. The pathway begins with receptor-ligand interaction, or for basal GPCR signaling the pathway begins with the receptor activating its G protein in the absence of an agonist, and ends with regulation of a downstream cellular process, e.g. transcription." [GOC:bf, GOC:mah, Wikipedia:G_protein-coupled_receptor] biological_process
+ENSG00000128285 GO:0007193 adenylate cyclase-inhibiting G-protein coupled receptor signaling pathway "The series of molecular signals generated as a consequence of a G-protein coupled receptor binding to its physiological ligand, where the pathway proceeds through inhibition of adenylyl cyclase activity and a subsequent decrease in the concentration of cyclic AMP (cAMP)." [GOC:dph, GOC:mah, GOC:signaling, GOC:tb, ISBN:0815316194] biological_process
+ENSG00000128285 GO:0007218 neuropeptide signaling pathway "The series of molecular signals generated as a consequence of a peptide neurotransmitter binding to a cell surface receptor." [GOC:mah, ISBN:0815316194] biological_process
+ENSG00000128285 GO:0007631 feeding behavior "Behavior associated with the intake of food." [GOC:mah] biological_process
+ENSG00000128285 GO:0008188 neuropeptide receptor activity "Combining with a neuropeptide to initiate a change in cell activity." [GOC:ai] molecular_function
+ENSG00000128285 GO:0031513 nonmotile primary cilium "A primary cilium which contains a variable array of axonemal microtubules but does not contain molecular motors. Nonmotile primary cilia are found on many different cell types and function as sensory organelles that concentrate and organize sensory signaling molecules." [GOC:dgh, GOC:kmv, PMID:17009929, PMID:20144998] cellular_component
+ENSG00000128285 GO:0005929 cilium "A specialized eukaryotic organelle that consists of a filiform extrusion of the cell surface and of some cytoplasmic parts. Each cilium is largely bounded by an extrusion of the cytoplasmic (plasma) membrane, and contains a regular longitudinal array of microtubules, anchored to a basal body." [GOC:cilia, GOC:kmv, ISBN:0198547684] cellular_component
+ENSG00000128285 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000128285 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000128285 GO:0060170 ciliary membrane "The portion of the plasma membrane surrounding a cilium." [GOC:cilia, GOC:dph, GOC:rph] cellular_component
+ENSG00000128285 GO:0007204 positive regulation of cytosolic calcium ion concentration "Any process that increases the concentration of calcium ions in the cytosol." [GOC:ai] biological_process
+ENSG00000128285 GO:0051928 positive regulation of calcium ion transport "Any process that activates or increases the frequency, rate or extent of the directed movement of calcium ions into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:ai] biological_process
+ENSG00000128285 GO:0042562 hormone binding "Interacting selectively and non-covalently with any hormone, naturally occurring substances secreted by specialized cells that affect the metabolism or behavior of other cells possessing functional receptors for the hormone." [CHEBI:24621, GOC:jl] molecular_function
+ENSG00000128285 GO:0007166 cell surface receptor signaling pathway "A series of molecular signals initiated by activation of a receptor on the surface of a cell. The pathway begins with binding of an extracellular ligand to a cell surface receptor, or for receptors that signal in the absence of a ligand, by ligand-withdrawal or the activity of a constitutively active receptor. The pathway ends with regulation of a downstream cellular process, e.g. transcription." [GOC:bf, GOC:mah, GOC:pr, GOC:signaling] biological_process
+ENSG00000128285 GO:0008022 protein C-terminus binding "Interacting selectively and non-covalently with a protein C-terminus, the end of any peptide chain at which the 1-carboxy function of a constituent amino acid is not attached in peptide linkage to another amino-acid residue." [ISBN:0198506732] molecular_function
+ENSG00000128285 GO:0030273 melanin-concentrating hormone receptor activity "Combining with the cyclic peptide hormone melanin-concentrating hormone to initiate a change in cell activity." [GOC:mah] molecular_function
+ENSG00000196236 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000196236 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000196236 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000196236 GO:0051604 protein maturation "Any process leading to the attainment of the full functional capacity of a protein." [GOC:ai] biological_process
+ENSG00000196236 GO:0008233 peptidase activity "Catalysis of the hydrolysis of a peptide bond. A peptide bond is a covalent bond formed when the carbon atom from the carboxyl group of one amino acid shares electrons with the nitrogen atom from the amino group of a second amino acid." [GOC:jl, ISBN:0815332181] molecular_function
+ENSG00000196236 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000196236 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000196236 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000196236 GO:0003094 glomerular filtration "The process in which plasma is filtered through the glomerular membrane which consists of capillary endothelial cells, the basement membrane, and epithelial cells. The glomerular filtrate is the same as plasma except it has no significant amount of protein." [GOC:mtg_cardio, GOC:sart, ISBN:0721643949] biological_process
+ENSG00000196236 GO:0004177 aminopeptidase activity "Catalysis of the hydrolysis of N-terminal amino acid residues from in a polypeptide chain." [GOC:jl, ISBN:0198506732] molecular_function
+ENSG00000196236 GO:0006508 proteolysis "The hydrolysis of proteins into smaller polypeptides and/or amino acids by cleavage of their peptide bonds." [GOC:bf, GOC:mah] biological_process
+ENSG00000196236 GO:0008237 metallopeptidase activity "Catalysis of the hydrolysis of peptide bonds by a mechanism in which water acts as a nucleophile, one or two metal ions hold the water molecule in place, and charged amino acid side chains are ligands for the metal ions." [GOC:mah, http://merops.sanger.ac.uk/about/glossary.htm#CATTYPE] molecular_function
+ENSG00000196236 GO:0016485 protein processing "Any protein maturation process achieved by the cleavage of a peptide bond or bonds within a protein. Protein maturation is the process leading to the attainment of the full functional capacity of a protein." [GOC:curators, GOC:jl, GOC:jsg] biological_process
+ENSG00000196236 GO:0030145 manganese ion binding "Interacting selectively and non-covalently with manganese (Mn) ions." [GOC:ai] molecular_function
+ENSG00000196236 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000196236
+ENSG00000099960 GO:0003333 amino acid transmembrane transport "The directed movement of amino acids, organic acids containing one or more amino substituents across a membrane by means of some agent such as a transporter or pore." [GOC:dph, GOC:tb] biological_process
+ENSG00000099960 GO:0015171 amino acid transmembrane transporter activity "Catalysis of the transfer of amino acids from one side of a membrane to the other. Amino acids are organic molecules that contain an amino group and a carboxyl group." [GOC:ai, GOC:mtg_transport, ISBN:0815340729] molecular_function
+ENSG00000099960 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000099960 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000099960 GO:0022857 transmembrane transporter activity "Enables the transfer of a substance from one side of a membrane to the other." [GOC:mtg_transport, ISBN:0815340729] molecular_function
+ENSG00000099960 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000099960 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000099960 GO:0055085 transmembrane transport "The process in which a solute is transported from one side of a membrane to the other." [GOC:dph, GOC:jid] biological_process
+ENSG00000099960 GO:0006520 cellular amino acid metabolic process "The chemical reactions and pathways involving amino acids, carboxylic acids containing one or more amino groups, as carried out by individual cells." [CHEBI:33709, GOC:curators, ISBN:0198506732] biological_process
+ENSG00000099960 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000099960 GO:0015174 basic amino acid transmembrane transporter activity "Catalysis of the transfer of basic amino acids from one side of a membrane to the other. Basic amino acids have a pH above 7." [GOC:ai, GOC:mtg_transport, ISBN:0815340729] molecular_function
+ENSG00000099960 GO:0015802 basic amino acid transport "The directed movement of basic amino acids, amino acids with a pH above 7, into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:ai] biological_process
+ENSG00000099960 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000099960 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000100263 GO:0004252 serine-type endopeptidase activity "Catalysis of the hydrolysis of internal, alpha-peptide bonds in a polypeptide chain by a catalytic mechanism that involves a catalytic triad consisting of a serine nucleophile that is activated by a proton relay involving an acidic residue (e.g. aspartate or glutamate) and a basic residue (usually histidine)." [GOC:mah, http://merops.sanger.ac.uk/about/glossary.htm#CATTYPE, ISBN:0716720094] molecular_function
+ENSG00000100263 GO:0006508 proteolysis "The hydrolysis of proteins into smaller polypeptides and/or amino acids by cleavage of their peptide bonds." [GOC:bf, GOC:mah] biological_process
+ENSG00000100263 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000100263 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100263 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100263 GO:0008233 peptidase activity "Catalysis of the hydrolysis of a peptide bond. A peptide bond is a covalent bond formed when the carbon atom from the carboxyl group of one amino acid shares electrons with the nitrogen atom from the amino group of a second amino acid." [GOC:jl, ISBN:0815332181] molecular_function
+ENSG00000100263 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100263 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100263 GO:0045732 positive regulation of protein catabolic process "Any process that activates or increases the frequency, rate or extent of the chemical reactions and pathways resulting in the breakdown of a protein by the destruction of the native, active configuration, with or without the hydrolysis of peptide bonds." [GOC:go_curators] biological_process
+ENSG00000100263 GO:0000165 MAPK cascade "An intracellular protein kinase cascade containing at least a MAPK, a MAPKK and a MAP3K. The cascade can also contain two additional tiers: the upstream MAP4K and the downstream MAP Kinase-activated kinase (MAPKAPK). The kinases in each tier phosphorylate and activate the kinases in the downstream tier to transmit a signal within a cell." [GOC:bf, GOC:mtg_signaling_feb11, PMID:20811974, PMID:9561267] biological_process
+ENSG00000100263 GO:0001889 liver development "The process whose specific outcome is the progression of the liver over time, from its formation to the mature structure. The liver is an exocrine gland which secretes bile and functions in metabolism of protein and carbohydrate and fat, synthesizes substances involved in the clotting of the blood, synthesizes vitamin A, detoxifies poisonous substances, stores glycogen, and breaks down worn-out erythrocytes." [GOC:add, ISBN:068340007X] biological_process
+ENSG00000100263 GO:0050708 regulation of protein secretion "Any process that modulates the frequency, rate or extent of the controlled release of a protein from a cell." [GOC:ai] biological_process
+ENSG00000100263 GO:0009410 response to xenobiotic stimulus "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a xenobiotic compound stimulus. Xenobiotic compounds are compounds foreign to living organisms." [GOC:jl] biological_process
+ENSG00000100263 GO:0002673 regulation of acute inflammatory response "Any process that modulates the frequency, rate, or extent of an acute inflammatory response." [GOC:add] biological_process
+ENSG00000100263 GO:0032815 negative regulation of natural killer cell activation "Any process that stops, prevents, or reduces the frequency, rate or extent of natural killer cell activation." [GOC:mah] biological_process
+ENSG00000100263
+ENSG00000099957 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000099957 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000099957 GO:0055085 transmembrane transport "The process in which a solute is transported from one side of a membrane to the other." [GOC:dph, GOC:jid] biological_process
+ENSG00000099957 GO:0006461 protein complex assembly "The aggregation, arrangement and bonding together of a set of components to form a protein complex." [GOC:ai] biological_process
+ENSG00000099957 GO:0022607 cellular component assembly "The aggregation, arrangement and bonding together of a cellular component." [GOC:isa_complete] biological_process
+ENSG00000099957 GO:0065003 macromolecular complex assembly "The aggregation, arrangement and bonding together of a set of macromolecules to form a complex." [GOC:jl] biological_process
+ENSG00000099957 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000099957 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000099957 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000099957 GO:0022857 transmembrane transporter activity "Enables the transfer of a substance from one side of a membrane to the other." [GOC:mtg_transport, ISBN:0815340729] molecular_function
+ENSG00000099957 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000099957 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000099957 GO:0004871 signal transducer activity "Conveys a signal across a cell to trigger a change in cell function or state. A signal is a physical entity or change in state that is used to transfer information in order to trigger a response." [GOC:go_curators] molecular_function
+ENSG00000099957 GO:0001614 purinergic nucleotide receptor activity "Combining with a purine nucleotide and transmitting the signal from one side of the membrane to the other to initiate a change in cell activity." [GOC:mah, GOC:signaling] molecular_function
+ENSG00000099957 GO:0004888 transmembrane signaling receptor activity "Combining with an extracellular or intracellular signal and transmitting the signal from one side of the membrane to the other to initiate a change in cell activity." [GOC:go_curators, Wikipedia:Transmembrane_receptor] molecular_function
+ENSG00000099957 GO:0004931 extracellular ATP-gated cation channel activity "Catalysis of the transmembrane transfer of a cation by a channel that opens when extracellular ATP has been bound by the channel complex or one of its constituent parts." [GOC:bf, GOC:mah, PMID:9755289] molecular_function
+ENSG00000099957 GO:0005524 ATP binding "Interacting selectively and non-covalently with ATP, adenosine 5'-triphosphate, a universally important coenzyme and enzyme regulator." [ISBN:0198506732] molecular_function
+ENSG00000099957 GO:0005639 integral component of nuclear inner membrane "The component of the nuclear inner membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:go_curators] cellular_component
+ENSG00000099957 GO:0005887 integral component of plasma membrane "The component of the plasma membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000099957 GO:0006812 cation transport "The directed movement of cations, atoms or small molecules with a net positive charge, into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:ai] biological_process
+ENSG00000099957 GO:0006936 muscle contraction "A process in which force is generated within muscle tissue, resulting in a change in muscle geometry. Force generation involves a chemo-mechanical energy conversion step that is carried out by the actin/myosin complex activity, which generates force through ATP hydrolysis." [GOC:ef, GOC:mtg_muscle, ISBN:0198506732] biological_process
+ENSG00000099957 GO:0015267 channel activity "Catalysis of energy-independent facilitated diffusion, mediated by passage of a solute through a transmembrane aqueous pore or channel. Stereospecificity is not exhibited but this transport may be specific for a particular molecular species or class of molecules." [GOC:mtg_transport, ISBN:0815340729, TC:1.-.-.-.-] molecular_function
+ENSG00000099957 GO:0030054 cell junction "A cellular component that forms a specialized region of connection between two cells or between a cell and the extracellular matrix. At a cell junction, anchoring proteins extend through the plasma membrane to link cytoskeletal proteins in one cell to cytoskeletal proteins in neighboring cells or to proteins in the extracellular matrix." [GOC:mah, http://www.vivo.colostate.edu/hbooks/cmb/cells/pmemb/junctions_a.html, ISBN:0198506732] cellular_component
+ENSG00000099957 GO:0035590 purinergic nucleotide receptor signaling pathway "The series of molecular signals generated as a consequence of a receptor binding to an extracellular purine nucleotide to initiate a change in cell activity." [GOC:BHF, PMID:9755289] biological_process
+ENSG00000099957 GO:0042802 identical protein binding "Interacting selectively and non-covalently with an identical protein or proteins." [GOC:jl] molecular_function
+ENSG00000099957 GO:0051260 protein homooligomerization "The process of creating protein oligomers, compounds composed of a small number, usually between three and ten, of identical component monomers. Oligomers may be formed by the polymerization of a number of monomers or the depolymerization of a large protein polymer." [GOC:ai] biological_process
+ENSG00000099957 GO:0098655 cation transmembrane transport "A process in which a cation is transported from one side of a membrane to the other by means of some agent such as a transporter or pore." [GOC:dos, GOC:vw] biological_process
+ENSG00000099957 GO:0005216 ion channel activity "Catalysis of facilitated diffusion of an ion (by an energy-independent process) by passage through a transmembrane aqueous pore or channel without evidence for a carrier-mediated mechanism." [GOC:cy, GOC:mtg_transport, ISBN:0815340729] molecular_function
+ENSG00000099957 GO:0006811 ion transport "The directed movement of charged atoms or small charged molecules into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:ai] biological_process
+ENSG00000099957 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000099957 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000099957 GO:0043025 neuronal cell body "The portion of a neuron that includes the nucleus, but excludes cell projections such as axons and dendrites." [GOC:go_curators] cellular_component
+ENSG00000099957 GO:0014069 postsynaptic density "The postsynaptic density is a region that lies adjacent to the cytoplasmic face of the postsynaptic membrane at excitatory synapse. It forms a disc that consists of a range of proteins with different functions, some of which contact the cytoplasmic domains of ion channels in the postsynaptic membrane. The proteins making up the disc include receptors, and structural proteins linked to the actin cytoskeleton. They also include signalling machinery, such as protein kinases and phosphatases. The postsynaptic density may be part of a neuron or a muscle cell or a glial cell." [GOC:BHF, GOC:ef, GOC:jid, GOC:pr, GOC:sjp, http://molneuro.kaist.ac.kr/psd, PMID:14532281, Wikipedia:Postsynaptic_density] cellular_component
+ENSG00000099957 GO:0043197 dendritic spine "Protrusion from a dendrite. Spines are specialised subcellular compartments involved in the synaptic transmission. They are linked to the dendritic shaft by a restriction. Because of their bulb shape, they function as a biochemical and an electrical compartment. Spine remodeling is though to be involved in synaptic plasticity." [GOC:nln] cellular_component
+ENSG00000099957 GO:0051291 protein heterooligomerization "The process of creating protein oligomers, compounds composed of a small number, usually between three and ten, of component monomers that are not all identical. Oligomers may be formed by the polymerization of a number of monomers or the depolymerization of a large protein polymer." [GOC:ai] biological_process
+ENSG00000099957 GO:0034220 ion transmembrane transport "A process in which an ion is transported from one side of a membrane to the other by means of some agent such as a transporter or pore." [GOC:mah] biological_process
+ENSG00000100031 GO:0003840 gamma-glutamyltransferase activity "Catalysis of the reaction: (5-L-glutamyl)-peptide + an amino acid = peptide + 5-L-glutamyl-amino acid." [EC:2.3.2.2] molecular_function
+ENSG00000100031 GO:0006749 glutathione metabolic process "The chemical reactions and pathways involving glutathione, the tripeptide glutamylcysteinylglycine, which acts as a coenzyme for some enzymes and as an antioxidant in the protection of sulfhydryl groups in enzymes and other proteins; it has a specific role in the reduction of hydrogen peroxide (H2O2) and oxidized ascorbate, and it participates in the gamma-glutamyl cycle." [CHEBI:16856, ISBN:0198506732] biological_process
+ENSG00000100031 GO:0006520 cellular amino acid metabolic process "The chemical reactions and pathways involving amino acids, carboxylic acids containing one or more amino groups, as carried out by individual cells." [CHEBI:33709, GOC:curators, ISBN:0198506732] biological_process
+ENSG00000100031 GO:0006790 sulfur compound metabolic process "The chemical reactions and pathways involving the nonmetallic element sulfur or compounds that contain sulfur, such as the amino acids methionine and cysteine or the tripeptide glutathione." [GOC:ai] biological_process
+ENSG00000100031 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100031 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100031 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000100031 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100031 GO:0016746 transferase activity, transferring acyl groups "Catalysis of the transfer of an acyl group from one compound (donor) to another (acceptor)." [GOC:jl, ISBN:0198506732] molecular_function
+ENSG00000100031 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100031 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100031 GO:0008233 peptidase activity "Catalysis of the hydrolysis of a peptide bond. A peptide bond is a covalent bond formed when the carbon atom from the carboxyl group of one amino acid shares electrons with the nitrogen atom from the amino group of a second amino acid." [GOC:jl, ISBN:0815332181] molecular_function
+ENSG00000100031 GO:0051604 protein maturation "Any process leading to the attainment of the full functional capacity of a protein." [GOC:ai] biological_process
+ENSG00000100031 GO:0006629 lipid metabolic process "The chemical reactions and pathways involving lipids, compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent. Includes fatty acids; neutral fats, other fatty-acid esters, and soaps; long-chain (fatty) alcohols and waxes; sphingoids and other long-chain bases; glycolipids, phospholipids and sphingolipids; and carotenes, polyprenols, sterols, terpenes and other isoprenoids." [GOC:ma] biological_process
+ENSG00000100031 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000100031 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000100031 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000100031 GO:0005615 extracellular space "That part of a multicellular organism outside the cells proper, usually taken to be outside the plasma membranes, and occupied by fluid." [ISBN:0198547684] cellular_component
+ENSG00000100031 GO:0002682 regulation of immune system process "Any process that modulates the frequency, rate, or extent of an immune system process." [GOC:add] biological_process
+ENSG00000100031 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100031 GO:0006508 proteolysis "The hydrolysis of proteins into smaller polypeptides and/or amino acids by cleavage of their peptide bonds." [GOC:bf, GOC:mah] biological_process
+ENSG00000100031 GO:0006536 glutamate metabolic process "The chemical reactions and pathways involving glutamate, the anion of 2-aminopentanedioic acid." [GOC:go_curators] biological_process
+ENSG00000100031 GO:0006691 leukotriene metabolic process "The chemical reactions and pathways involving leukotriene, a pharmacologically active substance derived from a polyunsaturated fatty acid, such as arachidonic acid." [GOC:ma] biological_process
+ENSG00000100031 GO:0006750 glutathione biosynthetic process "The chemical reactions and pathways resulting in the formation of glutathione, the tripeptide glutamylcysteinylglycine, which acts as a coenzyme for some enzymes and as an antioxidant in the protection of sulfhydryl groups in enzymes and other proteins." [CHEBI:16856, GOC:ai, GOC:al, GOC:pde, ISBN:0198506732] biological_process
+ENSG00000100031 GO:0006751 glutathione catabolic process "The chemical reactions and pathways resulting in the breakdown of glutathione, the tripeptide glutamylcysteinylglycine, which acts as a coenzyme for some enzymes and as an antioxidant in the protection of sulfhydryl groups in enzymes and other proteins." [CHEBI:16856, GOC:ai, ISBN:0198506732] biological_process
+ENSG00000100031 GO:0006805 xenobiotic metabolic process "The chemical reactions and pathways involving a xenobiotic compound, a compound foreign to living organisms. Used of chemical compounds, e.g. a xenobiotic chemical, such as a pesticide." [GOC:cab2] biological_process
+ENSG00000100031 GO:0007283 spermatogenesis "The process of formation of spermatozoa, including spermatocytogenesis and spermiogenesis." [GOC:jid, ISBN:9780878933846] biological_process
+ENSG00000100031 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000100031 GO:0019344 cysteine biosynthetic process "The chemical reactions and pathways resulting in the formation of cysteine, 2-amino-3-mercaptopropanoic acid." [GOC:go_curators] biological_process
+ENSG00000100031 GO:0019369 arachidonic acid metabolic process "The chemical reactions and pathways involving arachidonic acid, a straight chain fatty acid with 20 carbon atoms and four double bonds per molecule. Arachidonic acid is the all-Z-(5,8,11,14)-isomer." [ISBN:0198506732] biological_process
+ENSG00000100031 GO:0019370 leukotriene biosynthetic process "The chemical reactions and pathways resulting in the formation of leukotriene, a pharmacologically active substance derived from a polyunsaturated fatty acid, such as arachidonic acid." [GOC:go_curators] biological_process
+ENSG00000100031 GO:0031362 anchored component of external side of plasma membrane "The component of the plasma membrane consisting of the gene products that are tethered to the membrane only by a covalently attached anchor, such as a lipid group embedded in the membrane. Gene products with peptide sequences that are embedded in the membrane are excluded from this grouping." [GOC:dos, GOC:mah] cellular_component
+ENSG00000100031 GO:0031638 zymogen activation "The proteolytic processing of an inactive enzyme to an active form." [GOC:hjd] biological_process
+ENSG00000100031 GO:0036374 glutathione hydrolase activity "Catalysis of the reaction: glutathione + H2O = L-cysteinylglycine + L-glutamate." [EC:3.4.19.13, GOC:imk] molecular_function
+ENSG00000100031 GO:0050727 regulation of inflammatory response "Any process that modulates the frequency, rate or extent of the inflammatory response, the immediate defensive reaction (by vertebrate tissue) to infection or injury caused by chemical or physical agents." [GOC:ai] biological_process
+ENSG00000100031 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000100031 GO:1901687 glutathione derivative biosynthetic process "The chemical reactions and pathways resulting in the formation of glutathione derivative." [GOC:pr, GOC:TermGenie] biological_process
+ENSG00000100031
+ENSG00000274252 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000274252 GO:0006629 lipid metabolic process "The chemical reactions and pathways involving lipids, compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent. Includes fatty acids; neutral fats, other fatty-acid esters, and soaps; long-chain (fatty) alcohols and waxes; sphingoids and other long-chain bases; glycolipids, phospholipids and sphingolipids; and carotenes, polyprenols, sterols, terpenes and other isoprenoids." [GOC:ma] biological_process
+ENSG00000274252 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000274252 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000274252 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000274252 GO:0006520 cellular amino acid metabolic process "The chemical reactions and pathways involving amino acids, carboxylic acids containing one or more amino groups, as carried out by individual cells." [CHEBI:33709, GOC:curators, ISBN:0198506732] biological_process
+ENSG00000274252 GO:0006790 sulfur compound metabolic process "The chemical reactions and pathways involving the nonmetallic element sulfur or compounds that contain sulfur, such as the amino acids methionine and cysteine or the tripeptide glutathione." [GOC:ai] biological_process
+ENSG00000274252 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000274252 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000274252 GO:0016746 transferase activity, transferring acyl groups "Catalysis of the transfer of an acyl group from one compound (donor) to another (acceptor)." [GOC:jl, ISBN:0198506732] molecular_function
+ENSG00000274252 GO:0003840 gamma-glutamyltransferase activity "Catalysis of the reaction: (5-L-glutamyl)-peptide + an amino acid = peptide + 5-L-glutamyl-amino acid." [EC:2.3.2.2] molecular_function
+ENSG00000274252 GO:0006749 glutathione metabolic process "The chemical reactions and pathways involving glutathione, the tripeptide glutamylcysteinylglycine, which acts as a coenzyme for some enzymes and as an antioxidant in the protection of sulfhydryl groups in enzymes and other proteins; it has a specific role in the reduction of hydrogen peroxide (H2O2) and oxidized ascorbate, and it participates in the gamma-glutamyl cycle." [CHEBI:16856, ISBN:0198506732] biological_process
+ENSG00000274252 GO:0019370 leukotriene biosynthetic process "The chemical reactions and pathways resulting in the formation of leukotriene, a pharmacologically active substance derived from a polyunsaturated fatty acid, such as arachidonic acid." [GOC:go_curators] biological_process
+ENSG00000274252 GO:0031362 anchored component of external side of plasma membrane "The component of the plasma membrane consisting of the gene products that are tethered to the membrane only by a covalently attached anchor, such as a lipid group embedded in the membrane. Gene products with peptide sequences that are embedded in the membrane are excluded from this grouping." [GOC:dos, GOC:mah] cellular_component
+ENSG00000274252 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000274252 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100427 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100427 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100427 GO:0005768 endosome "A membrane-bounded organelle to which materials ingested by endocytosis are delivered." [ISBN:0198506732, PMID:19696797] cellular_component
+ENSG00000100427 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100427 GO:0006461 protein complex assembly "The aggregation, arrangement and bonding together of a set of components to form a protein complex." [GOC:ai] biological_process
+ENSG00000100427 GO:0022607 cellular component assembly "The aggregation, arrangement and bonding together of a cellular component." [GOC:isa_complete] biological_process
+ENSG00000100427 GO:0065003 macromolecular complex assembly "The aggregation, arrangement and bonding together of a set of macromolecules to form a complex." [GOC:jl] biological_process
+ENSG00000100427 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100427 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100427 GO:0016192 vesicle-mediated transport "A cellular transport process in which transported substances are moved in membrane-bounded vesicles; transported substances are enclosed in the vesicle lumen or located in the vesicle membrane. The process begins with a step that directs a substance to the forming vesicle, and includes vesicle budding and coating. Vesicles are then targeted to, and fuse with, an acceptor membrane." [GOC:ai, GOC:mah, ISBN:08789310662000] biological_process
+ENSG00000100427 GO:0008565 protein transporter activity "Enables the directed movement of proteins into, out of or within a cell, or between cells." [ISBN:0198506732] molecular_function
+ENSG00000100427 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000100427 GO:0005783 endoplasmic reticulum "The irregular network of unit membranes, visible only by electron microscopy, that occurs in the cytoplasm of many eukaryotic cells. The membranes form a complex meshwork of tubular channels, which are often expanded into slitlike cavities called cisternae. The ER takes two forms, rough (or granular), with ribosomes adhering to the outer surface, and smooth (with no ribosomes attached)." [ISBN:0198506732] cellular_component
+ENSG00000100427 GO:0005764 lysosome "A small lytic vacuole that has cell cycle-independent morphology and is found in most animal cells and that contains a variety of hydrolases, most of which have their maximal activities in the pH range 5-6. The contained enzymes display latency if properly isolated. About 40 different lysosomal hydrolases are known and lysosomes have a great variety of morphologies and functions." [GOC:mah, ISBN:0198506732] cellular_component
+ENSG00000100427 GO:0005773 vacuole "A closed structure, found only in eukaryotic cells, that is completely surrounded by unit membrane and contains liquid material. Cells contain one or several vacuoles, that may have different functions from each other. Vacuoles have a diverse array of functions. They can act as a storage organelle for nutrients or waste products, as a degradative compartment, as a cost-effective way of increasing cell size, and as a homeostatic regulator controlling both turgor pressure and pH of the cytosol." [GOC:mtg_sensu, ISBN:0198506732] cellular_component
+ENSG00000100427 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100427 GO:0005215 transporter activity "Enables the directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells." [GOC:ai, GOC:dgf] molecular_function
+ENSG00000100427 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100427 GO:0005769 early endosome "A membrane-bounded organelle that receives incoming material from primary endocytic vesicles that have been generated by clathrin-dependent and clathrin-independent endocytosis; vesicles fuse with the early endosome to deliver cargo for sorting into recycling or degradation pathways." [GOC:mah, NIF_Subcellular:nlx_subcell_20090701, PMID:19696797] cellular_component
+ENSG00000100427 GO:0005901 caveola "A membrane raft that forms small pit, depression, or invagination that communicates with the outside of a cell and extends inward, indenting the cytoplasm and the cell membrane. Examples include any of the minute pits or incuppings of the cell membrane formed during pinocytosis. Such caveolae may be pinched off to form free vesicles within the cytoplasm." [GOC:mah, ISBN:0721662544, PMID:16645198] cellular_component
+ENSG00000100427 GO:0006811 ion transport "The directed movement of charged atoms or small charged molecules into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:ai] biological_process
+ENSG00000100427 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000100427 GO:0016323 basolateral plasma membrane "The region of the plasma membrane that includes the basal end and sides of the cell. Often used in reference to animal polarized epithelial membranes, where the basal membrane is the part attached to the extracellular matrix, or in plant cells, where the basal membrane is defined with respect to the zygotic axis." [GOC:go_curators] cellular_component
+ENSG00000100427 GO:0031410 cytoplasmic vesicle "A vesicle formed of membrane or protein, found in the cytoplasm of a cell." [GOC:mah] cellular_component
+ENSG00000100427 GO:0032388 positive regulation of intracellular transport "Any process that activates or increases the frequency, rate or extent of the directed movement of substances within cells." [GOC:mah] biological_process
+ENSG00000100427 GO:0032403 protein complex binding "Interacting selectively and non-covalently with any protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:mah] molecular_function
+ENSG00000100427 GO:0045121 membrane raft "Any of the small (10-200 nm), heterogeneous, highly dynamic, sterol- and sphingolipid-enriched membrane domains that compartmentalize cellular processes. Small rafts can sometimes be stabilized to form larger platforms through protein-protein and protein-lipid interactions." [PMID:16645198] cellular_component
+ENSG00000100427 GO:0047484 regulation of response to osmotic stress "Any process that modulates the rate or extent of the response to osmotic stress." [GOC:ai] biological_process
+ENSG00000100427 GO:0048471 perinuclear region of cytoplasm "Cytoplasm situated near, or occurring around, the nucleus." [GOC:jid] cellular_component
+ENSG00000100427 GO:0051259 protein oligomerization "The process of creating protein oligomers, compounds composed of a small number, usually between three and ten, of component monomers; protein oligomers may be composed of different or identical monomers. Oligomers may be formed by the polymerization of a number of monomers or the depolymerization of a large protein polymer." [GOC:ai] biological_process
+ENSG00000100427 GO:0055037 recycling endosome "Organelle consisting of networks of 60nm tubules organized around the microtubule organizing centre in some cell types. They transport molecules (e.g., receptors, transporters, lipids) derived from endosomes, the Golgi apparatus, or the cytoplasm to the plasma membrane. Transported molecules may be recycled for reuse, or may be newly synthesized." [GOC:dph, GOC:jid, GOC:kmv, GOC:rph, PMID:10930469, PMID:15601896, PMID:16246101, PMID:21556374, PMID:21562044] cellular_component
+ENSG00000100427 GO:0071397 cellular response to cholesterol "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a cholesterol stimulus." [GOC:mah] biological_process
+ENSG00000100427 GO:0072584 caveolin-mediated endocytosis "An endocytosis process that begins when material is taken up into plasma membrane caveolae, which then pinch off to form endocytic caveolar carriers." [GOC:BHF, GOC:mah, PMID:17318224, PMID:18498251, PMID:8970738, PMID:9234965] biological_process
+ENSG00000100427 GO:0005911 cell-cell junction "A cell junction that forms a connection between two cells; excludes direct cytoplasmic junctions such as ring canals." [GOC:dgh, GOC:hb, GOC:mah] cellular_component
+ENSG00000100427 GO:0016324 apical plasma membrane "The region of the plasma membrane located at the apical end of the cell." [GOC:curators] cellular_component
+ENSG00000100427 GO:0015031 protein transport "The directed movement of proteins into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:ai] biological_process
+ENSG00000100427
+ENSG00000182944
+ENSG00000182944 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000182944 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000182944 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000182944 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000182944 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000182944 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000182944 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000182944 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000182944 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000182944 GO:0000166 nucleotide binding "Interacting selectively and non-covalently with a nucleotide, any compound consisting of a nucleoside that is esterified with (ortho)phosphate or an oligophosphate at any hydroxyl group on the ribose or deoxyribose." [GOC:mah, ISBN:0198547684] molecular_function
+ENSG00000182944 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000182944 GO:0005516 calmodulin binding "Interacting selectively and non-covalently with calmodulin, a calcium-binding protein with many roles, both in the calcium-bound and calcium-free states." [GOC:krc] molecular_function
+ENSG00000182944 GO:0006351 transcription, DNA-templated "The cellular synthesis of RNA on a template of DNA." [GOC:jl, GOC:txnOH] biological_process
+ENSG00000182944 GO:0006355 regulation of transcription, DNA-templated "Any process that modulates the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000182944 GO:0008270 zinc ion binding "Interacting selectively and non-covalently with zinc (Zn) ions." [GOC:ai] molecular_function
+ENSG00000182944 GO:0042802 identical protein binding "Interacting selectively and non-covalently with an identical protein or proteins." [GOC:jl] molecular_function
+ENSG00000182944 GO:0044822 poly(A) RNA binding "Interacting non-covalently with a poly(A) RNA, a RNA molecule which has a tail of adenine bases." [GOC:jl] molecular_function
+ENSG00000182944 GO:0003676 nucleic acid binding "Interacting selectively and non-covalently with any nucleic acid." [GOC:jl] molecular_function
+ENSG00000182944 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100296 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100296 GO:0048856 anatomical structure development "The biological process whose specific outcome is the progression of an anatomical structure from an initial condition to its mature state. This process begins with the formation of the structure and ends with the mature structure, whatever form that may be including its natural destruction. An anatomical structure is any biological entity that occupies space and is distinguished from its surroundings. Anatomical structures can be macroscopic such as a carpel, or microscopic such as an acrosome." [GO_REF:0000021, GOC:mtg_15jun06] biological_process
+ENSG00000100296 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100296 GO:0006913 nucleocytoplasmic transport "The directed movement of molecules between the nucleus and the cytoplasm." [GOC:go_curators] biological_process
+ENSG00000100296 GO:0044403 symbiosis, encompassing mutualism through parasitism "An interaction between two organisms living together in more or less intimate association. The term host is usually used for the larger (macro) of the two members of a symbiosis. The smaller (micro) member is called the symbiont organism. Microscopic symbionts are often referred to as endosymbionts. The various forms of symbiosis include parasitism, in which the association is disadvantageous or destructive to one of the organisms; mutualism, in which the association is advantageous, or often necessary to one or both and not harmful to either; and commensalism, in which one member of the association benefits while the other is not affected. However, mutualism, parasitism, and commensalism are often not discrete categories of interactions and should rather be perceived as a continuum of interaction ranging from parasitism to mutualism. In fact, the direction of a symbiotic interaction can change during the lifetime of the symbionts due to developmental changes as well as changes in the biotic/abiotic environment in which the interaction occurs." [GOC:cc, http://www.free-definition.com] biological_process
+ENSG00000100296 GO:0030154 cell differentiation "The process in which relatively unspecialized cells, e.g. embryonic or regenerative cells, acquire specialized structural and/or functional features that characterize the cells, tissues, or organs of the mature organism or some other relatively stable phase of the organism's life history. Differentiation includes the processes involved in commitment of a cell to a specific fate and its subsequent development to the mature state." [ISBN:0198506732] biological_process
+ENSG00000100296 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100296 GO:0006397 mRNA processing "Any process involved in the conversion of a primary mRNA transcript into one or more mature mRNA(s) prior to translation into polypeptide." [GOC:mah] biological_process
+ENSG00000100296 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100296 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100296 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100296 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100296 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100296 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100296 GO:0000346 transcription export complex "The transcription export (TREX) complex couples transcription elongation by RNA polymerase II to mRNA export. The complex associates with the polymerase and travels with it along the length of the transcribed gene. TREX is composed of the THO transcription elongation complex as well as other proteins that couple THO to mRNA export proteins. The TREX complex is known to be found in a wide range of eukaryotes, including S. cerevisiae and metazoans." [GOC:krc, PMID:11979277] cellular_component
+ENSG00000100296 GO:0000347 THO complex "The THO complex is a nuclear complex that is required for transcription elongation through genes containing tandemly repeated DNA sequences. The THO complex is also part of the TREX (TRanscription EXport) complex that is involved in coupling transcription to export of mRNAs to the cytoplasm. In S. cerevisiae, it is composed of four subunits: Hpr1p, Tho2p, Thp1p, and Mft1p, while the human complex is composed of 7 subunits." [GOC:krc, PMID:11060033, PMID:11979277, PMID:16983072] cellular_component
+ENSG00000100296 GO:0000445 THO complex part of transcription export complex "The THO complex when it is part of the TREX (TRanscription EXport) complex that is involved in coupling transcription to export of mRNAs to the cytoplasm. In S. cerevisiae, it is composed of four subunits: Hpr1, Tho2, Thp1, and Mft1, while the human complex is composed of 7 subunits." [GOC:krc, PMID:11060033, PMID:11979277, PMID:16983072] cellular_component
+ENSG00000100296 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100296 GO:0006406 mRNA export from nucleus "The directed movement of mRNA from the nucleus to the cytoplasm." [GOC:ma] biological_process
+ENSG00000100296 GO:0008380 RNA splicing "The process of removing sections of the primary RNA transcript to remove sequences not present in the mature form of the RNA and joining the remaining sections to form the mature form of the RNA." [GOC:krc, GOC:mah] biological_process
+ENSG00000100296 GO:0030224 monocyte differentiation "The process in which a relatively unspecialized myeloid precursor cell acquires the specialized features of a monocyte." [GOC:mah] biological_process
+ENSG00000100296 GO:0032786 positive regulation of DNA-templated transcription, elongation "Any process that activates or increases the frequency, rate or extent of transcription elongation, the extension of an RNA molecule after transcription initiation and promoter clearance by the addition of ribonucleotides catalyzed by a DNA-dependent RNA polymerase." [GOC:mah, GOC:txnOH] biological_process
+ENSG00000100296 GO:0046784 viral mRNA export from host cell nucleus "The directed movement of intronless viral mRNA from the host nucleus to the cytoplasm for translation." [PMID:11598019] biological_process
+ENSG00000100296 GO:0060215 primitive hemopoiesis "A first transient wave of blood cell production that, in vertebrates, gives rise to erythrocytes (red blood cells) and myeloid cells." [GOC:bf, GOC:dph, PMID:15378083, PMID:15617691] biological_process
+ENSG00000100296 GO:2000002 negative regulation of DNA damage checkpoint "Any process that stops, prevents, or reduces the frequency, rate or extent of a DNA damage checkpoint." [GOC:BHF, GOC:obol] biological_process
+ENSG00000100296 GO:0010468 regulation of gene expression "Any process that modulates the frequency, rate or extent of gene expression. Gene expression is the process in which a gene's coding sequence is converted into a mature gene product or products (proteins or RNA). This includes the production of an RNA transcript as well as any processing to produce a mature RNA product or an mRNA (for protein-coding genes) and the translation of that mRNA into protein. Some protein processing events may be included when they are required to form an active form of a product from an inactive precursor form." [GOC:dph, GOC:tb] biological_process
+ENSG00000100296 GO:0000902 cell morphogenesis "The developmental process in which the size or shape of a cell is generated and organized." [GOC:clt, GOC:dph, GOC:go_curators, GOC:tb] biological_process
+ENSG00000100296 GO:0001824 blastocyst development "The process whose specific outcome is the progression of the blastocyst over time, from its formation to the mature structure. The mammalian blastocyst is a hollow ball of cells containing two cell types, the inner cell mass and the trophectoderm." [GOC:dph, ISBN:0124020607, ISBN:0198542771] biological_process
+ENSG00000100296 GO:0003729 mRNA binding "Interacting selectively and non-covalently with messenger RNA (mRNA), an intermediate molecule between DNA and protein. mRNA includes UTR and coding sequences, but does not contain introns." [GOC:kmv, SO:0000234] molecular_function
+ENSG00000100296 GO:0010793 regulation of mRNA export from nucleus "Any process that modulates the frequency, rate or extent of the directed movement of mRNA from the nucleus to the cytoplasm." [GOC:dph, GOC:tb] biological_process
+ENSG00000100296 GO:0017145 stem cell division "The self-renewing division of a stem cell. A stem cell is an undifferentiated cell, in the embryo or adult, that can undergo unlimited division and give rise to one or several different cell types." [GOC:jid, ISBN:0582227089] biological_process
+ENSG00000100296 GO:0045650 negative regulation of macrophage differentiation "Any process that stops, prevents, or reduces the frequency, rate or extent of macrophage differentiation." [GOC:go_curators] biological_process
+ENSG00000100296 GO:2000035 regulation of stem cell division "Any process that modulates the frequency, rate or extent of stem cell division." [GOC:obol] biological_process
+ENSG00000100296
+ENSG00000070010 GO:0006511 ubiquitin-dependent protein catabolic process "The chemical reactions and pathways resulting in the breakdown of a protein or peptide by hydrolysis of its peptide bonds, initiated by the covalent attachment of a ubiquitin group, or multiple ubiquitin groups, to the protein." [GOC:go_curators] biological_process
+ENSG00000070010 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000070010 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000070010 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000070010 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000070010 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000070010 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000070010 GO:0008233 peptidase activity "Catalysis of the hydrolysis of a peptide bond. A peptide bond is a covalent bond formed when the carbon atom from the carboxyl group of one amino acid shares electrons with the nitrogen atom from the amino group of a second amino acid." [GOC:jl, ISBN:0815332181] molecular_function
+ENSG00000070010 GO:0048856 anatomical structure development "The biological process whose specific outcome is the progression of an anatomical structure from an initial condition to its mature state. This process begins with the formation of the structure and ends with the mature structure, whatever form that may be including its natural destruction. An anatomical structure is any biological entity that occupies space and is distinguished from its surroundings. Anatomical structures can be macroscopic such as a carpel, or microscopic such as an acrosome." [GO_REF:0000021, GOC:mtg_15jun06] biological_process
+ENSG00000070010 GO:0001501 skeletal system development "The process whose specific outcome is the progression of the skeleton over time, from its formation to the mature structure. The skeleton is the bony framework of the body in vertebrates (endoskeleton) or the hard outer envelope of insects (exoskeleton or dermoskeleton)." [GOC:dph, GOC:jid, GOC:tb, http://www.stedmans.com/] biological_process
+ENSG00000070010 GO:0004843 ubiquitin-specific protease activity "Catalysis of the thiol-dependent hydrolysis of a peptide bond formed by the C-terminal glycine of ubiquitin." [GOC:jh2, ISBN:0120793709] molecular_function
+ENSG00000070010 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000070010 GO:0043161 proteasome-mediated ubiquitin-dependent protein catabolic process "The chemical reactions and pathways resulting in the breakdown of a protein or peptide by hydrolysis of its peptide bonds, initiated by the covalent attachment of ubiquitin, and mediated by the proteasome." [GOC:go_curators] biological_process
+ENSG00000070010 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000070010 GO:0032403 protein complex binding "Interacting selectively and non-covalently with any protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:mah] molecular_function
+ENSG00000070010 GO:0005102 receptor binding "Interacting selectively and non-covalently with one or more specific sites on a receptor molecule, a macromolecule that undergoes combination with a hormone, neurotransmitter, drug or intracellular messenger to initiate a change in cell function." [GOC:bf, GOC:ceb, ISBN:0198506732] molecular_function
+ENSG00000166862 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000166862 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000166862 GO:0055085 transmembrane transport "The process in which a solute is transported from one side of a membrane to the other." [GOC:dph, GOC:jid] biological_process
+ENSG00000166862 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000166862 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000166862 GO:0007267 cell-cell signaling "Any process that mediates the transfer of information from one cell to another." [GOC:mah] biological_process
+ENSG00000166862 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000166862 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000166862 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000166862 GO:0005891 voltage-gated calcium channel complex "A protein complex that forms a transmembrane channel through which calcium ions may pass in response to changes in membrane potential." [GOC:mah] cellular_component
+ENSG00000166862 GO:0007268 synaptic transmission "The process of communication from a neuron to a target (neuron, muscle, or secretory cell) across a synapse." [GOC:jl, MeSH:D009435] biological_process
+ENSG00000166862 GO:0030666 endocytic vesicle membrane "The lipid bilayer surrounding an endocytic vesicle." [GOC:mah] cellular_component
+ENSG00000166862 GO:0032281 alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid selective glutamate receptor complex "An assembly of four or five subunits which form a structure with an extracellular N-terminus and a large loop that together form the ligand binding domain. The C-terminus is intracellular. The ionotropic glutamate receptor complex itself acts as a ligand gated ion channel; on binding glutamate, charged ions pass through a channel in the center of the receptor complex. The AMPA receptors mediate fast synaptic transmission in the CNS and are composed of subunits GluR1-4, products from separate genes. These subunits have an extracellular N-terminus and an intracellular C-terminus." [GOC:ef] cellular_component
+ENSG00000166862 GO:0051899 membrane depolarization "The process in which membrane potential changes in the depolarizing direction from the resting potential, usually from negative to positive. For example, the initial depolarization during the rising phase of an action potential is in the direction from the negative resting potential towards the positive membrane potential that will be the peak of the action potential." [Wikipedia:Depolarization] biological_process
+ENSG00000166862 GO:0070588 calcium ion transmembrane transport "A process in which a calcium ion is transported from one side of a membrane to the other by means of some agent such as a transporter or pore." [GOC:mah] biological_process
+ENSG00000166862 GO:2000311 regulation of alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionate selective glutamate receptor activity "Any process that modulates the frequency, rate or extent of alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionate selective glutamate receptor activity." [GOC:BHF] biological_process
+ENSG00000166862 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000166862 GO:0005245 voltage-gated calcium channel activity "Catalysis of the transmembrane transfer of a calcium ion by a voltage-gated channel. A voltage-gated channel is a channel whose open state is dependent on the voltage across the membrane in which it is embedded." [GOC:mtg_transport, GOC:tb, ISBN:0815340729] molecular_function
+ENSG00000166862 GO:0042391 regulation of membrane potential "Any process that modulates the establishment or extent of a membrane potential, the electric potential existing across any membrane arising from charges in the membrane itself and from the charges present in the media on either side of the membrane." [GOC:jl, GOC:mtg_cardio, GOC:tb, ISBN:0198506732] biological_process
+ENSG00000166862 GO:0060081 membrane hyperpolarization "The process in which membrane potential changes in the hyperpolarizing direction from the resting potential, usually from negative to more negative." [GOC:dph] biological_process
+ENSG00000166862 GO:0007528 neuromuscular junction development "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of a neuromuscular junction." [GOC:mtg_OBO2OWL_2013] biological_process
+ENSG00000166862 GO:0050877 neurological system process "A organ system process carried out by any of the organs or tissues of neurological system." [GOC:ai, GOC:mtg_cardio] biological_process
+ENSG00000166862 GO:0019226 transmission of nerve impulse "The neurological system process in which a signal is transmitted through the nervous system by synaptic transmission and the sequential electrochemical polarization and depolarization that travels across the membrane of a nerve cell (neuron) in response to stimulation." [GOC:curators, ISBN:0815316194] biological_process
+ENSG00000166862 GO:0035255 ionotropic glutamate receptor binding "Interacting selectively and non-covalently with an ionotropic glutamate receptor. Ionotropic glutamate receptors bind glutamate and exert an effect through the regulation of ion channels." [GOC:bf, ISBN:0198506732] molecular_function
+ENSG00000099940 GO:0043005 neuron projection "A prolongation or process extending from a nerve cell, e.g. an axon or dendrite." [GOC:jl, http://www.cogsci.princeton.edu/~wn/] cellular_component
+ENSG00000099940 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000099940 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000099940 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000099940 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000099940 GO:0016192 vesicle-mediated transport "A cellular transport process in which transported substances are moved in membrane-bounded vesicles; transported substances are enclosed in the vesicle lumen or located in the vesicle membrane. The process begins with a step that directs a substance to the forming vesicle, and includes vesicle budding and coating. Vesicles are then targeted to, and fuse with, an acceptor membrane." [GOC:ai, GOC:mah, ISBN:08789310662000] biological_process
+ENSG00000099940 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000099940 GO:0005815 microtubule organizing center "An intracellular structure that can catalyze gamma-tubulin-dependent microtubule nucleation and that can anchor microtubules by interacting with their minus ends, plus ends or sides." [GOC:vw, http://en.wikipedia.org/wiki/Microtubule_organizing_center, ISBN:0815316194, PMID:17072892, PMID:17245416] cellular_component
+ENSG00000099940 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000099940 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000099940 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000099940 GO:0061024 membrane organization "A process which results in the assembly, arrangement of constituent parts, or disassembly of a membrane. A membrane is a double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:dph, GOC:tb] biological_process
+ENSG00000099940 GO:0000046 autophagic vacuole fusion "The fusion of an autophagic vacuole with a vacuole (yeast) or lysosome (e.g. mammals and insects). In the case of yeast, inner membrane-bounded structures (autophagic bodies) appear in the vacuole." [PMID:11099404] biological_process
+ENSG00000099940 GO:0005484 SNAP receptor activity "Acting as a marker to identify a membrane and interacting selectively with one or more SNAREs on another membrane to mediate membrane fusion." [GOC:mah, PMID:14570579] molecular_function
+ENSG00000099940 GO:0005813 centrosome "A structure comprised of a core structure (in most organisms, a pair of centrioles) and peripheral material from which a microtubule-based structure, such as a spindle apparatus, is organized. Centrosomes occur close to the nucleus during interphase in many eukaryotic cells, though in animal cells it changes continually during the cell-division cycle." [GOC:mah, ISBN:0198547684] cellular_component
+ENSG00000099940 GO:0006887 exocytosis "A process of secretion by a cell that results in the release of intracellular molecules (e.g. hormones, matrix proteins) contained within a membrane-bounded vesicle by fusion of the vesicle with the plasma membrane of a cell. This is the process in which most molecules are secreted from eukaryotic cells." [GOC:mah, ISBN:0716731363] biological_process
+ENSG00000099940 GO:0006903 vesicle targeting "The process in which vesicles are directed to specific destination membranes. Targeting involves coordinated interactions among cytoskeletal elements (microtubules or actin filaments), motor proteins, molecules at the vesicle membrane and target membrane surfaces, and vesicle cargo." [GOC:mah, PMID:17335816] biological_process
+ENSG00000099940 GO:0015031 protein transport "The directed movement of proteins into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:ai] biological_process
+ENSG00000099940 GO:0030054 cell junction "A cellular component that forms a specialized region of connection between two cells or between a cell and the extracellular matrix. At a cell junction, anchoring proteins extend through the plasma membrane to link cytoskeletal proteins in one cell to cytoskeletal proteins in neighboring cells or to proteins in the extracellular matrix." [GOC:mah, http://www.vivo.colostate.edu/hbooks/cmb/cells/pmemb/junctions_a.html, ISBN:0198506732] cellular_component
+ENSG00000099940 GO:0031201 SNARE complex "A protein complex involved in membrane fusion; a stable ternary complex consisting of a four-helix bundle, usually formed from one R-SNARE and three Q-SNAREs with an ionic layer sandwiched between hydrophobic layers. One well-characterized example is the neuronal SNARE complex formed of synaptobrevin 2, syntaxin 1a, and SNAP-25." [GOC:bhm, GOC:pr, PMID:10872468, PMID:19450911] cellular_component
+ENSG00000099940 GO:0045202 synapse "The junction between a nerve fiber of one neuron and another neuron or muscle fiber or glial cell; the site of interneuronal communication. As the nerve fiber approaches the synapse it enlarges into a specialized structure, the presynaptic nerve ending, which contains mitochondria and synaptic vesicles. At the tip of the nerve ending is the presynaptic membrane; facing it, and separated from it by a minute cleft (the synaptic cleft) is a specialized area of membrane on the receiving cell, known as the postsynaptic membrane. In response to the arrival of nerve impulses, the presynaptic nerve ending secretes molecules of neurotransmitters into the synaptic cleft. These diffuse across the cleft and transmit the signal to the postsynaptic membrane." [ISBN:0198506732] cellular_component
+ENSG00000099940 GO:0061025 membrane fusion "The membrane organization process that joins two lipid bilayers to form a single membrane." [GOC:dph, GOC:tb] biological_process
+ENSG00000099940 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100101
+ENSG00000100417 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100417 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000100417 GO:0005975 carbohydrate metabolic process "The chemical reactions and pathways involving carbohydrates, any of a group of organic compounds based of the general formula Cx(H2O)y. Includes the formation of carbohydrate derivatives by the addition of a carbohydrate residue to another molecule." [GOC:mah, ISBN:0198506732] biological_process
+ENSG00000100417 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000100417 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100417 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000100417 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100417 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000100417 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100417 GO:0016853 isomerase activity "Catalysis of the geometric or structural changes within one molecule. Isomerase is the systematic name for any enzyme of EC class 5." [ISBN:0198506732] molecular_function
+ENSG00000100417 GO:0004615 phosphomannomutase activity "Catalysis of the reaction: alpha-D-mannose 1-phosphate = D-mannose 6-phosphate." [EC:5.4.2.8, RHEA:11143] molecular_function
+ENSG00000100417 GO:0006013 mannose metabolic process "The chemical reactions and pathways involving mannose, the aldohexose manno-hexose, the C-2 epimer of glucose. The D-(+)-form is widely distributed in mannans and hemicelluloses and is of major importance in the core oligosaccharide of N-linked oligosaccharides of glycoproteins." [ISBN:0198506732] biological_process
+ENSG00000100417 GO:0006488 dolichol-linked oligosaccharide biosynthetic process "The chemical reactions and pathways resulting in the formation of dolichol-linked oligosaccharide, usually by a stepwise addition of glycosyl chains to endoplasmic reticulum membrane-bound dolichol-P." [GOC:jl, ISBN:0471331309] biological_process
+ENSG00000100417 GO:0009298 GDP-mannose biosynthetic process "The chemical reactions and pathways resulting in the formation of GDP-mannose, a substance composed of mannose in glycosidic linkage with guanosine diphosphate." [GOC:ai] biological_process
+ENSG00000100417 GO:0018279 protein N-linked glycosylation via asparagine "The glycosylation of protein via the N4 atom of peptidyl-asparagine forming N4-glycosyl-L-asparagine; the most common form is N-acetylglucosaminyl asparagine; N-acetylgalactosaminyl asparagine and N4 glucosyl asparagine also occur. This modification typically occurs in extracellular peptides with an N-X-(ST) motif. Partial modification has been observed to occur with cysteine, rather than serine or threonine, in the third position; secondary structure features are important, and proline in the second or fourth positions inhibits modification." [GOC:jsg, RESID:AA0151, RESID:AA0420, RESID:AA0421] biological_process
+ENSG00000100417 GO:0019307 mannose biosynthetic process "The chemical reactions and pathways resulting in the formation of mannose, the aldohexose manno-hexose, the C-2 epimer of glucose." [GOC:ai] biological_process
+ENSG00000100417 GO:0043687 post-translational protein modification "The process of covalently altering one or more amino acids in a protein after the protein has been completely translated and released from the ribosome." [GOC:jsg] biological_process
+ENSG00000100417 GO:0044267 cellular protein metabolic process "The chemical reactions and pathways involving a specific protein, rather than of proteins in general, occurring at the level of an individual cell. Includes cellular protein modification." [GOC:jl] biological_process
+ENSG00000100417 GO:0046872 metal ion binding "Interacting selectively and non-covalently with any metal ion." [GOC:ai] molecular_function
+ENSG00000100417 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100417 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100417 GO:0003824 catalytic activity "Catalysis of a biochemical reaction at physiological temperatures. In biologically catalyzed reactions, the reactants are known as substrates, and the catalysts are naturally occurring macromolecular substances known as enzymes. Enzymes possess specific binding sites for substrates, and are usually composed wholly or largely of protein, but RNA that has catalytic activity (ribozyme) is often also regarded as enzymatic." [ISBN:0198506732] molecular_function
+ENSG00000100417 GO:0008152 metabolic process "The chemical reactions and pathways, including anabolism and catabolism, by which living organisms transform chemical substances. Metabolic processes typically transform small molecules, but also include macromolecular processes such as DNA repair and replication, and protein synthesis and degradation." [GOC:go_curators, ISBN:0198547684] biological_process
+ENSG00000100417 GO:0043025 neuronal cell body "The portion of a neuron that includes the nucleus, but excludes cell projections such as axons and dendrites." [GOC:go_curators] cellular_component
+ENSG00000273899 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000273899 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000273899 GO:0019843 rRNA binding "Interacting selectively and non-covalently with ribosomal RNA." [GOC:jl] molecular_function
+ENSG00000273899 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000273899 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000273899 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000273899 GO:0044822 poly(A) RNA binding "Interacting non-covalently with a poly(A) RNA, a RNA molecule which has a tail of adenine bases." [GOC:jl] molecular_function
+ENSG00000128185 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000128185 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000128185 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000128185 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000128185 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000128185
+ENSG00000100068 GO:0017147 Wnt-protein binding "Interacting selectively and non-covalently with Wnt-protein, a secreted growth factor involved in signaling." [GOC:jl] molecular_function
+ENSG00000100068 GO:0042813 Wnt-activated receptor activity "Combining with a Wnt protein and transmitting the signal across the plasma membrane to initiate a change in cell activity." [GOC:go_curators] molecular_function
+ENSG00000100068 GO:0044332 Wnt signaling pathway involved in dorsal/ventral axis specification "The series of molecular signals initiated by binding of Wnt protein to a frizzled family receptor on the surface of the target cell contributing to the establishment, maintenance and elaboration of the dorsal/ventral axis." [GOC:jl, GOC:yaf] biological_process
+ENSG00000100068 GO:0060070 canonical Wnt signaling pathway "The series of molecular signals initiated by binding of a Wnt protein to a frizzled family receptor on the surface of the target cell, followed by propagation of the signal via beta-catenin, and ending with a change in transcription of target genes. In this pathway, the activated receptor signals via downstream effectors that result in the inhibition of beta-catenin phosphorylation, thereby preventing degradation of beta-catenin. Stabilized beta-catenin can then accumulate and travel to the nucleus to trigger changes in transcription of target genes." [GOC:bf, GOC:dph, PMID:11532397, PMID:19619488] biological_process
+ENSG00000100068 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000100068 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100068 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100068 GO:0004871 signal transducer activity "Conveys a signal across a cell to trigger a change in cell function or state. A signal is a physical entity or change in state that is used to transfer information in order to trigger a response." [GOC:go_curators] molecular_function
+ENSG00000100068
+ENSG00000100410 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100410 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000100410 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100410 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100410 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100410 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000100410 GO:0005654 nucleoplasm "That part of the nuclear content other than the chromosomes or the nucleolus." [GOC:ma, ISBN:0124325653] cellular_component
+ENSG00000100410 GO:0001071 nucleic acid binding transcription factor activity "Interacting selectively and non-covalently with a DNA or RNA sequence in order to modulate transcription. The transcription factor may or may not also interact selectively with a protein or macromolecular complex." [GOC:txnOH] molecular_function
+ENSG00000100410 GO:0003677 DNA binding "Any molecular function by which a gene product interacts selectively and non-covalently with DNA (deoxyribonucleic acid)." [GOC:dph, GOC:jl, GOC:tb, GOC:vw] molecular_function
+ENSG00000100410 GO:0006397 mRNA processing "Any process involved in the conversion of a primary mRNA transcript into one or more mature mRNA(s) prior to translation into polypeptide." [GOC:mah] biological_process
+ENSG00000100410 GO:0000398 mRNA splicing, via spliceosome "The joining together of exons from one or more primary transcripts of messenger RNA (mRNA) and the excision of intron sequences, via a spliceosomal mechanism, so that mRNA consisting only of the joined exons is produced." [GOC:krc, ISBN:0198506732, ISBN:0879695897] biological_process
+ENSG00000100410 GO:0003700 sequence-specific DNA binding transcription factor activity "Interacting selectively and non-covalently with a specific DNA sequence in order to modulate transcription. The transcription factor may or may not also interact selectively with a protein or macromolecular complex." [GOC:curators, GOC:txnOH] molecular_function
+ENSG00000100410 GO:0005686 U2 snRNP "A ribonucleoprotein complex that contains small nuclear RNA U2." [GOC:krc, GOC:mah] cellular_component
+ENSG00000100410 GO:0005689 U12-type spliceosomal complex "Any spliceosomal complex that forms during the splicing of a messenger RNA primary transcript to excise an intron; the series of U12-type spliceosomal complexes is involved in the splicing of the majority of introns that contain atypical AT-AC terminal dinucleotides, as well as other non-canonical introns. The entire splice site signal, not just the terminal dinucleotides, is involved in determining which spliceosome utilizes the site." [GOC:krc, GOC:mah, PMID:11574683, PMID:11971955] cellular_component
+ENSG00000100410 GO:0006351 transcription, DNA-templated "The cellular synthesis of RNA on a template of DNA." [GOC:jl, GOC:txnOH] biological_process
+ENSG00000100410 GO:0008380 RNA splicing "The process of removing sections of the primary RNA transcript to remove sequences not present in the mature form of the RNA and joining the remaining sections to form the mature form of the RNA." [GOC:krc, GOC:mah] biological_process
+ENSG00000100410 GO:0010467 gene expression "The process in which a gene's sequence is converted into a mature gene product or products (proteins or RNA). This includes the production of an RNA transcript as well as any processing to produce a mature RNA product or an mRNA (for protein-coding genes) and the translation of that mRNA into protein. Some protein processing events may be included when they are required to form an active form of a product from an inactive precursor form." [GOC:dph, GOC:tb] biological_process
+ENSG00000100410 GO:0016607 nuclear speck "A discrete extra-nucleolar subnuclear domain, 20-50 in number, in which splicing factors are seen to be localized by immunofluorescence microscopy." [http://www.cellnucleus.com/] cellular_component
+ENSG00000100410 GO:0044822 poly(A) RNA binding "Interacting non-covalently with a poly(A) RNA, a RNA molecule which has a tail of adenine bases." [GOC:jl] molecular_function
+ENSG00000100410 GO:0045893 positive regulation of transcription, DNA-templated "Any process that activates or increases the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000100410 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100410 GO:0016363 nuclear matrix "The dense fibrillar network lying on the inner side of the nuclear membrane." [ISBN:0582227089] cellular_component
+ENSG00000100290 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100290 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100290 GO:0008219 cell death "Any biological process that results in permanent cessation of all vital functions of a cell. A cell should be considered dead when any one of the following molecular or morphological criteria is met: (1) the cell has lost the integrity of its plasma membrane; (2) the cell, including its nucleus, has undergone complete fragmentation into discrete bodies (frequently referred to as \"apoptotic bodies\"); and/or (3) its corpse (or its fragments) have been engulfed by an adjacent cell in vivo." [GOC:mah, GOC:mtg_apoptosis] biological_process
+ENSG00000100290 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100290 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100290 GO:0006915 apoptotic process "A programmed cell death process which begins when a cell receives an internal (e.g. DNA damage) or external signal (e.g. an extracellular death ligand), and proceeds through a series of biochemical events (signaling pathway phase) which trigger an execution phase. The execution phase is the last step of an apoptotic process, and is typically characterized by rounding-up of the cell, retraction of pseudopodes, reduction of cellular volume (pyknosis), chromatin condensation, nuclear fragmentation (karyorrhexis), plasma membrane blebbing and fragmentation of the cell into apoptotic bodies. When the execution phase is completed, the cell has died." [GOC:dhl, GOC:ecd, GOC:go_curators, GOC:mtg_apoptosis, GOC:tb, ISBN:0198506732, PMID:18846107, PMID:21494263] biological_process
+ENSG00000100290 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000100290 GO:0032464 positive regulation of protein homooligomerization "Any process that activates or increases the frequency, rate or extent of protein homooligomerization." [GOC:mah] biological_process
+ENSG00000100290 GO:0090200 positive regulation of release of cytochrome c from mitochondria "Any process that increases the rate, frequency or extent of release of cytochrome c from mitochondria, the process in which cytochrome c is enabled to move from the mitochondrial intermembrane space into the cytosol, which is an early step in apoptosis and leads to caspase activation." [GOC:BHF, GOC:dph, GOC:mtg_apoptosis, GOC:tb] biological_process
+ENSG00000100290 GO:0007283 spermatogenesis "The process of formation of spermatozoa, including spermatocytogenesis and spermiogenesis." [GOC:jid, ISBN:9780878933846] biological_process
+ENSG00000100290 GO:0008584 male gonad development "The process whose specific outcome is the progression of the male gonad over time, from its formation to the mature structure." [GOC:jid] biological_process
+ENSG00000100290 GO:0005740 mitochondrial envelope "The double lipid bilayer enclosing the mitochondrion and separating its contents from the cell cytoplasm; includes the intermembrane space." [GOC:ai, GOC:pz] cellular_component
+ENSG00000100290 GO:0008637 apoptotic mitochondrial changes "The morphological and physiological alterations undergone by mitochondria during apoptosis." [GOC:mah, GOC:mtg_apoptosis] biological_process
+ENSG00000100290 GO:0046982 protein heterodimerization activity "Interacting selectively and non-covalently with a nonidentical protein to form a heterodimer." [GOC:ai] molecular_function
+ENSG00000100290 GO:0051400 BH domain binding "Interacting selectively and non-covalently with the Bcl-2 homology (BH) domain of a protein. Bcl-2-related proteins share homology in one to four conserved regions designated the Bcl-2 homology (BH) domains BH1, BH2, BH3 and BH4. These domains contribute at multiple levels to the function of these proteins in cell death and survival. Anti-apoptotic members of the Bcl-2 family have four BH domains (BH1-BH4). Pro-apoptotic members have fewer BH domains." [PMID:11048732, PMID:12133724, PMID:9020082, PMID:9704409] molecular_function
+ENSG00000183172
+ENSG00000183172 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000183172 GO:0042592 homeostatic process "Any biological process involved in the maintenance of an internal steady state." [GOC:jl, ISBN:0395825172] biological_process
+ENSG00000183172 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000183172 GO:0055085 transmembrane transport "The process in which a solute is transported from one side of a membrane to the other." [GOC:dph, GOC:jid] biological_process
+ENSG00000183172 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000183172 GO:0006851 mitochondrial calcium ion transport "The directed movement of calcium ions (Ca2+) into, out of or within a mitochondrion." [GOC:ai] biological_process
+ENSG00000183172 GO:0031305 integral component of mitochondrial inner membrane "The component of the mitochondrial inner membrane consisting of the gene products that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:mah] cellular_component
+ENSG00000183172 GO:0036444 calcium ion transmembrane import into mitochondrion "A process in which a calcium ion (Ca2+) is transported from one side of a membrane to the other into the mitochondrion by means of some agent such as a transporter or pore." [GOC:vw] biological_process
+ENSG00000183172 GO:0051560 mitochondrial calcium ion homeostasis "Any process involved in the maintenance of an internal steady state of calcium ions within the cytoplasm of a cell or between mitochondria and their surroundings." [GOC:ai, GOC:mah] biological_process
+ENSG00000183172 GO:1990246 uniplex complex "A calcium channel complex in the mitochondrial inner membrane capable of highly-selective calcium channel activity. Its components include the EF-hand-containing proteins mitochondrial calcium uptake 1 (MICU1) and MICU2, the pore-forming subunit mitochondrial calcium uniporter (MCU) and its paralog MCUb, and the MCU regulator EMRE." [PMID:24231807] cellular_component
+ENSG00000183172 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000183172 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000100012 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100012 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000100012 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100012 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100012 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100012 GO:0008289 lipid binding "Interacting selectively and non-covalently with a lipid." [GOC:ai] molecular_function
+ENSG00000100012 GO:0005622 intracellular "The living contents of a cell; the matter contained within (but not including) the plasma membrane, usually taken to exclude large vacuoles and masses of secretory or ingested material. In eukaryotes it includes the nucleus and cytoplasm." [ISBN:0198506732] cellular_component
+ENSG00000100012 GO:0005215 transporter activity "Enables the directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells." [GOC:ai, GOC:dgf] molecular_function
+ENSG00000100012 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000100012 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100012
+ENSG00000186575 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000186575 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000186575 GO:0007010 cytoskeleton organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of cytoskeletal structures." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000186575 GO:0008283 cell proliferation "The multiplication or reproduction of cells, resulting in the expansion of a cell population." [GOC:mah, GOC:mb] biological_process
+ENSG00000186575 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000186575 GO:0005856 cytoskeleton "Any of the various filamentous elements that form the internal framework of cells, and typically remain after treatment of the cells with mild detergent to remove membrane constituents and soluble components of the cytoplasm. The term embraces intermediate filaments, microfilaments, microtubules, the microtrabecular lattice, and other structures characterized by a polymeric filamentous nature and long-range order within the cell. The various elements of the cytoskeleton not only serve in the maintenance of cellular shape but also have roles in other cellular functions, including cellular movement, cell division, endocytosis, and movement of organelles." [GOC:mah, ISBN:0198547684, PMID:16959967] cellular_component
+ENSG00000186575 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000186575 GO:0005768 endosome "A membrane-bounded organelle to which materials ingested by endocytosis are delivered." [ISBN:0198506732, PMID:19696797] cellular_component
+ENSG00000186575 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000186575 GO:0005730 nucleolus "A small, dense body one or more of which are present in the nucleus of eukaryotic cells. It is rich in RNA and protein, is not bounded by a limiting membrane, and is not seen during mitosis. Its prime function is the transcription of the nucleolar DNA into 45S ribosomal-precursor RNA, the processing of this RNA into 5.8S, 18S, and 28S components of ribosomal RNA, and the association of these components with 5S RNA and proteins synthesized outside the nucleolus. This association results in the formation of ribonucleoprotein precursors; these pass into the cytoplasm and mature into the 40S and 60S subunits of the ribosome." [ISBN:0198506732] cellular_component
+ENSG00000186575 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000186575 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000186575 GO:0008092 cytoskeletal protein binding "Interacting selectively and non-covalently with any protein component of any cytoskeleton (actin, microtubule, or intermediate filament cytoskeleton)." [GOC:mah] molecular_function
+ENSG00000186575 GO:0001953 negative regulation of cell-matrix adhesion "Any process that stops, prevents, or reduces the rate or extent of cell adhesion to the extracellular matrix." [GOC:hjd] biological_process
+ENSG00000186575 GO:0003779 actin binding "Interacting selectively and non-covalently with monomeric or multimeric forms of actin, including actin filaments." [GOC:clt] molecular_function
+ENSG00000186575 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000186575 GO:0005769 early endosome "A membrane-bounded organelle that receives incoming material from primary endocytic vesicles that have been generated by clathrin-dependent and clathrin-independent endocytosis; vesicles fuse with the early endosome to deliver cargo for sorting into recycling or degradation pathways." [GOC:mah, NIF_Subcellular:nlx_subcell_20090701, PMID:19696797] cellular_component
+ENSG00000186575 GO:0008156 negative regulation of DNA replication "Any process that stops, prevents, or reduces the frequency, rate or extent of DNA replication." [GOC:go_curators] biological_process
+ENSG00000186575 GO:0008285 negative regulation of cell proliferation "Any process that stops, prevents or reduces the rate or extent of cell proliferation." [GOC:go_curators] biological_process
+ENSG00000186575 GO:0014010 Schwann cell proliferation "The multiplication or reproduction of Schwann cells, resulting in the expansion of their population. Schwann cells are a type of glial cell in the peripheral nervous system." [GOC:ef, ISBN:0878932585] biological_process
+ENSG00000186575 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000186575 GO:0019898 extrinsic component of membrane "The component of a membrane consisting of gene products and protein complexes that are loosely bound to one of its surfaces, but not integrated into the hydrophobic region." [GOC:dos, GOC:jl, GOC:mah] cellular_component
+ENSG00000186575 GO:0022408 negative regulation of cell-cell adhesion "Any process that stops, prevents or reduces the rate or extent of cell adhesion to another cell." [GOC:isa_complete] biological_process
+ENSG00000186575 GO:0030036 actin cytoskeleton organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of cytoskeletal structures comprising actin filaments and their associated proteins." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000186575 GO:0030336 negative regulation of cell migration "Any process that stops, prevents, or reduces the frequency, rate or extent of cell migration." [GOC:go_curators] biological_process
+ENSG00000186575 GO:0035330 regulation of hippo signaling "Any process that modulates the frequency, rate or extent of hippo signaling." [GOC:bf] biological_process
+ENSG00000186575 GO:0042518 negative regulation of tyrosine phosphorylation of Stat3 protein "Any process that stops, prevents, or reduces the frequency, rate or extent of the introduction of a phosphate group to a tyrosine residue of a Stat3 protein." [GOC:jl, PMID:11426647] biological_process
+ENSG00000186575 GO:0042524 negative regulation of tyrosine phosphorylation of Stat5 protein "Any process that stops, prevents, or reduces the frequency, rate or extent of the introduction of a phosphate group to a tyrosine residue of a Stat5 protein." [GOC:jl, PMID:11426647] biological_process
+ENSG00000186575 GO:0046426 negative regulation of JAK-STAT cascade "Any process that stops, prevents, or reduces the frequency, rate or extent of the JAK-STAT signaling pathway activity." [GOC:bf] biological_process
+ENSG00000186575 GO:0048471 perinuclear region of cytoplasm "Cytoplasm situated near, or occurring around, the nucleus." [GOC:jid] cellular_component
+ENSG00000186575 GO:0051496 positive regulation of stress fiber assembly "Any process that activates or increases the frequency, rate or extent of the assembly of a stress fiber, a bundle of microfilaments and other proteins found in fibroblasts." [GOC:ai] biological_process
+ENSG00000186575 GO:0007420 brain development "The process whose specific outcome is the progression of the brain over time, from its formation to the mature structure. Brain development begins with patterning events in the neural tube and ends with the mature structure that is the center of thought and emotion. The brain is responsible for the coordination and control of bodily activities and the interpretation of information from the senses (sight, hearing, smell, etc.)." [GOC:dph, GOC:jid, GOC:tb, UBERON:0000955] biological_process
+ENSG00000186575 GO:0042127 regulation of cell proliferation "Any process that modulates the frequency, rate or extent of cell proliferation." [GOC:jl] biological_process
+ENSG00000186575 GO:0045177 apical part of cell "The region of a polarized cell that forms a tip or is distal to a base. For example, in a polarized epithelial cell, the apical region has an exposed surface and lies opposite to the basal lamina that separates the epithelium from other tissue." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000186575 GO:0001726 ruffle "Projection at the leading edge of a crawling cell; the protrusions are supported by a microfilament meshwork." [ISBN:0124325653] cellular_component
+ENSG00000186575 GO:0001707 mesoderm formation "The process that gives rise to the mesoderm. This process pertains to the initial formation of the structure from unspecified parts." [GOC:go_curators] biological_process
+ENSG00000186575 GO:0006469 negative regulation of protein kinase activity "Any process that stops, prevents, or reduces the frequency, rate or extent of protein kinase activity." [GOC:go_curators] biological_process
+ENSG00000186575 GO:0007398 ectoderm development "The process whose specific outcome is the progression of the ectoderm over time, from its formation to the mature structure. In animal embryos, the ectoderm is the outer germ layer of the embryo, formed during gastrulation." [GOC:dph, GOC:tb] biological_process
+ENSG00000186575 GO:0030027 lamellipodium "A thin sheetlike process extended by the leading edge of a crawling fibroblast; contains a dense meshwork of actin filaments." [ISBN:0815316194] cellular_component
+ENSG00000186575 GO:0030864 cortical actin cytoskeleton "The portion of the actin cytoskeleton, comprising filamentous actin and associated proteins, that lies just beneath the plasma membrane." [GOC:mah] cellular_component
+ENSG00000186575 GO:0070306 lens fiber cell differentiation "The process in which a relatively unspecialized cell acquires specialized features of a lens fiber cell, any of the elongated, tightly packed cells that make up the bulk of the mature lens in the camera-type eye. The cytoplasm of a lens fiber cell is devoid of most intracellular organelles including the cell nucleus, and contains primarily crystallins, a group of water-soluble proteins expressed in vary large quantities." [GOC:mah, PMID:7693735] biological_process
+ENSG00000186575 GO:0042475 odontogenesis of dentin-containing tooth "The process whose specific outcome is the progression of a dentin-containing tooth over time, from its formation to the mature structure. A dentin-containing tooth is a hard, bony organ borne on the jaw or other bone of a vertebrate, and is composed mainly of dentin, a dense calcified substance, covered by a layer of enamel." [GOC:cjm, GOC:mah, GOC:mtg_sensu, PMID:10333884, PMID:15355794] biological_process
+ENSG00000186575 GO:0005912 adherens junction "A cell junction at which anchoring proteins (cadherins or integrins) extend through the plasma membrane and are attached to actin filaments." [GOC:mah, http://www.vivo.colostate.edu/hbooks/cmb/cells/pmemb/junctions_a.html, ISBN:0198506732] cellular_component
+ENSG00000186575 GO:0030175 filopodium "Thin, stiff protrusion extended by the leading edge of a motile cell such as a crawling fibroblast or amoeba, or an axonal or dendritic growth cone, or a dendritic shaft." [GOC:mah, GOC:pr, ISBN:0815316194] cellular_component
+ENSG00000186575 GO:0021766 hippocampus development "The progression of the hippocampus over time from its initial formation until its mature state." [GO_REF:0000021, GOC:cls, GOC:dgh, GOC:dph, GOC:jid, GOC:mtg_15jun06, ISBN:0878937420, UBERON:0002421] biological_process
+ENSG00000186575 GO:0031647 regulation of protein stability "Any process that affects the structure and integrity of a protein by altering the likelihood of its degradation or aggregation." [GOC:dph, GOC:mah, GOC:tb] biological_process
+ENSG00000186575 GO:0043409 negative regulation of MAPK cascade "Any process that stops, prevents, or reduces the frequency, rate or extent of signal transduction mediated by the MAPKKK cascade." [GOC:go_curators] biological_process
+ENSG00000186575 GO:0045597 positive regulation of cell differentiation "Any process that activates or increases the frequency, rate or extent of cell differentiation." [GOC:go_curators] biological_process
+ENSG00000186575 GO:1900180 regulation of protein localization to nucleus "Any process that modulates the frequency, rate or extent of protein localization to nucleus." [GOC:TermGenie] biological_process
+ENSG00000186575 GO:0032154 cleavage furrow "In animal cells, the first sign of cleavage, or cytokinesis, is the appearance of a shallow groove in the cell surface near the old metaphase plate. A contractile ring containing actin and myosin is located just inside the plasma membrane at the location of the furrow. Ring contraction is associated with centripetal growth of the membrane that deepens the cleavage furrow and divides the cytoplasm of the two daughter cells. While the term 'cleavage furrow' was initially associated with animal cells, such a structure occurs in many other types of cells, including unicellular protists." [ISBN:0805319409] cellular_component
+ENSG00000186575 GO:0045216 cell-cell junction organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of a cell-cell junction. A cell-cell junction is a specialized region of connection between two cells." [GOC:ai, GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000186575 GO:2000177 regulation of neural precursor cell proliferation "Any process that modulates the frequency, rate or extent of neural precursor cell proliferation." [GOC:dph, GOC:yaf] biological_process
+ENSG00000175329 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000175329 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000175329 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000175329 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000175329 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000175329 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000175329 GO:0006351 transcription, DNA-templated "The cellular synthesis of RNA on a template of DNA." [GOC:jl, GOC:txnOH] biological_process
+ENSG00000175329 GO:0043565 sequence-specific DNA binding "Interacting selectively and non-covalently with DNA of a specific nucleotide composition, e.g. GC-rich DNA binding, or with a specific sequence motif or type of DNA e.g. promotor binding or rDNA binding." [GOC:jl] molecular_function
+ENSG00000175329 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000175329 GO:0003677 DNA binding "Any molecular function by which a gene product interacts selectively and non-covalently with DNA (deoxyribonucleic acid)." [GOC:dph, GOC:jl, GOC:tb, GOC:vw] molecular_function
+ENSG00000175329 GO:0006357 regulation of transcription from RNA polymerase II promoter "Any process that modulates the frequency, rate or extent of transcription from an RNA polymerase II promoter." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000175329 GO:1901738 regulation of vitamin A metabolic process "Any process that modulates the frequency, rate or extent of vitamin A metabolic process." [GOC:TermGenie, PMID:18093975] biological_process
+ENSG00000128283 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000128283 GO:0005856 cytoskeleton "Any of the various filamentous elements that form the internal framework of cells, and typically remain after treatment of the cells with mild detergent to remove membrane constituents and soluble components of the cytoplasm. The term embraces intermediate filaments, microfilaments, microtubules, the microtrabecular lattice, and other structures characterized by a polymeric filamentous nature and long-range order within the cell. The various elements of the cytoskeleton not only serve in the maintenance of cellular shape but also have roles in other cellular functions, including cellular movement, cell division, endocytosis, and movement of organelles." [GOC:mah, ISBN:0198547684, PMID:16959967] cellular_component
+ENSG00000128283 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000128283 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000128283 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000128283 GO:0005794 Golgi apparatus "A compound membranous cytoplasmic organelle of eukaryotic cells, consisting of flattened, ribosome-free vesicles arranged in a more or less regular stack. The Golgi apparatus differs from the endoplasmic reticulum in often having slightly thicker membranes, appearing in sections as a characteristic shallow semicircle so that the convex side (cis or entry face) abuts the endoplasmic reticulum, secretory vesicles emerging from the concave side (trans or exit face). In vertebrate cells there is usually one such organelle, while in invertebrates and plants, where they are known usually as dictyosomes, there may be several scattered in the cytoplasm. The Golgi apparatus processes proteins produced on the ribosomes of the rough endoplasmic reticulum; such processing includes modification of the core oligosaccharides of glycoproteins, and the sorting and packaging of proteins for transport to a variety of cellular locations. Three different regions of the Golgi are now recognized both in terms of structure and function: cis, in the vicinity of the cis face, trans, in the vicinity of the trans face, and medial, lying between the cis and trans regions." [ISBN:0198506732] cellular_component
+ENSG00000128283 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000128283 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000128283 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000128283 GO:0005925 focal adhesion "Small region on the surface of a cell that anchors the cell to the extracellular matrix and that forms a point of termination of actin filaments." [ISBN:0124325653, ISBN:0815316208] cellular_component
+ENSG00000128283 GO:0008360 regulation of cell shape "Any process that modulates the surface configuration of a cell." [GOC:dph, GOC:go_curators, GOC:tb] biological_process
+ENSG00000128283 GO:0015629 actin cytoskeleton "The part of the cytoskeleton (the internal framework of a cell) composed of actin and associated proteins. Includes actin cytoskeleton-associated complexes." [GOC:jl, ISBN:0395825172, ISBN:0815316194] cellular_component
+ENSG00000128283 GO:0031274 positive regulation of pseudopodium assembly "Any process that activates or increases the frequency, rate or extent of the assembly of pseudopodia." [GOC:pg] biological_process
+ENSG00000128283 GO:0007266 Rho protein signal transduction "A series of molecular signals within the cell that are mediated by a member of the Rho family of proteins switching to a GTP-bound active state." [GOC:bf] biological_process
+ENSG00000128283
+ENSG00000211652 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000211652 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000211655 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000211655 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100362 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000100362 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100362 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100362 GO:0005509 calcium ion binding "Interacting selectively and non-covalently with calcium ions (Ca2+)." [GOC:ai] molecular_function
+ENSG00000100362 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100362 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100362 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100362 GO:0030424 axon "The long process of a neuron that conducts nerve impulses, usually away from the cell body to the terminals and varicosities, which are sites of storage and release of neurotransmitter." [GOC:nln, ISBN:0198506732] cellular_component
+ENSG00000100362 GO:0046982 protein heterodimerization activity "Interacting selectively and non-covalently with a nonidentical protein to form a heterodimer." [GOC:ai] molecular_function
+ENSG00000100362 GO:0043025 neuronal cell body "The portion of a neuron that includes the nucleus, but excludes cell projections such as axons and dendrites." [GOC:go_curators] cellular_component
+ENSG00000100362 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100362 GO:0042803 protein homodimerization activity "Interacting selectively and non-covalently with an identical protein to form a homodimer." [GOC:jl] molecular_function
+ENSG00000100362 GO:0051480 cytosolic calcium ion homeostasis "Any process involved in the maintenance of an internal steady state of calcium ions within the cytosol of a cell or between the cytosol and its surroundings." [GOC:ai, GOC:mah, GOC:rph] biological_process
+ENSG00000100362 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000099956 GO:0000228 nuclear chromosome "A chromosome found in the nucleus of a eukaryotic cell." [GOC:mah] cellular_component
+ENSG00000099956 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000099956 GO:0005730 nucleolus "A small, dense body one or more of which are present in the nucleus of eukaryotic cells. It is rich in RNA and protein, is not bounded by a limiting membrane, and is not seen during mitosis. Its prime function is the transcription of the nucleolar DNA into 45S ribosomal-precursor RNA, the processing of this RNA into 5.8S, 18S, and 28S components of ribosomal RNA, and the association of these components with 5S RNA and proteins synthesized outside the nucleolus. This association results in the formation of ribonucleoprotein precursors; these pass into the cytoplasm and mature into the 40S and 60S subunits of the ribosome." [ISBN:0198506732] cellular_component
+ENSG00000099956 GO:0006338 chromatin remodeling "Dynamic structural changes to eukaryotic chromatin occurring throughout the cell division cycle. These changes range from the local changes necessary for transcriptional regulation to global changes necessary for chromosome segregation." [GOC:jid, PMID:12697820] biological_process
+ENSG00000099956 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000099956 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000099956 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000099956 GO:0005694 chromosome "A structure composed of a very long molecule of DNA and associated proteins (e.g. histones) that carries hereditary information." [ISBN:0198547684] cellular_component
+ENSG00000099956 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000099956 GO:0003677 DNA binding "Any molecular function by which a gene product interacts selectively and non-covalently with DNA (deoxyribonucleic acid)." [GOC:dph, GOC:jl, GOC:tb, GOC:vw] molecular_function
+ENSG00000099956 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000099956 GO:0006259 DNA metabolic process "Any cellular metabolic process involving deoxyribonucleic acid. This is one of the two main types of nucleic acid, consisting of a long, unbranched macromolecule formed from one, or more commonly, two, strands of linked deoxyribonucleotides." [ISBN:0198506732] biological_process
+ENSG00000099956 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000099956 GO:0044403 symbiosis, encompassing mutualism through parasitism "An interaction between two organisms living together in more or less intimate association. The term host is usually used for the larger (macro) of the two members of a symbiosis. The smaller (micro) member is called the symbiont organism. Microscopic symbionts are often referred to as endosymbionts. The various forms of symbiosis include parasitism, in which the association is disadvantageous or destructive to one of the organisms; mutualism, in which the association is advantageous, or often necessary to one or both and not harmful to either; and commensalism, in which one member of the association benefits while the other is not affected. However, mutualism, parasitism, and commensalism are often not discrete categories of interactions and should rather be perceived as a continuum of interaction ranging from parasitism to mutualism. In fact, the direction of a symbiotic interaction can change during the lifetime of the symbionts due to developmental changes as well as changes in the biotic/abiotic environment in which the interaction occurs." [GOC:cc, http://www.free-definition.com] biological_process
+ENSG00000099956 GO:0008134 transcription factor binding "Interacting selectively and non-covalently with a transcription factor, any protein required to initiate or regulate transcription." [ISBN:0198506732] molecular_function
+ENSG00000099956 GO:0030154 cell differentiation "The process in which relatively unspecialized cells, e.g. embryonic or regenerative cells, acquire specialized structural and/or functional features that characterize the cells, tissues, or organs of the mature organism or some other relatively stable phase of the organism's life history. Differentiation includes the processes involved in commitment of a cell to a specific fate and its subsequent development to the mature state." [ISBN:0198506732] biological_process
+ENSG00000099956 GO:0048856 anatomical structure development "The biological process whose specific outcome is the progression of an anatomical structure from an initial condition to its mature state. This process begins with the formation of the structure and ends with the mature structure, whatever form that may be including its natural destruction. An anatomical structure is any biological entity that occupies space and is distinguished from its surroundings. Anatomical structures can be macroscopic such as a carpel, or microscopic such as an acrosome." [GO_REF:0000021, GOC:mtg_15jun06] biological_process
+ENSG00000099956 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000099956 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000099956 GO:0005654 nucleoplasm "That part of the nuclear content other than the chromosomes or the nucleolus." [GOC:ma, ISBN:0124325653] cellular_component
+ENSG00000099956 GO:0000988 protein binding transcription factor activity "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules), in order to modulate transcription. A protein binding transcription factor may or may not also interact with the template nucleic acid (either DNA or RNA) as well." [GOC:txnOH] molecular_function
+ENSG00000099956 GO:0000790 nuclear chromatin "The ordered and organized complex of DNA, protein, and sometimes RNA, that forms the chromosome in the nucleus." [GOC:elh, PMID:20404130] cellular_component
+ENSG00000099956 GO:0002039 p53 binding "Interacting selectively and non-covalently with one of the p53 family of proteins." [GOC:hjd] molecular_function
+ENSG00000099956 GO:0003713 transcription coactivator activity "Interacting selectively and non-covalently with a activating transcription factor and also with the basal transcription machinery in order to increase the frequency, rate or extent of transcription. Cofactors generally do not bind the template nucleic acid, but rather mediate protein-protein interactions between activating transcription factors and the basal transcription machinery." [GOC:txnOH, PMID:10213677, PMID:16858867] molecular_function
+ENSG00000099956 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000099956 GO:0006281 DNA repair "The process of restoring DNA after damage. Genomes are subject to damage by chemical and physical agents in the environment (e.g. UV and ionizing radiations, chemical mutagens, fungal and bacterial toxins, etc.) and by free radicals or alkylating agents endogenously generated in metabolism. DNA is also damaged because of errors during its replication. A variety of different DNA repair pathways have been reported that include direct reversal, base excision repair, nucleotide excision repair, photoreactivation, bypass, double-strand break repair pathway, and mismatch repair pathway." [PMID:11563486] biological_process
+ENSG00000099956 GO:0006337 nucleosome disassembly "The controlled breakdown of nucleosomes, the beadlike structural units of eukaryotic chromatin composed of histones and DNA." [GOC:mah] biological_process
+ENSG00000099956 GO:0006351 transcription, DNA-templated "The cellular synthesis of RNA on a template of DNA." [GOC:jl, GOC:txnOH] biological_process
+ENSG00000099956 GO:0006357 regulation of transcription from RNA polymerase II promoter "Any process that modulates the frequency, rate or extent of transcription from an RNA polymerase II promoter." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000099956 GO:0007399 nervous system development "The process whose specific outcome is the progression of nervous tissue over time, from its formation to its mature state." [GOC:dgh] biological_process
+ENSG00000099956 GO:0008285 negative regulation of cell proliferation "Any process that stops, prevents or reduces the rate or extent of cell proliferation." [GOC:go_curators] biological_process
+ENSG00000099956 GO:0015074 DNA integration "The process in which a segment of DNA is incorporated into another, usually larger, DNA molecule such as a chromosome." [GOC:mah] biological_process
+ENSG00000099956 GO:0016514 SWI/SNF complex "A SWI/SNF-type complex that contains nine or more proteins, including both conserved (core) and nonconserved components; the Swi2/Snf2 ATPase is one of the core components." [GOC:mah, PMID:12672490] cellular_component
+ENSG00000099956 GO:0030957 Tat protein binding "Interacting selectively and non-covalently with Tat, a viral transactivating regulatory protein from the human immunodeficiency virus, or the equivalent protein from another virus." [GOC:mah, PMID:9094689] molecular_function
+ENSG00000099956 GO:0039692 single stranded viral RNA replication via double stranded DNA intermediate "A viral genome replication where the template is single-stranded RNA (ssRNA), and which proceeds via a double stranded DNA (dsDNA) intermediate molecule. Viral genomic RNA is first reverse transcribed into dsDNA, which integrates into the host chromosomal DNA, where it is transcribed by host RNA polymerase II." [GOC:bf, GOC:jl, ISBN:0198506732, VZ:1937] biological_process
+ENSG00000099956 GO:0043044 ATP-dependent chromatin remodeling "Dynamic structural changes to eukaryotic chromatin that require energy from the hydrolysis of ATP, ranging from local changes necessary for transcriptional regulation to global changes necessary for chromosome segregation, mediated by ATP-dependent chromatin-remodelling factors." [GOC:jl, PMID:12042764] biological_process
+ENSG00000099956 GO:0043923 positive regulation by host of viral transcription "Any process is which a host organism activates or increases the frequency, rate or extent of viral transcription, the synthesis of either RNA on a template of DNA or DNA on a template of RNA." [GOC:jl] biological_process
+ENSG00000099956 GO:0044772 mitotic cell cycle phase transition "The cell cycle process by which a cell commits to entering the next mitotic cell cycle phase." [GOC:mtg_cell_cycle] biological_process
+ENSG00000099956 GO:0045944 positive regulation of transcription from RNA polymerase II promoter "Any process that activates or increases the frequency, rate or extent of transcription from an RNA polymerase II promoter." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000099956 GO:0051091 positive regulation of sequence-specific DNA binding transcription factor activity "Any process that activates or increases the frequency, rate or extent of activity of a transcription factor, any factor involved in the initiation or regulation of transcription." [GOC:ai] biological_process
+ENSG00000099956 GO:0071564 npBAF complex "A SWI/SNF-type complex that is found in neural stem or progenitor cells, and in human contains actin and proteins encoded by the ARID1A/BAF250A or ARID1B/BAF250B, SMARCD1/BAF60A, SMARCD3/BAF60C, SMARCA2/BRM/BAF190B, SMARCA4/BRG1/BAF190A, SMARCB1/BAF47, SMARCC1/BAF155, SMARCE1/BAF57, SMARCC2/BAF170, PHF10/BAF45A, ACTL6A/BAF53A genes. The npBAF complex is essential for the self-renewal/proliferative capacity of the multipotent neural stem cells." [GOC:mah, GOC:ss, PMID:17640523] cellular_component
+ENSG00000099956 GO:0071565 nBAF complex "A SWI/SNF-type complex that is found in post-mitotic neurons, and in human contains actin and proteins encoded by the ARID1A/BAF250A or ARID1B/BAF250B, SMARCD1/BAF60A, SMARCD3/BAF60C, SMARCA2/BRM/BAF190B, SMARCA4/BRG1/BAF190A, SMARCB1/BAF47, SMARCC1/BAF155, SMARCE1/BAF57, SMARCC2/BAF170, DPF1/BAF45B, DPF3/BAF45C, ACTL6B/BAF53B genes. The nBAF complex along with CREST plays a role regulating the activity of genes essential for dendrite growth." [GOC:mah, GOC:ss, PMID:17640523] cellular_component
+ENSG00000099956 GO:0000978 RNA polymerase II core promoter proximal region sequence-specific DNA binding "Interacting selectively and non-covalently with a sequence of DNA that is in cis with and relatively close to a core promoter for RNA polymerase II." [GOC:txnOH] molecular_function
+ENSG00000099956 GO:0000980 RNA polymerase II distal enhancer sequence-specific DNA binding "Interacting selectively and non-covalently with a RNA polymerase II (Pol II) distal enhancer. In mammalian cells, enhancers are distal sequences that increase the utilization of some promoters, and can function in either orientation and in any location (upstream or downstream) relative to the core promoter." [GOC:txnOH] molecular_function
+ENSG00000099956 GO:0031492 nucleosomal DNA binding "Interacting selectively and non-covalently with the DNA portion of a nucleosome." [GOC:mah] molecular_function
+ENSG00000099956 GO:0001824 blastocyst development "The process whose specific outcome is the progression of the blastocyst over time, from its formation to the mature structure. The mammalian blastocyst is a hollow ball of cells containing two cell types, the inner cell mass and the trophectoderm." [GOC:dph, ISBN:0124020607, ISBN:0198542771] biological_process
+ENSG00000099956 GO:0001741 XY body "A structure found in a male mammalian spermatocyte containing an unpaired X chromosome that has become densely heterochromatic, silenced and localized at the nuclear periphery." [GOC:hjd] cellular_component
+ENSG00000099956 GO:0001835 blastocyst hatching "The hatching of the cellular blastocyst from the zona pellucida." [GOC:dph, ISBN:0124020607, ISBN:0198542771] biological_process
+ENSG00000131100 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000131100 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000131100 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000131100 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000131100 GO:0055085 transmembrane transport "The process in which a solute is transported from one side of a membrane to the other." [GOC:dph, GOC:jid] biological_process
+ENSG00000131100 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000131100 GO:0019899 enzyme binding "Interacting selectively and non-covalently with any enzyme." [GOC:jl] molecular_function
+ENSG00000131100 GO:0016887 ATPase activity "Catalysis of the reaction: ATP + H2O = ADP + phosphate + 2 H+. May or may not be coupled to another reaction." [EC:3.6.1.3, GOC:jl] molecular_function
+ENSG00000131100 GO:0022857 transmembrane transporter activity "Enables the transfer of a substance from one side of a membrane to the other." [GOC:mtg_transport, ISBN:0815340729] molecular_function
+ENSG00000131100 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000131100 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000131100 GO:0042592 homeostatic process "Any biological process involved in the maintenance of an internal steady state." [GOC:jl, ISBN:0395825172] biological_process
+ENSG00000131100 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000131100 GO:0005768 endosome "A membrane-bounded organelle to which materials ingested by endocytosis are delivered." [ISBN:0198506732, PMID:19696797] cellular_component
+ENSG00000131100 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000131100 GO:0005765 lysosomal membrane "The lipid bilayer surrounding the lysosome and separating its contents from the cell cytoplasm." [GOC:ai] cellular_component
+ENSG00000131100 GO:0006879 cellular iron ion homeostasis "Any process involved in the maintenance of an internal steady state of iron ions at the level of a cell." [GOC:ai, GOC:mah] biological_process
+ENSG00000131100 GO:0008152 metabolic process "The chemical reactions and pathways, including anabolism and catabolism, by which living organisms transform chemical substances. Metabolic processes typically transform small molecules, but also include macromolecular processes such as DNA repair and replication, and protein synthesis and degradation." [GOC:go_curators, ISBN:0198547684] biological_process
+ENSG00000131100 GO:0008286 insulin receptor signaling pathway "The series of molecular signals generated as a consequence of the insulin receptor binding to insulin." [GOC:ceb] biological_process
+ENSG00000131100 GO:0015992 proton transport "The directed movement of protons (hydrogen ions) into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:jl] biological_process
+ENSG00000131100 GO:0016324 apical plasma membrane "The region of the plasma membrane located at the apical end of the cell." [GOC:curators] cellular_component
+ENSG00000131100 GO:0016469 proton-transporting two-sector ATPase complex "A large protein complex that catalyzes the synthesis or hydrolysis of ATP by a rotational mechanism, coupled to the transport of protons across a membrane. The complex comprises a membrane sector (F0, V0, or A0) that carries out proton transport and a cytoplasmic compartment sector (F1, V1, or A1) that catalyzes ATP synthesis or hydrolysis. Two major types have been characterized: V-type ATPases couple ATP hydrolysis to the transport of protons across a concentration gradient, whereas F-type ATPases, also known as ATP synthases, normally run in the reverse direction to utilize energy from a proton concentration or electrochemical gradient to synthesize ATP. A third type, A-type ATPases have been found in archaea, and are closely related to eukaryotic V-type ATPases but are reversible." [GOC:mah, ISBN:0716743663, PMID:16691483] cellular_component
+ENSG00000131100 GO:0033178 proton-transporting two-sector ATPase complex, catalytic domain "A protein complex that forms part of a proton-transporting two-sector ATPase complex and catalyzes ATP hydrolysis or synthesis. The catalytic domain (F1, V1, or A1) comprises a hexameric catalytic core and a central stalk, and is peripherally associated with the membrane when the two-sector ATPase is assembled." [GOC:mah, PMID:10838056] cellular_component
+ENSG00000131100 GO:0033572 transferrin transport "The directed movement of transferrin into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:mlg] biological_process
+ENSG00000131100 GO:0046961 proton-transporting ATPase activity, rotational mechanism "Catalysis of the transfer of protons from one side of a membrane to the other according to the reaction: ATP + H2O + H+(in) = ADP + phosphate + H+(out), by a rotational mechanism." [EC:3.6.3.14] molecular_function
+ENSG00000131100 GO:0051117 ATPase binding "Interacting selectively and non-covalently with an ATPase, any enzyme that catalyzes the hydrolysis of ATP." [GOC:ai] molecular_function
+ENSG00000131100 GO:0051701 interaction with host "An interaction between two organisms living together in more or less intimate association. The term host is used for the larger (macro) of the two members of a symbiosis; the various forms of symbiosis include parasitism, commensalism and mutualism." [GOC:cc] biological_process
+ENSG00000131100 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000131100 GO:0090382 phagosome maturation "A process that is carried out at the cellular level which results in the arrangement of constituent parts of a phagosome within a cell. Phagosome maturation begins with endocytosis and formation of the early phagosome and ends with the formation of the hybrid organelle, the phagolysosome." [GOC:kmv, GOC:tb] biological_process
+ENSG00000131100 GO:0015991 ATP hydrolysis coupled proton transport "The transport of protons against an electrochemical gradient, using energy from ATP hydrolysis." [GOC:mah, GOC:vw] biological_process
+ENSG00000131100 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000131100 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000131100 GO:0005902 microvillus "Thin cylindrical membrane-covered projections on the surface of an animal cell containing a core bundle of actin filaments. Present in especially large numbers on the absorptive surface of intestinal cells." [ISBN:0813516194] cellular_component
+ENSG00000131100 GO:0008553 hydrogen-exporting ATPase activity, phosphorylative mechanism "Catalysis of the transfer of protons from one side of a membrane to the other according to the reaction: ATP + H2O + H+(in) -> ADP + phosphate + H+(out). These transporters use a phosphorylative mechanism, which have a phosphorylated intermediate state during the ion transport cycle." [EC:3.6.3.6] molecular_function
+ENSG00000211684
+ENSG00000099958 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000099958 GO:0018279 protein N-linked glycosylation via asparagine "The glycosylation of protein via the N4 atom of peptidyl-asparagine forming N4-glycosyl-L-asparagine; the most common form is N-acetylglucosaminyl asparagine; N-acetylgalactosaminyl asparagine and N4 glucosyl asparagine also occur. This modification typically occurs in extracellular peptides with an N-X-(ST) motif. Partial modification has been observed to occur with cysteine, rather than serine or threonine, in the third position; secondary structure features are important, and proline in the second or fourth positions inhibits modification." [GOC:jsg, RESID:AA0151, RESID:AA0420, RESID:AA0421] biological_process
+ENSG00000099958 GO:0030176 integral component of endoplasmic reticulum membrane "The component of the endoplasmic reticulum membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:mah] cellular_component
+ENSG00000099958 GO:0030433 ER-associated ubiquitin-dependent protein catabolic process "The chemical reactions and pathways resulting in the breakdown of proteins transported from the endoplasmic reticulum and targeted to cytoplasmic proteasomes for degradation. This process acts on misfolded proteins as well as in the regulated degradation of correctly folded proteins." [GOC:mah, GOC:rb, PMID:14607247, PMID:19520858] biological_process
+ENSG00000099958 GO:0030968 endoplasmic reticulum unfolded protein response "The series of molecular signals generated as a consequence of the presence of unfolded proteins in the endoplasmic reticulum (ER) or other ER-related stress; results in changes in the regulation of transcription and translation." [GOC:mah, PMID:12042763] biological_process
+ENSG00000099958 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000099958 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000099958 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000099958 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000099958 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000099958 GO:0005975 carbohydrate metabolic process "The chemical reactions and pathways involving carbohydrates, any of a group of organic compounds based of the general formula Cx(H2O)y. Includes the formation of carbohydrate derivatives by the addition of a carbohydrate residue to another molecule." [GOC:mah, ISBN:0198506732] biological_process
+ENSG00000099958 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000099958 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000099958
+ENSG00000070371 GO:0004871 signal transducer activity "Conveys a signal across a cell to trigger a change in cell function or state. A signal is a physical entity or change in state that is used to transfer information in order to trigger a response." [GOC:go_curators] molecular_function
+ENSG00000070371 GO:0005198 structural molecule activity "The action of a molecule that contributes to the structural integrity of a complex or assembly within or outside a cell." [GOC:mah] molecular_function
+ENSG00000070371 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000070371 GO:0005802 trans-Golgi network "The network of interconnected tubular and cisternal structures located within the Golgi apparatus on the side distal to the endoplasmic reticulum, from which secretory vesicles emerge. The trans-Golgi network is important in the later stages of protein secretion where it is thought to play a key role in the sorting and targeting of secreted proteins to the correct destination." [GOC:vw, ISBN:0815316194] cellular_component
+ENSG00000070371 GO:0005819 spindle "The array of microtubules and associated molecules that forms between opposite poles of a eukaryotic cell during mitosis or meiosis and serves to move the duplicated chromosomes apart." [ISBN:0198547684] cellular_component
+ENSG00000070371 GO:0005905 coated pit "A part of the endomembrane system in the form of an invagination of a membrane upon which a clathrin coat forms, and that can be converted by vesicle budding into a clathrin-coated vesicle. Coated pits form on the plasma membrane, where they are involved in receptor-mediated selective transport of many proteins and other macromolecules across the cell membrane, in the trans-Golgi network, and on some endosomes." [GOC:mah, ISBN:0198506732, NIF_Subcellular:sao1969557946, PMID:10559856, PMID:17284835] cellular_component
+ENSG00000070371 GO:0006886 intracellular protein transport "The directed movement of proteins in a cell, including the movement of proteins between specific compartments or structures within a cell, such as organelles of a eukaryotic cell." [GOC:mah] biological_process
+ENSG00000070371 GO:0006898 receptor-mediated endocytosis "An endocytosis process in which cell surface receptors ensure specificity of transport. A specific receptor on the cell surface binds tightly to the extracellular macromolecule (the ligand) that it recognizes; the plasma-membrane region containing the receptor-ligand complex then undergoes endocytosis, forming a transport vesicle containing the receptor-ligand complex and excluding most other plasma-membrane proteins. Receptor-mediated endocytosis generally occurs via clathrin-coated pits and vesicles." [GOC:mah, ISBN:0716731363] biological_process
+ENSG00000070371 GO:0007067 mitotic nuclear division "A cell cycle process comprising the steps by which the nucleus of a eukaryotic cell divides; the process involves condensation of chromosomal DNA into a highly compacted form. Canonically, mitosis produces two daughter nuclei whose chromosome complement is identical to that of the mother cell." [GOC:dph, GOC:ma, GOC:mah, ISBN:0198547684] biological_process
+ENSG00000070371 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000070371 GO:0009653 anatomical structure morphogenesis "The process in which anatomical structures are generated and organized. Morphogenesis pertains to the creation of form." [GOC:go_curators, ISBN:0521436125] biological_process
+ENSG00000070371 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000070371 GO:0030130 clathrin coat of trans-Golgi network vesicle "A clathrin coat found on a vesicle of the trans-Golgi network." [GOC:mah] cellular_component
+ENSG00000070371 GO:0030132 clathrin coat of coated pit "The coat found on coated pits and the coated vesicles derived from coated pits; comprises clathrin and the AP-2 adaptor complex." [GOC:mah] cellular_component
+ENSG00000070371 GO:0030135 coated vesicle "Small membrane-bounded organelle formed by pinching off of a coated region of membrane. Some coats are made of clathrin, whereas others are made from other proteins." [ISBN:0815316194] cellular_component
+ENSG00000070371 GO:0030136 clathrin-coated vesicle "A vesicle with a coat formed of clathrin connected to the membrane via one of the clathrin adaptor complexes." [GOC:mah, PMID:11252894] cellular_component
+ENSG00000070371 GO:0046326 positive regulation of glucose import "Any process that activates or increases the frequency, rate or extent of the import of the hexose monosaccharide glucose into a cell or organelle." [GOC:ai, GOC:dph, GOC:tb] biological_process
+ENSG00000070371 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000070371 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000070371 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000070371 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000070371 GO:0016023 cytoplasmic membrane-bounded vesicle "A membrane-bounded vesicle found in the cytoplasm of the cell." [GOC:ai, GOC:mah] cellular_component
+ENSG00000070371 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000070371 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000070371 GO:0016192 vesicle-mediated transport "A cellular transport process in which transported substances are moved in membrane-bounded vesicles; transported substances are enclosed in the vesicle lumen or located in the vesicle membrane. The process begins with a step that directs a substance to the forming vesicle, and includes vesicle budding and coating. Vesicles are then targeted to, and fuse with, an acceptor membrane." [GOC:ai, GOC:mah, ISBN:08789310662000] biological_process
+ENSG00000070371 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000070371 GO:0005488 binding "The selective, non-covalent, often stoichiometric, interaction of a molecule with one or more specific sites on another molecule." [GOC:ceb, GOC:mah, ISBN:0198506732] molecular_function
+ENSG00000054611 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000054611 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000054611 GO:0030234 enzyme regulator activity "Binds to and modulates the activity of an enzyme." [GOC:mah] molecular_function
+ENSG00000054611 GO:0005097 Rab GTPase activator activity "Increases the rate of GTP hydrolysis by a GTPase of the Rab family." [GOC:mah] molecular_function
+ENSG00000054611 GO:0032851 positive regulation of Rab GTPase activity "Any process that activates or increases the activity of a GTPase of the Rab family." [GOC:mah] biological_process
+ENSG00000054611 GO:0042803 protein homodimerization activity "Interacting selectively and non-covalently with an identical protein to form a homodimer." [GOC:jl] molecular_function
+ENSG00000054611 GO:0032313 regulation of Rab GTPase activity "Any process that modulates the activity of a GTPase of the Rab family." [GOC:mah] biological_process
+ENSG00000054611
+ENSG00000183530
+ENSG00000189306 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000189306 GO:0000166 nucleotide binding "Interacting selectively and non-covalently with a nucleotide, any compound consisting of a nucleoside that is esterified with (ortho)phosphate or an oligophosphate at any hydroxyl group on the ribose or deoxyribose." [GOC:mah, ISBN:0198547684] molecular_function
+ENSG00000189306 GO:0044822 poly(A) RNA binding "Interacting non-covalently with a poly(A) RNA, a RNA molecule which has a tail of adenine bases." [GOC:jl] molecular_function
+ENSG00000189306 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000189306
+ENSG00000100078 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100078 GO:0006629 lipid metabolic process "The chemical reactions and pathways involving lipids, compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent. Includes fatty acids; neutral fats, other fatty-acid esters, and soaps; long-chain (fatty) alcohols and waxes; sphingoids and other long-chain bases; glycolipids, phospholipids and sphingolipids; and carotenes, polyprenols, sterols, terpenes and other isoprenoids." [GOC:ma] biological_process
+ENSG00000100078 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100078 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000100078 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000100078 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100078 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000100078 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100078 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000100078 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100078 GO:0005615 extracellular space "That part of a multicellular organism outside the cells proper, usually taken to be outside the plasma membranes, and occupied by fluid." [ISBN:0198547684] cellular_component
+ENSG00000100078 GO:0005576 extracellular region "The space external to the outermost structure of a cell. For cells without external protective or external encapsulating structures this refers to space outside of the plasma membrane. This term covers the host cell environment outside an intracellular parasite." [GOC:go_curators] cellular_component
+ENSG00000100078 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100078 GO:0005509 calcium ion binding "Interacting selectively and non-covalently with calcium ions (Ca2+)." [GOC:ai] molecular_function
+ENSG00000100078 GO:0005814 centriole "A cellular organelle, found close to the nucleus in many eukaryotic cells, consisting of a small cylinder with microtubular walls, 300-500 nm long and 150-250 nm in diameter. It contains nine short, parallel, peripheral microtubular fibrils, each fibril consisting of one complete microtubule fused to two incomplete microtubules. Cells usually have two centrioles, lying at right angles to each other. At division, each pair of centrioles generates another pair and the twin pairs form the pole of the mitotic spindle." [ISBN:0198547684] cellular_component
+ENSG00000100078 GO:0006644 phospholipid metabolic process "The chemical reactions and pathways involving phospholipids, any lipid containing phosphoric acid as a mono- or diester." [ISBN:0198506732] biological_process
+ENSG00000100078 GO:0016042 lipid catabolic process "The chemical reactions and pathways resulting in the breakdown of lipids, compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent." [GOC:go_curators] biological_process
+ENSG00000100078 GO:0036148 phosphatidylglycerol acyl-chain remodeling "Remodeling the acyl chains of phosphatidylglycerol, through sequential deacylation and re-acylation reactions, to generate phosphatidylglycerol containing different types of fatty acid acyl chains." [CHEBI:17517, GOC:mw, PMID:15485873, PMID:18458083] biological_process
+ENSG00000100078 GO:0036151 phosphatidylcholine acyl-chain remodeling "Remodeling the acyl chains of phosphatidylcholine, through sequential deacylation and re-acylation reactions, to generate phosphatidylcholine containing different types of fatty acid acyl chains." [CHEBI:49183, GOC:mw, PMID:18195019, PMID:18458083] biological_process
+ENSG00000100078 GO:0036152 phosphatidylethanolamine acyl-chain remodeling "Remodeling the acyl chains of phosphatidylethanolamine, through sequential deacylation and re-acylation reactions, to generate phosphatidylethanolamine containing different types of fatty acid acyl chains." [CHEBI:16038, GOC:mw, PMID:18287005, PMID:18458083] biological_process
+ENSG00000100078 GO:0046474 glycerophospholipid biosynthetic process "The chemical reactions and pathways resulting in the formation of glycerophospholipids, any derivative of glycerophosphate that contains at least one O-acyl, O-alkyl, or O-alkenyl group attached to the glycerol residue." [ISBN:0198506732] biological_process
+ENSG00000100078 GO:0047498 calcium-dependent phospholipase A2 activity "Catalysis of the reaction: phosphatidylcholine + H2O = 1-acylglycerophosphocholine + a carboxylate. This reaction requires Ca2+." [EC:3.1.1.4] molecular_function
+ENSG00000100078 GO:0060271 cilium morphogenesis "A process that is carried out at the cellular level and in which the structure of a cilium is organized." [GOC:BHF, GOC:dph] biological_process
+ENSG00000100078 GO:0004623 phospholipase A2 activity "Catalysis of the reaction: phosphatidylcholine + H2O = 1-acylglycerophosphocholine + a carboxylate." [EC:3.1.1.4] molecular_function
+ENSG00000100078 GO:0001675 acrosome assembly "The formation of the acrosome from the spermatid Golgi." [GOC:dph, GOC:hjd, GOC:tb] biological_process
+ENSG00000100078 GO:0048468 cell development "The process whose specific outcome is the progression of the cell over time, from its formation to the mature structure. Cell development does not include the steps involved in committing a cell to a specific fate." [GOC:go_curators] biological_process
+ENSG00000100078 GO:0042629 mast cell granule "Coarse, bluish-black staining cytoplasmic granules, bounded by a plasma membrane and found in mast cells and basophils. Contents include histamine, heparin, chondroitin sulfates, chymase and tryptase." [GOC:jl, http://www.ijp-online.com/archives/1969/001/02/r0000-0000tc.htm, PMID:12360215] cellular_component
+ENSG00000100078 GO:0043303 mast cell degranulation "The regulated exocytosis of secretory granules containing preformed mediators such as histamine, serotonin, and neutral proteases by a mast cell." [ISBN:0781735149] biological_process
+ENSG00000100078 GO:0007288 sperm axoneme assembly "The assembly and organization of the sperm flagellar axoneme, the bundle of microtubules and associated proteins that forms the core of the eukaryotic sperm flagellum, and is responsible for movement." [GOC:bf, ISBN:0198547684] biological_process
+ENSG00000100078 GO:0046470 phosphatidylcholine metabolic process "The chemical reactions and pathways involving phosphatidylcholines, any of a class of glycerophospholipids in which the phosphatidyl group is esterified to the hydroxyl group of choline. They are important constituents of cell membranes." [ISBN:0198506732] biological_process
+ENSG00000100078 GO:0019372 lipoxygenase pathway "The chemical reactions and pathways by which an unsaturated fatty acid (such as arachidonic acid or linolenic acid) is converted to other compounds, and in which the first step is hydroperoxide formation catalyzed by lipoxygenase." [GOC:mah, PMID:17163881] biological_process
+ENSG00000182858 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000182858 GO:0016757 transferase activity, transferring glycosyl groups "Catalysis of the transfer of a glycosyl group from one compound (donor) to another (acceptor)." [GOC:jl, ISBN:0198506732] molecular_function
+ENSG00000182858 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000182858 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000182858 GO:0005975 carbohydrate metabolic process "The chemical reactions and pathways involving carbohydrates, any of a group of organic compounds based of the general formula Cx(H2O)y. Includes the formation of carbohydrate derivatives by the addition of a carbohydrate residue to another molecule." [GOC:mah, ISBN:0198506732] biological_process
+ENSG00000182858 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000182858 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000182858 GO:0006457 protein folding "The process of assisting in the covalent and noncovalent assembly of single chain polypeptides or multisubunit complexes into the correct tertiary structure." [GOC:go_curators, GOC:rb] biological_process
+ENSG00000182858 GO:0005783 endoplasmic reticulum "The irregular network of unit membranes, visible only by electron microscopy, that occurs in the cytoplasm of many eukaryotic cells. The membranes form a complex meshwork of tubular channels, which are often expanded into slitlike cavities called cisternae. The ER takes two forms, rough (or granular), with ribosomes adhering to the outer surface, and smooth (with no ribosomes attached)." [ISBN:0198506732] cellular_component
+ENSG00000182858 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000182858 GO:0000009 alpha-1,6-mannosyltransferase activity "Catalysis of the transfer of a mannose residue to an oligosaccharide, forming an alpha-(1->6) linkage." [GOC:mcc, PMID:2644248] molecular_function
+ENSG00000182858 GO:0005789 endoplasmic reticulum membrane "The lipid bilayer surrounding the endoplasmic reticulum." [GOC:mah] cellular_component
+ENSG00000182858 GO:0006487 protein N-linked glycosylation "A protein glycosylation process in which a carbohydrate or carbohydrate derivative unit is added to a protein via the N4 atom of peptidyl-asparagine, the omega-N of arginine, or the N1' atom peptidyl-tryptophan." [GOC:pr, RESID:AA0151, RESID:AA0156, RESID:AA0327] biological_process
+ENSG00000182858 GO:0006488 dolichol-linked oligosaccharide biosynthetic process "The chemical reactions and pathways resulting in the formation of dolichol-linked oligosaccharide, usually by a stepwise addition of glycosyl chains to endoplasmic reticulum membrane-bound dolichol-P." [GOC:jl, ISBN:0471331309] biological_process
+ENSG00000182858 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000182858 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000182858 GO:0018279 protein N-linked glycosylation via asparagine "The glycosylation of protein via the N4 atom of peptidyl-asparagine forming N4-glycosyl-L-asparagine; the most common form is N-acetylglucosaminyl asparagine; N-acetylgalactosaminyl asparagine and N4 glucosyl asparagine also occur. This modification typically occurs in extracellular peptides with an N-X-(ST) motif. Partial modification has been observed to occur with cysteine, rather than serine or threonine, in the third position; secondary structure features are important, and proline in the second or fourth positions inhibits modification." [GOC:jsg, RESID:AA0151, RESID:AA0420, RESID:AA0421] biological_process
+ENSG00000182858 GO:0043687 post-translational protein modification "The process of covalently altering one or more amino acids in a protein after the protein has been completely translated and released from the ribosome." [GOC:jsg] biological_process
+ENSG00000182858 GO:0044267 cellular protein metabolic process "The chemical reactions and pathways involving a specific protein, rather than of proteins in general, occurring at the level of an individual cell. Includes cellular protein modification." [GOC:jl] biological_process
+ENSG00000182858 GO:0052917 dol-P-Man:Man(7)GlcNAc(2)-PP-Dol alpha-1,6-mannosyltransferase activity "Catalysis of the reaction: alpha-D-man-(1->2)-alpha-D-Man-(1->2)-alpha-D-Man-(1->3)-(alpha-D-Man-(1->2)-alpha-D-Man-(1->3)-alpha-D-Man-(1->6))-beta-D-Man-(1->4)-beta-D-GlcNAc-(1->4)-D-GlcNAc-diphosphodolichol + dolichyl D-mannosyl phosphate = H(+) + alpha-D-Man-(1->2)-alpha-D-Man-(1->2)-alpha-D-Man-(1->3)-(alpha-D-Man-(1->2)-alpha-D-Man-(1->3)-(alpha-D-Man-(1->6))-alpha-D-Man-(1->6))-beta-D-Man-(1->4)-beta-D-GlcNAc-(1->4)-D-GlcNAc-diphosphodolichol + dolichyl phosphate." [EC:2.4.1.260, RHEA:29538] molecular_function
+ENSG00000182858 GO:0097502 mannosylation "The covalent attachment of a mannose residue to a substrate molecule." [GOC:cjm] biological_process
+ENSG00000100281
+ENSG00000100281 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100281 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100281 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000100281 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100281 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100281 GO:0007034 vacuolar transport "The directed movement of substances into, out of or within a vacuole." [GOC:ai] biological_process
+ENSG00000100281 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100281 GO:0003677 DNA binding "Any molecular function by which a gene product interacts selectively and non-covalently with DNA (deoxyribonucleic acid)." [GOC:dph, GOC:jl, GOC:tb, GOC:vw] molecular_function
+ENSG00000100281 GO:0008333 endosome to lysosome transport "The directed movement of substances from endosomes to lysosomes." [GOC:ai, ISBN:0716731363] biological_process
+ENSG00000100281 GO:0016055 Wnt signaling pathway "The series of molecular signals initiated by binding of a Wnt protein to a frizzled family receptor on the surface of the target cell and ending with a change in cell state." [GOC:dph, GOC:go_curators, PMID:11532397] biological_process
+ENSG00000100281 GO:0016589 NURF complex "An ISWI complex that contains an ATPase subunit of the ISWI family (SNF2L in mammals), a NURF301 homolog (BPTF in humans), and additional subunits, though the composition of these additional subunits varies slightly with species. NURF is involved in regulation of transcription from TRNA polymerase II promoters." [GOC:bf, GOC:krc, PMID:10779516, PMID:11279013, PMID:15284901, PMID:16568949, PMID:21810179] cellular_component
+ENSG00000100281 GO:0030178 negative regulation of Wnt signaling pathway "Any process that stops, prevents, or reduces the frequency, rate or extent of the Wnt signaling pathway." [GOC:dph, GOC:go_curators, GOC:tb] biological_process
+ENSG00000172346 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000172346 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000172346 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000172346 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000172346 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000172346 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000172346 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000172346 GO:0003677 DNA binding "Any molecular function by which a gene product interacts selectively and non-covalently with DNA (deoxyribonucleic acid)." [GOC:dph, GOC:jl, GOC:tb, GOC:vw] molecular_function
+ENSG00000172346 GO:0006355 regulation of transcription, DNA-templated "Any process that modulates the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000172346 GO:0006397 mRNA processing "Any process involved in the conversion of a primary mRNA transcript into one or more mature mRNA(s) prior to translation into polypeptide." [GOC:mah] biological_process
+ENSG00000172346 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000172346 GO:0003676 nucleic acid binding "Interacting selectively and non-covalently with any nucleic acid." [GOC:jl] molecular_function
+ENSG00000100124 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100124 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100124 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100124 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100124 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100124 GO:0030496 midbody "A thin cytoplasmic bridge formed between daughter cells at the end of cytokinesis. The midbody forms where the contractile ring constricts, and may persist for some time before finally breaking to complete cytokinesis." [ISBN:0815316194] cellular_component
+ENSG00000100124 GO:0045648 positive regulation of erythrocyte differentiation "Any process that activates or increases the frequency, rate or extent of erythrocyte differentiation." [GOC:go_curators] biological_process
+ENSG00000100124 GO:1902531 regulation of intracellular signal transduction "Any process that modulates the frequency, rate or extent of intracellular signal transduction." [GOC:dph, GOC:signaling, GOC:tb, GOC:TermGenie] biological_process
+ENSG00000100124 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100124 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100124 GO:0032403 protein complex binding "Interacting selectively and non-covalently with any protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:mah] molecular_function
+ENSG00000100124 GO:0045859 regulation of protein kinase activity "Any process that modulates the frequency, rate or extent of protein kinase activity." [GOC:go_curators] biological_process
+ENSG00000100124 GO:0006913 nucleocytoplasmic transport "The directed movement of molecules between the nucleus and the cytoplasm." [GOC:go_curators] biological_process
+ENSG00000100124 GO:0019887 protein kinase regulator activity "Modulates the activity of a protein kinase, an enzyme which phosphorylates a protein." [GOC:ai] molecular_function
+ENSG00000100124
+ENSG00000166897 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000166897 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000166897 GO:0005615 extracellular space "That part of a multicellular organism outside the cells proper, usually taken to be outside the plasma membranes, and occupied by fluid." [ISBN:0198547684] cellular_component
+ENSG00000166897 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000166897 GO:0030234 enzyme regulator activity "Binds to and modulates the activity of an enzyme." [GOC:mah] molecular_function
+ENSG00000166897 GO:0004864 protein phosphatase inhibitor activity "Stops, prevents or reduces the activity of a protein phosphatase, an enzyme that hydrolyzes phosphate groups from phosphorylated proteins." [GOC:ai] molecular_function
+ENSG00000166897 GO:0010923 negative regulation of phosphatase activity "Any process that decreases the rate or frequency of phosphatase activity. Phosphatases catalyze the hydrolysis of phosphoric monoesters, releasing inorganic phosphate." [GOC:BHF, GOC:dph, GOC:tb] biological_process
+ENSG00000166897 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000166897 GO:0019902 phosphatase binding "Interacting selectively and non-covalently with any phosphatase." [GOC:jl] molecular_function
+ENSG00000166897 GO:0019899 enzyme binding "Interacting selectively and non-covalently with any enzyme." [GOC:jl] molecular_function
+ENSG00000166897 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100209 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100209 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100209 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100209 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000100209 GO:0022607 cellular component assembly "The aggregation, arrangement and bonding together of a cellular component." [GOC:isa_complete] biological_process
+ENSG00000100209 GO:0006457 protein folding "The process of assisting in the covalent and noncovalent assembly of single chain polypeptides or multisubunit complexes into the correct tertiary structure." [GOC:go_curators, GOC:rb] biological_process
+ENSG00000100209 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100209 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000100209 GO:0005815 microtubule organizing center "An intracellular structure that can catalyze gamma-tubulin-dependent microtubule nucleation and that can anchor microtubules by interacting with their minus ends, plus ends or sides." [GOC:vw, http://en.wikipedia.org/wiki/Microtubule_organizing_center, ISBN:0815316194, PMID:17072892, PMID:17245416] cellular_component
+ENSG00000100209 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100209 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000100209 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100209 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100209 GO:0005813 centrosome "A structure comprised of a core structure (in most organisms, a pair of centrioles) and peripheral material from which a microtubule-based structure, such as a spindle apparatus, is organized. Centrosomes occur close to the nucleus during interphase in many eukaryotic cells, though in animal cells it changes continually during the cell-division cycle." [GOC:mah, ISBN:0198547684] cellular_component
+ENSG00000100209 GO:0016226 iron-sulfur cluster assembly "The incorporation of iron and exogenous sulfur into a metallo-sulfur cluster." [GOC:jl, GOC:mah, GOC:pde, GOC:vw] biological_process
+ENSG00000100209 GO:0046872 metal ion binding "Interacting selectively and non-covalently with any metal ion." [GOC:ai] molecular_function
+ENSG00000100209 GO:0051087 chaperone binding "Interacting selectively and non-covalently with a chaperone protein, a class of proteins that bind to nascent or unfolded polypeptides and ensure correct folding or transport." [http://www.onelook.com] molecular_function
+ENSG00000100209 GO:0051259 protein oligomerization "The process of creating protein oligomers, compounds composed of a small number, usually between three and ten, of component monomers; protein oligomers may be composed of different or identical monomers. Oligomers may be formed by the polymerization of a number of monomers or the depolymerization of a large protein polymer." [GOC:ai] biological_process
+ENSG00000100209 GO:0006461 protein complex assembly "The aggregation, arrangement and bonding together of a set of components to form a protein complex." [GOC:ai] biological_process
+ENSG00000100209 GO:0065003 macromolecular complex assembly "The aggregation, arrangement and bonding together of a set of macromolecules to form a complex." [GOC:jl] biological_process
+ENSG00000077935 GO:0005524 ATP binding "Interacting selectively and non-covalently with ATP, adenosine 5'-triphosphate, a universally important coenzyme and enzyme regulator." [ISBN:0198506732] molecular_function
+ENSG00000077935 GO:0030893 meiotic cohesin complex "A cohesin complex that mediates sister chromatid cohesion during meiosis; has a subunit composition distinct from that of the mitotic cohesin complex." [GOC:mah, PMID:12750522] cellular_component
+ENSG00000077935 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000077935 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000077935 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000077935 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000077935 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000077935 GO:0051276 chromosome organization "A process that is carried out at the cellular level that results in the assembly, arrangement of constituent parts, or disassembly of chromosomes, structures composed of a very long molecule of DNA and associated proteins that carries hereditary information. This term covers covalent modifications at the molecular level as well as spatial relationships among the major components of a chromosome." [GOC:ai, GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000077935 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000077935 GO:0005694 chromosome "A structure composed of a very long molecule of DNA and associated proteins (e.g. histones) that carries hereditary information." [ISBN:0198547684] cellular_component
+ENSG00000077935 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000077935 GO:0016192 vesicle-mediated transport "A cellular transport process in which transported substances are moved in membrane-bounded vesicles; transported substances are enclosed in the vesicle lumen or located in the vesicle membrane. The process begins with a step that directs a substance to the forming vesicle, and includes vesicle budding and coating. Vesicles are then targeted to, and fuse with, an acceptor membrane." [GOC:ai, GOC:mah, ISBN:08789310662000] biological_process
+ENSG00000077935 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000077935 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000077935 GO:0003677 DNA binding "Any molecular function by which a gene product interacts selectively and non-covalently with DNA (deoxyribonucleic acid)." [GOC:dph, GOC:jl, GOC:tb, GOC:vw] molecular_function
+ENSG00000077935 GO:0000775 chromosome, centromeric region "The region of a chromosome that includes the centromeric DNA and associated proteins. In monocentric chromosomes, this region corresponds to a single area of the chromosome, whereas in holocentric chromosomes, it is evenly distributed along the chromosome." [GOC:cjm, GOC:elh, GOC:kmv, GOC:pr] cellular_component
+ENSG00000077935 GO:0000795 synaptonemal complex "A proteinaceous scaffold found between homologous chromosomes during meiosis." [GOC:elh] cellular_component
+ENSG00000077935 GO:0000800 lateral element "A proteinaceous core found between sister chromatids during meiotic prophase." [GOC:elh] cellular_component
+ENSG00000077935 GO:0034991 nuclear meiotic cohesin complex "A cohesin complex that mediates sister chromatid cohesion in the nucleus during meiosis; has a subunit composition distinct from that of the meiotic cohesin complex." [GOC:mah] cellular_component
+ENSG00000077935 GO:0000794 condensed nuclear chromosome "A highly compacted molecule of DNA and associated proteins resulting in a cytologically distinct structure that remains in the nucleus." [GOC:elh] cellular_component
+ENSG00000077935 GO:0007126 meiotic nuclear division "One of the two nuclear divisions that occur as part of the meiotic cell cycle." [GOC:dph, GOC:mah, PMID:9334324] biological_process
+ENSG00000077935 GO:0007062 sister chromatid cohesion "The cell cycle process in which the sister chromatids of a replicated chromosome are associated with each other." [GOC:jh, GOC:mah, ISBN:0815316194] biological_process
+ENSG00000133488 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000133488 GO:0008289 lipid binding "Interacting selectively and non-covalently with a lipid." [GOC:ai] molecular_function
+ENSG00000133488 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000133488 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000133488 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000133488 GO:0005622 intracellular "The living contents of a cell; the matter contained within (but not including) the plasma membrane, usually taken to exclude large vacuoles and masses of secretory or ingested material. In eukaryotes it includes the nucleus and cytoplasm." [ISBN:0198506732] cellular_component
+ENSG00000133488 GO:0005215 transporter activity "Enables the directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells." [GOC:ai, GOC:dgf] molecular_function
+ENSG00000133488 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000133488
+ENSG00000079974 GO:0005525 GTP binding "Interacting selectively and non-covalently with GTP, guanosine triphosphate." [GOC:ai] molecular_function
+ENSG00000079974 GO:0007264 small GTPase mediated signal transduction "Any series of molecular signals in which a small monomeric GTPase relays one or more of the signals." [GOC:mah] biological_process
+ENSG00000079974 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000079974 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000079974 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000079974 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000079974 GO:0003924 GTPase activity "Catalysis of the reaction: GTP + H2O = GDP + phosphate." [ISBN:0198547684] molecular_function
+ENSG00000079974 GO:0006184 GTP catabolic process "The chemical reactions and pathways resulting in the breakdown of GTP, guanosine triphosphate." [ISBN:0198506732] biological_process
+ENSG00000079974 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000079974 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000079974 GO:0034655 nucleobase-containing compound catabolic process "The chemical reactions and pathways resulting in the breakdown of nucleobases, nucleosides, nucleotides and nucleic acids." [GOC:mah] biological_process
+ENSG00000079974 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000079974 GO:0006886 intracellular protein transport "The directed movement of proteins in a cell, including the movement of proteins between specific compartments or structures within a cell, such as organelles of a eukaryotic cell." [GOC:mah] biological_process
+ENSG00000079974 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000079974 GO:0006913 nucleocytoplasmic transport "The directed movement of molecules between the nucleus and the cytoplasm." [GOC:go_curators] biological_process
+ENSG00000079974 GO:0005622 intracellular "The living contents of a cell; the matter contained within (but not including) the plasma membrane, usually taken to exclude large vacuoles and masses of secretory or ingested material. In eukaryotes it includes the nucleus and cytoplasm." [ISBN:0198506732] cellular_component
+ENSG00000079974 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000079974 GO:0015031 protein transport "The directed movement of proteins into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:ai] biological_process
+ENSG00000079974 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000079974 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000079974 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000079974 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000079974
+ENSG00000278558
+ENSG00000278558 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000278558 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000133454 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000133454 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000133454 GO:0003774 motor activity "Catalysis of movement along a polymeric molecule such as a microfilament or microtubule, coupled to the hydrolysis of a nucleoside triphosphate." [GOC:mah, ISBN:0815316194] molecular_function
+ENSG00000133454 GO:0005524 ATP binding "Interacting selectively and non-covalently with ATP, adenosine 5'-triphosphate, a universally important coenzyme and enzyme regulator." [ISBN:0198506732] molecular_function
+ENSG00000133454 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000133454 GO:0016459 myosin complex "A protein complex, formed of one or more myosin heavy chains plus associated light chains and other proteins, that functions as a molecular motor; uses the energy of ATP hydrolysis to move actin filaments or to move vesicles or other cargo on fixed actin filaments; has magnesium-ATPase activity and binds actin. Myosin classes are distinguished based on sequence features of the motor, or head, domain, but also have distinct tail regions that are believed to bind specific cargoes." [GOC:mah, http://www.mrc-lmb.cam.ac.uk/myosin/Review/Reviewframeset.html, ISBN:96235764] cellular_component
+ENSG00000133454 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000133454 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000133454 GO:0000166 nucleotide binding "Interacting selectively and non-covalently with a nucleotide, any compound consisting of a nucleoside that is esterified with (ortho)phosphate or an oligophosphate at any hydroxyl group on the ribose or deoxyribose." [GOC:mah, ISBN:0198547684] molecular_function
+ENSG00000133454 GO:0006412 translation "The cellular metabolic process in which a protein is formed, using the sequence of a mature mRNA molecule to specify the sequence of amino acids in a polypeptide chain. Translation is mediated by the ribosome, and begins with the formation of a ternary complex between aminoacylated initiator methionine tRNA, GTP, and initiation factor 2, which subsequently associates with the small subunit of the ribosome and an mRNA. Translation ends with the release of a polypeptide chain from the ribosome." [GOC:go_curators] biological_process
+ENSG00000133454 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000133454 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000133454 GO:0030018 Z disc "Platelike region of a muscle sarcomere to which the plus ends of actin filaments are attached." [GOC:mtg_muscle, ISBN:0815316194] cellular_component
+ENSG00000133454 GO:0001570 vasculogenesis "The differentiation of endothelial cells from progenitor cells during blood vessel development, and the de novo formation of blood vessels and tubes." [PMID:8999798] biological_process
+ENSG00000133454 GO:0001701 in utero embryonic development "The process whose specific outcome is the progression of the embryo in the uterus over time, from formation of the zygote in the oviduct, to birth. An example of this process is found in Mus musculus." [GOC:go_curators, GOC:mtg_sensu] biological_process
+ENSG00000133454 GO:0031941 filamentous actin "A two-stranded helical polymer of the protein actin." [GOC:mah] cellular_component
+ENSG00000133454 GO:0048739 cardiac muscle fiber development "The process whose specific outcome is the progression of cardiac muscle fiber over time, from its formation to the mature structure." [GOC:dph, GOC:jid, GOC:lm] biological_process
+ENSG00000133454 GO:0008152 metabolic process "The chemical reactions and pathways, including anabolism and catabolism, by which living organisms transform chemical substances. Metabolic processes typically transform small molecules, but also include macromolecular processes such as DNA repair and replication, and protein synthesis and degradation." [GOC:go_curators, ISBN:0198547684] biological_process
+ENSG00000133454
+ENSG00000133454 GO:0003779 actin binding "Interacting selectively and non-covalently with monomeric or multimeric forms of actin, including actin filaments." [GOC:clt] molecular_function
+ENSG00000133454 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000133454 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000133454 GO:0016461 unconventional myosin complex "A portmanteau term for myosins other than myosin II." [GOC:ma] cellular_component
+ENSG00000133454 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000133454 GO:0008092 cytoskeletal protein binding "Interacting selectively and non-covalently with any protein component of any cytoskeleton (actin, microtubule, or intermediate filament cytoskeleton)." [GOC:mah] molecular_function
+ENSG00000211685 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000211685 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000219438 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000219438 GO:0005576 extracellular region "The space external to the outermost structure of a cell. For cells without external protective or external encapsulating structures this refers to space outside of the plasma membrane. This term covers the host cell environment outside an intracellular parasite." [GOC:go_curators] cellular_component
+ENSG00000219438 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000219438
+ENSG00000188130 GO:0000165 MAPK cascade "An intracellular protein kinase cascade containing at least a MAPK, a MAPKK and a MAP3K. The cascade can also contain two additional tiers: the upstream MAP4K and the downstream MAP Kinase-activated kinase (MAPKAPK). The kinases in each tier phosphorylate and activate the kinases in the downstream tier to transmit a signal within a cell." [GOC:bf, GOC:mtg_signaling_feb11, PMID:20811974, PMID:9561267] biological_process
+ENSG00000188130 GO:0004707 MAP kinase activity "Catalysis of the reaction: protein + ATP = protein phosphate + ADP. This reaction is the phosphorylation of proteins. Mitogen-activated protein kinase; a family of protein kinases that perform a crucial step in relaying signals from the plasma membrane to the nucleus. They are activated by a wide range of proliferation- or differentiation-inducing signals; activation is strong with agonists such as polypeptide growth factors and tumor-promoting phorbol esters, but weak (in most cell backgrounds) by stress stimuli." [GOC:ma, ISBN:0198547684] molecular_function
+ENSG00000188130 GO:0005524 ATP binding "Interacting selectively and non-covalently with ATP, adenosine 5'-triphosphate, a universally important coenzyme and enzyme regulator." [ISBN:0198506732] molecular_function
+ENSG00000188130 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000188130 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000188130 GO:0006468 protein phosphorylation "The process of introducing a phosphate group on to a protein." [GOC:hb] biological_process
+ENSG00000188130 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000188130 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000188130 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000188130 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000188130 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000188130 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000188130 GO:0004871 signal transducer activity "Conveys a signal across a cell to trigger a change in cell function or state. A signal is a physical entity or change in state that is used to transfer information in order to trigger a response." [GOC:go_curators] molecular_function
+ENSG00000188130 GO:0016301 kinase activity "Catalysis of the transfer of a phosphate group, usually from ATP, to a substrate molecule." [ISBN:0198506732] molecular_function
+ENSG00000188130 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000188130 GO:0004672 protein kinase activity "Catalysis of the phosphorylation of an amino acid residue in a protein, usually according to the reaction: a protein + ATP = a phosphoprotein + ADP." [MetaCyc:PROTEIN-KINASE-RXN] molecular_function
+ENSG00000188130 GO:0016772 transferase activity, transferring phosphorus-containing groups "Catalysis of the transfer of a phosphorus-containing group from one compound (donor) to another (acceptor)." [GOC:jl, ISBN:0198506732] molecular_function
+ENSG00000188130 GO:0004713 protein tyrosine kinase activity "Catalysis of the reaction: ATP + a protein tyrosine = ADP + protein tyrosine phosphate." [EC:2.7.10] molecular_function
+ENSG00000188130 GO:0030154 cell differentiation "The process in which relatively unspecialized cells, e.g. embryonic or regenerative cells, acquire specialized structural and/or functional features that characterize the cells, tissues, or organs of the mature organism or some other relatively stable phase of the organism's life history. Differentiation includes the processes involved in commitment of a cell to a specific fate and its subsequent development to the mature state." [ISBN:0198506732] biological_process
+ENSG00000188130 GO:0048856 anatomical structure development "The biological process whose specific outcome is the progression of an anatomical structure from an initial condition to its mature state. This process begins with the formation of the structure and ends with the mature structure, whatever form that may be including its natural destruction. An anatomical structure is any biological entity that occupies space and is distinguished from its surroundings. Anatomical structures can be macroscopic such as a carpel, or microscopic such as an acrosome." [GO_REF:0000021, GOC:mtg_15jun06] biological_process
+ENSG00000188130 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000188130 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000188130 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000188130 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000188130 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000188130 GO:0005654 nucleoplasm "That part of the nuclear content other than the chromosomes or the nucleolus." [GOC:ma, ISBN:0124325653] cellular_component
+ENSG00000188130 GO:0000287 magnesium ion binding "Interacting selectively and non-covalently with magnesium (Mg) ions." [GOC:ai] molecular_function
+ENSG00000188130 GO:0004674 protein serine/threonine kinase activity "Catalysis of the reactions: ATP + protein serine = ADP + protein serine phosphate, and ATP + protein threonine = ADP + protein threonine phosphate." [GOC:bf] molecular_function
+ENSG00000188130 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000188130 GO:0006351 transcription, DNA-templated "The cellular synthesis of RNA on a template of DNA." [GOC:jl, GOC:txnOH] biological_process
+ENSG00000188130 GO:0006355 regulation of transcription, DNA-templated "Any process that modulates the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000188130 GO:0006975 DNA damage induced protein phosphorylation "The widespread phosphorylation of various molecules, triggering many downstream processes, that occurs in response to the detection of DNA damage." [GOC:go_curators] biological_process
+ENSG00000188130 GO:0007050 cell cycle arrest "A regulatory process that halts progression through the cell cycle during one of the normal phases (G1, S, G2, M)." [GOC:dph, GOC:mah, GOC:tb] biological_process
+ENSG00000188130 GO:0007265 Ras protein signal transduction "A series of molecular signals within the cell that are mediated by a member of the Ras superfamily of proteins switching to a GTP-bound active state." [GOC:bf] biological_process
+ENSG00000188130 GO:0007517 muscle organ development "The process whose specific outcome is the progression of the muscle over time, from its formation to the mature structure. The muscle is an organ consisting of a tissue made up of various elongated cells that are specialized to contract and thus to produce movement and mechanical work." [GOC:jid, ISBN:0198506732] biological_process
+ENSG00000188130 GO:0010952 positive regulation of peptidase activity "Any process that increases the frequency, rate or extent of peptidase activity, the hydrolysis of peptide bonds within proteins." [GOC:dph, GOC:tb] biological_process
+ENSG00000188130 GO:0018105 peptidyl-serine phosphorylation "The phosphorylation of peptidyl-serine to form peptidyl-O-phospho-L-serine." [RESID:AA0037] biological_process
+ENSG00000188130 GO:0042692 muscle cell differentiation "The process in which a relatively unspecialized cell acquires specialized features of a muscle cell." [CL:0000187, GOC:go_curators] biological_process
+ENSG00000188130 GO:0045445 myoblast differentiation "The process in which a relatively unspecialized cell acquires specialized features of a myoblast. A myoblast is a mononucleate cell type that, by fusion with other myoblasts, gives rise to the myotubes that eventually develop into striated muscle fibers." [CL:0000056, GOC:go_curators, GOC:mtg_muscle] biological_process
+ENSG00000188130 GO:0048011 neurotrophin TRK receptor signaling pathway "A series of molecular signals initiated by the binding of a neurotrophin to a receptor on the surface of the target cell where the receptor possesses tyrosine kinase activity, and ending with regulation of a downstream cellular process, e.g. transcription." [GOC:bf, GOC:ceb, GOC:jc, GOC:signaling, PMID:12065629, Wikipedia:Trk_receptor] biological_process
+ENSG00000188130 GO:0051149 positive regulation of muscle cell differentiation "Any process that activates or increases the frequency, rate or extent of muscle cell differentiation." [CL:0000187, GOC:ai] biological_process
+ENSG00000188130 GO:0045786 negative regulation of cell cycle "Any process that stops, prevents or reduces the rate or extent of progression through the cell cycle." [GOC:dph, GOC:go_curators, GOC:tb] biological_process
+ENSG00000184949
+ENSG00000128274 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000128274 GO:0016757 transferase activity, transferring glycosyl groups "Catalysis of the transfer of a glycosyl group from one compound (donor) to another (acceptor)." [GOC:jl, ISBN:0198506732] molecular_function
+ENSG00000128274 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000128274 GO:0007009 plasma membrane organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of the plasma membrane." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000128274 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000128274 GO:0061024 membrane organization "A process which results in the assembly, arrangement of constituent parts, or disassembly of a membrane. A membrane is a double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:dph, GOC:tb] biological_process
+ENSG00000128274 GO:0006629 lipid metabolic process "The chemical reactions and pathways involving lipids, compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent. Includes fatty acids; neutral fats, other fatty-acid esters, and soaps; long-chain (fatty) alcohols and waxes; sphingoids and other long-chain bases; glycolipids, phospholipids and sphingolipids; and carotenes, polyprenols, sterols, terpenes and other isoprenoids." [GOC:ma] biological_process
+ENSG00000128274 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000128274 GO:0005975 carbohydrate metabolic process "The chemical reactions and pathways involving carbohydrates, any of a group of organic compounds based of the general formula Cx(H2O)y. Includes the formation of carbohydrate derivatives by the addition of a carbohydrate residue to another molecule." [GOC:mah, ISBN:0198506732] biological_process
+ENSG00000128274 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000128274 GO:0006486 protein glycosylation "A protein modification process that results in the addition of a carbohydrate or carbohydrate derivative unit to a protein amino acid, e.g. the addition of glycan chains to proteins." [GOC:curators, GOC:pr] biological_process
+ENSG00000128274 GO:0006688 glycosphingolipid biosynthetic process "The chemical reactions and pathways resulting in the formation of glycosphingolipid, a compound with residues of sphingoid and at least one monosaccharide." [GOC:go_curators] biological_process
+ENSG00000128274 GO:0008378 galactosyltransferase activity "Catalysis of the transfer of a galactosyl group to an acceptor molecule, typically another carbohydrate or a lipid." [ISBN:0198506732] molecular_function
+ENSG00000128274 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000128274 GO:0030173 integral component of Golgi membrane "The component of the Golgi membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:go_curators] cellular_component
+ENSG00000128274 GO:0050512 lactosylceramide 4-alpha-galactosyltransferase activity "Catalysis of the reaction: beta-D-galactosyl-(1,4)-D-glucosylceramide + UDP-galactose = alpha-D-galactosyl-(1,4)-beta-D-galactosyl-(1,4)-D-glucosylceramide + UDP." [EC:2.4.1.228, MetaCyc:2.4.1.228-RXN] molecular_function
+ENSG00000128274 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000128274 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000128274 GO:0015643 toxic substance binding "Interacting selectively and non-covalently with a toxic substance, a poisonous substance that causes damage to biological systems." [GOC:bf, GOC:curators, GOC:jl, GOC:pr] molecular_function
+ENSG00000128274 GO:0001576 globoside biosynthetic process "The chemical reactions and pathways resulting in the formation of a ceramide with a core structure of GalNAc-beta-(1->3)-Gal-alpha-(1->4)-Glc(I)." [ISBN:0198506732] biological_process
+ENSG00000100106 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100106 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100106 GO:0008092 cytoskeletal protein binding "Interacting selectively and non-covalently with any protein component of any cytoskeleton (actin, microtubule, or intermediate filament cytoskeleton)." [GOC:mah] molecular_function
+ENSG00000100106 GO:0019899 enzyme binding "Interacting selectively and non-covalently with any enzyme." [GOC:jl] molecular_function
+ENSG00000100106 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000100106 GO:0007010 cytoskeleton organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of cytoskeletal structures." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100106 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100106 GO:0005856 cytoskeleton "Any of the various filamentous elements that form the internal framework of cells, and typically remain after treatment of the cells with mild detergent to remove membrane constituents and soluble components of the cytoplasm. The term embraces intermediate filaments, microfilaments, microtubules, the microtrabecular lattice, and other structures characterized by a polymeric filamentous nature and long-range order within the cell. The various elements of the cytoskeleton not only serve in the maintenance of cellular shape but also have roles in other cellular functions, including cellular movement, cell division, endocytosis, and movement of organelles." [GOC:mah, ISBN:0198547684, PMID:16959967] cellular_component
+ENSG00000100106 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100106 GO:0007067 mitotic nuclear division "A cell cycle process comprising the steps by which the nucleus of a eukaryotic cell divides; the process involves condensation of chromosomal DNA into a highly compacted form. Canonically, mitosis produces two daughter nuclei whose chromosome complement is identical to that of the mother cell." [GOC:dph, GOC:ma, GOC:mah, ISBN:0198547684] biological_process
+ENSG00000100106 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100106 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100106 GO:0005925 focal adhesion "Small region on the surface of a cell that anchors the cell to the extracellular matrix and that forms a point of termination of actin filaments." [ISBN:0124325653, ISBN:0815316208] cellular_component
+ENSG00000100106 GO:0015629 actin cytoskeleton "The part of the cytoskeleton (the internal framework of a cell) composed of actin and associated proteins. Includes actin cytoskeleton-associated complexes." [GOC:jl, ISBN:0395825172, ISBN:0815316194] cellular_component
+ENSG00000100106 GO:0017049 GTP-Rho binding "Interacting selectively and non-covalently with the GTP-bound form of the Rho protein." [GOC:mah] molecular_function
+ENSG00000100106 GO:0030047 actin modification "Covalent modification of an actin molecule." [GOC:mah] biological_process
+ENSG00000100106 GO:0031625 ubiquitin protein ligase binding "Interacting selectively and non-covalently with a ubiquitin protein ligase enzyme, any of the E3 proteins." [GOC:vp] molecular_function
+ENSG00000100106 GO:0045159 myosin II binding "Interacting selectively and non-covalently with a class II myosin, any member of the class of 'conventional' double-headed myosins that includes muscle myosin." [GOC:mah, http://www.mrc-lmb.cam.ac.uk/myosin/Review/Reviewframeset.html] molecular_function
+ENSG00000100106 GO:0051015 actin filament binding "Interacting selectively and non-covalently with an actin filament, also known as F-actin, a helical filamentous polymer of globular G-actin subunits." [ISBN:0198506732] molecular_function
+ENSG00000100106 GO:0051016 barbed-end actin filament capping "The binding of a protein or protein complex to the barbed (or plus) end of an actin filament, thus preventing the addition, exchange or removal of further actin subunits." [ISBN:071673706X] biological_process
+ENSG00000100106 GO:1900026 positive regulation of substrate adhesion-dependent cell spreading "Any process that activates or increases the frequency, rate or extent of substrate adhesion-dependent cell spreading." [GOC:TermGenie, GOC:yaf] biological_process
+ENSG00000100106
+ENSG00000184164 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000184164 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000184164 GO:0005509 calcium ion binding "Interacting selectively and non-covalently with calcium ions (Ca2+)." [GOC:ai] molecular_function
+ENSG00000184164 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000184164 GO:0005783 endoplasmic reticulum "The irregular network of unit membranes, visible only by electron microscopy, that occurs in the cytoplasm of many eukaryotic cells. The membranes form a complex meshwork of tubular channels, which are often expanded into slitlike cavities called cisternae. The ER takes two forms, rough (or granular), with ribosomes adhering to the outer surface, and smooth (with no ribosomes attached)." [ISBN:0198506732] cellular_component
+ENSG00000184164 GO:0005615 extracellular space "That part of a multicellular organism outside the cells proper, usually taken to be outside the plasma membranes, and occupied by fluid." [ISBN:0198547684] cellular_component
+ENSG00000184164 GO:0005794 Golgi apparatus "A compound membranous cytoplasmic organelle of eukaryotic cells, consisting of flattened, ribosome-free vesicles arranged in a more or less regular stack. The Golgi apparatus differs from the endoplasmic reticulum in often having slightly thicker membranes, appearing in sections as a characteristic shallow semicircle so that the convex side (cis or entry face) abuts the endoplasmic reticulum, secretory vesicles emerging from the concave side (trans or exit face). In vertebrate cells there is usually one such organelle, while in invertebrates and plants, where they are known usually as dictyosomes, there may be several scattered in the cytoplasm. The Golgi apparatus processes proteins produced on the ribosomes of the rough endoplasmic reticulum; such processing includes modification of the core oligosaccharides of glycoproteins, and the sorting and packaging of proteins for transport to a variety of cellular locations. Three different regions of the Golgi are now recognized both in terms of structure and function: cis, in the vicinity of the cis face, trans, in the vicinity of the trans face, and medial, lying between the cis and trans regions." [ISBN:0198506732] cellular_component
+ENSG00000211676
+ENSG00000184381 GO:0002376 immune system process "Any process involved in the development or functioning of the immune system, an organismal system for calibrated responses to potential internal or invasive threats." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05] biological_process
+ENSG00000184381 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000184381 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000184381 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000184381 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000184381 GO:0006629 lipid metabolic process "The chemical reactions and pathways involving lipids, compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent. Includes fatty acids; neutral fats, other fatty-acid esters, and soaps; long-chain (fatty) alcohols and waxes; sphingoids and other long-chain bases; glycolipids, phospholipids and sphingolipids; and carotenes, polyprenols, sterols, terpenes and other isoprenoids." [GOC:ma] biological_process
+ENSG00000184381 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000184381 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000184381 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000184381 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000184381 GO:0008219 cell death "Any biological process that results in permanent cessation of all vital functions of a cell. A cell should be considered dead when any one of the following molecular or morphological criteria is met: (1) the cell has lost the integrity of its plasma membrane; (2) the cell, including its nucleus, has undergone complete fragmentation into discrete bodies (frequently referred to as \"apoptotic bodies\"); and/or (3) its corpse (or its fragments) have been engulfed by an adjacent cell in vivo." [GOC:mah, GOC:mtg_apoptosis] biological_process
+ENSG00000184381 GO:0040011 locomotion "Self-propelled movement of a cell or organism from one location to another." [GOC:dgh] biological_process
+ENSG00000184381 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000184381 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000184381 GO:0004623 phospholipase A2 activity "Catalysis of the reaction: phosphatidylcholine + H2O = 1-acylglycerophosphocholine + a carboxylate." [EC:3.1.1.4] molecular_function
+ENSG00000184381 GO:0005516 calmodulin binding "Interacting selectively and non-covalently with calmodulin, a calcium-binding protein with many roles, both in the calcium-bound and calcium-free states." [GOC:krc] molecular_function
+ENSG00000184381 GO:0006644 phospholipid metabolic process "The chemical reactions and pathways involving phospholipids, any lipid containing phosphoric acid as a mono- or diester." [ISBN:0198506732] biological_process
+ENSG00000184381 GO:0006935 chemotaxis "The directed movement of a motile cell or organism, or the directed growth of a cell guided by a specific chemical concentration gradient. Movement may be towards a higher concentration (positive chemotaxis) or towards a lower concentration (negative chemotaxis)." [ISBN:0198506732] biological_process
+ENSG00000184381 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000184381 GO:0016042 lipid catabolic process "The chemical reactions and pathways resulting in the breakdown of lipids, compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent." [GOC:go_curators] biological_process
+ENSG00000184381 GO:0032049 cardiolipin biosynthetic process "The chemical reactions and pathways resulting in the formation of cardiolipin, 1,3-bis(3-phosphatidyl)glycerol." [CHEBI:28494, GOC:mah] biological_process
+ENSG00000184381 GO:0035965 cardiolipin acyl-chain remodeling "Remodeling the acyl chains of premature (de novo synthesized) cardiolipin (1,3-bis(3-phosphatidyl)glycerol), through sequential deacylation and re-acylation reactions, to generate mature cardiolipin containing high-levels of unsaturated fatty acids." [GOC:bf, GOC:rb, PMID:19244244] biological_process
+ENSG00000184381 GO:0036151 phosphatidylcholine acyl-chain remodeling "Remodeling the acyl chains of phosphatidylcholine, through sequential deacylation and re-acylation reactions, to generate phosphatidylcholine containing different types of fatty acid acyl chains." [CHEBI:49183, GOC:mw, PMID:18195019, PMID:18458083] biological_process
+ENSG00000184381 GO:0036152 phosphatidylethanolamine acyl-chain remodeling "Remodeling the acyl chains of phosphatidylethanolamine, through sequential deacylation and re-acylation reactions, to generate phosphatidylethanolamine containing different types of fatty acid acyl chains." [CHEBI:16038, GOC:mw, PMID:18287005, PMID:18458083] biological_process
+ENSG00000184381 GO:0038096 Fc-gamma receptor signaling pathway involved in phagocytosis "An Fc-gamma receptor signaling pathway that contributes to the endocytic engulfment of external particulate material by phagocytes." [GOC:phg, PMID:12488490, PMID:15466916] biological_process
+ENSG00000184381 GO:0045087 innate immune response "Innate immune responses are defense responses mediated by germline encoded components that directly recognize components of potential pathogens." [GO_REF:0000022, GOC:add, GOC:ebc, GOC:mtg_15nov05, GOC:mtg_sensu] biological_process
+ENSG00000184381 GO:0046474 glycerophospholipid biosynthetic process "The chemical reactions and pathways resulting in the formation of glycerophospholipids, any derivative of glycerophosphate that contains at least one O-acyl, O-alkyl, or O-alkenyl group attached to the glycerol residue." [ISBN:0198506732] biological_process
+ENSG00000184381 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000184381 GO:0008152 metabolic process "The chemical reactions and pathways, including anabolism and catabolism, by which living organisms transform chemical substances. Metabolic processes typically transform small molecules, but also include macromolecular processes such as DNA repair and replication, and protein synthesis and degradation." [GOC:go_curators, ISBN:0198547684] biological_process
+ENSG00000184381 GO:0007204 positive regulation of cytosolic calcium ion concentration "Any process that increases the concentration of calcium ions in the cytosol." [GOC:ai] biological_process
+ENSG00000184381 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000184381 GO:0060135 maternal process involved in female pregnancy "A reproductive process occurring in the mother that allows an embryo or fetus to develop within it." [GOC:dph] biological_process
+ENSG00000184381 GO:0034976 response to endoplasmic reticulum stress "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a stress acting at the endoplasmic reticulum. ER stress usually results from the accumulation of unfolded or misfolded proteins in the ER lumen." [GOC:cjm, GOC:mah] biological_process
+ENSG00000184381 GO:0035774 positive regulation of insulin secretion involved in cellular response to glucose stimulus "Any process that increases the frequency, rate or extent of the regulated release of insulin that contributes to the response of a cell to glucose." [GOC:bf, GOC:yaf] biological_process
+ENSG00000184381 GO:0090238 positive regulation of arachidonic acid secretion "Any process that increases the rate, frequency, or extent of arachidonic acid secretion, the controlled release of arachidonic acid from a cell or a tissue." [GOC:BHF, GOC:dph, GOC:tb] biological_process
+ENSG00000184381 GO:0045921 positive regulation of exocytosis "Any process that activates or increases the frequency, rate or extent of exocytosis." [GOC:go_curators] biological_process
+ENSG00000184381 GO:0045909 positive regulation of vasodilation "Any process that activates or increases the frequency, rate or extent of vasodilation." [GOC:go_curators] biological_process
+ENSG00000184381 GO:0001934 positive regulation of protein phosphorylation "Any process that activates or increases the frequency, rate or extent of addition of phosphate groups to amino acids within a protein." [GOC:hjd] biological_process
+ENSG00000184381 GO:2000304 positive regulation of ceramide biosynthetic process "Any process that activates or increases the frequency, rate or extent of ceramide biosynthetic process." [GOC:dph] biological_process
+ENSG00000184381 GO:0047499 calcium-independent phospholipase A2 activity "Catalysis of the reaction: phosphatidylcholine + H2O = 1-acylglycerophosphocholine + a carboxylate. This reaction does not require Ca2+." [EC:3.1.1.4] molecular_function
+ENSG00000184381 GO:0090037 positive regulation of protein kinase C signaling "Any process that increases the frequency, rate, or extent of a series of reactions, mediated by the intracellular serine/threonine kinase protein kinase C, which occurs as a result of a single trigger reaction or compound." [GOC:dph, GOC:tb] biological_process
+ENSG00000184381 GO:0051967 negative regulation of synaptic transmission, glutamatergic "Any process that stops, prevents, or reduces the frequency, rate or extent of glutamatergic synaptic transmission, the process of communication from a neuron to another neuron across a synapse using the neurotransmitter glutamate." [GOC:ai] biological_process
+ENSG00000184381 GO:0014832 urinary bladder smooth muscle contraction "A process in which force is generated within smooth muscle tissue, resulting in a change in muscle geometry. This process occurs in the urinary bladder. Force generation involves a chemo-mechanical energy conversion step that is carried out by the actin/myosin complex activity, which generates force through ATP hydrolysis. The urinary bladder is a musculomembranous sac along the urinary tract." [GOC:mr, GOC:mtg_muscle, http://en.wikipedia.org/wiki/Rhea_(bird), MA:0001697, MSH:D001743, PMID:11768524, PMID:18276178, PMID:538956] biological_process
+ENSG00000184381 GO:0043008 ATP-dependent protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules) using energy from the hydrolysis of ATP." [GOC:jl] molecular_function
+ENSG00000184381 GO:1901339 regulation of store-operated calcium channel activity "Any process that modulates the frequency, rate or extent of store-operated calcium channel activity." [GOC:TermGenie] biological_process
+ENSG00000184381 GO:0007613 memory "The activities involved in the mental information processing system that receives (registers), modifies, stores, and retrieves informational stimuli. The main stages involved in the formation and retrieval of memory are encoding (processing of received information by acquisition), storage (building a permanent record of received information as a result of consolidation) and retrieval (calling back the stored information and use it in a suitable way to execute a given task)." [GOC:curators, http://www.onelook.com/, ISBN:0582227089] biological_process
+ENSG00000184381 GO:0090200 positive regulation of release of cytochrome c from mitochondria "Any process that increases the rate, frequency or extent of release of cytochrome c from mitochondria, the process in which cytochrome c is enabled to move from the mitochondrial intermembrane space into the cytosol, which is an early step in apoptosis and leads to caspase activation." [GOC:BHF, GOC:dph, GOC:mtg_apoptosis, GOC:tb] biological_process
+ENSG00000184381
+ENSG00000235568 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000235568 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000235568 GO:0002376 immune system process "Any process involved in the development or functioning of the immune system, an organismal system for calibrated responses to potential internal or invasive threats." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05] biological_process
+ENSG00000235568 GO:0030154 cell differentiation "The process in which relatively unspecialized cells, e.g. embryonic or regenerative cells, acquire specialized structural and/or functional features that characterize the cells, tissues, or organs of the mature organism or some other relatively stable phase of the organism's life history. Differentiation includes the processes involved in commitment of a cell to a specific fate and its subsequent development to the mature state." [ISBN:0198506732] biological_process
+ENSG00000235568 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000235568 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000235568 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000235568 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000235568 GO:0004871 signal transducer activity "Conveys a signal across a cell to trigger a change in cell function or state. A signal is a physical entity or change in state that is used to transfer information in order to trigger a response." [GOC:go_curators] molecular_function
+ENSG00000235568 GO:0001819 positive regulation of cytokine production "Any process that activates or increases the frequency, rate or extent of production of a cytokine." [GOC:add, ISBN:0781735149] biological_process
+ENSG00000235568 GO:0004888 transmembrane signaling receptor activity "Combining with an extracellular or intracellular signal and transmitting the signal from one side of the membrane to the other to initiate a change in cell activity." [GOC:go_curators, Wikipedia:Transmembrane_receptor] molecular_function
+ENSG00000235568 GO:0006954 inflammatory response "The immediate defensive reaction (by vertebrate tissue) to infection or injury caused by chemical or physical agents. The process is characterized by local vasodilation, extravasation of plasma into intercellular spaces and accumulation of white blood cells and macrophages." [GO_REF:0000022, GOC:mtg_15nov05, ISBN:0198506732] biological_process
+ENSG00000235568 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000235568 GO:0030183 B cell differentiation "The process in which a precursor cell type acquires the specialized features of a B cell. A B cell is a lymphocyte of B lineage with the phenotype CD19-positive and capable of B cell mediated immunity." [GO_REF:0000022, GOC:mah, GOC:mtg_15nov05] biological_process
+ENSG00000235568 GO:0035556 intracellular signal transduction "The process in which a signal is passed on to downstream components within the cell, which become activated themselves to further propagate the signal and finally trigger a change in the function or state of the cell." [GOC:bf, GOC:jl, GOC:signaling, ISBN:3527303782] biological_process
+ENSG00000235568 GO:0050861 positive regulation of B cell receptor signaling pathway "Any process that activates or increases the frequency, rate or extent of signaling pathways initiated by the cross-linking of an antigen receptor on a B cell." [GOC:ai] biological_process
+ENSG00000235568 GO:0051091 positive regulation of sequence-specific DNA binding transcription factor activity "Any process that activates or increases the frequency, rate or extent of activity of a transcription factor, any factor involved in the initiation or regulation of transcription." [GOC:ai] biological_process
+ENSG00000235568 GO:0007166 cell surface receptor signaling pathway "A series of molecular signals initiated by activation of a receptor on the surface of a cell. The pathway begins with binding of an extracellular ligand to a cell surface receptor, or for receptors that signal in the absence of a ligand, by ligand-withdrawal or the activity of a constitutively active receptor. The pathway ends with regulation of a downstream cellular process, e.g. transcription." [GOC:bf, GOC:mah, GOC:pr, GOC:signaling] biological_process
+ENSG00000235568 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000235568 GO:0009986 cell surface "The external part of the cell wall and/or plasma membrane." [GOC:jl, GOC:mtg_sensu, GOC:sm] cellular_component
+ENSG00000235568 GO:0045121 membrane raft "Any of the small (10-200 nm), heterogeneous, highly dynamic, sterol- and sphingolipid-enriched membrane domains that compartmentalize cellular processes. Small rafts can sometimes be stabilized to form larger platforms through protein-protein and protein-lipid interactions." [PMID:16645198] cellular_component
+ENSG00000235568 GO:0050853 B cell receptor signaling pathway "A series of molecular signals initiated by the cross-linking of an antigen receptor on a B cell." [GOC:add] biological_process
+ENSG00000235568 GO:0045577 regulation of B cell differentiation "Any process that modulates the frequency, rate or extent of B cell differentiation." [GOC:go_curators] biological_process
+ENSG00000235568
+ENSG00000128298 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000128298 GO:0061024 membrane organization "A process which results in the assembly, arrangement of constituent parts, or disassembly of a membrane. A membrane is a double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:dph, GOC:tb] biological_process
+ENSG00000128298 GO:0022607 cellular component assembly "The aggregation, arrangement and bonding together of a cellular component." [GOC:isa_complete] biological_process
+ENSG00000128298 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000128298 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000128298 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000128298 GO:0008092 cytoskeletal protein binding "Interacting selectively and non-covalently with any protein component of any cytoskeleton (actin, microtubule, or intermediate filament cytoskeleton)." [GOC:mah] molecular_function
+ENSG00000128298 GO:0030674 protein binding, bridging "The binding activity of a molecule that brings together two or more protein molecules, or a protein and another macromolecule or complex, through a selective, non-covalent, often stoichiometric interaction, permitting those molecules to function in a coordinated way." [GOC:bf, GOC:mah, GOC:vw] molecular_function
+ENSG00000128298 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000128298 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000128298 GO:0008289 lipid binding "Interacting selectively and non-covalently with a lipid." [GOC:ai] molecular_function
+ENSG00000128298 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000128298 GO:0005543 phospholipid binding "Interacting selectively and non-covalently with phospholipids, a class of lipids containing phosphoric acid as a mono- or diester." [ISBN:0198506732] molecular_function
+ENSG00000128298 GO:0008093 cytoskeletal adaptor activity "The binding activity of a molecule that brings together a cytoskeletal protein and one or more other molecules, permitting them to function in a coordinated way." [GOC:mtg_MIT_16mar07] molecular_function
+ENSG00000128298 GO:0012506 vesicle membrane "The lipid bilayer surrounding any membrane-bounded vesicle in the cell." [GOC:mah] cellular_component
+ENSG00000128298 GO:0017124 SH3 domain binding "Interacting selectively and non-covalently with a SH3 domain (Src homology 3) of a protein, small protein modules containing approximately 50 amino acid residues found in a great variety of intracellular or membrane-associated proteins." [GOC:go_curators, Pfam:PF00018] molecular_function
+ENSG00000128298 GO:0031410 cytoplasmic vesicle "A vesicle formed of membrane or protein, found in the cytoplasm of a cell." [GOC:mah] cellular_component
+ENSG00000128298 GO:0032956 regulation of actin cytoskeleton organization "Any process that modulates the frequency, rate or extent of the formation, arrangement of constituent parts, or disassembly of cytoskeletal structures comprising actin filaments and their associated proteins." [GOC:mah] biological_process
+ENSG00000128298 GO:0044291 cell-cell contact zone "Extended zone of intimate apposition between two cells containing one or more types of intercellular junctions, e.g., the intercalated disk of muscle." [NIF_Subcellular:sao1299635018] cellular_component
+ENSG00000128298 GO:0046847 filopodium assembly "The assembly of a filopodium, a thin, stiff protrusion extended by the leading edge of a motile cell such as a crawling fibroblast or amoeba, or an axonal growth cone." [GOC:dph, GOC:mah, GOC:tb, PMID:16337369, PMID:18464790] biological_process
+ENSG00000128298 GO:0071439 clathrin complex "A protein complex that consists of three clathrin heavy chains and three clathrin light chains, organized into a symmetrical three-legged structure called a triskelion. In clathrin-coated vesicles clathrin is the main component of the coat and forms a polymeric mechanical scaffold on the vesicle surface." [GOC:mah, PMID:16493411] cellular_component
+ENSG00000128298 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000128298 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000128298 GO:0007009 plasma membrane organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of the plasma membrane." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000221963 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000221963 GO:0008289 lipid binding "Interacting selectively and non-covalently with a lipid." [GOC:ai] molecular_function
+ENSG00000221963 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000221963 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000221963 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000221963 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000221963 GO:0005576 extracellular region "The space external to the outermost structure of a cell. For cells without external protective or external encapsulating structures this refers to space outside of the plasma membrane. This term covers the host cell environment outside an intracellular parasite." [GOC:go_curators] cellular_component
+ENSG00000221963 GO:0006869 lipid transport "The directed movement of lipids into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore. Lipids are compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent." [ISBN:0198506732] biological_process
+ENSG00000221963 GO:0042157 lipoprotein metabolic process "The chemical reactions and pathways involving any conjugated, water-soluble protein in which the nonprotein group consists of a lipid or lipids." [ISBN:0198506732] biological_process
+ENSG00000205856
+ENSG00000100206 GO:0003677 DNA binding "Any molecular function by which a gene product interacts selectively and non-covalently with DNA (deoxyribonucleic acid)." [GOC:dph, GOC:jl, GOC:tb, GOC:vw] molecular_function
+ENSG00000100206 GO:0005524 ATP binding "Interacting selectively and non-covalently with ATP, adenosine 5'-triphosphate, a universally important coenzyme and enzyme regulator." [ISBN:0198506732] molecular_function
+ENSG00000100206 GO:0006200 ATP catabolic process "The chemical reactions and pathways resulting in the breakdown of ATP, adenosine 5'-triphosphate, a universally important coenzyme and enzyme regulator." [GOC:ai] biological_process
+ENSG00000100206 GO:0006259 DNA metabolic process "Any cellular metabolic process involving deoxyribonucleic acid. This is one of the two main types of nucleic acid, consisting of a long, unbranched macromolecule formed from one, or more commonly, two, strands of linked deoxyribonucleotides." [ISBN:0198506732] biological_process
+ENSG00000100206 GO:0008094 DNA-dependent ATPase activity "Catalysis of the reaction: ATP + H2O = ADP + phosphate; this reaction requires the presence of single- or double-stranded DNA, and it drives another reaction." [EC:3.6.1.3, GOC:jl] molecular_function
+ENSG00000100206 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100206 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100206 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000100206 GO:0034655 nucleobase-containing compound catabolic process "The chemical reactions and pathways resulting in the breakdown of nucleobases, nucleosides, nucleotides and nucleic acids." [GOC:mah] biological_process
+ENSG00000100206 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000100206 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100206 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100206 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100206 GO:0005694 chromosome "A structure composed of a very long molecule of DNA and associated proteins (e.g. histones) that carries hereditary information." [ISBN:0198547684] cellular_component
+ENSG00000100206 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100206 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100206 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100206 GO:0007126 meiotic nuclear division "One of the two nuclear divisions that occur as part of the meiotic cell cycle." [GOC:dph, GOC:mah, PMID:9334324] biological_process
+ENSG00000100206 GO:0007131 reciprocal meiotic recombination "The cell cycle process in which double strand breaks are formed and repaired through a double Holliday junction intermediate. This results in the equal exchange of genetic material between non-sister chromatids in a pair of homologous chromosomes. These reciprocal recombinant products ensure the proper segregation of homologous chromosomes during meiosis I and create genetic diversity." [PMID:2087779] biological_process
+ENSG00000100206 GO:0007283 spermatogenesis "The process of formation of spermatozoa, including spermatocytogenesis and spermiogenesis." [GOC:jid, ISBN:9780878933846] biological_process
+ENSG00000100206 GO:0007292 female gamete generation "Generation of the female gamete; specialised haploid cells produced by meiosis and along with a male gamete takes part in sexual reproduction." [GOC:dph, ISBN:0198506732] biological_process
+ENSG00000100206 GO:0016887 ATPase activity "Catalysis of the reaction: ATP + H2O = ADP + phosphate + 2 H+. May or may not be coupled to another reaction." [EC:3.6.1.3, GOC:jl] molecular_function
+ENSG00000100206 GO:0000166 nucleotide binding "Interacting selectively and non-covalently with a nucleotide, any compound consisting of a nucleoside that is esterified with (ortho)phosphate or an oligophosphate at any hydroxyl group on the ribose or deoxyribose." [GOC:mah, ISBN:0198547684] molecular_function
+ENSG00000100206 GO:0003697 single-stranded DNA binding "Interacting selectively and non-covalently with single-stranded DNA." [GOC:elh] molecular_function
+ENSG00000100206 GO:0006281 DNA repair "The process of restoring DNA after damage. Genomes are subject to damage by chemical and physical agents in the environment (e.g. UV and ionizing radiations, chemical mutagens, fungal and bacterial toxins, etc.) and by free radicals or alkylating agents endogenously generated in metabolism. DNA is also damaged because of errors during its replication. A variety of different DNA repair pathways have been reported that include direct reversal, base excision repair, nucleotide excision repair, photoreactivation, bypass, double-strand break repair pathway, and mismatch repair pathway." [PMID:11563486] biological_process
+ENSG00000100206 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000100206 GO:0009432 SOS response "An error-prone process for repairing damaged microbial DNA." [GOC:jl, PMID:16000023] biological_process
+ENSG00000100206 GO:0001556 oocyte maturation "A developmental process, independent of morphogenetic (shape) change, that is required for an oocyte to attain its fully functional state. Oocyte maturation commences after reinitiation of meiosis commonly starting with germinal vesicle breakdown, and continues up to the second meiotic arrest prior to fertilization." [GOC:devbiol, http://ovary.stanford.edu] biological_process
+ENSG00000100206 GO:0001541 ovarian follicle development "The process whose specific outcome is the progression of the ovarian follicle over time, from its formation to the mature structure." [GOC:go_curators] biological_process
+ENSG00000100206 GO:0007129 synapsis "The meiotic cell cycle process where side by side pairing and physical juxtaposition of homologous chromosomes is created during meiotic prophase. Synapsis begins when the chromosome arms begin to pair from the clustered telomeres and ends when synaptonemal complex or linear element assembly is complete." [GOC:mtg_cell_cycle, PMID:22582262, PMID:23117617] biological_process
+ENSG00000100206 GO:0007286 spermatid development "The process whose specific outcome is the progression of a spermatid over time, from its formation to the mature structure." [GOC:dph, GOC:go_curators] biological_process
+ENSG00000100206 GO:0000794 condensed nuclear chromosome "A highly compacted molecule of DNA and associated proteins resulting in a cytologically distinct structure that remains in the nucleus." [GOC:elh] cellular_component
+ENSG00000100206 GO:0007276 gamete generation "The generation and maintenance of gametes in a multicellular organism. A gamete is a haploid reproductive cell." [GOC:ems, GOC:mtg_sensu] biological_process
+ENSG00000100206 GO:0007141 male meiosis I "A cell cycle process comprising the steps by which a cell progresses through male meiosis I, the first meiotic division in the male germline." [GOC:dph, GOC:mah] biological_process
+ENSG00000100206 GO:0000781 chromosome, telomeric region "The terminal region of a linear chromosome that includes the telomeric DNA repeats and associated proteins." [GOC:elh] cellular_component
+ENSG00000100129 GO:0001731 formation of translation preinitiation complex "The joining of the small ribosomal subunit, ternary complex, and mRNA." [GOC:hjd] biological_process
+ENSG00000100129 GO:0003743 translation initiation factor activity "Functions in the initiation of ribosome-mediated translation of mRNA into a polypeptide." [ISBN:0198506732] molecular_function
+ENSG00000100129 GO:0005852 eukaryotic translation initiation factor 3 complex "A complex of several polypeptides that plays at least two important roles in protein synthesis: First, eIF3 binds to the 40S ribosome and facilitates loading of the Met-tRNA/eIF2.GTP ternary complex to form the 43S preinitiation complex. Subsequently, eIF3 apparently assists eIF4 in recruiting mRNAs to the 43S complex. The eIF3 complex contains five conserved core subunits, and may contain several additional proteins; the non-core subunits are thought to mediate association of the complex with specific sets of mRNAs." [PMID:15904532] cellular_component
+ENSG00000100129 GO:0006446 regulation of translational initiation "Any process that modulates the frequency, rate or extent of translational initiation." [GOC:go_curators] biological_process
+ENSG00000100129 GO:0016282 eukaryotic 43S preinitiation complex "A protein complex composed of the 40S ribosomal subunit plus eIF1A, eIF3, and eIF2-GTP-bound methionyl-initiator methionine tRNA." [GOC:hjd, PMID:15145049] cellular_component
+ENSG00000100129 GO:0033290 eukaryotic 48S preinitiation complex "A protein complex composed of the small ribosomal subunit, eIF3, eIF1A, methionyl-initiatior methionine and a capped mRNA. The complex is initially positioned at the 5'-end of the capped mRNA." [GOC:hjd, PMID:15145049] cellular_component
+ENSG00000100129 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100129 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100129 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100129 GO:0022607 cellular component assembly "The aggregation, arrangement and bonding together of a cellular component." [GOC:isa_complete] biological_process
+ENSG00000100129 GO:0022618 ribonucleoprotein complex assembly "The aggregation, arrangement and bonding together of proteins and RNA molecules to form a ribonucleoprotein complex." [GOC:jl] biological_process
+ENSG00000100129 GO:0065003 macromolecular complex assembly "The aggregation, arrangement and bonding together of a set of macromolecules to form a complex." [GOC:jl] biological_process
+ENSG00000100129 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100129 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000100129 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100129 GO:0006413 translational initiation "The process preceding formation of the peptide bond between the first two amino acids of a protein. This includes the formation of a complex of the ribosome, mRNA, and an initiation complex that contains the first aminoacyl-tRNA." [ISBN:019879276X] biological_process
+ENSG00000100129 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000100129 GO:0044822 poly(A) RNA binding "Interacting non-covalently with a poly(A) RNA, a RNA molecule which has a tail of adenine bases." [GOC:jl] molecular_function
+ENSG00000100129 GO:0008135 translation factor activity, nucleic acid binding "Functions during translation by interacting selectively and non-covalently with nucleic acids during polypeptide synthesis at the ribosome." [GOC:ai, GOC:vw] molecular_function
+ENSG00000100129 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100129
+ENSG00000100129 GO:0005654 nucleoplasm "That part of the nuclear content other than the chromosomes or the nucleolus." [GOC:ma, ISBN:0124325653] cellular_component
+ENSG00000100129 GO:0005730 nucleolus "A small, dense body one or more of which are present in the nucleus of eukaryotic cells. It is rich in RNA and protein, is not bounded by a limiting membrane, and is not seen during mitosis. Its prime function is the transcription of the nucleolar DNA into 45S ribosomal-precursor RNA, the processing of this RNA into 5.8S, 18S, and 28S components of ribosomal RNA, and the association of these components with 5S RNA and proteins synthesized outside the nucleolus. This association results in the formation of ribonucleoprotein precursors; these pass into the cytoplasm and mature into the 40S and 60S subunits of the ribosome." [ISBN:0198506732] cellular_component
+ENSG00000100129 GO:0001650 fibrillar center "A structure found most metazoan nucleoli, but not usually found in lower eukaryotes; surrounded by the dense fibrillar component; the zone of transcription from multiple copies of the pre-rRNA genes is in the border region between these two structures." [PMID:10754561] cellular_component
+ENSG00000185651 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000185651 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000185651 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000185651 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000185651 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000185651 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000185651 GO:0019899 enzyme binding "Interacting selectively and non-covalently with any enzyme." [GOC:jl] molecular_function
+ENSG00000185651 GO:0016874 ligase activity "Catalysis of the joining of two substances, or two groups within a single molecule, with the concomitant hydrolysis of the diphosphate bond in ATP or a similar triphosphate." [EC:6, GOC:mah] molecular_function
+ENSG00000185651 GO:0008283 cell proliferation "The multiplication or reproduction of cells, resulting in the expansion of a cell population." [GOC:mah, GOC:mb] biological_process
+ENSG00000185651 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000185651 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000185651 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000185651 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000185651 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000185651 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000185651 GO:0000988 protein binding transcription factor activity "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules), in order to modulate transcription. A protein binding transcription factor may or may not also interact with the template nucleic acid (either DNA or RNA) as well." [GOC:txnOH] molecular_function
+ENSG00000185651 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000185651 GO:0000151 ubiquitin ligase complex "A protein complex that includes a ubiquitin-protein ligase and other proteins that may confer substrate specificity on the complex." [GOC:jh2, PMID:9529603] cellular_component
+ENSG00000185651 GO:0000209 protein polyubiquitination "Addition of multiple ubiquitin groups to a protein, forming a ubiquitin chain." [ISBN:0815316194] biological_process
+ENSG00000185651 GO:0003713 transcription coactivator activity "Interacting selectively and non-covalently with a activating transcription factor and also with the basal transcription machinery in order to increase the frequency, rate or extent of transcription. Cofactors generally do not bind the template nucleic acid, but rather mediate protein-protein interactions between activating transcription factors and the basal transcription machinery." [GOC:txnOH, PMID:10213677, PMID:16858867] molecular_function
+ENSG00000185651 GO:0004842 ubiquitin-protein transferase activity "Catalysis of the transfer of ubiquitin from one protein to another via the reaction X-Ub + Y --> Y-Ub + X, where both X-Ub and Y-Ub are covalent linkages." [GOC:BioGRID, GOC:jh2, PMID:9635407] molecular_function
+ENSG00000185651 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000185651 GO:0005524 ATP binding "Interacting selectively and non-covalently with ATP, adenosine 5'-triphosphate, a universally important coenzyme and enzyme regulator." [ISBN:0198506732] molecular_function
+ENSG00000185651 GO:0006351 transcription, DNA-templated "The cellular synthesis of RNA on a template of DNA." [GOC:jl, GOC:txnOH] biological_process
+ENSG00000185651 GO:0006355 regulation of transcription, DNA-templated "Any process that modulates the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000185651 GO:0006511 ubiquitin-dependent protein catabolic process "The chemical reactions and pathways resulting in the breakdown of a protein or peptide by hydrolysis of its peptide bonds, initiated by the covalent attachment of a ubiquitin group, or multiple ubiquitin groups, to the protein." [GOC:go_curators] biological_process
+ENSG00000185651 GO:0016567 protein ubiquitination "The process in which one or more ubiquitin groups are added to a protein." [GOC:ai] biological_process
+ENSG00000185651 GO:0016881 acid-amino acid ligase activity "Catalysis of the ligation of an acid to an amino acid via a carbon-nitrogen bond, with the concomitant hydrolysis of the diphosphate bond in ATP or a similar triphosphate." [GOC:jl, GOC:mah] molecular_function
+ENSG00000185651 GO:0031398 positive regulation of protein ubiquitination "Any process that activates or increases the frequency, rate or extent of the addition of ubiquitin groups to a protein." [GOC:mah] biological_process
+ENSG00000185651 GO:0031625 ubiquitin protein ligase binding "Interacting selectively and non-covalently with a ubiquitin protein ligase enzyme, any of the E3 proteins." [GOC:vp] molecular_function
+ENSG00000185651 GO:0044822 poly(A) RNA binding "Interacting non-covalently with a poly(A) RNA, a RNA molecule which has a tail of adenine bases." [GOC:jl] molecular_function
+ENSG00000185651 GO:0051443 positive regulation of ubiquitin-protein transferase activity "Any process that activates, maintains or increases the rate of ubiquitin transferase activity." [GOC:ai, GOC:tb] biological_process
+ENSG00000185651 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000185651 GO:0070979 protein K11-linked ubiquitination "A protein ubiquitination process in which ubiquitin monomers are attached to a protein, and then ubiquitin polymers are formed by linkages between lysine residues at position 11 of the ubiquitin monomers. K11-linked polyubiquitination targets the substrate protein for degradation. The anaphase-promoting complex promotes the degradation of mitotic regulators by assembling K11-linked polyubiquitin chains." [GOC:jsg, GOC:pr, GOC:sp, PMID:18485873, PMID:20655260, PMID:21113135] biological_process
+ENSG00000185651 GO:0071383 cellular response to steroid hormone stimulus "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a steroid hormone stimulus." [GOC:mah] biological_process
+ENSG00000185651 GO:0071385 cellular response to glucocorticoid stimulus "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a glucocorticoid stimulus. Glucocorticoids are hormonal C21 corticosteroids synthesized from cholesterol with the ability to bind with the cortisol receptor and trigger similar effects. Glucocorticoids act primarily on carbohydrate and protein metabolism, and have anti-inflammatory effects." [GOC:mah] biological_process
+ENSG00000185651 GO:0097027 ubiquitin-protein transferase activator activity "Increases the activity of a ubiquitin-protein transferase, an enzyme that catalyzes the covalent attachment of ubiquitin to lysine in a substrate protein." [GOC:rb, PMID:18321851] molecular_function
+ENSG00000185651 GO:0030234 enzyme regulator activity "Binds to and modulates the activity of an enzyme." [GOC:mah] molecular_function
+ENSG00000185651 GO:0015031 protein transport "The directed movement of proteins into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:ai] biological_process
+ENSG00000185651 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000211660 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000211660 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000138942 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000138942 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000138942 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000138942 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000138942 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000138942 GO:0006914 autophagy "The process in which cells digest parts of their own cytoplasm; allows for both recycling of macromolecular constituents under conditions of cellular stress and remodeling the intracellular structure for cell differentiation." [ISBN:0198547684, PMID:11099404, PMID:9412464] biological_process
+ENSG00000138942 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000138942 GO:0005783 endoplasmic reticulum "The irregular network of unit membranes, visible only by electron microscopy, that occurs in the cytoplasm of many eukaryotic cells. The membranes form a complex meshwork of tubular channels, which are often expanded into slitlike cavities called cisternae. The ER takes two forms, rough (or granular), with ribosomes adhering to the outer surface, and smooth (with no ribosomes attached)." [ISBN:0198506732] cellular_component
+ENSG00000138942 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000138942 GO:0005741 mitochondrial outer membrane "The outer, i.e. cytoplasm-facing, lipid bilayer of the mitochondrial envelope." [GOC:ai] cellular_component
+ENSG00000138942 GO:0008270 zinc ion binding "Interacting selectively and non-covalently with zinc (Zn) ions." [GOC:ai] molecular_function
+ENSG00000138942 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000138942 GO:0016567 protein ubiquitination "The process in which one or more ubiquitin groups are added to a protein." [GOC:ai] biological_process
+ENSG00000138942 GO:0016874 ligase activity "Catalysis of the joining of two substances, or two groups within a single molecule, with the concomitant hydrolysis of the diphosphate bond in ATP or a similar triphosphate." [EC:6, GOC:mah] molecular_function
+ENSG00000138942 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000138942 GO:0046872 metal ion binding "Interacting selectively and non-covalently with any metal ion." [GOC:ai] molecular_function
+ENSG00000063515 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000063515 GO:0001071 nucleic acid binding transcription factor activity "Interacting selectively and non-covalently with a DNA or RNA sequence in order to modulate transcription. The transcription factor may or may not also interact selectively with a protein or macromolecular complex." [GOC:txnOH] molecular_function
+ENSG00000063515 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000063515 GO:0003677 DNA binding "Any molecular function by which a gene product interacts selectively and non-covalently with DNA (deoxyribonucleic acid)." [GOC:dph, GOC:jl, GOC:tb, GOC:vw] molecular_function
+ENSG00000063515 GO:0003700 sequence-specific DNA binding transcription factor activity "Interacting selectively and non-covalently with a specific DNA sequence in order to modulate transcription. The transcription factor may or may not also interact selectively with a protein or macromolecular complex." [GOC:curators, GOC:txnOH] molecular_function
+ENSG00000063515 GO:0006357 regulation of transcription from RNA polymerase II promoter "Any process that modulates the frequency, rate or extent of transcription from an RNA polymerase II promoter." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000063515 GO:0009653 anatomical structure morphogenesis "The process in which anatomical structures are generated and organized. Morphogenesis pertains to the creation of form." [GOC:go_curators, ISBN:0521436125] biological_process
+ENSG00000063515 GO:0043565 sequence-specific DNA binding "Interacting selectively and non-covalently with DNA of a specific nucleotide composition, e.g. GC-rich DNA binding, or with a specific sequence motif or type of DNA e.g. promotor binding or rDNA binding." [GOC:jl] molecular_function
+ENSG00000063515 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000063515 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000167074 GO:0003700 sequence-specific DNA binding transcription factor activity "Interacting selectively and non-covalently with a specific DNA sequence in order to modulate transcription. The transcription factor may or may not also interact selectively with a protein or macromolecular complex." [GOC:curators, GOC:txnOH] molecular_function
+ENSG00000167074 GO:0006351 transcription, DNA-templated "The cellular synthesis of RNA on a template of DNA." [GOC:jl, GOC:txnOH] biological_process
+ENSG00000167074 GO:0006357 regulation of transcription from RNA polymerase II promoter "Any process that modulates the frequency, rate or extent of transcription from an RNA polymerase II promoter." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000167074 GO:0048511 rhythmic process "Any process pertinent to the generation and maintenance of rhythms in the physiology of an organism." [GOC:jid] biological_process
+ENSG00000167074 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000167074 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000167074 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000167074 GO:0001071 nucleic acid binding transcription factor activity "Interacting selectively and non-covalently with a DNA or RNA sequence in order to modulate transcription. The transcription factor may or may not also interact selectively with a protein or macromolecular complex." [GOC:txnOH] molecular_function
+ENSG00000167074 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000167074 GO:0043565 sequence-specific DNA binding "Interacting selectively and non-covalently with DNA of a specific nucleotide composition, e.g. GC-rich DNA binding, or with a specific sequence motif or type of DNA e.g. promotor binding or rDNA binding." [GOC:jl] molecular_function
+ENSG00000167074 GO:0003677 DNA binding "Any molecular function by which a gene product interacts selectively and non-covalently with DNA (deoxyribonucleic acid)." [GOC:dph, GOC:jl, GOC:tb, GOC:vw] molecular_function
+ENSG00000167074 GO:0006355 regulation of transcription, DNA-templated "Any process that modulates the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000167074 GO:0045944 positive regulation of transcription from RNA polymerase II promoter "Any process that activates or increases the frequency, rate or extent of transcription from an RNA polymerase II promoter." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000167074 GO:0003690 double-stranded DNA binding "Interacting selectively and non-covalently with double-stranded DNA." [GOC:elh] molecular_function
+ENSG00000167074 GO:0046982 protein heterodimerization activity "Interacting selectively and non-covalently with a nonidentical protein to form a heterodimer." [GOC:ai] molecular_function
+ENSG00000167074 GO:0042803 protein homodimerization activity "Interacting selectively and non-covalently with an identical protein to form a homodimer." [GOC:jl] molecular_function
+ENSG00000167074 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000167074
+ENSG00000197077
+ENSG00000198792 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000198792 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000198792
+ENSG00000234965 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000234965 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000234965
+ENSG00000183246 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000183246 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100320 GO:0000166 nucleotide binding "Interacting selectively and non-covalently with a nucleotide, any compound consisting of a nucleoside that is esterified with (ortho)phosphate or an oligophosphate at any hydroxyl group on the ribose or deoxyribose." [GOC:mah, ISBN:0198547684] molecular_function
+ENSG00000100320 GO:0000381 regulation of alternative mRNA splicing, via spliceosome "Any process that modulates the frequency, rate or extent of alternative splicing of nuclear mRNAs." [GOC:krc] biological_process
+ENSG00000100320 GO:0003714 transcription corepressor activity "Interacting selectively and non-covalently with a repressing transcription factor and also with the basal transcription machinery in order to stop, prevent, or reduce the frequency, rate or extent of transcription. Cofactors generally do not bind the template nucleic acid, but rather mediate protein-protein interactions between repressive transcription factors and the basal transcription machinery." [GOC:txnOH, PMID:10213677, PMID:16858867] molecular_function
+ENSG00000100320 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000100320 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100320 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100320 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100320 GO:0006397 mRNA processing "Any process involved in the conversion of a primary mRNA transcript into one or more mature mRNA(s) prior to translation into polypeptide." [GOC:mah] biological_process
+ENSG00000100320 GO:0008134 transcription factor binding "Interacting selectively and non-covalently with a transcription factor, any protein required to initiate or regulate transcription." [ISBN:0198506732] molecular_function
+ENSG00000100320 GO:0008380 RNA splicing "The process of removing sections of the primary RNA transcript to remove sequences not present in the mature form of the RNA and joining the remaining sections to form the mature form of the RNA." [GOC:krc, GOC:mah] biological_process
+ENSG00000100320 GO:0016070 RNA metabolic process "The cellular chemical reactions and pathways involving RNA, ribonucleic acid, one of the two main type of nucleic acid, consisting of a long, unbranched macromolecule formed from ribonucleotides joined in 3',5'-phosphodiester linkage." [ISBN:0198506732] biological_process
+ENSG00000100320 GO:0030520 intracellular estrogen receptor signaling pathway "Any series of molecular signals generated as a consequence of an intracellular estrogen receptor binding to one of its physiological ligands. The pathway begins with receptor-ligand binding, and ends with regulation of a downstream cellular process (e.g. transcription)." [GOC:mah, GOC:signaling] biological_process
+ENSG00000100320 GO:0042127 regulation of cell proliferation "Any process that modulates the frequency, rate or extent of cell proliferation." [GOC:jl] biological_process
+ENSG00000100320 GO:0044822 poly(A) RNA binding "Interacting non-covalently with a poly(A) RNA, a RNA molecule which has a tail of adenine bases." [GOC:jl] molecular_function
+ENSG00000100320 GO:0045892 negative regulation of transcription, DNA-templated "Any process that stops, prevents, or reduces the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000100320 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100320 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100320 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000100320 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100320 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100320 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100320 GO:0000988 protein binding transcription factor activity "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules), in order to modulate transcription. A protein binding transcription factor may or may not also interact with the template nucleic acid (either DNA or RNA) as well." [GOC:txnOH] molecular_function
+ENSG00000100320 GO:0003676 nucleic acid binding "Interacting selectively and non-covalently with any nucleic acid." [GOC:jl] molecular_function
+ENSG00000100320 GO:0043484 regulation of RNA splicing "Any process that modulates the frequency, rate or extent of RNA splicing, the process of removing sections of the primary RNA transcript to remove sequences not present in the mature form of the RNA and joining the remaining sections to form the mature form of the RNA." [GOC:jl] biological_process
+ENSG00000100320 GO:0003729 mRNA binding "Interacting selectively and non-covalently with messenger RNA (mRNA), an intermediate molecule between DNA and protein. mRNA includes UTR and coding sequences, but does not contain introns." [GOC:kmv, SO:0000234] molecular_function
+ENSG00000100320 GO:0048813 dendrite morphogenesis "The process in which the anatomical structures of a dendrite are generated and organized. A dendrite is a freely branching protoplasmic process of a nerve cell." [GOC:jl, ISBN:0198506732] biological_process
+ENSG00000100320 GO:0050885 neuromuscular process controlling balance "Any process that an organism uses to control its balance, the orientation of the organism (or the head of the organism) in relation to the source of gravity. In humans and animals, balance is perceived through visual cues, the labyrinth system of the inner ears and information from skin pressure receptors and muscle and joint receptors." [GOC:ai, GOC:dph, http://www.onelook.com/] biological_process
+ENSG00000100320 GO:0021942 radial glia guided migration of Purkinje cell "The migration of postmitotic a Purkinje cell along radial glial cells from the ventricular zone to the Purkinje cell layer." [GO_REF:0000021, GOC:cls, GOC:dgh, GOC:dph, GOC:jid, GOC:mtg_15jun06, PMID:15157725] biological_process
+ENSG00000100320 GO:0010724 regulation of definitive erythrocyte differentiation "Any process that modulates the rate, frequency, or extent of definitive erythrocyte differentiation. Definitive erythrocyte differentiation occurs as part of the process of definitive hemopoiesis." [GOC:add, GOC:dph, GOC:tb] biological_process
+ENSG00000056487 GO:0008270 zinc ion binding "Interacting selectively and non-covalently with zinc (Zn) ions." [GOC:ai] molecular_function
+ENSG00000056487 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000056487 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000056487 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000056487
+ENSG00000100312 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100312 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100312 GO:0000003 reproduction "The production of new individuals that contain some portion of genetic material inherited from one or more parent organisms." [GOC:go_curators, GOC:isa_complete, GOC:jl, ISBN:0198506732] biological_process
+ENSG00000100312 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100312 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100312 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100312 GO:0016192 vesicle-mediated transport "A cellular transport process in which transported substances are moved in membrane-bounded vesicles; transported substances are enclosed in the vesicle lumen or located in the vesicle membrane. The process begins with a step that directs a substance to the forming vesicle, and includes vesicle budding and coating. Vesicles are then targeted to, and fuse with, an acceptor membrane." [GOC:ai, GOC:mah, ISBN:08789310662000] biological_process
+ENSG00000100312 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100312 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100312 GO:0005576 extracellular region "The space external to the outermost structure of a cell. For cells without external protective or external encapsulating structures this refers to space outside of the plasma membrane. This term covers the host cell environment outside an intracellular parasite." [GOC:go_curators] cellular_component
+ENSG00000100312 GO:0008233 peptidase activity "Catalysis of the hydrolysis of a peptide bond. A peptide bond is a covalent bond formed when the carbon atom from the carboxyl group of one amino acid shares electrons with the nitrogen atom from the amino group of a second amino acid." [GOC:jl, ISBN:0815332181] molecular_function
+ENSG00000100312 GO:0016810 hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds "Catalysis of the hydrolysis of any carbon-nitrogen bond, C-N, with the exception of peptide bonds." [GOC:jl] molecular_function
+ENSG00000100312 GO:0003677 DNA binding "Any molecular function by which a gene product interacts selectively and non-covalently with DNA (deoxyribonucleic acid)." [GOC:dph, GOC:jl, GOC:tb, GOC:vw] molecular_function
+ENSG00000100312 GO:0002077 acrosome matrix dispersal "The proteolytic digestion of components in the acrosomal matrix that allows for their release into the egg. The dispersal of the components allows for the inner acrosomal membrane to interact with the egg." [GOC:dph, PMID:3886029] biological_process
+ENSG00000100312 GO:0004040 amidase activity "Catalysis of the reaction: a monocarboxylic acid amide + H2O = a monocarboxylate + NH3." [EC:3.5.1.4] molecular_function
+ENSG00000100312 GO:0004252 serine-type endopeptidase activity "Catalysis of the hydrolysis of internal, alpha-peptide bonds in a polypeptide chain by a catalytic mechanism that involves a catalytic triad consisting of a serine nucleophile that is activated by a proton relay involving an acidic residue (e.g. aspartate or glutamate) and a basic residue (usually histidine)." [GOC:mah, http://merops.sanger.ac.uk/about/glossary.htm#CATTYPE, ISBN:0716720094] molecular_function
+ENSG00000100312 GO:0005507 copper ion binding "Interacting selectively and non-covalently with copper (Cu) ions." [GOC:ai] molecular_function
+ENSG00000100312 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100312 GO:0005537 mannose binding "Interacting selectively and non-covalently with mannose, a monosaccharide hexose, stereoisomeric with glucose, that occurs naturally only in polymerized forms called mannans." [CHEBI:37684, GOC:jl, ISBN:0192800981] molecular_function
+ENSG00000100312 GO:0007190 activation of adenylate cyclase activity "Any process that initiates the activity of the inactive enzyme adenylate cyclase." [GOC:ai] biological_process
+ENSG00000100312 GO:0007338 single fertilization "The union of male and female gametes to form a zygote." [GOC:ems, GOC:mtg_sensu] biological_process
+ENSG00000100312 GO:0007340 acrosome reaction "The discharge, by sperm, of a single, anterior secretory granule following the sperm's attachment to the zona pellucida surrounding the oocyte. The process begins with the fusion of the outer acrosomal membrane with the sperm plasma membrane and ends with the exocytosis of the acrosomal contents into the egg." [GOC:dph, PMID:3886029] biological_process
+ENSG00000100312 GO:0008144 drug binding "Interacting selectively and non-covalently with a drug, any naturally occurring or synthetic substance, other than a nutrient, that, when administered or applied to an organism, affects the structure or functioning of the organism; in particular, any such substance used in the diagnosis, prevention, or treatment of disease." [GOC:jl, ISBN:0198506732] molecular_function
+ENSG00000100312 GO:0008270 zinc ion binding "Interacting selectively and non-covalently with zinc (Zn) ions." [GOC:ai] molecular_function
+ENSG00000100312 GO:0032504 multicellular organism reproduction "The biological process in which new individuals are produced by one or two multicellular organisms. The new individuals inherit some proportion of their genetic material from the parent or parents." [GOC:isa_complete, GOC:jid] biological_process
+ENSG00000100312 GO:0042806 fucose binding "Interacting selectively and non-covalently with fucose, the pentose 6-deoxygalactose." [CHEBI:33984, ISBN:0582227089] molecular_function
+ENSG00000100312 GO:0043159 acrosomal matrix "A structural framework, or 'dense core' at the interior of an acrosome. May regulate the distribution of hydrolases within the acrosome and their release during the acrosome reaction." [GOC:jl, PMID:8949900, PMID:9139729] cellular_component
+ENSG00000100312 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100312 GO:0006508 proteolysis "The hydrolysis of proteins into smaller polypeptides and/or amino acids by cleavage of their peptide bonds." [GOC:bf, GOC:mah] biological_process
+ENSG00000100312 GO:0003824 catalytic activity "Catalysis of a biochemical reaction at physiological temperatures. In biologically catalyzed reactions, the reactants are known as substrates, and the catalysts are naturally occurring macromolecular substances known as enzymes. Enzymes possess specific binding sites for substrates, and are usually composed wholly or largely of protein, but RNA that has catalytic activity (ribozyme) is often also regarded as enzymatic." [ISBN:0198506732] molecular_function
+ENSG00000100312 GO:0001669 acrosomal vesicle "A structure in the head of a spermatozoon that contains acid hydrolases, and is concerned with the breakdown of the outer membrane of the ovum during fertilization. It lies just beneath the plasma membrane and is derived from the lysosome." [ISBN:0124325653, ISBN:0198506732] cellular_component
+ENSG00000100312 GO:0016023 cytoplasmic membrane-bounded vesicle "A membrane-bounded vesicle found in the cytoplasm of the cell." [GOC:ai, GOC:mah] cellular_component
+ENSG00000100312 GO:0007339 binding of sperm to zona pellucida "The process in which the sperm binds to the zona pellucida glycoprotein layer of the egg. The process begins with the attachment of the sperm plasma membrane to the zona pellucida and includes attachment of the acrosome inner membrane to the zona pellucida after the acrosomal reaction takes place." [GOC:dph, ISBN:0878932437] biological_process
+ENSG00000100312 GO:0007341 penetration of zona pellucida "The infiltration by sperm of the zona pellucida to reach the oocyte. The process involves digestive enzymes from a modified lysosome called the acrosome, situated at the head of the sperm." [GOC:jl, http://arbl.cvmbs.colostate.edu/hbooks/pathphys/reprod/fert/fert.html] biological_process
+ENSG00000100312 GO:0048545 response to steroid hormone "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a steroid hormone stimulus." [GOC:go_curators] biological_process
+ENSG00000100312 GO:0005798 Golgi-associated vesicle "Any vesicle associated with the Golgi complex and involved in mediating transport within the Golgi or between the Golgi and other parts of the cell." [GOC:mah] cellular_component
+ENSG00000275004 GO:0003677 DNA binding "Any molecular function by which a gene product interacts selectively and non-covalently with DNA (deoxyribonucleic acid)." [GOC:dph, GOC:jl, GOC:tb, GOC:vw] molecular_function
+ENSG00000275004 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000275004 GO:0006351 transcription, DNA-templated "The cellular synthesis of RNA on a template of DNA." [GOC:jl, GOC:txnOH] biological_process
+ENSG00000275004 GO:0006355 regulation of transcription, DNA-templated "Any process that modulates the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000275004 GO:0046872 metal ion binding "Interacting selectively and non-covalently with any metal ion." [GOC:ai] molecular_function
+ENSG00000275004 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000275004 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000275004 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000275004 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000275004 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000275004 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000275004 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000211644 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000211644 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000099910 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000099910 GO:0051301 cell division "The process resulting in division and partitioning of components of a cell to form more cells; may or may not be accompanied by the physical separation of a cell into distinct, individually membrane-bounded daughter cells." [GOC:di, GOC:go_curators, GOC:pr] biological_process
+ENSG00000099910 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000099910 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000099910 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000099910 GO:0005815 microtubule organizing center "An intracellular structure that can catalyze gamma-tubulin-dependent microtubule nucleation and that can anchor microtubules by interacting with their minus ends, plus ends or sides." [GOC:vw, http://en.wikipedia.org/wiki/Microtubule_organizing_center, ISBN:0815316194, PMID:17072892, PMID:17245416] cellular_component
+ENSG00000099910 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000099910 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000099910 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000099910 GO:0007059 chromosome segregation "The process in which genetic material, in the form of chromosomes, is organized into specific structures and then physically separated and apportioned to two or more sets. In eukaryotes, chromosome segregation begins with the condensation of chromosomes, includes chromosome separation, and ends when chromosomes have completed movement to the spindle poles." [GOC:jl, GOC:mah, GOC:mtg_cell_cycle, GOC:vw] biological_process
+ENSG00000099910 GO:0051276 chromosome organization "A process that is carried out at the cellular level that results in the assembly, arrangement of constituent parts, or disassembly of chromosomes, structures composed of a very long molecule of DNA and associated proteins that carries hereditary information. This term covers covalent modifications at the molecular level as well as spatial relationships among the major components of a chromosome." [GOC:ai, GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000099910 GO:0000070 mitotic sister chromatid segregation "The cell cycle process in which replicated homologous chromosomes are organized and then physically separated and apportioned to two sets during the mitotic cell cycle. Each replicated chromosome, composed of two sister chromatids, aligns at the cell equator, paired with its homologous partner. One homolog of each morphologic type goes into each of the resulting chromosome sets." [GOC:ai, GOC:jl] biological_process
+ENSG00000099910 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000099910 GO:0005813 centrosome "A structure comprised of a core structure (in most organisms, a pair of centrioles) and peripheral material from which a microtubule-based structure, such as a spindle apparatus, is organized. Centrosomes occur close to the nucleus during interphase in many eukaryotic cells, though in animal cells it changes continually during the cell-division cycle." [GOC:mah, ISBN:0198547684] cellular_component
+ENSG00000099910 GO:0005827 polar microtubule "Any of the spindle microtubules that come from each pole and overlap at the spindle midzone. This interdigitating structure consisting of antiparallel microtubules is responsible for pushing the poles of the spindle apart." [ISBN:0815316194] cellular_component
+ENSG00000099910 GO:0006513 protein monoubiquitination "Addition of a single ubiquitin group to a protein." [GOC:ai] biological_process
+ENSG00000099910 GO:0007094 mitotic spindle assembly checkpoint "A cell cycle checkpoint that delays the metaphase/anaphase transition of a mitotic nuclear division until the spindle is correctly assembled and chromosomes are attached to the spindle." [GOC:mtg_cell_cycle, PMID:12360190] biological_process
+ENSG00000099910 GO:0031463 Cul3-RING ubiquitin ligase complex "A ubiquitin ligase complex in which a cullin from the Cul3 subfamily and a RING domain protein form the catalytic core; substrate specificity is conferred by a BTB-domain-containing protein." [PMID:15571813, PMID:15688063] cellular_component
+ENSG00000099910 GO:0072686 mitotic spindle "A spindle that forms as part of mitosis. Mitotic and meiotic spindles contain distinctive complements of proteins associated with microtubules." [GOC:mah, GOC:vw, PMID:11408572, PMID:18367542, PMID:8027178] cellular_component
+ENSG00000100092 GO:0005096 GTPase activator activity "Increases the activity of a GTPase, an enzyme that catalyzes the hydrolysis of GTP." [GOC:mah] molecular_function
+ENSG00000100092 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100092 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000100092 GO:0043547 positive regulation of GTPase activity "Any process that activates or increases the activity of a GTPase." [GOC:jl] biological_process
+ENSG00000100092 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100092 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100092 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100092 GO:0030234 enzyme regulator activity "Binds to and modulates the activity of an enzyme." [GOC:mah] molecular_function
+ENSG00000100092 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100092 GO:0005622 intracellular "The living contents of a cell; the matter contained within (but not including) the plasma membrane, usually taken to exclude large vacuoles and masses of secretory or ingested material. In eukaryotes it includes the nucleus and cytoplasm." [ISBN:0198506732] cellular_component
+ENSG00000100092 GO:0017124 SH3 domain binding "Interacting selectively and non-covalently with a SH3 domain (Src homology 3) of a protein, small protein modules containing approximately 50 amino acid residues found in a great variety of intracellular or membrane-associated proteins." [GOC:go_curators, Pfam:PF00018] molecular_function
+ENSG00000100092
+ENSG00000211651 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000211651 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000128294 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000128294 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000128294 GO:0005794 Golgi apparatus "A compound membranous cytoplasmic organelle of eukaryotic cells, consisting of flattened, ribosome-free vesicles arranged in a more or less regular stack. The Golgi apparatus differs from the endoplasmic reticulum in often having slightly thicker membranes, appearing in sections as a characteristic shallow semicircle so that the convex side (cis or entry face) abuts the endoplasmic reticulum, secretory vesicles emerging from the concave side (trans or exit face). In vertebrate cells there is usually one such organelle, while in invertebrates and plants, where they are known usually as dictyosomes, there may be several scattered in the cytoplasm. The Golgi apparatus processes proteins produced on the ribosomes of the rough endoplasmic reticulum; such processing includes modification of the core oligosaccharides of glycoproteins, and the sorting and packaging of proteins for transport to a variety of cellular locations. Three different regions of the Golgi are now recognized both in terms of structure and function: cis, in the vicinity of the cis face, trans, in the vicinity of the trans face, and medial, lying between the cis and trans regions." [ISBN:0198506732] cellular_component
+ENSG00000128294 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000128294 GO:0005783 endoplasmic reticulum "The irregular network of unit membranes, visible only by electron microscopy, that occurs in the cytoplasm of many eukaryotic cells. The membranes form a complex meshwork of tubular channels, which are often expanded into slitlike cavities called cisternae. The ER takes two forms, rough (or granular), with ribosomes adhering to the outer surface, and smooth (with no ribosomes attached)." [ISBN:0198506732] cellular_component
+ENSG00000128294 GO:0008476 protein-tyrosine sulfotransferase activity "Catalysis of the reaction: 3'-phosphoadenosine 5'-phosphosulfate + protein tyrosine = adenosine 3',5'-bisphosphate + protein tyrosine-O-sulfate." [EC:2.8.2.20] molecular_function
+ENSG00000128294 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000128294 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000128294 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000128294 GO:0008146 sulfotransferase activity "Catalysis of the transfer of a sulfate group from 3'-phosphoadenosine 5'-phosphosulfate to the hydroxyl group of an acceptor, producing the sulfated derivative and 3'-phosphoadenosine 5'-phosphate." [EC:2.8.2, GOC:curators] molecular_function
+ENSG00000128294 GO:0006478 peptidyl-tyrosine sulfation "The sulfation of peptidyl-tyrosine residues to form peptidyl-O4'-sulfo-L-tyrosine." [RESID:AA0172] biological_process
+ENSG00000128294 GO:0007342 fusion of sperm to egg plasma membrane "The binding and fusion of a sperm, having penetrated the zona pellucida, with the plasma membrane of the oocyte. Binding occurs at the posterior (post-acrosomal) region of the sperm head." [GOC:jl, http://arbl.cvmbs.colostate.edu/hbooks/pathphys/reprod/fert/fert.html] biological_process
+ENSG00000128294 GO:0060468 prevention of polyspermy "The negative regulation of fertilization process that takes place as part of egg activation, ensuring that only a single sperm fertilizes the egg." [GOC:dph] biological_process
+ENSG00000128294 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000128294 GO:0006790 sulfur compound metabolic process "The chemical reactions and pathways involving the nonmetallic element sulfur or compounds that contain sulfur, such as the amino acids methionine and cysteine or the tripeptide glutathione." [GOC:ai] biological_process
+ENSG00000128294 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000128294
+ENSG00000100116 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100116 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000100116 GO:0006520 cellular amino acid metabolic process "The chemical reactions and pathways involving amino acids, carboxylic acids containing one or more amino groups, as carried out by individual cells." [CHEBI:33709, GOC:curators, ISBN:0198506732] biological_process
+ENSG00000100116 GO:0008890 glycine C-acetyltransferase activity "Catalysis of the reaction: acetyl-CoA + glycine = L-2-amino-3-oxobutanoate + CoA + H(+)." [EC:2.3.1.29, RHEA:20739] molecular_function
+ENSG00000100116 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000100116 GO:0019518 L-threonine catabolic process to glycine "The chemical reactions and pathways resulting in the breakdown of L-threonine (the L-enantiomer of 2-amino-3-hydroxybutyric acid) to form to form 2-amino-3-oxobutanoate, which is subsequently converted to glycine." [GOC:bf, GOC:mah, MetaCyc:THREONINE-DEG2-PWY] biological_process
+ENSG00000100116 GO:0030170 pyridoxal phosphate binding "Interacting selectively and non-covalently with pyridoxal 5' phosphate, 3-hydroxy-5-(hydroxymethyl)-2-methyl4-pyridine carboxaldehyde 5' phosphate, the biologically active form of vitamin B6." [GOC:mah, ISBN:0198506732] molecular_function
+ENSG00000100116 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100116 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100116 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100116 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000100116 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000100116 GO:0016746 transferase activity, transferring acyl groups "Catalysis of the transfer of an acyl group from one compound (donor) to another (acceptor)." [GOC:jl, ISBN:0198506732] molecular_function
+ENSG00000100116 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100116 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100116 GO:0008152 metabolic process "The chemical reactions and pathways, including anabolism and catabolism, by which living organisms transform chemical substances. Metabolic processes typically transform small molecules, but also include macromolecular processes such as DNA repair and replication, and protein synthesis and degradation." [GOC:go_curators, ISBN:0198547684] biological_process
+ENSG00000100116 GO:0003824 catalytic activity "Catalysis of a biochemical reaction at physiological temperatures. In biologically catalyzed reactions, the reactants are known as substrates, and the catalysts are naturally occurring macromolecular substances known as enzymes. Enzymes possess specific binding sites for substrates, and are usually composed wholly or largely of protein, but RNA that has catalytic activity (ribozyme) is often also regarded as enzymatic." [ISBN:0198506732] molecular_function
+ENSG00000100116
+ENSG00000184979 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000184979 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000184979 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000184979 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000184979 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000184979 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000184979 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000184979 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000184979 GO:0008233 peptidase activity "Catalysis of the hydrolysis of a peptide bond. A peptide bond is a covalent bond formed when the carbon atom from the carboxyl group of one amino acid shares electrons with the nitrogen atom from the amino group of a second amino acid." [GOC:jl, ISBN:0815332181] molecular_function
+ENSG00000184979 GO:0004843 ubiquitin-specific protease activity "Catalysis of the thiol-dependent hydrolysis of a peptide bond formed by the C-terminal glycine of ubiquitin." [GOC:jh2, ISBN:0120793709] molecular_function
+ENSG00000184979 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000184979 GO:0006511 ubiquitin-dependent protein catabolic process "The chemical reactions and pathways resulting in the breakdown of a protein or peptide by hydrolysis of its peptide bonds, initiated by the covalent attachment of a ubiquitin group, or multiple ubiquitin groups, to the protein." [GOC:go_curators] biological_process
+ENSG00000184979 GO:0019221 cytokine-mediated signaling pathway "A series of molecular signals initiated by the binding of a cytokine to a receptor on the surface of a cell, and ending with regulation of a downstream cellular process, e.g. transcription." [GOC:mah, GOC:signaling, PMID:19295629] biological_process
+ENSG00000184979 GO:0060337 type I interferon signaling pathway "A series of molecular signals initiated by the binding of a type I interferon to a receptor on the surface of a cell, and ending with regulation of a downstream cellular process, e.g. transcription. Type I interferons include the interferon-alpha, beta, delta, episilon, zeta, kappa, tau, and omega gene families." [GOC:add, GOC:dph, GOC:signaling, PR:000025848] biological_process
+ENSG00000184979 GO:0060338 regulation of type I interferon-mediated signaling pathway "Any process that modulates the rate, frequency or extent of a type I interferon-mediated signaling pathway. A type I interferon-mediated signaling pathway is the series of molecular events generated as a consequence of a type I interferon binding to a cell surface receptor." [GOC:dph] biological_process
+ENSG00000184979 GO:0036459 ubiquitinyl hydrolase activity "Catalysis of the thiol-dependent hydrolysis of an ester, thioester, amide, peptide or isopeptide bond formed by the C-terminal glycine of ubiquitin." [EC:3.4.19.12, GOC:bf, GOC:ka] molecular_function
+ENSG00000185252 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000185252 GO:0005856 cytoskeleton "Any of the various filamentous elements that form the internal framework of cells, and typically remain after treatment of the cells with mild detergent to remove membrane constituents and soluble components of the cytoplasm. The term embraces intermediate filaments, microfilaments, microtubules, the microtrabecular lattice, and other structures characterized by a polymeric filamentous nature and long-range order within the cell. The various elements of the cytoskeleton not only serve in the maintenance of cellular shape but also have roles in other cellular functions, including cellular movement, cell division, endocytosis, and movement of organelles." [GOC:mah, ISBN:0198547684, PMID:16959967] cellular_component
+ENSG00000185252 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000185252 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000185252 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000185252 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000185252 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000185252 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000185252 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000185252 GO:0001071 nucleic acid binding transcription factor activity "Interacting selectively and non-covalently with a DNA or RNA sequence in order to modulate transcription. The transcription factor may or may not also interact selectively with a protein or macromolecular complex." [GOC:txnOH] molecular_function
+ENSG00000185252 GO:0003677 DNA binding "Any molecular function by which a gene product interacts selectively and non-covalently with DNA (deoxyribonucleic acid)." [GOC:dph, GOC:jl, GOC:tb, GOC:vw] molecular_function
+ENSG00000185252 GO:0003700 sequence-specific DNA binding transcription factor activity "Interacting selectively and non-covalently with a specific DNA sequence in order to modulate transcription. The transcription factor may or may not also interact selectively with a protein or macromolecular complex." [GOC:curators, GOC:txnOH] molecular_function
+ENSG00000185252 GO:0006351 transcription, DNA-templated "The cellular synthesis of RNA on a template of DNA." [GOC:jl, GOC:txnOH] biological_process
+ENSG00000185252 GO:0006355 regulation of transcription, DNA-templated "Any process that modulates the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000185252 GO:0007275 multicellular organismal development "The biological process whose specific outcome is the progression of a multicellular organism over time from an initial condition (e.g. a zygote or a young adult) to a later condition (e.g. a multicellular animal or an aged adult)." [GOC:dph, GOC:ems, GOC:isa_complete, GOC:tb] biological_process
+ENSG00000185252 GO:0015629 actin cytoskeleton "The part of the cytoskeleton (the internal framework of a cell) composed of actin and associated proteins. Includes actin cytoskeleton-associated complexes." [GOC:jl, ISBN:0395825172, ISBN:0815316194] cellular_component
+ENSG00000185252 GO:0046872 metal ion binding "Interacting selectively and non-covalently with any metal ion." [GOC:ai] molecular_function
+ENSG00000185252 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000185252 GO:0003676 nucleic acid binding "Interacting selectively and non-covalently with any nucleic acid." [GOC:jl] molecular_function
+ENSG00000185252 GO:0005622 intracellular "The living contents of a cell; the matter contained within (but not including) the plasma membrane, usually taken to exclude large vacuoles and masses of secretory or ingested material. In eukaryotes it includes the nucleus and cytoplasm." [ISBN:0198506732] cellular_component
+ENSG00000185252
+ENSG00000211643 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000211643 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000099942 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000099942 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000099942 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000099942 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000099942 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000099942 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000099942 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000099942 GO:0005768 endosome "A membrane-bounded organelle to which materials ingested by endocytosis are delivered." [ISBN:0198506732, PMID:19696797] cellular_component
+ENSG00000099942 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000099942 GO:0030674 protein binding, bridging "The binding activity of a molecule that brings together two or more protein molecules, or a protein and another macromolecule or complex, through a selective, non-covalent, often stoichiometric interaction, permitting those molecules to function in a coordinated way." [GOC:bf, GOC:mah, GOC:vw] molecular_function
+ENSG00000099942 GO:0004871 signal transducer activity "Conveys a signal across a cell to trigger a change in cell function or state. A signal is a physical entity or change in state that is used to transfer information in order to trigger a response." [GOC:go_curators] molecular_function
+ENSG00000099942 GO:0000186 activation of MAPKK activity "The initiation of the activity of the inactive enzyme MAP kinase kinase (MAPKK)." [PMID:9561267] biological_process
+ENSG00000099942 GO:0005070 SH3/SH2 adaptor activity "Interacting selectively and non-covalently and simultaneously with one or more signal transduction molecules, usually acting as a scaffold to bring these molecules into close proximity either using their own SH2/SH3 domains (e.g. Grb2) or those of their target molecules (e.g. SAM68)." [GOC:mah, GOC:so] molecular_function
+ENSG00000099942 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000099942 GO:0007254 JNK cascade "An intracellular protein kinase cascade containing at least a JNK (a MAPK), a JNKK (a MAPKK) and a JUN3K (a MAP3K). The cascade can also contain two additional tiers: the upstream MAP4K and the downstream MAP Kinase-activated kinase (MAPKAPK). The kinases in each tier phosphorylate and activate the kinases in the downstream tier to transmit a signal within a cell." [GOC:bf, GOC:signaling, PMID:11790549, PMID:20811974] biological_process
+ENSG00000099942 GO:0007265 Ras protein signal transduction "A series of molecular signals within the cell that are mediated by a member of the Ras superfamily of proteins switching to a GTP-bound active state." [GOC:bf] biological_process
+ENSG00000099942 GO:0009967 positive regulation of signal transduction "Any process that activates or increases the frequency, rate or extent of signal transduction." [GOC:sm] biological_process
+ENSG00000099942 GO:0035556 intracellular signal transduction "The process in which a signal is passed on to downstream components within the cell, which become activated themselves to further propagate the signal and finally trigger a change in the function or state of the cell." [GOC:bf, GOC:jl, GOC:signaling, ISBN:3527303782] biological_process
+ENSG00000099942 GO:0044822 poly(A) RNA binding "Interacting non-covalently with a poly(A) RNA, a RNA molecule which has a tail of adenine bases." [GOC:jl] molecular_function
+ENSG00000099942 GO:0048011 neurotrophin TRK receptor signaling pathway "A series of molecular signals initiated by the binding of a neurotrophin to a receptor on the surface of the target cell where the receptor possesses tyrosine kinase activity, and ending with regulation of a downstream cellular process, e.g. transcription." [GOC:bf, GOC:ceb, GOC:jc, GOC:signaling, PMID:12065629, Wikipedia:Trk_receptor] biological_process
+ENSG00000099942 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000099942 GO:0009952 anterior/posterior pattern specification "The regionalization process in which specific areas of cell differentiation are determined along the anterior-posterior axis. The anterior-posterior axis is defined by a line that runs from the head or mouth of an organism to the tail or opposite end of the organism." [GOC:dph, GOC:go_curators, GOC:isa_complete, GOC:tb] biological_process
+ENSG00000099942 GO:0007507 heart development "The process whose specific outcome is the progression of the heart over time, from its formation to the mature structure. The heart is a hollow, muscular organ, which, by contracting rhythmically, keeps up the circulation of the blood." [GOC:jid, UBERON:0000948] biological_process
+ENSG00000099942 GO:0001568 blood vessel development "The process whose specific outcome is the progression of a blood vessel over time, from its formation to the mature structure. The blood vessel is the vasculature carrying blood." [GOC:hjd, UBERON:0001981] biological_process
+ENSG00000099942 GO:0009887 organ morphogenesis "Morphogenesis of an organ. An organ is defined as a tissue or set of tissues that work together to perform a specific function or functions. Morphogenesis is the process in which anatomical structures are generated and organized. Organs are commonly observed as visibly distinct structures, but may also exist as loosely associated clusters of cells that work together to perform a specific function or functions." [GOC:dgh, GOC:go_curators, ISBN:0471245208, ISBN:0721662544] biological_process
+ENSG00000099942 GO:0007389 pattern specification process "Any developmental process that results in the creation of defined areas or spaces within an organism to which cells respond and eventually are instructed to differentiate." [GOC:go_curators, GOC:isa_complete, ISBN:0521436125] biological_process
+ENSG00000099942 GO:0048538 thymus development "The process whose specific outcome is the progression of the thymus over time, from its formation to the mature structure. The thymus is a symmetric bi-lobed organ involved primarily in the differentiation of immature to mature T cells, with unique vascular, nervous, epithelial, and lymphoid cell components." [GOC:add, ISBN:0781735149] biological_process
+ENSG00000099942 GO:0060017 parathyroid gland development "The process whose specific outcome is the progression of the parathyroid gland over time, from its formation to the mature structure. The parathyroid gland is an organ specialised for secretion of parathyroid hormone." [GOC:dph, ISBN:0721662544] biological_process
+ENSG00000099942 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000277971 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000277971 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100341 GO:0006629 lipid metabolic process "The chemical reactions and pathways involving lipids, compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent. Includes fatty acids; neutral fats, other fatty-acid esters, and soaps; long-chain (fatty) alcohols and waxes; sphingoids and other long-chain bases; glycolipids, phospholipids and sphingolipids; and carotenes, polyprenols, sterols, terpenes and other isoprenoids." [GOC:ma] biological_process
+ENSG00000100341 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100341 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000100341 GO:0016042 lipid catabolic process "The chemical reactions and pathways resulting in the breakdown of lipids, compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent." [GOC:go_curators] biological_process
+ENSG00000100341 GO:0016787 hydrolase activity "Catalysis of the hydrolysis of various bonds, e.g. C-O, C-N, C-C, phosphoric anhydride bonds, etc. Hydrolase is the systematic name for any enzyme of EC class 3." [ISBN:0198506732] molecular_function
+ENSG00000100341 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100341 GO:0008152 metabolic process "The chemical reactions and pathways, including anabolism and catabolism, by which living organisms transform chemical substances. Metabolic processes typically transform small molecules, but also include macromolecular processes such as DNA repair and replication, and protein synthesis and degradation." [GOC:go_curators, ISBN:0198547684] biological_process
+ENSG00000093072 GO:0005615 extracellular space "That part of a multicellular organism outside the cells proper, usually taken to be outside the plasma membranes, and occupied by fluid." [ISBN:0198547684] cellular_component
+ENSG00000093072 GO:0008152 metabolic process "The chemical reactions and pathways, including anabolism and catabolism, by which living organisms transform chemical substances. Metabolic processes typically transform small molecules, but also include macromolecular processes such as DNA repair and replication, and protein synthesis and degradation." [GOC:go_curators, ISBN:0198547684] biological_process
+ENSG00000093072 GO:0019239 deaminase activity "Catalysis of the removal of an amino group from a substrate, producing ammonia (NH3)." [GOC:jl] molecular_function
+ENSG00000093072 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000093072 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000093072 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000093072 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000093072 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000093072 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000093072 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000093072 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000093072 GO:0034655 nucleobase-containing compound catabolic process "The chemical reactions and pathways resulting in the breakdown of nucleobases, nucleosides, nucleotides and nucleic acids." [GOC:mah] biological_process
+ENSG00000093072 GO:0016810 hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds "Catalysis of the hydrolysis of any carbon-nitrogen bond, C-N, with the exception of peptide bonds." [GOC:jl] molecular_function
+ENSG00000093072 GO:0004000 adenosine deaminase activity "Catalysis of the reaction: adenosine + H2O = inosine + NH3." [EC:3.5.4.4] molecular_function
+ENSG00000093072 GO:0006154 adenosine catabolic process "The chemical reactions and pathways resulting in the breakdown of adenosine, adenine riboside, a ribonucleoside found widely distributed in cells of every type as the free nucleoside and in combination in nucleic acids and various nucleoside coenzymes." [GOC:go_curators] biological_process
+ENSG00000093072 GO:0007275 multicellular organismal development "The biological process whose specific outcome is the progression of a multicellular organism over time from an initial condition (e.g. a zygote or a young adult) to a later condition (e.g. a multicellular animal or an aged adult)." [GOC:dph, GOC:ems, GOC:isa_complete, GOC:tb] biological_process
+ENSG00000093072 GO:0008083 growth factor activity "The function that stimulates a cell to grow or proliferate. Most growth factors have other actions besides the induction of cell growth or proliferation." [ISBN:0815316194] molecular_function
+ENSG00000093072 GO:0008201 heparin binding "Interacting selectively and non-covalently with heparin, any member of a group of glycosaminoglycans found mainly as an intracellular component of mast cells and which consist predominantly of alternating alpha-(1->4)-linked D-galactose and N-acetyl-D-glucosamine-6-sulfate residues." [GOC:jl, ISBN:0198506732] molecular_function
+ENSG00000093072 GO:0008270 zinc ion binding "Interacting selectively and non-covalently with zinc (Zn) ions." [GOC:ai] molecular_function
+ENSG00000093072 GO:0031685 adenosine receptor binding "Interacting selectively and non-covalently with an adenosine receptor." [GOC:mah, GOC:nln] molecular_function
+ENSG00000093072 GO:0042803 protein homodimerization activity "Interacting selectively and non-covalently with an identical protein to form a homodimer." [GOC:jl] molecular_function
+ENSG00000093072 GO:0043103 hypoxanthine salvage "Any process that generates hypoxanthine, 6-hydroxy purine, from derivatives of it without de novo synthesis." [GOC:jl, ISBN:0198506732] biological_process
+ENSG00000093072 GO:0043394 proteoglycan binding "Interacting selectively and non-covalently with a proteoglycan, any glycoprotein in which the carbohydrate units are glycosaminoglycans." [ISBN:0198506732] molecular_function
+ENSG00000093072 GO:0046103 inosine biosynthetic process "The chemical reactions and pathways resulting in the formation of inosine, hypoxanthine riboside, a nucleoside found free but not in combination in nucleic acids except in the anticodons of some tRNAs." [GOC:go_curators] biological_process
+ENSG00000205702
+ENSG00000205702 GO:0005506 iron ion binding "Interacting selectively and non-covalently with iron (Fe) ions." [GOC:ai] molecular_function
+ENSG00000205702 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000205702 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000205702 GO:0016705 oxidoreductase activity, acting on paired donors, with incorporation or reduction of molecular oxygen "Catalysis of an oxidation-reduction (redox) reaction in which hydrogen or electrons are transferred from each of two donors, and molecular oxygen is reduced or incorporated into a donor." [GOC:mah] molecular_function
+ENSG00000205702 GO:0016491 oxidoreductase activity "Catalysis of an oxidation-reduction (redox) reaction, a reversible chemical reaction in which the oxidation state of an atom or atoms within a molecule is altered. One substrate acts as a hydrogen or electron donor and becomes oxidized, while the other acts as hydrogen or electron acceptor and becomes reduced." [GOC:go_curators] molecular_function
+ENSG00000205702 GO:0020037 heme binding "Interacting selectively and non-covalently with heme, any compound of iron complexed in a porphyrin (tetrapyrrole) ring." [CHEBI:30413, GOC:ai] molecular_function
+ENSG00000205702 GO:0055114 oxidation-reduction process "A metabolic process that results in the removal or addition of one or more electrons to or from a substance, with or without the concomitant removal or addition of a proton or protons." [GOC:dhl, GOC:ecd, GOC:jh2, GOC:jid, GOC:mlg, GOC:rph] biological_process
+ENSG00000205702 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100258 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100258 GO:0005783 endoplasmic reticulum "The irregular network of unit membranes, visible only by electron microscopy, that occurs in the cytoplasm of many eukaryotic cells. The membranes form a complex meshwork of tubular channels, which are often expanded into slitlike cavities called cisternae. The ER takes two forms, rough (or granular), with ribosomes adhering to the outer surface, and smooth (with no ribosomes attached)." [ISBN:0198506732] cellular_component
+ENSG00000100258 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100258 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000100258 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000188263
+ENSG00000244486 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000244486 GO:0005925 focal adhesion "Small region on the surface of a cell that anchors the cell to the extracellular matrix and that forms a point of termination of actin filaments." [ISBN:0124325653, ISBN:0815316208] cellular_component
+ENSG00000244486 GO:0007155 cell adhesion "The attachment of a cell, either to another cell or to an underlying substrate such as the extracellular matrix, via cell adhesion molecules." [GOC:hb, GOC:pf] biological_process
+ENSG00000244486 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000244486 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000244486 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000244486 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000244486 GO:0005044 scavenger receptor activity "Combining with any modified low-density lipoprotein (LDL) or other polyanionic ligand and delivering the ligand into the cell via endocytosis. Ligands include acetylated and oxidized LDL, Gram-positive and Gram-negative bacteria, apoptotic cells, beta-amyloid fibrils, and advanced glycation end products (AGEs)." [GOC:bf, PMID:11790542, PMID:12379907, PMID:12621157, PMID:20981357] molecular_function
+ENSG00000244486 GO:0006898 receptor-mediated endocytosis "An endocytosis process in which cell surface receptors ensure specificity of transport. A specific receptor on the cell surface binds tightly to the extracellular macromolecule (the ligand) that it recognizes; the plasma-membrane region containing the receptor-ligand complex then undergoes endocytosis, forming a transport vesicle containing the receptor-ligand complex and excluding most other plasma-membrane proteins. Receptor-mediated endocytosis generally occurs via clathrin-coated pits and vesicles." [GOC:mah, ISBN:0716731363] biological_process
+ENSG00000244486 GO:0007157 heterophilic cell-cell adhesion via plasma membrane cell adhesion molecules "The attachment of an adhesion molecule in one cell to a nonidentical adhesion molecule in an adjacent cell." [ISBN:0198506732] biological_process
+ENSG00000093000 GO:0005643 nuclear pore "Any of the numerous similar discrete openings in the nuclear envelope of a eukaryotic cell, where the inner and outer nuclear membranes are joined." [ISBN:0198547684] cellular_component
+ENSG00000093000 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000093000 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000093000 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000093000 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000093000 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000093000 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000093000 GO:0044403 symbiosis, encompassing mutualism through parasitism "An interaction between two organisms living together in more or less intimate association. The term host is usually used for the larger (macro) of the two members of a symbiosis. The smaller (micro) member is called the symbiont organism. Microscopic symbionts are often referred to as endosymbionts. The various forms of symbiosis include parasitism, in which the association is disadvantageous or destructive to one of the organisms; mutualism, in which the association is advantageous, or often necessary to one or both and not harmful to either; and commensalism, in which one member of the association benefits while the other is not affected. However, mutualism, parasitism, and commensalism are often not discrete categories of interactions and should rather be perceived as a continuum of interaction ranging from parasitism to mutualism. In fact, the direction of a symbiotic interaction can change during the lifetime of the symbionts due to developmental changes as well as changes in the biotic/abiotic environment in which the interaction occurs." [GOC:cc, http://www.free-definition.com] biological_process
+ENSG00000093000 GO:0061024 membrane organization "A process which results in the assembly, arrangement of constituent parts, or disassembly of a membrane. A membrane is a double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:dph, GOC:tb] biological_process
+ENSG00000093000 GO:0005975 carbohydrate metabolic process "The chemical reactions and pathways involving carbohydrates, any of a group of organic compounds based of the general formula Cx(H2O)y. Includes the formation of carbohydrate derivatives by the addition of a carbohydrate residue to another molecule." [GOC:mah, ISBN:0198506732] biological_process
+ENSG00000093000 GO:0005654 nucleoplasm "That part of the nuclear content other than the chromosomes or the nucleolus." [GOC:ma, ISBN:0124325653] cellular_component
+ENSG00000093000 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000093000 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000093000 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000093000 GO:0007049 cell cycle "The progression of biochemical and morphological phases and events that occur in a cell during successive cell replication or nuclear replication events. Canonically, the cell cycle comprises the replication and segregation of genetic material followed by the division of the cell, but in endocycles or syncytial cells nuclear replication or nuclear division may not be followed by cell division." [GOC:go_curators, GOC:mtg_cell_cycle] biological_process
+ENSG00000093000 GO:0000278 mitotic cell cycle "Progression through the phases of the mitotic cell cycle, the most common eukaryotic cell cycle, which canonically comprises four successive phases called G1, S, G2, and M and includes replication of the genome and the subsequent segregation of chromosomes into daughter cells. In some variant cell cycles nuclear replication or nuclear division may not be followed by cell division, or G1 and G2 phases may be absent." [GOC:mah, ISBN:0815316194, Reactome:69278] biological_process
+ENSG00000093000 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000093000 GO:0007077 mitotic nuclear envelope disassembly "The cell cycle process in which the controlled breakdown of the nuclear envelope during mitotic cell division occurs." [GOC:bf] biological_process
+ENSG00000093000 GO:0008645 hexose transport "The directed movement of hexose into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore. Hexoses are any aldoses with a chain of six carbon atoms in the molecule." [GOC:ai] biological_process
+ENSG00000093000 GO:0010827 regulation of glucose transport "Any process that modulates the frequency, rate or extent of glucose transport. Glucose transport is the directed movement of the hexose monosaccharide glucose into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:dph, GOC:tb] biological_process
+ENSG00000093000 GO:0015031 protein transport "The directed movement of proteins into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:ai] biological_process
+ENSG00000093000 GO:0015758 glucose transport "The directed movement of the hexose monosaccharide glucose into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:ai] biological_process
+ENSG00000093000 GO:0016032 viral process "A multi-organism process in which a virus is a participant. The other participant is the host. Includes infection of a host cell, replication of the viral genome, and assembly of progeny virus particles. In some cases the viral genetic material may integrate into the host genome and only subsequently, under particular circumstances, 'complete' its life cycle." [GOC:bf, GOC:jl, GOC:mah] biological_process
+ENSG00000093000 GO:0019221 cytokine-mediated signaling pathway "A series of molecular signals initiated by the binding of a cytokine to a receptor on the surface of a cell, and ending with regulation of a downstream cellular process, e.g. transcription." [GOC:mah, GOC:signaling, PMID:19295629] biological_process
+ENSG00000093000 GO:0031965 nuclear membrane "Either of the lipid bilayers that surround the nucleus and form the nuclear envelope; excludes the intermembrane space." [GOC:mah, GOC:pz] cellular_component
+ENSG00000093000 GO:0046907 intracellular transport "The directed movement of substances within a cell." [GOC:ai] biological_process
+ENSG00000093000 GO:0051028 mRNA transport "The directed movement of mRNA, messenger ribonucleic acid, into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:ai] biological_process
+ENSG00000093000 GO:0055085 transmembrane transport "The process in which a solute is transported from one side of a membrane to the other." [GOC:dph, GOC:jid] biological_process
+ENSG00000093000
+ENSG00000185339 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000185339 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000185339 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000185339 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000185339 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000185339 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000185339 GO:0005768 endosome "A membrane-bounded organelle to which materials ingested by endocytosis are delivered." [ISBN:0198506732, PMID:19696797] cellular_component
+ENSG00000185339 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000185339 GO:0005615 extracellular space "That part of a multicellular organism outside the cells proper, usually taken to be outside the plasma membranes, and occupied by fluid." [ISBN:0198547684] cellular_component
+ENSG00000185339 GO:0005576 extracellular region "The space external to the outermost structure of a cell. For cells without external protective or external encapsulating structures this refers to space outside of the plasma membrane. This term covers the host cell environment outside an intracellular parasite." [GOC:go_curators] cellular_component
+ENSG00000185339 GO:0006766 vitamin metabolic process "The chemical reactions and pathways involving vitamins. Vitamin is a general term for a number of unrelated organic substances that occur in many foods in small amounts and that are necessary in trace amounts for the normal metabolic functioning of the body. Vitamins may be water-soluble or fat-soluble and usually serve as components of coenzyme systems." [GOC:ai] biological_process
+ENSG00000185339 GO:0006767 water-soluble vitamin metabolic process "The chemical reactions and pathways involving any of a diverse group of vitamins that are soluble in water." [GOC:jl] biological_process
+ENSG00000185339 GO:0006824 cobalt ion transport "The directed movement of cobalt (Co) ions into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:ai] biological_process
+ENSG00000185339 GO:0009235 cobalamin metabolic process "The chemical reactions and pathways involving cobalamin (vitamin B12), a water-soluble vitamin characterized by possession of a corrin nucleus containing a cobalt atom." [GOC:go_curators] biological_process
+ENSG00000185339 GO:0015889 cobalamin transport "The directed movement of cobalamin (vitamin B12), a water-soluble vitamin characterized by possession of a corrin nucleus containing a cobalt atom, into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:ai] biological_process
+ENSG00000185339 GO:0031419 cobalamin binding "Interacting selectively and non-covalently with cobalamin (vitamin B12), a water-soluble vitamin characterized by possession of a corrin nucleus containing a cobalt atom." [GOC:mah] molecular_function
+ENSG00000185339 GO:0043202 lysosomal lumen "The volume enclosed within the lysosomal membrane." [GOC:jl, PMID:15213228] cellular_component
+ENSG00000185339 GO:0046872 metal ion binding "Interacting selectively and non-covalently with any metal ion." [GOC:ai] molecular_function
+ENSG00000185339 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000185339
+ENSG00000169548 GO:0003677 DNA binding "Any molecular function by which a gene product interacts selectively and non-covalently with DNA (deoxyribonucleic acid)." [GOC:dph, GOC:jl, GOC:tb, GOC:vw] molecular_function
+ENSG00000169548 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000169548 GO:0006351 transcription, DNA-templated "The cellular synthesis of RNA on a template of DNA." [GOC:jl, GOC:txnOH] biological_process
+ENSG00000169548 GO:0006355 regulation of transcription, DNA-templated "Any process that modulates the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000169548 GO:0046872 metal ion binding "Interacting selectively and non-covalently with any metal ion." [GOC:ai] molecular_function
+ENSG00000169548 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000169548 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000169548 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000169548 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000169548 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000169548 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000169548 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000182902 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000182902 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000182902 GO:0022857 transmembrane transporter activity "Enables the transfer of a substance from one side of a membrane to the other." [GOC:mtg_transport, ISBN:0815340729] molecular_function
+ENSG00000182902 GO:0015293 symporter activity "Enables the active transport of a solute across a membrane by a mechanism whereby two or more species are transported together in the same direction in a tightly coupled process not directly linked to a form of energy other than chemiosmotic energy." [GOC:mtg_transport, ISBN:0815340729, PMID:10839820] molecular_function
+ENSG00000182902 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000182902 GO:0055085 transmembrane transport "The process in which a solute is transported from one side of a membrane to the other." [GOC:dph, GOC:jid] biological_process
+ENSG00000182902 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000182902 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000182902 GO:0005743 mitochondrial inner membrane "The inner, i.e. lumen-facing, lipid bilayer of the mitochondrial envelope. It is highly folded to form cristae." [GOC:ai] cellular_component
+ENSG00000100207 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100207 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100207 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100207 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000100207 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100207 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100207 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100207 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100207 GO:0000988 protein binding transcription factor activity "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules), in order to modulate transcription. A protein binding transcription factor may or may not also interact with the template nucleic acid (either DNA or RNA) as well." [GOC:txnOH] molecular_function
+ENSG00000100207 GO:0003677 DNA binding "Any molecular function by which a gene product interacts selectively and non-covalently with DNA (deoxyribonucleic acid)." [GOC:dph, GOC:jl, GOC:tb, GOC:vw] molecular_function
+ENSG00000100207 GO:0003713 transcription coactivator activity "Interacting selectively and non-covalently with a activating transcription factor and also with the basal transcription machinery in order to increase the frequency, rate or extent of transcription. Cofactors generally do not bind the template nucleic acid, but rather mediate protein-protein interactions between activating transcription factors and the basal transcription machinery." [GOC:txnOH, PMID:10213677, PMID:16858867] molecular_function
+ENSG00000100207 GO:0006351 transcription, DNA-templated "The cellular synthesis of RNA on a template of DNA." [GOC:jl, GOC:txnOH] biological_process
+ENSG00000100207 GO:0006355 regulation of transcription, DNA-templated "Any process that modulates the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000100207 GO:0008270 zinc ion binding "Interacting selectively and non-covalently with zinc (Zn) ions." [GOC:ai] molecular_function
+ENSG00000100207 GO:0044822 poly(A) RNA binding "Interacting non-covalently with a poly(A) RNA, a RNA molecule which has a tail of adenine bases." [GOC:jl] molecular_function
+ENSG00000100207 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000100207 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100207
+ENSG00000099974 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000099974 GO:0016829 lyase activity "Catalysis of the cleavage of C-C, C-O, C-N and other bonds by other means than by hydrolysis or oxidation, or conversely adding a group to a double bond. They differ from other enzymes in that two substrates are involved in one reaction direction, but only one in the other direction. When acting on the single substrate, a molecule is eliminated and this generates either a new double bond or a new ring." [EC:4.-.-.-, ISBN:0198547684] molecular_function
+ENSG00000099974 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000099974 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000099974 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000099974 GO:0008152 metabolic process "The chemical reactions and pathways, including anabolism and catabolism, by which living organisms transform chemical substances. Metabolic processes typically transform small molecules, but also include macromolecular processes such as DNA repair and replication, and protein synthesis and degradation." [GOC:go_curators, ISBN:0198547684] biological_process
+ENSG00000099974 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000099974 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000099984
+ENSG00000099984 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000099984 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000183066
+ENSG00000183066 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000183066 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000183066 GO:0022607 cellular component assembly "The aggregation, arrangement and bonding together of a cellular component." [GOC:isa_complete] biological_process
+ENSG00000183066 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000183066 GO:0005856 cytoskeleton "Any of the various filamentous elements that form the internal framework of cells, and typically remain after treatment of the cells with mild detergent to remove membrane constituents and soluble components of the cytoplasm. The term embraces intermediate filaments, microfilaments, microtubules, the microtrabecular lattice, and other structures characterized by a polymeric filamentous nature and long-range order within the cell. The various elements of the cytoskeleton not only serve in the maintenance of cellular shape but also have roles in other cellular functions, including cellular movement, cell division, endocytosis, and movement of organelles." [GOC:mah, ISBN:0198547684, PMID:16959967] cellular_component
+ENSG00000183066 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000183066 GO:0007343 egg activation "The process in which the egg becomes metabolically active, initiates protein and DNA synthesis and undergoes structural changes to its cortex and/or cytoplasm." [GOC:bf, PMID:9630751] biological_process
+ENSG00000183066 GO:0033011 perinuclear theca "A condensed cytoplasmic structure that covers the nucleus of mammalian spermatozoa except for a narrow zone around the insertion of the tail. It shows two distinct regions, a subacrosomal layer and, continuing caudally beyond the acrosomic system, the postacrosomal sheath. The perinuclear theca has been considered a cytoskeletal scaffold responsible for maintaining the overall architecture of the mature sperm head; however, recent studies indicate that the bulk of its constituent proteins are not traditional cytoskeletal proteins but rather a variety of cytosolic proteins." [PMID:17289678, PMID:8025156] cellular_component
+ENSG00000183066 GO:0035039 male pronucleus assembly "The conversion at fertilization of the inactive sperm nucleus into a male pronucleus with its chromosomes processed for the first zygotic division." [GOC:bf, PMID:11735001] biological_process
+ENSG00000183066 GO:0050699 WW domain binding "Interacting selectively and non-covalently with a WW domain of a protein, a small module composed of 40 amino acids and plays a role in mediating protein-protein interactions via proline-rich regions." [PMID:14531730] molecular_function
+ENSG00000183066 GO:0051321 meiotic cell cycle "Progression through the phases of the meiotic cell cycle, in which canonically a cell replicates to produce four offspring with half the chromosomal content of the progenitor cell via two nuclear divisions." [GOC:ai] biological_process
+ENSG00000183066 GO:0007049 cell cycle "The progression of biochemical and morphological phases and events that occur in a cell during successive cell replication or nuclear replication events. Canonically, the cell cycle comprises the replication and segregation of genetic material followed by the division of the cell, but in endocycles or syncytial cells nuclear replication or nuclear division may not be followed by cell division." [GOC:go_curators, GOC:mtg_cell_cycle] biological_process
+ENSG00000100014 GO:0046983 protein dimerization activity "The formation of a protein dimer, a macromolecular structure consists of two noncovalently associated identical or nonidentical subunits." [ISBN:0198506732] molecular_function
+ENSG00000100014 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100014 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100014 GO:0005921 gap junction "A cell-cell junction that is composed of an array of small channels that permit small molecules to pass from one cell to another. At gap junctions, the membranes of two adjacent cells are separated by a uniform narrow gap of about 2-4 nm that is spanned by channel-forming proteins called connexins, which form hexagonal tubes called connexons." [GOC:mah, GOC:mtg_muscle, http://www.vivo.colostate.edu/hbooks/cmb/cells/pmemb/junctions_g.html, ISBN:0815332181] cellular_component
+ENSG00000100014 GO:0007049 cell cycle "The progression of biochemical and morphological phases and events that occur in a cell during successive cell replication or nuclear replication events. Canonically, the cell cycle comprises the replication and segregation of genetic material followed by the division of the cell, but in endocycles or syncytial cells nuclear replication or nuclear division may not be followed by cell division." [GOC:go_curators, GOC:mtg_cell_cycle] biological_process
+ENSG00000100014 GO:0051301 cell division "The process resulting in division and partitioning of components of a cell to form more cells; may or may not be accompanied by the physical separation of a cell into distinct, individually membrane-bounded daughter cells." [GOC:di, GOC:go_curators, GOC:pr] biological_process
+ENSG00000100014 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100014 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100014 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100014 GO:0005815 microtubule organizing center "An intracellular structure that can catalyze gamma-tubulin-dependent microtubule nucleation and that can anchor microtubules by interacting with their minus ends, plus ends or sides." [GOC:vw, http://en.wikipedia.org/wiki/Microtubule_organizing_center, ISBN:0815316194, PMID:17072892, PMID:17245416] cellular_component
+ENSG00000100014 GO:0015630 microtubule cytoskeleton "The part of the cytoskeleton (the internal framework of a cell) composed of microtubules and associated proteins." [GOC:jl, ISBN:0395825172] cellular_component
+ENSG00000100014 GO:0015629 actin cytoskeleton "The part of the cytoskeleton (the internal framework of a cell) composed of actin and associated proteins. Includes actin cytoskeleton-associated complexes." [GOC:jl, ISBN:0395825172, ISBN:0815316194] cellular_component
+ENSG00000100014 GO:0030036 actin cytoskeleton organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of cytoskeletal structures comprising actin filaments and their associated proteins." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100014 GO:0016477 cell migration "The controlled self-propelled movement of a cell from one site to a destination guided by molecular cues. Cell migration is a central process in the development and maintenance of multicellular organisms." [GOC:cjm, GOC:dph, GOC:ems, GOC:pf, http://en.wikipedia.org/wiki/Cell_migration] biological_process
+ENSG00000100014 GO:0031941 filamentous actin "A two-stranded helical polymer of the protein actin." [GOC:mah] cellular_component
+ENSG00000100014 GO:0030835 negative regulation of actin filament depolymerization "Any process that stops, prevents, or reduces the frequency, rate or extent of actin depolymerization." [GOC:mah] biological_process
+ENSG00000100014 GO:0007026 negative regulation of microtubule depolymerization "Any process that stops, prevents, or reduces the frequency, rate or extent of microtubule depolymerization; prevention of depolymerization of a microtubule can result from binding by 'capping' at the plus end (e.g. by interaction with another cellular protein of structure) or by exposing microtubules to a stabilizing drug such as taxol." [GOC:mah, ISBN:0815316194] biological_process
+ENSG00000100014
+ENSG00000100393 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000100393 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100393 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100393 GO:0002376 immune system process "Any process involved in the development or functioning of the immune system, an organismal system for calibrated responses to potential internal or invasive threats." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05] biological_process
+ENSG00000100393 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000100393 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000100393 GO:0008134 transcription factor binding "Interacting selectively and non-covalently with a transcription factor, any protein required to initiate or regulate transcription." [ISBN:0198506732] molecular_function
+ENSG00000100393 GO:0016746 transferase activity, transferring acyl groups "Catalysis of the transfer of an acyl group from one compound (donor) to another (acceptor)." [GOC:jl, ISBN:0198506732] molecular_function
+ENSG00000100393 GO:0044403 symbiosis, encompassing mutualism through parasitism "An interaction between two organisms living together in more or less intimate association. The term host is usually used for the larger (macro) of the two members of a symbiosis. The smaller (micro) member is called the symbiont organism. Microscopic symbionts are often referred to as endosymbionts. The various forms of symbiosis include parasitism, in which the association is disadvantageous or destructive to one of the organisms; mutualism, in which the association is advantageous, or often necessary to one or both and not harmful to either; and commensalism, in which one member of the association benefits while the other is not affected. However, mutualism, parasitism, and commensalism are often not discrete categories of interactions and should rather be perceived as a continuum of interaction ranging from parasitism to mutualism. In fact, the direction of a symbiotic interaction can change during the lifetime of the symbionts due to developmental changes as well as changes in the biotic/abiotic environment in which the interaction occurs." [GOC:cc, http://www.free-definition.com] biological_process
+ENSG00000100393 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100393 GO:0048856 anatomical structure development "The biological process whose specific outcome is the progression of an anatomical structure from an initial condition to its mature state. This process begins with the formation of the structure and ends with the mature structure, whatever form that may be including its natural destruction. An anatomical structure is any biological entity that occupies space and is distinguished from its surroundings. Anatomical structures can be macroscopic such as a carpel, or microscopic such as an acrosome." [GO_REF:0000021, GOC:mtg_15jun06] biological_process
+ENSG00000100393 GO:0008219 cell death "Any biological process that results in permanent cessation of all vital functions of a cell. A cell should be considered dead when any one of the following molecular or morphological criteria is met: (1) the cell has lost the integrity of its plasma membrane; (2) the cell, including its nucleus, has undergone complete fragmentation into discrete bodies (frequently referred to as \"apoptotic bodies\"); and/or (3) its corpse (or its fragments) have been engulfed by an adjacent cell in vivo." [GOC:mah, GOC:mtg_apoptosis] biological_process
+ENSG00000100393 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000100393 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100393 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100393 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100393 GO:0005654 nucleoplasm "That part of the nuclear content other than the chromosomes or the nucleolus." [GOC:ma, ISBN:0124325653] cellular_component
+ENSG00000100393 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100393 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100393 GO:0000988 protein binding transcription factor activity "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules), in order to modulate transcription. A protein binding transcription factor may or may not also interact with the template nucleic acid (either DNA or RNA) as well." [GOC:txnOH] molecular_function
+ENSG00000100393 GO:0003677 DNA binding "Any molecular function by which a gene product interacts selectively and non-covalently with DNA (deoxyribonucleic acid)." [GOC:dph, GOC:jl, GOC:tb, GOC:vw] molecular_function
+ENSG00000100393 GO:0007049 cell cycle "The progression of biochemical and morphological phases and events that occur in a cell during successive cell replication or nuclear replication events. Canonically, the cell cycle comprises the replication and segregation of genetic material followed by the division of the cell, but in endocycles or syncytial cells nuclear replication or nuclear division may not be followed by cell division." [GOC:go_curators, GOC:mtg_cell_cycle] biological_process
+ENSG00000100393 GO:0000086 G2/M transition of mitotic cell cycle "The mitotic cell cycle transition by which a cell in G2 commits to M phase. The process begins when the kinase activity of M cyclin/CDK complex reaches a threshold high enough for the cell cycle to proceed. This is accomplished by activating a positive feedback loop that results in the accumulation of unphosphorylated and active M cyclin/CDK complex." [GOC:mtg_cell_cycle] biological_process
+ENSG00000100393 GO:0000122 negative regulation of transcription from RNA polymerase II promoter "Any process that stops, prevents, or reduces the frequency, rate or extent of transcription from an RNA polymerase II promoter." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000100393 GO:0000278 mitotic cell cycle "Progression through the phases of the mitotic cell cycle, the most common eukaryotic cell cycle, which canonically comprises four successive phases called G1, S, G2, and M and includes replication of the genome and the subsequent segregation of chromosomes into daughter cells. In some variant cell cycles nuclear replication or nuclear division may not be followed by cell division, or G1 and G2 phases may be absent." [GOC:mah, ISBN:0815316194, Reactome:69278] biological_process
+ENSG00000100393 GO:0001047 core promoter binding "Interacting selectively and non-covalently with the regulatory region composed of the transcription start site and binding sites for the basal transcription machinery. Binding may occur as a sequence specific interaction or as an interaction observed only once a factor has been recruited to the DNA by other factors." [GOC:txnOH] molecular_function
+ENSG00000100393 GO:0001102 RNA polymerase II activating transcription factor binding "Interacting selectively and non-covalently with an RNA polymerase II transcription activating factor, a protein involved in positive regulation of transcription." [GOC:txnOH] molecular_function
+ENSG00000100393 GO:0001666 response to hypoxia "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a stimulus indicating lowered oxygen tension. Hypoxia, defined as a decline in O2 levels below normoxic levels of 20.8 - 20.95%, results in metabolic adaptation at both the cellular and organismal level." [GOC:hjd] biological_process
+ENSG00000100393 GO:0003682 chromatin binding "Interacting selectively and non-covalently with chromatin, the network of fibers of DNA, protein, and sometimes RNA, that make up the chromosomes of the eukaryotic nucleus during interphase." [GOC:jl, ISBN:0198506732, PMID:20404130] molecular_function
+ENSG00000100393 GO:0003713 transcription coactivator activity "Interacting selectively and non-covalently with a activating transcription factor and also with the basal transcription machinery in order to increase the frequency, rate or extent of transcription. Cofactors generally do not bind the template nucleic acid, but rather mediate protein-protein interactions between activating transcription factors and the basal transcription machinery." [GOC:txnOH, PMID:10213677, PMID:16858867] molecular_function
+ENSG00000100393 GO:0004402 histone acetyltransferase activity "Catalysis of the reaction: acetyl-CoA + histone = CoA + acetyl-histone." [EC:2.3.1.48] molecular_function
+ENSG00000100393 GO:0004468 lysine N-acetyltransferase activity, acting on acetyl phosphate as donor "Catalysis of the reaction: acetyl phosphate + L-lysine = phosphate + N6-acetyl-L-lysine." [EC:2.3.1.32] molecular_function
+ENSG00000100393 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100393 GO:0006325 chromatin organization "Any process that results in the specification, formation or maintenance of the physical structure of eukaryotic chromatin." [GOC:mah] biological_process
+ENSG00000100393 GO:0006351 transcription, DNA-templated "The cellular synthesis of RNA on a template of DNA." [GOC:jl, GOC:txnOH] biological_process
+ENSG00000100393 GO:0006355 regulation of transcription, DNA-templated "Any process that modulates the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000100393 GO:0006475 internal protein amino acid acetylation "The addition of an acetyl group to a non-terminal amino acid in a protein." [GOC:mah] biological_process
+ENSG00000100393 GO:0006915 apoptotic process "A programmed cell death process which begins when a cell receives an internal (e.g. DNA damage) or external signal (e.g. an extracellular death ligand), and proceeds through a series of biochemical events (signaling pathway phase) which trigger an execution phase. The execution phase is the last step of an apoptotic process, and is typically characterized by rounding-up of the cell, retraction of pseudopodes, reduction of cellular volume (pyknosis), chromatin condensation, nuclear fragmentation (karyorrhexis), plasma membrane blebbing and fragmentation of the cell into apoptotic bodies. When the execution phase is completed, the cell has died." [GOC:dhl, GOC:ecd, GOC:go_curators, GOC:mtg_apoptosis, GOC:tb, ISBN:0198506732, PMID:18846107, PMID:21494263] biological_process
+ENSG00000100393 GO:0007219 Notch signaling pathway "A series of molecular signals initiated by the binding of an extracellular ligand to the receptor Notch on the surface of a target cell, and ending with regulation of a downstream cellular process, e.g. transcription." [GOC:go_curators, GOC:signaling] biological_process
+ENSG00000100393 GO:0007399 nervous system development "The process whose specific outcome is the progression of nervous tissue over time, from its formation to its mature state." [GOC:dgh] biological_process
+ENSG00000100393 GO:0007623 circadian rhythm "Any biological process in an organism that recurs with a regularity of approximately 24 hours." [GOC:bf, GOC:go_curators] biological_process
+ENSG00000100393 GO:0008013 beta-catenin binding "Interacting selectively and non-covalently with the beta subunit of the catenin complex." [GOC:bf] molecular_function
+ENSG00000100393 GO:0008270 zinc ion binding "Interacting selectively and non-covalently with zinc (Zn) ions." [GOC:ai] molecular_function
+ENSG00000100393 GO:0016032 viral process "A multi-organism process in which a virus is a participant. The other participant is the host. Includes infection of a host cell, replication of the viral genome, and assembly of progeny virus particles. In some cases the viral genetic material may integrate into the host genome and only subsequently, under particular circumstances, 'complete' its life cycle." [GOC:bf, GOC:jl, GOC:mah] biological_process
+ENSG00000100393 GO:0016407 acetyltransferase activity "Catalysis of the transfer of an acetyl group to an acceptor molecule." [GOC:ai] molecular_function
+ENSG00000100393 GO:0018076 N-terminal peptidyl-lysine acetylation "The acetylation of the N-terminal lysine of proteins." [GOC:ai] biological_process
+ENSG00000100393 GO:0018393 internal peptidyl-lysine acetylation "The addition of an acetyl group to a non-terminal lysine residue in a protein." [GOC:mah] biological_process
+ENSG00000100393 GO:0032481 positive regulation of type I interferon production "Any process that activates or increases the frequency, rate, or extent of type I interferon production. Type I interferons include the interferon-alpha, beta, delta, episilon, zeta, kappa, tau, and omega gene families." [GOC:add, GOC:mah] biological_process
+ENSG00000100393 GO:0033613 activating transcription factor binding "Interacting selectively and non-covalently with an activating transcription factor, any protein whose activity is required to initiate or upregulate transcription." [GOC:mah, GOC:txnOH] molecular_function
+ENSG00000100393 GO:0035257 nuclear hormone receptor binding "Interacting selectively and non-covalently with a nuclear hormone receptor, a ligand-dependent receptor found in the nucleus of the cell." [GOC:bf] molecular_function
+ENSG00000100393 GO:0042771 intrinsic apoptotic signaling pathway in response to DNA damage by p53 class mediator "A series of molecular signals in which an intracellular signal is conveyed to trigger the apoptotic death of a cell. The pathway is induced by the cell cycle regulator phosphoprotein p53, or an equivalent protein, in response to the detection of DNA damage, and ends when the execution phase of apoptosis is triggered." [GOC:go_curators, GOC:mtg_apoptosis] biological_process
+ENSG00000100393 GO:0043627 response to estrogen "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of stimulus by an estrogen, C18 steroid hormones that can stimulate the development of female sexual characteristics." [GOC:jl, ISBN:0198506732] biological_process
+ENSG00000100393 GO:0043923 positive regulation by host of viral transcription "Any process is which a host organism activates or increases the frequency, rate or extent of viral transcription, the synthesis of either RNA on a template of DNA or DNA on a template of RNA." [GOC:jl] biological_process
+ENSG00000100393 GO:0043967 histone H4 acetylation "The modification of histone H4 by the addition of an acetyl group." [GOC:jl] biological_process
+ENSG00000100393 GO:0043969 histone H2B acetylation "The modification of histone H2B by the addition of an acetyl group." [GOC:jl] biological_process
+ENSG00000100393 GO:0045087 innate immune response "Innate immune responses are defense responses mediated by germline encoded components that directly recognize components of potential pathogens." [GO_REF:0000022, GOC:add, GOC:ebc, GOC:mtg_15nov05, GOC:mtg_sensu] biological_process
+ENSG00000100393 GO:0045944 positive regulation of transcription from RNA polymerase II promoter "Any process that activates or increases the frequency, rate or extent of transcription from an RNA polymerase II promoter." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000100393 GO:0050681 androgen receptor binding "Interacting selectively and non-covalently with an androgen receptor." [GOC:ai] molecular_function
+ENSG00000100393 GO:0051091 positive regulation of sequence-specific DNA binding transcription factor activity "Any process that activates or increases the frequency, rate or extent of activity of a transcription factor, any factor involved in the initiation or regulation of transcription." [GOC:ai] biological_process
+ENSG00000100393 GO:0051726 regulation of cell cycle "Any process that modulates the rate or extent of progression through the cell cycle." [GOC:ai, GOC:dph, GOC:tb] biological_process
+ENSG00000100393 GO:0060765 regulation of androgen receptor signaling pathway "Any process that modulates the rate, frequency, or extent of the androgen receptor signaling pathway." [GOC:dph] biological_process
+ENSG00000100393 GO:0061418 regulation of transcription from RNA polymerase II promoter in response to hypoxia "Any process that modulates the frequency, rate or extent of transcription from an RNA polymerase II promoter as a result of a hypoxia stimulus." [GOC:dph, PMID:12511571] biological_process
+ENSG00000100393 GO:0071456 cellular response to hypoxia "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a stimulus indicating lowered oxygen tension. Hypoxia, defined as a decline in O2 levels below normoxic levels of 20.8 - 20.95%, results in metabolic adaptation at both the cellular and organismal level." [GOC:mah] biological_process
+ENSG00000100393 GO:0090043 regulation of tubulin deacetylation "Any process that modulates the frequency, rate or extent of tubulin deacetylation. Tubulin deacetylation is the removal of an acetyl group from a protein amino acid." [GOC:BHF, GOC:dph, GOC:tb] biological_process
+ENSG00000100393 GO:0003712 transcription cofactor activity "Interacting selectively and non-covalently with a regulatory transcription factor and also with the basal transcription machinery in order to modulate transcription. Cofactors generally do not bind the template nucleic acid, but rather mediate protein-protein interactions between regulatory transcription factors and the basal transcription machinery." [GOC:txnOH, PMID:10213677, PMID:16858867] molecular_function
+ENSG00000100393 GO:0016573 histone acetylation "The modification of a histone by the addition of an acetyl group." [GOC:ai] biological_process
+ENSG00000100393 GO:0000123 histone acetyltransferase complex "A protein complex that possesses histone acetyltransferase activity." [GOC:mah] cellular_component
+ENSG00000100393 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100393 GO:0005667 transcription factor complex "A protein complex that is capable of associating with DNA by direct binding, or via other DNA-binding proteins or complexes, and regulating transcription." [GOC:jl] cellular_component
+ENSG00000100393 GO:0045893 positive regulation of transcription, DNA-templated "Any process that activates or increases the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000100393 GO:0000979 RNA polymerase II core promoter sequence-specific DNA binding "Interacting selectively and non-covalently with the regulatory region composed of the transcription start site and binding sites for transcription factors of the RNA polymerase II basal transcription machinery." [GOC:txnOH] molecular_function
+ENSG00000100393 GO:0032092 positive regulation of protein binding "Any process that activates or increases the frequency, rate or extent of protein binding." [GOC:mah] biological_process
+ENSG00000100393 GO:0007519 skeletal muscle tissue development "The developmental sequence of events leading to the formation of adult skeletal muscle tissue. The main events are: the fusion of myoblasts to form myotubes that increase in size by further fusion to them of myoblasts, the formation of myofibrils within their cytoplasm and the establishment of functional neuromuscular junctions with motor neurons. At this stage they can be regarded as mature muscle fibers." [GOC:mtg_muscle] biological_process
+ENSG00000100393 GO:0007507 heart development "The process whose specific outcome is the progression of the heart over time, from its formation to the mature structure. The heart is a hollow, muscular organ, which, by contracting rhythmically, keeps up the circulation of the blood." [GOC:jid, UBERON:0000948] biological_process
+ENSG00000100393 GO:0001756 somitogenesis "The formation of mesodermal clusters that are arranged segmentally along the anterior posterior axis of an embryo." [ISBN:0721662544] biological_process
+ENSG00000100393 GO:0030324 lung development "The process whose specific outcome is the progression of the lung over time, from its formation to the mature structure. In all air-breathing vertebrates the lungs are developed from the ventral wall of the oesophagus as a pouch which divides into two sacs. In amphibians and many reptiles the lungs retain very nearly this primitive sac-like character, but in the higher forms the connection with the esophagus becomes elongated into the windpipe and the inner walls of the sacs become more and more divided, until, in the mammals, the air spaces become minutely divided into tubes ending in small air cells, in the walls of which the blood circulates in a fine network of capillaries. In mammals the lungs are more or less divided into lobes, and each lung occupies a separate cavity in the thorax." [GOC:jid, UBERON:0002048] biological_process
+ENSG00000100393 GO:0009887 organ morphogenesis "Morphogenesis of an organ. An organ is defined as a tissue or set of tissues that work together to perform a specific function or functions. Morphogenesis is the process in which anatomical structures are generated and organized. Organs are commonly observed as visibly distinct structures, but may also exist as loosely associated clusters of cells that work together to perform a specific function or functions." [GOC:dgh, GOC:go_curators, ISBN:0471245208, ISBN:0721662544] biological_process
+ENSG00000100393 GO:0031490 chromatin DNA binding "Interacting selectively and non-covalently with DNA that is assembled into chromatin." [GOC:mah] molecular_function
+ENSG00000100393 GO:0001085 RNA polymerase II transcription factor binding "Interacting selectively and non-covalently with an RNA polymerase II transcription factor, any protein required to initiate or regulate transcription by RNA polymerase II." [GOC:txnOH] molecular_function
+ENSG00000100393 GO:0002039 p53 binding "Interacting selectively and non-covalently with one of the p53 family of proteins." [GOC:hjd] molecular_function
+ENSG00000100393 GO:0097157 pre-mRNA intronic binding "Interacting selectively and non-covalently with an intronic sequence of a pre-messenger RNA (pre-mRNA)." [GOC:ans, PMID:16260624] molecular_function
+ENSG00000241360 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000241360 GO:0005856 cytoskeleton "Any of the various filamentous elements that form the internal framework of cells, and typically remain after treatment of the cells with mild detergent to remove membrane constituents and soluble components of the cytoplasm. The term embraces intermediate filaments, microfilaments, microtubules, the microtrabecular lattice, and other structures characterized by a polymeric filamentous nature and long-range order within the cell. The various elements of the cytoskeleton not only serve in the maintenance of cellular shape but also have roles in other cellular functions, including cellular movement, cell division, endocytosis, and movement of organelles." [GOC:mah, ISBN:0198547684, PMID:16959967] cellular_component
+ENSG00000241360 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000241360 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000241360 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000241360 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000241360 GO:0016791 phosphatase activity "Catalysis of the hydrolysis of phosphoric monoesters, releasing inorganic phosphate." [EC:3.1.3, EC:3.1.3.41, GOC:curators] molecular_function
+ENSG00000241360 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000241360 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000241360 GO:0051186 cofactor metabolic process "The chemical reactions and pathways involving a cofactor, a substance that is required for the activity of an enzyme or other protein. Cofactors may be inorganic, such as the metal atoms zinc, iron, and copper in certain forms, or organic, in which case they are referred to as coenzymes. Cofactors may either be bound tightly to active sites or bind loosely with the substrate." [GOC:ai] biological_process
+ENSG00000241360 GO:0022607 cellular component assembly "The aggregation, arrangement and bonding together of a cellular component." [GOC:isa_complete] biological_process
+ENSG00000241360 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000241360 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000241360 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000241360 GO:0000287 magnesium ion binding "Interacting selectively and non-covalently with magnesium (Mg) ions." [GOC:ai] molecular_function
+ENSG00000241360 GO:0004647 phosphoserine phosphatase activity "Catalysis of the reaction: L(or D)-O-phosphoserine + H2O = L(or D)-serine + phosphate." [EC:3.1.3.3] molecular_function
+ENSG00000241360 GO:0004721 phosphoprotein phosphatase activity "Catalysis of the reaction: a phosphoprotein + H2O = a protein + phosphate. Together with protein kinases, these enzymes control the state of phosphorylation of cell proteins and thereby provide an important mechanism for regulating cellular activity." [EC:3.1.3.16, ISBN:0198547684] molecular_function
+ENSG00000241360 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000241360 GO:0006470 protein dephosphorylation "The process of removing one or more phosphoric residues from a protein." [GOC:hb] biological_process
+ENSG00000241360 GO:0007088 regulation of mitosis "Any process that modulates the frequency, rate or extent of mitosis." [GOC:go_curators] biological_process
+ENSG00000241360 GO:0030836 positive regulation of actin filament depolymerization "Any process that activates or increases the frequency, rate or extent of actin depolymerization." [GOC:mah] biological_process
+ENSG00000241360 GO:0031072 heat shock protein binding "Interacting selectively and non-covalently with a heat shock protein, any protein synthesized or activated in response to heat shock." [GOC:mah, GOC:vw] molecular_function
+ENSG00000241360 GO:0031247 actin rod assembly "The assembly of actin rods, a cellular structure consisting of parallel, hexagonally arranged actin tubules." [GOC:pg, PMID:14706699] biological_process
+ENSG00000241360 GO:0032361 pyridoxal phosphate catabolic process "The chemical reactions and pathways resulting in the breakdown of pyridoxal phosphate, pyridoxal phosphorylated at the hydroxymethyl group of C-5, the active form of vitamin B6." [GOC:mah] biological_process
+ENSG00000241360 GO:0032465 regulation of cytokinesis "Any process that modulates the frequency, rate or extent of the division of the cytoplasm of a cell and its separation into two daughter cells." [GOC:mah] biological_process
+ENSG00000241360 GO:0033883 pyridoxal phosphatase activity "Catalysis of the reaction: pyridoxal 5'-phosphate + H2O = pyridoxal + phosphate." [EC:3.1.3.74] molecular_function
+ENSG00000241360 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000241360 GO:0071318 cellular response to ATP "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of an ATP (adenosine 5'-triphosphate) stimulus." [GOC:mah] biological_process
+ENSG00000241360 GO:0015629 actin cytoskeleton "The part of the cytoskeleton (the internal framework of a cell) composed of actin and associated proteins. Includes actin cytoskeleton-associated complexes." [GOC:jl, ISBN:0395825172, ISBN:0815316194] cellular_component
+ENSG00000241360 GO:0030027 lamellipodium "A thin sheetlike process extended by the leading edge of a crawling fibroblast; contains a dense meshwork of actin filaments." [ISBN:0815316194] cellular_component
+ENSG00000241360 GO:0030496 midbody "A thin cytoplasmic bridge formed between daughter cells at the end of cytokinesis. The midbody forms where the contractile ring constricts, and may persist for some time before finally breaking to complete cytokinesis." [ISBN:0815316194] cellular_component
+ENSG00000241360 GO:0032154 cleavage furrow "In animal cells, the first sign of cleavage, or cytokinesis, is the appearance of a shallow groove in the cell surface near the old metaphase plate. A contractile ring containing actin and myosin is located just inside the plasma membrane at the location of the furrow. Ring contraction is associated with centripetal growth of the membrane that deepens the cleavage furrow and divides the cytoplasm of the two daughter cells. While the term 'cleavage furrow' was initially associated with animal cells, such a structure occurs in many other types of cells, including unicellular protists." [ISBN:0805319409] cellular_component
+ENSG00000241360 GO:0032587 ruffle membrane "The portion of the plasma membrane surrounding a ruffle." [GOC:mah] cellular_component
+ENSG00000241360 GO:0070938 contractile ring "A cytoskeletal structure composed of filamentous protein that forms beneath the membrane of many cells or organelles, in the plane of cell or organelle division. Ring contraction is associated with centripetal growth of the membrane that divides the cytoplasm of the two daughter cells or organelles." [GOC:mah, ISBN:0123645859, ISBN:0792354923, PMID:10791428, PMID:17913889] cellular_component
+ENSG00000241360 GO:0008152 metabolic process "The chemical reactions and pathways, including anabolism and catabolism, by which living organisms transform chemical substances. Metabolic processes typically transform small molecules, but also include macromolecular processes such as DNA repair and replication, and protein synthesis and degradation." [GOC:go_curators, ISBN:0198547684] biological_process
+ENSG00000241360
+ENSG00000184459 GO:0008289 lipid binding "Interacting selectively and non-covalently with a lipid." [GOC:ai] molecular_function
+ENSG00000184459 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000184459 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000184459 GO:0001530 lipopolysaccharide binding "Interacting selectively and non-covalently with lipopolysaccharide." [PMID:11079463] molecular_function
+ENSG00000184459 GO:0005543 phospholipid binding "Interacting selectively and non-covalently with phospholipids, a class of lipids containing phosphoric acid as a mono- or diester." [ISBN:0198506732] molecular_function
+ENSG00000184459 GO:0005576 extracellular region "The space external to the outermost structure of a cell. For cells without external protective or external encapsulating structures this refers to space outside of the plasma membrane. This term covers the host cell environment outside an intracellular parasite." [GOC:go_curators] cellular_component
+ENSG00000184459 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000185022 GO:0003700 sequence-specific DNA binding transcription factor activity "Interacting selectively and non-covalently with a specific DNA sequence in order to modulate transcription. The transcription factor may or may not also interact selectively with a protein or macromolecular complex." [GOC:curators, GOC:txnOH] molecular_function
+ENSG00000185022 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000185022 GO:0006355 regulation of transcription, DNA-templated "Any process that modulates the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000185022 GO:0043565 sequence-specific DNA binding "Interacting selectively and non-covalently with DNA of a specific nucleotide composition, e.g. GC-rich DNA binding, or with a specific sequence motif or type of DNA e.g. promotor binding or rDNA binding." [GOC:jl] molecular_function
+ENSG00000185022 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000185022 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000185022 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000185022 GO:0001071 nucleic acid binding transcription factor activity "Interacting selectively and non-covalently with a DNA or RNA sequence in order to modulate transcription. The transcription factor may or may not also interact selectively with a protein or macromolecular complex." [GOC:txnOH] molecular_function
+ENSG00000185022 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000185022 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000185022 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000185022 GO:0005654 nucleoplasm "That part of the nuclear content other than the chromosomes or the nucleolus." [GOC:ma, ISBN:0124325653] cellular_component
+ENSG00000185022 GO:0006366 transcription from RNA polymerase II promoter "The synthesis of RNA from a DNA template by RNA polymerase II, originating at an RNA polymerase II promoter. Includes transcription of messenger RNA (mRNA) and certain small nuclear RNAs (snRNAs)." [GOC:jl, GOC:txnOH, ISBN:0321000382] biological_process
+ENSG00000185022 GO:0007567 parturition "The reproductive process in which the parent is separated from its offspring either by giving birth to live young or by laying eggs." [ISBN:0198506732] biological_process
+ENSG00000185022 GO:0007596 blood coagulation "The sequential process in which the multiple coagulation factors of the blood interact, ultimately resulting in the formation of an insoluble fibrin clot; it may be divided into three stages: stage 1, the formation of intrinsic and extrinsic prothrombin converting principle; stage 2, the formation of thrombin; stage 3, the formation of stable fibrin polymers." [http://www.graylab.ac.uk/omd/, ISBN:0198506732] biological_process
+ENSG00000185022 GO:0003677 DNA binding "Any molecular function by which a gene product interacts selectively and non-covalently with DNA (deoxyribonucleic acid)." [GOC:dph, GOC:jl, GOC:tb, GOC:vw] molecular_function
+ENSG00000185022 GO:0001701 in utero embryonic development "The process whose specific outcome is the progression of the embryo in the uterus over time, from formation of the zygote in the oviduct, to birth. An example of this process is found in Mus musculus." [GOC:go_curators, GOC:mtg_sensu] biological_process
+ENSG00000185022 GO:0035914 skeletal muscle cell differentiation "The process in which a relatively unspecialized cell acquires specialized features of a skeletal muscle cell, a somatic cell located in skeletal muscle." [CL:0000188, GOC:BHF, GOC:vk] biological_process
+ENSG00000185022 GO:0045604 regulation of epidermal cell differentiation "Any process that modulates the frequency, rate or extent of epidermal cell differentiation." [GOC:go_curators] biological_process
+ENSG00000015475 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000015475 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000015475 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000015475 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000015475 GO:0008219 cell death "Any biological process that results in permanent cessation of all vital functions of a cell. A cell should be considered dead when any one of the following molecular or morphological criteria is met: (1) the cell has lost the integrity of its plasma membrane; (2) the cell, including its nucleus, has undergone complete fragmentation into discrete bodies (frequently referred to as \"apoptotic bodies\"); and/or (3) its corpse (or its fragments) have been engulfed by an adjacent cell in vivo." [GOC:mah, GOC:mtg_apoptosis] biological_process
+ENSG00000015475 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000015475 GO:0007005 mitochondrion organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of a mitochondrion; includes mitochondrial morphogenesis and distribution, and replication of the mitochondrial genome as well as synthesis of new mitochondrial components." [GOC:dph, GOC:jl, GOC:mah, GOC:sgd_curators, PMID:9786946] biological_process
+ENSG00000015475 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000015475 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000015475 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000015475 GO:0001836 release of cytochrome c from mitochondria "The process that results in the movement of cytochrome c from the mitochondrial intermembrane space into the cytosol, which is part of the apoptotic signaling pathway and leads to caspase activation." [GOC:add, GOC:mah, GOC:mtg_apoptosis, ISBN:0721639976, PMID:12925707, PMID:9560217] biological_process
+ENSG00000015475 GO:0005123 death receptor binding "Interacting selectively and non-covalently with any member of the death receptor (DR) family. The DR family falls within the tumor necrosis factor receptor superfamily and is characterized by a cytoplasmic region of ~80 residues termed the death domain (DD)." [GOC:ceb, GOC:rl, PMID:15654015] molecular_function
+ENSG00000015475 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000015475 GO:0005741 mitochondrial outer membrane "The outer, i.e. cytoplasm-facing, lipid bilayer of the mitochondrial envelope." [GOC:ai] cellular_component
+ENSG00000015475 GO:0006915 apoptotic process "A programmed cell death process which begins when a cell receives an internal (e.g. DNA damage) or external signal (e.g. an extracellular death ligand), and proceeds through a series of biochemical events (signaling pathway phase) which trigger an execution phase. The execution phase is the last step of an apoptotic process, and is typically characterized by rounding-up of the cell, retraction of pseudopodes, reduction of cellular volume (pyknosis), chromatin condensation, nuclear fragmentation (karyorrhexis), plasma membrane blebbing and fragmentation of the cell into apoptotic bodies. When the execution phase is completed, the cell has died." [GOC:dhl, GOC:ecd, GOC:go_curators, GOC:mtg_apoptosis, GOC:tb, ISBN:0198506732, PMID:18846107, PMID:21494263] biological_process
+ENSG00000015475 GO:0008625 extrinsic apoptotic signaling pathway via death domain receptors "A series of molecular signals in which a signal is conveyed from the cell surface to trigger the apoptotic death of a cell. The pathway starts with a ligand binding to a death domain receptor on the cell surface, and ends when the execution phase of apoptosis is triggered." [GOC:mah, GOC:mtg_apoptosis] biological_process
+ENSG00000015475 GO:0008637 apoptotic mitochondrial changes "The morphological and physiological alterations undergone by mitochondria during apoptosis." [GOC:mah, GOC:mtg_apoptosis] biological_process
+ENSG00000015475 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000015475 GO:0032461 positive regulation of protein oligomerization "Any process that activates or increases the frequency, rate or extent of protein oligomerization." [GOC:mah] biological_process
+ENSG00000015475 GO:0032464 positive regulation of protein homooligomerization "Any process that activates or increases the frequency, rate or extent of protein homooligomerization." [GOC:mah] biological_process
+ENSG00000015475 GO:0042770 signal transduction in response to DNA damage "A cascade of processes induced by the detection of DNA damage within a cell." [GOC:go_curators] biological_process
+ENSG00000015475 GO:0043065 positive regulation of apoptotic process "Any process that activates or increases the frequency, rate or extent of cell death by apoptotic process." [GOC:jl, GOC:mtg_apoptosis] biological_process
+ENSG00000015475 GO:0051402 neuron apoptotic process "Any apoptotic process in a neuron, the basic cellular unit of nervous tissue. Each neuron consists of a body, an axon, and dendrites. Their purpose is to receive, conduct, and transmit impulses in the nervous system." [CL:0000540, GOC:mtg_apoptosis, MeSH:A.08.663] biological_process
+ENSG00000015475 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000015475 GO:0090150 establishment of protein localization to membrane "The directed movement of a protein to a specific location in a membrane." [GOC:ascb_2009, GOC:dph, GOC:tb] biological_process
+ENSG00000015475 GO:0090200 positive regulation of release of cytochrome c from mitochondria "Any process that increases the rate, frequency or extent of release of cytochrome c from mitochondria, the process in which cytochrome c is enabled to move from the mitochondrial intermembrane space into the cytosol, which is an early step in apoptosis and leads to caspase activation." [GOC:BHF, GOC:dph, GOC:mtg_apoptosis, GOC:tb] biological_process
+ENSG00000015475 GO:0097193 intrinsic apoptotic signaling pathway "A series of molecular signals in which an intracellular signal is conveyed to trigger the apoptotic death of a cell. The pathway starts with reception of an intracellular signal (e.g. DNA damage, endoplasmic reticulum stress, oxidative stress etc.), and ends when the execution phase of apoptosis is triggered. The intrinsic apoptotic signaling pathway is crucially regulated by permeabilization of the mitochondrial outer membrane (MOMP)." [GOC:mtg_apoptosis, GOC:yaf, PMID:11919192, PMID:17340152, PMID:18852119] biological_process
+ENSG00000015475 GO:1900740 positive regulation of protein insertion into mitochondrial membrane involved in apoptotic signaling pathway "Any process that activates or increases the frequency, rate or extent of protein insertion into mitochondrial membrane involved in apoptotic signaling pathway." [GOC:mtg_apoptosis, GOC:TermGenie] biological_process
+ENSG00000015475 GO:1901030 positive regulation of mitochondrial outer membrane permeabilization involved in apoptotic signaling pathway "Any process that activates or increases the frequency, rate or extent of mitochondrial outer membrane permeabilization involved in apoptotic signaling pathway." [GOC:BHF, GOC:mtg_apoptosis, GOC:TermGenie] biological_process
+ENSG00000015475 GO:2001238 positive regulation of extrinsic apoptotic signaling pathway "Any process that activates or increases the frequency, rate or extent of extrinsic apoptotic signaling pathway." [GOC:mtg_apoptosis] biological_process
+ENSG00000015475 GO:2001244 positive regulation of intrinsic apoptotic signaling pathway "Any process that activates or increases the frequency, rate or extent of intrinsic apoptotic signaling pathway." [GOC:mtg_apoptosis] biological_process
+ENSG00000015475 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000015475 GO:0031625 ubiquitin protein ligase binding "Interacting selectively and non-covalently with a ubiquitin protein ligase enzyme, any of the E3 proteins." [GOC:vp] molecular_function
+ENSG00000015475 GO:0051260 protein homooligomerization "The process of creating protein oligomers, compounds composed of a small number, usually between three and ten, of identical component monomers. Oligomers may be formed by the polymerization of a number of monomers or the depolymerization of a large protein polymer." [GOC:ai] biological_process
+ENSG00000015475 GO:0097284 hepatocyte apoptotic process "Any apoptotic process in a hepatocyte, the main structural component of the liver." [CL:0000182, GOC:jc, GOC:mtg_apoptosis, PMID:15856020] biological_process
+ENSG00000015475 GO:0042127 regulation of cell proliferation "Any process that modulates the frequency, rate or extent of cell proliferation." [GOC:jl] biological_process
+ENSG00000015475 GO:1902108 regulation of mitochondrial membrane permeability involved in apoptotic process "Any regulation of mitochondrial membrane permeability that is involved in apoptotic process." [GOC:mtg_apoptosis, GOC:pm, GOC:TermGenie, PMID:19168129] biological_process
+ENSG00000015475 GO:2000045 regulation of G1/S transition of mitotic cell cycle "Any cell cycle regulatory process that controls the commitment of a cell from G1 to S phase of the mitotic cell cycle." [GOC:mtg_cell_cycle] biological_process
+ENSG00000015475 GO:0006919 activation of cysteine-type endopeptidase activity involved in apoptotic process "Any process that initiates the activity of the inactive enzyme cysteine-type endopeptidase in the context of an apoptotic process." [GOC:al, GOC:dph, GOC:jl, GOC:mtg_apoptosis, GOC:tb, PMID:14744432, PMID:18328827, Wikipedia:Caspase] biological_process
+ENSG00000015475 GO:0006626 protein targeting to mitochondrion "The process of directing proteins towards and into the mitochondrion, usually mediated by mitochondrial proteins that recognize signals contained within the imported protein." [GOC:mcc, ISBN:0716731363] biological_process
+ENSG00000015475 GO:0097191 extrinsic apoptotic signaling pathway "A series of molecular signals in which a signal is conveyed from the cell surface to trigger the apoptotic death of a cell. The pathway starts with either a ligand binding to a cell surface receptor, or a ligand being withdrawn from a cell surface receptor (e.g. in the case of signaling by dependence receptors), and ends when the execution phase of apoptosis is triggered." [GOC:mtg_apoptosis, GOC:yaf, PMID:17340152] biological_process
+ENSG00000015475 GO:0032592 integral component of mitochondrial membrane "The component of the mitochondrial membrane consisting of the gene products that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:mah] cellular_component
+ENSG00000015475 GO:0097345 mitochondrial outer membrane permeabilization "The process by which the mitochondrial outer membrane becomes permeable to the passing of proteins and other molecules from the intermembrane space to the cytosol as part of the apoptotic signaling pathway." [GOC:BHF, GOC:mtg_apoptosis, GOC:pg, PMID:21041309] biological_process
+ENSG00000015475 GO:0032459 regulation of protein oligomerization "Any process that modulates the frequency, rate or extent of protein oligomerization." [GOC:mah] biological_process
+ENSG00000015475 GO:0032355 response to estradiol "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of stimulus by estradiol, a C18 steroid hormone hydroxylated at C3 and C17 that acts as a potent estrogen." [GOC:mah, ISBN:0911910123] biological_process
+ENSG00000015475 GO:0007420 brain development "The process whose specific outcome is the progression of the brain over time, from its formation to the mature structure. Brain development begins with patterning events in the neural tube and ends with the mature structure that is the center of thought and emotion. The brain is responsible for the coordination and control of bodily activities and the interpretation of information from the senses (sight, hearing, smell, etc.)." [GOC:dph, GOC:jid, GOC:tb, UBERON:0000955] biological_process
+ENSG00000015475 GO:0034349 glial cell apoptotic process "Any apoptotic process in a glial cell, a non-neuronal cell of the nervous system." [CL:0000125, GOC:mtg_apoptosis, GOC:sart] biological_process
+ENSG00000100311 GO:0008083 growth factor activity "The function that stimulates a cell to grow or proliferate. Most growth factors have other actions besides the induction of cell growth or proliferation." [ISBN:0815316194] molecular_function
+ENSG00000100311 GO:0051781 positive regulation of cell division "Any process that activates or increases the frequency, rate or extent of cell division." [GOC:ai] biological_process
+ENSG00000100311 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100311 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100311 GO:0048856 anatomical structure development "The biological process whose specific outcome is the progression of an anatomical structure from an initial condition to its mature state. This process begins with the formation of the structure and ends with the mature structure, whatever form that may be including its natural destruction. An anatomical structure is any biological entity that occupies space and is distinguished from its surroundings. Anatomical structures can be macroscopic such as a carpel, or microscopic such as an acrosome." [GO_REF:0000021, GOC:mtg_15jun06] biological_process
+ENSG00000100311 GO:0040011 locomotion "Self-propelled movement of a cell or organism from one location to another." [GOC:dgh] biological_process
+ENSG00000100311 GO:0048870 cell motility "Any process involved in the controlled self-propelled movement of a cell that results in translocation of the cell from one place to another." [GOC:dgh, GOC:dph, GOC:isa_complete, GOC:mlg] biological_process
+ENSG00000100311 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000100311 GO:0002376 immune system process "Any process involved in the development or functioning of the immune system, an organismal system for calibrated responses to potential internal or invasive threats." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05] biological_process
+ENSG00000100311 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000100311 GO:0007267 cell-cell signaling "Any process that mediates the transfer of information from one cell to another." [GOC:mah] biological_process
+ENSG00000100311 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100311 GO:0030198 extracellular matrix organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of an extracellular matrix." [GOC:mah] biological_process
+ENSG00000100311 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000100311 GO:0030234 enzyme regulator activity "Binds to and modulates the activity of an enzyme." [GOC:mah] molecular_function
+ENSG00000100311 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100311 GO:0005615 extracellular space "That part of a multicellular organism outside the cells proper, usually taken to be outside the plasma membranes, and occupied by fluid." [ISBN:0198547684] cellular_component
+ENSG00000100311 GO:0005576 extracellular region "The space external to the outermost structure of a cell. For cells without external protective or external encapsulating structures this refers to space outside of the plasma membrane. This term covers the host cell environment outside an intracellular parasite." [GOC:go_curators] cellular_component
+ENSG00000100311 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100311 GO:0016192 vesicle-mediated transport "A cellular transport process in which transported substances are moved in membrane-bounded vesicles; transported substances are enclosed in the vesicle lumen or located in the vesicle membrane. The process begins with a step that directs a substance to the forming vesicle, and includes vesicle budding and coating. Vesicles are then targeted to, and fuse with, an acceptor membrane." [GOC:ai, GOC:mah, ISBN:08789310662000] biological_process
+ENSG00000100311 GO:0000139 Golgi membrane "The lipid bilayer surrounding any of the compartments of the Golgi apparatus." [GOC:mah] cellular_component
+ENSG00000100311 GO:0001892 embryonic placenta development "The embryonically driven process whose specific outcome is the progression of the placenta over time, from its formation to the mature structure. The placenta is an organ of metabolic interchange between fetus and mother, partly of embryonic origin and partly of maternal origin." [GOC:add, ISBN:068340007X] biological_process
+ENSG00000100311 GO:0001938 positive regulation of endothelial cell proliferation "Any process that activates or increases the rate or extent of endothelial cell proliferation." [GOC:add] biological_process
+ENSG00000100311 GO:0002548 monocyte chemotaxis "The movement of a monocyte in response to an external stimulus." [GOC:add, PMID:11696603, PMID:15173832] biological_process
+ENSG00000100311 GO:0002576 platelet degranulation "The regulated exocytosis of secretory granules containing preformed mediators such as histamine and serotonin by a platelet." [GOC:add] biological_process
+ENSG00000100311 GO:0003104 positive regulation of glomerular filtration "Any process that activates or increases the frequency, rate or extent of glomerular filtration. Glomerular filtration is the processs whereby blood is filtered by the glomerulus into the renal tubule." [GOC:mtg_cardio] biological_process
+ENSG00000100311 GO:0005161 platelet-derived growth factor receptor binding "Interacting selectively and non-covalently with the platelet-derived growth factor receptor." [GOC:ai] molecular_function
+ENSG00000100311 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100311 GO:0005518 collagen binding "Interacting selectively and non-covalently with collagen, a group of fibrous proteins of very high tensile strength that form the main component of connective tissue in animals. Collagen is highly enriched in glycine (some regions are 33% glycine) and proline, occurring predominantly as 3-hydroxyproline (about 20%)." [GOC:ai, ISBN:0198506732] molecular_function
+ENSG00000100311 GO:0005788 endoplasmic reticulum lumen "The volume enclosed by the membranes of the endoplasmic reticulum." [ISBN:0198547684] cellular_component
+ENSG00000100311 GO:0006468 protein phosphorylation "The process of introducing a phosphate group on to a protein." [GOC:hb] biological_process
+ENSG00000100311 GO:0007173 epidermal growth factor receptor signaling pathway "A series of molecular signals initiated by binding of a ligand to the tyrosine kinase receptor EGFR (ERBB1) on the surface of a cell. The pathway ends with regulation of a downstream cellular process, e.g. transcription." [GOC:ceb, PR:000006933] biological_process
+ENSG00000100311 GO:0007179 transforming growth factor beta receptor signaling pathway "A series of molecular signals initiated by the binding of an extracellular ligand to a transforming growth factor beta receptor on the surface of a target cell, and ending with regulation of a downstream cellular process, e.g. transcription." [GOC:BHF, GOC:mah, GOC:signaling] biological_process
+ENSG00000100311 GO:0007507 heart development "The process whose specific outcome is the progression of the heart over time, from its formation to the mature structure. The heart is a hollow, muscular organ, which, by contracting rhythmically, keeps up the circulation of the blood." [GOC:jid, UBERON:0000948] biological_process
+ENSG00000100311 GO:0007596 blood coagulation "The sequential process in which the multiple coagulation factors of the blood interact, ultimately resulting in the formation of an insoluble fibrin clot; it may be divided into three stages: stage 1, the formation of intrinsic and extrinsic prothrombin converting principle; stage 2, the formation of thrombin; stage 3, the formation of stable fibrin polymers." [http://www.graylab.ac.uk/omd/, ISBN:0198506732] biological_process
+ENSG00000100311 GO:0008284 positive regulation of cell proliferation "Any process that activates or increases the rate or extent of cell proliferation." [GOC:go_curators] biological_process
+ENSG00000100311 GO:0008543 fibroblast growth factor receptor signaling pathway "The series of molecular signals generated as a consequence of a fibroblast growth factor receptor binding to one of its physiological ligands." [GOC:ceb] biological_process
+ENSG00000100311 GO:0009611 response to wounding "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a stimulus indicating damage to the organism." [GOC:go_curators] biological_process
+ENSG00000100311 GO:0009986 cell surface "The external part of the cell wall and/or plasma membrane." [GOC:jl, GOC:mtg_sensu, GOC:sm] cellular_component
+ENSG00000100311 GO:0010512 negative regulation of phosphatidylinositol biosynthetic process "Any process that decreases the frequency, rate or extent of the chemical reactions and pathways resulting in the formation of phosphatidylinositol." [GOC:dph, GOC:tb, GOC:vw] biological_process
+ENSG00000100311 GO:0010544 negative regulation of platelet activation "Any process that decreases the rate or frequency of platelet activation. Platelet activation is a series of progressive, overlapping events triggered by exposure of the platelets to subendothelial tissue." [GOC:BHF, GOC:dph, GOC:tb] biological_process
+ENSG00000100311 GO:0014068 positive regulation of phosphatidylinositol 3-kinase signaling "Any process that activates or increases the frequency, rate or extent of signal transduction mediated by the phosphatidylinositol 3-kinase cascade." [GOC:ef] biological_process
+ENSG00000100311 GO:0014911 positive regulation of smooth muscle cell migration "Any process that activates, maintains or increases the frequency, rate or extent of smooth muscle cell migration." [CL:0000192, GOC:mtg_muscle] biological_process
+ENSG00000100311 GO:0016176 superoxide-generating NADPH oxidase activator activity "Increases the activity of the enzyme superoxide-generating NADPH oxidase." [GOC:ai] molecular_function
+ENSG00000100311 GO:0016323 basolateral plasma membrane "The region of the plasma membrane that includes the basal end and sides of the cell. Often used in reference to animal polarized epithelial membranes, where the basal membrane is the part attached to the extracellular matrix, or in plant cells, where the basal membrane is defined with respect to the zygotic axis." [GOC:go_curators] cellular_component
+ENSG00000100311 GO:0018105 peptidyl-serine phosphorylation "The phosphorylation of peptidyl-serine to form peptidyl-O-phospho-L-serine." [RESID:AA0037] biological_process
+ENSG00000100311 GO:0018108 peptidyl-tyrosine phosphorylation "The phosphorylation of peptidyl-tyrosine to form peptidyl-O4'-phospho-L-tyrosine." [RESID:AA0039] biological_process
+ENSG00000100311 GO:0030168 platelet activation "A series of progressive, overlapping events triggered by exposure of the platelets to subendothelial tissue. These events include shape change, adhesiveness, aggregation, and release reactions. When carried through to completion, these events lead to the formation of a stable hemostatic plug." [http://www.graylab.ac.uk/omd/] biological_process
+ENSG00000100311 GO:0030335 positive regulation of cell migration "Any process that activates or increases the frequency, rate or extent of cell migration." [GOC:go_curators] biological_process
+ENSG00000100311 GO:0031093 platelet alpha granule lumen "The volume enclosed by the membrane of the platelet alpha granule." [GOC:mah, PMID:8467233] cellular_component
+ENSG00000100311 GO:0031954 positive regulation of protein autophosphorylation "Any process that activates or increases the frequency, rate or extent of the phosphorylation by a protein of one or more of its own residues." [GOC:mah] biological_process
+ENSG00000100311 GO:0032147 activation of protein kinase activity "Any process that initiates the activity of an inactive protein kinase." [GOC:mah] biological_process
+ENSG00000100311 GO:0032148 activation of protein kinase B activity "Any process that initiates the activity of the inactive enzyme protein kinase B." [GOC:pg] biological_process
+ENSG00000100311 GO:0035793 positive regulation of metanephric mesenchymal cell migration by platelet-derived growth factor receptor-beta signaling pathway "Any process that increases the frequency, rate or extent of metanephric mesenchymal cell migration as a result of the series of molecular signals generated as a consequence of a platelet-derived growth factor receptor-beta binding to one of its physiological ligands." [GOC:bf, GOC:mtg_kidney_jan10, GOC:yaf, PMID:10734101] biological_process
+ENSG00000100311 GO:0038001 paracrine signaling "The transfer of information from one cell to another, where the signal travels from the signal-producing cell to the receiving cell by passive diffusion or bulk flow in intercellular fluid. The signaling cell and the receiving cell are usually in the vicinity of each other." [GOC:mtg_signaling_feb11, ISBN:3527303782] biological_process
+ENSG00000100311 GO:0038095 Fc-epsilon receptor signaling pathway "A series of molecular signals initiated by the binding of the Fc portion of immunoglobulin E (IgE) to an Fc-epsilon receptor on the surface of a signal-receiving cell, and ending with regulation of a downstream cellular process, e.g. transcription. The Fc portion of an immunoglobulin is its C-terminal constant region." [GOC:phg, PMID:12413516, PMID:15048725] biological_process
+ENSG00000100311 GO:0042056 chemoattractant activity "Providing the environmental signal that initiates the directed movement of a motile cell or organism towards a higher concentration of that signal." [GOC:go_curators, ISBN:0198506732] molecular_function
+ENSG00000100311 GO:0042802 identical protein binding "Interacting selectively and non-covalently with an identical protein or proteins." [GOC:jl] molecular_function
+ENSG00000100311 GO:0042803 protein homodimerization activity "Interacting selectively and non-covalently with an identical protein to form a homodimer." [GOC:jl] molecular_function
+ENSG00000100311 GO:0043406 positive regulation of MAP kinase activity "Any process that activates or increases the frequency, rate or extent of MAP kinase activity." [GOC:dph, GOC:go_curators] biological_process
+ENSG00000100311 GO:0043410 positive regulation of MAPK cascade "Any process that activates or increases the frequency, rate or extent of signal transduction mediated by the MAPK cascade." [GOC:go_curators] biological_process
+ENSG00000100311 GO:0043536 positive regulation of blood vessel endothelial cell migration "Any process that activates or increases the frequency, rate or extent of the migration of the endothelial cells of blood vessels." [GOC:go_curators] biological_process
+ENSG00000100311 GO:0043552 positive regulation of phosphatidylinositol 3-kinase activity "Any process that activates or increases the frequency, rate or extent of phosphatidylinositol 3-kinase activity." [GOC:bf] biological_process
+ENSG00000100311 GO:0045087 innate immune response "Innate immune responses are defense responses mediated by germline encoded components that directly recognize components of potential pathogens." [GO_REF:0000022, GOC:add, GOC:ebc, GOC:mtg_15nov05, GOC:mtg_sensu] biological_process
+ENSG00000100311 GO:0045737 positive regulation of cyclin-dependent protein serine/threonine kinase activity "Any process that activates or increases the frequency, rate or extent of CDK activity." [GOC:go_curators, GOC:pr] biological_process
+ENSG00000100311 GO:0045740 positive regulation of DNA replication "Any process that activates or increases the frequency, rate or extent of DNA replication." [GOC:go_curators] biological_process
+ENSG00000100311 GO:0045840 positive regulation of mitosis "Any process that activates or increases the frequency, rate or extent of mitosis." [GOC:go_curators] biological_process
+ENSG00000100311 GO:0045892 negative regulation of transcription, DNA-templated "Any process that stops, prevents, or reduces the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000100311 GO:0045893 positive regulation of transcription, DNA-templated "Any process that activates or increases the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000100311 GO:0046982 protein heterodimerization activity "Interacting selectively and non-covalently with a nonidentical protein to form a heterodimer." [GOC:ai] molecular_function
+ENSG00000100311 GO:0048008 platelet-derived growth factor receptor signaling pathway "The series of molecular signals generated as a consequence of a platelet-derived growth factor receptor binding to one of its physiological ligands." [GOC:ceb] biological_process
+ENSG00000100311 GO:0048011 neurotrophin TRK receptor signaling pathway "A series of molecular signals initiated by the binding of a neurotrophin to a receptor on the surface of the target cell where the receptor possesses tyrosine kinase activity, and ending with regulation of a downstream cellular process, e.g. transcription." [GOC:bf, GOC:ceb, GOC:jc, GOC:signaling, PMID:12065629, Wikipedia:Trk_receptor] biological_process
+ENSG00000100311 GO:0048015 phosphatidylinositol-mediated signaling "A series of molecular signals in which a cell uses a phosphatidylinositol-mediated signaling to convert a signal into a response. Phosphatidylinositols include phosphatidylinositol (PtdIns) and its phosphorylated derivatives." [GOC:bf, GOC:ceb, ISBN:0198506732] biological_process
+ENSG00000100311 GO:0048146 positive regulation of fibroblast proliferation "Any process that activates or increases the frequency, rate or extent of multiplication or reproduction of fibroblast cells." [GOC:jid] biological_process
+ENSG00000100311 GO:0048407 platelet-derived growth factor binding "Interacting selectively and non-covalently with platelet-derived growth factor." [GOC:dgh] molecular_function
+ENSG00000100311 GO:0048661 positive regulation of smooth muscle cell proliferation "Any process that activates or increases the rate or extent of smooth muscle cell proliferation." [CL:0000192, GOC:ebc] biological_process
+ENSG00000100311 GO:0050731 positive regulation of peptidyl-tyrosine phosphorylation "Any process that activates or increases the frequency, rate or extent of the phosphorylation of peptidyl-tyrosine." [GOC:ai] biological_process
+ENSG00000100311 GO:0050918 positive chemotaxis "The directed movement of a motile cell or organism towards a higher concentration of a chemical." [GOC:ai, GOC:bf, GOC:isa_complete] biological_process
+ENSG00000100311 GO:0050921 positive regulation of chemotaxis "Any process that activates or increases the frequency, rate or extent of the directed movement of a motile cell or organism in response to a specific chemical concentration gradient." [GOC:ai] biological_process
+ENSG00000100311 GO:0060326 cell chemotaxis "The directed movement of a motile cell guided by a specific chemical concentration gradient. Movement may be towards a higher concentration (positive chemotaxis) or towards a lower concentration (negative chemotaxis)." [GOC:dph] biological_process
+ENSG00000100311 GO:0061098 positive regulation of protein tyrosine kinase activity "Any process that increases the rate, frequency, or extent of protein tyrosine kinase activity." [GOC:dph, GOC:tb] biological_process
+ENSG00000100311 GO:0070374 positive regulation of ERK1 and ERK2 cascade "Any process that activates or increases the frequency, rate or extent of signal transduction mediated by the ERK1 and ERK2 cascade." [GOC:mah] biological_process
+ENSG00000100311 GO:0071363 cellular response to growth factor stimulus "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a growth factor stimulus." [GOC:mah] biological_process
+ENSG00000100311 GO:0071506 cellular response to mycophenolic acid "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a mycophenolic acid stimulus." [CHEBI:168396, GOC:mah, GOC:yaf] biological_process
+ENSG00000100311 GO:0072126 positive regulation of glomerular mesangial cell proliferation "Any process that increases the frequency, rate or extent of glomerular mesangial cell proliferation." [GOC:mtg_kidney_jan10] biological_process
+ENSG00000100311 GO:0072255 metanephric glomerular mesangial cell development "The process whose specific outcome is the progression of a glomerular mesangial cell in the metanephros over time, from its formation to the mature structure." [GOC:mtg_kidney_jan10] biological_process
+ENSG00000100311 GO:0072593 reactive oxygen species metabolic process "The chemical reactions and pathways involving a reactive oxygen species, any molecules or ions formed by the incomplete one-electron reduction of oxygen. They contribute to the microbicidal activity of phagocytes, regulation of signal transduction and gene expression, and the oxidative damage to biopolymers." [CHEBI:26523, GOC:mah] biological_process
+ENSG00000100311 GO:0090280 positive regulation of calcium ion import "Any process that increases the rate, frequency, or extent of the directed movement of calcium ions into a cell or organelle." [GOC:BHF] biological_process
+ENSG00000100311 GO:1900127 positive regulation of hyaluronan biosynthetic process "Any process that activates or increases the frequency, rate or extent of hyaluronan biosynthetic process." [GOC:TermGenie, GOC:yaf] biological_process
+ENSG00000100311 GO:2000379 positive regulation of reactive oxygen species metabolic process "Any process that activates or increases the frequency, rate or extent of reactive oxygen species metabolic process." [GOC:mah] biological_process
+ENSG00000100311 GO:2000573 positive regulation of DNA biosynthetic process "Any process that activates or increases the frequency, rate or extent of DNA biosynthetic process." [GOC:obol] biological_process
+ENSG00000100311 GO:2000591 positive regulation of metanephric mesenchymal cell migration "Any process that activates or increases the frequency, rate or extent of metanephric mesenchymal cell migration." [GOC:mtg_kidney_jan10, GOC:obol, GOC:yaf] biological_process
+ENSG00000100311 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000100311 GO:0030036 actin cytoskeleton organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of cytoskeletal structures comprising actin filaments and their associated proteins." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100311 GO:0006929 substrate-dependent cell migration "The orderly movement of a cell from one site to another along a substrate such as the extracellular matrix; the migrating cell forms a protrusion that attaches to the substrate." [ISBN:0815316194, PMID:11944043, PMID:14657486] biological_process
+ENSG00000100311 GO:0001568 blood vessel development "The process whose specific outcome is the progression of a blood vessel over time, from its formation to the mature structure. The blood vessel is the vasculature carrying blood." [GOC:hjd, UBERON:0001981] biological_process
+ENSG00000100311 GO:0048514 blood vessel morphogenesis "The process in which the anatomical structures of blood vessels are generated and organized. The blood vessel is the vasculature carrying blood." [GOC:jid] biological_process
+ENSG00000100311 GO:0030031 cell projection assembly "Formation of a prolongation or process extending from a cell, e.g. a flagellum or axon." [GOC:jl, GOC:mah, http://www.cogsci.princeton.edu/~wn/] biological_process
+ENSG00000100311 GO:0030336 negative regulation of cell migration "Any process that stops, prevents, or reduces the frequency, rate or extent of cell migration." [GOC:go_curators] biological_process
+ENSG00000100311 GO:0045743 positive regulation of fibroblast growth factor receptor signaling pathway "Any process that activates or increases the frequency, rate or extent of fibroblast growth factor receptor signaling pathway activity." [GOC:go_curators] biological_process
+ENSG00000100311 GO:0060445 branching involved in salivary gland morphogenesis "The process in which the branching structure of the salivary gland is generated and organized." [GOC:dph] biological_process
+ENSG00000100311 GO:0060664 epithelial cell proliferation involved in salivary gland morphogenesis "The multiplication or reproduction of epithelial cells of the submandibular salivary gland, resulting in the expansion of a cell population and the shaping of the gland." [GOC:dph, PMID:17336109] biological_process
+ENSG00000100311 GO:0050730 regulation of peptidyl-tyrosine phosphorylation "Any process that modulates the frequency, rate or extent of the phosphorylation of peptidyl-tyrosine." [GOC:ai] biological_process
+ENSG00000100311 GO:0072262 metanephric glomerular mesangial cell proliferation involved in metanephros development "The multiplication or reproduction of glomerular mesangial cells in the metanephros, resulting in the expansion of the population." [GOC:mtg_kidney_jan10] biological_process
+ENSG00000100311 GO:0045977 positive regulation of mitotic cell cycle, embryonic "Any process that activates or increases the frequency, rate or extent of progression through the embryonic mitotic cell cycle." [GOC:dph, GOC:go_curators, GOC:tb] biological_process
+ENSG00000100311 GO:0072264 metanephric glomerular endothelium development "The process whose specific outcome is the progression of the metanephric glomerular endothelium over time, from its formation to the mature structure. The metanephric glomerular endothelium is an epithelial tissue that covers the internal surfaces of the glomerulus of the metanephros." [GOC:mtg_kidney_jan10] biological_process
+ENSG00000100311 GO:0042127 regulation of cell proliferation "Any process that modulates the frequency, rate or extent of cell proliferation." [GOC:jl] biological_process
+ENSG00000100311 GO:0042493 response to drug "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a drug stimulus. A drug is a substance used in the diagnosis, treatment or prevention of a disease." [GOC:jl] biological_process
+ENSG00000100311 GO:0014070 response to organic cyclic compound "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of an organic cyclic compound stimulus." [CHEBI:33832, GOC:ef] biological_process
+ENSG00000100311 GO:0032355 response to estradiol "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of stimulus by estradiol, a C18 steroid hormone hydroxylated at C3 and C17 that acts as a potent estrogen." [GOC:mah, ISBN:0911910123] biological_process
+ENSG00000100311 GO:0010033 response to organic substance "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of an organic substance stimulus." [GOC:sm] biological_process
+ENSG00000100311 GO:0005102 receptor binding "Interacting selectively and non-covalently with one or more specific sites on a receptor molecule, a macromolecule that undergoes combination with a hormone, neurotransmitter, drug or intracellular messenger to initiate a change in cell function." [GOC:bf, GOC:ceb, ISBN:0198506732] molecular_function
+ENSG00000100311 GO:0006260 DNA replication "The cellular metabolic process in which a cell duplicates one or more molecules of DNA. DNA replication begins when specific sequences, known as origins of replication, are recognized and bound by initiation proteins, and ends when the original DNA molecule has been completely duplicated and the copies topologically separated. The unit of replication usually corresponds to the genome of the cell, an organelle, or a virus. The template for replication can either be an existing DNA molecule or RNA." [GOC:mah] biological_process
+ENSG00000100311 GO:0043627 response to estrogen "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of stimulus by an estrogen, C18 steroid hormones that can stimulate the development of female sexual characteristics." [GOC:jl, ISBN:0198506732] biological_process
+ENSG00000100311 GO:0001666 response to hypoxia "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a stimulus indicating lowered oxygen tension. Hypoxia, defined as a decline in O2 levels below normoxic levels of 20.8 - 20.95%, results in metabolic adaptation at both the cellular and organismal level." [GOC:hjd] biological_process
+ENSG00000100311 GO:0042060 wound healing "The series of events that restore integrity to a damaged tissue, following an injury." [GOC:bf, PMID:15269788] biological_process
+ENSG00000100311 GO:0032868 response to insulin "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of an insulin stimulus. Insulin is a polypeptide hormone produced by the islets of Langerhans of the pancreas in mammals, and by the homologous organs of other organisms." [GOC:mah, ISBN:0198506732] biological_process
+ENSG00000100311 GO:0016049 cell growth "The process in which a cell irreversibly increases in size over time by accretion and biosynthetic production of matter similar to that already present." [GOC:ai] biological_process
+ENSG00000185133
+ENSG00000185133 GO:0046856 phosphatidylinositol dephosphorylation "The process of removing one or more phosphate groups from a phosphatidylinositol." [ISBN:0198506732] biological_process
+ENSG00000185133 GO:0006629 lipid metabolic process "The chemical reactions and pathways involving lipids, compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent. Includes fatty acids; neutral fats, other fatty-acid esters, and soaps; long-chain (fatty) alcohols and waxes; sphingoids and other long-chain bases; glycolipids, phospholipids and sphingolipids; and carotenes, polyprenols, sterols, terpenes and other isoprenoids." [GOC:ma] biological_process
+ENSG00000185133 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000185133 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000185133 GO:0016791 phosphatase activity "Catalysis of the hydrolysis of phosphoric monoesters, releasing inorganic phosphate." [EC:3.1.3, EC:3.1.3.41, GOC:curators] molecular_function
+ENSG00000185133 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000185133 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000185133 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000185133 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000185133 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000185133 GO:0001726 ruffle "Projection at the leading edge of a crawling cell; the protrusions are supported by a microfilament meshwork." [ISBN:0124325653] cellular_component
+ENSG00000185133 GO:0004445 inositol-polyphosphate 5-phosphatase activity "Catalysis of the reactions: D-myo-inositol 1,4,5-trisphosphate + H2O = myo-inositol 1,4-bisphosphate + phosphate, and 1D-myo-inositol 1,3,4,5-tetrakisphosphate + H2O = 1D-myo-inositol 1,3,4-trisphosphate + phosphate." [EC:3.1.3.56] molecular_function
+ENSG00000185133 GO:0006644 phospholipid metabolic process "The chemical reactions and pathways involving phospholipids, any lipid containing phosphoric acid as a mono- or diester." [ISBN:0198506732] biological_process
+ENSG00000185133 GO:0006661 phosphatidylinositol biosynthetic process "The chemical reactions and pathways resulting in the formation of phosphatidylinositol, any glycophospholipid in which the sn-glycerol 3-phosphate residue is esterified to the 1-hydroxyl group of 1D-myo-inositol." [CHEBI:28874, ISBN:0198506732] biological_process
+ENSG00000185133 GO:0017124 SH3 domain binding "Interacting selectively and non-covalently with a SH3 domain (Src homology 3) of a protein, small protein modules containing approximately 50 amino acid residues found in a great variety of intracellular or membrane-associated proteins." [GOC:go_curators, Pfam:PF00018] molecular_function
+ENSG00000185133 GO:0043647 inositol phosphate metabolic process "The chemical reactions and pathways involving inositol phosphate, 1,2,3,4,5,6-cyclohexanehexol, with one or more phosphate groups attached." [CHEBI:24848, GOC:jl] biological_process
+ENSG00000185133 GO:0052658 inositol-1,4,5-trisphosphate 5-phosphatase activity "Catalysis of the reaction: 1D-myo-inositol 1,4,5-trisphosphate + H2O = 1D-myo-inositol 1,4-bisphosphate + phosphate." [EC:3.1.3.56, RHEA:19800] molecular_function
+ENSG00000185133 GO:0052659 inositol-1,3,4,5-tetrakisphosphate 5-phosphatase activity "Catalysis of the reaction: 1D-myo-inositol 1,3,4,5-tetrakisphosphate + H2O = 1D-myo-inositol 1,3,4-trisphosphate + phosphate." [EC:3.1.3.56, RHEA:11395] molecular_function
+ENSG00000185133 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000185133 GO:0043198 dendritic shaft "Cylindric portion of the dendrite, directly stemming from the perikaryon, and carrying the dendritic spines." [GOC:nln] cellular_component
+ENSG00000185133 GO:0030426 growth cone "The migrating motile tip of a growing nerve cell axon or dendrite." [ISBN:0815316194] cellular_component
+ENSG00000185133 GO:0010977 negative regulation of neuron projection development "Any process that decreases the rate, frequency or extent of neuron projection development. Neuron projection development is the process whose specific outcome is the progression of a neuron projection over time, from its formation to the mature structure. A neuron projection is any process extending from a neural cell, such as axons or dendrites (collectively called neurites)." [GOC:dph, GOC:tb] biological_process
+ENSG00000185133 GO:0033137 negative regulation of peptidyl-serine phosphorylation "Any process that stops, prevents, or reduces the frequency, rate or extent of the phosphorylation of peptidyl-serine." [GOC:mah] biological_process
+ENSG00000185133 GO:0034485 phosphatidylinositol-3,4,5-trisphosphate 5-phosphatase activity "Catalysis of the reaction: phosphatidylinositol-3,4,5-trisphosphate + H2O = phosphatidylinositol-3,4-bisphosphate + phosphate." [GOC:pf] molecular_function
+ENSG00000185133 GO:0031115 negative regulation of microtubule polymerization "Any process that stops, prevents, or reduces the frequency, rate or extent of microtubule polymerization." [GOC:mah] biological_process
+ENSG00000183963
+ENSG00000183963 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000183963 GO:0015629 actin cytoskeleton "The part of the cytoskeleton (the internal framework of a cell) composed of actin and associated proteins. Includes actin cytoskeleton-associated complexes." [GOC:jl, ISBN:0395825172, ISBN:0815316194] cellular_component
+ENSG00000183963 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000183963 GO:0005856 cytoskeleton "Any of the various filamentous elements that form the internal framework of cells, and typically remain after treatment of the cells with mild detergent to remove membrane constituents and soluble components of the cytoplasm. The term embraces intermediate filaments, microfilaments, microtubules, the microtrabecular lattice, and other structures characterized by a polymeric filamentous nature and long-range order within the cell. The various elements of the cytoskeleton not only serve in the maintenance of cellular shape but also have roles in other cellular functions, including cellular movement, cell division, endocytosis, and movement of organelles." [GOC:mah, ISBN:0198547684, PMID:16959967] cellular_component
+ENSG00000183963 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000183963 GO:0003779 actin binding "Interacting selectively and non-covalently with monomeric or multimeric forms of actin, including actin filaments." [GOC:clt] molecular_function
+ENSG00000183963 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000183963 GO:0006939 smooth muscle contraction "A process in which force is generated within smooth muscle tissue, resulting in a change in muscle geometry. Force generation involves a chemo-mechanical energy conversion step that is carried out by the actin/myosin complex activity, which generates force through ATP hydrolysis. Smooth muscle differs from striated muscle in the much higher actin/myosin ratio, the absence of conspicuous sarcomeres and the ability to contract to a much smaller fraction of its resting length." [GOC:ef, GOC:jl, GOC:mtg_muscle, ISBN:0198506732] biological_process
+ENSG00000183963 GO:0007517 muscle organ development "The process whose specific outcome is the progression of the muscle over time, from its formation to the mature structure. The muscle is an organ consisting of a tissue made up of various elongated cells that are specialized to contract and thus to produce movement and mechanical work." [GOC:jid, ISBN:0198506732] biological_process
+ENSG00000183963 GO:0008307 structural constituent of muscle "The action of a molecule that contributes to the structural integrity of a muscle fiber." [GOC:mah] molecular_function
+ENSG00000183963 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000183963 GO:0005198 structural molecule activity "The action of a molecule that contributes to the structural integrity of a complex or assembly within or outside a cell." [GOC:mah] molecular_function
+ENSG00000183963 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000183963 GO:0048856 anatomical structure development "The biological process whose specific outcome is the progression of an anatomical structure from an initial condition to its mature state. This process begins with the formation of the structure and ends with the mature structure, whatever form that may be including its natural destruction. An anatomical structure is any biological entity that occupies space and is distinguished from its surroundings. Anatomical structures can be macroscopic such as a carpel, or microscopic such as an acrosome." [GO_REF:0000021, GOC:mtg_15jun06] biological_process
+ENSG00000183963 GO:0008092 cytoskeletal protein binding "Interacting selectively and non-covalently with any protein component of any cytoskeleton (actin, microtubule, or intermediate filament cytoskeleton)." [GOC:mah] molecular_function
+ENSG00000183963 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000161179 GO:0005975 carbohydrate metabolic process "The chemical reactions and pathways involving carbohydrates, any of a group of organic compounds based of the general formula Cx(H2O)y. Includes the formation of carbohydrate derivatives by the addition of a carbohydrate residue to another molecule." [GOC:mah, ISBN:0198506732] biological_process
+ENSG00000161179 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000161179 GO:0016810 hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds "Catalysis of the hydrolysis of any carbon-nitrogen bond, C-N, with the exception of peptide bonds." [GOC:jl] molecular_function
+ENSG00000161179 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100053 GO:0007601 visual perception "The series of events required for an organism to receive a visual stimulus, convert it to a molecular signal, and recognize and characterize the signal. Visual stimuli are detected in the form of photons and are processed to form an image." [GOC:ai] biological_process
+ENSG00000100053 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100053 GO:0050877 neurological system process "A organ system process carried out by any of the organs or tissues of neurological system." [GOC:ai, GOC:mtg_cardio] biological_process
+ENSG00000100053 GO:0005212 structural constituent of eye lens "The action of a molecule that contributes to the structural integrity of the lens of an eye." [GOC:mah] molecular_function
+ENSG00000100053
+ENSG00000100029 GO:0005730 nucleolus "A small, dense body one or more of which are present in the nucleus of eukaryotic cells. It is rich in RNA and protein, is not bounded by a limiting membrane, and is not seen during mitosis. Its prime function is the transcription of the nucleolar DNA into 45S ribosomal-precursor RNA, the processing of this RNA into 5.8S, 18S, and 28S components of ribosomal RNA, and the association of these components with 5S RNA and proteins synthesized outside the nucleolus. This association results in the formation of ribonucleoprotein precursors; these pass into the cytoplasm and mature into the 40S and 60S subunits of the ribosome." [ISBN:0198506732] cellular_component
+ENSG00000100029 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100029 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100029 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100029 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100029 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100029 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000100029 GO:0008283 cell proliferation "The multiplication or reproduction of cells, resulting in the expansion of a cell population." [GOC:mah, GOC:mb] biological_process
+ENSG00000100029 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100029 GO:0005654 nucleoplasm "That part of the nuclear content other than the chromosomes or the nucleolus." [GOC:ma, ISBN:0124325653] cellular_component
+ENSG00000100029 GO:0000463 maturation of LSU-rRNA from tricistronic rRNA transcript (SSU-rRNA, 5.8S rRNA, LSU-rRNA) "Any process involved in the maturation of a precursor Large SubUnit (LSU) ribosomal RNA (rRNA) molecule into a mature LSU-rRNA molecule from the pre-rRNA molecule originally produced as a tricistronic rRNA transcript that contains the Small Subunit (SSU) rRNA, 5.8S rRNA, and Large Subunit (LSU) in that order from 5' to 3' along the primary transcript." [GOC:curators] biological_process
+ENSG00000100029 GO:0000466 maturation of 5.8S rRNA from tricistronic rRNA transcript (SSU-rRNA, 5.8S rRNA, LSU-rRNA) "Any process involved in the maturation of an rRNA molecule originally produced as part of a tricistronic rRNA transcript that contained the Small SubUnit (SSU) rRNA, the 5.8S rRNA, and the Large SubUnit (LSU) rRNA, in that order, from 5' to 3' along the primary transcript." [GOC:curators, PMID:10690410] biological_process
+ENSG00000100029 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100029 GO:0006364 rRNA processing "Any process involved in the conversion of a primary ribosomal RNA (rRNA) transcript into one or more mature rRNA molecules." [GOC:curators] biological_process
+ENSG00000100029 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000100029 GO:0030687 preribosome, large subunit precursor "A preribosomal complex consisting of 27SA, 27SB, and/or 7S pre-rRNA, 5S rRNA, ribosomal proteins including late-associating large subunit proteins, and associated proteins; a precursor of the eukaryotic cytoplasmic large ribosomal subunit." [PMID:10567516] cellular_component
+ENSG00000100029 GO:0042273 ribosomal large subunit biogenesis "A cellular process that results in the biosynthesis of constituent macromolecules, assembly, and arrangement of constituent parts of a large ribosomal subunit; includes transport to the sites of protein synthesis." [GOC:jl] biological_process
+ENSG00000100029 GO:0043021 ribonucleoprotein complex binding "Interacting selectively and non-covalently with any complex of RNA and protein." [GOC:bf, GOC:go_curators, GOC:vk] molecular_function
+ENSG00000100029 GO:0044822 poly(A) RNA binding "Interacting non-covalently with a poly(A) RNA, a RNA molecule which has a tail of adenine bases." [GOC:jl] molecular_function
+ENSG00000100029 GO:0051726 regulation of cell cycle "Any process that modulates the rate or extent of progression through the cell cycle." [GOC:ai, GOC:dph, GOC:tb] biological_process
+ENSG00000100029 GO:0070545 PeBoW complex "A protein complex that is involved in coordinating ribosome biogenesis with cell cycle progression. In human, it is composed of Pes1, Bop1, and WDR12; in Saccharomyces the proteins are known as Nop7p, Erb1 and Ytm1 respectively." [GOC:ab, GOC:mah, PMID:16043514, PMID:17353269] cellular_component
+ENSG00000100029 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100029 GO:0042254 ribosome biogenesis "A cellular process that results in the biosynthesis of constituent macromolecules, assembly, and arrangement of constituent parts of ribosome subunits; includes transport to the sites of protein synthesis." [GOC:ma] biological_process
+ENSG00000100029 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100029 GO:0033365 protein localization to organelle "A process in which a protein is transported to, or maintained in, a location within an organelle." [GOC:mah] biological_process
+ENSG00000100029 GO:0000793 condensed chromosome "A highly compacted molecule of DNA and associated proteins resulting in a cytologically distinct structure." [GOC:elh] cellular_component
+ENSG00000100029 GO:0007000 nucleolus organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of the nucleolus." [GOC:dph, GOC:jid, GOC:jl, GOC:mah] biological_process
+ENSG00000100029
+ENSG00000100029 GO:0005694 chromosome "A structure composed of a very long molecule of DNA and associated proteins (e.g. histones) that carries hereditary information." [ISBN:0198547684] cellular_component
+ENSG00000198951 GO:0006629 lipid metabolic process "The chemical reactions and pathways involving lipids, compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent. Includes fatty acids; neutral fats, other fatty-acid esters, and soaps; long-chain (fatty) alcohols and waxes; sphingoids and other long-chain bases; glycolipids, phospholipids and sphingolipids; and carotenes, polyprenols, sterols, terpenes and other isoprenoids." [GOC:ma] biological_process
+ENSG00000198951 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000198951 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000198951 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000198951 GO:0005975 carbohydrate metabolic process "The chemical reactions and pathways involving carbohydrates, any of a group of organic compounds based of the general formula Cx(H2O)y. Includes the formation of carbohydrate derivatives by the addition of a carbohydrate residue to another molecule." [GOC:mah, ISBN:0198506732] biological_process
+ENSG00000198951 GO:0016798 hydrolase activity, acting on glycosyl bonds "Catalysis of the hydrolysis of any glycosyl bond." [GOC:jl] molecular_function
+ENSG00000198951 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000198951 GO:0005764 lysosome "A small lytic vacuole that has cell cycle-independent morphology and is found in most animal cells and that contains a variety of hydrolases, most of which have their maximal activities in the pH range 5-6. The contained enzymes display latency if properly isolated. About 40 different lysosomal hydrolases are known and lysosomes have a great variety of morphologies and functions." [GOC:mah, ISBN:0198506732] cellular_component
+ENSG00000198951 GO:0005773 vacuole "A closed structure, found only in eukaryotic cells, that is completely surrounded by unit membrane and contains liquid material. Cells contain one or several vacuoles, that may have different functions from each other. Vacuoles have a diverse array of functions. They can act as a storage organelle for nutrients or waste products, as a degradative compartment, as a cost-effective way of increasing cell size, and as a homeostatic regulator controlling both turgor pressure and pH of the cytosol." [GOC:mtg_sensu, ISBN:0198506732] cellular_component
+ENSG00000198951 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000198951 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000198951 GO:0004557 alpha-galactosidase activity "Catalysis of the hydrolysis of terminal, non-reducing alpha-D-galactose residues in alpha-D-galactosides, including galactose oligosaccharides, galactomannans and galactohydrolase." [EC:3.2.1.22] molecular_function
+ENSG00000198951 GO:0008456 alpha-N-acetylgalactosaminidase activity "Catalysis of the hydrolysis of terminal non-reducing N-acetyl-D-galactosamine residues in N-acetyl-alpha-D-galactosaminides." [EC:3.2.1.49] molecular_function
+ENSG00000198951 GO:0009311 oligosaccharide metabolic process "The chemical reactions and pathways involving oligosaccharides, molecules with between two and (about) 20 monosaccharide residues connected by glycosidic linkages." [ISBN:0198506732] biological_process
+ENSG00000198951 GO:0016052 carbohydrate catabolic process "The chemical reactions and pathways resulting in the breakdown of carbohydrates, any of a group of organic compounds based of the general formula Cx(H2O)y." [ISBN:0198506732] biological_process
+ENSG00000198951 GO:0016139 glycoside catabolic process "The chemical reactions and pathways resulting in the breakdown of glycosides, compounds in which a glycosyl group is substituted into a hydroxyl, thiol or selenol group in another compound." [GOC:go_curators] biological_process
+ENSG00000198951 GO:0019377 glycolipid catabolic process "The chemical reactions and pathways resulting in the breakdown of glycolipid, a class of 1,2-di-O-acylglycerols joined at oxygen 3 by a glycosidic linkage to a carbohydrate part (usually a mono-, di- or tri-saccharide)." [CHEBI:33563, GOC:go_curators] biological_process
+ENSG00000198951 GO:0042803 protein homodimerization activity "Interacting selectively and non-covalently with an identical protein to form a homodimer." [GOC:jl] molecular_function
+ENSG00000198951 GO:0046477 glycosylceramide catabolic process "The chemical reactions and pathways resulting in the breakdown of glycosylceramides, any compound formed by the replacement of the glycosidic hydroxyl group of a cyclic form of a monosaccharide (or derivative) by a ceramide group." [GOC:ai] biological_process
+ENSG00000198951 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000198951 GO:0004553 hydrolase activity, hydrolyzing O-glycosyl compounds "Catalysis of the hydrolysis of any O-glycosyl bond." [GOC:mah] molecular_function
+ENSG00000100228 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100228 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100228 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000100228 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100228 GO:0005794 Golgi apparatus "A compound membranous cytoplasmic organelle of eukaryotic cells, consisting of flattened, ribosome-free vesicles arranged in a more or less regular stack. The Golgi apparatus differs from the endoplasmic reticulum in often having slightly thicker membranes, appearing in sections as a characteristic shallow semicircle so that the convex side (cis or entry face) abuts the endoplasmic reticulum, secretory vesicles emerging from the concave side (trans or exit face). In vertebrate cells there is usually one such organelle, while in invertebrates and plants, where they are known usually as dictyosomes, there may be several scattered in the cytoplasm. The Golgi apparatus processes proteins produced on the ribosomes of the rough endoplasmic reticulum; such processing includes modification of the core oligosaccharides of glycoproteins, and the sorting and packaging of proteins for transport to a variety of cellular locations. Three different regions of the Golgi are now recognized both in terms of structure and function: cis, in the vicinity of the cis face, trans, in the vicinity of the trans face, and medial, lying between the cis and trans regions." [ISBN:0198506732] cellular_component
+ENSG00000100228 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100228 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100228 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100228 GO:0005525 GTP binding "Interacting selectively and non-covalently with GTP, guanosine triphosphate." [GOC:ai] molecular_function
+ENSG00000100228 GO:0007264 small GTPase mediated signal transduction "Any series of molecular signals in which a small monomeric GTPase relays one or more of the signals." [GOC:mah] biological_process
+ENSG00000100228 GO:0015031 protein transport "The directed movement of proteins into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:ai] biological_process
+ENSG00000100228 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000100228 GO:0003924 GTPase activity "Catalysis of the reaction: GTP + H2O = GDP + phosphate." [ISBN:0198547684] molecular_function
+ENSG00000100228 GO:0006184 GTP catabolic process "The chemical reactions and pathways resulting in the breakdown of GTP, guanosine triphosphate." [ISBN:0198506732] biological_process
+ENSG00000100228 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000100228 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100228 GO:0034655 nucleobase-containing compound catabolic process "The chemical reactions and pathways resulting in the breakdown of nucleobases, nucleosides, nucleotides and nucleic acids." [GOC:mah] biological_process
+ENSG00000100228 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000100228 GO:0006886 intracellular protein transport "The directed movement of proteins in a cell, including the movement of proteins between specific compartments or structures within a cell, such as organelles of a eukaryotic cell." [GOC:mah] biological_process
+ENSG00000100228 GO:0006913 nucleocytoplasmic transport "The directed movement of molecules between the nucleus and the cytoplasm." [GOC:go_curators] biological_process
+ENSG00000100228 GO:0005622 intracellular "The living contents of a cell; the matter contained within (but not including) the plasma membrane, usually taken to exclude large vacuoles and masses of secretory or ingested material. In eukaryotes it includes the nucleus and cytoplasm." [ISBN:0198506732] cellular_component
+ENSG00000100228 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100228 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000205593 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000205593 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000205593 GO:0005768 endosome "A membrane-bounded organelle to which materials ingested by endocytosis are delivered." [ISBN:0198506732, PMID:19696797] cellular_component
+ENSG00000205593 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000205593 GO:0017112 Rab guanyl-nucleotide exchange factor activity "Stimulates the exchange of guanyl nucleotides associated with a GTPase of the Rab family. Under normal cellular physiological conditions, the concentration of GTP is higher than that of GDP, favoring the replacement of GDP by GTP in association with the GTPase." [GOC:mah] molecular_function
+ENSG00000205593 GO:0032851 positive regulation of Rab GTPase activity "Any process that activates or increases the activity of a GTPase of the Rab family." [GOC:mah] biological_process
+ENSG00000205593 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000205593
+ENSG00000128228 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000128228 GO:0005783 endoplasmic reticulum "The irregular network of unit membranes, visible only by electron microscopy, that occurs in the cytoplasm of many eukaryotic cells. The membranes form a complex meshwork of tubular channels, which are often expanded into slitlike cavities called cisternae. The ER takes two forms, rough (or granular), with ribosomes adhering to the outer surface, and smooth (with no ribosomes attached)." [ISBN:0198506732] cellular_component
+ENSG00000128228 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000128228 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000128228 GO:0071218 cellular response to misfolded protein "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a misfolded protein stimulus." [GOC:mah] biological_process
+ENSG00000128228 GO:0034976 response to endoplasmic reticulum stress "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a stress acting at the endoplasmic reticulum. ER stress usually results from the accumulation of unfolded or misfolded proteins in the ER lumen." [GOC:cjm, GOC:mah] biological_process
+ENSG00000128228 GO:0042981 regulation of apoptotic process "Any process that modulates the occurrence or rate of cell death by apoptotic process." [GOC:jl, GOC:mtg_apoptosis] biological_process
+ENSG00000128228 GO:0051087 chaperone binding "Interacting selectively and non-covalently with a chaperone protein, a class of proteins that bind to nascent or unfolded polypeptides and ensure correct folding or transport." [http://www.onelook.com] molecular_function
+ENSG00000128228 GO:0071712 ER-associated misfolded protein catabolic process "The chemical reactions and pathways resulting in the breakdown of misfolded proteins transported from the endoplasmic reticulum and targeted to cytoplasmic proteasomes for degradation." [GOC:mah, GOC:vw, PMID:14607247, PMID:19520858] biological_process
+ENSG00000128228 GO:0051787 misfolded protein binding "Interacting selectively and non-covalently with a misfolded protein." [GOC:ai] molecular_function
+ENSG00000128228 GO:0051117 ATPase binding "Interacting selectively and non-covalently with an ATPase, any enzyme that catalyzes the hydrolysis of ATP." [GOC:ai] molecular_function
+ENSG00000099901 GO:0046907 intracellular transport "The directed movement of substances within a cell." [GOC:ai] biological_process
+ENSG00000099901 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000099901 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000099901
+ENSG00000099901 GO:0005092 GDP-dissociation inhibitor activity "Prevents the dissociation of GDP from a GTPase, thereby preventing GTP from binding." [GOC:mah] molecular_function
+ENSG00000099901 GO:0005096 GTPase activator activity "Increases the activity of a GTPase, an enzyme that catalyzes the hydrolysis of GTP." [GOC:mah] molecular_function
+ENSG00000099901 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000099901 GO:0005635 nuclear envelope "The double lipid bilayer enclosing the nucleus and separating its contents from the rest of the cytoplasm; includes the intermembrane space, a gap of width 20-40 nm (also called the perinuclear space)." [ISBN:0198547684] cellular_component
+ENSG00000099901 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000099901 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000099901 GO:0008536 Ran GTPase binding "Interacting selectively and non-covalently with Ran, a conserved Ras-like GTP-binding protein, implicated in nucleocytoplasmic transport, cell cycle progression, spindle assembly, nuclear organization and nuclear envelope (NE) assembly." [GOC:rn, PMID:12787777, PMID:14726649] molecular_function
+ENSG00000099901 GO:0016032 viral process "A multi-organism process in which a virus is a participant. The other participant is the host. Includes infection of a host cell, replication of the viral genome, and assembly of progeny virus particles. In some cases the viral genetic material may integrate into the host genome and only subsequently, under particular circumstances, 'complete' its life cycle." [GOC:bf, GOC:jl, GOC:mah] biological_process
+ENSG00000099901 GO:0043547 positive regulation of GTPase activity "Any process that activates or increases the activity of a GTPase." [GOC:jl] biological_process
+ENSG00000099901 GO:0050790 regulation of catalytic activity "Any process that modulates the activity of an enzyme." [GOC:ai] biological_process
+ENSG00000099901 GO:0044403 symbiosis, encompassing mutualism through parasitism "An interaction between two organisms living together in more or less intimate association. The term host is usually used for the larger (macro) of the two members of a symbiosis. The smaller (micro) member is called the symbiont organism. Microscopic symbionts are often referred to as endosymbionts. The various forms of symbiosis include parasitism, in which the association is disadvantageous or destructive to one of the organisms; mutualism, in which the association is advantageous, or often necessary to one or both and not harmful to either; and commensalism, in which one member of the association benefits while the other is not affected. However, mutualism, parasitism, and commensalism are often not discrete categories of interactions and should rather be perceived as a continuum of interaction ranging from parasitism to mutualism. In fact, the direction of a symbiotic interaction can change during the lifetime of the symbionts due to developmental changes as well as changes in the biotic/abiotic environment in which the interaction occurs." [GOC:cc, http://www.free-definition.com] biological_process
+ENSG00000099901 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000099901 GO:0019899 enzyme binding "Interacting selectively and non-covalently with any enzyme." [GOC:jl] molecular_function
+ENSG00000099901 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000099901 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000099901 GO:0030234 enzyme regulator activity "Binds to and modulates the activity of an enzyme." [GOC:mah] molecular_function
+ENSG00000099901 GO:0005813 centrosome "A structure comprised of a core structure (in most organisms, a pair of centrioles) and peripheral material from which a microtubule-based structure, such as a spindle apparatus, is organized. Centrosomes occur close to the nucleus during interphase in many eukaryotic cells, though in animal cells it changes continually during the cell-division cycle." [GOC:mah, ISBN:0198547684] cellular_component
+ENSG00000099901 GO:0007051 spindle organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of the spindle, the array of microtubules and associated molecules that forms between opposite poles of a eukaryotic cell during DNA segregation and serves to move the duplicated chromosomes apart." [GOC:mah] biological_process
+ENSG00000099901 GO:0046604 positive regulation of mitotic centrosome separation "Any process that activates or increases the frequency, rate or extent of centrosome separation." [GOC:ai] biological_process
+ENSG00000215568
+ENSG00000100401 GO:0005098 Ran GTPase activator activity "Increases the rate of GTP hydrolysis by a GTPase of the Ran family." [GOC:mah] molecular_function
+ENSG00000100401 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000100401 GO:0032853 positive regulation of Ran GTPase activity "Any process that activates or increases the activity of a GTPase of the Ran family." [GOC:mah] biological_process
+ENSG00000100401 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100401 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100401 GO:0030234 enzyme regulator activity "Binds to and modulates the activity of an enzyme." [GOC:mah] molecular_function
+ENSG00000100401 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100401 GO:0005856 cytoskeleton "Any of the various filamentous elements that form the internal framework of cells, and typically remain after treatment of the cells with mild detergent to remove membrane constituents and soluble components of the cytoplasm. The term embraces intermediate filaments, microfilaments, microtubules, the microtrabecular lattice, and other structures characterized by a polymeric filamentous nature and long-range order within the cell. The various elements of the cytoskeleton not only serve in the maintenance of cellular shape but also have roles in other cellular functions, including cellular movement, cell division, endocytosis, and movement of organelles." [GOC:mah, ISBN:0198547684, PMID:16959967] cellular_component
+ENSG00000100401 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100401 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000100401 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100401 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100401 GO:0007049 cell cycle "The progression of biochemical and morphological phases and events that occur in a cell during successive cell replication or nuclear replication events. Canonically, the cell cycle comprises the replication and segregation of genetic material followed by the division of the cell, but in endocycles or syncytial cells nuclear replication or nuclear division may not be followed by cell division." [GOC:go_curators, GOC:mtg_cell_cycle] biological_process
+ENSG00000100401 GO:0000278 mitotic cell cycle "Progression through the phases of the mitotic cell cycle, the most common eukaryotic cell cycle, which canonically comprises four successive phases called G1, S, G2, and M and includes replication of the genome and the subsequent segregation of chromosomes into daughter cells. In some variant cell cycles nuclear replication or nuclear division may not be followed by cell division, or G1 and G2 phases may be absent." [GOC:mah, ISBN:0815316194, Reactome:69278] biological_process
+ENSG00000100401 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100401 GO:0005643 nuclear pore "Any of the numerous similar discrete openings in the nuclear envelope of a eukaryotic cell, where the inner and outer nuclear membranes are joined." [ISBN:0198547684] cellular_component
+ENSG00000100401 GO:0046826 negative regulation of protein export from nucleus "Any process that stops, prevents, or reduces the frequency, rate or extent of the directed movement of proteins from the nucleus into the cytoplasm." [GOC:bf] biological_process
+ENSG00000100401 GO:0000776 kinetochore "A multisubunit complex that is located at the centromeric region of DNA and provides an attachment point for the spindle microtubules." [GOC:elh] cellular_component
+ENSG00000100401 GO:0048471 perinuclear region of cytoplasm "Cytoplasm situated near, or occurring around, the nucleus." [GOC:jid] cellular_component
+ENSG00000100401
+ENSG00000130638 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000130638 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000130638 GO:0008219 cell death "Any biological process that results in permanent cessation of all vital functions of a cell. A cell should be considered dead when any one of the following molecular or morphological criteria is met: (1) the cell has lost the integrity of its plasma membrane; (2) the cell, including its nucleus, has undergone complete fragmentation into discrete bodies (frequently referred to as \"apoptotic bodies\"); and/or (3) its corpse (or its fragments) have been engulfed by an adjacent cell in vivo." [GOC:mah, GOC:mtg_apoptosis] biological_process
+ENSG00000130638 GO:0048856 anatomical structure development "The biological process whose specific outcome is the progression of an anatomical structure from an initial condition to its mature state. This process begins with the formation of the structure and ends with the mature structure, whatever form that may be including its natural destruction. An anatomical structure is any biological entity that occupies space and is distinguished from its surroundings. Anatomical structures can be macroscopic such as a carpel, or microscopic such as an acrosome." [GO_REF:0000021, GOC:mtg_15jun06] biological_process
+ENSG00000130638 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000130638 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000130638 GO:0005615 extracellular space "That part of a multicellular organism outside the cells proper, usually taken to be outside the plasma membranes, and occupied by fluid." [ISBN:0198547684] cellular_component
+ENSG00000130638 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000130638 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000130638 GO:0007399 nervous system development "The process whose specific outcome is the progression of nervous tissue over time, from its formation to its mature state." [GOC:dgh] biological_process
+ENSG00000130638 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000130638 GO:0030425 dendrite "A neuron projection that has a short, tapering, often branched, morphology, receives and integrates signals from other neurons or from sensory stimuli, and conducts a nerve impulse towards the axon or the cell body. In most neurons, the impulse is conveyed from dendrites to axon via the cell body, but in some types of unipolar neuron, the impulse does not travel via the cell body." [GOC:dos, GOC:mah, GOC:nln, ISBN:0198506732] cellular_component
+ENSG00000130638 GO:0031175 neuron projection development "The process whose specific outcome is the progression of a neuron projection over time, from its formation to the mature structure. A neuron projection is any process extending from a neural cell, such as axons or dendrites (collectively called neurites)." [GOC:mah] biological_process
+ENSG00000130638 GO:0043025 neuronal cell body "The portion of a neuron that includes the nucleus, but excludes cell projections such as axons and dendrites." [GOC:go_curators] cellular_component
+ENSG00000130638 GO:0048471 perinuclear region of cytoplasm "Cytoplasm situated near, or occurring around, the nucleus." [GOC:jid] cellular_component
+ENSG00000130638 GO:0005488 binding "The selective, non-covalent, often stoichiometric, interaction of a molecule with one or more specific sites on another molecule." [GOC:ceb, GOC:mah, ISBN:0198506732] molecular_function
+ENSG00000130638 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000130638 GO:0042802 identical protein binding "Interacting selectively and non-covalently with an identical protein or proteins." [GOC:jl] molecular_function
+ENSG00000130638
+ENSG00000128191 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000128191 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000128191 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000128191 GO:0005856 cytoskeleton "Any of the various filamentous elements that form the internal framework of cells, and typically remain after treatment of the cells with mild detergent to remove membrane constituents and soluble components of the cytoplasm. The term embraces intermediate filaments, microfilaments, microtubules, the microtrabecular lattice, and other structures characterized by a polymeric filamentous nature and long-range order within the cell. The various elements of the cytoskeleton not only serve in the maintenance of cellular shape but also have roles in other cellular functions, including cellular movement, cell division, endocytosis, and movement of organelles." [GOC:mah, ISBN:0198547684, PMID:16959967] cellular_component
+ENSG00000128191 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000128191 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000128191 GO:0005654 nucleoplasm "That part of the nuclear content other than the chromosomes or the nucleolus." [GOC:ma, ISBN:0124325653] cellular_component
+ENSG00000128191 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000128191 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000128191 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000128191 GO:0003725 double-stranded RNA binding "Interacting selectively and non-covalently with double-stranded RNA." [GOC:jl] molecular_function
+ENSG00000128191 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000128191 GO:0010467 gene expression "The process in which a gene's sequence is converted into a mature gene product or products (proteins or RNA). This includes the production of an RNA transcript as well as any processing to produce a mature RNA product or an mRNA (for protein-coding genes) and the translation of that mRNA into protein. Some protein processing events may be included when they are required to form an active form of a product from an inactive precursor form." [GOC:dph, GOC:tb] biological_process
+ENSG00000128191 GO:0015630 microtubule cytoskeleton "The part of the cytoskeleton (the internal framework of a cell) composed of microtubules and associated proteins." [GOC:jl, ISBN:0395825172] cellular_component
+ENSG00000128191 GO:0031053 primary miRNA processing "Any process involved in the conversion of a primary microRNA transcript into a pre-microRNA molecule." [GOC:sl, PMID:15211354] biological_process
+ENSG00000128191 GO:0046872 metal ion binding "Interacting selectively and non-covalently with any metal ion." [GOC:ai] molecular_function
+ENSG00000128191 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000128191 GO:0072091 regulation of stem cell proliferation "Any process that modulates the frequency, rate or extent of stem cell proliferation. A stem cell is a cell that retains the ability to divide and proliferate throughout life to provide progenitor cells that can differentiate into specialized cells." [GOC:mtg_kidney_jan10] biological_process
+ENSG00000128191
+ENSG00000187792 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000187792 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000187792 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000187792 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000187792 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000187792 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000187792 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000187792 GO:0003677 DNA binding "Any molecular function by which a gene product interacts selectively and non-covalently with DNA (deoxyribonucleic acid)." [GOC:dph, GOC:jl, GOC:tb, GOC:vw] molecular_function
+ENSG00000187792 GO:0006351 transcription, DNA-templated "The cellular synthesis of RNA on a template of DNA." [GOC:jl, GOC:txnOH] biological_process
+ENSG00000187792 GO:0006355 regulation of transcription, DNA-templated "Any process that modulates the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000187792 GO:0046872 metal ion binding "Interacting selectively and non-covalently with any metal ion." [GOC:ai] molecular_function
+ENSG00000187792 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000128335 GO:0005576 extracellular region "The space external to the outermost structure of a cell. For cells without external protective or external encapsulating structures this refers to space outside of the plasma membrane. This term covers the host cell environment outside an intracellular parasite." [GOC:go_curators] cellular_component
+ENSG00000128335 GO:0006869 lipid transport "The directed movement of lipids into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore. Lipids are compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent." [ISBN:0198506732] biological_process
+ENSG00000128335 GO:0008289 lipid binding "Interacting selectively and non-covalently with a lipid." [GOC:ai] molecular_function
+ENSG00000128335 GO:0042157 lipoprotein metabolic process "The chemical reactions and pathways involving any conjugated, water-soluble protein in which the nonprotein group consists of a lipid or lipids." [ISBN:0198506732] biological_process
+ENSG00000128335 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000128335 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000128335 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000128335 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000128335 GO:0005102 receptor binding "Interacting selectively and non-covalently with one or more specific sites on a receptor molecule, a macromolecule that undergoes combination with a hormone, neurotransmitter, drug or intracellular messenger to initiate a change in cell function." [GOC:bf, GOC:ceb, ISBN:0198506732] molecular_function
+ENSG00000128335 GO:0005789 endoplasmic reticulum membrane "The lipid bilayer surrounding the endoplasmic reticulum." [GOC:mah] cellular_component
+ENSG00000128335 GO:0006629 lipid metabolic process "The chemical reactions and pathways involving lipids, compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent. Includes fatty acids; neutral fats, other fatty-acid esters, and soaps; long-chain (fatty) alcohols and waxes; sphingoids and other long-chain bases; glycolipids, phospholipids and sphingolipids; and carotenes, polyprenols, sterols, terpenes and other isoprenoids." [GOC:ma] biological_process
+ENSG00000128335 GO:0006953 acute-phase response "An acute inflammatory response that involves non-antibody proteins whose concentrations in the plasma increase in response to infection or injury of homeothermic animals." [ISBN:0198506732] biological_process
+ENSG00000128335 GO:0007275 multicellular organismal development "The biological process whose specific outcome is the progression of a multicellular organism over time from an initial condition (e.g. a zygote or a young adult) to a later condition (e.g. a multicellular animal or an aged adult)." [GOC:dph, GOC:ems, GOC:isa_complete, GOC:tb] biological_process
+ENSG00000128335 GO:0008035 high-density lipoprotein particle binding "Interacting selectively and non-covalently with high-density lipoprotein particle, a lipoprotein particle with a high density (typically 1.063-1.21 g/ml) and a diameter of 5-10 nm that contains APOAs and may contain APOCs and APOE." [GOC:mah] molecular_function
+ENSG00000128335 GO:0008203 cholesterol metabolic process "The chemical reactions and pathways involving cholesterol, cholest-5-en-3 beta-ol, the principal sterol of vertebrates and the precursor of many steroids, including bile acids and steroid hormones. It is a component of the plasma membrane lipid bilayer and of plasma lipoproteins and can be found in all animal tissues." [ISBN:0198506732] biological_process
+ENSG00000128335 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000128335 GO:0060135 maternal process involved in female pregnancy "A reproductive process occurring in the mother that allows an embryo or fetus to develop within it." [GOC:dph] biological_process
+ENSG00000128335 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000128335 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000187905
+ENSG00000100403 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100403 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000100403 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100403 GO:0044403 symbiosis, encompassing mutualism through parasitism "An interaction between two organisms living together in more or less intimate association. The term host is usually used for the larger (macro) of the two members of a symbiosis. The smaller (micro) member is called the symbiont organism. Microscopic symbionts are often referred to as endosymbionts. The various forms of symbiosis include parasitism, in which the association is disadvantageous or destructive to one of the organisms; mutualism, in which the association is advantageous, or often necessary to one or both and not harmful to either; and commensalism, in which one member of the association benefits while the other is not affected. However, mutualism, parasitism, and commensalism are often not discrete categories of interactions and should rather be perceived as a continuum of interaction ranging from parasitism to mutualism. In fact, the direction of a symbiotic interaction can change during the lifetime of the symbionts due to developmental changes as well as changes in the biotic/abiotic environment in which the interaction occurs." [GOC:cc, http://www.free-definition.com] biological_process
+ENSG00000100403 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100403 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100403 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100403 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100403 GO:0016032 viral process "A multi-organism process in which a virus is a participant. The other participant is the host. Includes infection of a host cell, replication of the viral genome, and assembly of progeny virus particles. In some cases the viral genetic material may integrate into the host genome and only subsequently, under particular circumstances, 'complete' its life cycle." [GOC:bf, GOC:jl, GOC:mah] biological_process
+ENSG00000100403 GO:0044822 poly(A) RNA binding "Interacting non-covalently with a poly(A) RNA, a RNA molecule which has a tail of adenine bases." [GOC:jl] molecular_function
+ENSG00000100403 GO:0046872 metal ion binding "Interacting selectively and non-covalently with any metal ion." [GOC:ai] molecular_function
+ENSG00000100403 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000167037 GO:0005097 Rab GTPase activator activity "Increases the rate of GTP hydrolysis by a GTPase of the Rab family." [GOC:mah] molecular_function
+ENSG00000167037 GO:0005794 Golgi apparatus "A compound membranous cytoplasmic organelle of eukaryotic cells, consisting of flattened, ribosome-free vesicles arranged in a more or less regular stack. The Golgi apparatus differs from the endoplasmic reticulum in often having slightly thicker membranes, appearing in sections as a characteristic shallow semicircle so that the convex side (cis or entry face) abuts the endoplasmic reticulum, secretory vesicles emerging from the concave side (trans or exit face). In vertebrate cells there is usually one such organelle, while in invertebrates and plants, where they are known usually as dictyosomes, there may be several scattered in the cytoplasm. The Golgi apparatus processes proteins produced on the ribosomes of the rough endoplasmic reticulum; such processing includes modification of the core oligosaccharides of glycoproteins, and the sorting and packaging of proteins for transport to a variety of cellular locations. Three different regions of the Golgi are now recognized both in terms of structure and function: cis, in the vicinity of the cis face, trans, in the vicinity of the trans face, and medial, lying between the cis and trans regions." [ISBN:0198506732] cellular_component
+ENSG00000167037 GO:0032851 positive regulation of Rab GTPase activity "Any process that activates or increases the activity of a GTPase of the Rab family." [GOC:mah] biological_process
+ENSG00000167037 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000167037 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000167037 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000167037 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000167037 GO:0030234 enzyme regulator activity "Binds to and modulates the activity of an enzyme." [GOC:mah] molecular_function
+ENSG00000167037 GO:0032313 regulation of Rab GTPase activity "Any process that modulates the activity of a GTPase of the Rab family." [GOC:mah] biological_process
+ENSG00000258555 GO:0046983 protein dimerization activity "The formation of a protein dimer, a macromolecular structure consists of two noncovalently associated identical or nonidentical subunits." [ISBN:0198506732] molecular_function
+ENSG00000258555 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000258555 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000258555 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000258555 GO:0019899 enzyme binding "Interacting selectively and non-covalently with any enzyme." [GOC:jl] molecular_function
+ENSG00000258555 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000258555 GO:0003013 circulatory system process "A organ system process carried out by any of the organs or tissues of the circulatory system. The circulatory system is an organ system that moves extracellular fluids to and from tissue within a multicellular organism." [GOC:mtg_cardio] biological_process
+ENSG00000258555 GO:0050877 neurological system process "A organ system process carried out by any of the organs or tissues of neurological system." [GOC:ai, GOC:mtg_cardio] biological_process
+ENSG00000258555 GO:0048856 anatomical structure development "The biological process whose specific outcome is the progression of an anatomical structure from an initial condition to its mature state. This process begins with the formation of the structure and ends with the mature structure, whatever form that may be including its natural destruction. An anatomical structure is any biological entity that occupies space and is distinguished from its surroundings. Anatomical structures can be macroscopic such as a carpel, or microscopic such as an acrosome." [GO_REF:0000021, GOC:mtg_15jun06] biological_process
+ENSG00000258555 GO:0007267 cell-cell signaling "Any process that mediates the transfer of information from one cell to another." [GOC:mah] biological_process
+ENSG00000258555 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000258555 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000258555 GO:0008219 cell death "Any biological process that results in permanent cessation of all vital functions of a cell. A cell should be considered dead when any one of the following molecular or morphological criteria is met: (1) the cell has lost the integrity of its plasma membrane; (2) the cell, including its nucleus, has undergone complete fragmentation into discrete bodies (frequently referred to as \"apoptotic bodies\"); and/or (3) its corpse (or its fragments) have been engulfed by an adjacent cell in vivo." [GOC:mah, GOC:mtg_apoptosis] biological_process
+ENSG00000258555 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000258555 GO:0016192 vesicle-mediated transport "A cellular transport process in which transported substances are moved in membrane-bounded vesicles; transported substances are enclosed in the vesicle lumen or located in the vesicle membrane. The process begins with a step that directs a substance to the forming vesicle, and includes vesicle budding and coating. Vesicles are then targeted to, and fuse with, an acceptor membrane." [GOC:ai, GOC:mah, ISBN:08789310662000] biological_process
+ENSG00000258555 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000258555 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000258555 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000258555 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000258555 GO:0004871 signal transducer activity "Conveys a signal across a cell to trigger a change in cell function or state. A signal is a physical entity or change in state that is used to transfer information in order to trigger a response." [GOC:go_curators] molecular_function
+ENSG00000258555 GO:0001609 G-protein coupled adenosine receptor activity "Combining with adenosine and transmitting the signal across the membrane by activating an associated G-protein; promotes the exchange of GDP for GTP on the alpha subunit of a heterotrimeric G-protein complex." [GOC:bf, GOC:mah, PMID:9755289] molecular_function
+ENSG00000258555 GO:0001973 adenosine receptor signaling pathway "The series of molecular signals generated as a consequence of a receptor binding to extracellular adenosine and transmitting the signal to a heterotrimeric G-protein complex to initiate a change in cell activity." [GOC:dph] biological_process
+ENSG00000258555 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000258555 GO:0005887 integral component of plasma membrane "The component of the plasma membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000258555 GO:0006171 cAMP biosynthetic process "The chemical reactions and pathways resulting in the formation of the nucleotide cAMP (cyclic AMP, adenosine 3',5'-cyclophosphate)." [ISBN:0198506732] biological_process
+ENSG00000258555 GO:0006909 phagocytosis "An endocytosis process that results in the engulfment of external particulate material by phagocytes. The particles are initially contained within phagocytic vacuoles (phagosomes), which then fuse with primary lysosomes to effect digestion of the particles." [ISBN:0198506732] biological_process
+ENSG00000258555 GO:0006915 apoptotic process "A programmed cell death process which begins when a cell receives an internal (e.g. DNA damage) or external signal (e.g. an extracellular death ligand), and proceeds through a series of biochemical events (signaling pathway phase) which trigger an execution phase. The execution phase is the last step of an apoptotic process, and is typically characterized by rounding-up of the cell, retraction of pseudopodes, reduction of cellular volume (pyknosis), chromatin condensation, nuclear fragmentation (karyorrhexis), plasma membrane blebbing and fragmentation of the cell into apoptotic bodies. When the execution phase is completed, the cell has died." [GOC:dhl, GOC:ecd, GOC:go_curators, GOC:mtg_apoptosis, GOC:tb, ISBN:0198506732, PMID:18846107, PMID:21494263] biological_process
+ENSG00000258555 GO:0006954 inflammatory response "The immediate defensive reaction (by vertebrate tissue) to infection or injury caused by chemical or physical agents. The process is characterized by local vasodilation, extravasation of plasma into intercellular spaces and accumulation of white blood cells and macrophages." [GO_REF:0000022, GOC:mtg_15nov05, ISBN:0198506732] biological_process
+ENSG00000258555 GO:0006968 cellular defense response "A defense response that is mediated by cells." [GOC:ebc] biological_process
+ENSG00000258555 GO:0007169 transmembrane receptor protein tyrosine kinase signaling pathway "A series of molecular signals initiated by the binding of an extracellular ligand to a receptor on the surface of the target cell where the receptor possesses tyrosine kinase activity, and ending with regulation of a downstream cellular process, e.g. transcription." [GOC:ceb, GOC:signaling] biological_process
+ENSG00000258555 GO:0007188 adenylate cyclase-modulating G-protein coupled receptor signaling pathway "The series of molecular signals generated as a consequence of a G-protein coupled receptor binding to its physiological ligand, where the pathway proceeds through activation or inhibition of adenylyl cyclase activity and a subsequent change in the concentration of cyclic AMP (cAMP)." [GOC:mah, GOC:signaling, ISBN:0815316194] biological_process
+ENSG00000258555 GO:0007190 activation of adenylate cyclase activity "Any process that initiates the activity of the inactive enzyme adenylate cyclase." [GOC:ai] biological_process
+ENSG00000258555 GO:0007417 central nervous system development "The process whose specific outcome is the progression of the central nervous system over time, from its formation to the mature structure. The central nervous system is the core nervous system that serves an integrating and coordinating function. In vertebrates it consists of the brain, spinal cord and spinal nerves. In those invertebrates with a central nervous system it typically consists of a brain, cerebral ganglia and a nerve cord." [GOC:bf, GOC:jid, ISBN:0582227089] biological_process
+ENSG00000258555 GO:0007596 blood coagulation "The sequential process in which the multiple coagulation factors of the blood interact, ultimately resulting in the formation of an insoluble fibrin clot; it may be divided into three stages: stage 1, the formation of intrinsic and extrinsic prothrombin converting principle; stage 2, the formation of thrombin; stage 3, the formation of stable fibrin polymers." [http://www.graylab.ac.uk/omd/, ISBN:0198506732] biological_process
+ENSG00000258555 GO:0007600 sensory perception "The series of events required for an organism to receive a sensory stimulus, convert it to a molecular signal, and recognize and characterize the signal. This is a neurological process." [GOC:ai, GOC:dph] biological_process
+ENSG00000258555 GO:0008015 blood circulation "The flow of blood through the body of an animal, enabling the transport of nutrients to the tissues and the removal of waste products." [GOC:mtg_heart, ISBN:0192800825] biological_process
+ENSG00000258555 GO:0010579 positive regulation of adenylate cyclase activity involved in G-protein coupled receptor signaling pathway "Any process that increases the frequency, rate or extent of adenylate cyclase (AC) activity that is an integral part of a G-protein coupled receptor signaling pathway." [GOC:dph, GOC:signaling, GOC:tb] biological_process
+ENSG00000258555 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000258555 GO:0042802 identical protein binding "Interacting selectively and non-covalently with an identical protein or proteins." [GOC:jl] molecular_function
+ENSG00000258555 GO:0048011 neurotrophin TRK receptor signaling pathway "A series of molecular signals initiated by the binding of a neurotrophin to a receptor on the surface of the target cell where the receptor possesses tyrosine kinase activity, and ending with regulation of a downstream cellular process, e.g. transcription." [GOC:bf, GOC:ceb, GOC:jc, GOC:signaling, PMID:12065629, Wikipedia:Trk_receptor] biological_process
+ENSG00000258555 GO:0004930 G-protein coupled receptor activity "Combining with an extracellular signal and transmitting the signal across the membrane by activating an associated G-protein; promotes the exchange of GDP for GTP on the alpha subunit of a heterotrimeric G-protein complex." [GOC:bf, http://www.iuphar-db.org, Wikipedia:GPCR] molecular_function
+ENSG00000258555 GO:0007186 G-protein coupled receptor signaling pathway "A series of molecular signals that proceeds with an activated receptor promoting the exchange of GDP for GTP on the alpha-subunit of an associated heterotrimeric G-protein complex. The GTP-bound activated alpha-G-protein then dissociates from the beta- and gamma-subunits to further transmit the signal within the cell. The pathway begins with receptor-ligand interaction, or for basal GPCR signaling the pathway begins with the receptor activating its G protein in the absence of an agonist, and ends with regulation of a downstream cellular process, e.g. transcription." [GOC:bf, GOC:mah, Wikipedia:G_protein-coupled_receptor] biological_process
+ENSG00000133466 GO:0005581 collagen trimer "A protein complex consisting of three collagen chains assembled into a left-handed triple helix. These trimers typically assemble into higher order structures." [GOC:dos, GOC:mah, ISBN:0721639976, PMID:19693541, PMID:21421911] cellular_component
+ENSG00000133466 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000133466 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000133466 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000133466 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000133466 GO:0005615 extracellular space "That part of a multicellular organism outside the cells proper, usually taken to be outside the plasma membranes, and occupied by fluid." [ISBN:0198547684] cellular_component
+ENSG00000133466 GO:0042802 identical protein binding "Interacting selectively and non-covalently with an identical protein or proteins." [GOC:jl] molecular_function
+ENSG00000133466 GO:0070208 protein heterotrimerization "The formation of a protein heterotrimer, a macromolecular structure consisting of three noncovalently associated subunits, of which not all are identical." [GOC:hjd] biological_process
+ENSG00000133466 GO:0051259 protein oligomerization "The process of creating protein oligomers, compounds composed of a small number, usually between three and ten, of component monomers; protein oligomers may be composed of different or identical monomers. Oligomers may be formed by the polymerization of a number of monomers or the depolymerization of a large protein polymer." [GOC:ai] biological_process
+ENSG00000133466
+ENSG00000099899 GO:0000166 nucleotide binding "Interacting selectively and non-covalently with a nucleotide, any compound consisting of a nucleoside that is esterified with (ortho)phosphate or an oligophosphate at any hydroxyl group on the ribose or deoxyribose." [GOC:mah, ISBN:0198547684] molecular_function
+ENSG00000099899 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000099899 GO:0008168 methyltransferase activity "Catalysis of the transfer of a methyl group to an acceptor molecule." [ISBN:0198506732] molecular_function
+ENSG00000099899 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000099899 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000099899 GO:0001510 RNA methylation "Posttranscriptional addition of a methyl group to either a nucleotide or 2'-O ribose in a polyribonucleotide. Usually uses S-adenosylmethionine as a cofactor." [GOC:hjd] biological_process
+ENSG00000099899 GO:0006396 RNA processing "Any process involved in the conversion of one or more primary RNA transcripts into one or more mature RNA molecules." [GOC:mah] biological_process
+ENSG00000099899 GO:0008173 RNA methyltransferase activity "Catalysis of the transfer of a methyl group from a donor to a nucleoside residue in an RNA molecule." [GOC:mah] molecular_function
+ENSG00000099899 GO:0044822 poly(A) RNA binding "Interacting non-covalently with a poly(A) RNA, a RNA molecule which has a tail of adenine bases." [GOC:jl] molecular_function
+ENSG00000099899 GO:0003723 RNA binding "Interacting selectively and non-covalently with an RNA molecule or a portion thereof." [GOC:mah] molecular_function
+ENSG00000099899 GO:0003676 nucleic acid binding "Interacting selectively and non-covalently with any nucleic acid." [GOC:jl] molecular_function
+ENSG00000099899 GO:0004719 protein-L-isoaspartate (D-aspartate) O-methyltransferase activity "Catalysis of the reaction: S-adenosyl-L-methionine + protein L-beta-aspartate = S-adenosyl-L-homocysteine + protein L-beta-aspartate methyl ester." [EC:2.1.1.77] molecular_function
+ENSG00000099899 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000099899 GO:0008176 tRNA (guanine-N7-)-methyltransferase activity "Catalysis of the reaction: S-adenosyl-L-methionine + tRNA = S-adenosyl-L-homocysteine + tRNA containing N7-methylguanine." [EC:2.1.1.33] molecular_function
+ENSG00000099899 GO:0006400 tRNA modification "The covalent alteration of one or more nucleotides within a tRNA molecule to produce a tRNA molecule with a sequence that differs from that coded genetically." [GOC:curators] biological_process
+ENSG00000099899 GO:0006399 tRNA metabolic process "The chemical reactions and pathways involving tRNA, transfer RNA, a class of relatively small RNA molecules responsible for mediating the insertion of amino acids into the sequence of nascent polypeptide chains during protein synthesis. Transfer RNA is characterized by the presence of many unusual minor bases, the function of which has not been completely established." [ISBN:0198506732] biological_process
+ENSG00000099899 GO:0031167 rRNA methylation "The posttranscriptional addition of methyl groups to specific residues in an rRNA molecule." [GOC:mah] biological_process
+ENSG00000099899 GO:0008152 metabolic process "The chemical reactions and pathways, including anabolism and catabolism, by which living organisms transform chemical substances. Metabolic processes typically transform small molecules, but also include macromolecular processes such as DNA repair and replication, and protein synthesis and degradation." [GOC:go_curators, ISBN:0198547684] biological_process
+ENSG00000099899
+ENSG00000128271 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000128271 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000128271 GO:0004930 G-protein coupled receptor activity "Combining with an extracellular signal and transmitting the signal across the membrane by activating an associated G-protein; promotes the exchange of GDP for GTP on the alpha subunit of a heterotrimeric G-protein complex." [GOC:bf, http://www.iuphar-db.org, Wikipedia:GPCR] molecular_function
+ENSG00000128271 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000128271 GO:0004871 signal transducer activity "Conveys a signal across a cell to trigger a change in cell function or state. A signal is a physical entity or change in state that is used to transfer information in order to trigger a response." [GOC:go_curators] molecular_function
+ENSG00000128271 GO:0007186 G-protein coupled receptor signaling pathway "A series of molecular signals that proceeds with an activated receptor promoting the exchange of GDP for GTP on the alpha-subunit of an associated heterotrimeric G-protein complex. The GTP-bound activated alpha-G-protein then dissociates from the beta- and gamma-subunits to further transmit the signal within the cell. The pathway begins with receptor-ligand interaction, or for basal GPCR signaling the pathway begins with the receptor activating its G protein in the absence of an agonist, and ends with regulation of a downstream cellular process, e.g. transcription." [GOC:bf, GOC:mah, Wikipedia:G_protein-coupled_receptor] biological_process
+ENSG00000128271 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000128271 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000128271
+ENSG00000128271 GO:0019899 enzyme binding "Interacting selectively and non-covalently with any enzyme." [GOC:jl] molecular_function
+ENSG00000128271 GO:0003013 circulatory system process "A organ system process carried out by any of the organs or tissues of the circulatory system. The circulatory system is an organ system that moves extracellular fluids to and from tissue within a multicellular organism." [GOC:mtg_cardio] biological_process
+ENSG00000128271 GO:0050877 neurological system process "A organ system process carried out by any of the organs or tissues of neurological system." [GOC:ai, GOC:mtg_cardio] biological_process
+ENSG00000128271 GO:0048856 anatomical structure development "The biological process whose specific outcome is the progression of an anatomical structure from an initial condition to its mature state. This process begins with the formation of the structure and ends with the mature structure, whatever form that may be including its natural destruction. An anatomical structure is any biological entity that occupies space and is distinguished from its surroundings. Anatomical structures can be macroscopic such as a carpel, or microscopic such as an acrosome." [GO_REF:0000021, GOC:mtg_15jun06] biological_process
+ENSG00000128271 GO:0007267 cell-cell signaling "Any process that mediates the transfer of information from one cell to another." [GOC:mah] biological_process
+ENSG00000128271 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000128271 GO:0008219 cell death "Any biological process that results in permanent cessation of all vital functions of a cell. A cell should be considered dead when any one of the following molecular or morphological criteria is met: (1) the cell has lost the integrity of its plasma membrane; (2) the cell, including its nucleus, has undergone complete fragmentation into discrete bodies (frequently referred to as \"apoptotic bodies\"); and/or (3) its corpse (or its fragments) have been engulfed by an adjacent cell in vivo." [GOC:mah, GOC:mtg_apoptosis] biological_process
+ENSG00000128271 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000128271 GO:0016192 vesicle-mediated transport "A cellular transport process in which transported substances are moved in membrane-bounded vesicles; transported substances are enclosed in the vesicle lumen or located in the vesicle membrane. The process begins with a step that directs a substance to the forming vesicle, and includes vesicle budding and coating. Vesicles are then targeted to, and fuse with, an acceptor membrane." [GOC:ai, GOC:mah, ISBN:08789310662000] biological_process
+ENSG00000128271 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000128271 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000128271 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000128271 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000128271 GO:0001609 G-protein coupled adenosine receptor activity "Combining with adenosine and transmitting the signal across the membrane by activating an associated G-protein; promotes the exchange of GDP for GTP on the alpha subunit of a heterotrimeric G-protein complex." [GOC:bf, GOC:mah, PMID:9755289] molecular_function
+ENSG00000128271 GO:0001973 adenosine receptor signaling pathway "The series of molecular signals generated as a consequence of a receptor binding to extracellular adenosine and transmitting the signal to a heterotrimeric G-protein complex to initiate a change in cell activity." [GOC:dph] biological_process
+ENSG00000128271 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000128271 GO:0005887 integral component of plasma membrane "The component of the plasma membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000128271 GO:0006171 cAMP biosynthetic process "The chemical reactions and pathways resulting in the formation of the nucleotide cAMP (cyclic AMP, adenosine 3',5'-cyclophosphate)." [ISBN:0198506732] biological_process
+ENSG00000128271 GO:0006909 phagocytosis "An endocytosis process that results in the engulfment of external particulate material by phagocytes. The particles are initially contained within phagocytic vacuoles (phagosomes), which then fuse with primary lysosomes to effect digestion of the particles." [ISBN:0198506732] biological_process
+ENSG00000128271 GO:0006915 apoptotic process "A programmed cell death process which begins when a cell receives an internal (e.g. DNA damage) or external signal (e.g. an extracellular death ligand), and proceeds through a series of biochemical events (signaling pathway phase) which trigger an execution phase. The execution phase is the last step of an apoptotic process, and is typically characterized by rounding-up of the cell, retraction of pseudopodes, reduction of cellular volume (pyknosis), chromatin condensation, nuclear fragmentation (karyorrhexis), plasma membrane blebbing and fragmentation of the cell into apoptotic bodies. When the execution phase is completed, the cell has died." [GOC:dhl, GOC:ecd, GOC:go_curators, GOC:mtg_apoptosis, GOC:tb, ISBN:0198506732, PMID:18846107, PMID:21494263] biological_process
+ENSG00000128271 GO:0006954 inflammatory response "The immediate defensive reaction (by vertebrate tissue) to infection or injury caused by chemical or physical agents. The process is characterized by local vasodilation, extravasation of plasma into intercellular spaces and accumulation of white blood cells and macrophages." [GO_REF:0000022, GOC:mtg_15nov05, ISBN:0198506732] biological_process
+ENSG00000128271 GO:0006968 cellular defense response "A defense response that is mediated by cells." [GOC:ebc] biological_process
+ENSG00000128271 GO:0007169 transmembrane receptor protein tyrosine kinase signaling pathway "A series of molecular signals initiated by the binding of an extracellular ligand to a receptor on the surface of the target cell where the receptor possesses tyrosine kinase activity, and ending with regulation of a downstream cellular process, e.g. transcription." [GOC:ceb, GOC:signaling] biological_process
+ENSG00000128271 GO:0007188 adenylate cyclase-modulating G-protein coupled receptor signaling pathway "The series of molecular signals generated as a consequence of a G-protein coupled receptor binding to its physiological ligand, where the pathway proceeds through activation or inhibition of adenylyl cyclase activity and a subsequent change in the concentration of cyclic AMP (cAMP)." [GOC:mah, GOC:signaling, ISBN:0815316194] biological_process
+ENSG00000128271 GO:0007190 activation of adenylate cyclase activity "Any process that initiates the activity of the inactive enzyme adenylate cyclase." [GOC:ai] biological_process
+ENSG00000128271 GO:0007417 central nervous system development "The process whose specific outcome is the progression of the central nervous system over time, from its formation to the mature structure. The central nervous system is the core nervous system that serves an integrating and coordinating function. In vertebrates it consists of the brain, spinal cord and spinal nerves. In those invertebrates with a central nervous system it typically consists of a brain, cerebral ganglia and a nerve cord." [GOC:bf, GOC:jid, ISBN:0582227089] biological_process
+ENSG00000128271 GO:0007596 blood coagulation "The sequential process in which the multiple coagulation factors of the blood interact, ultimately resulting in the formation of an insoluble fibrin clot; it may be divided into three stages: stage 1, the formation of intrinsic and extrinsic prothrombin converting principle; stage 2, the formation of thrombin; stage 3, the formation of stable fibrin polymers." [http://www.graylab.ac.uk/omd/, ISBN:0198506732] biological_process
+ENSG00000128271 GO:0007600 sensory perception "The series of events required for an organism to receive a sensory stimulus, convert it to a molecular signal, and recognize and characterize the signal. This is a neurological process." [GOC:ai, GOC:dph] biological_process
+ENSG00000128271 GO:0008015 blood circulation "The flow of blood through the body of an animal, enabling the transport of nutrients to the tissues and the removal of waste products." [GOC:mtg_heart, ISBN:0192800825] biological_process
+ENSG00000128271 GO:0010579 positive regulation of adenylate cyclase activity involved in G-protein coupled receptor signaling pathway "Any process that increases the frequency, rate or extent of adenylate cyclase (AC) activity that is an integral part of a G-protein coupled receptor signaling pathway." [GOC:dph, GOC:signaling, GOC:tb] biological_process
+ENSG00000128271 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000128271 GO:0042802 identical protein binding "Interacting selectively and non-covalently with an identical protein or proteins." [GOC:jl] molecular_function
+ENSG00000128271 GO:0048011 neurotrophin TRK receptor signaling pathway "A series of molecular signals initiated by the binding of a neurotrophin to a receptor on the surface of the target cell where the receptor possesses tyrosine kinase activity, and ending with regulation of a downstream cellular process, e.g. transcription." [GOC:bf, GOC:ceb, GOC:jc, GOC:signaling, PMID:12065629, Wikipedia:Trk_receptor] biological_process
+ENSG00000100385 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000100385 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100385 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100385 GO:0044403 symbiosis, encompassing mutualism through parasitism "An interaction between two organisms living together in more or less intimate association. The term host is usually used for the larger (macro) of the two members of a symbiosis. The smaller (micro) member is called the symbiont organism. Microscopic symbionts are often referred to as endosymbionts. The various forms of symbiosis include parasitism, in which the association is disadvantageous or destructive to one of the organisms; mutualism, in which the association is advantageous, or often necessary to one or both and not harmful to either; and commensalism, in which one member of the association benefits while the other is not affected. However, mutualism, parasitism, and commensalism are often not discrete categories of interactions and should rather be perceived as a continuum of interaction ranging from parasitism to mutualism. In fact, the direction of a symbiotic interaction can change during the lifetime of the symbionts due to developmental changes as well as changes in the biotic/abiotic environment in which the interaction occurs." [GOC:cc, http://www.free-definition.com] biological_process
+ENSG00000100385 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100385 GO:0006461 protein complex assembly "The aggregation, arrangement and bonding together of a set of components to form a protein complex." [GOC:ai] biological_process
+ENSG00000100385 GO:0022607 cellular component assembly "The aggregation, arrangement and bonding together of a cellular component." [GOC:isa_complete] biological_process
+ENSG00000100385 GO:0065003 macromolecular complex assembly "The aggregation, arrangement and bonding together of a set of macromolecules to form a complex." [GOC:jl] biological_process
+ENSG00000100385 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000100385 GO:0004871 signal transducer activity "Conveys a signal across a cell to trigger a change in cell function or state. A signal is a physical entity or change in state that is used to transfer information in order to trigger a response." [GOC:go_curators] molecular_function
+ENSG00000100385 GO:0004911 interleukin-2 receptor activity "Combining with interleukin-2 and transmitting the signal from one side of the membrane to the other to initiate a change in cell activity." [GOC:jl, GOC:signaling] molecular_function
+ENSG00000100385 GO:0005887 integral component of plasma membrane "The component of the plasma membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000100385 GO:0009897 external side of plasma membrane "The leaflet the plasma membrane that faces away from the cytoplasm and any proteins embedded or anchored in it or attached to its surface." [GOC:dos, GOC:tb] cellular_component
+ENSG00000100385 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000100385 GO:0016032 viral process "A multi-organism process in which a virus is a participant. The other participant is the host. Includes infection of a host cell, replication of the viral genome, and assembly of progeny virus particles. In some cases the viral genetic material may integrate into the host genome and only subsequently, under particular circumstances, 'complete' its life cycle." [GOC:bf, GOC:jl, GOC:mah] biological_process
+ENSG00000100385 GO:0019221 cytokine-mediated signaling pathway "A series of molecular signals initiated by the binding of a cytokine to a receptor on the surface of a cell, and ending with regulation of a downstream cellular process, e.g. transcription." [GOC:mah, GOC:signaling, PMID:19295629] biological_process
+ENSG00000100385 GO:0019976 interleukin-2 binding "Interacting selectively and non-covalently with interleukin-2." [GOC:jl] molecular_function
+ENSG00000100385 GO:0038110 interleukin-2-mediated signaling pathway "A series of molecular signals initiated by the binding of interleukin-2 to a receptor on the surface of a cell, and ending with regulation of a downstream cellular process, e.g. transcription." [GOC:nhn, GOC:signaling] biological_process
+ENSG00000100385 GO:0043066 negative regulation of apoptotic process "Any process that stops, prevents, or reduces the frequency, rate or extent of cell death by apoptotic process." [GOC:jl, GOC:mtg_apoptosis] biological_process
+ENSG00000100385 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000138944 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000138944 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000130540 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000130540 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000130540 GO:0006629 lipid metabolic process "The chemical reactions and pathways involving lipids, compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent. Includes fatty acids; neutral fats, other fatty-acid esters, and soaps; long-chain (fatty) alcohols and waxes; sphingoids and other long-chain bases; glycolipids, phospholipids and sphingolipids; and carotenes, polyprenols, sterols, terpenes and other isoprenoids." [GOC:ma] biological_process
+ENSG00000130540 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000130540 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000130540 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000130540 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000130540 GO:0006805 xenobiotic metabolic process "The chemical reactions and pathways involving a xenobiotic compound, a compound foreign to living organisms. Used of chemical compounds, e.g. a xenobiotic chemical, such as a pesticide." [GOC:cab2] biological_process
+ENSG00000130540 GO:0008146 sulfotransferase activity "Catalysis of the transfer of a sulfate group from 3'-phosphoadenosine 5'-phosphosulfate to the hydroxyl group of an acceptor, producing the sulfated derivative and 3'-phosphoadenosine 5'-phosphate." [EC:2.8.2, GOC:curators] molecular_function
+ENSG00000130540 GO:0008202 steroid metabolic process "The chemical reactions and pathways involving steroids, compounds with a 1,2,cyclopentanoperhydrophenanthrene nucleus." [ISBN:0198547684] biological_process
+ENSG00000130540 GO:0050427 3'-phosphoadenosine 5'-phosphosulfate metabolic process "The chemical reactions and pathways involving 3'-phosphoadenosine 5'-phosphosulfate, a naturally occurring mixed anhydride. It is an intermediate in the formation of a variety of sulfo compounds in biological systems." [ISBN:0198506732] biological_process
+ENSG00000130540 GO:0006790 sulfur compound metabolic process "The chemical reactions and pathways involving the nonmetallic element sulfur or compounds that contain sulfur, such as the amino acids methionine and cysteine or the tripeptide glutathione." [GOC:ai] biological_process
+ENSG00000130540 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000130540 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000130540
+ENSG00000100330 GO:0004725 protein tyrosine phosphatase activity "Catalysis of the reaction: protein tyrosine phosphate + H2O = protein tyrosine + phosphate." [EC:3.1.3.48] molecular_function
+ENSG00000100330 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100330 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100330 GO:0035335 peptidyl-tyrosine dephosphorylation "The removal of phosphoric residues from peptidyl-O-phospho-tyrosine to form peptidyl-tyrosine." [GOC:bf] biological_process
+ENSG00000100330 GO:0046872 metal ion binding "Interacting selectively and non-covalently with any metal ion." [GOC:ai] molecular_function
+ENSG00000100330 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100330 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100330 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000100330 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100330 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100330 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100330 GO:0016791 phosphatase activity "Catalysis of the hydrolysis of phosphoric monoesters, releasing inorganic phosphate." [EC:3.1.3, EC:3.1.3.41, GOC:curators] molecular_function
+ENSG00000100330 GO:0006629 lipid metabolic process "The chemical reactions and pathways involving lipids, compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent. Includes fatty acids; neutral fats, other fatty-acid esters, and soaps; long-chain (fatty) alcohols and waxes; sphingoids and other long-chain bases; glycolipids, phospholipids and sphingolipids; and carotenes, polyprenols, sterols, terpenes and other isoprenoids." [GOC:ma] biological_process
+ENSG00000100330 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000100330 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000100330 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000100330 GO:0004438 phosphatidylinositol-3-phosphatase activity "Catalysis of the reaction: 1-phosphatidyl-1D-myo-inositol 3-phosphate + H2O = 1-phosphatidyl-1D-myo-inositol + phosphate." [EC:3.1.3.64] molecular_function
+ENSG00000100330 GO:0004722 protein serine/threonine phosphatase activity "Catalysis of the reaction: protein serine phosphate + H2O = protein serine + phosphate, and protein threonine phosphate + H2O = protein threonine + phosphate." [GOC:bf] molecular_function
+ENSG00000100330 GO:0006470 protein dephosphorylation "The process of removing one or more phosphoric residues from a protein." [GOC:hb] biological_process
+ENSG00000100330 GO:0006644 phospholipid metabolic process "The chemical reactions and pathways involving phospholipids, any lipid containing phosphoric acid as a mono- or diester." [ISBN:0198506732] biological_process
+ENSG00000100330 GO:0006661 phosphatidylinositol biosynthetic process "The chemical reactions and pathways resulting in the formation of phosphatidylinositol, any glycophospholipid in which the sn-glycerol 3-phosphate residue is esterified to the 1-hydroxyl group of 1D-myo-inositol." [CHEBI:28874, ISBN:0198506732] biological_process
+ENSG00000100330 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000100330 GO:0019898 extrinsic component of membrane "The component of a membrane consisting of gene products and protein complexes that are loosely bound to one of its surfaces, but not integrated into the hydrophobic region." [GOC:dos, GOC:jl, GOC:mah] cellular_component
+ENSG00000100330 GO:0046856 phosphatidylinositol dephosphorylation "The process of removing one or more phosphate groups from a phosphatidylinositol." [ISBN:0198506732] biological_process
+ENSG00000100330 GO:0052629 phosphatidylinositol-3,5-bisphosphate 3-phosphatase activity "Catalysis of the reaction: 1-phosphatidyl-1D-myo-inositol 3,5-bisphosphate + H2O = a 1-phosphatidyl-1D-myo-inositol 5-phosphate + phosphate + 2 H+." [MetaCyc:RXN-10958, PMID:19901554] molecular_function
+ENSG00000100330 GO:0016311 dephosphorylation "The process of removing one or more phosphoric (ester or anhydride) residues from a molecule." [ISBN:0198506732] biological_process
+ENSG00000100330
+ENSG00000138964 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000138964 GO:0007155 cell adhesion "The attachment of a cell, either to another cell or to an underlying substrate such as the extracellular matrix, via cell adhesion molecules." [GOC:hb, GOC:pf] biological_process
+ENSG00000138964 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000138964 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000138964 GO:0005856 cytoskeleton "Any of the various filamentous elements that form the internal framework of cells, and typically remain after treatment of the cells with mild detergent to remove membrane constituents and soluble components of the cytoplasm. The term embraces intermediate filaments, microfilaments, microtubules, the microtrabecular lattice, and other structures characterized by a polymeric filamentous nature and long-range order within the cell. The various elements of the cytoskeleton not only serve in the maintenance of cellular shape but also have roles in other cellular functions, including cellular movement, cell division, endocytosis, and movement of organelles." [GOC:mah, ISBN:0198547684, PMID:16959967] cellular_component
+ENSG00000138964 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000138964 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000138964 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000138964 GO:0008092 cytoskeletal protein binding "Interacting selectively and non-covalently with any protein component of any cytoskeleton (actin, microtubule, or intermediate filament cytoskeleton)." [GOC:mah] molecular_function
+ENSG00000138964 GO:0003779 actin binding "Interacting selectively and non-covalently with monomeric or multimeric forms of actin, including actin filaments." [GOC:clt] molecular_function
+ENSG00000138964 GO:0007160 cell-matrix adhesion "The binding of a cell to the extracellular matrix via adhesion molecules." [GOC:hb] biological_process
+ENSG00000138964 GO:0030054 cell junction "A cellular component that forms a specialized region of connection between two cells or between a cell and the extracellular matrix. At a cell junction, anchoring proteins extend through the plasma membrane to link cytoskeletal proteins in one cell to cytoskeletal proteins in neighboring cells or to proteins in the extracellular matrix." [GOC:mah, http://www.vivo.colostate.edu/hbooks/cmb/cells/pmemb/junctions_a.html, ISBN:0198506732] cellular_component
+ENSG00000138964 GO:0031532 actin cytoskeleton reorganization "A process that is carried out at the cellular level which results in dynamic structural changes to the arrangement of constituent parts of cytoskeletal structures comprising actin filaments and their associated proteins." [GOC:ecd, GOC:mah] biological_process
+ENSG00000138964 GO:0007010 cytoskeleton organization "A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of cytoskeletal structures." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000138964 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000138964
+ENSG00000075240
+ENSG00000075240 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000075240 GO:0008219 cell death "Any biological process that results in permanent cessation of all vital functions of a cell. A cell should be considered dead when any one of the following molecular or morphological criteria is met: (1) the cell has lost the integrity of its plasma membrane; (2) the cell, including its nucleus, has undergone complete fragmentation into discrete bodies (frequently referred to as \"apoptotic bodies\"); and/or (3) its corpse (or its fragments) have been engulfed by an adjacent cell in vivo." [GOC:mah, GOC:mtg_apoptosis] biological_process
+ENSG00000075240 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000075240 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000075240 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000075240 GO:0006915 apoptotic process "A programmed cell death process which begins when a cell receives an internal (e.g. DNA damage) or external signal (e.g. an extracellular death ligand), and proceeds through a series of biochemical events (signaling pathway phase) which trigger an execution phase. The execution phase is the last step of an apoptotic process, and is typically characterized by rounding-up of the cell, retraction of pseudopodes, reduction of cellular volume (pyknosis), chromatin condensation, nuclear fragmentation (karyorrhexis), plasma membrane blebbing and fragmentation of the cell into apoptotic bodies. When the execution phase is completed, the cell has died." [GOC:dhl, GOC:ecd, GOC:go_curators, GOC:mtg_apoptosis, GOC:tb, ISBN:0198506732, PMID:18846107, PMID:21494263] biological_process
+ENSG00000075240 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000100292 GO:0006788 heme oxidation "The chemical reactions and pathways resulting in the loss of electrons from one or more atoms in heme." [GOC:mah] biological_process
+ENSG00000100292 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100292 GO:0051186 cofactor metabolic process "The chemical reactions and pathways involving a cofactor, a substance that is required for the activity of an enzyme or other protein. Cofactors may be inorganic, such as the metal atoms zinc, iron, and copper in certain forms, or organic, in which case they are referred to as coenzymes. Cofactors may either be bound tightly to active sites or bind loosely with the substrate." [GOC:ai] biological_process
+ENSG00000100292 GO:0004392 heme oxygenase (decyclizing) activity "Catalysis of the reaction: heme + 3 donor-H2 + 3 O2 = biliverdin + Fe2+ + CO + 3 acceptor + 3 H2O." [EC:1.14.99.3] molecular_function
+ENSG00000100292 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100292 GO:0016491 oxidoreductase activity "Catalysis of an oxidation-reduction (redox) reaction, a reversible chemical reaction in which the oxidation state of an atom or atoms within a molecule is altered. One substrate acts as a hydrogen or electron donor and becomes oxidized, while the other acts as hydrogen or electron acceptor and becomes reduced." [GOC:go_curators] molecular_function
+ENSG00000100292 GO:0055114 oxidation-reduction process "A metabolic process that results in the removal or addition of one or more electrons to or from a substance, with or without the concomitant removal or addition of a proton or protons." [GOC:dhl, GOC:ecd, GOC:jh2, GOC:jid, GOC:mlg, GOC:rph] biological_process
+ENSG00000100292 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000100292 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100292 GO:0055085 transmembrane transport "The process in which a solute is transported from one side of a membrane to the other." [GOC:dph, GOC:jid] biological_process
+ENSG00000100292 GO:0042592 homeostatic process "Any biological process involved in the maintenance of an internal steady state." [GOC:jl, ISBN:0395825172] biological_process
+ENSG00000100292 GO:0006461 protein complex assembly "The aggregation, arrangement and bonding together of a set of components to form a protein complex." [GOC:ai] biological_process
+ENSG00000100292 GO:0022607 cellular component assembly "The aggregation, arrangement and bonding together of a cellular component." [GOC:isa_complete] biological_process
+ENSG00000100292 GO:0065003 macromolecular complex assembly "The aggregation, arrangement and bonding together of a set of macromolecules to form a complex." [GOC:jl] biological_process
+ENSG00000100292 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100292 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100292 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000100292 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000100292 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100292 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000100292 GO:0002376 immune system process "Any process involved in the development or functioning of the immune system, an organismal system for calibrated responses to potential internal or invasive threats." [GO_REF:0000022, GOC:add, GOC:mtg_15nov05] biological_process
+ENSG00000100292 GO:0019899 enzyme binding "Interacting selectively and non-covalently with any enzyme." [GOC:jl] molecular_function
+ENSG00000100292 GO:0008219 cell death "Any biological process that results in permanent cessation of all vital functions of a cell. A cell should be considered dead when any one of the following molecular or morphological criteria is met: (1) the cell has lost the integrity of its plasma membrane; (2) the cell, including its nucleus, has undergone complete fragmentation into discrete bodies (frequently referred to as \"apoptotic bodies\"); and/or (3) its corpse (or its fragments) have been engulfed by an adjacent cell in vivo." [GOC:mah, GOC:mtg_apoptosis] biological_process
+ENSG00000100292 GO:0005783 endoplasmic reticulum "The irregular network of unit membranes, visible only by electron microscopy, that occurs in the cytoplasm of many eukaryotic cells. The membranes form a complex meshwork of tubular channels, which are often expanded into slitlike cavities called cisternae. The ER takes two forms, rough (or granular), with ribosomes adhering to the outer surface, and smooth (with no ribosomes attached)." [ISBN:0198506732] cellular_component
+ENSG00000100292 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100292 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100292 GO:0005615 extracellular space "That part of a multicellular organism outside the cells proper, usually taken to be outside the plasma membranes, and occupied by fluid." [ISBN:0198547684] cellular_component
+ENSG00000100292 GO:0004871 signal transducer activity "Conveys a signal across a cell to trigger a change in cell function or state. A signal is a physical entity or change in state that is used to transfer information in order to trigger a response." [GOC:go_curators] molecular_function
+ENSG00000100292 GO:0008283 cell proliferation "The multiplication or reproduction of cells, resulting in the expansion of a cell population." [GOC:mah, GOC:mb] biological_process
+ENSG00000100292 GO:0048646 anatomical structure formation involved in morphogenesis "The developmental process pertaining to the initial formation of an anatomical structure from unspecified parts. This process begins with the specific processes that contribute to the appearance of the discrete structure and ends when the structural rudiment is recognizable. An anatomical structure is any biological entity that occupies space and is distinguished from its surroundings. Anatomical structures can be macroscopic such as a carpel, or microscopic such as an acrosome." [GOC:dph, GOC:jid, GOC:tb] biological_process
+ENSG00000100292 GO:0001525 angiogenesis "Blood vessel formation when new vessels emerge from the proliferation of pre-existing blood vessels." [ISBN:0878932453] biological_process
+ENSG00000100292 GO:0001935 endothelial cell proliferation "The multiplication or reproduction of endothelial cells, resulting in the expansion of a cell population. Endothelial cells are thin flattened cells which line the inside surfaces of body cavities, blood vessels, and lymph vessels, making up the endothelium." [GOC:add, ISBN:0781735149] biological_process
+ENSG00000100292 GO:0002246 wound healing involved in inflammatory response "The series of events that restore integrity to damaged tissue that contribute to an inflammatory response." [GOC:jal, ISBN:0721601871] biological_process
+ENSG00000100292 GO:0002686 negative regulation of leukocyte migration "Any process that stops, prevents, or reduces the frequency, rate, or extent of leukocyte migration." [GOC:add] biological_process
+ENSG00000100292 GO:0005789 endoplasmic reticulum membrane "The lipid bilayer surrounding the endoplasmic reticulum." [GOC:mah] cellular_component
+ENSG00000100292 GO:0006778 porphyrin-containing compound metabolic process "The chemical reactions and pathways involving any member of a large group of derivatives or analogs of porphyrin. Porphyrins consists of a ring of four pyrrole nuclei linked each to the next at their alpha positions through a methine group." [GOC:jl, ISBN:0198506732] biological_process
+ENSG00000100292 GO:0006879 cellular iron ion homeostasis "Any process involved in the maintenance of an internal steady state of iron ions at the level of a cell." [GOC:ai, GOC:mah] biological_process
+ENSG00000100292 GO:0006979 response to oxidative stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of oxidative stress, a state often resulting from exposure to high levels of reactive oxygen species, e.g. superoxide anions, hydrogen peroxide (H2O2), and hydroxyl radicals." [GOC:jl, PMID:12115731] biological_process
+ENSG00000100292 GO:0007588 excretion "The elimination by an organism of the waste products that arise as a result of metabolic activity. These products include water, carbon dioxide (CO2), and nitrogenous compounds." [ISBN:0192801023] biological_process
+ENSG00000100292 GO:0014806 smooth muscle hyperplasia "A process, occurring in smooth muscle, in which there is an increase in cell number by cell division, often leading to an increase in the size of an organ." [GOC:mtg_muscle] biological_process
+ENSG00000100292 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000100292 GO:0020037 heme binding "Interacting selectively and non-covalently with heme, any compound of iron complexed in a porphyrin (tetrapyrrole) ring." [CHEBI:30413, GOC:ai] molecular_function
+ENSG00000100292 GO:0034101 erythrocyte homeostasis "Any process of regulating the production and elimination of erythrocytes within an organism." [GOC:add, PMID:10694114, PMID:14754397] biological_process
+ENSG00000100292 GO:0034383 low-density lipoprotein particle clearance "The process in which a low-density lipoprotein particle is removed from the blood via receptor-mediated endocytosis and its constituent parts degraded." [GOC:BHF, GOC:mah] biological_process
+ENSG00000100292 GO:0035094 response to nicotine "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a nicotine stimulus." [CHEBI:17688, GOC:bf, GOC:ef, ISBN:0198506732, ISBN:0582227089] biological_process
+ENSG00000100292 GO:0035556 intracellular signal transduction "The process in which a signal is passed on to downstream components within the cell, which become activated themselves to further propagate the signal and finally trigger a change in the function or state of the cell." [GOC:bf, GOC:jl, GOC:signaling, ISBN:3527303782] biological_process
+ENSG00000100292 GO:0042167 heme catabolic process "The chemical reactions and pathways resulting in the breakdown of heme, any compound of iron complexed in a porphyrin (tetrapyrrole) ring." [GOC:jl] biological_process
+ENSG00000100292 GO:0042542 response to hydrogen peroxide "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a hydrogen peroxide (H2O2) stimulus." [GOC:jl] biological_process
+ENSG00000100292 GO:0042803 protein homodimerization activity "Interacting selectively and non-covalently with an identical protein to form a homodimer." [GOC:jl] molecular_function
+ENSG00000100292 GO:0043123 positive regulation of I-kappaB kinase/NF-kappaB signaling "Any process that activates or increases the frequency, rate or extent of I-kappaB kinase/NF-kappaB signaling." [GOC:jl] biological_process
+ENSG00000100292 GO:0043619 regulation of transcription from RNA polymerase II promoter in response to oxidative stress "Modulation of the frequency, rate or extent of transcription from an RNA polymerase II promoter as a result of a stimulus indicating the organism is under oxidative stress, a state often resulting from exposure to high levels of reactive oxygen species, e.g. superoxide anions, hydrogen peroxide (H2O2), and hydroxyl radicals." [GOC:jl] biological_process
+ENSG00000100292 GO:0045080 positive regulation of chemokine biosynthetic process "Any process that activates or increases the frequency, rate or extent of the chemical reactions and pathways resulting in the formation of chemokines." [GOC:go_curators] biological_process
+ENSG00000100292 GO:0045765 regulation of angiogenesis "Any process that modulates the frequency, rate or extent of angiogenesis." [GOC:go_curators] biological_process
+ENSG00000100292 GO:0045909 positive regulation of vasodilation "Any process that activates or increases the frequency, rate or extent of vasodilation." [GOC:go_curators] biological_process
+ENSG00000100292 GO:0046872 metal ion binding "Interacting selectively and non-covalently with any metal ion." [GOC:ai] molecular_function
+ENSG00000100292 GO:0048471 perinuclear region of cytoplasm "Cytoplasm situated near, or occurring around, the nucleus." [GOC:jid] cellular_component
+ENSG00000100292 GO:0048661 positive regulation of smooth muscle cell proliferation "Any process that activates or increases the rate or extent of smooth muscle cell proliferation." [CL:0000192, GOC:ebc] biological_process
+ENSG00000100292 GO:0048662 negative regulation of smooth muscle cell proliferation "Any process that stops, prevents or reduces the rate or extent of smooth muscle cell proliferation." [CL:0000192, GOC:ebc] biological_process
+ENSG00000100292 GO:0051090 regulation of sequence-specific DNA binding transcription factor activity "Any process that modulates the frequency, rate or extent of the activity of a transcription factor, any factor involved in the initiation or regulation of transcription." [GOC:ai] biological_process
+ENSG00000100292 GO:0051260 protein homooligomerization "The process of creating protein oligomers, compounds composed of a small number, usually between three and ten, of identical component monomers. Oligomers may be formed by the polymerization of a number of monomers or the depolymerization of a large protein polymer." [GOC:ai] biological_process
+ENSG00000100292 GO:0055072 iron ion homeostasis "Any process involved in the maintenance of an internal steady state of iron ions within an organism or cell." [GOC:ai, GOC:jid, GOC:mah] biological_process
+ENSG00000100292 GO:0071456 cellular response to hypoxia "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a stimulus indicating lowered oxygen tension. Hypoxia, defined as a decline in O2 levels below normoxic levels of 20.8 - 20.95%, results in metabolic adaptation at both the cellular and organismal level." [GOC:mah] biological_process
+ENSG00000100292 GO:1902042 negative regulation of extrinsic apoptotic signaling pathway via death domain receptors "Any process that stops, prevents or reduces the frequency, rate or extent of extrinsic apoptotic signaling pathway via death domain receptors." [GOC:TermGenie, PMID:17245429] biological_process
+ENSG00000100292 GO:0071243 cellular response to arsenic-containing substance "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of an arsenic stimulus from compounds containing arsenic, including arsenates, arsenites, and arsenides." [GOC:mah] biological_process
+ENSG00000100292 GO:0042168 heme metabolic process "The chemical reactions and pathways involving heme, any compound of iron complexed in a porphyrin (tetrapyrrole) ring." [GOC:jl, ISBN:0124325653] biological_process
+ENSG00000100292 GO:0034395 regulation of transcription from RNA polymerase II promoter in response to iron "Any process that modulates the frequency, rate or extent of transcription from an RNA polymerase II promoter in response to an iron stimulus." [GO_REF:0000021, GOC:mah] biological_process
+ENSG00000100292 GO:0071276 cellular response to cadmium ion "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a cadmium (Cd) ion stimulus." [GOC:mah] biological_process
+ENSG00000100292 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000100292 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100292 GO:0043392 negative regulation of DNA binding "Any process that stops or reduces the frequency, rate or extent of DNA binding. DNA binding is any process in which a gene product interacts selectively with DNA (deoxyribonucleic acid)." [GOC:dph, GOC:jl, GOC:tb] biological_process
+ENSG00000100292 GO:0007264 small GTPase mediated signal transduction "Any series of molecular signals in which a small monomeric GTPase relays one or more of the signals." [GOC:mah] biological_process
+ENSG00000100292 GO:0008630 intrinsic apoptotic signaling pathway in response to DNA damage "A series of molecular signals in which an intracellular signal is conveyed to trigger the apoptotic death of a cell. The pathway is induced by the detection of DNA damage, and ends when the execution phase of apoptosis is triggered." [GOC:go_curators, GOC:mtg_apoptosis] biological_process
+ENSG00000100292 GO:0043524 negative regulation of neuron apoptotic process "Any process that stops, prevents, or reduces the frequency, rate or extent of cell death by apoptotic process in neurons." [GOC:go_curators, GOC:mtg_apoptosis] biological_process
+ENSG00000100292 GO:0008285 negative regulation of cell proliferation "Any process that stops, prevents or reduces the rate or extent of cell proliferation." [GOC:go_curators] biological_process
+ENSG00000100292 GO:0043627 response to estrogen "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of stimulus by an estrogen, C18 steroid hormones that can stimulate the development of female sexual characteristics." [GOC:jl, ISBN:0198506732] biological_process
+ENSG00000100292 GO:0001666 response to hypoxia "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a stimulus indicating lowered oxygen tension. Hypoxia, defined as a decline in O2 levels below normoxic levels of 20.8 - 20.95%, results in metabolic adaptation at both the cellular and organismal level." [GOC:hjd] biological_process
+ENSG00000100292 GO:0045766 positive regulation of angiogenesis "Any process that activates or increases angiogenesis." [GOC:go_curators] biological_process
+ENSG00000100292 GO:0008217 regulation of blood pressure "Any process that modulates the force with which blood travels through the circulatory system. The process is controlled by a balance of processes that increase pressure and decrease pressure." [GOC:dph, GOC:mtg_cardio, ISBN:0721643949] biological_process
+ENSG00000100292 GO:0010656 negative regulation of muscle cell apoptotic process "Any process that decreases the rate or frequency of muscle cell apoptotic process, a form of programmed cell death induced by external or internal signals that trigger the activity of proteolytic caspases whose actions dismantle a muscle cell and result in its death." [GOC:dph, GOC:mtg_apoptosis, GOC:tb] biological_process
+ENSG00000100292 GO:0005901 caveola "A membrane raft that forms small pit, depression, or invagination that communicates with the outside of a cell and extends inward, indenting the cytoplasm and the cell membrane. Examples include any of the minute pits or incuppings of the cell membrane formed during pinocytosis. Such caveolae may be pinched off to form free vesicles within the cytoplasm." [GOC:mah, ISBN:0721662544, PMID:16645198] cellular_component
+ENSG00000100292 GO:0043305 negative regulation of mast cell degranulation "Any process that stops, prevents, or reduces the rate of mast cell degranulation." [ISBN:0781735149] biological_process
+ENSG00000100292 GO:0032764 negative regulation of mast cell cytokine production "Any process that stops, prevents, or reduces the frequency, rate, or extent of mast cell cytokine production." [GOC:mah] biological_process
+ENSG00000100292 GO:0031670 cellular response to nutrient "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a nutrient stimulus." [GOC:mah] biological_process
+ENSG00000100292 GO:0004630 phospholipase D activity "Catalysis of the reaction: a phosphatidylcholine + H2O = choline + a phosphatidate." [EC:3.1.4.4] molecular_function
+ENSG00000100292 GO:0043433 negative regulation of sequence-specific DNA binding transcription factor activity "Any process that stops, prevents, or reduces the frequency, rate or extent of the activity of a transcription factor, any factor involved in the initiation or regulation of transcription." [GOC:jl] biological_process
+ENSG00000100292 GO:0005730 nucleolus "A small, dense body one or more of which are present in the nucleus of eukaryotic cells. It is rich in RNA and protein, is not bounded by a limiting membrane, and is not seen during mitosis. Its prime function is the transcription of the nucleolar DNA into 45S ribosomal-precursor RNA, the processing of this RNA into 5.8S, 18S, and 28S components of ribosomal RNA, and the association of these components with 5S RNA and proteins synthesized outside the nucleolus. This association results in the formation of ribonucleoprotein precursors; these pass into the cytoplasm and mature into the 40S and 60S subunits of the ribosome." [ISBN:0198506732] cellular_component
+ENSG00000100191 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100191 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100191 GO:0022857 transmembrane transporter activity "Enables the transfer of a substance from one side of a membrane to the other." [GOC:mtg_transport, ISBN:0815340729] molecular_function
+ENSG00000100191 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100191 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100191 GO:0006814 sodium ion transport "The directed movement of sodium ions (Na+) into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:ai] biological_process
+ENSG00000100191 GO:0008643 carbohydrate transport "The directed movement of carbohydrate into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore. Carbohydrates are any of a group of organic compounds based of the general formula Cx(H2O)y." [GOC:ai] biological_process
+ENSG00000100191 GO:0015293 symporter activity "Enables the active transport of a solute across a membrane by a mechanism whereby two or more species are transported together in the same direction in a tightly coupled process not directly linked to a form of energy other than chemiosmotic energy." [GOC:mtg_transport, ISBN:0815340729, PMID:10839820] molecular_function
+ENSG00000100191 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000100191 GO:0055085 transmembrane transport "The process in which a solute is transported from one side of a membrane to the other." [GOC:dph, GOC:jid] biological_process
+ENSG00000100191 GO:0005215 transporter activity "Enables the directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells." [GOC:ai, GOC:dgf] molecular_function
+ENSG00000100191 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000073169
+ENSG00000249222 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000249222 GO:0022857 transmembrane transporter activity "Enables the transfer of a substance from one side of a membrane to the other." [GOC:mtg_transport, ISBN:0815340729] molecular_function
+ENSG00000249222 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000249222 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000249222 GO:0000276 mitochondrial proton-transporting ATP synthase complex, coupling factor F(o) "All non-F1 subunits of the mitochondrial hydrogen-transporting ATP synthase, including integral and peripheral mitochondrial inner membrane proteins." [GOC:mtg_sensu, PMID:10838056] cellular_component
+ENSG00000249222 GO:0015078 hydrogen ion transmembrane transporter activity "Catalysis of the transfer of hydrogen ions from one side of a membrane to the other." [GOC:ai] molecular_function
+ENSG00000249222 GO:0015986 ATP synthesis coupled proton transport "The transport of protons across a membrane to generate an electrochemical gradient (proton-motive force) that powers ATP synthesis." [ISBN:0716731363] biological_process
+ENSG00000249222 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000249222 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000249222 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000249222 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000249222 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000249222 GO:0055085 transmembrane transport "The process in which a solute is transported from one side of a membrane to the other." [GOC:dph, GOC:jid] biological_process
+ENSG00000100284 GO:0006886 intracellular protein transport "The directed movement of proteins in a cell, including the movement of proteins between specific compartments or structures within a cell, such as organelles of a eukaryotic cell." [GOC:mah] biological_process
+ENSG00000100284 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100284 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100284 GO:0005622 intracellular "The living contents of a cell; the matter contained within (but not including) the plasma membrane, usually taken to exclude large vacuoles and masses of secretory or ingested material. In eukaryotes it includes the nucleus and cytoplasm." [ISBN:0198506732] cellular_component
+ENSG00000100284 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100284 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100284 GO:0016192 vesicle-mediated transport "A cellular transport process in which transported substances are moved in membrane-bounded vesicles; transported substances are enclosed in the vesicle lumen or located in the vesicle membrane. The process begins with a step that directs a substance to the forming vesicle, and includes vesicle budding and coating. Vesicles are then targeted to, and fuse with, an acceptor membrane." [GOC:ai, GOC:mah, ISBN:08789310662000] biological_process
+ENSG00000100284 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000100284 GO:0005768 endosome "A membrane-bounded organelle to which materials ingested by endocytosis are delivered." [ISBN:0198506732, PMID:19696797] cellular_component
+ENSG00000100284 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100284 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100284 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100284 GO:0005769 early endosome "A membrane-bounded organelle that receives incoming material from primary endocytic vesicles that have been generated by clathrin-dependent and clathrin-independent endocytosis; vesicles fuse with the early endosome to deliver cargo for sorting into recycling or degradation pathways." [GOC:mah, NIF_Subcellular:nlx_subcell_20090701, PMID:19696797] cellular_component
+ENSG00000100284 GO:0006897 endocytosis "A vesicle-mediated transport process in which cells take up external materials or membrane constituents by the invagination of a small region of the plasma membrane to form a new membrane-bounded vesicle." [GOC:mah, ISBN:0198506732, ISBN:0716731363] biological_process
+ENSG00000100284 GO:0015031 protein transport "The directed movement of proteins into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:ai] biological_process
+ENSG00000100284 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000100284 GO:0016197 endosomal transport "The directed movement of substances into, out of or mediated by an endosome, a membrane-bounded organelle that carries materials newly ingested by endocytosis. It passes many of the materials to lysosomes for degradation." [ISBN:0198506732] biological_process
+ENSG00000100284 GO:0030276 clathrin binding "Interacting selectively and non-covalently with a clathrin heavy or light chain, the main components of the coat of coated vesicles and coated pits, and which also occurs in synaptic vesicles." [GOC:jl, GOC:mah, ISBN:0198506732] molecular_function
+ENSG00000100284 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000100284
+ENSG00000100346 GO:0005245 voltage-gated calcium channel activity "Catalysis of the transmembrane transfer of a calcium ion by a voltage-gated channel. A voltage-gated channel is a channel whose open state is dependent on the voltage across the membrane in which it is embedded." [GOC:mtg_transport, GOC:tb, ISBN:0815340729] molecular_function
+ENSG00000100346 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100346 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000100346 GO:0005891 voltage-gated calcium channel complex "A protein complex that forms a transmembrane channel through which calcium ions may pass in response to changes in membrane potential." [GOC:mah] cellular_component
+ENSG00000100346 GO:0006810 transport "The directed movement of substances (such as macromolecules, small molecules, ions) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter or pore." [GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000100346 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000100346 GO:0007411 axon guidance "The chemotaxis process that directs the migration of an axon growth cone to a specific target site in response to a combination of attractive and repulsive cues." [ISBN:0878932437] biological_process
+ENSG00000100346 GO:0034765 regulation of ion transmembrane transport "Any process that modulates the frequency, rate or extent of the directed movement of ions from one side of a membrane to the other." [GOC:mah] biological_process
+ENSG00000100346 GO:0070509 calcium ion import "The directed movement of calcium ions into a cell or organelle." [GOC:mah] biological_process
+ENSG00000100346 GO:0070588 calcium ion transmembrane transport "A process in which a calcium ion is transported from one side of a membrane to the other by means of some agent such as a transporter or pore." [GOC:mah] biological_process
+ENSG00000100346 GO:0086010 membrane depolarization during action potential "The process in which membrane potential changes in the depolarizing direction from the negative resting potential towards the positive membrane potential that will be the peak of the action potential." [GOC:BHF, GOC:mtg_cardiac_conduct_nov11] biological_process
+ENSG00000100346 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100346 GO:0055085 transmembrane transport "The process in which a solute is transported from one side of a membrane to the other." [GOC:dph, GOC:jid] biological_process
+ENSG00000100346 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100346 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100346 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100346 GO:0022857 transmembrane transporter activity "Enables the transfer of a substance from one side of a membrane to the other." [GOC:mtg_transport, ISBN:0815340729] molecular_function
+ENSG00000100346 GO:0005216 ion channel activity "Catalysis of facilitated diffusion of an ion (by an energy-independent process) by passage through a transmembrane aqueous pore or channel without evidence for a carrier-mediated mechanism." [GOC:cy, GOC:mtg_transport, ISBN:0815340729] molecular_function
+ENSG00000100346 GO:0006811 ion transport "The directed movement of charged atoms or small charged molecules into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore." [GOC:ai] biological_process
+ENSG00000100346 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000100346 GO:0019228 neuronal action potential "An action potential that occurs in a neuron." [GOC:dph, GOC:isa_complete, GOC:tb] biological_process
+ENSG00000100346 GO:0030431 sleep "Any process in which an organism enters and maintains a periodic, readily reversible state of reduced awareness and metabolic activity. Usually accompanied by physical relaxation, the onset of sleep in humans and other mammals is marked by a change in the electrical activity of the brain." [ISBN:0192800981] biological_process
+ENSG00000211640 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000211640 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100243 GO:0016491 oxidoreductase activity "Catalysis of an oxidation-reduction (redox) reaction, a reversible chemical reaction in which the oxidation state of an atom or atoms within a molecule is altered. One substrate acts as a hydrogen or electron donor and becomes oxidized, while the other acts as hydrogen or electron acceptor and becomes reduced." [GOC:go_curators] molecular_function
+ENSG00000100243 GO:0055114 oxidation-reduction process "A metabolic process that results in the removal or addition of one or more electrons to or from a substance, with or without the concomitant removal or addition of a proton or protons." [GOC:dhl, GOC:ecd, GOC:jh2, GOC:jid, GOC:mlg, GOC:rph] biological_process
+ENSG00000100243 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100243 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100243 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100243 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100243 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000100243 GO:0005975 carbohydrate metabolic process "The chemical reactions and pathways involving carbohydrates, any of a group of organic compounds based of the general formula Cx(H2O)y. Includes the formation of carbohydrate derivatives by the addition of a carbohydrate residue to another molecule." [GOC:mah, ISBN:0198506732] biological_process
+ENSG00000100243 GO:0003013 circulatory system process "A organ system process carried out by any of the organs or tissues of the circulatory system. The circulatory system is an organ system that moves extracellular fluids to and from tissue within a multicellular organism." [GOC:mtg_cardio] biological_process
+ENSG00000100243 GO:0006629 lipid metabolic process "The chemical reactions and pathways involving lipids, compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent. Includes fatty acids; neutral fats, other fatty-acid esters, and soaps; long-chain (fatty) alcohols and waxes; sphingoids and other long-chain bases; glycolipids, phospholipids and sphingolipids; and carotenes, polyprenols, sterols, terpenes and other isoprenoids." [GOC:ma] biological_process
+ENSG00000100243 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000100243 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100243 GO:0005811 lipid particle "An intracellular non-membrane-bounded organelle comprising a matrix of coalesced lipids surrounded by a phospholipid monolayer. May include associated proteins." [GOC:mah, GOC:tb] cellular_component
+ENSG00000100243 GO:0005783 endoplasmic reticulum "The irregular network of unit membranes, visible only by electron microscopy, that occurs in the cytoplasm of many eukaryotic cells. The membranes form a complex meshwork of tubular channels, which are often expanded into slitlike cavities called cisternae. The ER takes two forms, rough (or granular), with ribosomes adhering to the outer surface, and smooth (with no ribosomes attached)." [ISBN:0198506732] cellular_component
+ENSG00000100243 GO:0005739 mitochondrion "A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration." [GOC:giardia, ISBN:0198506732] cellular_component
+ENSG00000100243 GO:0005737 cytoplasm "All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures." [ISBN:0198547684] cellular_component
+ENSG00000100243 GO:0004128 cytochrome-b5 reductase activity, acting on NAD(P)H "Catalysis of the reaction: NAD(P)H + H+ + 2 ferricytochrome b(5) = NAD(P)+ + 2 ferrocytochrome b(5)." [EC:1.6.2.2, ISBN:0198547684] molecular_function
+ENSG00000100243 GO:0005741 mitochondrial outer membrane "The outer, i.e. cytoplasm-facing, lipid bilayer of the mitochondrial envelope." [GOC:ai] cellular_component
+ENSG00000100243 GO:0005833 hemoglobin complex "An iron-containing, oxygen carrying complex. In vertebrates it is made up of two pairs of associated globin polypeptide chains, each chain carrying a noncovalently bound heme prosthetic group." [GOC:jl, ISBN:0198506732] cellular_component
+ENSG00000100243 GO:0006695 cholesterol biosynthetic process "The chemical reactions and pathways resulting in the formation of cholesterol, cholest-5-en-3 beta-ol, the principal sterol of vertebrates and the precursor of many steroids, including bile acids and steroid hormones." [GOC:ai] biological_process
+ENSG00000100243 GO:0006766 vitamin metabolic process "The chemical reactions and pathways involving vitamins. Vitamin is a general term for a number of unrelated organic substances that occur in many foods in small amounts and that are necessary in trace amounts for the normal metabolic functioning of the body. Vitamins may be water-soluble or fat-soluble and usually serve as components of coenzyme systems." [GOC:ai] biological_process
+ENSG00000100243 GO:0006767 water-soluble vitamin metabolic process "The chemical reactions and pathways involving any of a diverse group of vitamins that are soluble in water." [GOC:jl] biological_process
+ENSG00000100243 GO:0008015 blood circulation "The flow of blood through the body of an animal, enabling the transport of nutrients to the tissues and the removal of waste products." [GOC:mtg_heart, ISBN:0192800825] biological_process
+ENSG00000100243 GO:0016020 membrane "Double layer of lipid molecules that encloses all cells, and, in eukaryotes, many organelles; may be a single or double lipid bilayer; also includes associated proteins." [GOC:mah, ISBN:0815316194] cellular_component
+ENSG00000100243 GO:0019852 L-ascorbic acid metabolic process "The chemical reactions and pathways involving L-ascorbic acid, (2R)-2-[(1S)-1,2-dihydroxyethyl]-4-hydroxy-5-oxo-2,5-dihydrofuran-3-olate; L-ascorbic acid is vitamin C and has co-factor and anti-oxidant activities in many species." [CHEBI:38290, GOC:jl, ISBN:0198506732] biological_process
+ENSG00000100243 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000100243 GO:0071949 FAD binding "Interacting selectively and non-covalently with the oxidized form, FAD, of flavin-adenine dinucleotide, the coenzyme or the prosthetic group of various flavoprotein oxidoreductase enzymes." [GOC:mah] molecular_function
+ENSG00000100243 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100243 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100243 GO:0005743 mitochondrial inner membrane "The inner, i.e. lumen-facing, lipid bilayer of the mitochondrial envelope. It is highly folded to form cristae." [GOC:ai] cellular_component
+ENSG00000100243 GO:0005789 endoplasmic reticulum membrane "The lipid bilayer surrounding the endoplasmic reticulum." [GOC:mah] cellular_component
+ENSG00000100243 GO:0050660 flavin adenine dinucleotide binding "Interacting selectively and non-covalently with FAD, flavin-adenine dinucleotide, the coenzyme or the prosthetic group of various flavoprotein oxidoreductase enzymes, in either the oxidized form, FAD, or the reduced form, FADH2." [CHEBI:24040, GOC:ai, GOC:imk, ISBN:0198506732] molecular_function
+ENSG00000100243 GO:0051287 NAD binding "Interacting selectively and non-covalently with nicotinamide adenine dinucleotide, a coenzyme involved in many redox and biosynthetic reactions; binding may be to either the oxidized form, NAD+, or the reduced form, NADH." [GOC:ai] molecular_function
+ENSG00000100243 GO:0043531 ADP binding "Interacting selectively and non-covalently with ADP, adenosine 5'-diphosphate." [GOC:jl] molecular_function
+ENSG00000100243 GO:0016208 AMP binding "Interacting selectively and non-covalently with AMP, adenosine monophosphate." [GOC:go_curators] molecular_function
+ENSG00000211638 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000211638 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000159307 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000159307 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000159307 GO:0030154 cell differentiation "The process in which relatively unspecialized cells, e.g. embryonic or regenerative cells, acquire specialized structural and/or functional features that characterize the cells, tissues, or organs of the mature organism or some other relatively stable phase of the organism's life history. Differentiation includes the processes involved in commitment of a cell to a specific fate and its subsequent development to the mature state." [ISBN:0198506732] biological_process
+ENSG00000159307 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000159307 GO:0048856 anatomical structure development "The biological process whose specific outcome is the progression of an anatomical structure from an initial condition to its mature state. This process begins with the formation of the structure and ends with the mature structure, whatever form that may be including its natural destruction. An anatomical structure is any biological entity that occupies space and is distinguished from its surroundings. Anatomical structures can be macroscopic such as a carpel, or microscopic such as an acrosome." [GO_REF:0000021, GOC:mtg_15jun06] biological_process
+ENSG00000159307 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000159307 GO:0005615 extracellular space "That part of a multicellular organism outside the cells proper, usually taken to be outside the plasma membranes, and occupied by fluid." [ISBN:0198547684] cellular_component
+ENSG00000159307 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000159307 GO:0005509 calcium ion binding "Interacting selectively and non-covalently with calcium ions (Ca2+)." [GOC:ai] molecular_function
+ENSG00000159307 GO:0006954 inflammatory response "The immediate defensive reaction (by vertebrate tissue) to infection or injury caused by chemical or physical agents. The process is characterized by local vasodilation, extravasation of plasma into intercellular spaces and accumulation of white blood cells and macrophages." [GO_REF:0000022, GOC:mtg_15nov05, ISBN:0198506732] biological_process
+ENSG00000159307 GO:0007512 adult heart development "The process whose specific outcome is the progression of the adult heart over time, from its formation to the mature structure." [GOC:bf] biological_process
+ENSG00000159307 GO:0007596 blood coagulation "The sequential process in which the multiple coagulation factors of the blood interact, ultimately resulting in the formation of an insoluble fibrin clot; it may be divided into three stages: stage 1, the formation of intrinsic and extrinsic prothrombin converting principle; stage 2, the formation of thrombin; stage 3, the formation of stable fibrin polymers." [http://www.graylab.ac.uk/omd/, ISBN:0198506732] biological_process
+ENSG00000159307 GO:0009791 post-embryonic development "The process whose specific outcome is the progression of the organism over time, from the completion of embryonic development to the mature structure. See embryonic development." [GOC:go_curators] biological_process
+ENSG00000159307 GO:0009897 external side of plasma membrane "The leaflet the plasma membrane that faces away from the cytoplasm and any proteins embedded or anchored in it or attached to its surface." [GOC:dos, GOC:tb] cellular_component
+ENSG00000159307 GO:0009986 cell surface "The external part of the cell wall and/or plasma membrane." [GOC:jl, GOC:mtg_sensu, GOC:sm] cellular_component
+ENSG00000159307 GO:0019897 extrinsic component of plasma membrane "The component of a plasma membrane consisting of gene products and protein complexes that are loosely bound to one of its surfaces, but not integrated into the hydrophobic region." [GOC:curators, GOC:dos] cellular_component
+ENSG00000159307 GO:0042802 identical protein binding "Interacting selectively and non-covalently with an identical protein or proteins." [GOC:jl] molecular_function
+ENSG00000159307 GO:0045446 endothelial cell differentiation "The process in which a mesodermal, bone marrow or neural crest cell acquires specialized features of an endothelial cell, a thin flattened cell. A layer of such cells lines the inside surfaces of body cavities, blood vessels, and lymph vessels, making up the endothelium." [CL:0000115, GOC:go_curators] biological_process
+ENSG00000159307 GO:0046982 protein heterodimerization activity "Interacting selectively and non-covalently with a nonidentical protein to form a heterodimer." [GOC:ai] molecular_function
+ENSG00000159307 GO:0051260 protein homooligomerization "The process of creating protein oligomers, compounds composed of a small number, usually between three and ten, of identical component monomers. Oligomers may be formed by the polymerization of a number of monomers or the depolymerization of a large protein polymer." [GOC:ai] biological_process
+ENSG00000159307 GO:0006461 protein complex assembly "The aggregation, arrangement and bonding together of a set of components to form a protein complex." [GOC:ai] biological_process
+ENSG00000159307 GO:0022607 cellular component assembly "The aggregation, arrangement and bonding together of a cellular component." [GOC:isa_complete] biological_process
+ENSG00000159307 GO:0065003 macromolecular complex assembly "The aggregation, arrangement and bonding together of a set of macromolecules to form a complex." [GOC:jl] biological_process
+ENSG00000159307 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000133475 GO:0003840 gamma-glutamyltransferase activity "Catalysis of the reaction: (5-L-glutamyl)-peptide + an amino acid = peptide + 5-L-glutamyl-amino acid." [EC:2.3.2.2] molecular_function
+ENSG00000133475 GO:0006749 glutathione metabolic process "The chemical reactions and pathways involving glutathione, the tripeptide glutamylcysteinylglycine, which acts as a coenzyme for some enzymes and as an antioxidant in the protection of sulfhydryl groups in enzymes and other proteins; it has a specific role in the reduction of hydrogen peroxide (H2O2) and oxidized ascorbate, and it participates in the gamma-glutamyl cycle." [CHEBI:16856, ISBN:0198506732] biological_process
+ENSG00000133475 GO:0006520 cellular amino acid metabolic process "The chemical reactions and pathways involving amino acids, carboxylic acids containing one or more amino groups, as carried out by individual cells." [CHEBI:33709, GOC:curators, ISBN:0198506732] biological_process
+ENSG00000133475 GO:0006790 sulfur compound metabolic process "The chemical reactions and pathways involving the nonmetallic element sulfur or compounds that contain sulfur, such as the amino acids methionine and cysteine or the tripeptide glutathione." [GOC:ai] biological_process
+ENSG00000133475 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000133475 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000133475 GO:0044281 small molecule metabolic process "The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule." [GOC:curators, GOC:pde, GOC:vw] biological_process
+ENSG00000133475 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000133475 GO:0016746 transferase activity, transferring acyl groups "Catalysis of the transfer of an acyl group from one compound (donor) to another (acceptor)." [GOC:jl, ISBN:0198506732] molecular_function
+ENSG00000133475 GO:0005783 endoplasmic reticulum "The irregular network of unit membranes, visible only by electron microscopy, that occurs in the cytoplasm of many eukaryotic cells. The membranes form a complex meshwork of tubular channels, which are often expanded into slitlike cavities called cisternae. The ER takes two forms, rough (or granular), with ribosomes adhering to the outer surface, and smooth (with no ribosomes attached)." [ISBN:0198506732] cellular_component
+ENSG00000133475 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000133475 GO:0006750 glutathione biosynthetic process "The chemical reactions and pathways resulting in the formation of glutathione, the tripeptide glutamylcysteinylglycine, which acts as a coenzyme for some enzymes and as an antioxidant in the protection of sulfhydryl groups in enzymes and other proteins." [CHEBI:16856, GOC:ai, GOC:al, GOC:pde, ISBN:0198506732] biological_process
+ENSG00000133475 GO:0006805 xenobiotic metabolic process "The chemical reactions and pathways involving a xenobiotic compound, a compound foreign to living organisms. Used of chemical compounds, e.g. a xenobiotic chemical, such as a pesticide." [GOC:cab2] biological_process
+ENSG00000133475 GO:0019370 leukotriene biosynthetic process "The chemical reactions and pathways resulting in the formation of leukotriene, a pharmacologically active substance derived from a polyunsaturated fatty acid, such as arachidonic acid." [GOC:go_curators] biological_process
+ENSG00000133475 GO:0031362 anchored component of external side of plasma membrane "The component of the plasma membrane consisting of the gene products that are tethered to the membrane only by a covalently attached anchor, such as a lipid group embedded in the membrane. Gene products with peptide sequences that are embedded in the membrane are excluded from this grouping." [GOC:dos, GOC:mah] cellular_component
+ENSG00000133475 GO:0048471 perinuclear region of cytoplasm "Cytoplasm situated near, or occurring around, the nucleus." [GOC:jid] cellular_component
+ENSG00000133475 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000133475 GO:1901687 glutathione derivative biosynthetic process "The chemical reactions and pathways resulting in the formation of glutathione derivative." [GOC:pr, GOC:TermGenie] biological_process
+ENSG00000133475 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000133475 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000133475 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000133475 GO:0006629 lipid metabolic process "The chemical reactions and pathways involving lipids, compounds soluble in an organic solvent but not, or sparingly, in an aqueous solvent. Includes fatty acids; neutral fats, other fatty-acid esters, and soaps; long-chain (fatty) alcohols and waxes; sphingoids and other long-chain bases; glycolipids, phospholipids and sphingolipids; and carotenes, polyprenols, sterols, terpenes and other isoprenoids." [GOC:ma] biological_process
+ENSG00000211641 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000211641 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000133477
+ENSG00000100387 GO:0006950 response to stress "Any process that results in a change in state or activity of a cell or an organism (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a disturbance in organismal or cellular homeostasis, usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation)." [GOC:mah] biological_process
+ENSG00000100387 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100387 GO:0006464 cellular protein modification process "The covalent alteration of one or more amino acids occurring in proteins, peptides and nascent polypeptides (co-translational, post-translational modifications) occurring at the level of an individual cell. Includes the modification of charged tRNAs that are destined to occur in a protein (pre-translation modification)." [GOC:go_curators] biological_process
+ENSG00000100387 GO:0009056 catabolic process "The chemical reactions and pathways resulting in the breakdown of substances, including the breakdown of carbon compounds with the liberation of energy for use by the cell or organism." [ISBN:0198547684] biological_process
+ENSG00000100387 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100387 GO:0019899 enzyme binding "Interacting selectively and non-covalently with any enzyme." [GOC:jl] molecular_function
+ENSG00000100387 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100387 GO:0043234 protein complex "Any macromolecular complex composed (only) of two or more polypeptide subunits along with any covalently attached molecules (such as lipid anchors or oligosaccharide) or non-protein prosthetic groups (such as nucleotides or metal ions). Prosthetic group in this context refers to a tightly bound cofactor. The component polypeptide subunits may be identical." [GOC:go_curators] cellular_component
+ENSG00000100387 GO:0016874 ligase activity "Catalysis of the joining of two substances, or two groups within a single molecule, with the concomitant hydrolysis of the diphosphate bond in ATP or a similar triphosphate." [EC:6, GOC:mah] molecular_function
+ENSG00000100387 GO:0044403 symbiosis, encompassing mutualism through parasitism "An interaction between two organisms living together in more or less intimate association. The term host is usually used for the larger (macro) of the two members of a symbiosis. The smaller (micro) member is called the symbiont organism. Microscopic symbionts are often referred to as endosymbionts. The various forms of symbiosis include parasitism, in which the association is disadvantageous or destructive to one of the organisms; mutualism, in which the association is advantageous, or often necessary to one or both and not harmful to either; and commensalism, in which one member of the association benefits while the other is not affected. However, mutualism, parasitism, and commensalism are often not discrete categories of interactions and should rather be perceived as a continuum of interaction ranging from parasitism to mutualism. In fact, the direction of a symbiotic interaction can change during the lifetime of the symbionts due to developmental changes as well as changes in the biotic/abiotic environment in which the interaction occurs." [GOC:cc, http://www.free-definition.com] biological_process
+ENSG00000100387 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100387 GO:0007165 signal transduction "The cellular process in which a signal is conveyed to trigger a change in the activity or state of a cell. Signal transduction begins with reception of a signal (e.g. a ligand binding to a receptor or receptor activation by a stimulus such as light), or for signal transduction in the absence of ligand, signal-withdrawal or the activity of a constitutively active receptor. Signal transduction ends with regulation of a downstream cellular process, e.g. regulation of transcription or regulation of a metabolic process. Signal transduction covers signaling from receptors located on the surface of the cell and signaling via molecules located within the cell. For signaling between cells, signal transduction is restricted to events at and within the receiving cell." [GOC:go_curators, GOC:mtg_signaling_feb11] biological_process
+ENSG00000100387 GO:0006259 DNA metabolic process "Any cellular metabolic process involving deoxyribonucleic acid. This is one of the two main types of nucleic acid, consisting of a long, unbranched macromolecule formed from one, or more commonly, two, strands of linked deoxyribonucleotides." [ISBN:0198506732] biological_process
+ENSG00000100387 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100387 GO:0005829 cytosol "The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes." [GOC:hgd, GOC:jl] cellular_component
+ENSG00000100387 GO:0005654 nucleoplasm "That part of the nuclear content other than the chromosomes or the nucleolus." [GOC:ma, ISBN:0124325653] cellular_component
+ENSG00000100387 GO:0004842 ubiquitin-protein transferase activity "Catalysis of the transfer of ubiquitin from one protein to another via the reaction X-Ub + Y --> Y-Ub + X, where both X-Ub and Y-Ub are covalent linkages." [GOC:BioGRID, GOC:jh2, PMID:9635407] molecular_function
+ENSG00000100387 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100387 GO:0006281 DNA repair "The process of restoring DNA after damage. Genomes are subject to damage by chemical and physical agents in the environment (e.g. UV and ionizing radiations, chemical mutagens, fungal and bacterial toxins, etc.) and by free radicals or alkylating agents endogenously generated in metabolism. DNA is also damaged because of errors during its replication. A variety of different DNA repair pathways have been reported that include direct reversal, base excision repair, nucleotide excision repair, photoreactivation, bypass, double-strand break repair pathway, and mismatch repair pathway." [PMID:11563486] biological_process
+ENSG00000100387 GO:0006513 protein monoubiquitination "Addition of a single ubiquitin group to a protein." [GOC:ai] biological_process
+ENSG00000100387 GO:0007219 Notch signaling pathway "A series of molecular signals initiated by the binding of an extracellular ligand to the receptor Notch on the surface of a target cell, and ending with regulation of a downstream cellular process, e.g. transcription." [GOC:go_curators, GOC:signaling] biological_process
+ENSG00000100387 GO:0008270 zinc ion binding "Interacting selectively and non-covalently with zinc (Zn) ions." [GOC:ai] molecular_function
+ENSG00000100387 GO:0016032 viral process "A multi-organism process in which a virus is a participant. The other participant is the host. Includes infection of a host cell, replication of the viral genome, and assembly of progeny virus particles. In some cases the viral genetic material may integrate into the host genome and only subsequently, under particular circumstances, 'complete' its life cycle." [GOC:bf, GOC:jl, GOC:mah] biological_process
+ENSG00000100387 GO:0016567 protein ubiquitination "The process in which one or more ubiquitin groups are added to a protein." [GOC:ai] biological_process
+ENSG00000100387 GO:0019005 SCF ubiquitin ligase complex "A ubiquitin ligase complex in which a cullin from the Cul1 subfamily and a RING domain protein form the catalytic core; substrate specificity is conferred by a Skp1 adaptor and an F-box protein. SCF complexes are involved in targeting proteins for degradation by the proteasome. The best characterized complexes are those from yeast and mammals (with core subunits named Cdc53/Cul1, Rbx1/Hrt1/Roc1)." [PMID:15571813, PMID:15688063] cellular_component
+ENSG00000100387 GO:0019788 NEDD8 ligase activity "Catalysis of ATP-dependent isopeptide bond formation between the carboxy-terminal residues of the small ubiquitin-related modifier NEDD8 and a substrate lysine residue." [GOC:mah] molecular_function
+ENSG00000100387 GO:0031146 SCF-dependent proteasomal ubiquitin-dependent protein catabolic process "The chemical reactions and pathways resulting in the breakdown of a protein or peptide by hydrolysis of its peptide bonds, initiated by the covalent attachment of ubiquitin, with ubiquitin-protein ligation catalyzed by an SCF (Skp1/Cul1/F-box protein) complex, and mediated by the proteasome." [PMID:15380083] biological_process
+ENSG00000100387 GO:0031461 cullin-RING ubiquitin ligase complex "Any ubiquitin ligase complex in which the catalytic core consists of a member of the cullin family and a RING domain protein; the core is associated with one or more additional proteins that confer substrate specificity." [PMID:15571813, PMID:15688063] cellular_component
+ENSG00000100387 GO:0031462 Cul2-RING ubiquitin ligase complex "A ubiquitin ligase complex in which a cullin from the Cul2 subfamily and a RING domain protein form the catalytic core; substrate specificity is conferred by an elongin-BC adaptor and a SOCS/BC box protein." [PMID:15571813, PMID:15688063] cellular_component
+ENSG00000100387 GO:0031463 Cul3-RING ubiquitin ligase complex "A ubiquitin ligase complex in which a cullin from the Cul3 subfamily and a RING domain protein form the catalytic core; substrate specificity is conferred by a BTB-domain-containing protein." [PMID:15571813, PMID:15688063] cellular_component
+ENSG00000100387 GO:0031464 Cul4A-RING E3 ubiquitin ligase complex "A ubiquitin ligase complex in which a cullin from the Cul4A subfamily and a RING domain protein form the catalytic core; substrate specificity is conferred by an adaptor protein." [PMID:15571813, PMID:15688063] cellular_component
+ENSG00000100387 GO:0031465 Cul4B-RING E3 ubiquitin ligase complex "A ubiquitin ligase complex in which a cullin from the Cul4B subfamily and a RING domain protein form the catalytic core; substrate specificity is conferred by unknown subunits." [PMID:15571813, PMID:15688063] cellular_component
+ENSG00000100387 GO:0031466 Cul5-RING ubiquitin ligase complex "A ubiquitin ligase complex in which a cullin from the Cul5 subfamily and a RING domain protein form the catalytic core; substrate specificity is conferred by an elongin-BC adaptor and a SOCS/BC box protein." [PMID:15571813, PMID:15688063] cellular_component
+ENSG00000100387 GO:0031467 Cul7-RING ubiquitin ligase complex "A ubiquitin ligase complex in which a cullin from the Cul7 subfamily and a RING domain protein form the catalytic core; substrate specificity is conferred by a Skp1 linker and an F-box protein." [PMID:15571813, PMID:15688063] cellular_component
+ENSG00000100387 GO:0031625 ubiquitin protein ligase binding "Interacting selectively and non-covalently with a ubiquitin protein ligase enzyme, any of the E3 proteins." [GOC:vp] molecular_function
+ENSG00000100387 GO:0043161 proteasome-mediated ubiquitin-dependent protein catabolic process "The chemical reactions and pathways resulting in the breakdown of a protein or peptide by hydrolysis of its peptide bonds, initiated by the covalent attachment of ubiquitin, and mediated by the proteasome." [GOC:go_curators] biological_process
+ENSG00000100387 GO:0045116 protein neddylation "Covalent attachment of the ubiquitin-like protein NEDD8 (RUB1) to another protein." [PMID:11698580] biological_process
+ENSG00000100387 GO:0061418 regulation of transcription from RNA polymerase II promoter in response to hypoxia "Any process that modulates the frequency, rate or extent of transcription from an RNA polymerase II promoter as a result of a hypoxia stimulus." [GOC:dph, PMID:12511571] biological_process
+ENSG00000100387 GO:0071456 cellular response to hypoxia "Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a stimulus indicating lowered oxygen tension. Hypoxia, defined as a decline in O2 levels below normoxic levels of 20.8 - 20.95%, results in metabolic adaptation at both the cellular and organismal level." [GOC:mah] biological_process
+ENSG00000100387 GO:0030163 protein catabolic process "The chemical reactions and pathways resulting in the breakdown of a protein by the destruction of the native, active configuration, with or without the hydrolysis of peptide bonds." [GOC:mah] biological_process
+ENSG00000100387 GO:0032403 protein complex binding "Interacting selectively and non-covalently with any protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:mah] molecular_function
+ENSG00000100387 GO:0030891 VCB complex "A protein complex that possesses ubiquitin ligase activity; the complex is usually pentameric; for example, in mammals the subunits are pVHL, elongin B, elongin C, cullin-2 (Cul2), and Rbx1." [GOC:mah, PMID:11865071] cellular_component
+ENSG00000100122 GO:0007601 visual perception "The series of events required for an organism to receive a visual stimulus, convert it to a molecular signal, and recognize and characterize the signal. Visual stimuli are detected in the form of photons and are processed to form an image." [GOC:ai] biological_process
+ENSG00000100122 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100122 GO:0050877 neurological system process "A organ system process carried out by any of the organs or tissues of neurological system." [GOC:ai, GOC:mtg_cardio] biological_process
+ENSG00000100122 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100122 GO:0005212 structural constituent of eye lens "The action of a molecule that contributes to the structural integrity of the lens of an eye." [GOC:mah] molecular_function
+ENSG00000128408 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000128408 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000128408 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000128408 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000128408 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000176635 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000176635 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000176635 GO:0005694 chromosome "A structure composed of a very long molecule of DNA and associated proteins (e.g. histones) that carries hereditary information." [ISBN:0198547684] cellular_component
+ENSG00000176635 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000176635 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000176635 GO:0007126 meiotic nuclear division "One of the two nuclear divisions that occur as part of the meiotic cell cycle." [GOC:dph, GOC:mah, PMID:9334324] biological_process
+ENSG00000176635 GO:0051177 meiotic sister chromatid cohesion "The cell cycle process in which sister chromatids of a replicated chromosome are joined along the entire length of the chromosome during meiosis." [GOC:ai] biological_process
+ENSG00000176635 GO:0051276 chromosome organization "A process that is carried out at the cellular level that results in the assembly, arrangement of constituent parts, or disassembly of chromosomes, structures composed of a very long molecule of DNA and associated proteins that carries hereditary information. This term covers covalent modifications at the molecular level as well as spatial relationships among the major components of a chromosome." [GOC:ai, GOC:dph, GOC:jl, GOC:mah] biological_process
+ENSG00000176635 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000176635
+ENSG00000130487 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000130487 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100100 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100100 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100100 GO:0019899 enzyme binding "Interacting selectively and non-covalently with any enzyme." [GOC:jl] molecular_function
+ENSG00000100100 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100100 GO:0005886 plasma membrane "The membrane surrounding a cell that separates the cell from its external environment. It consists of a phospholipid bilayer and associated proteins." [ISBN:0716731363] cellular_component
+ENSG00000100100 GO:0014067 negative regulation of phosphatidylinositol 3-kinase signaling "Any process that stops, prevents, or reduces the frequency, rate or extent of signal transduction mediated by the phosphatidylinositol 3-kinase cascade." [GOC:ef] biological_process
+ENSG00000100100 GO:0016021 integral component of membrane "The component of a membrane consisting of gene products and protein complexes that have some part that penetrates at least one leaflet of the membrane bilayer. This component includes gene products that are buried in the bilayer with no exposure outside the bilayer." [GOC:dos, GOC:go_curators] cellular_component
+ENSG00000100100 GO:0036313 phosphatidylinositol 3-kinase catalytic subunit binding "Interacting selectively and non-covalently with the catalytic subunit of a phosphatidylinositol 3-kinase. The catalytic subunit catalyzes the addition of a phosphate group to an inositol lipid at the 3' position of the inositol ring." [GOC:bf, PMID:17475214] molecular_function
+ENSG00000100100 GO:0043553 negative regulation of phosphatidylinositol 3-kinase activity "Any process that stops, prevents, or reduces the frequency, rate or extent of phosphatidylinositol 3-kinase activity." [GOC:bf] biological_process
+ENSG00000100100 GO:0070062 extracellular vesicular exosome "A membrane-bounded vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane." [GOC:BHF, GOC:mah, PMID:15908444, PMID:17641064] cellular_component
+ENSG00000100100 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100100
+ENSG00000100105 GO:0008150 biological_process "Any process specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms. A process is a collection of molecular events with a defined beginning and end." [GOC:go_curators, GOC:isa_complete] biological_process
+ENSG00000100105 GO:0009058 biosynthetic process "The chemical reactions and pathways resulting in the formation of substances; typically the energy-requiring part of metabolism in which simpler substances are transformed into more complex ones." [GOC:curators, ISBN:0198547684] biological_process
+ENSG00000100105 GO:0034641 cellular nitrogen compound metabolic process "The chemical reactions and pathways involving various organic and inorganic nitrogenous compounds, as carried out by individual cells." [GOC:mah] biological_process
+ENSG00000100105 GO:0005575 cellular_component "The part of a cell or its extracellular environment in which a gene product is located. A gene product may be located in one or more parts of a cell and its location may be as specific as a particular macromolecular complex, that is, a stable, persistent association of macromolecules that function together." [GOC:go_curators, NIF_Subcellular:sao-1337158144] cellular_component
+ENSG00000100105 GO:0005634 nucleus "A membrane-bounded organelle of eukaryotic cells in which chromosomes are housed and replicated. In most cells, the nucleus contains all of the cell's chromosomes except the organellar chromosomes, and is the site of RNA synthesis and processing. In some species, or in specialized cell types, RNA metabolism or DNA replication may be absent." [GOC:go_curators] cellular_component
+ENSG00000100105 GO:0043226 organelle "Organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton, and prokaryotic structures such as anammoxosomes and pirellulosomes. Excludes the plasma membrane." [GOC:go_curators] cellular_component
+ENSG00000100105 GO:0003674 molecular_function "Elemental activities, such as catalysis or binding, describing the actions of a gene product at the molecular level. A given gene product may exhibit one or more molecular functions." [GOC:go_curators] molecular_function
+ENSG00000100105 GO:0003677 DNA binding "Any molecular function by which a gene product interacts selectively and non-covalently with DNA (deoxyribonucleic acid)." [GOC:dph, GOC:jl, GOC:tb, GOC:vw] molecular_function
+ENSG00000100105 GO:0006351 transcription, DNA-templated "The cellular synthesis of RNA on a template of DNA." [GOC:jl, GOC:txnOH] biological_process
+ENSG00000100105 GO:0006355 regulation of transcription, DNA-templated "Any process that modulates the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000100105 GO:0045892 negative regulation of transcription, DNA-templated "Any process that stops, prevents, or reduces the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000100105 GO:0046872 metal ion binding "Interacting selectively and non-covalently with any metal ion." [GOC:ai] molecular_function
+ENSG00000100105 GO:0043167 ion binding "Interacting selectively and non-covalently with ions, charged atoms or groups of atoms." [GOC:jl] molecular_function
+ENSG00000100105 GO:0005515 protein binding "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators] molecular_function
+ENSG00000100105 GO:0003682 chromatin binding "Interacting selectively and non-covalently with chromatin, the network of fibers of DNA, protein, and sometimes RNA, that make up the chromosomes of the eukaryotic nucleus during interphase." [GOC:jl, ISBN:0198506732, PMID:20404130] molecular_function
+ENSG00000100105 GO:0007283 spermatogenesis "The process of formation of spermatozoa, including spermatocytogenesis and spermiogenesis." [GOC:jid, ISBN:9780878933846] biological_process
+ENSG00000100105 GO:0008584 male gonad development "The process whose specific outcome is the progression of the male gonad over time, from its formation to the mature structure." [GOC:jid] biological_process
+ENSG00000100105 GO:0010468 regulation of gene expression "Any process that modulates the frequency, rate or extent of gene expression. Gene expression is the process in which a gene's coding sequence is converted into a mature gene product or products (proteins or RNA). This includes the production of an RNA transcript as well as any processing to produce a mature RNA product or an mRNA (for protein-coding genes) and the translation of that mRNA into protein. Some protein processing events may be included when they are required to form an active form of a product from an inactive precursor form." [GOC:dph, GOC:tb] biological_process
+ENSG00000100105 GO:0045893 positive regulation of transcription, DNA-templated "Any process that activates or increases the frequency, rate or extent of cellular DNA-templated transcription." [GOC:go_curators, GOC:txnOH] biological_process
+ENSG00000100105 GO:0030217 T cell differentiation "The process in which a precursor cell type acquires characteristics of a more mature T-cell. A T cell is a type of lymphocyte whose definin characteristic is the expression of a T cell receptor complex." [GO_REF:0000022, GOC:jid, GOC:mah, GOC:mtg_15nov05] biological_process
diff --git a/flotilla/test/example_data/human_grch38_chr22_genes.txt b/flotilla/test/example_data/human_grch38_chr22_genes.txt
new file mode 100644
index 00000000..fd83f9f1
--- /dev/null
+++ b/flotilla/test/example_data/human_grch38_chr22_genes.txt
@@ -0,0 +1,490 @@
+ENSG00000008735
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diff --git a/flotilla/test/test_embark.py b/flotilla/test/test_embark.py
new file mode 100644
index 00000000..22ff9248
--- /dev/null
+++ b/flotilla/test/test_embark.py
@@ -0,0 +1,3 @@
+def test_embark():
+ pass
+ # test_study = flotilla.embark(shalek2013_datapackage_path)
diff --git a/flotilla/test/test_ipython_notebook.ipynb b/flotilla/test/test_ipython_notebook.ipynb
new file mode 100644
index 00000000..7fd0d767
--- /dev/null
+++ b/flotilla/test/test_ipython_notebook.ipynb
@@ -0,0 +1,1370 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:abed0a66eaad3598f317f3533448c8f558f40cc290929cac3dfb6fd108394801"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "import flotilla\n",
+ "import seaborn as sns\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "sns.set_context('talk')\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 1
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "study = flotilla.embark('http://sauron.ucsd.edu/flotilla_projects/neural_diff_chr22/datapackage.json')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "study = flotilla.embark('neural_diff_chr22')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stderr",
+ "text": [
+ "initializing study\n",
+ "predictor ExtraTreesClassifier is of type \n",
+ "added ExtraTreesClassifier to default predictors\n",
+ "predictor ExtraTreesRegressor is of type \n",
+ "added ExtraTreesRegressor to default predictors\n",
+ "predictor GradientBoostingClassifier is of type \n",
+ "added GradientBoostingClassifier to default predictors\n",
+ "predictor GradientBoostingRegressor is of type \n",
+ "added GradientBoostingRegressor to default predictors\n",
+ "loading expression data\n",
+ "initializing expression\n",
+ "done initializing expression\n",
+ "loading splicing data\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "dropping set(['M_M3_11', 'M_M2_07', 'M_M1_03', 'M_M6_1', 'M_M2_02', 'M_M2_01', 'P_P2_06', 'S_MSA_23', 'M_M1_04', 'M_M3_1', 'M_M4_2', 'S_MSA_21', 'S_MSA_05', 'M_M4_13', 'M_M4_10', 'S_MSA_28', 'M_M6_2', 'M_M2_06', 'M_M3_3'])\n",
+ "dropping set(['M_M3_11', 'M_M2_07', 'M_M1_03', 'M_M6_1', 'M_M2_02', 'M_M2_01', 'P_P2_06', 'S_MSA_23', 'M_M1_04', 'M_M3_1', 'M_M4_2', 'S_MSA_21', 'S_MSA_05', 'M_M4_13', 'M_M4_10', 'S_MSA_28', 'M_M6_2', 'M_M2_06', 'M_M3_3'])\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stderr",
+ "text": [
+ "initializing splicing\n",
+ "done initializing splicing\n",
+ "initializing expression\n",
+ "done initializing expression\n",
+ "subclasses initialized\n",
+ "package validated\n"
+ ]
+ }
+ ],
+ "prompt_number": 2
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "%pdb"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Automatic pdb calling has been turned ON\n"
+ ]
+ }
+ ],
+ "prompt_number": 7
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "study.interactive_pca(sample_subsets=['all_samples'], \n",
+ " feature_subsets=['protein_coding'], \n",
+ " savefile='figure1_a.pdf')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "savefile : figure1_a.pdf\n",
+ "y_pc : 2\n",
+ "data_type : expression\n",
+ "featurewise : False\n",
+ "show_point_labels : False\n",
+ "sample_subset : all_samples\n",
+ "feature_subset : protein_coding\n",
+ "x_pc : 1\n",
+ "list_link : \n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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8qhJ22j1UQLujgSMKaKsKMAD4DpgZu1TY2O5o4MuUAOBG3H2Gu+8Uu08zwgKyV6OiYo0P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xht2rRh9+7drF27NmxjgBcE/O233zKDgM2bN2fz5s3s2bMnrONGknAHAaVgpaam0qZNG7766quC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\nPAWB7tZA9FpuVEqBCiGEEEJUK0opK7AD6Ky1LnBq3w88o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F3qtBfGNFDx1LZrn5pKksfD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\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C4D337LTf5gqAAC0tLdy18ukemam306mnWACUJEnSQPAs8Pby99MjYm1ErKV4MvwLwJmZ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+ "text": [
+ ""
+ ]
+ },
+ {
+ "metadata": {},
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+ "png": 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Cr7hjTvWvvRI3enwH3yEYP5rqTNxDuu/8V0mfyVEERmuKWwTiv8AkLVwc3oTI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v5hTv1vaIDQ0FDS0tKw2Wzs2rXLL+cMJH5Zkyal/FYI0QOYBOwAArZH+p69+wiJ7nTYvuzB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\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IGD16ND4+7Z8YsFosbPjhK/ZnbUHpqCYt1MkggxLwjoitU8kYkwiiaKag8Dc+fm4toiqA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\nTo4cOUJMTAydOnXi0KFDbDp8iKKCAvR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4wDMejO5iW/I2Pvv2U4K6Xcjfd6lAdWF/+b6L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\n97nKokr81f4OD0etqKjAaDQ2KlABRMbEEB0by6rkHUw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kc68QnjECS7jvr8aHhVsxZo/j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8wqath3JWGI3h/M7FRCQNyHkMX/8mjo4iGED96ihqjsetjUrw824Bpj5Y6g8c4Tru36riKs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\nR7WkqiUrEzX/kdi2pJAJrz+/MaqWc2bWzsxq/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u/LL1GXVzCsV6/z7omLjKRPp04sX7CA\nn775hvVr1rB//34SEhK47rrrGDJkiMdB5aHOSCpwTMh+kCTpotWwJEn7gadbxCo3IS+/kA8+/YqT\nJZUEJvU/b9dJoVAQKHrx2+b9bNq2k4fvvoXYmMalz7mK3PxcjqQfIbJf4+wNTAzk3U/f4V9Pv+5W\nO3DmigrenTSJPjaZkHPCQVV2e4NLT9rtdrRVZ512UXoDgVYr/5sylVufnkpsYt05062JWq1mwrPv\n8J/pk1Arc4nwPz83W5ZrVvLdecICUZdxzT1tf14jSZIZR1lmhBDBQohIYJskSTe61jIPTaFbp26k\ndOiBlHUIv0g/cvfk8tCdD3scVK1MpdmM1g0Keiqar0XeIlRHVd3XQn17qmt5aBFkWWbOa/9ioM5R\nGCbBYGTp+j/pffXVBHuEh92S6s24f154XggRAwSdlmHx0PYY1v8KFq9YTFV0FVp9/bqPFYXlBGmD\naRftnk6q05grKlg993sO79iBurSU4Lj2+IWFERsRXmN7Hy8vOsa2ozgnh6r160mVvngyzgAAIABJ\nREFUIX/7Dq68/TZiOnZsXeM9uCX1hawvB1YLIdwjvrAVkGWZ9Vt2MOWVN3nm37MoNsYQEJ9Sq1PG\nPzYJS0gnps+awz9ffJ2lK9Zhs9la2eqGs3LjSl547wWCL2mYYN+56Aw6iFLwz+n/5FSu+0QYff7K\nK/S32QnRO1JUKrRaDraLJTWxE8nx8Q3qo1NsLPmdBHvi4yiqdnQZ1GpGGI18/eZbWK1O2zR3CiqV\nignPvsPqk0bKqhTk23w5ZotiuzUJm9pAmj2GPTZBhi2MIpsXGYVVFBo7cu1fwEEFIIQYJYRYUV0C\nPgfIBIqEELlCiLlCiN4uNtFDI7nnxnswpZuxVlnxU/mR3LHuqpwenI+PXwBlVteXprcr3CvN2oOH\ntsz8mR+SUFaG8Rz5gst1Oj596SW3m9t4qBcJ8Ag0tmEUCgXPPjqN3C25WKvq/vyZy8yUH6zg6Ynu\nGxNiqarii5dfYdbEiajXruMKu50h3t50zc2j0yGJg4ckcotLzrvHarOx68gRvA8fJuXYMS7TaBmu\n1ZJ88iRLXpnO2488wrF9+1z0ijy4Cw2ZCW4DdgghXsFRzcZ9PTDNwGazMeeHhWzevhubIRjfyGQC\noxpWPUCj1RPYoSd2m42f1+9nwdIVJHeK54Hbx6HTuUep9Oy8bD788gOKFcVE9otociSUb7gPFn8L\n02e9QtcO3Rg/brzLdZsqc3LxNRjICAulwNsHg58v7UJD0Tei+oNCoSAhMhJrWBgnQkM5XlCIV0UF\n0adOEVFeTtbRo8QK0YKvom42bNhAYWEhNpvtTLU/WZYJ6tiHVWWFxMdGYjToaWfUIjSO30dFpYVS\nUxWZFSZ2n0wjITGZn3/+GYVCceZHpVIRGxtLcnLbcQgIIe7DkQ4zF/gWh4OqEjAAUTj0XNYKIe6o\njk7w0AZQqVREhkSSdTiT26+609Xm/C3R6fVUqbyx2SpQ1SBk3xqUm63ofMJc8mwPHv5qpO3dy8lt\nWxl8QQl4g1pND5OJuW+9zW1TnnKRdR5qQgjxOY6A+HMn6qePNcBrQogSQJYkqe1qN/yNCQ4I5tlH\npvHqB9MJuywMdQ3V1itKTJTtK+PVp15Fp3WdTmR9fDl9Omn793Nr2NmIqZ/z8rgmOBid1Uq3o0eZ\nfzKLQb37EOTrg81u55f16xlVXoHBYjmvvbdGQ3+Nhvk5Ocz+1+s8P2e2q16WBzegId6FmcCXwH+B\nx4QQbwJfni7F/FfhqZf+jdkrAt9O/Zrch1Klwi8qHohHKsjhsWde4T9vuVb3tKS0hI+++ojM/AwC\nuwQSami+ppRGryGidwRp2alMmj6JAZcO4KaRN11UhrQ1yMzMpCI8jIMxMUSGhRFlMDQrFVGtUtEu\nNBRCQymvrORYSDD5GZkcP3GCmI4dXZbmaDaba4zQM5kqMGo1KJVKlEolew6f4NLOjpBglVLh0HdR\nKlGrlVSazWg0DqfpaUeX1WqlsrKy9V6Ic3gauFuSpO9quf5xtbDoDDzpM22Kfj378el3n9Izue0V\npKiPo1u2EHjO+JFfVk67Af1d7uS/kOE33MPqH95jaMfW32CRZZnfUuGOKZNb/dkXIoRI42zmdF0D\nvyxJUsNCdj14aGW+nzmT4caaRZqjDAYOH9hP5uHDRHvSa9yJGBybbRuBQ5wdf8791300Nzw0iajw\nKJ5/7AVefu8lwvuEo9KcXUNVllVStreU15/+N3q93oVW1s+gsdewcd8+MioqiKlhrFEAyoxMMqIy\nCEhMJDXrJKoTJzB41SznUG6xcNJqpV/PtrN57qFlaMjsWJYkaYsQohdwLzAN+LcQ4lcc6YAbJEna\nU2cPbYCwsFDS8k3Nrniy9pt3ycs4wum5beLieQQHB3Prrbfy8MMP13v/1KlTWbDg/IIdfn5+jBkz\nhilTpqCpjg5auHAhH330EZmZmYSFhfHQQw9xww03nLmnqKSIWV/P4kRuJn6JfkTEn9UdsJgtbPhu\nI5l7M9DotYj+Hek2otuZ122tsrJp3mbSd6UDENU5ir639EGjOz8yySfMF58wX7ZmbmX9K+vp37Mv\nN426uVWcVWVlZSxbtgxvb2+0ej1J7dujdLIDyUuno1NMDEczM0Gt5ocffqBfv35ERUU59TkNYciQ\nIRedO7h9PWnrv6dfoo6y/DRK8aG40MCWPVWosOGjMhOgKCGMMqICSlm04wj3PTWD4LDoVrffyUQB\n9Y05a3CUjW/zbN6xD5PKWOO4ZK8oYnDvFBdY1TJ07tAZe6Xd7Rw3zUGWZb554w0U+w/Q9ZxyyzlV\nFn6a9z0TX38do7f7aG916tGPjNQDrNq1nMvj1a3mmLfZ7Cw8ZGfI9eMJDK5Zw6KVeQh4GbgUxyZd\ndi3t3FNAy8PfnuyMDAJNZjR1CBBfqtWxYu733Dnt2Va0zEM9XAlMwDH+/Ay8eTqLRQhxM/C0JEmH\nXGifBycRHhLOkw8+xZufvEFkX4dOsM1qI39HAa9Nfc3tHVQA4tKezPr2W3778kt+27EDY1k5gy4Y\nc64NDiY/O4cToaFUFhVx7QUOqlGBgewpLeWERoNfZATTnn2GiHburcHloeVp8EqgeoD8X3UY6jXA\nHcC7gA5wA6nV5vHUxPEsWr6an39bRUCn3ihVTV8khUTFMu6WW7lh1JXYbDa2bt3K888/T2hoKDfe\nWL+uc0pKCm+/7Vhf22w2Dh48yLPPPouPjw+PPfYY27dvZ+rUqTzzzDP079+fVatWMW3aNGJiYhCJ\ngllfzeJEfib+if7nOadOs2neZopOFnLVo1dhMVtY++VatAYtSYOTANg4dxNFJ4u44uFhIMP6r/9k\n5+Kd9Lq+10V9AQRE+0M0bM3cxp+vbKBPz77c3MLOqqVLl9KlSxd0Oh15WSfJyc8nPLjxOlv1YZdl\n1Fot0dHRREREsHr1asaNG+fSRXRVZSULPn8LU+Zurk1UoVJaMFJEKEW0D4YV5V2I0BbRSZXJmfWl\nCq7vZOPbt56k64DRXD7mNrcSv28km4AZQoh7JEkquPCiEMIfeKm6XZvmx0W/89uf2/CPv6TG66VZ\nh9mxcxePT7irlS1rGYIDg5Gtdleb4TSKcnP55KWXSKwwEXdOUQeAcI2G/lWVvPfoPxj7wP0k9+3r\nIisv5oobx7MtOIwfF3/HkNgqgnxaNqoqvaCKDacM3HDPP4hLqvlvvbWRJGlpdTTVAWBWtZCxBw9t\nhkObtxAu1+1D9dZoKM7PbyWLPDQESZJk4D9CiCXAp8CN1fOdvdVNPI7xvxBx0XFc2f8q1hxeTVBC\nELl783jwjgfx9fZ1tWkNRqPVcvX99wOQe+IEy7/6is1paQRWmOluNKBVqQgsLSW1oICo0lLAsYGX\nbjJxSKFAFxREv/+7mZsHDWrLaxMPTqbRK21JkqzAj8CPQggdNZdHbZNcfeXlnDyVzaaD+wlO6Nak\nPqxVZgJ8vXj0vrvOfNBiY2NZvnw5K1eubJCTSqPREBl5tvJeTEwMmzZtYuXKlTz22GMsWLCAQYMG\ncdtttwFw9913s/z35bw440Xie8Y5nFMJNVdsMZeaSduWxtAHhhDczuHUSRyUyIFVB0ganER5YTlp\n29K45pmx+IY6BsjuI7txYPXBeu0OiA6AaNiRuY0NL29gSL8hXHfVdS0y4BgMBsxmMzqdDr3RQGVF\n48q5NhS73Y5C6dBmsVqtqFQqlzmozCYTC2bPJPN4Kh1D9ATEJbLbpsViU4JChaxQI6u0xLcPxFwZ\nwcaSSJQ2CwrZBnYrOqWdzp3MnDywgTc3riOlV3+GXXMbSqXrhZIbyX3AIuCkEGIHcByoAPRANI7I\nh0xglMssdAIfz/6e7UeyCOxQe+qbX7TgyKljvPjvmTz3z4ddknLrTBxaaW3u77FG/vj6G3YsX8Yg\nrQ4vg6HGNn5aHaPVdtb/579sXraM26ZORatzD+2JnoOvpnOvwcyd9SrWk2kMaq/E4OTSf0XlFtak\nKwnv0JNJ/3jC7SLoJEk6JITYCphdbYsHD40lqU9v5v/8M3XlopZYLAREt350uIf6kSTpuBDidFTV\nKiHEB3jS/P6SXHvltazZtBp7ezveeNG1U1dXm9RkQqKiuHXKFAAO79jB0jlfocnPp6/RSJXFgm9J\nCWkmE/tVKrpfPohHbr0VjdY99Js9uBf1zQjXAqbaLkqSVAlsdqpFrYwsyxw6cpSlK9dxLP0ElRo/\nguKbPjiodXrMsoZ/PPsq0ZHhXDWoH927JKJWq7FYGlYxtianjlqtPqNJVF5eTo8ePc7YP3v+bDKy\nM0ALEZfVXU44+2gOyBDe8Ww6RUhcCDuX7KKiuIKsgycJiAw446ACiOsZR1zPhhcT8a92Vq0/to41\nL6/mjhvvdLrGzIgRI1i6dClFRUXs3buXa/r0OXNt57FjpLRv75RjtUpFqclEVlYWWVlZjBrV+n6P\n3FOZ/PfjT7Ha7fh7G4kUPVF5G9F76QgxatGqVTU7AiOCzvxXlmXMVVZKyisx+lQSVlrOjoPprN/1\nEv5eeiY8/Git+eHuhiRJh4UQXYCrgSE4Kt2E4HBU7cYhqj5fkqQq11nZPN773xwOnSrHv32Xetv6\nhLenoDCbZ2e8w6vPPN72HVWuNqCZFOfn89nLrxBdUsLIBnymVEol/b29yU7P5K2HHua6hx4ksVfN\nUautjcHLm7snv8apzGMs+PwdDJWn6N9OjU7TPEdiqcnKmuOgD47jjqefxNc/0EkWOx9Jki5ztQ0e\nPDSFkKgoig16bHY7qlo2o3ZWVjL2ppta2TIPDaWGqCotbf9r0kMN+PsEUJJdQkpiD1eb4jQ69uhB\nxx49OLpnD3Pffhsfq5V9RUUEXNKDJydO9ERNeaiTOp1UkiRdJYTQCSHCJEm6SI9BCKECoiRJSneG\nMUKIKcDDQARwCkeI/WvO6Ptc8vILWLh8NfsPHaHMVIVN440hJBpD/GU4I/tXo/fCq0MfsivKmfXD\n7xS9+wF7N61m4NCryDhxkpiouh1J8jnh2bIss2fPHhYtWsSYMWMAeOuttwCHKPr0mdMx+5goyi9C\n9K9f+LIsvwydt+48gT6Dn0PorqKogpKcYryDvNk6fytp244hyzLterSj59hLUGsbt8sd2D4Qe4yd\nLxZ/wZadW5hw6wSnDUgqlYpRo0bx0YwZGPX6Fl2YB/r6snLRIiY//zz6WiIinI0sy+z683fWL5uP\nwVrAtVGg1+sox4sKWzbmYiMlRXpyZTV2lKBUgUKNQq0jLDSIqkoL+YWFKOwWFHYrsmxDiR2D0oIf\nlYRTwSVhFRgxkVds5vNXHkDjF8HwG+8ltqP7ixVKkmSp1sXbIEnSqQuvCyFUQohYZ41NrclXPyzk\n0KkyfKMaLmRrDAijAnjtvY+Z9sRDLWdcq9B2Jy07V6xg6ZezGarT4VWLWHFthOl1jLbbWfXBh+zt\nkcINjz3mNhO48Oj2PPjce2Qc2c/Crz/C25JL//ZqtOrGOavKzVbWHJdR+ccw7onHCQyNrP8mN0QI\nEQiYJEmqdRPPgwd3YNi4ceyZM4eUGhzmFRYLclgYUR06uMAyD42hOqpqDOCPY33k4S+GXq8n35SP\nv4+/q01xOvFdu3L3iy/y8Sef0D4ujusfecTVJnloA9TqdRBCGHFU9rsd0AghTgCPS5L0wznNYoBU\nnKBJVR3S+iIwENgG9AN+F0JskyRpWXP7B5j+6mtklVRhVRnQBEZTnFdIdI+hZ66f2LmSqJQhdR4n\n970ShQKKKqxk1nAdGTL2bSbz4DZkuyPySbbLRCX2oLgSXvnoa9RWE2NHDGXEkP412rl161a6dXOk\nG9rtdqxWK71792bixIln2siyzLQ3p6EMV7Bp7iZ0XjqSh9bvXLBWWs9zUAGoqhcaNqudyvIqMvdl\n0q57LEMnDKGyoopN32+isrySQXcNrLf/C1GqlIR3DyM18wjvf/E+j93zWKP7qIlKk4n/PjuN2MJC\nrEmJmKqqMFSHi54bFeWMYz+lki45ubzz6KOMf+FFQmNaTni8vLSYpd/9l8zU/cR5lTMqVotadVqw\n3o6OUgIprfV+i1XBysPJROrK6KU5hkoJ1LOOjAjQMSYATFUnWTvnJfJtfnS5dACDxtzmduk30Ppj\nU2tSXlHBmk3bCUyqeWyoC2NAGBmpJ9l/KJXOnRJawDoPdfHT+zPJ27aNq728muxcUimVDPT2JnXX\nbt574gkemjEDXSs5xhtCTIfOPPzCB6Qd2s0vcz6kna6QS6K19b5eq83O+mMWyrQR3PjYkwSHt43i\nDUKI8cAYHBowS4B5wE/A5YBNCPE1MKE6qtyDB7fjkmHDWPn9vBqv7TCbGfvA/a1skYf6EEIYcKyH\n+kmSNLB6zvMJcBOOOU2hEOIdSZKmu9BMD06mqLgQ346+7Du8j9FDR7vaHKcT0a4dFquVERMmuNoU\nD22EulagMzlbYeIUcAvwnRBipCRJy89p56yt3iLAimMAPr2slnHijsGOPXuJ7H0Nvn6O1IKS9Iab\nHuGnRROoZ0RyEEqgyGTl+0M1v30RHbvR+fKxZB/YRHhSH7RGb7R6Iyd2riQwIYXKijLmfP0NV13e\nt0Y9oK5du/L6668DjtQ/Hx8fgoKCzmvzy+8/k551HOnHw4R3DKf/Hf3RGuvP6VVpVdgvECa2WRzO\nNI1ejUIJei89A+4YgELpeH8uuboHa75ci+3Wfhc5uBqKf7Q/hzYcwlxpRq9rXrzaoS1b+GnWLAYo\nlKhCQ0nV69FrNPXf2EQM3t5UxcQw/NQpZk+bxiXDhzP01luc+ozckxn88uV7VBVl0SvCymWJOmhC\nXJ9GKaNT2vFXlKJq5CfToFUxKF6FLJtIPbyYj55dTlj7JMbe+Zi7pQK29tjUamzduQ+FT9MrmxnD\n4/lt1TqPk6oVqTSZ+Pi554jOL6C/k6r0JRiNBJaW8dYjj3DX1KeJ6uhekQ5xnbrx2PT/snnFz8xb\nMg9zeQl3XnY2RXzujjJu7uF4L7IKqlh7ysA1tz9Gx25tJ3NOCPE08CzwBVCFoxjDZMAG3ICjaMy/\ngFeAp1xjpQcP9eMbEkxlXj66CyLOy/V6oj1RVO7If4DhwL+rj1/HIW0wBdgPdAaeFEKoJUl60SUW\nenAq+UX5lFSVEmEMJ+NkOlar1S03iZuLXZYJdUGFdA9tk7o+AdcC4yRJ+qP6eKkQwgx8LoRIkiSp\n9nCOJiBJ0hYhxFvABhzOKQXwkTMr6sz+9H989MW3HDl4CJVfBBHdBp13/dyoqHOPjRolvdv74t3p\nujPXAr003H3z9aw5UkROmeVM+6MHdqHW6fEJDMOn/9gz7WVZxieyI4WHNhIZEsBzb8yoVbBap9MR\nF1e7BpQsyyz4bgGHNx+hz7jeJPRu+ILUK8ALc5kZu82OUuV4fkVxBQoUeAd6o/fW4x3kfcZBBRAQ\nFYAsy1SZqjBomr6rr1Gp0WmbLgpstVqZ+8ablB48yDA/P47FxqIJ8Kd7VFSLpsV0io0l02jkoJ8v\nfU9lk7FsGe9v2cK9L76At59fs/p+/7130ZcdR1l+kv6xChafNBPmd3ahe+5ir6HHHRNN6BRVTb7/\n5h7edAjV0SEUPt+4mU9fnkBQTCduevAZd/nSbJGxSQgRjmO3cigOoeRvgUeqNSFaBX8/H7A1TLuu\nJqxVlfgF115uvE3QhlyLO1as4LevvqK/UkVgI9P76iNAp2OkzcYP06cTc2lPrnnY/YTxLxt6DV17\nD+WfjzyAlGNBhJ6/WbAlw0qFbycmzXjeXcaOxvAgMF6SpLkAQoivgK3ATZIkza8+Vw7MwuOk8uDG\nVJpMFzmoALBasNlsbjeueOAa4HpJklZUH9+EYyxaXH28VAixF4cD/cXWN8+DM5FlmTf/8waBnQMA\n8Io3MvOL93n8vidcbFnL4C4yBh7cn7oSgfTAyQvOPQ5UAk7XiRJCDASeBEbicJ5dA9wnhLiuzhsb\ngZeXkScnjufDGU9zdR+BnLWbgsNbsVbVHamfHOGFt+7iCbZeq0KE1b0wsdmsFB7diSV9O4OTInj/\n5ad4YfJEAgNqd27U9wGeO3cue3btZcDwAUR0qlvf6kJC40JAhlNHzkqMZR/JJjA6AK1BS3C7YEpy\nirHbzkZbFZ0qRqvXovdpegRUvpRPz+SeTR6cMiSJNx96mMCjRwnqkkx61y50SEqkY3Q0ylYY8KKD\ng+mSmEhB5yTs3brSxWrlo0mT2Pzrr03uM23/DvZuXs3AoFMMFxq89c5ZxF1iSCNE7RwfslGn5Jok\nJQnW/bz/3IOUFLpFqeqWGpu+w1EpMAjoiWMMur0Z/TWaLokdUZia/h5X5qYx5qrLnWiRh5o4snMn\nbz/6KLtnz+FqvYHAFqrIp1WpuMLbG/2Onbw54UFWfv/9eZqF7oDBy4cPP/uGdGUcR/MczvGbe3iz\nNcOCPn4Atz/2clt0UAGEATtOH0iStB1HFNWBc9ocqG7nwYNbsnPFCtT5NX+ndEbJFy+/3MoWeWgA\naqDknGMZyLqgTSYQ0GoWeWgxZn45E1uwFZ2XYx7hE+bL8bLj/PTbTy62rGWw2+31N/LggbqdVNuA\nKUKIM1ujkiRVAOOBCUKIu3AMnM7iJmCZJEm/SZIkS5K0EPgNR1qPU1Gr1YwaNog3nn+SaRPvovzI\nRszlJbW219dRyeiiazJn3hWbzUrBnpU8esc1vP3SFMZdMxydrv6UvPoWIfPnz2fs2LFMmTiF/K35\n7J6/m7L8MszlDmfb0dVHz2t/7rFXgBfB0UFsm7+NvPR8ju9KZ/+K/SQN6QxAVOcolEoV675aT2FW\nIdmp2Wz6biNJgxPPOJjq6r+m4/wj+YggwZ3X31Xva6+JP77+hh9fnUHfAH/yu3cjLimJxNhYtK28\n8FEqlcSFh5OcmEhBt250jYtn//fzmP3qq2cqLzaGHz57h6eHGs9zTp0b1eROxxH+Gka2K+ermS/V\n+FpaGaePTUKIrkAPHNpWJkmS0nBEVK12ntn1o1KpGNS3F6XZjdd7N5UWERsaSMgFqcFtDXfdY5Nl\nmQ2LFvHWxImsfuddhlptXNoM/anG0M5gYLRWS+GvS3jrgQn89MEHVJrcR7NboVBw9z9nsKPQjyqr\nnYJyCwW6WEbdNrH+m92XQ8CFgj0dAOmc4y7ARUVlPHhwB3avWsWK2bMZYPSq8Xq0QY9fegZzZsxw\nO+f335xfgQ+FEPHVx98DjwshFADVc5+pOCqwe2jDfDDnA46Xp+EXc75YekhSCKt2rWT+X8xRJQOV\nlR4JRw8Noy4n1T+AEUCOEGLh6ZOSJK0CJuJIi/nRibbYcZRWPRcb1KES7QRy8gqwWqxo9bVHRJkt\ntXt9L7qm4MwqS6VSo1BpyM0rbPAEQKFQ1LvoOXz4MN988w2jRo5i1cLV7Fy5i59ems+2Bdsa9IyE\nHgn4R/ixbOYyNn2/iZjEGOIvdaQXKlVKkgd0xlZl5de3lrD6szUERQXTfWT3BvV9IXkH8+gS0oUH\nb21axbFv33iD7OXLudLbm8zoaLp36NAs/alNu3Zx37Rp3DdtGpt2Ny2TVKVSkRgbQ1FoCCleXoSl\nHuW9xx/HamlcmpZGraQtRb2qlIpay1i3Mi0xNvUBjgDvCyEKhBAngTuADKdY3AhuuXYkFGc2etFg\nzjrA4xPubCGr/r6YKyr46f2ZvPXABE7M+5Er7TJ9vL3RtPJnQaFQ0MnLi5FaLT7bdvDRwxP537Tn\nyMnMbFU7akOhUHDNnY+yOd3KnxlK/u+hZ11tUnN5HIfTe391qh+SJB2XJMkKIIR4HcdYM8eFNnrw\ncBE2m42vX/83m774gquMdTvSk4xGgo6k8ubEiRTm5LSilR7q4CGgApCEEFuAKGAckCGEWIMjimoY\njvmOhzbKB7M/IK30KIEdat5YDOsexqrdK/9SEVWy3U5BQYGrzfDQRqg1FEWSpJ1CCAGMBoIvuPax\nEGIdjkXchSGoTeUnYLkQYjjwB44ohitwiJI6DYvFwsbtu1jz51ZO5eZTpfHFP3kQSlXtUTn7TpUT\nFaC7KOXPbLEhZVecd27gLZPOOw7sPJC5f2xj3sLfCAsKoG+vSxjY+xIMhppT5157rf5spe3bt593\nbLVaefzlSYT2CQUg/vL4865feCyuEAhErf0njUgiiaRar9fX/+njsrwyIvSR3DtufK191cWCDz9E\ns/8ASV7Vu4BWK1VWK7omOqm+X7KEuUuWnDn+9yefcPPIkYwbObLRfcmy7HBKKRREGwxoy03Mevpp\nHn3zzQb3Mer/JvDDVx8yIt6Cn7HlhN+dQXpBFZtyfbhrkuulV1pobArDEUn1LRACdAJWAXnAe04w\nu8EoFAoG9b2M1QdO4BvasCpoVZUVRIcG4eXlXF2kvzM2m42FH3/Mkc1b6I6CkUYDaOuPhG0NIo0G\nIoGynBzmPTsNdVQUt015qtkaec0lrlNXfrUZUeoNePm41pbmIknSCiFERxyLw5q+MEcC79IC8gce\nPDSVsuJi/vPMsySbTKQ0sOBJO4OBEKuV/z75FGMmPEByv34tbKWHupAkKR8YIoToh2NDLhFYh2Mz\nPweYC3wtSVKR66z00Bw+/+HzagdVYJ3tQruFsWrnSnx9fLmi3xWtZF3LYLVasdvtpKenE+URT/fQ\nAOrMl5IkqRj45sLzQgg/IEOSpKedZYgkSWuEEHcC7wAJOLRhxkuStKPuO+vGarWyeeceVqzZSF5h\nMSaLDYxB+IRE4dWhAzUHQZ9PRZWdzcdK6BblTYBRg0IBRRVWDmVXnBFNrw2lSoV/dEegI+sWz+G7\nz2eBLIPCEXClVCrPCKgvXLiQdu3aNfo1qtVqrr5iDEv3/4q08QhpW9NqbTtm6tX4hvjWet2ZlB0t\n46WpTdM7qDSbSd26jRHniBF3Pnac/UBIRAQRAQGNSrO50EF1mtPnGuOoKiqv4FhmBgknstBUR7uE\n6nUYc3I5snMnHVJSGtSPSOnLQwmd+fr9l1BlZjGgnQJjDdpnriS3pJINWRp2US1/AAAgAElEQVSC\n23fnselT3EZgtY6xaQCwrQljkxXIkSTptJdxvxDiO+AqWtlJBXBZSjIrth0EGuakMpcW06lTfP0N\nPTSYR+66izFaHSOrx6Cf8/K4JvisT9RdjgdrNBTn5PCPe+/lwy+/xOCkCoNNRaXRo9I1vcCGOyFJ\nUjYwUwihEEIE44j2LpMkqUSSpG4uNs+Dh/PISk3li1emM1SjwcfQuM+gUa1mlNHI6v9+TFZqKlfe\ncUcLWemhoUiS9Cfw5+ljIYQe8JckyWlVzz20PmmZaWw7uJWIXg3TFA7tHsr8pT/RJ6UP3ka3qrTd\nKHbt2kWlycTJkxdKynrwUDN1roiFEDfjKO9uAxbgWBR+AdxafX0BcJckSWXOMKa6is5cZ/RVVFzC\nxEn/xGK1g1qPxuCDUu1YYEeJ3jXec2LnyhrPR6UMIau4iqziAvwNalQKKKiwkllH+5oIDA7Gr++w\nsydsdiyV5fgGhqJTK6i0Nl0TIMg/CLsVeoxOocuw5FrbeQe23gCnVKjQapoWeWCpqkJ3gbie1mYj\nJfUoJ0tK2OMfgG9QIDHBwfWmoG3avbtGB9Vp5i5ZQruoKHp3q33NIcsy2cXF5OTk4FNWRvfMExfl\nygaiIDfzRIOdVABePn488OzbnMo8xsLZ7yOXZjEgVoGPwbXOquziSjZkaQmO6cI90yZh9G4dx6YT\nWA5053zdmIZwBFALIRTnVPNTA+XONK6h7JPSUOobXqVP7+VLWrp7pH39Fag0m7FUVLDTYmVnueNP\nINVs4ue8PIDznEXncvq6K9r7yjJ71q3jshEjGvQaWwpZlvmryNsIIUYBk4G+gO6c8wU4Ir7fliRp\nk4vM8+DhPH6YOZORej3aJm4mqZRKLvf25rcVK+h/3XUYXezw/jsjhHgVeEeSpDwhhApH1OYDgEYI\nkQe8KUnSv534PJdXN/678OOvPxKYXHcE1bkoFAr0sTpWbVzF1UOvbkHLWg6TycTRo0exWiwEBQWx\nY8cOevTo4WqzPLg5ta7uhRCP43BKGXHsHn4CrAEG4HBS3QQkAW+3vJmN540P/keVQocuIBydj/8Z\nB1VzKTJZya+wNkkxXqvTY/DyOfvj64dvSCRRPa9EF92Fd2f9r8l2LVu3DL8oXwy+BnxDfWv9Uapa\nT0dFNsrsObinSfd6+/pS6e3FvJzzNWl/zssjIjeP7ocP4793Hz+vXMne1FRySkqQZZlfVp+vc/3L\n6tV8+M1FATcXcbrNuffLssyCNWvYf+wYew8cQLH/AGnr1hN/joPq9CLRardzAJmUIYOb9HrDo9tz\n/zNvc/2kt9hQEs2vh2yUmBqnceUMThZVMf+ATJq+B/e/+DG3PPK82zmohBArhRArqv897wfHWDX7\ndJtGdLsERzTVc0IIbbWQ+s3A7BZ4CfXy++p1+IQ0PBxaa/AiLeMEVqu1Ba36+6DT67nlxhs5brVg\ntjne0wT9+ZEJFzqSLjxurfZW2c7asjK6denqFg4qu8WMtdIlvl2nIoS4D4cMQQYOHbzROCQIxgDP\n4NCAXVu9meehDVNaXkrqyVSyirPO/BxKP9SmxlOLxYI5v6DJDqpzibfZ+fOXX5xglYdm8ARw2pPx\nPHAn8BQwHEfGybNCCGfqL7i8unFdnMzOYdIr7zP1/bn8sHCZq81pFmXlZai1jduI1hi05OS3Tc24\nsrIyFi5cSHKyI4AiNjaWEydOsG/fPhdb5sHdqetT8gRwnyRJn8OZNJo1wE2SJP1Yfa4M+BqHd9+t\neH7yo3y74Fe27dxLpUKHxj8cr8CwM6l1NVFbBFRLtJdlmYrifCrzT1AqbaSziOfuh5smNGu1WsnK\nzSIiIbxJ958mfVc6m+ZtBqD3uMuI7RbbrP6COgby/eLv6ZrYtUn3T3jtNSaNH0+B2Uyg/mINr4Cy\nMtR5eXQuryAnOJh9fr5YFQpySkoJ8fE+kw5Y0YAqWOe2KTabycrOwVpRjlxSgti7D011VFdNCYZm\nm43lFRXcMnkyBq+GJJDWTnBYFOOnvEFhXjY/fPIGxpMZ9G+vbnHBcrPFxh+pMkHxPZjw0iR0Nbzf\nbsRR4B4c49FKzv+1DAC2APk0osKfJEnlQoirgA9wLECzgWmSJC1yltEN5Y91mzCrfdHXoZNXE+qQ\nDsz68jseHe8288o2zbBbb6Xv2LHMn/kB2WlpBFVW0lmnw1iHJl5tEVDObm+12zlSUUG6SoUuOIgx\nEyYQlZDQqL5agkM7NxCmrSC/spLigjz8Ahv3+tyMp4G7JUn6rpbrHwshHgJm4KQIcA+tiyzLzF08\nl7Vb1+LXxRfNORHMpoJKTEcquPXa2+jbo68LrWwYGo0G74gIyoqK8G5GcRmAw2oV/7juOidZ5sEJ\n3IWj8vBn1cfLhRBpwHSg2dFU51Q3vkqSpCogTQhxOqLK5ZSVV/DSGx/g1aE3aHUsW7eRhPax9Oia\n6GrTmkSfnr35bf8yguMaXom5/EQ5V97u9GL3Lc6BAwfYu3cv3bp1Q6c7E4xMcnIyhw8f5sSJEwwe\nPBitm+h9enAv6loFhQDrzznegEO079w0mmOAe4VZVKPTabn75mu5++ZrycnNZ9nqP9l7cA9lFZVU\nySqUPsF4B4Wj1ujq78wJ2KwWygpysJXmorZX4W3Q0j2hPcPH3UJMdGSz+l62bhnaiOZNSnYt2c2u\nJbvOHK/6ZDXdR3an+8imy26otWpyy3Ox2+11Ogdrw9vXlw9nz+aLV6ajz8qip9FYa3RBRF4eEXl5\ndAay9+1lr68fKi8jg3v35ruFC6msqqrzWT7e3hw+cYK40FCK9u6lQ3YOWpsNx6s/6+u48PldvbxY\noVRw74xXCY1umH5QQwgIDuP+qW+yb+tafpz7P65qX4W/V8uIqx/Nq2JHkT+3PPoMYVGN10RrbSRJ\nGi+EmAd8DBwAnjydciyEmALMlCSpsel+SJK0GxjkVGObwE+LfsOvY+MXRd5BYew5sIEKkwljI/VI\nPNSM0dub256eCkDq7t2s/vFHSrJz0FeY6KBWEa7XN0obrzmUWiwcMpvJ12rR+ftx6dgxXHPllajV\n7qFjZ7PZ+HXuJ1zXUYOpys63H07nwefedbVZzSEKqC8UeA1uGk3uoXasVivf/PIN2/ZsRROhIbLv\nxdow2lAdPsHezF31LT8snsfIoaMY1ndYq33em8JtU57ioylT6Gu2E6Jv/NzWYrezoqKcQePGofN8\nh7gTfsDmC85tB2Kc1P+51Y3HAZU4smeed1L/TaasvIKnXvo3+vY90Ggdf9P+HXvx0ZdzmXj3zaR0\naXuOqiv6Xcnilb9CXMPa2212NJVaYiKd9etuedLT09m6dSuBgYH07NmzxnGzY8eOlJaWsnDhQqKi\noujZsyeaZjrYPfy1qGt2uxV4pnrRVwY8hyM9cBRnJ26jgIMtaqETCA0J4vYbx5w5Liwq5s+tu9i6\ncw/FZeWYzBZsGi90AeEY/YKaPQmRZRlzWRGm/JMoKksw6jX4Gg306ZrEwF6jCQ117u7y7oO78Y1o\nuq/wQgfV2fOOc81xVCm9lRw/cZy4mAaOxhegMxiYMONVNv/6K4vnzWOgSo2frvbJlwqIzM0jMjeP\nKqWSzPx8unXrxrHjx8nNzb2ovY+PD3FxcSjsdqJ378FoaViKXZXNxhpTBbG9ejF54sQWm7gmXzqQ\n+KQezJo+iSERpQT7One34WC2hROqBB575VW3nnxfiCRJS6t3/94G9gkh7pckqW3HgAMHpFQsGt8m\n/y60Ie35YdFy7rxprJMt85DQrRsJ1bp1+aeyWb9gPisOHMRWWkKU1UYHoxGdEwsL2Ox2MitMpAJ2\nby8C27VjwDVjiU9OdrvPqt1u5+MZ/6R/WDlqlRYfg5J26lPM+/hf3PTAVFeb11Q2ATOEEPdIknRR\nzWwhhD/wUnU7D22AvMI8vpj3GcdPpqOPNZypiFwbSqWSkM6h2O12Fu9YxMLlv9AtsRu3XXs7ep37\nRRv7BQUx+aOP+OSFF8g+mU2XRlR7LaisZK1s585p04ju2LEFrfTQCJKEEJk4AgYGAnvPuTYU51VX\nd5vqxuciyzLPvPoWutju6IxnNTqVShUBiX344LNveenJh4mKCHOViU1CrVaTEBNPbnEuRr/6P6NF\nmYVcMdD9o6isVis7duwgIyMDX19funbtWu8mmo+PD5dccgl5eXksWrQIo9FIr169CAxsuGaXh78u\ndf31PAwsBk7L8Ftw6DK8IYQYiCPFZjgwvkUtbAEC/P0YfcUgRl/hCJqQZZm09Ez+WLuJI2m7KTNX\nYlEa8I3phErdMKeA3Waj+ISEurIEL4OWjtFRDBk+kiSR0KQoosbg5+NHqbkEjb7xHuj03ek1OqhO\ns2vJLgKi/Juc+mevtBPgF9Cke8/lslGjSB44kE9ffJG4wiLiG7DLp7XbiT+RxZHdu/EOC6Nbt27s\n27cPm80GOLz4SqWSffv24atSYQxvWKWNospK1gB3Pf88ka2QYmPw8uaRFz/k/ecfYqzejEHrnIVw\nVlEVGYo47p08wyn9tTbVFf7GCyFGAJ9Ua1C1nuhaC5CTlw/ahi8sLkTr5UtObr4TLfJQE0HhYYx9\n8EHAMTHbu24dm5ctpyw/j0CTmc56PV5N2BG02u0crignQ6VG7e9P8uWDGD96NEafhovotzZFBbl8\n+fZzXBpYRGTA2e/LLhEa9pzcySf/epI7H5+Oto7NBTflPmARcFIIsQOHXksFoMdRdvNSIBPHZp0H\nN+Zkzkk+mvMRheZC/IQf4e0aJ42gVCoJ7hAMHeBQtsTk1ycTFxHHw7c/jMHNIo40Wi0PvfYay7+c\nzfIVKxhsNKKpZw66t6Kc/NBQJr/0Elr3TvX/O7EG+BAIxxEocLkQ4nNJksxCiM9xaANPcdKz3Kq6\n8WkWLV9FlTEcf+PF339KpQr/Tpfx4WdfM+PZJ1xgXfMYPfhqZi6Y2SAnVWV2FVfcc0UrWNV47HY7\nqampHDx4EJvNRkRERJME0YODgwkODsZkMrFx40aqqqoIDQ0lJSUFo7Hpc2IPbZtanVSSJO0WQnQE\nhgD+wAZJko4JIfYBE6vvvb0OvYY2g0KhIL5dDPHtzoZS7j14mNnfL6BM6YN3ZIc67y/LSUdTfoq7\nxo6kX6/Wr1YwtO9QPvhxJkb/xn+QN31/YQRxzW2a6qRSWVT4+/o36d4L8fLx4dE33+SzF19ClZFB\nuwZODh9ITOL13bvIy8sjOTmZ3bt3ExcXR3F1tT6AB5K7NKivMouFdWo1j7/9FvpWHDi1Oh33PDGd\nb99+kmuSmt+f1WZnbZaRSTOmN78zF3NOVNVbOHYW247a7QV0T07k619+B+KbdH959nEGXTfUuUZ5\nqBO1Wk3K4MGkDB4MwNG9e1n5/TyKsrKIt1rpaDTWG/mUbTaz225DExTEZddew7XDhrlNGl9drFn8\nLdtWLWJEgh3vGjZJukZoCC9J5/1p9zPipnvpctng1jeyiUiSdFgI0QW4Gsc8KA5HlIEJRzT5h8BP\n1RouHtyUz374jO0HtxHUJYgIQ/N0OwH8wnzxC/OloCCPya9P5obhNzC0r/uNuVfedSeJffswZ8YM\nRhlqd1RtqCin3dCh3HzHHa1soYe6kCRpOIAQwgcQOKKbbNWXg4GJkiR94qTHuVV149OYzFWo6ohO\nViiU2Gz2Wq+7Mza7FUUD95tlQKlwn/1XWZZJT09n7969VFZWEhwcTFJSUp2/q4ZiMBhISnIscoqK\nivj999+x2+1ER0fTtWvX83StPPz1qXMWXO2xXwH4S5KUXX1uJQ6xYoQQKiFErCRJ6S1vauvSJbEj\nT//jAab8a1a9bS2mcu67+TpSujjBe9AEOsV3Qlupw26zt2r1vvooySmhq2iaaHptKBQK7nnhed67\n7z4aqp7UOzSU/4uP57ujR5FlmR49emC1WklLSwPg/+Lj6R1ad9j/aQ6YzNz09JRWdVCdJigskrD4\nrpwq2k64f/MG6m2ZFkaOm9AmFsINoTqq6r7qFBxbfe3dFX8/X5LiokjNycQ7tHEaZ6bSYnxVZi7r\n0TCHq4eWIb5LF+K7dMFms/HnggUsWb6cBHMVnWpIvck1m9kiy7Tr2oUJ99+PoY2UfD92aDcLZs9E\neJVwY7KWugIYQ3y13JhkY9Ovs1j720/c9MAUgsMaXrnSlUiSZAHmCyEW4FgYaoGy6vHGg5uz6+Au\nth/ZTkSvhkVJNwZjoBeGPkbm/fo9vbv3xsvYvKIpLUGMENzxzLPMe/VVhtcwthyqqCDo0ku50uOg\nclskSSoVQhzHkdViACySJI2p57bGcm5143/hcIjdjEOw3WXcMPoKVq57GYtfCJoLqt/Kskzh4S08\n9eCdLrKueSxdsxTv8IZ93+tDdfy29jdGDxndwlbVTXZ2Njt27MBkMuHv70/Hjh1bVEPK398ff39/\nZFkmLy+PpUuXApCQkOA0p5gH96bWmaUQwiiE+BQowRHuniGEuPGCZjFAWksa6Ere/98cyorPl6I4\nsXPlRce+UQnM+f7n1jTtIq4bcS35hxuf5tN73GVOaVMT5akV3HmD87/jivPyUNgbXLgNgHHxCdyc\n3OVMRENZWRnh4eH8X3wC4+IbnrKnUSooyMxs1LOdyeVjb+dQfvP1aHKrDCRf6nKN8CYjhLhZCLFA\nCPGjEOKOaof5HBxV/Yqqz7eNFf8FTHrgTvzshZTlnay/cTXmsmKsJ/YwfeqkFrTMQ2NQqVQMvOEG\nJs+ahc+wIfxWXobVfnbXd1tFBWmxMTw26yPGPf54m3BQFeSd4uMZT7BmzgzGxFXQNbJh6fAqpZJ+\n7bUMCc3lx3cn89V7z2MqL2tha5uPEGJU9UZdBY6KnxlAoRAiVwgxVwjR27UWeqiLyJBI7GYbsty4\n+UJDsVvtqBUat9SnOk2M6Ig+rGbNngyViusmTmxlizw0hNNjjxDCBOTgSC0uEkLkOXvskSSpHEdq\n3xU41nyLcFF143NRqVRMn/o4ZalbsFSercAtyzIF0mbuuH4UIqG96wxsIjabjdSMow3OfvGP9Wfl\nnyta2KqasdlsbNq0ifnz57Nz507at29PSkoK7du3bzWRc4VCQUhICN26daNLly6UlJTwyy+/sGTJ\nEgoLC1vFBg+uoa6wm5nAlcAEHJoLK4DvhBAXqre5l4KrE7m8f28s5fVvmJZnZ5LSLbkVLKqdAZcO\nRFGibLHJWGMpyy8lKSEJrca5Qt+VJhMfv/AC/RpRrtSsVnMoNpZuI0ZwVa9eHEtNpbiggBuGDyfp\nimGUNkKDobuXF8u//ZZT6a4JHiwpyEWrbH54s91mpaqy0gkWtT5CiMeBbwAjjsiGT3DoNwzAodNw\nE5BEG626pVAomP70JEKUJZSeOlZv+/LCXOTsA7z58lR0Ok8ZX3dDoVBw5R13cP3kyfxhcky0d1VU\nEHb5IO554YU2odUkyzKL5szk2zeeYGDgSYZ00KBVNz5q10unZlQnNZ0VB/nPiw/y59IfWsBa5yCE\nuA/4CYdj6h/AaByLuDHAsziyMNYKIW52mZEe6iQkKIRbr76VU3+eoiSnxKl9F6QVULSjiCceeMLt\nd/TNZnON5202K+Zyl2Z0eaiBesaeZ2iBsUeSpN2SJA2SJEkvSVI7SZLqTyNpBYIC/Znx7BOUHtmM\nzeoobFSUup3brx3O5X0vdbF1TWPJ6l/RRjR8rqZUKqnUVpF6LLUFrTofWZbZtWsX8+fPR6FQkJKS\nghDC5el2SqWSiIgIUlJSiIuLY+3atfz222+YTKb6b/bQ5qhrlnktcI8kSV9IkrRUkqS7gE+Bz6tz\npP/yDO7Xi5GjRlBy4vCZc1EpQ85r4x8taOev4I4bnR1923gG9BpAUWZRo+5pqCZVYylNLePuG+5u\n9H11kX/qFG//4zH6V1nqFSU2qdUcj4hgV4cEjnVJJiopkcR2sfTt3p1Ppk/nf6+8wtCePRGJieR0\n68puITgSE02pXkddbj6FQsFwnZ4vn3ueA5tat6iTLMv88tWHXBLdfEdE70gr381qs3pUTwD3SZJ0\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Mm0Sn3kOsZocgCGrgY2AqoBAE4TzwtCiKv1YaFgycpqGpZc2Ic2mZDSoNs0eee/Q5Xlv0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\njEAmkxEfH8+RI0coKCio8j1mQONUdZA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ilSxf/h2V5aUMuu5WR4vYZC5V3ZOVfYp1W3ay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jM1uRt58t47iI/1fKfOz777L2bZTCCeExwuKj3Cui1rGTu061qrOxwOVq5cSWZm5olnuFKp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\nZSWDR46k76hRLt897jXwQnz8A/j+05kMDq8ktp2LRXexI7eK7KoQ7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zhszZ5nt1oIDGh/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N/ElEUkNdngDyAq+xf8jpWcDrNKyhmBmq+qCqjlfVZGAUUAx8KiLRIS6bqRuLP5HN4o+J\nZBZ/IpvFHxPJLP5ENos/plask8ocRFXfBhYAfwp1WQLsY6CviIwGBgEfhrg8ASMipwG7fIOhqi4G\n7gbaAC1DVTZjasLiT+Sx+GMaCos/kcfij2koLP5EHos/wWOdVKYiVwGnAu1CXZBAUdU84D3gZeB9\nVS0IcZECaTawF3hKRNqIiEdEugB3AD+ranpIS2dMzVj8iSwWf0xDYvEnslj8MQ2JxZ/IYvEnSKyT\nypRLVbcCtwGxoS5LgL0GdMYZalom4pdAVdVsYAzQCliGs7Tr1zhzvhtaEkbTwFn8iSwWf0xDYvEn\nslj8MQ2JxZ/IYvHHGGOMMcYYY4wxxhhjjDHGGGOMMcYYY4wxxhhjjDHGGGOMMcYYY4wxxhhjjDHG\nGGOMMcYYY4wxxhhjGjBPqAsQqUSkN/AccDiQCfxFVe939w0GngEGA9nAK8AtqloaouLWmIh8ABzj\ns8kLdFfVrSKShFO/U919HwMXqWpuPRezTqqo483Ag4Dve3aBqr5Rn2WsCxE5C5gFdAI2A/ep6ksi\nchcw0+/wKGCdqvaq52KaOhCRvwLbVHWWz7Y+wN+BIcAG4C5VfTNERayTCup3MvAw0A3YAfyfqj4Q\noiLWWQV1bBAx1p9bryW4sSjU5TG10wjaPw29fs8D0/02RwNfqepxDeE7RERmAlcArQHFqcN77r6I\nfw8bsyre24hvH1RRv4i/NvFVXptARI7A+fvsgxN/fqeqr1d8lvBT0fWXu+9i4E6gI7AReFhVnw9V\nWcNZTKgLEIlEJBb4AOdLfDzQH5gvInOAb4D3gKeBsUAv4BOcP7QnQlDc2hKgr6quLWffU0BT4BAg\nDvgMuAm4v/6KFxCV1bEncLmqvljPZQoIt5H9PM5F7hzgZOBNEVnqfmE/4HNsHLAA+GMIimpqwW2I\njQMu5sD3Mgp4BycGjQFGAh+LyHJV/TkERa2VSurXBngD5wLrXZwY+5H7uX4/BEWttYrq6GooMdbf\nX3AabRG/7HRj1dDbPw29fgCqOgOYUfZYRFKBhcC9DeE7REROBa7GuQm5ErgeeF1EDgH20ADew8aq\nivc2mghvH1RWP1XdSYRfm5TjgDaB22n1AfAo8AgwAif+rFDVJSErZQ1Udv3lHvJnYCLOddcZwKsi\n8r2qLi3ndI1ao+6kEpEuOD24d+D0aqYC/1TVy6t46vFAiao+5D5eIiIjge1AXyBZVcsu+H8RkdeB\n46jnL8Da1k9EooF2wPpy9qUCZwFdVDXT3XYazoVUvQtGHV09gJcDV9LaqcNndALOXdEv3MfvishP\n7vbFfsfeD/yiqm8FrOCmSnV4b8H54k7EuVPoazhOx8Y9qloEzBWRuTiNttsCVPRqCVL9xgKqqm+7\nj79yv/hDMgIwGHUMtxjrU64u1L6uiMhUoDPOhb6N4g4xa/9UKCLqB3X/m/TxLPCKqn4rIiOI/O+Q\nY4E3VHWZe56ncW7CdcVp94XNe9hYBem97UqYtA+CVL+dhMm1CQS8TVBmPBCvqg+7jxeIyOc48ade\nO6mCdP3lBb5Q1XnuvjdE5Amcz6h1UvmJCnUBwkBzYBjOB2QQcLbbIKnMEcAaEXlTRDJFZD0wVlW3\nA2uAUX7HD6LizpBgq039OgMlOMEhW0RWiMjZ7r7DcO5EXSki20RkF07DZWNwil8tga4jOHcr7nPf\n3y0i8qDbsRUKtanfWzh3YwAQkWScOh/wORSRfsCFOKM0TP2rzXuLqt6pqlfgDAX3NRRYqaoFPtuW\n4QybDoWA1k9V31TVwQAi4hGRsUA/YG7AS159gX4PwzHGlqlVXd273A8D5+FMU7CRVOHB2j8Hi6T6\nQS3/JsuIyHHAocDv3U0R/x2iqlep6vWwb6T4ZTidjL8Snu9hYxXQ9zYM2weB/uxCeF2bQODaBGXi\ngCK/w6NwZr6EQkCvv1T1EVU9zd0eLSJnAEnAd8EofKRr1COpfNzk5vpY7fZ29hCRLyo49gGgDU5v\n97nAmTjDob8QkQ3uvOGyHvAOOEMZuwMXBLcKlapJ/e7HGfZdBNwMfAtMBv4pIuk4dU/D+aPqArQC\nZgMPATcEsxJVCGQd5+Hcbbsf530egDN0uBS4O6i1qFiN6qeqZQ1ORORw4AWcOvuPlnoQJ9/G7mAU\n2lRLrd/bcqQCWX7b8oCEAJSztgJZP2BfbF2P03j5nNDfgQpkHcM1xpapaaz9A07Ol7tUdYNIqNqa\npgLW/tkvEusHtYw/IuLB+fu8zx01BQ3oO0ScvDD/xBm5eb+q5rjHhON72FgF+r0Nt/ZBwOonIvGE\n37UJBLZN8DUQLyIzgBdxRs9P4MDRVvUt4NdfbkfX1zif0ZeATcEqfCSzTipAVff4PCx2t1X4hSwi\nzwL/U9XX3E0LROQznKDxnjhz+m8BbscJMuerqv+Xfr2paf1caT6//1tEpgOTcP6oAG5X1Xxgk4g8\nh5NXJWQCWUdVnQ3E+uxbIiJ/xsnhEJIvgtrUT0RSgD8Bp+Ak8PuLqnp99vfCGeJ+ScALbKqtlp/d\niuTgTCHz1QzIqOX56izA9Ss752Ygxh0J+DrOxeXNdTlnHcsTyDoWuz/DKsaWqcX35W1Auqr+y2ez\nTfcLE9b+OVCk1Q/qFH8m4Fz0vuqzrcF8h6jqayLyFs4UordFZKGqfhiO72FjFej31t0XNu2DINQv\nrK5NILBtAlXdLiKTca5dHsdJT/IJTlwKiWBcf6nqN+5IuWHA28BVOB3mxod1UpWvqgb0KpzcL75i\n2P9H9BJO7oIRqroiwGULhErrJ05y4lJV9c2VEo+zys1qn8f57u++dQ8Xta6jOIn7Wqjqev99gS9m\nrVVVv+Y4SfkWAz1UtbwG5kXAZ+okYzThoy4X8EuBWSISp6qF7rb+wFd1L1bA1Lp+IvIU0FpVpwGo\n6jIR+RBnlaZwUpf3MFJibJmq6joBGC0iee7jOGCUiJylqscHt2imFhp1+4fIrx9UP/5cjJP/pthn\nW8R/h4jIz8DTqvqsW7fPxMlN1Bf4kMh4Dxur2r63/UTkWCAtzNsHtf7sipMbLtyvTaAObQKc0au5\nqtq/7GARWYAzGilc1Pr6S5xV5Zep6u3qrCb6vYh8jROPjB/rpCpfZxHxnxNbZhZO0rp7ReQinC+7\nI3FWabrDHdp3Es4Hc1d9FLYWKqvffTh/gJPFSda7AZiCU7+bVPVXEVkG/FFErscZjXQpTo93OKl1\nHXFyMnwoIifgDDHtD1wL3BvkMtdEVfUrwEmyeK5v772f03B6+k14qTT+6IHLKXs48AtzDrAN+J2I\nzMJZQWQ4Tt6xcFGX+n2AM+pxBPADTo6AMwm/O1C1rqOqLoyQGFumqroe47tBRL4CXlTVsEj+ag7S\nmNs/DaF+UI34497Fnwic7rd/DpH9HXIfzvfEZSLyEU4+n5OBIcA1EfQeNla1fm+BloR/+6Au9YuE\naxOoQ5tARDridMwdjTNF7mKctAdvBLPANVSX668PgLtE5CXgN5zvl2PxWW3V7GedVOUncF2nqrHl\nbN9HRE7CucD/K7AW58P4k4jcACQD2/zm2c5R1QkBKnNN1Lh+IpIAtMUJEEnACuAMVS1L3HciTr13\n4eQueFZVnw5oqWsm4HUUkbtwGqiHAFuBp1T1+UAXvJpqU7/3gNFAod/nsKyB2hpnlZBQzvM2tYw/\nfs/3HUJcIs4Sxi8AN+KMyjndHf4eCoGu32ci8jucBktbnIupF1Q1lJ2tAa2jK9xibJm61tWEF2v/\nlCOC6ge1/5schJNn6lvfjQ3hO0REmuDk8luCM1VxOc57+GOYvoeNVUDfW3d/OLUPglG/cLo2gQC3\nCVR1k4hchjMFuQPO/8NJ6pNzrJ4F9PoLJw9wV5yRqS1wvl/u1v0rUhpjjDHGGGOMMcYYY4wxxhhj\njDHGGGOMMcYYY4wxxhhjjDHGGGOMMcYYY4wxxhhjjDHGGGOMMcYYY4wxxhhjjDHGGGOMMcYYY4wx\nxhhjjDHGGGOMMcYYY4wxxhhjjDGmPonIOhE5z/39HyLyYqjLZIxpHCz+GGNCxeKPMSZULP6YSBYV\n6gKYRsHr97sXQETGiUhpaIpkjGkkLP4YY0LF4o8xJlQs/piIFRPqAphGxxPqAhhjGi2LP8aYULH4\nY4wJFYs/JqJYJ5WpNhHpAfwFGAPkAK8BN+GMyHsYOAtoCnwJ3KSqv1VyrrHucYhICXAy8BZwo6r+\nn7vdA2wA/gpsAW4H3gYuA+KB94ErVDXTPX4A8GdgBLAb+Adwr6oWB+r/wBgTGhZ/jDGhYvHHGBMq\nFn9MY2TT/Uy1iEgz4AsgDxgGTMMJijcCfweG4gS64cAO4CsRSazklN+5zwfo4p77feA0n2OGAR1w\ngjFAN+BQ4GjgeKA/8JJbvrbAV8A8YDBwHnAG8GjtamyMCRcWf4wxoWLxxxgTKhZ/TGNlnVSmus4A\n2gIXqOoyVf0CeBDogxMwz1PVH1R1GXA5kIgTyMqlqgXAdvf3je7j14HxIpLkHjYZWKiqa93H0e7r\nL1HV+cBVwCki0sZ9zV9U9V51fAncBVwYyP8EY0xIWPwxxoSKxR9jTKhY/DGNkk33M9U1FCcIZZZt\nUNU/i8gUnF7z5SLie3ws0LmGr/FfIBc4ESdgTgL+z2f/RlXd6vN4ofuzG3AYMFpE8nz2e4BYEUlV\n1T01LIsxJnxY/DHGhIrFH2NMqFj8MY2SdVKZ6ooHisrZHuv+PMxvvwdIr8kLqGqBiLwDTBKRpUAP\n4A2fQwr8nhLt/sx3f/8YuNnvGA+QiTEmkln8McaEisUfY0yoWPwxjZJN9zPV9SvQW0SalG0QkSeB\nGe7DRHeYpwKbgeeBrhWcy1vBdnB68E/AGcL6tapu9tnXRURSfB6PAoqBlW75uqsPoB/wsKraMqvG\nRDaLP8aYULH4Y4wJFYs/plGykVSmuv4J3Esj0hUAACAASURBVA08JSJ/wkmaNwNnqGkJ8LSIXA0U\nArOAFsCSCs5VtgxqAYCIHAEsUdV89icHvAm41u95McBLInIPkAo8A7ykqrki8ixwuYg8hLOqRE/g\naeCpOtbbGBN6Fn+MMaFi8ccYEyoWf0yjZCOpTLWo6k7gOGAgTvB7BLhDVd8CTgeWAZ8D83GC5vEV\n9KB72d+Tvwj4CZgLDHJfpwT4t7v/Tb/nbgC+cV/nfWAOcI37vN+ACcB4YCnwLPC0qj5Uh2obY8KA\nxR9jTKhY/DHGhIrFH2OMCRMi8pyIvOy37QIRWVvRc4wxJhAs/hhjQsXijzEmVCz+mHBi0/1M2BCR\nQ4DuwFnAMSEujjGmEbH4Y4wJFYs/xphQsfhjwpFN9zPh5FycZVBfVNXv/fb5DlM1xphAs/hjjAkV\niz/GmFCx+GOMMcYYY4wxxhhjjDHGGGOMMcYYY4wxxhhjjDHGGGOMMcYYY4wxxhhjjDHGGGOMMcYY\nY4wxxhhjjDHGGGOMMcYYY4wxxhhjjDHGGGOMMcYYY4wxxhhjjDHGGGOMMSbseEJdAGOMMaYxEJE4\nIMpvs1dVC0JRHmOMMcaYYBORGCDGf7uq5oegOCYCHPRhMY2biPwDOK+SQx4FngbWVnLMP1T1Ivd8\nTYAbgHOAbkAJ8AvwnKq+WEEZXgAuBG5W1T/57bsA+LvfU0qADcCzqvqI3/HTgbuBrsBW95iHKim7\nMaaeVCPedFHVDe6xRwJzgV9UdWA55yp1fz1FVT8sZ/+DwB3AXFU9qpz9a4BZqvqS3/aLgNuALjgx\n5C+q+mgF9akwdrnWAO39tq3DiY3GmBALdUwSkZbAk8AJQAKwECee/ODzvLOBu3Dixm7g38Dtqprr\n7r8XuKeSOoxT1a8r2W+MCbJqxJoLgRfxiTnVOOccnBtfZfGkOfBH4DQgBVDgXlV9290fT8UDVkpV\ntVBEooFZbnla4Vz/Pa2qT/m9dlXXW3OAkX6v4QWiq1M30/hYJ5UpzzZgegX71rM/oP0R+KycY7YA\niEhTd/8A4AngW5zP3BjgGREZrqqX+z7RDZiTgSJgKlDehR44nV7b3d+b4QTgh0VkZ1nnl4hMAl5y\nz/ElMAF4UETWq+qrFdbeGFOfKos3231+PxsnLvQTkd6quqKC50wGDrogdLeD0yg6gIhcgtMJ5fXb\nPhV4HngcmA0MBO4VkWJV/bPfsZXGLhGJBdoCZwAbfXbZKCpjwksoY9LbOLHoaiAPuBP4XET6qupm\nERkLvAK8ANyM0766G6cddJHf+Y+poDxLK9hujKlflcUaqcX5vBwYT14DDseJERtxYsRbIjJOVecB\nK4FOFZxrLnAUMBO4CbgXZ5DBicATIpKpqi9Dta+3OgHXAj9gTDVYJ5UpT4GqflnRThHp4v76a2XH\nAfcDQ4CRqrrEZ/v7IrIQeFVEXlbVb3z2nQAkAffhXAx2UdV15Zx7gd+dhfdFZCRwKs6dB4Df4/Tk\n3+I+/kRE+gLHAtZJZUx4qDTewL5h4lNwOsZvAs7EubPnbxFwiohEq2qJz/N7A72AH/G5a+iOOLiE\ng0c3lbkdeEVVb3Yf/1dEAO4QkSdVtdTn2Kpi1yHua3+gqoWV1dcYE1IhiUnutiOBi8su7ETkF5wL\nycnAU8CVwDeqeql7qo/dacQ34tdJVVUdjDEhV2GsEZGKOo8q48HtpBKRXjjtkimq+o677RNgGU4H\n9zycG/xxfufoBPwLeF5EPDgx51FVfdjd/5F77knAy+62Sq+33Jt07YH/quqqWtTLNEL+uTGMgXJG\nGtSUO4rqUuD//DqoAFDVN1Q12q+DCpw7k3NxphSW4IxIqK58nLuaiEgfnAbgC+7jKPd1j1fVC2pW\nG2NMEFUn3hwLtMQZ1fQZFceFd4BUYJzf9snAKpy7gL5+wpla88cKztcPpyHnaxHQGhjkt72q2NUZ\n2OwOn7fvXmPCV6hiUrL7M91n2073Z9mUmL7AAr9zZWE3nY2JRHW+3qrEAPf8+2a8uDfWFgG93cdL\nVPWHsn/A/4DrgJfcjvKWQBrlx5xoqPb1Vkf353oR8bidX8ZUyr7UTHmiKpqn7JfgLs7NOeWr1B0l\ncBiQCHxS3RcVkSScYaTXquouEZmL0/Ar7wKyic9rJwFn4VxQ3upuG+L+7CgirwC9RWQz8AdVfaa6\nZTLGBF1F8aZAVcsacGfjjB7YICL/AV4Skf6q6t/ptAknh8tk4Auf7ZNxptG08T3Y5+5iF/bHDl8Z\nQDu/bYe4P7sCi93nVyd2dQZKRORrYISIFLhluk5V95Tz2saY0AhVTFqCk1/zLjdH3k6cGJIDvAeg\nqgMA3Iu8RKA/cA1OXqoDVFCHQr8RoMaY0Kkw1gTg3F8DR6lqTtkGN270x03LUo6rgB447RlUdSfu\ngBY3N1VT4Hj3X1m6lupcb3UGsnE67ScAXhH5FLimuvm2TONjd3NNeTrh5ELI9f8nIok+xz1XzjFl\nDbSy6TPra/C6p+F0nP7HffxvYKiIdC/n2BU+r7kd+DNOzoe57v4O7s/ncUY2jMNp5P3FzT9jjAkP\nFcWbswBEJAE4BSe3Ajh/x4U402v8eXEu/CaVbXCHzA91t9f07t2bwNUiMkZEmonIGJzcDuAkNS5T\nndjV2f23GqeRdgtwMvCpjawyJqyEJCa5q3xOwrmIXIbTtjkfmKmq/ovV9AP24uT6TAUe4WDl1eH2\nqqtvjKknFcWas+t6YlVN910gwW1nPIIzwur//I8XkRY46QruUdWsck55Bc6Nu9dx8tq97W6vzvVW\nZ6C5+/tEYAZODJzr3uQz5iA2ksqUZxvORVd58nx+vx/4yG9/2UirYvdnUQ1e92yc1R+8IpICfIXT\nwDsTZ76zr0k4q0cAxANH4Fw8foAzDL9slNUsn578eW7eqmuBv9WgXMaY4Kko3qx2f56CM2Lgczcu\nAMzHGal0dznP+w/wBxEZ6U4nnoQzze4HEbmyhmW7HWeY+hz3cQFOI+8unIZkmerErijgdVW90H08\nR0TSgbdw7lp+UMOyGWOCIyQxSURa4Yw0WI5zsZiHkzbhTyKyVlV9Y8QqnJWyeuIkV//KHcm1w+eY\nI8opy6ZK6m2MqV+VxZqTA/Ui4iTT/BswCmd1v9fLOewmnGl85a68jnPTbglOIvZ7gPdxEqtX53or\nHieh+mmqWuyW6VecUaYX4OTbM+YA1kllylPgu9yxPzdxMMDqSo4r60DqiLPsuv85WuHkXbhaVZ8R\nkdY4K9FEA/5TX6ZycCfVYr8hovNEpBB4XER6sL8zbY7f8+bgDGc1xoSHSuMNTgdQFE7y4AOIyGD/\nnHequlpEfsZJavwNzrSad2tTMHeY/CQRaYuzMt8qnKnMd+FMy6G6sUtVf1fOS5RNh+6LdVIZEy7q\nOyaVTSEsW8RhmE9n0xcishQn0fG+GOGmXvgO+E5EfsQZxT4FeNbnGFtFy5jwVmGs8bnWKpfbQR7j\nTsmr7LircW6urQXGqur8co5JxLk2+kNZJ5I/VU3HuW6bLyL5OCOl+lCN6y1VfQ5n9o3v+X4UkR04\n7R9jDmJTDEyw/IgzqurECvaf5P783v15BlCKMwpqnM+/+4CB7koSVSlrMLZm/zRD/1UrPDj5HYwx\nYc5thB0H/IED48LROPGlvOk14E6vcTvDR+KMTqjN618jIkep6jY3wWg2MMZ97Z/dw+oSu+Ldn3tr\nUz5jTP0KckzqBmz3Gw0FTqxJE5HWIlIqIv5J2n9zfzbHGNNYPM7Bueji2D+TBRG5C2dxmCeAQeV1\nULnOxMnv+w/fjSIy1Y05rf2OL4s5SdTteisOa/+YCthIKlOeOq82oap5IvIP4EoR+buq7rvj6M4/\nvgv4RVV/dDefDcxW1dm+5xGR5e6xZ+Jc9FVmBM6qWoozeqsEZxjtEvdcHpzG5dyKTmCMqXeVxZvT\ngVjgCVXd7rtDRL7AGal0RznPexv4HU7MyOTAO3w1iW+TgdE40/fKctFcCHzgLhABVceuqcD9IpIF\nPKmqd/kcVnax6Vs+Y0xo1XdMKrMeaCcincpGirvtln7AClXd4SYkPhpn6k2ZMe7PpVXUyxgTXupy\nvbUHGFv2wE1s3g34r/u4K860vDtU9eEqzjUV+FZVt/ltX+T+PIb9OfjAiTmFOFOT11PF9ZY7GnSp\nqk73Ke9xOCuazqlWbU2jY51UpjwJInI05ScZ3oazQkN13I5zgbdARB7HCV5pOPOe2+GsDoGIdMbp\nYJrhfwK3UfYDTgD17aQa7eZzAedzfARwG/Csqu5yz/sUcIc7DfAnnIvJbsA51Sy/MSb4Kktmfjaw\nwP9i0PUBcKKIHKaq//Pdoao/i8gq4DLgZb/VrGqSPP1vOKt2zcRJZHwdTgy7F6odu87Eyd/3FnCz\niBQBP+AMcb8XeE1Vf61BmYwxwRWqmPR34HrgIxF5CGeEwXScWHGFe8xjwKMisguYB3THuRD9TlX/\nW5NKGmNCrjrtkYtFxD+VwDbgc+B6EXkJZyW/E3DaJ2Ud2JNwRlUtEZFj/J6fp6oLYN8qoEfhxJYD\nqOoqEXkfeFJEUnGmDI7BuY57XFX3Anurcb31FnCvO71vNk4qmPtwYunH1fg/MI2QdVIZf16cJZE/\nr2D/v3FWpaqSqma5ifPuAi7CCUqZOD3rZ6tq2V2/aTi98BXljfkAeEBE+rL/rsM/ffYXA+twAt4f\nfLbfhLOE8xVAK5y7jCf454swxoSMlwruJIpIO/Y3hsrzoftzKvC/cva/jROrfKfVVPh65VHVf4lI\nG5zOqbY4MeR4VV3uHlKT2HUFTsPyYv6fvfsOj6LcHjj+3VQIEEAIHSnCQUSwIyo2BFFs+LPde+2i\nYkEFr2JvgP1awd672L2iiGDBdhWUolI8oIAoRRBDS9/s7493VpZlw0ayu5NyPs+TJ+7MuzMni3kz\nc+Z9zwvXAL/hVsK5vrLxGGOSzrc+SVWXe9dMt+L6hvq4h3tHqOr/vDb3ikgWbvn3y4BVuBvAq2Id\n0xhTbcX7PQ3vi7UYwzeq2ltELgEuxV2LLALOi0j6dMUVNZ8Y4/2LcUkkgF1wo0MrqmF3Ki6BdSNu\n5NMi3OisyKRWvPutMWxaCOJC4A9cf2irjRpjjDHGGGOMMcYYY4wxxhhjjDHGGGOMMcYYY4wxxhhj\njDHGGGOMMcYYY4wxxhhjjDHGGGOMMcYYY4ypvv7OUtwmQUTkaeC0rTS5C7fc+ZPAJ6raL8YxyoGb\nVPWmiG1DgQsAwa04NRt4RFWfjfH+PYGrgf1xqzX8DnwE3BqxclW4bV/gPqAH8Ctwl6o+FNXmWmAY\n0Aj4GhgesXofItIWeAg4BNgAvAyMVNXiiDaDcavadAIWeD/faxH7s4A7cStNZACfABeq6tIYP98B\n3meXFrU9HbgBt9pgHvAzcIuqPhfV7lI2rei1BLhNVZ+M2H8WcAXQEVgOjFPV/3j7AkB2dEwRSsLL\nT4vIKbiVOzp5x3lYVW/dynuNqRLrf5LX/4hIH+B2YHeg3PuZLlHVX2J8Bk8CAVU9M2r7vt55dgcK\ncCuq/ltVN0Qfw2v/IG4VnU4R25oDY4HDcH3RLOB6VZ0S0WY34G5gb6AYmOTFujLWeYxJBOt/fL3+\nycF9vscBucA84HZVfTmize7ez7s7UOKd51JVXRR9Hq/9Zv2PiKQBWbHaeoqBdLayuriqFm3l/cZs\nM+t/fO1/coF7gKO9WKfjrm2mefsz+Bv9QlXu87yVWu8GBgANcP9eI1X104rOb1IvLX4TkyQrgP4V\nfD3CpqVHDxKRYys4xl9Ll4rIdbibkgnA/+E6knnA0yJyW+SbRORE4H9AE2AEcCRwM64T/FZE9oto\n2xG3fOnvwPHAM8BYL0kTbnMZrkMYC5wElAEfikietz8deBfYEddpXAX8E3gs4hh7427GvsFdQH0I\njBeRQyJCvxu3fPv1wBlAG2CKiNSL+vka4JZ4j7W06w245NJdwGBgBvCMiBwV8f4RwC24f4fBuE7/\nce/mMfz5PYZbXv4Y4EHgRhEZ7h3iQNzNZUVfp3jHOdb7PP/rHed14GYR+VeMuI1JJOt/Etz/iEgn\nYDJQ6v38FwG7ARO9i6/Iz2BX3DL1oajtnYAPgDXe/ktxF1HPE4P3WZ0XfRzgFWAf3Od7MlAETBCR\nLt778oApuAdVJ+GWj+6H+/czJtms//Hn+mccrl+5ETgBdzP6gogc6L13O1yyOoi7ThkB9AImecmn\nzVTQ/5zG1q9/DgAej9PGmGSy/sef/udlYCDuHux43L/DZBHZ3ttf6X6hKvd53vXY+8BeuGusf+Ku\nkSaGr5FM9VBhxtIkXbGqflTRTi97DqDAHSIyQVVLK2ibBYwE7lbVayJ2vSkiQWC4iNykqoVep/cU\n8KSqDo06zmPAZ7is/Z7e5hHARuBYL4sdvtG5HnhSRDKBK3EjgG72jjMVWIbL7N+Ay5r3AvZS1W+9\nNiFc4ucG7wnd1cAcVT3VO++73pP+63EdbgvgXOAqVR3nHWMWsBDXwTzldVqTgF2BHGJ3XucCL6vq\nPd4xPgD6AmcC74hIfe+c16nqnV6b8DEPB770ft7nVPUy75jviwjAVSJyP/At0CfGuc/GddDve69v\n8T63y73XE0VkJ+BQ4MUY7zcmUaz/SXD/4+3fABwZfuInIguBz4GjvM9jMO6CtHsFH/1FuBGVg1U1\n6B1jHvCViPSKejqaBTyKe7oa+Tl2Bw4CBqjqh962icAqYBBwP+5CtClwZniEhHcx+4iI9FTV7yuI\nz5hEsP4nxdc/ItIYl7C+QFWf8Da/IyILcDdyU3E3uTnAUaq63nvfT96+nYG4/Q/uRj36+icAXAu0\nBaYBS3EP9yJlAc/hEv3GJJP1P6nvf3rhRnb3U9VPvM0TROQHYDguWTSKOP1CIu7zcCPKegJ7qOpM\nr81kXL90EW4WjakGbCSVf2L9YsUyEjf8cmu/NM1xwxWXx9j3EO5CIsd7fTFQGOt4qlqG+yW+S9yU\nNYAjgHejhllOBLYXl5nZG9gO9+Q+fJz1uBuzQyOOsSjcQUYcIwAM8DraQ3GZfKLa7Ot1SofikqqR\n5/kZ+DHiPKW4DmgU7klALLm4pxLhYwSBfDb9LhyEG377BLih66parqo9VfU6r00P3B+TSDNww0p3\nUdX1qjot8gv3mZ8MnKKqv3s3kt0iz+PFc5iqnlFB7MYkivU/iet/BnibdgY+j4p1uve9m/d9Ke5p\n6FW4fifazsBX4QSVZ4b3/ZCotlfjni4+zeZT93vg/n2/iNhWjJu6E34w1dj7/ntEm9Xe9/QYcRmT\nSNb/pP76p6t3jC+itm9g0+/8TsD34QSVZ533Pfqhdsz+R1VXx7j+aQIcDJyoqoWq+nOMNoOA9bib\na2OSyfqf1Pc/O3vfo++dZuFGsFHJfiER93k9gT/CCSqvzUbc6LdumGrDRlL5J01EsolRFyyqQ5qN\nG5Z5rYg8raqro9vjfhlXAFeLSCEwQVWXeceahesYww4FPog8h9dJhS9SFgPhJ+v1gM7Aw9Ehht+K\nezIG7pc70gIgPG2tR/R+VV0hIutxF06dcXVToo+hXlydvGMUxJj/vMA7BqpagqsHE669EH1TB25q\n3WneyIJvcYmjnrhRTeCm56wGBorILUAH70njNbppfnY+0DrquO29752AmZE7vATUk8CzqhruoHfz\nvrcTkeeAHUXkN1ztq+gnCcYkmvU/iet/xPvva3BDxiP19L4v9877La7fQUTOY0t/svW+Be+93YHL\ncU8Hj4n62V7z4g4/5W2KezpYn03T+d7DXeTdK26qQiPcSIc5uH9zY5LJ+p8UX/+o6jds6hcycDdy\n/8T1USO8NheF23ujytviRn4uJKJf2Fr/E827yX0MGK2qCytosxtuyvGB3s9hTDJZ/5P6+6/wQ7nW\nbD76sj2utu8WYvULCbrPewXYrPaU93l3xU2NNNWEjaTyz/a4jHr0nNuN3h/1sBDu5qEcGBPrQF4G\n/gRctvlh4FcRURF5SkQGR2TlATrgOsJIL0XFUIgr6NfM278mqv1a73su7ilCRW1yvf9uHmN/uE3j\nSpynsXeMP7dyjMo6F/gDV4/lT1yNhjdVdby3vy3uhu0/uKGu/XBJp/EiEh4x8QowTEQOEJGG4or3\nhUdZ1Y9xzjNw2fnIocDhPy6PAQ/gRnC9DYwTkbP/xs9jzLaw/ifB/Y+qfqeq4QvIcLHSp3EXsW/G\neG8srwAHi8gQEWksIt1wT2JDeH2L93k+hhviP7PiQwFu2vBy3KiHp8PxqZvONwxXY2IZ7oloD+As\nVa3sU2ZjtpX1P/5c/4TdhnsYNxZ3UxY9ugrcqE/FlTm4UTdNP/47/Q+42jBBXH2YitwLvKaqX1X6\nJzBm21n/k/r+52PctdD9IrK9iGwnrp7WfsS+b4Jt7xe2ep+nqr94o7QA8BKWT+JGpT225eGMXyxJ\n5Z8VuHn70V/7EFUgTlX/wBW6HCIiOxODqn6hql28Y4wE5uIK+L2Bq5kUztRn4jrcSFdGnP/EGIcP\nRr1Oi96u3mp1UW0i3xd9jMq0iT5PRccoi7G9Ii/h1WLBFfAcAxwtInd4++vhniqco6rPeXOnT8HV\ncwk/ZbwSV/jwE9xQ+ElsKmwcXdwvE5fAekBVI/8IhIsN3qSqD6rqZ95TzJls/uTFmGSw/ieJ/Y+I\nnIx7CtsMV99lXXSbWFT1dWA0ri7Dn7inm78CP7Hp3+V8oB2bEuNbcyVuOuL9wFARucGLry/uwu1F\n3JPI43D/ZhNFpH0FxzImUaz/8ef6J+w+3PS7m3B1Mh+N0eZQXK2qSbiiw+FVzird/4grxD4cN0I8\nZpzew7++VJAEMCYJrP9Jcf+jqoXAsbhVQxfjkuTn4upNFUa3r2K/EO8+L/I8e+EKxh8LDFXVL7fh\nfCZJbLqff4ojM7nR3HTjzTwADMWtsHDoFm/weMecBvzHG754E25Y9rG4OccrcU8RIt/z1xBsL6kS\nFs6kN4k6TThDvxovky8ijVV1bVSb8NDYfFyHES3cJjwMtKLzrPLaRO+PPs9WiVse/nDgOFUNj2z4\n3LuQulhErmdTZ/lJ+H2qWiYin+PNqfbmLh8rIq2AVrih8HvinrhELzV/Iu6CblzU9i3OE/H6wsr8\nPMZUgfU/Seh/RKQprubUkbiLrxFRyem4VPVGEbkL2AE3CmoN7intL16fcytuEYZy7zPOAALe08Ay\njahn5X22C3HFT9vglsi+CddXzVXVUyJi/xKXEDsbV3DVmGSx/ifF1z+RvGk7S4Gp3siRS0VkmHdt\nE24zA5ghIu/irmvOFZG5/I3+B5fQKsP1iRUZCUxW1ejpRsYki/U/PvQ/qvo/EemMm1kSwo3gfgpY\nEqP5NvULlbjPu05Vi8WVYRmFG+n5Da6w/A9/51wm+WwkVQ3h/eEfAfQXkaMj94nIZSJSLiKNot5T\nhHsCAO6GB9zqdP0jMvvRDop4/wbcTcuOUW26et/nAPO9/47VJvwLP5+oYnTezVZDr83PuGJ4sY6x\nETdHez6QK26ViYrOE09n73t0zZXZuBUkGrOps8yKapPmxYKIXCQiB6vqClWd5X1OB+Dq0USvinUu\nrqP9LWp7RecJhM9jTHVh/U/8/se72fsYt6zxQFU9/e8mqETkKBE5U93iC7NUdSXu6W4W7uJ3R9x0\n5PFsmh5wNZumL1wrIg96N5PRFnjvBdcXbtZXeedaCUT/jMb4yvqfql//iMhIEdkQY9cC3PVNQxGZ\nKyKb1cT0PvvFuBvSbmy9//mrpIE3zels3Cpb0bX6wm0640oqPFmZn8EYP1j/k5D+p52I3Ag0VtV5\nqjrfKy2wP+7aJrJtVfqFePd54WTbo7hE2OWquo8lqKonS1L552/X/VDVD3CFb/8TtSs8PPHkGG8L\nF+5d7H1/EDf6Z2R0QxHpgBuaHWkicIy4ArxhxwEzVHWFd+51uKWLw8fJww3TDBegew/oJm4J0shj\nlLKpiOAnuHnd4WMEcE8fJnlDWT/ADZP9R0SbHrjigZUtdLfY+75P1PadcaMVfsfdZIIb5h4+Tw4u\nCTU1IvbzIvbXx1vaNLLop/eHoC9uyG+0T3HDZyPPE8ANvZ8ao70xiWT9T+L7n0txBUAPUNVtXUZ9\nD2CM95Qv7EJc3agvcEVAo6coPMGm6QuP41YDFBFpF3Xs/dm0hPxioHfkebwL0BZsuvA1Jlms/0n9\n9c8MIEdEoq9/DgBWeEnqGcBBElFHxxuBsDOu75iBu36qqP95IuK4vXE1eGJd/4SdgBtpNWErbYxJ\nNOt/Ut//ZODq/PaJOMbhuMLs0SsLVqVfWOx9r/A+T0T2B84CTlXVe7fhHCZFbLqff+qLyCHEWF0C\n9we/IpcSlblW1S9F5DXgPhHZEdfhlOHm/l6Eu7h4y2v7mYjcCdzsdVpv4zq5XXEd5IdsvlrLHbjO\n9zUReQxXx+A4vOSKqhaKyO3ATSKyEvdU7krcENCnvGO8hnvaNt6bUtcKt2LMOFUNF+MbhRt6/iSu\nyPCJXkzne+dZKiJPAKNFpAQ3/HQU8I2qVqoj8z6nL3GF+7bzYu2LmwJztZfV/0ZE3sKtetUQN8w9\nPP0u/MfpMVyNhmtwTzMuwd3c3Rh1yoG4f98tkk6qulJExgJXeT/PbNxqHJ2J/cfOmESy/ifx/c8J\nuKWfO3gXnJF+UtVFUdtiffbP4YafPysiL+OWjz4BV9C8HDftL/qp4yAipi9477sBeEdcDYYC4FRg\nXyD8FPhu3AXgGyLyFK5w6eW4kVRPx4jLmESy/ifF1z/ezzYLeEFERuGm8Rzl/Xzha5x7cfU2XxWR\nZ3GjLS7H3dCOVbe8/deRB43ufyIchnsQF6soe2Sbb1S1YCttjEk0639Sf/+1WEQ+xN1/XYsb0XQL\nMFVVJ0Y13+Z+oTL3eSJyAvAbsFpE+kcdYo031dlUA74kqUTkCmBHVT3Te90a9wt1IO4P552qOtaP\n2FIkBLQEKnra/houO71Ftl9VF4rIZnmewAAAIABJREFUfcBlUbv+iUuWnIabOw1uvu99wD2qWhxx\njCtEZJrX/jFcMb8fcR3X/cBXEW1/EpHDcDc1r+J+sc9U1f9GtLnVG756Ma5Q8GfAyeH6Bqpa6h3j\nIdzwzSLcKhhXRRzjCxE5Hlfg7mTcqjKDozqLi3A3XKOBHOB9vE40hlCszw83V3k0riPPw2XdL1PV\n+6I+y5txWf+GuIu2frppWdkXRKSl9/m1wv0ROizG3OneQL6qLqggxn/j/picj5tb/h1wuLpla02S\nxOh/OgKP4FYZKcVdOFxQiy+crf9JTv/TFbdC3qDozw2XwB4VtS3W5/uTiByDuzh93ft5L1DVp2Mc\nM/I4fx1LVTd4F1734Ia0p+NuTo9W1fe8Nu+LyBG42lQv4f6//xg4UTevbWFSxOuXLsAt0b0CeEhV\nb/U3qqSw/seH6x/v5uwIXCLqHlxiej5whqo+67X5RkQG4/qqV3BT+KYC/9QtSxZUeC5Pb+AHdQWT\nt+CN4tyDzUdfGR94SYNrojanAYtVtVuMt9Rk1v/4d//1Ty+O8Ap6b7FpQSrgb/cL23qf1xW3wnqs\n/wc+wU01NNVArCxy0ojIQbh//OG4ZSXP8rZ/gJtqdQ5u7u5U4JQY2VVjjNkmW+l/PsdNY7gCNyLu\nbeBjVR3hU6jGmDpE3EpG/8VNyfwWN+ptCnCMN83EGGNSwpte9gVwh6q+6nc8xpi6KdUjqfbAZTWX\nhTd4o6j6Ax28Jy4/iMh44AzcfFxjjEmEWP1PI9wN4TFe/7PEG1ZtKywaY1IlHzdFJJ1NtUJDbH3q\niTHGJMNo3Cg4S1AZY3yT0iSVqt4F4NXACNsdNyVqacS2ubhV0YwxJiGi+p/wKNJCYE9V/SOi6a7E\nXhLXGGMSTlWni8hduKnlIVz/9KCqfrf1dxpjTOKIK4h9Jluu9maMMSnlV+H0AJvmkTbFFY6LVICb\nL2+MMYn2V/+jqmW4qX6ISFPgNlzhykN8i84YU6eIW23oclwtjQ+AI3HFqz9U1Td9Dc4YU5eEi2qv\n8TsQY0zd5leSKrLQ2UZcEbZIDYFKFW8VkSbDhg378/TTTyc3NzdR8Rlj/oZAIJDS+nZVtEWhRRE5\nC7gVVweml7e8b1zW/xjjvxrW/8RyAm458Ene63dEZBIwALfaUkzW/xjjv1rQ/wAgIt1wq1KfXcn2\n1v8Y47Pa0v/Ekha/SdKEP9TvgeZebaqwnXHFQyujybhx41i3LnowljHGxCciY3Ar2xyjqidXNkHl\nsf7HGFNV5UBW1LYgsD7O+6z/McYkylm4ZPnqSra3/scYkzS+T/fzlvScCtwmIkNxxY1PxJaANMYk\nx1/9j5ccvwzoqaoLfI3KGFNXvQFMFpGBwIe465/+uALGxhiTCoOBu/0OwhhjwN/pfpFTbk4GngDW\nAMuBC1R1hh+BGWNqvcj+Zx/cCIa5IhLZZpGqSvQbjTEm0VT1UxE5DbgH2AG3cMMQVZ3pb2TGmLpA\nRPKALsCXfsdijDHgU5JKVc+Mer0MVzDUGGOSKrL/UdU38HfaszHGoKrjgfF+x2GMqXtUdRWQ7ncc\nxhgTZjdnxhhjjDHGGGOMMcZ3lqQyxhhjjDHGGGOMMb6zJJUxxhhjjDHGGGOM8Z0lqYwxxhhjjDHG\nGGOM7yxJZYwxxhhjjDHGGGN8Z0kqY4wxxhhjjDHGGOM7S1IZY4wxxhhjjDHGGN9ZksoYY4wxxhhj\njDHG+C7D7wCMMcaY2i4UCvHwiEs5uF49vsr/kyOuv568Nm38DssYY4wxxphqxUZSGWOMMUn20Ysv\n0TI/n+D69fQoDzH+rrv9DskYY4wxxphqx5JUxhhjTBIFg0G+/fgjujVoAEBOZiYZq1ezVBf4HJkx\nxhhjjDHViyWpjDHGmCSaN20a7YpLNtvWu359Jj37rE8RmepERK4VkcKor2IR+dHv2OqaJ158wu8Q\njDHGmDrPklTGGGNMEq1cvJgmaZv/uc1OT6eosMCniEx1oqpjVLV++AtoDHwHXOtzaHXOJ1M/9jsE\nY4wxps6zwunG1FAlxcX8MusjWjfNqVT7X/PLkL36EQgEkhyZMSbSTvvuy3/ff58OEdtWFBbSpvuO\nvsVkqrXRwA+q+qrfgdQlJSUllIfK/Q7DGGOMqfMsSWVMDfTt1Al8MmE8B7YrIqth5X6Nl64O8t6b\nL3H8kEtp32WnJEdojAlr3aEDGxs2orS8nExvRNXs8nLOPe00nyMz1Y2I9ADOBCyDmWJfz/6a9Jx0\n1m9cT6MGjfwOx5iUEZFWwONAP6AIeAkYpqohXwMzxtRZNt3PmBpkzeqVPHjTRSye+hwn7BSiZeN6\nBNIzKvXVrWU2R+9QyAdPjeKF+2+gpLjY7x/HmDpj8HlD+brATe9bVlBI+1160TA31+eoTDV0MzBO\nVdf4HUhd8+bEN2i7d1seePYBv0MxJtVeBpYAzYA9gGOAU3yNyBhTp1mSypga4ouJ43n+9hEc3HI1\nvbfP2qZpe1kZaRzaNRMJzee+a85h0dyZSYjUGBOtc8+eFG/XlOJgkNkBGHzhhX6HZKoZEekGDAQe\n9DuWuubzbz6nvHE5DbdryK9/LGXturV+h2RMSohIT2A3YISqFqrqItyIqqn+RmaMqcssSWVMDfDa\nI7exYvqbHNsjnYb1qj5Lt1XjLI7vHuSD527nqw/eSECENYeIXCEiT0W8bi0i73srav0iIhf5GZ+p\nvQaedhrT16+neaeOZGZl+R2OqX7OAj5Q1dV+B1LXTJjyDs26NgOgsTTm6deeivMOY2qNPsBC4H4R\nWSMiy4FTgaX+hmWMqcssSWVMNffV5Ddg5Sx6b5+Z0ONmpKcxqFsm3055lZW/LUnosasjETlIREYB\n1wCRdRaeAVYD2wGDgBtF5HAfQjS1nOy6Kz+VFNNn0CC/QzHV02DgPb+DqIsyMjb9fQ2WBmnUyKbi\nmjqjJW4k1UIgDzgEGApc7GdQxpi6zZJUxlRz303/gt7tk7fGwa4tSpkz/ZOkHb8a2QN3AbYsvEFE\nWgP9gau8Ye4/AOOBM3yJ0NRqgUCAIgJ07tnT71BMNSMieUAX4Eu/Y6nIQ8+M5+axtXOE0UH7HMia\nn10ZsA2LNnBUv6N8jsiYlCkDflfV/6hqUFXn4mpUHepzXMaYOsySVMZUc+07dWXhqtKkHX9hfgad\npFfSjl9dqOpdqno+8L+IzbsD+aoaOax9LtA9pcGZOqM8LUCWTfUzUVR1laqmq+r3fsdSkYWLlrD0\nt98IhWrfgl/99xtAWn4661eto1OLjuQ1y/M7JGNSZSGQISKRhU4zgI0+xWOMMZakMqa6O+wfQ5lX\nkMeSNYlPVH2ztJSGHXan0067JfzY1VjkhVhTYF3U/gKgfurCMXXJtix4YIzfysrK2FBYQqheY2bP\nme93OElx5CFHsnzGCs7511C/QzEmlSbiRlNdJyJZXiH1k4Bn/Q3LGFOXWZLKmGouEAhw3nX3sThd\n+OznEoLl5VU+ZlFpkAnzy2jcYyDHnTMyAVHWKJHDADYCOVH7GwK2tJNJEktSmZrn82kzoGEeDfI6\n8O6UT/0OJyn232t/SteXkdvQ6lGZukNVN+Km9vXHPbSbAFyrqhN8DcwYU6clr9CNMSZh0tPTOeWS\nUfww7RNee/VJ9m9bTJsm2zZlaM7yEuZvaMw/LryKVu07JTjSGiOcKfgeaC4irVV1ubdtZ+Bbf8Iy\nxpjq5+PPviK3RRfSM7P4ffkav8NJioyMDNLT7NmtqXtU9TvgAL/jMKYmC0+FtxHziWF/jY2pQXbu\nfRCX3Pw4i7N68d6PZRSXVn5U1ZqNpbw+J0T2TkdwyZhHLEEFqOpCYCpwm4jUE5H9gBOBR/wKzhhj\nqpu1GwtJz3QPRorKyiktTV6dRF/ZzYUxxpht8OSdV/HiuFF+h1Fr2EgqY2qYzKwsTrrgGlYsXcRL\nD93MLk3X0zWv4lFVoVCIr5aUsja7HUNvvJH6DRqlMNpqKcTmU/5OBp4A1gDLgQtUdYYfgZk6wO6B\nTQ1UUhakXvhFVkOW/raczh239zOk5Kh9NeGNMcYkWSgUYmP+7xSsS/c7lFrDklTG1FCt2ndi+M2P\n8fYz9/DRT9M4uHPGFkNMS8rKmfBjOfsNOoU9D7YltQFU9cyo18uAw30Kxxhjqr2yYMSo3YxsVq35\ns1YmqWwglTHGmL/rvttu4IX3viYEZDa/nfOGX+F3SDWeTfczpgYLBAIMPuNSdhl4Ou/9GNxsafCS\nsnLemAcnDBttCSpjjDHbLnKEUSBAWVnQt1CMMcaY6uKWUTfw0NPjWVdYxvrCMu556Eluv2WM32HV\neNVqJJWIXAFcALQGVgAPqeqt/kZlTPW3a9/DKSsp5YtPX6JvR/dr/a7CqcNH06p9Z5+jM8YYU1OV\nl5dvPsQokFZ7a1IZY4wxlXTH7bfwzAsvb7H9yWeeIz0zi8sur3MrqCdM3CSViBwOHKSqV3ivzwVO\nB5oD84C7VbXK6xGLyADgRmB/3Mpa+wJTRORbVf2gqsc3prbbs9/RzJ7+GWsLlrJ8XYie+w6q0Qkq\nEdkHOAkIAm+r6qciMhoYBpQBzwJXqGqZj2EaY2oxEekLTFPVEr9j8cu69esJpWf+9TotM5vVa9b6\nGJExtZ+IpAHXAOcCebh7o8tV9cuINh2Bn1TVCuEYk2ITJ07kiSefqXD/Y48/Qc9euzBw4MAURlV7\nbHW6n4hcCbwD7OC9vgh4EPgDeNN7/xQROT4BseTjbjzTI+IK4UZUGWMq4dgzL2XabwHmr6vPQcec\n5nc420xE/gV8DgwCDgU+FpHxwPnAbcCtuASWjac1xiTTZGCqiNS+AkyV9N2cBaTVy/3rdYPGzZi3\n4CcfIzKmTrgNuBS3sMtwoASYLCK7R7WzSmrG+OC6a66K2+baq69MQSS1U7yRVBcB56nq497rS4CL\nVfXBcAMRGQrcBLxWlUBUdbqI3AX8D5ecCgAPqup3VTmuMXXJdi1aU5DWkHr1G21RRL2GGQVcp6q3\nAIjIScBLwJmq+oy3bREwDrC/AMaYZPoWmOmN5ByrqnWqINPUr6bTMK/dX68zsrJZs8xGUhmTZKcC\nZ6nqmwAi8ijwBvCCiPRSVZtza4yPysvL47YJBuvU5UJCxSuc3hw3miGsGRA9te9joEtVAxGR/YHL\ncatsZQDHAGeLyLFVPbYxdUlRKbTrsIPfYVRVO+CViNevAuXAjIhtP+CGwBtjTDKNBQ4DTgMWisiF\nItIwkScQkVYiMkFECkRkjYg8ICLV4knDqjX5ZGbX32zbxpIQGwsKfIrImDqhCTA3/EJVy4EhwHa4\naYDGGB8NOnDPuG2O7r9PCiKpneIlqb4FRnrzosElqKKXaj8K0ATEcgLwgapOUtWQqr4DTAIGJODY\nxtQZpcFymrSq8TNT5gNDRCRcZ+F8XH/VJ6JNH2BxiuMyxtQ9IVWdDuwF3AKMBFaKyKsicq6I9EzA\nOV4GluAeBu6Be1B3SgKOWyX5a9dRFNwyV5ae25LJU7/yISJj6ow5uKTUX1T1D+BC4CoR6c/m624a\nY1Lo39ffxr7dW1e4v0/3Nlx+wx0pjKh2iTfd70LgA+BHEXkLmAXc5M2HVqAXcATuYqqqyoGsqG1B\nYH0Cjm1MnZGelk56RvSvUo0zHFcPb5iIFOFu3O4C7hWRXXDTgc8ArvctQmNMneJN83tMRJ7CXfec\nCtwLZOPqaW4TL8m1G3CoV6B9kYj0A4qqHnXVfDt7LmkNmm2xvWGzVnw3Zx6DD+/nQ1TG1AlXAO+I\nyCDgU1W9AEBVXxORXYH3vC9jjA+aNmvBnrvuRJdmAZ79fNlm+07t24bmHXvSoFFjn6Kr+bY6kkpV\nZwLdgaeAvYHzcImk/wPOwhU6P1BVJyYgljeA/iIyUEQyRORQoD/u6aIxprLS0gikVYtZIttMVT8B\nBDdi4W6gr6peDgzFjaDaDxitqnf5FqQxpk5S1TJVfV1VBwNNgaqO5+8DLATu96b6LcclwJZW8bhV\ntmTZcjJzGm2xPT0zi8LiYh8iMqZuUNUPgZ2BF4GNUfuuxc1kKcSVPjDG+CAzs17M7aEQZEVNkzd/\nT7yRVKjqatzw9luSGYi3vPxpwD241QSXAEO8RJkxppICAWrFAHBVXQ48FLXteeB5fyIyxtRBn+Fu\nBGNS1WJgWhXP0RI3kuolXJ29bsAnwGrgvioeu0rKSoMEArGfZ4bKa8EfGmOqMVX9mQruv1R1Eq4s\nijHGB+Xl5Xw2fTbT5i3bYt/zXyxjrz9ncGYoVNMXsvJN3CSViBQCjwD/TvaKNqo6HhifzHMYU/sF\nasWCxCJyCO5JYQiYCEwFHgaOB9YBjwE3qardKRljkmU0sDLJ5ygDflfV/3iv54rIy8Ch+Jykyqmf\nTTAYu+pCTR+xa0x1JyL7ACfhyp+87T3QHw0Mw/UbzwJXqGqZj2EaUyeNu+NGps37tcL90+f+wqP3\n38nQS0amMKraI17hdHC3u/sAX4vIHkmOxxhTZQECNTxLJSJnAe/jVg7tgJsO/CHupu163GpbF+Nq\nNhhjTLJ8AEwVkWSuRrEQyIhazS+DqCk+fgiGQlT8ELhm/50xpjoTkX/hVlgfhLv2+VhExuMWkrkN\nuBWXwBqTgHONFZEiESmM+OoT/53G1F1Pv/xW3DaPPGWTP7ZV3JFUuFEMp+OWX/5IRCYAt6qqzYE2\nppoKhWr84KIrgUtU9UH4a1TVZOB0VX3O2/YLMAp3sWaMMcnyLTDTG8EwNgmjyifiRkVcJyK34ab7\nnYS79vLV76tWk5W9ZeF0gJJSG7xhTBKNAq5T1VsAROQk3JTgM1X1GW/bImAc7pqpKgQ4XFU/ruJx\njDER7FHOtqvMSCqAclW9F1fAD2CWiHwqIheKiCQpNmPMNqglU5874JJSYR/hhrvPitg2DUjm6AZj\njAE3cvMw4DRgoXft0zBRB1fVjbiREv1xU5knANeq6oREnWNbrfx9NVk5DWLuKygqrg0PRIyprtoB\nr0S8fhW3EvqMiG0/4OrYVVUX3KrtxphKuvj8s+O2uXTERSmIpHaqbJIKAFVdqqon45JVs4Ebgfki\nsjoJsRlj6q4lwNHhF17dqf64aTFhHYE/UxuWMaYOCqnqdGAvXBHjkcBKEXlVRM4VkZ5VPYGqfqeq\nB6hqPVXtoKoPxX9Xcq3fsJF1RRWPlgrlNGfql9NTGJExdcp8YIiIpHuvz8fdt0VOw+sDLK7KSUQk\nE2gPPC0i60VksYgMr8oxjakLzhh6MQMP3LvC/Uf278vJp5+Twohql7+VpApT1fmqehHQCjgQuCOh\nURlj6rrrgNtE5D0RGQOgqlNVtRBARM7HLegQf0K4McYkgKoGVfUx3ArEpwGZwL1sPsKz1njpjXfJ\nyutY4f7c1h15b8rU1AVkTN0yHFcgPV9EVuFGdN4F3Csi40TkAdxiMo9U8TydcNONxwK5wBnA9SIy\npIrHNabWu//RZzlo715bbB/Qdw/ueuAJHyKqPSpTk6rCyUNeXYbPvC9jjEkIVR0vIvNwNVl2idHk\ndtww+EtTGpgxps7zVtJ6HXhdRLKJ3UfVeHP0Jxp23qvC/WnpGawpKKakpISsrKwURmZM7aeqn3gl\nVQYDTYCpqvqliMzGJbAygNGqelcVz6NATsSmT0TkWeD/ALvLNiaOh595hX+f8w++nDEHgAN69+KO\nh1/0OaqaL26SSlXrpSIQY4yJpKrfAf+O3i4i7YFmqlqayPOJyBXABUBrYAXwkKremshzGGNqnM+A\nwop2qmoxrj5erVJSUkJhaTn147QLNGjG9Fk/sF/v3VMSlzF1iaouBx6K2vY8kLAlw0RkO6Ceqi6L\n2JwNrE3UOYypzQKBADfe+SAv3HEx5aEAZ13n+2z9WiFukkpEOuHmQfcBWuBGVq0CvgcmqOq7SY3Q\nGGM2p7iRCwkr8ikiA3A19vbHreS1LzBFRL5V1Q8SdR5jTM2iqofG2i4ifYFvw1OQa5slvy6H7Pi1\n4es1bs53cxdYksqYmusoYIyIHA7MwZVxORk4zteojKlBcps2I5jVhMzMTOo3aOR3OLXCVpNU3rLv\n7+ASUgtwc5b3x60skQe8ICILgKO9bL8xxlSZiDwFhNh8unH4dSZwq4iswxU0PisBp8zH9W/pbKrV\nF8KNqDLGmGiTSXCyvDpZk59PICP+FL7MrHr8uc4u/4ypwZ4DugKTgOa4QuyXqOrkrb3JGLO57Po5\nNGnazO8wao14I6nuAu5U1RvCG0TkVOB6Ve0qIo1wdWEeA45MXpjGmDqmPdAP+Ar4kU3JqsjvFdbL\n+7tUdbqI3AX8j03JsAe9KYfGmDpIRD5my2R5WBbwrIgU4pLl/VIaXJKlBdLcTx6H+3AS1hUbYzwR\n/U9Y9C9auG+qUv+jquXAtd6XMWYbZWTVIz07J35DUynxVvfrzpbznl8AOopIe1VdD1wFHJKM4Iwx\nddYAXH2oLnjLMKvqmap6BlAKXKWqZ6jqmYk4mYjsD1wOHI5L3h8DnC0ixybi+MaYGuln4CDcjeAn\nwNSIr3JgesTrWqVRwwZQXha3XbC01LU1xiTaeKADrg9aDyyJ+vol4r9rvV+XLefhJxNWisuYhAsE\n0ggE7KFNosQbSfUbrjbLgohtXXDJrXXe6+a4ztMYYxJCVUPAwyIyEbe6zPEicqaq/uA1qcQz/r/l\nBOADVZ3kvX5HRCbhkmVvJvhcpgIFRQUE0gMEygPUy7Y1O4y/VHWIiLwKPArMAy5X1Q3w10ILY72V\nsWqdlnnNCJUWxW1XUriBlp1apiAiY+oWVX1YRKYB3+BmsMz2OyY//b5qNTO/m+N3GMZUzJJUCRVv\nJNUY3I3i/SIyRESuB6YAz6vqWu8i7TngmWQHaoype1R1CS5R9ARuWeQbSeA0vwjluOk7kYJYAj5l\nPpv+Kdc8cg33fHQ3l99+Gfnr8v0OyRhU9X2gJ65/mCMiMQup1zZNGueSFiyO2y5YuJYdu3RKQUTG\n1D2qOgNYjbseqdPy12+gPOHPJ6uXsU+M9TsEUxWWoEqorSapVPVJ3LSXTsAVuLpTDwBDvCadgVu9\nfcYYk3CqGlLVh4E9gL64m8VE/yV4A+gvIgNFJMO7Ee0PvJzg85gYVv+5mpf++xLNum1HiBBNezZl\nzP1jCIVq9wWpqRlUda2qDgGGAo+LyNPEf8hXowUCAepnZ8ZvWLiO7l07Jz8gY+ooVW0RMYq8Tpo8\neTIjhl3AVx+9z+TJtbee+7Rp0/wOwZhqI950P7zl1zdbgl1E6olIc+B8r+CeMcYklTeqqn+Sjv2p\niJwG3APsgKvxMERVZybjfGZzdz58B3l75v01TDo7J5vCNoU8+eqTDDlxSJx3G5Maqvq+iPTELSqz\nDLciaK3VKq8ZKwo2kJ3TMOb+UChETnY6GRlxLyWNSYm3n3uQdp2EPfom5VIh5bz6v0v9jsNP48aN\nY+zYTSOMhg0bxkUXXcSwYcN8jCo5QiG7pa75bDRVomz1ykJE6gM3Avuq6v4ikgM8jqvfkg78KSL3\nADd7NWSMMSYhRKQvcDHQBwgXPVkFfA9MAJ5S1YJEnU9Vx+MKlZoUWrx0MQUZBeTWy91se5O2TZj9\ndZ0uwVHjbFizknpZ6QAUl4Vo0CTP54gST1XX4hZVaAYkrP8RkbHAOWxeb+9gVf0qUef4u44/8lBu\nf+I1sjv3irl/Y/5qdpEuKY7KmIr9ukjJyEgnSc+z/KAi8h5wpqqui9u6lolOUIWFt9WmRNW6DesI\nEiQUClldo5rK/tkSKt7jr4eBgcAd3uvbgYNx0/vmAjvhVsTKwCWzjDGmykTkH8CzuKLlzwJtgBOB\n94F8YDgwUkQGqup83wI1VbZi9QrS6seeORUM1cILtlr4OKesrIzn772OFqWL2LmVu6z4YkmQnE69\nOfq04TX6309EhgBH4f7lJgKv4qYHHwgEReQFYKiqxi/gFOdUwOGq+nEVj5MwO3Tanszywgr3l6xe\nwnFnnZfCiIyp2OTJk3n89Q8JBD4iO28H+vevFYmqAG5q8Q8icrGqvuV3QKkyZcqUmAmqsLFjx9Kt\nWzcGDBiQwqiS54W3XqBJ5yZ8+L8P6b9vrfh/15gqiVdT4RjgX6p6t/f6BOBsVb1bVd/3tp8OnJvM\nII0xdc4oYISqnqSq16rqWcBxwCBgJLAj8DFu1S1Tg+3Zc0/Kfi/bov5USVEJjXMa1+gER12gM7/k\nvquHsFPGz/RsnUkgECAQCNC3YwYNV37Ffdecy/JffvI7zG0iIlcB9wG/AouAm4CvgVa4/uhU3PLw\noxNwui5AtVspMDenfoW14eqlw3ZNm6Q4ImO2NG7cOIYNG0ZBUQkbC4u58MILGTdunN9hJUIIuBI3\nqnyciHwuIkf4HFNKXHPttXHbjLziyhREknzLVi7j+5++p02vNrw58Q2KiuKvrGpMbRcvSZUBRA4v\nDeHqMET6FWiayKCMMXVeB2BS5AZVnQTkAe1VNQjcCeztQ2wmgTIyMjh6wDGsmrf6r22hUIhV365i\n+FnDfYzMbE0oFOLVR2/jf6/fz3HdymjdJHpxTJAWWRzZqYA3H7iWj9+qkYsAn4erTTdMVS8FjsAl\nk65V1TdV9WXgIuBfVTmJiGQC7YGnRWS9iCwWkWrxP3/LlnmUFMRe5DQ7y2pRGf+NHTu2wilhtSVR\n5Y2g6g5MBV7y+og7ReRQEcmN8/4aqbAwfqKmLBikuLgkBdEkT2lZKbc9eBstd2tBWnoaTXo2YfTY\nRDz3MKZmi5ekeg94QETCS7e8AowQkQD8dWF1JfBZ8kI0xtRBPwGDIzeISG9cojyczeiGq1FlariB\n+w+kbf02rF/lnomsmruK4w8/gRbNWvgcWTLUjvl+n7z9LA1Wz6Rfl0zS0yu+lMjOTOOo7hksmfYe\nC3+YnsIIE6Il8NfiCd5y8EFgXkSbeWyqmbetOuGKsI8FcoEzgOu9qYa+apiTQ1lpacx96Wk2ytH4\n67WXX9xqImrs2LG8/ebrKYxfeDd8AAAgAElEQVQoeVR1vapeA3TE9RUDcCUQ1vgZV7J027VP3DZd\n9+rHvAU1c6Ru2EPPP0RO13pkZLukf07jHApyCnj34wk+R2aMv+Ilqc7HFQZVEZkOtMXVhVkqIp/i\nRlEdAlyY1CiNMXXNSGCMiPxXREaLyJPAFOAeVd0oIvfjalXd52uUJmEuPfvfbFxQSHFBMU1oSr99\n+vkdUlJUNHWqplnx2y+0ya18kqJ1oxArlv6cxIiS4kdcMfNI0dPydgZWVuUk6uSo6n9VNaSqn+D6\nt/+rynETobCoiPSMzJj7asn/yqaG+mXBD9w0Ov6Ik2uvvY4/VkZPAqm5VHWNqt6lqrviRp1XaSRn\nddWsdXu69614ZmP3vkeQt72wsaDiunk1wc+//kyjvM0HwzXbYTs+/drGf5i6batJKlX9Q1UPBg7A\nFQwtBz7HFU3/BVc3poeqLkh2oMaYukNVJwC7A0twU/oaAueo6hVek9W4enl3+hSiSbCMjAz26Lk7\ny75dxtn/PNvvcJKnPER5ec1fZvroUy/iw18bsGxN/KkW81aW8nNZa/Y59PgURJZQI4ChIjJXRJ4H\nUNUlqloGICK341Y8fq4qJxGR7USkTdTmbGBtVY6bCPn5a8nMrhdzX1mw5v9/bGqud154iNJK/D9Y\nGgzy1jM19nnWL7hRljGp6lJVfSWF8aRMegB23G9QzERV975HsuN+gwiVFpG3Xc2uOBPrwVUgECBU\nS0ZdG7OtKlVQQFW/BL6M3CYiTYCgqsYuVmCMMVWgqnNx9V5i7RuV4nCqhQ0bCwmWl9O4UQO/Q0mK\nQQcewZQPp9C+dXu/Q0maLEL8sWIleW1a+x1KlTTIbcIlYx5h/IOjmbfwRw7qlL7FtL+i0iCTF4bo\n2Gt/zj+55g24VtWPRKQrbgS5xGhyOHAvcGsVT3UUbuTo4cAc3MqBJ+OKs/uqoKiI9KZb1hsDKpUg\nMCZZeu25L9mvf0xRSXCr7bIy0um9f81cAU5Vu/kdg1/at2nBsj9XseN+g8jNa8vsyeMB2OXQk2jT\ndReCpSWkF/+JdOnkc6RV0yavDev+zCen6abruvyl+ezWfXcfozLGf3GTVCJyEvBPXB2Gt4AXgafx\nhpeKyFvA6aq6IXlhGmOMufHOsRQUl/DgrfFXvamJ8prnub80NdiyZcv4/vvvCYVC9OjRY7N9+WvW\n0KJTJ6a8P5G+UctmL126lD/++IOuXbvStWtX0tPTUxn2NsnIyODki29i4ffTeO3p+ziyaxkN6rnL\nilXrSvjw1/qcPvwmWrTp4HOk205VV+Lqv2xGRLYD+qhqQQJO8xzQFbdYRHNgMXCJqk5OwLGrpLQ0\nWOGFYmlZDf9lNTXa/keezNAff+a+R5/farvhw4bSc5/+KYoqsUSkL25lvz5sqn23GvgOmAA8laA+\nCBFJx9UYnqSqNyXimFVx2flncsWoOykIhWgju9BGdvlrX2lJEWv1a268rOY9/Ig2/KzhXDr6UrL7\n1CM9I53igmJCK+GfQ/7pd2jG+GqrSSoRGQH8B/gQKMYNaz8PaINLUpUCY4C7gXOTGqkxxtRhy1eu\nYl1xOWn1m/HR51/Tr2/tW9gwEAgQCNSsYszBYJBFixahqhQXF9OwYUM6dOhAdnY2ZWWbz9L4dMoU\n+u61F5/Ons1excWbJaJat25NixYtWLFiBfPmzSM9PZ127drRo0cPsrOzU/1j/S1devZmyNX38Nzt\nIzh2JwiWl/PRr/W4ZPQjZFXz2OPxipcfhat4PxF4FXgDN9qpTEReBIaqavG2nkNVy4Frva9qpbi0\nrMILxTLSyV+7jiaNa+XiYqYGuODf11EYqsejjz0ec//55w3lrPMuSXFUiSEi/8DVpnvT+94GN6pz\nIpAPDAdGishAVZ2fgFNeD+yFK8buu4yMDO64YSQ33/MwK1cU0KhVRwCKNqyl+NfvuOXq4bRo3szf\nIBMgOyubi8+8mHEvj6PVHi35Y9Yaxlw6psZdCxmTaPFGUl0KnK2qT8FfGf1PgRNU9XVv2wbgBSxJ\nZYxJEBFZxKZl0Lb2lzqkqp23sr/WeOz5V2nYrjsZ2fX47/sf1coklVP9L8xKSkqYN28eS5YsIRgM\n0qxZM7p06UJmZuwC0wAL5s4lKxSiSW4uvbvvxAf//S+HH3vsZm3S09Np27Ytbdu2JRQKsXr1aiZN\nmkQoFKJZs2b06tWL3NzqmRBo2qwFWQ2bAmspC0KzvNa1IUF1FXANbvR4CXATcBluvN9xuLpRtwOj\ncYs91Crr1m+gOBigosnFGQ2b8/nXMzny0ANTGpcxkf592eVk16vP2LGbD3i8+OKLufDCGj3SZhQw\nQlUfCG8QkfHAU0A74ArgCeBRXO3gbSYi+wLH4xLw1eaPcHp6OtdfdiF3P/w0P/3+C9m5zSlb9gN3\nj7qK+vVi18qribp17kaLhi34Y8kf7LHzHjRtXLPrbBmTCPGSVHnAFxGv/4crnh65ss1i3JLJxhiT\nKOfjLtD2BB6h4tWz6kxlyVVr1tJgB1cWp6C0nNLS0q0mRUzi/fbbb8ycOZOysjJatWpFjx49SEuL\nt0guzJo2jaULF3LwHnsA0Kp5M/LXr2PCa69x+LHHxpzaFwgEyMvLIy8vD4B169bx2WefUVJSQpcu\nXSp97lSZ8el71C/9A8ggOzONDauW8OtP82i3Q3e/Q6uK84AhqjoewCue/g3uQd2b3raNwEPUwiTV\n+LffJytv+wr3N2rRlk+/mm5JKuO7YcOG0a1bN0ZeNoJAIMAd/7mH/v1r5hS/CB1wU4D/oqqTRCQP\naK+qS0TkTmBGVU4iIrm4xNfJVNPV2kcMPZ2Lrx7Dhj+Xc/PIi2pVgiqsa6eufDRrCr0P6O13KMZU\nC/GSVN8AV4vIFcAG4DrcioCDgO+9NoOARAwzNcYYAFT1fW801TzgIVX9zu+Y/FYekY4LBdIpLS2r\nlUmq6riiTSgU4qOPPqK0tBQRqfTnXlRUxOR3JpCblUm/PffcbN+OnTrR6PffGf/MM/Q/4ghatGxZ\nwVGc3NxcdtppJ0KhEMuXL+eNN95g0KBB5OTkbPPPlQjBYJA3nriTjUtn06/zpmTbEZLG24+OYqd9\nDuPgwaf7GGGVtARmhl+o6gwRCeL6pbB5bKoVU2uEQiFmfj+H3G77VdgmLT2DPwtKWfH7alq1aJ7C\n6IzZ0oABAxjyf/3YvrPUhgQVwE/AYFzZFQBEpDfu4dxqb1M3YFUVz/MA8JyqfiMiUA0f/gUCAXba\nUfh+zjzymm3ndzhJMXfBXJps35TPvvmUnXfc2e9wjPFdvCTVBcC7wHLvdSmugN+dIrI/bkjoQGBI\nIoIRkVa4ulf9gCLgJWCYqla7DtMYk1yq+qOIfIPrC+q8nOwMyoNlpKVnkBUoIyenvt8hJVxRcVG1\nTFIFg0Hy8/PZfffKrbYTCoWY+fXX6Ny57N+rF00qmKLXtkULWmy3HZ9/+CGZDRty0MCBcetPBQIB\n2rRpQzAYZMmSJXTv7t9Ipd8WKS8/fBu9WxTQcYfNE3eZGWkc3T2N7+a+x7iZ0zh1+CgaN61x9UN+\nBM4BLo/Y1gX4LeL1zlQ80rPGevntiQQat4vbrkH7Hox74nnGXDU8BVEZs3UZmdlk1a81q9+OBF4T\nkQOA2UBb3JS8e1R1o4jcD5yFm4a8TbzFsXYAwk8SAlSj6X6R0gKBahpZ1f26/FfWFP5B61at+eHL\nORQVFVGvFo4WM+bv2OpcAW/0QlfgCNwwUFHVcbjRU0W4pNUpqvpMguJ5GVgCNAP2AI4BTknQsY0x\nNYyq9lZVjd+y9jvuyIGs/XUBG/5YSa/u4nc4SfHt99+SXi99i4LjfsvIyKBdu3bMmjWLDRsqXsg2\nFAox46uvGP/UU6St38BRfftWmKAKy8zI4OA992THli2Z8PJ4Jk+YQFFRxXnZkpIS5s+fz4YNG+ja\ntes2/0xVteTH73jlgRsY3LWYjs0qHlnWq00Wh7RewyNjhpO/pqoP/FNuBDBUROZ6U/1Q1SWqWgYg\nIrfjHqw952OMCVdcXMInX0yjUav4qzJm1c/h9/WlzNWfUhCZMVsXSEujtmQyVHUCsDvuvmhvoCFw\njqpe4TVZDfxLVe+swmkGeOfYKCKFuHuua0Vk3tbflnr60yKCoXRKSkr9DiXhnnzlSZrv7EajNurS\nkBfe3vqKlaaaqn7PWGu0eCOpUNUi3EoSkds+Bj5OZCAi0hPYDThUVUuARSISHlFljKmDRCQbaOIt\nAx+9Lx1oq6q/pD6y1OuzRy9eeP0dSlav5YyLa135GwDe+/g9mu/UjNfff42TjvyH3+FsZu+996ag\noIBp06axcOFCcnJyaN++PfXr16e0tJTpn3/O0sWL6b799hzVt+/fPn7zpk05bJ8+rMnP591XXiGn\nUSP6HnIIjXJzKSsrY/ny5axevZr69euz22670apVqyT8lJU36fWnGdw9jYz0+HWxGtbL4OD2xXw2\n4QWOOq3mjLhR1Y9EpCtuRa1YmeHDgXuBW1MaWJI99MzLZLXqVun2jTvuzGPPvcI9o69KYlTGVEbt\nSFCFqepc4KIK9o1KwPHPBs4OvxaRp4BFiTh2Is3Vn1lfmkZ2ix14+NnxXHx27Rq/sKFwPY3rNQag\nUYtGLP5+sb8BmW0TCmGZqsSJm6RKoT7AQuB+ETkRKMY9obze16iMMSknIjnAWNxTvUwR+Q23ys1r\nEc3a42o2bFl1upZq27oFK1auJDs7y+9QEu6rWV+xPrCelh1b8OmXn3H4QYPIbVi91uTIycnhoIMO\nAmDFihXMmjmTObNmUVRYSI+OHTlyv4rr91TWdk2acFifPvyRn8/7b79NsLycHbp3Z9fddmO//faL\nWWTdD9167s7s2RPYo13lird/tyqNA/vtk+SoEs9LkI+tYF+vFIeTdMFgkHkLF9N0x30r/Z70jEzy\nyzNZ+PMSunSOP/rKmKQJVNvZaqYKHn/+FRp33IX0jEzmzP8fGzcW0KCBv/UYEym3QS7FG4vJbpDN\nuhXr6NZhR79DSorxDz/MSeed53cYSRTC+p/EqT5LA7nCo7vhElV5wCHAUFwNLGNM3TIWNwx9KG56\n8UfAyyIyIKpdnfprIJ06UB4M+h1GwuWvy+fZ158hr4cb7r5dr6bcPPZmQqHq+UQqFAox4933+P6V\nV9lz4UIGrltP+ooVfD9vHvMWL2FtYeE2HbeotJSFy5bx/Y8/snrJEvquX8+AFSv5/b2JfPbCCxQV\nFCT4J9l2Bx51CoVNdmL+yvhTL75cUkqnPQbSpdfeKYjMVMXnX8+Ahi3+9vsatRFefWdS/IbGmGpL\nVc+sbqOoSkpKWF8cJD3DTSsPNG3HpKlf+hxVYp13yvms+WENoVCIjT9v5LRjT/M7pKT4/JNP/A4h\nuULVsapqzbXVkVTe6lrhz3trN4MhVe1cxVjKgN9VNbyKxVwReRk4FLivisc2xtQsg4ETVfVD7/X7\nIlIEPCUi3VV1vY+x+SYrM+P/2Tvv8KiqrQ+/Z2pmMplJ7wlJgEMCoVdB6dJEsYK96/0U6716bVyv\n12uvKHas4FUQFRRRkKr0IhA6BwgtnfQymcmU8/0xARJIhWRmEuZ9njzknLPPPmuGyZ69115r/VCr\nvCkAtmV4+f2XCe0TikLh2jfxM/hhCbfy+bzPuWtyi+hytBiyLPPOo4+SVFLKhJPKejYbSZlZrl8F\ngcz8SI4aDBhMJjpERKBU1L8fJMsyuSUl5OXloa2sJC47B/+qqtMNlEpGGAwUZWbx9tQHePDttzCF\neEcB8hseeJYZ0+4lOaJ+p1yV3UmJKpopV9/hRst8nCv7Dx1FG9B89SyNTk9JVv312nz48NE83LwG\n81rsdgeCcPrlKxBwOpwetKjlCQ0KpUNEAsf2HWH4oBGo2uE8D0CQZaxWa6MCMW0Vp8OB0+FdNVXb\nMo39FdwHPA/0Az6mfgWblnAcHgRUoigKNdT8VEBFC/Ttw4ePtoUfp1VFT/IoMBpX/ZcHWvqBbUFd\nVKZa4aYdsX3vdiq1Zoz6gFrnA2NNbFu/FVm+s9YE1dMsnT2bDiWldNTXnWqglmUSslwf3RK9nt2F\nRYRGRhBdh2OpzGLh0JEjRBQV0eNEfoOrkCCtlksVCma99BIPvvlmS7yU8+bQ7i04reU0lHEry1BS\ndIL83ExCI2LcZ1wLcCEuEvU6LQ5bcbPvczodKH17yD58tCTuXIN5LXq9jgCtEoe9CqVKg73oOONH\nTfa0WS3OrVffyqP/eoQrH7jS06a0GjpBID1tBykD+nvalFbBajFTWVbkaTPaDQ06qSRJWlw9SdsL\nfFit9tda/IYrmupfoii+AnQBpnBaFtWHDx8XDn8BT4iieLckSTYASZLMoijeBSwVRXEzsKqFnzkH\n2I1LXTQSWA1swMuUu7zIX9MibE7bhCHKUOc1wSCQnZdNdES0m62qH61eT1kT0xBNZjM9Dx3iqNlM\nhlMmNiz01LUKq5XDBw7Q8/CRJufdK8DjNansdjubly9g8+qlmChmUnLDe11atYJJooMfpj+Gwy+E\noROuo1u/oV7leGyAC26ROHb4EP7c8gmERjXrvooTmYzo2b2VrPLh48LDzWswr+a+227g9U/noA6K\noWdKJ/zr2SRqy0SERSA4FO02iurInj10UKlZvWB+u3RSybKMzVxMUeUFmejRKjRF3W+/KIpbaGWV\nPUmSKkRRHAO8BzyNazI4rVqC1SP8sX4L2w9lEx6b0GC74hO5RAUomDR2RFuZePvw4e08BCwB8kRR\nXCNJ0uUAkiStEkVxKq6Ip7SWelhbURdtj+NLSGAo+7P344//WdecVplAY6AHrKqfoddcwxsrVhBp\ntRLYxJD1DtnZ7Ag01XJS5RUXk5Sd3WQHld3pZLmlkrse+tc5WH1+WCrNbFq2gN3b1uM0F9HJVMWk\njhoUQtMK+Os0SiZ0AZu9iLTf3mPVj5+hMYTQZ8hoel081msn5RfiIjEsNJjQAA0WixmNX9MWgrIs\n4yw6zlUTfHuKPny0JO5ag3k7nZLiCVDLlJ1I556Hn/S0Oa2GooHSAG2dHz74kBEGA2szs8g+epSo\nDu1LZON/7z5Hj+AKbA6B7z95hWvvbb+fU3fRpJmhJEkDWtuQ6ufsAIa641kNsW7LdubOX4RVbcQY\n24Xs9Po2T13IssyO/QdZtmoNl40ZybgRQ9rlYtKHD3chSdJ2URRF4DIg9Ixrn4iiuAa4BchqoUf6\n1EU9RGRYJLZDdRffFhyg89O52aKGEQSBB19/nXcfe5xBFishfo07qjLDwwgMrO1siwwK4mBUFN0P\nH2m0+r/N6WSx2cz1j/+TsNjY87C+6VRZraz9bS57t29EYS2iS2AV42O11fW1zq2ehFqloF+8H/2Q\nsdnz2L/uCz5e/C1Kf5fDqu+wCR6PFDsTdy4SRVFU4orgXCJJ0n9a+3n18cjfbmPaq+8TnNI0hb/i\nI7uZMmm81/3f+fDRHnDXGszb6d+nJyv+WI1Go/a0KT6ayYZFi4gqK0dr8GeITsecN9/i0XfbR7np\nvKyjfD/zDZI0J+gY5dq42565nY9feIRr7/knIV6UCdDWaNb2pSiKoYAGKJckqbR1TPIMdrudBb+t\nYNW6jdi0gZgS+qJTNu3tEQQBU3QSTmcCP6/bzcLfV9C3Ryo3XzOxXUrF+/DhDiRJKgG+EUVROHPs\nkSRpD/BUCz7upLrot7jURbvgSifMxyfc0KosXrUYU5KpzmuaEDWLVv7CxJGXu9mqhvHz9+eR6W8z\n/dFHGWy1ElRPRJUDOBQXhyoyksSwsFrXdBoNMYmJpAkKko8fx89Wt6POXu2gumXaNGI6d2rpl3IW\nlRXl/PzVdPKO7aNHsJWJHbQIghJoWWehWqUgNVpHajTYHfns2zCLGb9/j9hjIGMm3+tV0VVuXCQ+\nC/QHFrvpeXUSHhrCqIsHsGrnYUzRiQ22tVSUEu4vMHKIbx3tw0dr0p7XYE0hMS6q/RelbqfxDesW\n/coYf1dkrlapRF9cTMbBg8R2av05TWtx7MBulv74JY7i44xOVKCr4TztFaOmkyWXedP/gV9IApde\ndycxHTp70Nq2SaOzQFEUJwCPARdRY/tUFMVCYDnwliRJG1vNwlamsKiET/83j/Tj2ShMMQR0GnTO\nUVAKhQJTdEegI9systn07KtEhwVz141XExvdvPoOPnxc6Lh57PGpi7qZcnM5r334GmZDBUH6oDrb\nBHcMYcnmJeTm53Hndd5VQF2j1fLwm2/y1kMPMdbhQHtGFEluSDBZoaEkxcdj1NXt4Ak2GAhI7oLk\nr0dXVERCRuZZJchXmM3c8M/H3eKgOnZgN3M/epmR8VUMTtbi0i9ofVRKBanRfqRGOzmctYp3pv3F\nnY+9TFBouFue31Rac5EoiuJg4FrgR7xgqTL5irGs3vACGdsOE9t75KnzmdtXEtNrxKnjrE2LmPn+\ndE+Y6MNHu6e9r8Gag1KpbFApt13QbqobnqYkPx9tRQWC4XTt0R5+fqyYM5dbpz3jQcuaT1lxISvm\nf8mxg3sIUZZycYwSfUTdkX0GPxWXdYFyyxFWfDqNYtlIYpcejLjyVvwD6t6Y9VGbBp1UoijejatG\n1FxcEQYZuNJgdEAMLhWs1aIo3iJJ0txWtrVF2SOlM+u7+RSWV6GLFgnsktCi/RtCoyA0ipJKM8/P\nmE2ARmbypAkM7OMrLOrDR2N4YOzxqYu6iSpbFZ/P/Yxdh3ZhSjERZKrbQQWuKNXIXpHsy9jLw/95\niAkjJjBu2Hg3WtswWp2Ou579N7OnTWNc9QSsQqvlQFwsoRER9AwObtSxplap6JaYSElEBDv8/YnO\nzyeioBCAHRUV9J4wgQ7durX6azmQtoFfv36Ha7sqUClbVx7a4ZT5dn0Wi3fkk19ahUmv4qLOgdw+\nNJbEUC0RAWZmvvx37nz8ZUIj4866Pzk5GYVCwZo1awgODq51TRTFR4C3gK8kSbqj+lyH6nMjAD2w\nD1eNqY/P7FsUxStxOYpmSJL0cHMWidVOrPeAcbgkD5cDf5MkKbf6eh/gQ6AHUAB8LEnSf0VRNAJf\nADcBU8/lPW1pBEGgd/duLP1jTb1tZFlGq1ZiMgbU28aHDx/nRnteg50LXrRH1Yq0Py/VvHdn0FNd\n25FjVKvJTT9EZXk5OkPdwjnegrm8lD9/+YaDe7ajtZXQM8JOn85+NLXsgcFPxfCOABYyT6zm65fW\nY1Mb6dKjPxdPuB6d/ux6rD5cNBZJ9RRwuyRJc+q5/okoivcBL+EaRL2e9CMZfPjVN5RWKTDGdyU4\nunXT8TQ6PcGd++B02Pl8wUq+/XEhd9xwDT27dWnV5/rw0cZx99jjUxdtZex2O1/+8AXb96Xhn6Qn\nalDTo0sDYwORY2SW7P6dxauWMPHSiYwePLoVrW064XGx9Jkwnp2/LSY4MZHiyEhS42KbXZ/HpNfT\nUxTJCA5mT1Y2UZJEYVgYN0xpfantdYu/J23VD0xKUbhlp/qr1Rn8sbeQR8clEBWoJavIysyVx3ns\nm318fGcqeq2Kq5IdfPXGE1xx68N07jHwrD4UCgXLly/nuuuuO/PSlbgyLWUAURT9gBXAH8BwXIu8\nMcB0URSDJEl65Yz7r8c1FlwtiuJOmrdI/BowApdW9/URLtXQEaIo6oCFuAQhbge6AV+IopgNDANm\nS5K0xVWKzztWKkkdYjDF1p6r1IyictptdO7a091m+fBxodDu1mDng+IC8FI5nE5Pm9Ci/DJzJtpj\nxwnyP1uEY4hSyYzHH+fB11/3SkdV5mGJX75+H9mcR49QO1ckahEEFc2slFSLmGAtMcEgy+UcPbKY\nL59fjsoYyeW3PkRkIyJtFyKNvdMxwM5G2vyJa4fS65n782KWrdmCqWNvgtStu1N8JgqliqCErjgc\ndt77+id6i3Hcf8cNbrXBh482hFvHHm9UF21P7Ni/g5n/m4m+o46oQZHn1IcgCIR2DEFOklm0ZSHL\nVy/n8f97nGBTcOM3tzIjpkzh7U2bcISF0jPh3BVrBEEgLiyMfK0fq3Jz+Pu/W7duvyzLfP/Jqziz\ntzMx2X3FaH/dfoJHxiXQN9EV8h4d5Eegv4r7Pt/NvqxyusUG4KdWck1XJ0u+nU7m4fEMn3RrrT56\n9ep1lpNKFMUQYAiwltMpcyOBIOBuSZJOrgD2i6LYEbgTeKXG/f7ARGAG8CjwHE1cJIqiuBqX86uv\nJEnbqvt7FFhV/axuuPIn75UkyQ7sFUVxOPA3wMZph7iAF6T7AWzetgt9UES915VqDcVl5W60yIeP\nC4p2tQY7X5yyjJcMja3CwcMHETRgsVrw07on1b61kGWZWS+8iDY9nT51OKgATBotF1utvP3ww9zz\n3/8SFu09BcZnvvQPNOYMRsQr0WnOzzFVF4IgkBDqR0IomK3Z/PrhUyiCErj9sZdb9Dltnca2TDfi\nmnzVuQoQRTEQ+E91O69m74F0lq7dRkjyIFRudlDVRKlUEdK5D9uP5LN4Rf1h9D58XOC4feyRJGmH\nJElDJUnykySpgyRJH7ZU3xcy23Zv48NvPyR8UBjGSON59ycIAqFdwlCLKp5941+UVZS1gJXnz9jb\nb+dobsNKsE3lSGYmccnJ6Ft5d/GfD/8fxsJtDE5wOajmbqvtcGitY4vNSUG5rdb1ThH+jO/fgdjg\n05Pz79PMTOiiIuOvRWxaNr9WX6NHj2b9+vWYzeaapycCe4DDNc4ZcMXlnzmWvAbcdca5y3HNRv8L\nnAAiadoiMbr6JwPYVeNaXvW/oYAI7Kl2UJ2kCpdQQx+gQhTFSuBmYJooinsbeW6rUlVlI/1YJlq9\n6zOYJW3nt/ef5rf3nyZLSjvVrtyuZP/Bw/V148OHj3On3azBWgJXIJVXBJm2Cv/7+X9E9Azn6wWz\nPW3KefPVf18g5PBhuuvrdlCdxKTVMk6t4ZOnn6a81Du0AA7t3Y7JeoyRnTToNK2vWKvXqri0swpl\nSTpZx33fpTVpzEl1N9/Y4y8AACAASURBVJAMZIuiuEEUxbmiKH4hiuK31buG2UBP4J7WNvR82ZK2\nm7LCnFrnMrev9NixPjSGbbv2NMFyHz4uSNrN2HMhY7FYmDlnJtEDo1AoWzaNTKvXEtQrmJfff6lF\n+z1Xunbvjk6j4VBm5nn1U2axkFFYwPW33tp44/PAarFQUZhL1yj3K9AOTQ7mg2XHWPLXMX76K5f0\nPDOyLBMT4o9Jf3ZE17AkDeuX/VTrXNeuXQkODmb16tU1T08Cajd01YUyA7tFUXxfFMVrRFGMkCQp\nS5Kk1We0vR5YJUlSMa60PDtNXCRKkrRFkqR4SZJqyjTeUP3sPZIkvSFJ0iU17k3FlVL8UrVjXCdJ\nkg6YDfxXkqSUup7pLj6aNRd1uKtY/761v7Jx/kws5SVYykvYOP8T9q39FQBTfFc+ntXuM418+PAE\nvnlQTWSh3bqoDh07xAnzCUI6hLJ973YqzG23HGrGwYNYDh4ksR7BmDPRKpWMUKuZ+8abrWxZ0zCZ\nQjhuCeBIQd2Kyw1xyBZNhi2k2fcdPGHlhD0AfSNOvQuNBlcNkiQdAFJxTdw24So42gFXzYWdwB1A\nt+p2Xs2UK8YhV5ZQWVrsaVOoqjRTnr6Vv90yxdOm+PDhlbSnsaclkWUZZxuZpcmyzPPvPk9QqglF\nK9U58jNosRqszJ7vHTuP48aMJWf3HtIOHKDcam3WvXaHg33HjnFsz14iAK22dSN+d2/+gytSaoew\nT+ltOK/j6/qHYQnvizWkB7JCXW/7v49P4L5R8Ri18NHyY/zts13c+H4aetlcZ3sAk7KS4oL8WtdH\njRrFsmXLgFO1py4FaoVcSZJUAAzAVRtqLDAPyBJFcZUoiqeUTERRNFVfP+nkWoRLya8XzVwkiqKo\nFEVxGvAM8KQkSWU1rvmLolgF7MDlwPoeL6Oiwswu6TD+weHsW/sre9csOqvN3jWL2Lf2V5RqDWZB\nz/q/0uroyYcPH+eKbx5UmyqbDVluIxOgZvLlvC8JSw0FwJhi5LO5n3rYonNn2Tff0LOZ8xeTRktx\nVhYOh6OVrGo6odFxPPLSTE6Y+rJgH+zNtlSnmjZOvtNEPk1zUjkcTnZlWZi/D8rDB/PoSzMJDKk/\nvf5CpNEky+pdwfmiKC7AFbJ+Un65pLWNa0k0GjWzP/uEF6d/RM6hI5jiu9YqAAq0+nFU90soOrwL\no7KKN//zhE8Rx4ePBmgvY0/L4/2TtLKKMl6c8SLOSCemwPNP8WuI4I7BbN3zFyWzSph6y9RG1fRa\nk6yDB4jLP0GM1cqhCjP2QBOdYmLQqOuv9yTLMkdycykvKKDj8Qz0Visrda1fj+LQ7i0kB7VcHarK\n6EuwhaYia1z/39aogWhzNqLN33FWW5VSwZX9IriyXwRVdic7j5fxy7Y83l1yhGCDmiHi2YqP0Xo7\nB3dtpN+wywBX2ueoUaN4+OGHEUVRiaseVKEkSdtEURSo8YciSdJh4GHgYVEUo4EJwBPA76IoJkiS\nZAWuwpUWuLD6tqW4CrD/iCulbwSQiCs9rxLXIvF94EdJkqpOPksUxc7ALFwRELdIkvRtzddRXf+u\nJy5xhhdxOdWG1bh+R6NvdivzzYJf0YR3JEtKq9NBdZK9axZhDIshomM35i9aykV9fUXUffhoSXzz\nIBdLly7liSefwmazsWzZaEaP9g7hlJbCUlVJoCYQAL1JT+6xvEbu8E6O7tlDSfphjP7NV6zr6pSZ\n8/rr3PTkk61gWfNQKpVcfffj2O12Ni79kYXrV+JnL6J/NAQb6p435TmDcGpNVDicFDiNhCjqTl88\nUWrlr2wlVZog+g8dy5jhE5sttHOh0KiTqgH55QJcijmn5Je9Ha1Ww/NPPMT+g4f59H/zKLSCISYZ\nja51w+vsNiulx/ehx8rd106if6/WlxP34aOt057GnpZDxu7wbvWXXdIuPpj9ASE9g/ELcE/xz9Cu\noRzPOsbjLz7Osw8/izGgdR1jdeFwONi8YiXj9f6oZJkux45hyVYjlZdTpFBwcUrKKQfa9iNH6JWQ\nQFF5BUczjpOQnUNSWXWwjSAg5xeQc/QokR3OvQh7Y6g1flhK7aA//4KgVcEpVEX0A+Xp1EHZLxhL\nzFCU5RmoLIWnzu/OKGPBllyevKIjSoWARqWgb6KJvokm7vxkB1vSS+p0UllkJWpN7c9T//79T76n\nw3Gl+v1c4/JJdb+ncC3qZgBIkpQFfFqt3Lce6AFsxhWtAHC4WmEPQAlcJUnSo01ZJIqiOAhXmuAa\nXBEOWXW9X5Ik7cVVOF0AfhBFMViSpMK62nqC9MPHMET3YPW30xttm7Z0LuPFlzDbPL8D7sNHe8Nd\n8yBRFO/CJRoTCxwHXpUkaeb59tsSvPfee8yYMePU8dSpU3nwwQd54IEHPGhVyxIWHE5JaQl6o47i\njCIGdrvI0yY1G2nrVn585x3G65vvoAKI1/lRKh3gm1df5cYnnmhh684NlUrFkPGTGTJ+MkUnclgy\n71Py9h8kTldBrxgNVYKWY3IshU4j/sZgusaGgyyTnqFnf2kRocoS4oVMlE4r2zJtZFj8iU7oxuTH\n78YUHOrpl+f1NJiDIYri3bh2EY8DDwGXAaNxFRd9BtckcLUoim0qb61Lp0Re//c/efaBWwisPE7R\n/g2UF+Q0fmMzqSgpoHD/RrQFB/jH7Vcz/YWnfQ4qH61Oe4iGbq9jz/mSk1eI04vz/fLy85gx612i\nLop0m4PqJMZoI/oUHf96Y5pbn3uS7956ix4OB6oaqY1+Nhvd0w+jLC5m1+HDOGvISx/NzSX/4AF6\nSgcIKqtd/P1inY6vXn4Zu91OazFm8j1sKg5n/ZGq83Z82oJTajmoTqH2pyq8X61TKqXAqr2F7Ms6\nWxVOlsHgV3tH0WJzsuyAjXxtEj0Gjazdl0rF0KFDAa7BVTR9QR3mRQP3VzuEanLyuEQUxVBcKoD/\nwZW+d/LnESBeFMXNuFLzcnGNSUWiKJ6oTv8bCCCKohqXDPx3kiRddqaDShTFn0VRPFMlUIsrWstS\nh90exelsntNJbmfS6T58eBp3zYNEUewNTAduB3TVfX8kimKP8+m3JTjTQXWSGTNm8N5773nAotbh\n/276P0r2FCPLMlXHbVw99mpPm9QsTmRmMv+dd5mg9681B2ouqTodmr37+OkD79ItcjgcVDkFkgdP\npMvwGyiJHcvXxxL4oyIFQ3xPundNpmNcBEpBQKlQ0Dk+ku7dkvGL7cny0i58k5FIRfxYuo68kS4X\nTcBqd3pFaqO309gW6lM0UX4Z1+SsTREbHcWzj03Faq3ifz/+wtadG7FpjJhiRBTnGHrndDopyzmM\nUJ5HSuckbr/nEQIM5+ZV9uHj3JDbg0pvux57zpX9hw5jlwVkWfZoWlt97NiXhtqkbvEi6U1FrVNT\nXlnhdgnnP777jqo9e+lQT9HLIfkFlJor2YNAalIiYQEBONPTEbPr3hzRKpX0s1iYOe1f3PdK60gS\n6w1Gpj73Pnu2rGbpkh+xVRQRrDLTNVxRbzh7fcjK+utPnHmtS5SBvokmXl6Yzr0j4kgK01FaaWfR\n9hOcKKtiVLcQsost7M1XUI4/emMYo++8nbiOddcRHz16ND///PNdQAWwqsalk38g7wK3AXNEUXwb\nKAC640q1Wy1JkiSK4v8BTuBdSZKKTnYgiuKw6n50uBaJGYC1+jgGl2NrtSiKtwDFQAQwXRTFhDPM\nPI6r9tRHoijeDGwAOgOvAN9KkmTGi+jdoxurdmfT89IpbJz/SYNte146BYe9CqN/25ZM9+HDC3HX\nPGg0sLyGkMRcURTfwZWSfHa+tptYtmxZnQ6qk8yYMYPk5OR2kfpnNBgR40X275YYP2x8m0v/2vDL\nL/RTKlG2QP3RFH9/lu/e1XjDFsbpdFJcXExOTg55eXmUl5e76sA6nciyjMFgwGg0kpCQgCiKyLLM\n9r82kLZrP8MGnp3qLggCW3fspUOnFMZO6guA1WqlpKSEtLQ0KioqEAQBhUKBIAgYjUbCw8OJjIzE\nZDJ55Rzf3TTmpIqhafLLb7WMOZ5Bq9Vw5w1Xc+cNsG7zNr5fuIRyhwpTh24oVU2brDsdDkoy9qO1\nl3H5yKGMH3mv7wPmw2MIbd9LdUGMPc1hj3SIUpuAKiCCHxYt5dqJYzxt0lmMvvhScgvyWLt+LYZE\nf4wRRreMg06Hk6IjRVizrTxw+wNudVAd2LqVtEW/MspgaLCd0Wwmb/9+/vHD90RERjLcWkV8eHi9\n7SP9/CjJy2P+e+9z1QNTW9rsU3Ttdwld+7lE546n72fjsgXkH8tAtpajEyrpEGAjPsQPjar+yaei\nqoz69gQV1qKzzj13TSdmr8nikxXHyC+z4adWEB0awNXDe7DbEklcZCcuu+FqQiNiGrX/4osvBlc0\n0m+SJJ00Q67+QZKkA6IoXgK8APwG+ANHcC3qTnoApwDzazqoqnkM1zgTV0/qS81F4vu4UgHPHLdk\nIFGSpFmiKEZV2xEN5OMq5v5Uoy/SzUwaO4Lla14BRdM22EqO7uHR269pZat8+LjgcMs8SJKk14HX\ngZP1/a4GAnA50z3Gc88916Q27cFJBTB26Dg2vb2JcQ+P87Qpzabb4MGsWrueyBboq9LhQBMY2AI9\n1Y8sy2RmZpKenk5JSckpR5ROpyMgIICwsDDi4+MbnL8KgkDvfhexID0da5UNraa2v6DCXAlKDd17\n9T11TqvVEh4eTvgZcz9ZljGbzRQUFHDkyBEsFguCICAIAsHBwSQlJREdHd2yb0IboDEn1UZc8st3\n1FUvoab8cmsY5wkG9+/N4P692XvgMJ/MmkOZwoApPrnBD2ppdjrK8lzuuHYSg/p6PDrWxwWP3B4U\nUC64sach7HY7M2bOwtR5EEqVmiWr1jD8ov6Ehpxdu8fT3DTpJq4Zew3fL57H9r+2Y1VY8Y/3JyC0\nZYUinA4nxZnFWHOsBGiNXD70coYPHOHWzQG73c73773HZU2QDf4u/RDfHTlC3759OZaRwavZ2Vyf\nlMTkpI713tNFp+PPTZtI3zmUpO7d623XUsQldSHu3tO1IIoL89m1cQV/7NyCtaIY2VpOjL+NTqEq\njLrT0wdN9ibsAXGniqafRFF5Ar/czbXOFZTbOJjvIDI6lusTk111HPpeTNe+l6A3NO0zsm/fvlO/\n+/v7I0lSrf+AM4uPS5KUhitFpk4kSRpRz6UY4ApJknY3YM6fuOrCvEUji0VJkl4FXm2ojTeg1WoY\n1CeVt155sdG223+fw4Srrielc6IbLPPh44LCrfMgURQH4xrPFMBXuCJHfbiJ9Mx0VP5qMnMz6RDT\nevUoW4Ok7t2Z71dHyv85kF5RwYDJk1ukr/pYtGgRDoeDpKQk4uLizrkfs7kCi6USjfpsd4pO50dZ\naQlWq7VRtWZBEPD398f/jILzsixTXl7Ohg0bMBgMjBnjfZvTrUljTqq7gV9wyS9vA47iqsvgh6u4\nXj9cg9iE1jTSE6R0TuTt/z7F8jUbmTP/VwIS+6A5oxic3Wal5OBfjL5kAFOuuNdDlvrw0S65YMee\nunjjg89RRXY5FdlpTOrLi29/yNsvPO1hy+rGz8+Pm6+8hZuvvIX8onx+WvoT0tb9VDjM6ON05O/L\np+Pw086Z9D/SSRqW1Oix0+Gk8GghjnwHAX5GhvUZztjbxqLVNE/uuKVY/r//UVJSgjLytI/kp/x8\nJoWG1jq2lpYwJz0dAI1GQ0VFBQBz0tPZZzbzbGr3eu/Pt1Tyyxdf8NBb7g8aDAwO5eLxk7l4vGvC\naLfbObBzM9vWLKHoUCZ6Zxk9wp2Ek4fu8K9Yowbh9AsFnCjMeeiOr0RwWDmWb2FPoQqbykhEXEf6\nXD+OBDEVRR2pAXfeeSdbtmyp0x5BENiyZQvqBtQSW5hTi0RcUU+X1NFGA66aVNVKXO2C26dcxVsv\nPd9oO9lh4+/33d76BvloEfZs20bX3r09bYaPpuHWeZAkSetEUdQA/XHVwpoKeKzw03PPPcfUqQ1H\nETcl2qotcDTzKD/9/hPxA+N446PXefGJlzAa3C8Ac64smTWLkCobqBt2VG2PiqRXPWUOTpKg07Hk\nu+8Q+/XFYGyd92D48OFs2rSJw9W1QrVaLSaTCZPJhE6na9Jm54F9e9iycS2XXtynzvYKQWDURb34\ncc4sLrpkOAlJnRvt82REVWlpKcXFxVRVVaFQKAgODqZ///7n9FrbMg06qarD5FNxFSRtkvxye2PU\nxQPp37MbT73wJs7Y7vj5u/5gbFUWyg5s5Pl/PkhURP0pGz58uBunw0kN9fU2iW/sOU1mdi7pWYUE\ni6edNmo/HcXqQJb9uYHRQwd50LrGCQ0K5a7JdwFQYa7ghyU/8HveEnK25xDaNRSVpnF1ObvVTvam\nHIzaAC4bchkjBo30ipoN+dnZ+DVSg+FYcTErjxwGXE4Wu92OTqejtNQlT7w9J4eN4REMrCf1Tyko\noBULqDcHlUpFSu+LSOntUh4qzMtm6fefs3nfLkZ3TMdQdhRZqQFZRnDaKDHbWH5ESeeew7nxbzfj\nH2Bq9BkvvvgiFkv9dcTd6KCCGotEYDcuNS0LroLnkUAqrnpT97QnBxW4Pqt33XMvH773ToPtLhk5\nlvDQEDdZ5eN8+fC115jx7beeNsNHE3DXPEgUxYXAbkmSnpQkyQlsFEXxT6Dreb2A82T06NHce+/f\n+OSTj+u8Pn7ipDaf6mexWHj/6/dJz04nelAUSrWSoF5BPP3mU/RO6cNtV9+GSnX+CrytxfaVK1k6\nbx6J5kr6+zeeHl7UhO9vf7WaYVYrHz38CAk9ezDx3nvxa0K0enMwGAyMHOkSYpFlmdLSUjIzM0/V\nojqZ/geg1+sJCAjAaDSi0+nIP5HHnyt+JyxQz5WXDm7QoWUM8GfSpYPZuC2NtL82M3TUWIKCQ6is\nrKSkpISysjLMZnOdtalSU1MxtpKTrq3Q6Ce/euI1vynyy+0VY4CB1//9Tx599mU0yUNQKJSUHfyL\nF556xDc58+F1yE4Hdlvb9934xh4Xc3/6Df8Y8azzxphOLF21xuudVDXx1/tz61W3cutVt5J+PJ0P\nZn2AIlyoFTUF1DrO25mHmNSFR+54BP9zlDZuLXpdMpSqPXtrnasZBQWwPSvz1O/x8fEcOnSI2NhY\ncnNzT53/ZN/eU06qM+8fbAjgWAO1qzxJcHgUU+5/Bmn3NrbMfRnZVsKM348C8NDYBAqdRu6e9j7+\nxqbXl4iKimotc5tNPYvEIFyLxE3AG7RjZ/kjD97P7ytWcWhPWp3XE5J78voL/3azVT7Oi7a9f3XB\n4aZ50EJgmiiKXwEHcEWMjgHuacFnnBP/+MffyczJY9HP82ud79nvIqa/+ZqHrDp/ikqK+GzupxzJ\nOYqxs4HoAae/97QGLVGDopBy9vHoi4/SrXM3br/mdrfW2mwIu93Oyjlz2L56NTEWK2P1epRNcFA1\nB5NWy3ggZ8dOPpz6AKb4OK6c+gDB4WEt+hxwbcicjKLq2rW2X9Zut1NYWEhubi7Svj3s370DpSAT\nExlGYKCJCosNfz91vY4qWZapqLQRGRGOX3Exy39dgFNQkpLai6ROnenUqRPBwcFesenqjTTqpBJF\ncQKu4qEX4do9PHm+ANeu4luSJLX7ujB6vY7rr5rI3OVbUPoFMKhPd5+DyodXolYqKcw56mkzzhvf\n2OPiRH4h2ujYs84rFAos9rYrYZsUl8Qbz7zBYy/+AzmubrXCisIKOoeJPHDbAx6wsHG6DRnMxmVL\nyTx2jBg/XYNtAwMDCQgI4OjRo2g0GpKSkkivTgGsD6vDwXpB5h+PP96SZrcosiyzbvH37DmUy09b\nTofx//uHA4zpFcmfv3zD+Bvv96CF58eF7iy/4867+fzruaRvX1PrfJeLxtEzOQl//5bd4fbRyshO\nr1WHbRHkU5oJ7QI3zYNm4nLArwSCgcPAvyRJ+vE8+20R3nr9FVBqWLp4EYJCQUr3Xnzz+UeeNuuc\nmffrd6zavIrAlECiOtRfatwYacIYaeJI3mH+/sKj3DjpRi7uV1fGufs4uH0782bMoJtDZrxeh9BM\nJWBBqcSiUuHXxOjwSJ2OSKA0K5vZjz9GTJ8+XPPQQ24bv5RKJRn7t7L29/kEUsxVcUp0GiWyfITC\n0gBySiI47NSj0PgTFxOFUe9KdyypsHA8MwfZVoFJYSaWHFJVFQhxUGG1s2HzPo5vC2bo+MmEhdVX\nEtNHg04qURTvxpWPPBf4lgbklyVJavcy8MMH9+e7hUuxmYu5/sFHPW2ODx91olU5yTxyyNNmnBe+\nsec0FpuN+iouWW3ekQZ2rlTZqrBYrfVOOFR+KvKycuu85i3c+swzvP/EE8hFJcTqzt7pvDulKwvM\nFSiVSnbtcskqZ2VlERUVRc+ePdm/fz/3il3Ous9st/N7VRX3vvgCmkaKbnqKspIivnjjaY4d3MPC\nrWfXmfh9ew6y/B3ZGUe55ZH/ota0TGFVd3KhO8uvHDeCZWs2EpbYlbSlrqG255gp+KlVXHv5WA9b\n56M5yLKM2unk0M6ddOrRPkV+Kh0Kys3tI7DRXfMgSZJkXCqjXqc0epI3X/4PD6DCJmj594O3tdnI\nk70H9rJs0zLiL4pv8j0B4QEYwgx8PvcL+nTti76FU9+aw7z33mOCRouqkTIHZ+IADsfGEBsaym6l\nkuRjx/GvavrfqVGjYbRGw9pNmzmQlobYq1czLW8eRQV5/D73E/KOH6KDfwWXJ6hRKk/PXwQBQoQy\nQigDJVQ5FOw+3JmioFgcDieUZdBXcQi12nlW3/5aFaM6g91RyvalH7Lq59lEJoiMmXwvpsDgVn1d\nbY3GIqmeAm6XJGlOPddryi+364UiuEICNWoVVXYHel3Du+Y+fHiCrKOHCKCMkrIq7Ha7V+eyN4Jv\n7AGKikuosDrrdVI5tUY2/JXGoL493WpXS2CtsvL0q09h6lq/optWr6VQX8iML9/lwdsfcqN1TUel\nUvHAa6/x4ZNPoSwoJKraUWVRqTgWHUVJeBg5CxdSVlZW677s7Gzy8/Pp3LkzxZ07UWKzYTJXAlDl\ncLDUVsXU114lMKzlw9tbgsIT2Xz2yj8JJb9OB9VJlqZl0y1qO+89N5Wp/56BxktSFpqCz1nuqgFm\n8vfDFJ1KtHh6nCk7sJGe3ZI9aJmP5iDLMp899xwjDUbmTn+HB19/DWNI+8sGqHQoKTdbPW1GS+Gb\nB1UjCAKdkjpw8NBRYqLrjz7ydpI7JWNUGSk6WkRQh6apMzudTgoPFNCxQ0ePOqgAegwYwIeLl3B7\naCgB1fWl6hKLmRQailWp5ERoCBvLyxETEoiNiiJQr+cnSSKgeyqWklJ0Viu79+1lkr8B4Yz7a/Y3\nMTiYHWYz5cFBJKSktMprs9vtbFnxE1vWLsPPXky/KJnBXdRQ7wz8NBqFk96K/awrUKPCyQDtgUbv\nUSkV9IvT0g8bJ0q38t2rU6nSBjFg2AT6DptQp7DMhUZjK9gYXMX5GuJPGpFdbk/YHU4cTrmtOwAu\nKLYuX86uJUu4JCr6rGu7CvJRJSUx5ra2uzNTkx8+f4sx8UpyS60smv0uk+74u6dNOld8Yw/w7qdf\no485O8rmJMaYznzzw8I256SSZZn/TP8Pfp216IMarmUQnBTM4QOHmb1gNrdceYubLGweSqWS/3v5\nJd588CGSQ0IwBwWiNhiIj4zk1eefP8tBdRKbzcaePXvIy86m8333caS4GP/KSvYdOMid//631zqo\nAH7+6l3sllLeXX2s0baz12Ty4mQD65Z8z/ArbnaDdS2G2xaJoijeBTyNS7XrOPCqJEkzz6fPlqJv\nz+78sTcXY7jrO9TpsGMK8KX5tSW+efVVwo8dJ1GvJ9xm4/0nnuDxjz5qd/NYp9OBuaLdZOL65kE1\nCAsO5sCBtp0lIAgCrz3zOt8v/p4/1/2BLsEPU3TdNRtlWaYwvRDnCSdXjruK4QOHu9fYOrjs3ns5\nZDazt6SEosxMQixW7LITB1BiMFAQFITd4M/OuDg0Oh1hwcEoN28mteNpNWcB6BwTAzExVNps7Cwv\nY3dMDHJVFcoqG06nA6tSidpu56i5gqN2G3/4+XHJddfRa2TLp8YV5Gbxy9fvsWH7Xq7sqmJiBw1K\nhYq528qZ0vt0OqPr2NDgccduNvywNrn9yeMwo5aSQ+Vc26mEPeu+4r3fvyMoogOX3/IggSHeWZPU\nHTT27XRKflmSpMIzL4qiGAj8p7pdu+fo8UxsqFEYAli9YSsjLh7gaZN81EPxiRMs++Ybju7ZS6TF\nQi+9HnPFQTbk5TFzn6vQ8b3JKQwMD+fwn2uYvnYdwXFxjL7xBuLEs4tUtwV+mPk6oi4fvUZDYqiS\nQwc2sW31b/S+ZLynTTsXLvixJzv3BJn5pQTXkQp2EqVSRZUmiFXrNjF8cNsZj9KPpVOmKCUyqGk7\noiGdQ9i0cZNXOqkqKyvZuXMn2dnZhPftQ0mlhd6dOzWrZoLD4SAxMhIiIym3Wtlnt7Nh2zZM6el0\n796d0DOKqXsDHZO7s2vPnia3P1CsZmRimxtb3bJIFEWxNzAdl5T8WuA64BtRFDdKkrTjfPpuCUYO\nGcDyjZ9BtZOqvDCPoV3rH5d8eB956en0qo7C8FeriS2tJH3nTsTevT1sWctRVHACECgsKcdqsaD1\naztRm/Vwwc+DanIg/Qgo2r5TVRAErht/HVePuZqvf/qazes3EdIrBI3udDpZRVEFZXvLGDd8PBPu\nm+BVNeQeeuQRwOVE+2X+fHJ272ahzUagwUCvTp24+owi6pOGDat1fEWNY51azVWXnK6zZXc4sBv8\nWZKbiwyEhabw4pQphLbChl1BbhYLvnwbR3EGF8fDiWDoGnV+Y4YgyyCce008pVJB9xg/uiNTYj7A\n3DceQRvSgavu/AemYO+bB7Y2jf21n5JfFkVxG3AUMAN+uHb7+uEKf5/QmkZ6C5998wOGWBGVWsPP\nS5b7nFRexrH9uKIDtgAAIABJREFU+1mzYAF5x46jLi8nRakkRacDg8tT/V36IebUKFT86o40rk9K\nYnJSRxKBsqwslr7wIiV6HQFh4QwaP46ugwZ5fchlldXK1+88S6T9CF2jT3/JjeqkYuWSr8g6ls6E\nG+/3qi+5JnDBjz3zf12GX0THRtsZYzvx+8o1bcpJBbiKFDSrvfcUw7VaraSlpZGVlYVCoSA6Opru\n3buzduVKjHpdrb+1eyZP5rVPP22wv3smTz71u59SSVVlJampqVgsFrZs2UJlZSVGo5HevXsTHOwd\nNQsumXgj5vISsP/Ewo2HG2zbPyWePpfeQKfubewz6r5F4mhguSRJq6uP54qi+A7QBfC4kyosNBjB\nebp+iMNSRpeO/TxokY/motD6gfN0fZQCQSA+ue2nazqdTnJycti+fRtbNqzlkn6pmCsref2VFxh0\n8TB69OhJWFhYW5v/nOSCnwed5ERBIdn5xSgDwljw2wquHD/S0yadN0qlktuuvo2JIyfyrzemEX1x\nNIIgYLPYMO8z8+a0t9CovbeO465du7ALAtfddBOCIJB1/Dib163Daa1iSI8e+OubXhZHlmW27ttH\nTnExnbp04ZpRo1Cr1ZSUlLB4yRJuvrllI7BzM44w+60nmdBZICDC5QqpGeV0rscbrUpUgrpF+jPp\n1UzoAiXmw3z8wkPc/fRbBIe23VTXc6FBJ1U98sthuOSXdwLv047ll2uya/8BckosBIe5dqIqlQEs\nWbWOscMHe9iyC5fSoiI2LvqV/du2YSstxVRlpYtaQ0+t9pRj6iRnOqhOcvLc5KSOBKjVDKzOsa48\ncYKdH33M0k8/QxkQQGznTgyZNImIuLjWf2HNYP2SH1i/bAFDY21EhNf+MhMEgZGd1OzL+JPpT2/l\nipvvp2O3vh6ytHl4auwRRVEJrAaWSJL0n5bsu7kUFZeg0Z2t6ncmCoUSu+Ps4ozeTMcOHYkP6kBe\nRi6BsXWHutckd3sul42a6AbLGqaoqIh169Zht9uJiYmhR48eCIKAw+Fg9bJlUFlJxzMWfgN79GDK\n+PHM/e23OvucMn48A2sUMVapVAxK6coP33zD5ddeS5curogVs9nMhg0bsFqt9OzZk6SkpNZ7oU1k\n7PX3kTpwFNmP3s+WvcfrbDOwexLPz5iNKahN1r9xyyJRkqTXgdfh1Bh0NRAAbDifflsKWZaptcSX\n8frNm3PFe1zhLceJ4xnI5WWgPx3hkKBUsnLOHMbfcYcHLWs+5eXlHD58mIyMDGw2G06nk/LSYo6m\nS0wY2qd6YWwiyKhnxeqV5Ofl4af3R6FQoNVqiYuLIzExEV0bqCvrW4O5sFqreO61GRgSeqPW6li0\nfA3JnRNJ7pToadNahJDAEMKCTztSrWYrPVJ7erWDCiA1NRWHw8GOHTsQBIHQ0FAuu+YaqqxW5n/7\nLWP6D0Bfh5hMXfyxdSsdU1MZ2r07JSUlHDx4EKvVSkBAAJMmTWpx2+d89ApXpijRqlvue6zIGYBN\nZcAmy5Q69RgV5hbp16RXMynZwTfvvcADz73XIn22FRqNm7zQ5ZcBKirMvPvJLAKTh5w6Z4rrwve/\n/E6PlM5ERXhv3ZD2hN1uZ/e6dWz6/XcqCgpRm80kAkP1epQaDdSjHLUxL69OB9VJ5qSn08EQwMDw\n03m/OqWSHgEB9ABkm40TW7fx86bNlPv5oTEa6TF4MP3Hj8PPAxMdh8PB6l++IW3DKpIM5VzbVY0g\n1C8DmxyhoVOohTVzXuNXIZgRE6eQOmC4+ww+Rzw09jwL9AcWt+IzmkRifCxZh4oICI1qsJ3DbkPn\npepvDfHYPY/x6kevUnAsn8D4+h1VOdtyGDtgLOOGjnOjdWdz9OhRNm/eTGpqKtrq99vhcPDX+vUc\nPnCAbomJJHboUOe9k8e7Um7PdFRdP2EC1407+3VFhYUyRKvh13nzMJhMXDJ6NHp/f1JSUnA4HBw6\ndIiMjAyGDh3awq+y+cQkisz6YQn333E9qzbWDvqZOOpi3vzgMw9Zdv64e5EoiuJgXOmDCuArXA4w\nj5Odk4dTeXqMUfmbSNsjtc/C6e3QSzXr5ZcZcYZgQWe9nqWrVtF71Cgi45uuNOYp9uzZw759+9Bq\ntYSEhNCpUycyjx9l8/o1xIQFctWlg2tFSwWZjFx16UVs2L6XzGNWBg8dQXBIGAUFBSxbtgybzUaf\nPn1ISEjw3ItqAhf6GqyouIR/vTwdVXQqGj9XkEBQlwG88dFX3HvjNQzo093DFrYMlioLGlxrGF2A\njuOHGq/16GkEQaBXr1706tULm83GwYMH2b9/P3a7naCQEGx2G679nMapsFjRGgykpaURHh7OkCFD\nMJlMrWZ7bIck8su3EBN0fnPnKqeCDDmatEJ/4qMjSO0QiSzDyi1VRBhkIhWFxAhZqBXn98WSW2Ij\nSex6Xn20RRp1Ul3o8ssOh4NnXn4bfUJvFMrTb5cgCJg69ef5N97jrf8+ha7t5757JbIss3nxYjYu\nXYa9uJhou50+ej1+SuVZ0VL18Ul1DarG2tR0UtVEEATCdTpOXrVXVHD0pwV88vPPOAMMpPTrx4jr\nr291mfgT2RksnTeT/KwjdA0yc5WoRRCattOiUioY3lGD3VHKtt8+YPmC2SR16c6oa+5EbzC2qt3n\nirvHnuoF4rXAj4DHcwMu6teTVdt/gEacVOVFefRP7uwmq1oOQRB48r4neX7685QXlmMIPvvv+cTe\nE4wdMJaJIy/3gIW12b17NykpKWi1WswVFWz44w/yc/PolpjAxCFDGr1/8vjxdIiJYeZ33yEIAvdc\ndx0DGpCBDzQaGTdoEMWlpSz54UdUfloGXHIJEVFRdOzYkS1btrTgqzs/lEolH8+axz/uvYF1W3bi\nlGUuHzeSaS/P8LRp5407F4mSJK0TRVGDy1H+IzAVl7qgR/l2wa/oIk5HLRiCI9i+8y9uve4KD1rV\nOshy+/JS2e12lBXlaA1nq6imKlVsWbyEiffe4wHLmocgCAiCgN1mY1faNgrysokOC2T80D71it4o\nFAoG9+lGlc3O5r/WUlBiJiIqlsCQsFN9ejsX8hps6Z8bmPfzbxiS+p5yUAEolCqCkwfz2fe/sv6v\n7Txw540olUpuueUWNm/eXKuP0NBQbrzxRu6///5Gn/fkk0+yYMGCWudMJhOXX345TzzxBOrqTIuF\nCxfywQcfkJGRQUREBPfddx/XXHNNk15TeXk5zz77LCtWrMBgMHD99dczdepUHLKr/oG9ys7GeZs4\nuvUoP89ayLBhw3jhhRc8rurXGGq1mpSUFERRZPann+GwWjEF1K/cfCYDu3ZlzZ9/cv9DD7mlpMHl\ntz7Mp6/9k7KqHJIjGl9HyTJUymoK5SCKZBNm2Q9ZqUGh9iM8NIRgTtAl8bQ4V7DJQIoYR2FpJdvy\nO+C0WRGcVfgrrARTRKBQjE6w0ZQhaGdWFZmKDtxxnfeP0y1Ng04qn/wyvPDWhzgDE9H7n72QV2m0\naON68MyLb/H6c0+0C3U4b2LjokX8ueAnEqxVDPXXo/KS8GyVQkFHQwAdAdnh5PjKP5jxxx8k9e3L\nVVOntuizrBYLq3/5hn1pGzHIJfSNhsBkNU3dnTjLdqWC/h209KeKrMJ1zHpxE7IumP5Dx9P7knFe\n8xl299gjiqIR+AK4CdfC0OMkxMeisJU32s5Rms+QAZ6NMjofnrj/CR579R8YBpztpFKbNV7hoAIY\nNmwY8+bMISczE60g0FcUGdipU7P6GNijR63UvqYQaDQyZtBALFYr29auZWVFBQFBQUy43Dvel5pc\n1K8XN3V3kFMiE942BRvOwh2LRFEUFwK7JUl6UpIkJ7BRFMU/AY9vnVosVg4cPk5g8unSBoIgUOFQ\nsWv/AVK7tD0HeX3kF+bjFJpbLM+7UalUyKq6I62L7XYSO3o+bbgx7HY7xcf2kJW2AtlaSkxEMCGR\nkdgF2HPwGEajkfAQEzpN7SVNhcVGbkExFeVlmHQqQv202IvSOH6gFLV/IAVh/sTFxXlt6uqFugbL\nzM7lnZmzKHVoCUq5uE5nokKhIKhTXw4W5PDAU//lhqtd34djx47liSeeAFyfmy1btvDss88SHh7O\ntdde2+ize/XqxVtvuXQwHA4H+/bt45lnniEgIICHH36YrVu38uSTT/L0008zZMgQVq1axbRp04iL\ni2PAgMZrLj7//PNIksSsWbOoqKjg73//OwEBAagEFbIss2HuRoqyihhz1aXcevVtPPnkk0yfPp2n\nn366OW+h26moqGDTpk3k5uSQnZnBxMHNK4UTGhRIz7h4Zn/xBQmdOtGnTx/iWrG8ikar5b5p0/ll\n9gwW7lnPyETw16qwOFUUy0ZKMFHq1OEUVCCokRVqtHo/jAEGwnVa9FpVrc9lqKl2FH3vZFd0alig\nnrBA1++yLGO22iirsJJTXk6VxYLgsIHTjhI7AYpKAikmUChBq3BSVmlnxRGBzn1GcM+U/2u198Kb\naSySym3yy97I19//Qq5VhTG6fvlHP4OJCmscb3/8FY/df6cbrWvf5GVm8vmnn/JQdMypNL6f8vOZ\nVEPlqqnHw6OimX/0SIPPGx4Vfc79x/vriQc+X74cU3AwI2+44VxfNuAqBLr1z8Vs/vM3ZHMhqSFV\nXJGkbTCl71yIDtISHQR2RxF7137JB4vnog8MZ/gVN9Gxq8cVf9w99rwPzJYkaYvoUnf0+Ha6IAjo\ntY3/n6ucFmKiItxgUeug1WhRKup2jmrqSeF1NztWr2bpt3MIKa9gQHQUueFh5BQXo9RqMfr5tfqO\nvMVmIyO/AH+1hhilBeu27Xy/cyepgwcz5tZbvUJGfu3i7yg4ksZAUUuQ3smPC7/GaApC7HWRp007\nZ9y4SFwITBNF8SvgAHAJMAbw+Nbp2x9/hSbq7LQ+Y0IqH335LTNe+lebiEhpCj8s/gFDjIG0PWn0\n7NrT0+a0GMoAA3KV7az/pwyFgitqKGt5G3a7nZ+/fJvjUhopQVYui9OgVCqAwuofV4RDQXEAGUUx\nVOBPhw5x2G12MrOyMFJOvCIDk2BGODlEhrp+bPYC9v7xKX/+8jXJvYcwdsrfvPFzfEGtwaqqbEz/\n5CsOZuQRkNCDQE3jm7H+IZE4g8L55rd1HDpyjOCQUKKjT0e0xMfHs3TpUlauXNkkJ5Vara51f1xc\nHBs3bmTlypU8/PDDLFiwgKFDh3LTTTcBcPvtt7NixQrmzZvXqJOqsLCQRYsW8eGHH9KjerPq5ptv\n5quvvmL8lPHk5uRy+K/DDLtzKKP7Xkrv3r156KGHmDVrVqN2e4KysjL27t1LdnY2giCQkJCAzWKB\niIhz+lvqEBPNvqxMunbtyv79+9m8eTNGo5EuXboQExPT4s7koqIiIpIHYdVFMn/vLvR+KuJjIjEG\nBBCg0xKhU6NqwWcKgoC/nwZ/Pw2RIbWjzOxOJxWVNorNFo6VlnEsMweLTUYc0J2QDh0oKSlp1fRH\nb6Wxma1b5Jdr4i2Fi0vLyvlj41ZCUhr3BvuHRLFf2sKhI8fpmOBdhbXbKv5GIw6lkjyLhfXlrmiS\nQ5ZKfsrPb/Tek21Otl+c2XhZj8WZGfTV+Z1T/wCjTCZKgfiUlEbvr4/sjMMs/e5Tik8cp5OhknEx\nGlRKBecaNdVUVDUkTyurstgy92V+tRuISerKmGvvwmAKatXn14Pbxh5RFKcAHYHbqk8JeEG6H0BU\neBhZ5nK0+rpTW2VZxqBre/WoapKRnYFDU3f0QoWl8Uiy1sThcPDBP/9J4IkCRvvrUQUYoKyMiLIy\nbIJAZn4+x/R6VHp/oiLCCWzBaE+LzUZmfj7m0lK0lRZic3Lwr6oufeTvjwgcWb2GV9es4d7nnycs\ntvEi+y2Nw+Fgw+8/sGXNUjroypggupyqKqWCa7o6WfPjO/z+4yyGXTaF1AHDvHER2BjuWiTOxFXv\naiUQDBwG/iVJ0o/n0ed5s2HrDo7klRFUR7SNUqmCwA588OW3TL3jRg9Y17KUlpWyQ9pBbP9Yvpj3\nOW8/O70tfl7rpP+o/2fvvMOjqtI//rnT+0ySSQ/pmYRA6BI6KAgiAoIN17br2lZX0XUta8W1u6vu\noqv+7BXFihSRokgv0muYJBAgpPdkerm/PwIhgYT0gvh5njwPd+bcc88dZs59z3ve9/uOZ/c339Jf\nq2VLZARDj+dR4/EgN5t7zEZAY7z30kP0VeVyQaqCpuwgQQCztBozGfhEWJPpRiXxM0K576ypNHKZ\nhH5RKvoBew79xFdvl3P17f/olPtoB12+BususnOO8eJr76CK6kOgJbZV50okEkwxvUGhZce+g3y1\naDlXTZ1Y975MJsPj8bSor8Z+8zKZDJ+v1kax2WwMHNhwEzcoKIjy8vJm+966dSt+v5/09PS61wYN\nGsRrr71GVnYmTrWLgIgAzDFmft21hSkXTWHKlNq/7sTpdFJQUEBBQQGlpaX4fD78fj9yuZywsDDS\n0tLqPjdNUhJb1q0jNT6+1U6lHRkZ9Bs4EJlMRkJCbWVrh8OB1Wpl27ZtCIKARCJBp9MRFhZGWFgY\nJpOpTfP0smXLcDgcREdHM2jwEAYPuYCsg/v5ddN6RgxKxaDtWrtaJpFg1Cqpqa4mI/sIw0ePIzY+\nCVEUKS8vZ+XKlQQFBTFu3LguHVd309w36GT55UYTRDuw/HJ9TgoXd2skw6dfL0Idkdzi9sa4ND7+\nckHzDX+nRWj1el7/6CMKe6eQ4/NS4nETe5rmU/2opsaOE1StWzCe3r65/uOVSio8Ho54Pew0m3lu\n7lwSBwxo1TUBXnr+af435y6WvfkwQ9SHcNurSYtUnXBQwfwdDRfqnXmsVkg5VuHh8iQP0TVb+OT5\nu7jnthsoLy5o9X21k66cey4GBgE2i8XiAK6nNqqheTGzTmbYoH7Yy5r+7N32GiLCmo70PBdYt3Ud\nqpDGFyBeqZfKqu7Th33/yTmkVFQySK87Y0dNLorE5uXTLyubxL17qdq7lz0HDnDw2DEcLTSIT8fn\n83GkqIg9Bw9ybN9+wnftpt9BK8lHj55yUNUjVqNhskLJO08+icvhaNM12zLGHeuW8/az9/Hmo3+k\nZufXzEh0MDiqYdSfVCJhbLycKTFVZC17g9cf/RMfvPQQ1j1bzyXdn5YuEiOaaXNWrFaraLVa/2G1\nWsOsVqvCarUmW63WN9rTZ3spK6/kvc++xhjXtDCxLjiSXZm5bN7e3EfU83nto9cI7BuAVCZFGibl\nu2Xd6h/sUIZPnUppYCA2jwfbiZT+tR4PNz7as1OIRlx0KVvy5RwucbWovVQAtcRDgKSyRVovoiiS\nUeBif5mSYRf1SH217liDdTlFJaW8MPdtjMnD0RjbXgVWkEhQGoJZuXkPP/y8Fp/Px/r161m3bh2j\nRo1qUR/1n02iKLJ7924WL15cd/7LL7/MbbfdVtemrKyMDRs20KdPn2b7zs3NJSAgoK7wCgCSE9c0\nQ3VxFbogHft/3s+3n3xH/wH9efyJx3F00bO9Pu+99x4LFy5kwYIFrFy5khUrVqBSqUhOTiYtLQ2X\ny0VqaiqBgYEIgsCWLVuAWn3KURdeyIodOxr0tzMn56zH27KyKLXZsJz4HE/2p1ariY+Px+Vy0a9f\nP/r06UNoaCgbN25ky5YtLFiwgO+//54333yTsrKyFt/f4MGDUavVHDt2jN27d5OdnU2AOZTLr76e\nbfsPU1BU2spPrP0cyytkb3YeM665AZ0hgKysLHbv3k1ubi4Gg6Eu+u58orlIqi4pv3ySniRcfDy/\nAHVo3xa3l8mV2B0te5D+TstQabXM+vvfuUYU2bt+A+sXL8ZRVobO6SRZ3nQa1OnOpLDUPry4e9dZ\nr3VPah/STzuvMWo8HqKVSorkcuRGA6NHjmT41KnI27Ab6fV6+eTVx8g7uJ+7R+lRyHrWjmaoUcll\nRijfUsUX/76XuIHjuGRWl+VFd9ncY7VabzlxPQAsFssHwGGr1frP9vbdXuJiohDda5t831lTSWzi\nuR29aTIY8eV7G31P9Ipo1N0nGGq31RDQglQ6hd9PTH6tM9Epk3GkrAy3Tkd8r15oWjA3+Px+so4f\nx1NZRa/CAmJtLS9drJRKkbvETnX8uF0utvz8PXu3rsNnLydW52R8qAJFpIR6Mk2NIpNKGBKtZAg+\nHO5j7Pn2JZZ/rkGpD2LImEmkpV/UI9IVm+DkIvFPVqv1DAv4t7JIPB2fz8ecf81FnzCk2d1wU/wA\n3v3sa+JjIgkO6nzB286g2lZNfnke4Um1RSoCYgJYs2UNMy9pmRjyuUB4bAzVO3eBICACgkqFPqBb\noqRbTNqw8aQMGs3P377Pgj1bMUmqGRQhwaA+23whIghnnwvLajxsyxexC0bS0idz36Wzeowe52l0\n6RqsO6iorOKpl17DYElH2oR22kn0KilyiUC5w0ujjzsRju3bQm7GNtZ88x5/F2rlMyZPnsy1LZTh\n2Lp1a50zwO/34/V6SU9P565G9Gazs7OZPXs2JpOJP//5z832bbfbUZ0osuX1enlr3lts27sNBFDq\nVLhsbnL35RLTP5oJd46nuqSaxV8vYr91P19//nWXRXb6/X5qamoICgpCq9ViNBqprKzEYDC0aAwx\nCQmsX7++Vdcsr65m0LBhzbYTBAG1Wo1arSYkJISqqioqKyvxer0UFha2WHTdbDYzadIkoPZ5V1JS\nQm5uLseOHSMptT/rt20iOTYCuUKBTqdDp1Gh7cD0P6/PT43TTY3NSU2NDa/HxcGcfPoPGc6xY8cw\nm830798fs9ncYzXzuoKzWoZdWX65pwkXx8VEsyu/GG1gy6IU3A47gfqWVZv7ndYhCAJpo0aSNqq2\nglZeTg4bFixg5+EcfNXVhHq9JKpUaJtwXKWHhDArPp4vDh1q9P1Z8fFNVvZz+3wcdtg5KkhApyMg\nuhfDJk/GMmhQux8Y77/0IP3Ux5kwrmGe8TUDdT3q+IahtUUDdmb/wuJPPFx2w91n3EtH09Wl33sq\nocFB4Gl6F83nrCEuOrILR9TxXDj8IhavWgINdSfxur3o5Ya6ijrdwbX33887jzzCRJUaTQvHofJ6\nST5yFK8gsN9ux6ZQolKe6agacKL0udvrZV9WFpYjR9G5WrfR4fP72WC3M/iSS1B1QvUf6+7N/LLo\nc7zVxSSb3FwScVITpm0pyGqFlKExUsCH21tAxqp3WLfoU/RBEUy48mai4iwdOv4O4De/SGyMF19/\nFzEwoUFFraaQSCQYEobw1L9e5z/PPNKTHY5NsmztMlSRp77TgiDgU/nIK8wjIrRdQXI9AkdNDYd3\n7CQ6IACVSsXRsFAisw+x8fvvGT59encP76zIFQomzbqDSbMg70g2P333IeU5hxka7ibSdKaD/Gy5\n+odK3OwoVhIe05dp9/wJc2jPfnb+lu2gsvIKvliwlJ37M9HFDER+Fv2pIK2cQdE6AjVypBKBSoeX\nrGIHBwvP3MwJT+pH6thpiD4f1ccziTTrufkPV7XYCZmWlsaLL74I1M4Der2eoKCG0V2iKPL+++8z\nd+5c0tPTefHFFzEYmq+SrVKpcLvdrNu6li8WfoE2SYs5NQgWglwlQ5CASqti1A2jECQCQb2CEBBY\n89Fa7n7ibu7+490kJ7Q8w6etSCQSZs+ejd/vp7i4mIKCAiIjI9m/fz+iWLshplKp2L17NyqVCrVa\nTUJCAi6Xqy5KTH3a533S3mnqOEitrtvs93q9pKSkkJ+fj91ux+FwoFQq2bNnT12Vz8DAQDweD1FR\nUQwbNqxhdForkUqlhIaGEhpaq+265eeF2MhmmHAMl0tChdNIJSby/Wp8J0TUJTIlRqOBIJMWlfzs\nzzyH20NpRQ2VVdWIXjeC34NM9KCX2AmkgnihCoXEj93nIcY8nn7Dx7f5Xn5rNGtNdGH55R4lXHz9\nFZex+bHn0QQEt8gZUX10Dw/8rds1Ts8LImJjufLee4ET1Te2bmXL0h+pKipCarORJAhEajQN/t+u\njq/Nbz7dUTUrPoGr4xvqbZS5XBzwuLGp1KgDAhgw9TKmjB+PUtWx2lCumnIiI3pW9NTZSAuX8sMh\na5ddrytLv5923T91Zv+tQS6Xo5bVGkWNzUOCs5w+ya2rMNfTUCqUDOufzq7cXZiiTHWvl+wt4W83\n3N+NI4OQqCju/Pe/+eDpZ4ivqSGpFY4gmSiSdjiHldG9GnVSnaSoqoqY/PxWO6jKXC7W+/1MveN2\n+gzvWHHykoJcvnjzeYIp5qIoOcrIjtfGU9RpwojUOI+w8t3H8RliuPaux9Domjf4u4Lf8iKxKX5c\ntZ6jZS5MMQktPkeuUuMOTuTlNz/kobtvaf6EHoTH62HNhtUEDwtu8LohxsC7X7zLE7Of6KaRdQyi\nKDLv9dcJSkhgnd/H1599RnBICGMGDCDrl18YMHEi6h5SObk5ImISuOHep3G7XCz48FWOH93B0OiW\n2VA/Z3kISR3HX++99ZxypHaXHdTRiKLIwaxDLFm5hqN5BTi8EpTmaAKb0f2VSwWGxxswqU9tEgVo\n5PSLlOJw+zhaXu+5KYBMqUIfWOtsMARH4HbYePmjBci8dswmPePHjGDEBQOa/A4olUri4uLOeh/3\n338/a9asYc6cOcyYMaPFn0FgUCAlpSXMXzWfsBFhCIJAXkYeAgK6QB0qnQpdkA5BcsrWC4gMQBRF\n9Kk6Xpv/GpaIJO68/q4u+Q5LJJIGzpvT8fv9VFdXU1ZWRnl5Obm5uTidTvx+P06vlwNHjmAyGgnU\nalE2ssnn9/upcDgoq6ykqLISeW4uZRUVyGQyTCYTQUFBJCYmYjKZukQ7z+v18ve/3oLEVU64XmB+\nxcnHehXXDCyHen43j1egtMTEki1SRIkcJDKkgohwwnVx8Zh0jh4vxOeyocZBmFBIoqSCb3Y1rrN6\nzUAdFybIWLP0HQ7s3MRVt//jvI6gOkmz3/IuKr/c44SLVSoll19yEYvW7sQUc/Yq0NUFOQxNSyY8\n9NzWhjlFvy/dAAAgAElEQVQXkUql9ElPp88JIcLqigrWffstK3fuRFJVTV+JhNATBtjV8QnE6PS8\nnXEAQRC4LTmFoSciqGo8Hna4XDi0WiJ6pzD9qqsIjY7u1LEn9x/OxgM/N3zI1uP0iKaTnK4p1RXt\nRb+IWq1iwqz2VS5sDV0x95wLXDxuFEs2HcAY2XDRaK8sJzE68pwyuJviusuvZ8szW2pjUzghCC/R\nERfVtLHYVQSEhHDf3P+y8M03Wbf5V0ZqNS3auBCBrMhI+sXGEmIyNdku2GDgQEgIxhobsham7GU7\n7OSYArjv2WdQdvACM+9IFp/PfYLLkkGj6BrxUJ1KxvgkKLMd5X9P3c2dj/8XraHpz6wr+a0sEluC\ny+Xm2x9WEJAystXnagNCyM7awd6DmfRNTuqE0XU8Pp+POa/OQddbi0TacEGgMWkoLyvnnS/e5tZZ\ntzXRQ8/l2LFjrF+zhkOZmQTodOQXFDB/6VIASisqyLBamT5xEv967jlCQ8MYPCydgQMH9tS0twYo\nlEquvv1hbr/hSobWM9Pm76ghtnfD45N2Trlo5KY//KWLR9p+usoOslgs46kVYE8GSoG5Vqv1xfb0\nWVVdw1eLlnMwM5tqhxufXIcmpBfquF609KmVEqpp4KA6iVImId6sbtJ+PolCrUVxQlfP5nHx2Yqt\nfPb9MrQKGeGhwcy8dAIJcae+RM092+fPn8+aNWv4/PPPSUpq2Tzn8/n4dtk3/LjhR0RRxK88telY\nmFVIYFQACrUCc4yZzA2Z+H3+uvmooqAShUqBNkCLLlBHXtFx7vvnvcy89AouHHZhi67fWUgkEoxG\nI0aj8QzHnlBQgG/jJhThYRwxmXBotaTVE1M/VlhERXExgTU1hJWUcNDl4prHu69SbHlJER+8/Agm\nsQydofk5UC4RCaMc1YnCXqIgx6XthSBTIgK52QdIk2ahkrVcn1QQBMbGyzlcspu5T/yFWx58EV0P\nsYO6i7Oubrqw/HJ94WIAOSBaLJZZVqu17eXS2smUCWM4mH2Y7KJj6EMa132xlRcTINRw6/Xn1u7h\nbxW9ycTkm29mMlBTVcWP77/Ptr37SPX5iNVoSA8JaZDaV+p0stXnwxQTzfSbbyY8JqbpzjuYidfc\nxrof9Pw87ytCtV70qp5nHIqiSEmNnwqvggdv+xsJaWcvsdtRdOHc0+OZMmE0S39eS60f/xTOwmxu\nf/Sv3TOoDqZWT+k046QHVdYSBIHpd97JtsQVbJg3j5ImhExP6uHVKJVkRUUSHhlFiMnIwtWrG20/\nbexYlDIZlqQk9shkRBaXEFJW1mSV0elmM7k2O6UJcdz92GMdbtDlH81m3twnmNFbQCHr+l28QK2c\nS+NdvPHMvdz1xNweEVF1PjnL3/p4PuqI3m3+Xhlj+/LBvG94+amHO3hkHU+NvYY5rzyJLEaGPlDf\naJuA+AAysg7wwhvP88DtD54TDpyqsjI+f+89Smw2QrQ6Jg4ezMKffqpzUNXn++XLuGbyZMxRvVix\neDErf/iBq2fMID6tabH8nsLSeW9gVHighe6OEHk163/8kpGXXN25A+tAusoOOqGttwC4/cS1hgE/\nWiyWDKvV+n1r+8vOOcZ7n31FZsY+QvuPRxs1AKMgcHznKgLjTgmMH9+5isgBF571eFjcqXTUhQsX\nMm3aKYH79T8thehTUcSu6nI0hqAG59fvr3DfhhPHtdHnO35dTmZeGWrBzaUTxgE0q+343XffMW3a\nNNRqNbm5pyqHa7VaAk7TePN6vcxf8gWbd25GEakgfkI8ucePs+27bSj+MBxbuY0DqzMYds0wju46\nyqYvN+Oyu1n++grSrxqK2+Fm+8Lt9B6XUjcn60MMaM06Fv76Pd8v+55JYydxydhLelwl0stuvZXX\nrVYU5RVIjEYkNHQASiQCAiB3OlhVVclNTzzRrffwyX8eZ0qsDU2ysfnG9Wi42V/EBu8gEEUGy3e0\noH3jxJkVmLWVfPzqY9z55OutGs9vjea24Luk/HJPFi6+77YbefS5V7FValEbGwqyue02hJJM5jzd\nsyuknK/oDAauvPdefD4fS959l+UbN3KRWlMnfLfFZscfE80dD/wdja579MRGXXotwyZexdLPXufQ\ngZ0k6u30CZOf0H1pnJZMcu1t7/L4+fWYh2KfgRmXTWbYxC4XkO2q0u89Hp/Ph68xw0kiobrGhuEc\n18ITRZF//vefaGJOLTYEQcAht/PVD19y1aU9Z1ExeOLFrPv2W2jCSeWUy8mOikRmCqBPRDiyFi5q\nNQoF/S0WjgcFsbOoCL/TgaTG1mjbI34fM2+5tUMNOlEU+fnbD9i/eWW3OahOYlDLuTTOyZtP3cn4\n6TcwYNSkbhvL+eYsP3QkF3X8BW0+XyqTU+ny43S6UKm6toR3a3C5Xfzj+YcxDTCh0p09jTUwMYiK\nwgqe+s9T/PP+bjdJG6W8qIiNixZh3bkTWXU1yQolpmQLokbDln37G3VQnWTR6tUEms3EGE0EFxay\n+l//ZoFaTVBUFKNnXE5samqPSjvx+Xx8/vpT6KozuXt0Qyf2NQN1bHY2PD7J6Fgpazd+R1HeMS7/\n09963KK+CbrKDhoN5Fit1nknjtdbLJYfgUlAq51UH3+5AIchDlVAKboW6vo2hd3lb/I93xlmkdCq\nHBypXE5gwgD8fj/fLVlGgExo9nuRmZnJrl27mDdvXoPXZ8yYwfPPPw+A2+PmnS/eIePQAdTRKkKH\nnUqXG37NMDbN38Ty15YjU8joN6kf1cXV7Fp6qrhTUXYRi19aglKrJGl4Iv0n929wLYlEgtliRhRF\nVhxYzo+/LCV94DCunXZtt36vRVGkuLiY7OxsSkpKiBwxgp179jAgPILE0zKNIoODCQkMZMnGcmLH\nj2frvn2oDx0iLi6OmJiYLtcidbtd7bJ7fCJk+BMxmAJqZWhq4kmSHELaxv8OpVyKy9n1VR17Gmf9\n+CwWixMYbLVa952lTR9gq9Vq7bCcg9Y4qSwWSyxw+KeffiIqKqqjhtAAr9fLPY88gybhAmTyWsPL\n7/dRcWA9/57z4Dm/SDxfOJqRwfwXXmCyRstmu52U6dMZOePy7h5WHaIosm31D2z6eTFqXzlDI8Gk\n6dqJ+ni5i+2FMmT6EMbP/CMJvQe06Dyhg5+M3TX3tJbOnn+KSkp56l+vIwvvjdrQ0EnucTqozv6V\ne2+7idTkluvH9CSOHD/Cq++8ijJOgSHszKiZkowSAgjkwdsfrKuK053sXbeOde++x2ittsHrXiA7\nJhqv0URCZESj+gstxe/3c6SoiOrSUpKO5aJxN5Q7OuJwUJmSwrUPPtDma9THumsTS754l1RDNanh\nPUcjTxRFthz1UugPYsbNfyPiLBpJHT3/nMRisWQDj55lkciJReLfrVZrl/8IO3r+uf3hZwmwpLer\nj/KjB7nr6vH0S01p93g6iydefhxflA+NqeUac+U55aQG9eHmK2/uxJGdHafDwTGrlZzduzmamYW9\nshKf3Y7K5SJOKiVSrW6wSPUCD2daMQUFkZ2dTWXlqQxVQRDo168fdrud0rx83j1N167a4+Gg00m5\nTIpUo0Wm0xIW1Yu4fmnE9umD6TQx6a4gc/dmFn72FsNC7UQHNj5XbXYmEyCtwSI/3uj71iI3eyoM\nXHnL/UTFd8x3tBPnny6xgywWSwBgtlqtmSeO5cBO4OOzpfw1Nf8cOpLLf/7vQ9wKI4YoCxJJ2yMQ\n5VKByamBGE5L+XN7/WzOqeRIWfuqqtcU5+EtOczkCWOZPmlcu/oCyMzJ5PE5jxM3IQZdcG2E5qHV\nh4gfe0r7tv7xrqW7Gzio6tN/cn/6T+531vNPHgfGB+DPF3n4zn8QHBTcWHedgt/vJyMjg6ysLHw+\nH1qtlpCQkLpKgF6vl/kffsj00aPPcKCt37Wb3kMG0+tEqqDL5aKoqIjy8nIATCYTQ4YMQXuavdUZ\nHM3cx/y3nufSRO8Z37WmcPjl5IuhFPtN+KQaoiLCCDTUPlNKKmrIKyhE5rMTIq0gjEJUksarWJ9O\nuc3DssNyrrv7SSJimtec7az5pyfQXCRVt5Rf7knCxQAymYxH7r2Df/73PQKTaw24ypw93HbjNb87\nqM4holNSGHrZZexc8D2+2Jge5aCCWqNxyLgpDBk3hZLC4yyb/zalBw7TO8BJcqii03ZIvD4/23I9\nHHfpiE+5gD/ddjNqbbd/r8/L0u8nqais4rX3PuVYYTn6mEHIVWfan3KVGlPKCP7z0bcEqAXu+uMf\niO51blSi8vl8/N+8t9iXs5+QwcHIFI0/iswpZmwVNu5/7m9cPnEGF4+6uItHeoqaykoWv/sel56m\nAVVuMJATHk5ibAy6dlSYOYlEIiEuLAxvcDCZOh2a4hJi8/Lq3o9Rq9m0bz/bV/7EoAltrwLjcbv5\n/I2nEUqtXJ4oQybtOQ4qqJ0P02PkOD3lLHrrMSJ6D+eyG+7u6p3iSGoF0s/GGmq1XM55mkt1aRES\nCQ5n+xaOncm+zH1UiBWEmhoXA26KgNgAtm/ezh9cf0Cl7HyH+eF9+9i/cRNHMzPx2G34HU6kHjeB\ngFmQMEClQiWVgkJR+9cIUqCsogJDQECj2oVyuRy73U6l48wqaXq5nCEnne2iiLeyivLiHWRu3sxm\nQcAhkyGolEjVasxhYaSkp5Oano6iA+bA+vh8Pn79aQG/rl1OoFDBTEv75ipLiILYQDsr35+DTRrI\nyIkz6D9iQk+NrOoSO8hqtZYD5Sf6TAbeobY4xP/a0l98TBRzn3uMtZu38/XCpTglWgzRKUilrdfP\n9PhENh6qYlC0ngCNrK66X3aJo1UOqu1LP+PY/l9PvSCKIIpIJAJSqZR//PWPrR7b6Tz22GN88+03\nIMLurbtPXconsnHhJqY+fBmG4FObcUd3H23SQQWwa+kuAiJbpklk6hWAO9jN3A/+y9N/f6btN9FK\n3nrrLZRKJVqtFkEQcLvdlJeXM3RorTSITCYjMCgIm92BTqthZ05O3blFthpMxcXkFxczdOhQlEol\nvXr1olevWnmd6upq3n77be688852Ve9rCdFJfbjjidf48s1nEXJzGRMrRSmvjaxy+qRUYqACE1V+\nDaKktsKfTKXGHBhAik6J9LRoU7NJh9mkw+v3U1HtZF9ZBV63A8HnQRA9GCU2jFRgEqpQSmqjBR1u\nH6tz/MgCYvnrU4+i1jaehn4+0dyMcV6WX26MqIgwkmLCya0qQ6bSEKSWMKR/n+ZP/J0exeiZM3nw\niy/463XXdfdQzoo5NJLr7nkKn8/Huh++4LsNPxOtqWZwVMc5q1wePxuO+KiSBHDhtGu5csiYDum3\ngzgv557cvAL+7+MvKaqoRh2RQmCy5aztJVIZgYkD8bidPPPGJxiUAjdcPZ3+qZ1fpritFJcW8+xr\nz6KMkxMxNLzZ9lqTFs0IDUu2LmbLzi08dMdD3SIW/8E//8k4ubyBMVIcEEBxTDT9e/Xq8EWOTCql\nd0wM+Xo9VpkUy9Fjde+lazUs/uxTeg9LR93GVOUH7rmVa5OdRCTULvjqiwz3pGOVXMqlyfC/X1ai\n1miZcOWf23S/beS8cpafbmi3CZ+HAGPrdD26ki8XzScopW1RQJoYNV/98BU3zLihg0fVEFtNDS88\n8ghjNDqGGQ0sLS+v1bs7sVD7vqSE6fWiC74vKanTwwNYUFZGnwH9cWg0hB7JYc+ePfh8vgbXEEWR\nrVu3Yjab6du3L3vj44jJL0DvcJzR38njYJWKYJWq9lhfu4ASHU6O7d/Pgo2byBo7litn39Pu+3e7\nXGz7ZTG7tqzBaysj0ehiWrwCidAxjnSFTML4RAleXyV7Vr3DusWfotQHMXjURPqNmNCTipF0mR1k\nsVhUwNMnrvlf4Ln2Vi0dnT6I0emD2L57P598+T3VMh2GqORWp48W2zwsO1CGUS1DLhUos3nwt9Kf\nnjr6MpLSJ+CoKMFbeoT0wQO45MKRdWOJiGj/Bt/s2bPJdeYS2r/xFEddYO3z7WQU1OYvtzTb5+Yv\nt3DVM1c2eK1+FFX9Y4VKQam7tNXjbg9ms5nCwkLcbjc6nQ65XN7AFsrYvRu3zY5Oe2bUapDBwKHM\nTOISG0YLuVwuCgsLKSsrq+uzs3G73VRU1dB/wiyOHM7my/170CqlhIeYkatU6HVadBoVYWoFUknL\nbT2ZRILZqMFsPHX/Pr+faoebCruT3BobLpeLvIJinF6RlCH9iY6Jo7S8klCFqsvTHnsaZ52Jz8fy\ny2fjjhuv4f6n/wsyJXfeclV3D+d32oBEIsEpCEQn99xFfH2kUiljp17H2KnXsX3NUr5cOI+Lol0E\nG9q3q5BV7GJHmZ4r/3w/0Ylnr17ZHZxvc4/H4+Uv99yHxBiBPqYvASHqFomKnjyWK1Q4bFUY40bx\nxrzFBKmX8MBdtxBg6n7h6fpUVFXw5KtPEHxBMApVyxccgiAQ3DuYmuIqnnjlCZ594Nku3/mWyeQ4\n/CIn97ZEIDc0pFMcVPUJDwwk2+WisqQEo71Wo8AriviF5vUzzobbYSMioGMzZdcdLOO15UcAGJQU\nCnRcRGaQToJ1/04mdFiPLeK8cpa3xvhuCtHnRa/ttgzsZrG57ATI21YxyRBiIHN/ZgeP6Ey0Oh0P\nPvMMGZs2syk7i2OVFax0uxHcbgIQqfK4sXm9aJtwpviDzQgREfQNDma608lLmU2PuaSkhLTERHol\nJnIAgaGHDjXazu3zUepyUeL1ctzjZoXTiaA8EUkVF8e0C4bQd9iwNt9zYW4O65d9RcHRQwjuSpJM\nHiZFKJBJJdSrV9ChyKQSBkYpGYgft7eQg2vfY9PST0BpJCahN8MvuYrA4LBOuXZL6Co7yGKxyICl\ngAfoa7VaG8+VbCOD+qUyqF8qv6zfwlcLf8SnDcYYmdjq51elo2XpUo3hc7vwFmVzQR8Lt865q1Mc\nkXqDHl2ABkNI99ld/o6Ihm0FV19dqxlaVlZGRkYGpaWl+Hw+tm3dSnZGBqE6HeMvGFLXfkBsbIPz\ni8vK2bB3L86aGoxBQUgkEtRqNQkJCYwaNarTilW43W62bdtGSUkJfn9tJJPBYMBgMJDWfyCDhgzl\nyKEsNq1fQ0pcBJHBHbfxIpVIMGlVGDVK9pWVk5NXzIjRFxIVHYvb7aaqqgqr1cq2bduA2nVraGgo\nAwcOPO+cVs3+Sk+WXz7xd16j12nRqaS4Pa4GJUt/59xCkEh6lBBoSxk0ZjJ9ho7jjafvZZK8En0L\n86ZPJ6vYTZ6qL/c9133lXlvC+TT3vPnh59R4pcRa2i5aDLXCxQHx/XHabbzw2tu8+PjfO2iEHcML\nbzyPebC5VQ6q+uiC9VQ4y/ng6w+4+aqu1Ya55el/8vrfH0BbUUF/lQpRr8NkNHXJbygyKIhcsxlN\nzhEO2Gwckkq56ZF/oGqHVsOlE8ZizVuHJbT2/+L0AgutPXbbKpnz46m1zYoduUToRG4YFdkh/UcH\nqki+qGt9Qeebs9zX2vCExhB6brpfRnYGblnbxyZIBMprynA6nZ2ukZeQlkbCaVX23G43x7Ozid67\nD6v1IBWlZfjsdvQqFVk2G4kn5oMZPj/52YfYW1KCXm/gmqlTmb9o0RnXUKvVTJs4kd6RURQdyGDg\n8drf73SzmTKXiwNuN3aFAn1YKFv1BqLi40jr04dLk5MxmNpfGr28uICV335AwdFsjEIVfUPhgjgl\ntYmKXVtJUSGTkBapIg0QxSoKStfw/X/WYxMM9ErszfgZf0RnDGi2n46mi+ygmdSmNqdZrdZO+/GO\nGzmUsSMuYPnqjSxZsQqnRIMxKhmpvHNSzf1+P9WFR6Aqn7SUJP5898MolZ2X1p55OBOZvuV2efrV\nQ/nl3cYr/9Zv0xq8eHG73SiaSAHuLAIDAxkxYgRVZWV88e+XcRYV0jsqCrsosjc7m4iwMILqRX27\nPB5yCgrw1NSQqtNTs2MXVq+bydddR78xnZ/VsWTJEqRSKcnJyU1+VjHxiUTHJbBz2yYWrtzI6Av6\nEGDsGAdkcVk567cdoG//gVw9bkrd6wqFArPZjLleJKvb7Wb//v2UlJRw6aW/if2wFtMiV7LFYkkH\niqxW62GLxfL2aecJgGi1WrtPTbILMep11NTUdPcwfqc99GDHTHMoVWr+9LdnmP+vv3JpGzU/d5Wo\nmP1Cz3ZQneR8mXtMASZMkQ1DnutHTbX22G2rILCbKlY2xcr1K3Gqneg17cuzN/UKYNumrVxeeTmB\np1Vc7UzkCgX3zf0vuVlZLP/0U0rz89Hk5xERGICiE3e3/KLI4bw8DhUWkatRM2rWNcwaM6bdv98p\n1/+Vt587jLzkOHHm9hm0n6w7zkdrz9x8P/naSUdVW9lx3IMiajADRk1uVz9toauc5RaLZTy12lbJ\nQCkw92yixR1NXn4hPkn7FzZyg5l1W3YQH9uzNvJ2Z+zmrY/fJGxE+yJjdElaHv/XY/zt9vsJD2k+\nXbkjUSgUxPXuTVzv3g1ed9jtvPHEE9gKi+hvMCAAEcXFRBQX4xUEjCEhCFOnsnLjRkpKSpDL5fTp\n04cUs5nL3B40VmuD/mweD19WV/HISy8RHhPTKfeyacW3fPTRR0TqQaWQUAT8XA3gabIi8fwdjdve\nndfeCxQTUVHO20//yiWz/kLqkNGNntuZdIEdNBJIAGoslgYSAx9ardZb29HvGQiCwKRxI5g0bgT7\nDmbz2TcLKalyYYhNQ6bomIg5v99PVa4VpbeKKWNGcumEP3fJxnRlVUWrfKvR/aLpP7n/WYXTo/u1\nch6ViFTWVBIc2HXi6SfZvHgJv3z9FWPkCgxKFRSXQHEJPiCjuhoxKQnzCbt0V2Ym/Y4cRe3x1J3f\nXyJl63vvs2n5Cm6e82Snpt1Onz6dgwcPkpOTg9PpRBRFpFIper0eo9GITqdDJpMhCAIDhwwnNW0Q\ny5csIECnYHBfS5vtL1EU2bLrAHaPwMxZNzZwkHm9Xqqrq6mqqqKqqgpRFBEEAaVSSWpqKqf9Ns8L\nzvoNOBECOh+YQe1O4mHgBmAltbuJFwC7gOc7d5g9h/CQYA5WVTbf8Hd6MD3fOXM29m5ZRZSu6bK8\nzaGWuCk6foTQqNiOG1QHc77NPTdeORWVchk/r12PNCgGfXDbKnXZK8pwFRykT1Icd99yewePsu1U\nVFXw3Y/fEj6yYxZ1Qf2CeOH153nxkZe63NkalZjIzXPmIIoiH73/Pqv37sXndqNXqUiJiSGoAyIM\n7A4n1iNHKKgoR5BKUeh03PXUHHT6jhPSFASB2x55mflvPsNB6z4ujDslFNoa1lvLG3VQneSjtceJ\nD9Ew0tL6KIRqh5efcwSSh4zn4qs6dK3UKjp7kXhC22oBcDu1894w4EeLxZJhtVpbXQK+Lbz96Vfo\nwpPa3Y8uMJQt2zdxw1XTesRGSObhTN6b/y42qZ2wkWFIZe2L0NEF6XFr3Dz7zjOEGyO44/o7CDJ1\nfaW7mqoqtv74I/u3bsVZUUmky0VqIxsTMlEksrCQMUGBmC65hPkLF2IwGkmPj2d8dQ2yeovEk2jl\ncqZrdXzx1FNI9Aai4uMZNvUyIuPjz2jbVrL278KgBKW8+78jzRFiVBJV4SVr3/YudVJ1lR1ktVpn\nA7PbNdg20Cc5geceuY9jefn867V3yC2vRK44M0Lx9A25kxzfuarR15UyCddfMZVxI1sXhdRehg8e\nwecLP8cX40Mqb9k8039yP4AzHFUDLu1Pv0v6ter6rhoXGlHTLQ6qZx57jLz9+4mUyVklOIFaSYTR\n8fEUmINApyMzK4uHv/4agPQhQ8g1GhG8HiRl5QgnNPOmm80U5OXz9qOPceeLL3TaeCUSCb1796Z3\nPYe/0+mksLCQgoICsrKy8Hq9iKKI3+9HLpczcOhIigvyWLZmK5PGDGn1880vivywajPJfQcQHxhc\ndw3hhHSDXC4nMDCQ+Ph4QkNDO10s/lygOTfl/dQaS0OsVuv2eq//3Wq1HrRYLBdQq9dQ0VkD7Glo\n1MpOy5H9nd9pjl++/4TszUu4OKntk9f4eAmfvvoI0/90H4l925de1omcd3PP1VMnMXPyeOZ99wOb\ntm5AMEWhD23ZLpqtrAh3URYpCTHc/tTf0ah7libMc68/S9CgoA5btCo1ShxhDt7+/G1u/0P3OOME\nQeC6G29k0aJFREZGIpfJ2LttO1utVuQSCf0SEjAHtNwx43A62ZWZSYXdjlavp8+AAQwIDmb//v2M\nHTu2Qx1U9e9h1p2Pc/ywlSWf/Q9vTQlpZjcxQcoW/1/NXZbTojYtdVL5fH4OFrmxVqrQB8Vww0N/\nxxhobv7ETqALneWjgRyr1TrvxPF6i8XyIzAJ6HQn1Y49GezfswulKf+M91q7QIwccCGiIYIPvviO\nm6+d2aHjbCkFRQV8+cMX5OQewaP0YO5jRq/ouN+PQq0gfEg4jmo7T77xJCpUpKWkccWkK9B1YmVc\nt8vFt6+9TmHOYaQ1NcT6YaRWg0wuh9OiOb1AaWAAJSYTPqWSoKAgJgQEcPGJqlvVTicH8/PxORwY\nbXZCi4tReU9p/oSr1YQDosdD6e5d/LhtG1UqFaqAAKbffhsR7XRYXT/7KVYv/IR92zeCs5JYvZvE\nYAVqRdOO8qYioOojnvhrafum+re5vFiLvByzKfnhmIkhYyZywUXTWtVfB3Be2EHhIcEEGA0UlXXM\nbUgR6ZvSfod7q68rlfLQXx7i+bdeIGJ4OBJpyzZ9+k/uR0CkqVZIXYD0q9KJ7terVdd2O92U7Szn\nuYeea8vQ24XX6+VIxkFiZXJQKPAFBSKqVCCV4khLw2I08u3y5cxfurTunB9//pkBqamkpabiNxoR\nfT4Er5dSmYxQIK+ggJ0/r2LARY0/fzoDlUpFTEwMMY1Ej9bU1FBYWIggCFRXV7P0ly1ER4ai0+sJ\nDTSiVjbuTrE7PRSWVWKrruFwbj6RMYkEBYcRFhZGeHg4Gs2ZgvK/c4rmnFTXA4+fNjnCiWeA1Wr9\n1aVWDrgAACAASURBVGKxzAEeA1Z0/PB6HlKppGMq4PzO77SC44etfPPey8SqyploadpBVV+4+J5J\nsY0uDJVyCTNT/az94mVW63sx6y+PojW0P/qjgzkv5x6ZTMaNV03jhiun8vmCpazasBFT4uAmNRv8\nfj8Vh3bQOyaMOztZb6GtrNq0Co/eg1HTsRW/TFEmdm/ahcPpQK3qHqecXC5nxowZbNy4kby8PAaP\nHIFGo8FWU8PWDRvYcvAgZp2OQSkpTYauW48cJSvvOBqdjiEjRhAaHo7X6yUzM5OqnBymT5/e6Ttq\nkXEWbnvsv7icTtYs+pQl+3fic1QSqXGSEipD14QB1lGU2zwcKPJT7FYj1wTSb+ho/jL+8p5QZaur\nFonrqNWFAcBisciBVODjdvbbLEUlpbzx4TwUho7bfdeHxbBp9zZ6J+1i+JD+Hdbv2fD5fHy3/Ds2\nbt+IW+rCEG8g6ILOjXBS69WoB6sRRZF9hXv59dUtaKVapk+8nBGDR3T49V6ZPZsL3F7S1CpowhlW\najRy3GxGolFjDgwiWa9r1GbVq1SkxsUhiiLVLhdHSkpw1dSgr7ERnZdXl7UkCAJmlRrziQAXV2Ul\n7z/2GLe++CKhvVq3kD6dsdNuYOy0G/B6vezesJKNv67BXl2O31VDoNxFrEkk3KhA2sLFfu2AJYit\njJb3+vwcL3NxuEpKtU+FRKlDZwpkwCUTmDKo88SbW8Bv2g4qLavg068XcSDrMLKQBGLT+7bq/KYc\n6G67jUf+9SahJh2zZkyhT3JCRwy3RURHxnDXDXfxf9/+H2EDQ1t+Xr/o1qf21aN0ZylP3jsHg67r\nRdu/eOUVxqQkIwsORqnXE242o1OcqkT+5dKlDRxUJ9m5fz/JMTFcPbk2jd/j9VJYWcnxsjKUDgc/\nfDmflGHpqHqAI0en06HT6eoE3d/8590M8WZTWWnkWHkEdjQEBgVTVFbNgJRocgvKqCgrQSfY6CXk\nofFXUSEN4bob/9jdt3JO0ZwFmASsP+21o9RWgDjJL8C/OnBMPRpB5JwU3f6dc5O8o4dY9PFc5I4C\nLo2VopQ3vVg9XRfmyW8yuWl0ZKN6MDKphAsTJVTYj/Hhs3dhikzk8j/9Da2+x5QOP6/nHkEQ+MOM\nSxk2MI0X/m8egUmDG21XnX+YqWPTmTppXNcOsBVkZB1AHdI5TiSJVkphcSGxvWI7pf+WIAgCI0aM\nwG63s3btWhwOB4mJiYydOBGA40ePsmjZMiZeMBSt5tTn4BdFVv26lYj4OGZefz1SqRSPx0NGRgYu\nl4v09HTCw7tW80apUnHxVbdwMbUOUOvuX9m6ejFVecXgqiJa6yYpRI6mntPqnkmxPPnN2Sue3TMp\ntsFxpd3DwWIvBQ4VEpWBoNBIht8wg14JvXtEilg9umSRaLVay4FyAIvFkgy8Q604+//a2mdLKCop\n5bHn/4MhKZ2gRtJszkZjC8S18/7DlmXf1B2v/vZ9jCYTf7zpJu68885m+3z44YdZsGBBg9eMRiNT\np07loYceqqtstGjRIt544w1yc3MJCQnBEKAnKq0XykgFAYNNHF5zmDDjKe2pQ6sP1ZVp9zg9rJi7\nkoqicuQqBZaRSejUOhLG1S5kvW4vK1/7ifLC8tr7TI0kvFcYSeOTGu0P4PCaw8SPjccYZsTv8/P6\nO6/x5eIvGZ0+mpmTZnbYdzomLp6SjAyaUtXK7NULWWRtVb+WXlMQBAwqFYao2hTzCrudXRo1admH\nkDdSKczu8SDVagkICWnrbZyBTCZj0JhLGDTmEqBWtyXv6CH2bvqJPVkZ+FzV4LYRqXGTGCxHr258\n6eL0y3H4lbhFOfGyfBSCr9F2FTYPmSU+CpwKBIUOuUpPfO80JqVfREh4+xxvHcxvzg7yer18tWg5\nm7ftwu4TUIXEY0rpWIeuQqMl0DIUu9vJ3E8XIvfZiesVwc3XziTA1Pk2blJsEsUHi7FX2c54r/68\nUZ9DqxuvqtnS9ga1EXMXRhx7vV6sVisrFi9B9HgYMngwpkacSZt3727UQXWS+UuXEhMZSXq/fshl\nMqKCgogKCsLt9eLX63np2WcZMW4cQ9PTMRh6TrVqtUaHTywjSFJNkOQgAEfLwimrMrM/8wjRvkOk\nyovq2le6/Ri6IS38XKc5J5UTaLC6sFqtyae1UdDVZTi6E0Fyjisa/c65QN7RQyz8aC4KZwEXxkhQ\nK84uzNxW4WKTRs603lBak8GHz96JKaLHOKt+n3uA+NheSGm67LLPUcWwwa3TLehqJo25hFc+fwXt\ngLZXomuSGoiJ6hxh39ai0WiYNGkSdrudDRs2UF1dTUJCApHR0cz8wx/45rPPmDluXF371du2M3DU\nSHrF1pYdPnjwIF6vl/T0dMLCuq/s+UkkEgkpA9JJGZAOUOtA276OzetWUJ1bjMpXTUqglxFJJm4a\nHdmkLtVNoyNJTzCSWeAgs1KOT24gMDSKwVdMZkbqoJ6+6dNli0SLxaICngZuAf4LPNeZVQN37TvI\n6+9/hiEpvVEdmDYhQGTyAPpeVBsU5vN4OLb9J15//XVCQkK48sorm+1iwIABvPLKK7Xn+3xkZGTw\n6KOPotfrmT17Ntu3b+fhhx/mkUceYeTIkXz5zZe89/Z7hKSHEtar+ciFzV9twV5lZ+LdE/E4Paz9\naC3h8RF1TqpN8zdjq7Qx4c7xIML6zzbgrnI1cFKdDYlUgjpQjSpOyYqflzN5zOQOS+m49qEHWfLu\nu+xev5F+9R3egDUmGn1UFBFB7VsImTQaUpKS2I1A6pEjDYSNqz0eNqpU/O3f/0LRidGdgiAQGZNA\nZMypCBi3203m7i3s2LCCyvx85J5qegd5CQwMIE8Mo8yvQ6rUkxYfgc8vsv2oBjw2zJJKwsQCCkqr\nOVghwy/XExQWzcCZk5jWe0BPiNY8G78ZO8jlcvPae5+SdeQ40sBe6OKGoOrkDQm5QkVAXG2FzKM1\nFTz0wv8I1Cm546ZZxPZqXzGPprAetjL3/bkoAjqvmEpjKHrJeeDZB3jgjgcINbc8gqu15OTksGvX\nLrweD9Z9++gdEUFidNMRYP+bN6/J9+q3Se/X0I5VyGT0jYsjPiyc5atXk5+Xh85gwGw2M2rUqG7f\nzKqpKkdpbmi7REvzyVMHovCUE1HPQQVgUEkpzjkznf53zk5zs/Mm4Dpg51naXEJtKebzApEOKNH8\nO7/TBJVlJcx/6zlktjwuaoFzCjpGuDhIp2BayilnVVB0b2b++YFONUSb4fe5B9i97yA+edNaKoqA\nCBavXM2fZs3owlG1jvjoeEwSI26nG4Wq49IRy4+UM3pI9xsrp6PRaJgwYQIul4u1a9dy6NAhYmJi\n0J2mExZhDqK8tBSH243T6WTkyJENyg73NORyOWnpF5KWXhtBU1lRxubl37Bwz1Yiw4K5drifzzc2\nNMKuTg8nOCSUH46Z6J8+lhvHTkGt6QRnZefRJYvEE9pXS6l1fvW1Wq1NT+jtRBRFPpy/gI07Mwjo\nPRKJpGPXt1KFEo3hVNXN1Iv/QGlhHm++/R5Tp05rNiVZLpcTERFRd9yrVy82b97MqlWrmD17NgsW\nLGDMmDFcd911ADx4/4Ps2r6LQ6uykXtkKIOVRI9ouGg6GY3grHZyeNthLrrtQswxtb+1lDEpZG3K\nAsBWbuPwtsNMf2QahpDaXfv+k/txYHVGo/2dfux2uKnKr0KlUhHqCePZ55/r8FSxfYWF5FSWc9he\nG6UhAp6kRC5OTMKorXWG7czJYUBsLAALV6/GK5Ui852KKvJKpcwcNaruuLH2Up+PHK0W6fFcBIeT\n6WYz1R43obGxKLtB81AQBPQh0cRfcAmlpaW4XC525OZQdayCQX3i6RtubvAs6GuJxSeKZB4pYL1V\nQqA5mPCkKBRKJcHBwWgDuzZKtY38JuygfRlZzH3nI5RR/TAlD++WMah1JtSWoXg9Lp597X0uGj6Y\na2dc2iF9+3w+flzzI6s2/Ixb4SJ4qJlwRes2mpqKmGpNe5fRxTPvPI1WomP6xOkMGzCsw+2j7du3\nU1hYSHFuLuMHDsSo1zeYP6DhfGJ3OBg4cCA7duyoe//04+SUhqXK65+vUauIjYqi+OhRVElJZGdn\n069fP4zG7ttIz963DaNYTu2jvyEywYNCdJ3xuiAIaLwlHD9sJTLu/KvS11aac1I9DfxssVjyqC2H\n3CB21mKxXAc8QW1FmvMDAQRJz1oUdSTH8o/x0v9eYu7Tc3vc4q+9VFVVcfDgQUS1ir1792IymYiK\nalsVtc5gyaevs2TZSv46Qo0+otY5NX9HTQMxz8aOv12X02zfJ4WLm+tvZaabawYqyK/Yy2uP3cKF\nU69l0JiOeZC3kt/nHuD7ZT+jD48DIM+6k10rvgSg/8XXEGHpjzYwhL0ZO87WRY/gikuv4KOVHxKc\n0nEpIu5CN1fdenWH9dfRKJVKLrzwQr576y2W/Por/U8rHxwZGsYvO3eg9fn44/33o++AqoBdidEU\nyMSrb2Xi1bfWRn5+PJfrFUqWbqv1r4zrF4Wl/wVMu3F2twmfdwBdtUicCUQCaVar9UwLt4PILyzm\nxdfexqMNJ9DSdUUzFFojLpeDex59lj9fdyVDB6Y12bYxu0Mmk+E74WSx2WwMHDiwwfshISGo1Wqe\nvfs51m1bx/bd26mwVeDwOpCaZOjDdKgNagoPFYEIYUmnFpDBccHsXLoLe6WdvIx8AiIC6hxUAHGD\n44gbHHfGmERRpKasBnuBHWyglqsJMgVx8eCLuWDAUJSKjt/gWfrBBxzatp1ep0f/CAIOjwe9KCLp\nKLtNEAARUSaryx6I0Ggpz8zkfw88wJ/nzEGl7RyHs8fj4dixY+Tk5GCz2fD7a6sZG41GTCYTqamp\nCILAoEGD8Hg8LJj/CbERZ2qqSYCD2UeYdf1Ndc5Cn89XZwtu3br1xK0KGI1G4uLiiIiI6EnRVee8\nHbT/YDavvvMpgSmjkPSAolMyuZKglOGs3nUAp+u7dm3wZedk88WizymsKEQeIiNgcGC3rpuUGiVh\ng8LweX3MX/cF8xd/Qa/waK6bfh1hwR0TnT1z5kw++egjbC4XuTlHOCaXUep0knH0GDqdFp1ajb9e\nmrBG1XyUruzE90IURexuN3aXm+z8fJwOB6LXS2lVNWESKbl793LPU091a/Eyh62Gbz/4D1f2bmqO\n8DeZbTU2Tsa8N55l9jNvd2cAwDnFWWdiq9W6/sQk+D7wkMVi2UKtboIRGAKEAy9ardZPO32kPYlG\n8vR/C+w7uJc3PnkDZaSSOa/O4f5b78eg7zk5wG0lPz+fnTt34na7SU5ORpTLUalU7Nmzh23bttG7\nd28SExO7Le3E5/Px4b//QZR4hPhAoUm9ha4k3CTnKqPI6hUfU5x3lEmz7ujS6/8+99Tm/B8vLMaU\nnETG+h84sG5J3Xubv3ub3qOmkDLyUqqdPgqLSwkN7rn57vM+nUdxTSnVhTUNXm+PPoNfKlJWWdYt\n5d+boyQ/nxWffEqu1Uofv58ZajVZefn8cvw4n373HSq1msvGjmVyeQU2p5N37rsPXXgEY6+6kuTT\nFuDnAhHR8dzx2H9YteBjUqOWUOoQGHP13aQMHNndQ2svXbVIHAkkADWWhs7MD61W663t7Bufz8fb\nn37Fjv1Z6OMGoOqo9L7GqGceiX4/xUcOUpRzgNQx0zCmjOC9b5azZPkvPPDXP6PTnpkGJ9azr0RR\nZM+ePSxevJipU6cC8PLLLzdoX1ZWxoYNG5g1axZGvZEp46YwZdwUoHYO3Wvdyy+bVpG1L5uSomKU\nOmWD8vBqY+0Y7BV2qooq0QXp2PrdVg5vy0EURWIGxjB42iBkClndmMqyS/GXiKT17se4K8YRHx3f\n6YvT9+fMwXDkCHc3olPnLyunaO9e9geYQKFAr9VR7XSiUyqZNnZss30PiI3F7fVSXFVFfFQUuNxo\nHXaiCgpRKFWgPPV96aPRUF5ewSv33MPd//43+lZUMG0Mv99PcXExP/zwA06ns84hpVQqUavVjBjR\nuF7Rli1b6v7tRcaOjKMADExpGEWnVNRW5K7fvj5DT1Q7tNlsZGdn10V45OXlodFomDJlCkFBHVeZ\ntjWc63aQ1+tl7rsfE5gyokc4qOpjiu7Nhl1bGXXBEZISWicZ4PV6+c/7r5JTlkNQ7yBCLZ2XXtcW\npDIpwZZap215VRlPv/VPLug9lJuuuKlDvsc33HQTX7/yCnm7djNMqSRNLsdz9Bg1Oi01Oh0alYp9\n+/eDXM4NV1zBdytWIJFI6n7bJ39jer2e0NBQBiYlsS8jAzxeNB43STU29NnZtdVGRRHRbiNXreGO\n5579f/bOOz6qMvvDz53eZ1JI72USSghICV2kKCqogGLZVde1u+5ase7ay2Ivu+7a9rfuuiquFV1B\nRelSlBIgQAbSey+TmcnU+/sjJBJIz6SAPJ8Pu96Ze997Jpm897znPed7hjRA5fP5ePPP93BOvBuZ\ntOMql65+ugqZhLnRzbz9zL3c9McXT7lEkIGg29WwxWL5yGw2r6NFQHQqEAHYgH8C71sslqwBtXCY\nIRzzvyc79Q31/LjvR7bv3kZtYx0elYfQqaFIZVLsjQ4eePl+tDIt4SHhzJp0JqNTRg/I7qA/EUWR\nuro68vLyKC0txePxoNPpiI+Pb9chSyqVkpqaisfjobS0lKysLKRSKUFBQcTHxxMWFjZoQav1n71D\njC+P1HAVo8Lap48e30K5o+Mobc+Fi3syXiuCIDA7Uc7Xmd9TOm0+ETGD1yEFBn/uMZvNc4EXgBSg\nhpZF6Qp/3qOniKLII8/+BUWo+YQAVSutryVMmM1jz/2FFx67f1h299u8czOF5UVow/zboSVwZAAP\nP/8Qf7rtoQHVYOgpDbW1rH33PxQeOoTaZmO0XM7YY+acXRs3sF+jweFyEZ+UxNsffYQ1Lo5lCYmc\ngxJHbS07XnyJ/6mUmMIjmHvF5cSmHF9ZNrw566KreHHHZgwBplMhQDVoi0SLxXIbcFt/7e2I7Jx8\nXn79HSTB8QSmTOnzODqFFHOoBokAhXXNVFrdJ54kQlHWDooP7Ww59HkRfSKRqWeQMH4WgkRCQEI6\nDU0N3PnwMyw5fz4Lzmr/Pfnpp58Ye1SfxOfztem0/e53vzvhdjk5Odx2222YTCauvfbaE96XyWSM\nGzWOcaPG4fF4uOq6K/G5ffh8vrbnu1TW8v9ejw+nzUVxVjGx6THMufEsnHYX2z/cjtPmZNbVM2m2\nNpPzbS5LLlzC0luWDtoiw+VyUXckh4xOSlwkQFhtLWG1tQDYFAoqRwSTr1ajDQggPiysU1trrFaK\nS0tRNTsJqakhoqmJ7jyfAKWSiTYb37//ARfecnOfPlN2djZr164lIiICvV6PVCrF5XK10+OrqKho\nd82OHTvagkqt7wcGBtJa3FBZZ213/p7sIkSvB4/H03Z+aGhou+tb0Wq1ZGVltY3vcDgoLS1l165d\n2Gw2JBIJ48aN67A9/UByMq/BrE02vBLlsAtQtSI3hvFj5r5eB6kOFxzGUnyY2Ol978Y3WKgNalST\nVGz6biNLz12KXtu5fERvuPjOO6kqLeWTv/wFe1kZaYKEcCCgqb1YfCpQFxdH1tFmDtnZ2YSHhxMQ\nEEB9fT0T5HLOqayCyqp21zm8Xrba7TTqdMy49FIuP9oBcChZ9c+XGGeqJUDbuQxLdyksIwwKkh1l\nfPPhm5xz6Q3+NfAUpEcpGxaLpYYWIc+XB9ac0/gbt9tNQUk+lrzDWPIs1NRW4/K6aPY48Um9yAPk\nGOOMBCkC212nMajRTGrRHahprOFf69/B87kHhUSBUqZEKVcSGRFFanwqyfHJhAaHDnpU2OfzUVNT\nQ1FRERUVFXg8HrxeL2q1mqCgIEaOHNlt1F0mkxETE0NMTExLK2arlYMHD7Jjxw4EQUAqlRIcHExU\nVBRhYWEDkgZ+aP9uFsX0Pfg33RzAuBg9ewqtHb4/LkbfpR5Vd4wNhZ/WfcEFV9/e5zH6ymDNPWaz\n2QR8RktGxEpa2s6vMZvNhywWy+cDee/j8Xg8PPTnl2mUB9NYWdJhgKqVg5v/h2FEJIERo7nzoad5\n6sE7MRr844T0F5vdxgtvvUCVs5K0S8d0Oz8UZhay/b8tu90ZyyZ3245ZqVESPDGYx/76GJNGT/Lb\nLmFvaKit5fv33qPg0CFk1iZGSqWkqtWgax/8/TA3hw9yc1EqlYwcOZKamhp8Ph8f5LZkjC1LSEQt\nlXKGvuV311RWxtonn6JBrSYgIpw5l1120gSsREFCQuq4oTbDb5zMi8TX//0hO7NyMCZNRirru5Dv\nmAgtKaEa1EczkBJHqCmpd7Ilp+EEpzw8eSyjzrwAAAEBhUaHQtU+QK3SGVGOnM7nG3axeduPPHz3\nrcjlLc/WtLQ0Vqxo2RsQBAG9Xk/QcWLgoijyj3/8g1deeYWMjAxWrFjRYecnURTZnbWb1etXU11f\nTaPHikQuabcB5XW3JMfJVTIECai0KmZcOaNN1uGMhePZ+M4mvFdMQ6lTEj09ivV71rNtz1aiwqK4\naP7iAe8uqlAo0MfGcKCklFHdlNiJAKKIzONF4vXS3Nx19ajL7QaPB7nXi4CID7oNUtU4m/lRgN9d\n0r0QfmfU19cDLb9jURRJTEwkNzeXM844o83P6iz7CVoyoHbs2IG1rpIZ45MJCjC2ZVMdS2piFHt+\n2s7kKdNPCHJ1Nr7b7SY1NRWbzYYoigiCgM/no7Gxsc+ftz+crGuwAJORUQmRHDyyC3tjHVFnzG17\nr2TPunYdQgf7uLE0B4WjiqXnX9HrzzUycSTzp85j8/bNSIIEAuICkcqGXyDO4/JQm1MDDQLXXHqN\n3wJUrYyIiODGp56i2W7ni9ffIDNzDzMVSrTyn581MuBSvYEPq6r4uKiIyZMnU1BQwJ49e7gsIZFl\nkSdKruy02agLMLH4jtuJGUZ+z9frf+CBOT/r8XUkn5I0UmjLY+lMXiUlVMHn+346HaTqAd2uuM1m\ncwJwGfCBxWLJPdqB5hlgHi07in+3WCz/HlgzT9MVHo+H/KI89lr2YcmzYG2y4va6cXmcuEUPUq0U\nuV6KJkCLMkKJSlDRmyI+lUGFytC+PMDn9ZHbkEvWT/vxbfDiawaFVIFCpkAulRMSPIJRyaMZYx5D\nSFBIvxePXq+XkpISCgsLqaurw+fzIYoiWq0Wk8lEUlJSvwNIgiBgMBjaObuiKNLY2IjFYmHnzpbd\nYYlEglarbQtuKRT9y14ZlT6R/Qe+Ii2ib+NssdR1GqAC2FNoZYulrs+Bqt1lAosWL+rTtf1hkOee\nmUC+xWJpbUWyxWw2rwHOAQY1SPXYc3+lSR2OLiCETR9234E+89uVnPu7p5DEjOOBJ1/gpScebFvw\nDQVer5d3PnmHnVk7MY0yEmrsPsspc/VeMldnth2vf2sD6eemk35u150L5So5EVPCySrZz22P/IGL\nF17CrEmz+v0ZuuPAtm2sefddFEcDU/M7CEy1sr2ysi0Y5XQ6MRqNHDr0sxDzB7m5xOr0ZBzT0l0n\nlzPlqKPXVFLKd08+RZ1Kxaip01jwm6uGdZq4VKZA0o+AyHDkZFwkPvfaP8ir9fRbeypALWNkqAbl\nMSVycqmEmEAVdXYPWWXH7JwLIFOq0Ad2/zcvCALGmJFYG2pZ/ugKnnnoHqClzCs+/kQNqFZEUeSu\nu+5i48aNPPLIIyxevLjt9fzifLZnbif7yCHsTjsOlwPBIBAQF0BwchBWqZUjO4/g8/qQSFtCMfYG\nOwICukAdKp0KXZCune5oQGQAoijicrhQG9QYQg0YQlt8hOrGap7/4HkkTglquRqDTs/YkelMHjuZ\nkGD/6e8B3PDkk6xf+SGr131PoNNNTHAgHr0Bu0qJWyoFqRRkMpDJUKvVBBoMRKjV3WpUhQcGEhYQ\ngMPtptZqpaypCZ/bDR4voteD1OtF43KjamqitqaGfI+H0Ph47r77rn4JqGdkZJCRkYHX66Wuro6a\nmhqkUik5OTl4PB5EUUStVrN3715EUWwRow8NpaqqCr1ej1KpxKhVU1tiJSigpQz8+FK/8aktm49r\nNvxIaHgEkyZNwm6309TUhMPhwGAwkJmZiSAILcLGGg0HDhxAJpMREBDAmWeeSUBAAAEBAUMmB3Gy\nr8HuuPFqDh7O4/EnnqAu/wDGmBS/N2voDU1VJXhq8jln9gwWn9f3Ku1Lzl3GxQsuYcP29Xy98Rus\nzVaU4Qpqc+pIOuvnqoPcDbntZAoG+vjI9zmYYox4arwE6gK4ZsG1jBs1sJtGKo2GS+64nYM7d7Lt\n5VeYLD/x+b8sIZFYnZ49Wi32qiruG5vO5JCO58gKg4HlL744oDb3BUEiaQtad4ZPIiB2U23l9fqQ\nyodf1cNwpMuVjNlsHgNspaXDTet2/rO0ZBv8g5YNl7fMZnO1xWJZPZCGnqY9DdYGXn/vdSprK2n2\nNCPVSZCb5OjCdWhUao5rSOR3JFIJukAtusATd/VEUaTSVknuvlw+2/wpglOCWq5i/OhxXLrw8h4v\nsKxWK7t27aK+vh5RFDEajQQFBRERETFoi7RWQc3jO0k4HA7Ky8s5cOBAmwM1bty4dqnkPeWsi67m\nvaJ8dhZlMSG69xlVr3yd36Nzehuk8nh9fHfEw6hZFxERO7ilfkMw92ymRby49f5yYBTwLz+M3WMq\nqqopa3QSlNT7BY5CrcWuj+C7TVtZMGfmAFjXPbmFubz09kuoYhVETO1Z96TjA1Q/v97yWneBKgBT\npAlfuI+PN3/E2k1rufeme9EOQBe50pwcVr7yCqb6BuZqNMg6CUwdyxuHDrY7lkqlNDc3n3BORicO\nm04uJ+Oo05ezcQPP/LCZOUuWMGkYpL93hCCRIJwiJfFwci4SN279CUu5lcC40f0eKzlU3S5A1YpE\nEAgzKNoHqfqA2hiIg2RefP0doGPh9GNZuXIlGzdu5P3336fGVsNDzz9Es9uBw9OMoAFlkBJ9oLbK\n/AAAIABJREFUsh6DzIDhuO24kPgRIEL5kQoiUlrmp4ojFQRGBaBQKwiODebwD4fbBbHqyxtQqBSo\n9CfqeKkNatRjf/a13E4363K/5+uf1iBxSVDJVWjVOn5z8dXERPSupMjn81FSUkJubi5WqxVRFPGp\nlCQtWIC9qYnMoiI8ThcjZDLSEhNR90CguDMEQUCjUKAJCoJjstZ8Ph9HiovJKi1F1KgJjp9A0ogR\nSCQSvj6qM6NQKIiOjiYhIaGdpEJPac1W76qzqc/nw2q1Ul9fT11dHfn5eezbuQOF1EdkaBAHc4oJ\nCjQRaNQgOxpMcnu9VNfbqKtrICo0gB2bv+dHmZL0iVMICQklIiICk8mEVqsdsgBUd5wqa7CRyfG8\n987b/PDjHt754BO08RPaZTUBA34cPvZMag5tY/oZo/n1XQ/6pSpCEARmTzmL2VPOwuly8s3mb/jv\njv9StqMcTbQKQ9jgdKDzeX3UFdXjrnAj1ossXrqE6ROmD4p+k9vtJmvLFn746is8lZVM6WIeyggJ\nwR4VzQ0zZ3XpIUQ2NvLcTTcxZsoUMs4/n4ARJzZFGAqWLb6AI9mrSA5pmec6kkvZ6FTiPtr1rzM5\nlQPlLibMPGcQLD756e6v9FHgW+Ayi8XiMpvNCuAq4GWLxbIcwGw2lwC309I++TSDxJadWzhcZCFy\nUiQBquHVFUoQBFQ6FSrdz5OVtcbK6m/WsOSci3uUeeRyuXj11VeJiYlBfnSBtm/fPkJDQ9vStY9P\n3f7iiy9O0BsYrPOdTicrV65k8eLFREdH9+wHdQxX/P5hNn31AR+t/5Izo1yMMAyt9teRSie7arRc\n8KvbSB47ufsL/M+gzj0Wi6WOlgUnZrM5BXgTcADdpzL5kfr6RiSyn/9u0udfyvZP3+jymvT5l7b9\nt1Sppqa2fsDs64qS8hKeeWMFEVMi2gkTd0Xh3sIOA1StZK7OJCDS1G3pH7RkOIaMCsHeYOeBZx7g\n+T8+7/fy3P++8gpnOl0oexCcGggSNRoSRJGvVq5k/Pz5w6kL1TEIp4ps40m7SFzz/UaM0aP8Mpas\ni27G0uPfE+lelKMD1MZAio8cQUt74fSO+PTTT1m0aBFPPfskDY5GlCYlEomAVCZFbpcTOrrjjaLW\nZgzBUUH88O4PJE9Iwml3cWT3EaZc2qLVFTkqEpVezeZ3txAQYMLj9nBk5xHC4kLJ25gH9K7ZQxNN\naEZp+PObKzhv1nksPGthj34eHo+H559/HoPBgF6vRyaTtQXvjvVJAEoKC9m6fTs2h4PIkJATAi7H\ntoY/lj35+R2+3nr+/iNHyK+qYuSYMSyePZtdu3YBLb7ZsSQnJ1NRUcGmTZuYMWMGo0b553t3LBKJ\nBKPRiFarZcea96jK28+5MV5MGjmQh8stobw8hEMlI9AGBONxe3HbaomUVBInVCGXikyNgsoGJ5u/\nycY5dgppv/rdsM5IPcoptQabNmkcaSOTWf7osyhGzhjUezfk7eWmXy9lwtiRAzK+UqFk0ZxFLJqz\niGZnM5988wm7du5Co9fQbG1uC3IfP3/059ha24Rao6Zpr435U+Yz/9qB9wkcNhv7t/zA/h+20Fhd\ng9jURITPS4ZGi0rbtV/kEQRkCjmVAQGE1tV1el66VstYUaRo3QY++H4dTrUKucFA4ujRjDvrLMIG\nWReulfEzF/CfLV+Q3MF+YpUvgMOeaKKjovF5fWwtF0iWFhAsPbFE+HC9lLMyzjpxkNOcQHfbB2cC\nz1osltan0mRAD3xwzDmrgIwBsO00XXDe7PO47/r7kRZIKdtejuXb9sLZxztMQ3XcWNpI5bZKYn2x\n/HXFaz0ujVMoFCiVyradM6fT2a3zOhS43W5yc3M5cOAASqWSiIiIPo8187zLuOnhv5MtGcPnB0XK\n63vWibxVFL2/5wBkVzTz8UEJrti53P7UW0MVoIIhmHvMZrPKbDY/S0vb+e+BaRaLpamby/xKfGwU\nPmffb+mxWTEnxvnPoF7wxvtvEJ4R3uMAFcD2DzvXHOnNOceiMWoQQuC7rd/16rqe4G2yoezl7uQN\nqd07xT05pxVBEAj3iuRlDU8ppOG/7usVrYvESIvFknncIvEmi8VyA/AULYvEYUPIiGCKd65t91rJ\nnnV9Oq5qciOKIqtWrWr3/qpVq7A2e9qfL9AWoOzN/URRRKmQtZVddcXhw4d5//33+WHDVrJ2ZLHr\nm138tGYnefvyu7yulcTxiWgNGvZv3E/O7hzGnjOWhIkt5YUSqYR5t8zF6/KQuW4vh7ZlExQZTMyo\nvgskC4KAwiTjk88+wWbvWdaZTCYjISEBmUxGY2MjNTU1VFdXU1NTQ05ODuXl5VitVrxeL5ExMSy6\n5BKCw8I4kHucH1Zc3O541YYNXb7feuzxeCipr+fSq69m7IQJSKVSPB4PDoeDxsZGamtrqa6uprq6\nGovFgsPhYMqUKaQMoH6MrbGeW665lCj7HhalSjBp5Kzc3fKsVEh8xEjLKcjaSkN1JVJbCZPl+9m8\nNxe55Ge/cV2umyWjBNSlm3j1oVvaBNWHMafcGkyv0xJoMuDzebs/2Y/IRNeABaiOR6VUccWiK3ju\nwed44NoHMdQaqdxeSfXh6jYNvL7idnqoyKqgekc1sd4YnrrtaVbct4JzZ587IAGqpsZGvv3Pf/jr\nXXfz8s038/Ytv6PoP+8ypryCeaLIfK2W0XoDqk78IhFoVKnIjolhf0oK4xMTaYiPY19CAhWBAXT2\nFygIAjE6LbP0eubL5MxssiFZv4H//ekhXrruOl697TbeW7GCgmOkEwaShtpq3n7uAWbHC/hEaPBp\nsHjj2O4ew1bvRCqNExk5MoURAXpCg42kpKZSZpzMVu8EtrvHcMQbS4NXjSjC7Dh448/LsTUOzYby\nyUR332g9cGx7jZmAFdh1zGt2wL9tm07TIxJjE7n/lgd497N3WfPNmqE2p0Pqsuv4/W/+QFpqWq+v\nveuuuwDaHLPKykq8Xi+ZmZno9XqSk5Pxer1tKa2tLao74/gdyN6cL4ois2fPpra2loaGBgDi4+Mx\nGo0kJiYSHh7ul5RxtVbH5bc+RLPDzpf/fpWtB7MYHeggOUTZqfM+3RzA1TMjeWdTSYfvXz0zsstS\nP4/Xx+4SN0UOPWmT5/L7C64c0javRxnUucdsNsto2Yl0A2MsFkvHP8wBxul0IQg/f48yv13Z7TWZ\n364kwpzeciCR4OhGKHeg0Gq0WF3WtlbtQ4noFgkyBXV/Yi+RaDSIXm+vduAzQkK4LCGhTZfqeC5L\nSOi01K8zqmVS4gYgY+E0J3AmsKgHi8Q7BtuwrvjV0oVs3vB9uy52fSWnykF84InyAS6Pj/2l7YMu\nMy/vW6yuPn8vly04i7Omd6+f1ZrR4/V6Wb3hKzZs20izxEHw6OAu555jMxDM88ydnqcxajjr+t7v\nch+f8eBodFCf3YDzoItfnXMlk27rnTbYJZdccsJrLpeL2tratn8lJSVt+k0arRbR60WvVKLT6tAp\nFRQUFLS7vqCkhOv++EcArl+2DJnXy7i4uBbNLY+H/KIiCiorabQ24RVF9u3bhyAISCSSto5cQUFB\nBAUFodFoBjUTKXvfjwTIXUQFdP3IlwkeVGJzl+ckhygoaaymsrSQiJiOM+OGCafkGmzS+HS+/jEb\nU3Tnf4f+pNnWiFHb93LY/hAeEs7yG5YjiiJbdm1hzbo11Duq0MaqMYT2rBxQFEXqiupxlTkZYRzB\njQtvZLR5zIDa/edHH0VeVo7S4SABgXqHjYtGhLRpb35eXc2Fx5Tofl5dzTmhoVj1eqw6HbvrajFH\nRiIoFGh1Og4ePMiF41r81OSoKD7PySF43Dgs9Q14nc1YiooZHRyE3u7AYG3ku+ISLjpm/C9ra7kw\nOJjI1vuVV3CGzc73Tz1NnVJJsQDPv/66X+ek5uZmdu/YxA9Hn6XhkXEcUGoQBTlavY4Ao45wTcdr\nM4VMSnxEMEQE4xNFGmxOChus2Gw2BJWbwNAm/vriU8ilMmbMWcDYCRl9Kpc+1eluNVEIjANavevz\ngU0Wi+XYlJYzgKIBsO00XdBkb+K1f79GcWURinAFY5a0157wZ0ppf46jZkTx1ldvIvmvlGkTpnLJ\necu6+lgd0uoUteL1eqmsrKSoqIjs7Gy8Xi9erxeVStXmRPVnR8Hr9dLQ0EBNTQ12u72ty5/JZCIu\nLo6IiIh+i6V3h0qt4eIb7sXj8bD5fx/w6fb1RCoamRAtRyY9cdFx5YyWqfv4QNVvZkby6xmRJ5wP\nYHN62FbowyYLYtZ5y1iSMdvvn6MfDPbcswSIBNIsFsvQRHmA3VnZ1FUU4bC3COF73d2b4nU727IS\nPG4Xu/apmTV14oDa2RG/vfi3PPK3hwmf1DMtKoCEyQlkre06Iyhhcu8WET6fD+oEJoyZ0KvreoJE\nJgNv73dClyW0aLodH6i6LCGRZQl9WCRJhOEQSP4lcFIuEkcEBXLXHXfw9odfEGiejEQi6bOmi0+E\ndZY6xmfMpcbmRiJAg8ODImk6jc3eHo9XUVbKT9+1D2L99N3nIPoQBIEHrutdpzipVMrCOYtYOGcR\necV5vPKPV9i/bz8lBzvfX1h030IMI3rTNqb3iKJI9cEqjGIAT93+FAad/+6nUCgICwsjLCysw/uW\nbt2GfvsOhMBAKnVakgKD2G+xYDAa2bZrF+u3bWs7/5m33uLS8xey/0gOOJtRuj1kSGVod+8hq6aa\nK/74R6KSk/1me38ZM3EWG1d/TEVDPaHGFgmIjjRfdjrcyARvp+8D5Fa7sKmjCY0cmrKhXjBkazCz\n2XwvkGqxWK7x99iLz5tLbmERlrz9GGNHDagmWFN1GZK6fB78410Ddo+eIAgCMybMYMaEGThdTt5f\n9R57tmciCRYISux4Q83n81F9sAqpXc7MyTNZdM2iQSnx93q9ZO7axa0jQjAdDUrtaXb8bBfg02qw\nxETjkstBJsNjNFCYlIRBrydUpUK6dStjUlPbrtl7sL02pwCEGY2EHdX7zS0rI2H0aKwuFzVNNnwq\nFfvDw8HjQfB48Pm8uCQSFD5f2xgmpZLJSiUen4+XS4rZtno1U887r1efVRRFGhoaKCoqoqysDJfL\nhd3WRHFhHs0OBya9hskTxmPUq5F3sPbqCRJBIECnIkCnAn7W13J7vdQ22tm+bQvfrl2LWqMlKiYO\ntUaLSqUiLCyMmJiYDjvX/lLo7tv+BvA3s9kcC0QD04DfQFvmwRRgBfCfAbTxNB2wbuv37NiyA02w\nGpldRl3uz/W9vdFMGIzzY6fHYq1sZNWXq7hw3kX9DvBIpVLCw8MJD2+/GG5sbKSgoIAjR47gcrkQ\nRZHAwEDCw8O7nNh9Ph9VVVVUVVXh8/mQSqWEhYUxfvx4goKChlRUUyaTMfvCXzP7wl9zaNcWvlr1\nHlp3DdNiJagV7RepV86IJCFE0yak/odz4jrMoKptcrOlWIIyIIrzbr6F8KjOOykNIYM990wHEoEm\ns7nd7t4/LRbL9X66R7eolQqEY8pa40aO5/CerV1eEzdyfNt/iz5vvwR0+0NwUDChhjCcDidKdc92\nhHJ3dDxnHH/OhAvO6LEddbm1LJq30O+7/JZdu6C+DqGPguxt3W1kMgKVSm5ISe20u013xHh8fPHG\nG1x40019un4gGYZV2f3hpN2omzoxHdEn8o8PPycwJQOJtO+LG7dPZEdB5x1ke8KomQtJzpjX7jVr\nsYW0xAguXnh2v0rl46PieeFPL/DnV/9MwaQ8jOEd63TqAgdeS648s4Ils5YwZ+qcAb/XsQiCwO+f\nfYZXbv0959fXE1Jfjw+oDAxkY0kJK1efKFm0+7AFHA4Wy+Uojgbfd1mtzLv6qmEVoAJQKJXc+shf\n+PsTtzNNqOxUt3O8MpeuXLb8Ghc5Yjw3PvD0yaBJNehrMLPZPBuYQ0sJ80f+Gvd47rrpN2zctpP3\nP/kCaXA8uuCON1P7SrOtEXtRFhPGpHDdXQ8Mq00dpULJby5uif19vOYj1u1cT/iE9oFnr8dL+fZy\nfn3RlUw7Y9qg2ieVSnnjnXf48IWXaCwrRet0MlmraetstzM+DnNgINEjRqA6up4bfVyp74Vnntnu\n+IIeHgfKZARqNMSG/BzM8fl8mKKj2VhYyMyiYpReL3ONRvZarZTLpKDVcue99zFmes9/Trm5uezb\nt6+t8ZXJZEIqgQN7fsSkUzE1LQGTYWCfF3KplNAAPaFTWpoD1TU0svfgERodLiZPm4XVamXLli04\nnU4EQeCMM87ok+bxyUx3XstzgBa4F9ABfwdau9j8G7gU+AZ4fKAMPE3HLJp7AYd2Z5Nfko+12opX\n9OLzeRGlUJVbjdqkRm1QIZUN3sTs9fjwuNx4nT5Elw8BCVJBis8iMtE8mYXPLRzQDCSDwUBaWhpp\naS2lhR6Ph/z8fLKzs3G5XERHR7fLyLLZbOTk5AAQFxfH/PnzUQ3RAr8npJ4xndQzplNamMOX/3oV\neXMFc2Lblx5NS9Ay7eafs+pEz88ip3aXyPeFcowRZq5+4E60huEluH8cgzr3WCyW24Db/DFWf5g4\nbgzRMdG4jHFoA0YQCch0gRzc/L8Ozx8543xSp7fsHLkcdux5P3H54t7tJPmT8846j3+te4eQFP+2\nX+8N7lovc6fP6/7EXvDtv/7FJ599xs2hPzuSHaW7d3scEoI9JoYbZs5iVXV1u3v0ZrxUnZZ/fPMt\n5UVFXPvII8g6aPk8dJxSUaqTeqNu2uRxBAUZef61/0OXMBGFeugSvlQ6Iypads19Xg91h39k2Xlz\nWHj2md1c2TMEQeDss87m3xv+hSFk6HaeRadv0ANU0JL98L+33iYYcMpk5EZF4tZoqGls5IN/dlw2\nbrFYKCsrI+DCCwnT6oiurCS0uZn1n68icdw4jEH+L5nuD6LPh0SmQNpFFKq7PUXR50Ol0bdtSA5z\nhmINNoGWdI9SP47ZIbOmTGD6pHH8c+Xn7Ni9BVXkaNT99Eu9bhcN+fuICNTyyIO3YTIO7yyUC+Ze\nyPqt6098QwSNQsvU8VMH3SYAvcnEtY89AkBFURE7Vq9m/aFsvLYmJIcOIYwIwVJbi1SpBJkMtUaD\nQafDoFKh6Ee2l8/nw+pyYbXZsDY14XO7EV0u3E1NqMsr2OBwINVqMUZHM2HeXC6ZNKlP2WVlZWV4\nPB6CgoIICQlhw9rVyPFw/uyJQ5aYEGA0cOaUcXi9Xrb8tB2pSsfUWXPbkijKyspOB6mO5ehu4SNH\n/x3Pq8DzFovlJ/+bdZqesPzu5e2ORVGkqqYKS2422fkWSo4UY3PZcHvdOD1O1Ho1Ur0UdYAajUnT\nbQCro4wpd7ObutI6XA1ufE1eZIIMhVSBXKYgNT6F+Oh4zPEppCSkoBlChxhaspCSkpJISkrC4/Gw\ndetWso6KDRcUFGC325k3bx5arf9b1Q8kETGJ3PDHl6irLEEj9fR4QnU6nVytHYFWPzhtcfvDL3Xu\nEQSBFQ8t58mXXqcsvwJj7Oi2INTxgaqRMxaSOv1cAKxleSibq1nx0HIM+qHpPAfgaHYgSHuh17Rs\nMuvf2tDtOb1BxIfL7UKp6H99f1NDA/949FFCa+uJlne9OOqZbSCXy2nUnKjx01uC5HKSyyt49qab\nufQPvychPb3fY/oF0XcqZVOd9Bt1KYnxPPvwcv749Iu4g5PRBgxtO293s4PGnB+544arGZWS6Nex\nhzLruZWhyM7xer08c/MtpHs8pKrVFIwIRjoihJSwUK7/05+6vNZqtbJy1Sqevf9+ckQfE61WjM3N\nvH7nnZx3/fWMmTG4Xdg6oqmhjtUrX6fkyH4mhjgJ1PV9MzF+hAp3xR5eeeC3JI+ZyNwl16DupivZ\nUDEUfpDFYnkewGw2/x+D0KdVKpVy7RVLuHzxuTz/2v9RlJOHKX4sEknvA4jW8nxktgruv+lq4mOj\nBsBa/1HXUMfbH75NQWk+hlF6CjML2f7fliYxGcsmEzM2BlmUhNseu43R5tFcvfjqIdtED42OZtEN\nN7QdN9TWsmfdOg7s+JHmuloMzU5C9Ho8gYHk63W4ZTJEuRylVkt4cDC6LrSWPF4v5Q0N1NfWIbhd\nCB4Pekcz2oYGmqqrKRMEBIOe6MREpi9dSvzIkX6ZY6dPn47H46GwsJC8vDzqauuIjzCRdbgApUqF\nXqtFq1GhVcn67fd1h8fnw+5w0+RwYm2y4XY2o9fIKayoprKykoSEBGbMmDFMuzkPLMM+17U7zGZz\nHJD33XffERU18JPSB59+SdZBC48/cOeA38vfNDU1cSjnIFlHssgvLsDebMPhagaNiC5Kh9bUPljj\n8/loKGvAWeFE5pOjVqgw6AyY482MNo8hIToB+bDawe+eQ4cO8ebf/86C889n/vz5Q23OKYNwEuTN\nDwQDOf+s3biNlavWYEiaiFyhotSS2Saknn72pUQkp+Pzeqk7spNp40dyzWWL/Xr/3tLc3Mw9Ty0n\nOCO4Vxmcmav3krk6s8P30s9NJ/3csb2yw1bThK7WwAO3PtCr646nLC+P/3vkUeYoFOj9lAFaERSI\nIyUFW1kZo/Py/TKmx+djs93OyAULmHP5ZX4Zsz+8+ODNjMuYyVkXXDFo9xyK+cdsNk8DXEMZLO/N\n/OPxeHjk2b9QJxjQh/S9W11/cFgbcJfs4/H7bico0P+ZvD9m7uDdTe8S0lGP8EGibEcZL93/8qAu\nKDweD09eex3TpVLC1S0B8GqjkbLgIP63cyfFpaWUl5e36PUdg8lkIiIiAp1Gw2WjRhNdWooUcHi9\nbLbbmLrsUjIWnj9on+N4aitLWfn3p5E4apgY5iHE6F9h4eJaJ7srZciNEfzq9w+h6aN+2Kno/5jN\n5n8CYleaVAPh//yUeYDX/7USo3kyMnnPf9/1uXuYNCqBa69Y4hc7BgKb3cZn33zKngOZOHBgTDKg\nMWo69IGO9X2slVaa8m3o5TqmTpzKgjPPRSEfWF3c3lCQnc2ad95BWVrGpGM2/e1yOTsiI5iVltZp\nYGlvXj7BJcVE1Na1BSQqmpvZIQiceeGFTDznbOQDrAEMUF1exHt/eZxgST1pkRocMiMNmLD6VPgE\nOUhkiBI5arUavV6HSadC0ctKJafbQ0OTk8amJpodDgSfG3xupKIHvcSBkQZUnjr2FDupE01cfeeT\nGAODux33VJx/WunyKWo2m/O6ub5tv9RisQzrFhmnAZ1Ox8T0SUxM/7nLjCiK5BXn8dW6rzi8/TDK\nKAX6cD1VWdWo3SqmjpvK3KXzMBmHdWlYj0lNTUWpUjFjGOwOnqZzTs89MG/WFMaOSubBp18mIHU6\nEeb0n7v4HaXuyE/cetXFpI8euNbfPSGvOI/nX38eY5qh1yXGrY7Y8U7auPPSGbugdwEqAG2Qjrqm\nGv703J+475b70PZBQ8rj8fDPp57mXLUahZ9KQrYbjahCQ0kLCqLQ62Wjzcasyqp+jyuTSJit0/HN\nmjWkTppERJJ/s1N6i4CPkrzBaQs9lFgslh8GauyBEC2WyWQ8cf/tPP+3/yOn5AiGyCR/Dd0j7PVV\nSGvzePHxB1AqB2bRsX3PdurzGtoFqXI35LbLCh/o45rSWjIPZjIhzf+NGzpDJpNx/xuv8+lf/sqe\nrCzGii1dQIIbGmhyufk/l4sxY8bgcrnIzs5uE1+vra3FYrFw16jRxJWWYvd42NbcjGTECC69/z7C\nY4dOWNxus/HKo3dwRbr0qP6m/0vzogKVRAVCg72YZx+4mT+99O9hkY3XyhD7QUOSDzsxfRSR9/yO\nh597ncCRPSt1ayg5zLyMsVy86OwBtq5vlFWW8eb7b1LZWIEmRoPxDAMmoaWiobNNutbX0s8diz5E\njz5Ej8/rY0PeBr7Z8g3xEfHccMWN6LX6Qf0sHRGbksKNTz3Fy3feCU4XdoWC/IhwPBoN6RERXWY+\njYyOIk8mZV9gICF19YTW1LDb6+GuN94YlOBUK8Fh0fzhiTfIPbiHr//7NlJHJZMiyknV/2yDKEKT\nTUVtUwD5PhNuQYFPokBvNBEebEQpbx9SaXa5KatqwGZtAJ8LpegkSFpHEg1ohWYECXB0uqlscPJj\nuQyJNoQFV11PTHL7Zmi/VLrb6nmni/dEYB4tgsMNfrPoNIOKIAgkRCdw61W3Iooiz/ztGY5sOcI1\nS64ZdLG+wUNAre5/uc1pBpTTcw8QEhzE/FnTWHeoHOOI9sKiXreLyGDjkAaoXG4Xr/7zFX7c9hMj\nL0ptawHf24WdXqNj9nVnsv3DHSBA3KjYdgGq3o5Xm19H2Dgl96xYztxpc1lyztJefS6vx4PK7faL\nhp4HyI2OpkkqYWJiIoIgEBsaSlFtLXv1epKKitG4XN2O0x3RiBRZsoc0SFV4OAs9jdSU2LA11g93\n3btuGexF4mCIFt918zW8+e//svNwFqa4wXGEbdUl6F01PPbIPQOaYVRVU41MObQ6Q1KFhJyinEEN\nUkGLsPild92Jx+Ph01dfZX/mXqYpFEwNCaGoycoHe/ei1+sZN24cNpuN3bt3A3BZQgITg4PZ3tSE\nLSiIyx5+iBH9ELH3FxqtllFp6WzJ30NGtIhePTDfm1qbm61FIlOmzRxWAaqj/CL9oPDQEEKD9DR7\nPUh70PBB7qwftgGqRmsjDz77IFHTIgnXtG/2VLi3sNMscmgJVAVEmogZ25L5KpFKCIwJhBioqa/h\n7sfu5vUVrw+o/T2lvKAAX00ttsBADiYmMjY+DnkP5nq5TIY5OrqlO2ltLdlFxYzKzuaTV1/l0rsG\nvyNjwshx3PzQqzTUVvP1h2+w6ZCFRL2dtAgFgiCgF5rRU0astAxoCVzV1BvIr43EJTNgTohGFMGS\nW4TK10CcpJhASVNbMOpYvD4fmSVuCuxaIhPSuPL+G056n8nfdKdJ9UhHr5vN5mTgeWAq8CbwoN8t\nO82gIwgCt193O9f+/rencIAKTjFh31OS03PPz5RWVCFTdBBUFQQcDseJrw8SP+z8gffsayLpAAAg\nAElEQVQ+/w/6VD3aUE1bgKqvxIyNaXPGOusU2hs0Rg2aaRq25G7hhyd+4O4blxM24sTW7R2hVKlQ\nh4VRXllFmLr3OhAi0KDVUBwSiqjVEBcZSfJxugwzRo7E7fWSazDgtFoJqW8gpLq6T/kCDo+Hg4LA\n4nPO6cPV/qHw8H4+/PvTLBkpweH28Lcnbufae54hIHjoSq/8wGAvEgdFtPj6Ky/B+MU3rP1hJ6bE\n8QO6OG8sySVS5+OB5XcMuF6TVqMhKKm92Pfx2poDeSyKIqZI05BmN8hkMi654w4aampY+fwLOEpL\nODeqRWx3Y3MzTqcTjUaDRCLhktg44kNC+VYisOCmGxk9dWhEmjtj2U0PUFqYy9qP/0FDYRGxGjtj\nwhXIZf37vja7fewtdVParCU4ysyyO68jMCS8+wsHmSH2gwSG0Fk2GozYXE6kPQhOyoazAL4AEkGC\n1+094a3tH+7o9vLtH+5o84uOxevyIpUOn6Dql2++xXSlEpXLhd5m41B+PgGBgYQZjV3+fkRRpN7h\noLyqCo/NRlxNDQa1mtUHDgyi9SdiDAxm2U0PIIoiO777nI++/Yz0IDvmkPZ+nCBAsLSRYGkjDp+c\nH7PdiKLIFMVelLITf+etHCh3cqBex5nnX8kFM845GTqNDgm9WlWYzWYT8DBwC7AVmGCxWDoPA5/m\npEOpUCJ0FPI9zWmGkF/q3LP6+00czCslIPnEXXmpTE4jWv72zw+4+TeDq0WUW5jLu1/+m4hpLanc\nujPbC88O5sKwu+PAhEDckR6efOUJXnj4ReSynunoXffE4/zz0ccoLypmnLb7JhAeoDowgGqjCZ9K\nicFoJCUoqEsHTS6VknJ0F7GysZGDNTX4HA5MNjshVVWovJ07Oa2UOZr5SS7llqefHpJuVV6vl1X/\nfJHKIztZOkqCTCpBLpOwKNHJOyvuYNyMBcy+8MpBt8sfDPYicTBFi5ctOpuo0BD+sfJTTL3Uf+kJ\nPp+P+tw9TElL4reXD45OzPIb7+HND94kc0cmI9JHIFcOni6UvcFO3f56Lph3AQtmLRi0+3aGITCQ\nS5bfzcGsLL5du5awpESW+ny8+8knjBk7lusuuwyrw4F+3DjmzZhBePjwC9IARMQkcNUdTyCKInu3\nfc+3363C21TNpDA3YabefWcLqpvZU61EZQxn5kWXsGTs5JNqcTjIfpDIEAapFEo5Xpu7R+cO51+h\nQWfgxYdf5O/v/o2CQwUow5WYIk1I+hBg8rq91BXU4qn2MiY5jWse9Vs1eL/JOHs+a994k7P0elIK\nCxGBWr2ew8FBeJRKTAEBRAYFtW2INNjtFFdU4LM7CLA1kVxZhfyoZl6Bw45mxNA2+GhFEAQy5l3E\n5LkX8s2Hb/K/3etYYJZ0KKaulrhRiXbUghul0LHv5vH6+DLbx5jpC7n9gitPqvlnKOjRE9xsNkuB\nm2npMGEFfmWxWPyeim42m+cCLwApQA3wisViWeHv+5ymG07xv5lT/OOdUgzW3DPcsNsdrPjLW1Q0\neTsMULViiDKzrzSfO/74FPf8/nrCQwfnwW5rtiFRSU+aB6xUJsGHD7EXbedkMhnXPf4Ymz7+mC//\n9xWTBIHQY7rreIF6vZ6qwADcSiUSlYqggEBG6nW9zkwRBIFQo5FQoxFRFGlobqagpgaX3Y7gdBJo\nbWJETQ3yY+y3u9384HQSlJrC3cuXD0nnl/071vP1R/9kcmgzE1PaB/80ShlLR8O+rC94eccmll57\nJ1EJqYNuoz8ZxEXioGQxTJs8jriYCJ566W8IQYlog3qWadgdzbZG7Pl7+M1lS5k2afC6TgqCwA2X\n30BBSQHvfPQOVY1V+LQ+TPEmlBr/BuEAbPU2rPlW5B4FkSER3Hfv/UOSReV2uyktLaWwsJCGhgZ8\nPh8+nw+1Wk1AQABqjQaTVktaUhKzzziDPfn5JIeE8vWPO4iKi+Pw4cPs3LkTaOmQqFKpiIyMJDY2\ndth0PxYEgfSpc0mfOheHrYmv3vsrWw/uZ1a0iyBd12XZZfUufihTkjx2Njf+4fpB1brxB0PhB/lT\nD68vzJ0xhf3//gK1ruvyJ0djHdEh3YtLDyV6rZ7lN96D2+1mzaY1/PDjFprcTYw9J62to19nTL5k\nMvXF9TSXNmPSmFgy62KmT5g+7EpT02bNQq3X89kbbzKiqYl0jYYgq5UgqxWAGoOBzJARJMTGUlRR\nga6ujtTSMmTH+DRFdjv7gJSMDG68/roh+iQdIwgC51x6A5bUsax69xUuGiV26P9GSSqRSzoOrnp9\nPj49KLL4uvuITx0m3ZiHOd16tWaz+Vxa2jDHAn8GnrVYLE5/G3LU+fsMuBFYCUwB1pjN5kMWi+Vz\nf9/vNJ1zciw7T3OqM1hzz3Bj1Tfr+fKb9ahj0jAFG7s93xAWh9sVxkPPv87EMWZuuPKSAQ8epZnT\nODNtFpu2bUIRocAUbRqWASuvx0tNdg0yu5zrL7+hTx1xZi5dSsbChXz08svsysklOiYaUatFUKow\nmYzEGwwo/BggEgQBk1qN6Wi3JJ/PR43NxpHaWjyOZiROJ1VFRXj1Oq545OEh0Y/xeDz855WHkdcf\nYWmqDKmk8+y0tAglKR4bq998hLDUKSy86rZh+V3piiFYJA5aBkNEWAgvP/lHXnnz3xw6sgtjQnqf\n2r+30lhyBL1o5fFH70WvG5oAR2xkLA/d9hAABw4fYNXaVVTUVOBVewhIDECh7nuQwl5vpyG3AZWo\nIi4qnqXXLCV8iErFsrOzycrKQiqVYjQaCQoKIjIysu3vy+Vy8dG775KekEDcMfPEuLg4AM6ZNJl1\n//sfk2fOZOzYnzUAnU4ntbW15OXl4XK5MJlMnHnmmcNmYazW6lh6/b00222895fH0FUXMCVWdsK8\n4vX52JDrRRE+hlufuA/ZSdaNGn65ftDolCQMUhduVzNyRecl9/biA9z86PJBtKzvyOVyFs1ZxKI5\ni7DZbbz/xfvUZtdxeO/hDs9PmpSE3q1ncngGF1110bDvpp40fjx3/+01Du3YwdfvvY+8ro7JKhVq\nmYygxkYsR47wH4uFIwcOcFlIKPEhIYiiyCGbnVyFnFHTpnLbVVehUPp/Q8FfmNOn4GhqZNPX/2BW\nwom/jwh5bafXrsvxcv4VfzgdoOoF3XX3Ww2cA2wCbgCKgVCz2XzCuRaLpbCftswE8i0Wy3tHj7eY\nzeY1R+9/Okh1mtP8ghjkuWfY8P6nX7F+VzYBI6f3aiEvV6gISp1CZnEBK159i/v+cP0AWtnCJect\nY8k5S/n828/Ytns7dq8NTbQGQ6hhSIMQXo+X+sJ63NVujGoTVy24mglj+i5iXFRUxN69e1EnJ2NO\nTcWybx9Gj4fJKbEdpnz7G4lEwgi9nhF6PUeKisiqriZ11kwkMhk//PgjcXFxpKWlDeoC8q0/3814\nXRkRCT1b7CtkEhakSDhQ+gOfvOlk6Q33DrCF/uOXsEiUSqXccdNv2L3vIK+/8wHKqDGoDQG9GsPt\nctKYs5P5s6awbBiJGI9KHsWo5FEAZOdk89Hqj6isq0AwCQTGByKVtwTkCjML27IaMpZNbqcD43Q4\nqTtch9wpJy4qnttvuIMRQcOnHKUzyoqLiTAFtAtQHYtWo2buxIls3rmT+OTkgTJxwFBptPz2nhX8\n+P0qPl/zAeebhTa9KofLyxcWgXMvu5VRE0/Obs6/VD+olbtu+S0Pv/AGgSkZHb5vLc9n7ozJaHtQ\njj/c0Gq0XHfpdS3/fncdm9Zuavd+YnoCt914G2fPHD5zaU9JnTyZ1MmTKc3P55O//AVTdQ2HK8r5\nIDeXOXPmUFBezoryci6IiSEwOoYZFy5i6eLFJ83mVfr0s8ncsZHKBgshxp4F1EpqXagjx5JySus9\n+5/utn9bVVhn0jJJdoZI//vDbgbahAvMZrMcGAX8q5/jnuY0pzn5GMy5Z9iwM3M/prjxfX5YG8Ji\nKT7SvRinv5BKpSxZsJQlC5bSZGvis28+Ze/uvdhFB7pYLfrgwSl98Xl91BfX4axwY1QZOW/a+czO\nmN2vErji4mJ27NiBXq/HbDa3jZWWlkZRfj5frF3LzLFjCTINfDcWl9vNup07iYqP5/JrftP2/RBF\nkYqKCj799FPi4+M544wzBtyWnKxdBLhKiDD1XlB+VLiSVQd347DbUGuGRxlRVwzhInFIRIvHp43k\n5Scf5MkX/06VtRpjZM8CF/a6KnyVFh5f/jvChnHpTUpiCg/e+iCiKLJ9z3be+fgdIqaFs++b/e26\nbK1/awPp56aTfu5YnHYnDbsbuO2a20mKTxpC608kJSWFlJSUduV+BQUFeL1efD4fer2ecmsjTpcL\nZSclbht27yFtSgZ79+4F2pf7paenD5tyv66YNOcCwuPMrHztcZaM9OHxiXyWLeO6e/9MYMjQdyrs\nB79IP6iV8NARxIQGUmNvQqnRnXhCYxmXXnjt4BvmZ1576TWu/N2VZO/OBgEyLslA59Iyf8b8oTat\nX0TExXHrc89x14038mXuic1wVhUWcsvChcxcMjiahf7kslv+xN8euo4l3Rc7ALC1QsXvnzx5NueG\nC9158GfRs+qvfjtTFoulDqgDMJvNKbSIkTqAv/Z3bH8iQq90TU4z/Dj92zspGLS5ZzixdOEC3v7g\nUwJTMpD0oPXy8dTl7GH2pHEDYFn36LQ6fr24RSC7obGBj9Z8xN5te5EECwQmBA5Ipo/H5aH6UA0q\nt4o50+Zxzm/P8Ys2k9vtZsuWLUycOLFDu6Pj4lh29dV8/fnnhNTUMDoxsd/37Iza+gY27tvLuYsX\nExgY2O49QRAICwsjLCyM7OxscnNzSUhI6GQk/3Bo12YSg/q+4xmt85CfvY+R46f40aoBY6gWiUMm\nWqxUKnjsvj/w7sdfsmnXHgISu55PrGX5hCidPPjkg0Oii9YXBEFgyvgpaDQa7nv4PnJ255xwTmvQ\nKsQ0gqeWP41BbxhsM3uMXC4nNjaW2NjYttdEUaS8vJysbdvItFgYlZyM7pgyGlEUycrLx+VykpiU\nRGxs7LAvJ+qKqIRUll53L9/9+2mavRJ+e89TJ3uACn6hftCxXHnJBTz59/dRHjcPORrrMCfGnTTZ\nN51RUV3Bk688SeKcBMYuTWt7vaG0gftX3M9Dtz2ERn3yZYq1snbtWr5cv77t2HdUHL2V1157jdGj\nRzNv3rxBtqx/KJRKwuNHUtOU2a0mXmldM0mjZ540z8fhRHc/sYeByywWS2XrC0fFzbdaLBb70eNI\nYB1w4tZiLzGbzSrgceA64GXgKYvF4urvuP5EOB2gOs1pBoNBnXuGC1MnjsWo1/LCG/8icOS0XmnD\n1B3ZyYXzpnHenJkDaGHPMBqMXLvsWkRR5NvN3/L5N58ROC4Ilc5/WgONpQ14in3c+qtbSUlI8du4\n0CKaLggCLpcLlarjjCG5XM7Ciy9m4zffsPfwYcYOQLlMVW0d27MPseyqq7pcQHq9XpqamjANQlaX\nQqXG7e37c9DjY1hrThzHkCwSh1q0GODXSxdi1Ov438afMCV0HKiyVhQQaxS459bfDbJ1/qEsv6zD\nAFUrmaszmXjWxGEdoOoMQRBQKhTYGhoIMhpRHtf5UxAElHIZBrmcnF27SUoaXllifSEudSxWwYgx\nKIDg0KihNscf/CL9oGOJjY5ELp5YXe2oKmDxxVcMgUX+Y83GNXzx3SpGTBiBXNX++W6MMGLXOVj+\n1N1cd/n1jB81fois7B+PPPJIu+ONGzd2eM7JFqQCGDVxJgVrdhHUQZLfsRQ3Ckw4f87gGHWK0d3W\n9mzgeA/9S+DY2V8O9PvpZjabZcBqIB0YY7FYHhluASo46ome3IH705zmZGA2gzT3DDdGmhMINGgR\nj9tx6g656OLsWVMHyKq+IQgCZ888mz/ft4K6PXV4PR235e0tTVVWdI16nvvjc34PUEGL3eeffz7Z\n2dnk5OTg9XZu96yzz6ZJFCmuqOz0nL7gdLnYkrWfpb/6VZcBqsrKSnbt2kVGRsYJmVYDwdhpZ5NV\n3fekoSKbiuik0X60aEB5GDhgsVjWt/6jJWNqxzHHh2nJ/D7lWHT2bNISI2iqKjnhPVezHXVzNffc\nevKW2zz66KPdnrNv275BsMS/1NbW8tlnn/Htt9+iCwhgnNmMvINd/OToaNJGjSIrN5ePP/6YrKys\nIbDWv8iVakKjBzabdBCZzS/UDzqWpNgo7A0/C1L7fF5UopOYqJM3U27tlrV8+cMXhE8NPyFA1YrG\noCZsahivr3yd7LzsQbZwYDg+k+pkRqc30eztvkKg2QMaXQ/rAk/TjuHRqqOFJUAksMhisZzoEQ0T\nBOB0MtVpTnMaf1NVXcsrb7/Lrfc/QZM8EKmsd6UXkqAEbn3waZ586XXyC4uHVVmyXqvn5itvoupA\ntV/Gsx5u4t5b7hvQVH+tVstFF11EUlIS+/bt49ChQzidHetlzzn3XHYetuByd9x6uC+s27mT85cs\n6TBF3Ov1UlhYyK5du5BKpSxdupTo6Gi/3bsrQiNjCU2ZwvaC3n1Wr8/HGouHjHmLUZw8LeBn8wtf\nJN7ym8sR606U22oqzOK+P9wwBBb5j54smETEYTWX9oTi4pb5P23sWAxBQazftQu3x3PCeYfy8v6f\nvfsOk6o8+zj+3d5gl6U3YWk3RRBQQAVUVFCsKBbEHjVqYk1MjCZ2Y48mee2aGFti711EjAKxIUUQ\neBBEikgRqbvssuX945yFYdi+s3tmZn+f69prd06b+9mZueec5zyF/33zDYcdfRStWrXCORdAtJHV\nvfcAOnWL/I0LCc6FZ01g28p5Oz6HG5fO5fQTjw04qvp556N3aD+ofbXnMIlJiXQc1oGnXnqqkSKL\nrPCWVHXdJholJidTUlr9d0NpGSQmxd2QcY0imjpIjgB6AFvCBiV93DnX8FNV1VhCXNUEN0UJwLof\nV9O6fbugQ5EmqqysjEVLvufDaZ+xZOlytm4rYjvJpLXuSnOrW2uozJbtyGzZjnX5W7j1kedJKikg\nKz2Fju3aMWr4UPbqZ4H2id/T+pOTkEP+xnwyc+o+xsL679YzYp/hpKY0TkVHXl4eeXl5rFmzhhkz\nZrBt2zY6d+5M69Y7B4hOSkri8GOO4b/vvsuYfSueiag2FixdStdevWiRu+sMa1u3bmXp0qUUFxfT\nr18/DjzwwEDG5DjmrMuZ9vazvPzRGxySV0KLzKorVFdtKOKTlWkcdcqF9Nkn+O6oUnMJCQl069KJ\nlVs3k5a1cyKEZmmJtG5VuxkAo82I0SN484U3q9xmz4P6MWf+HAb2i51pw/faay86d+7MjBkzyGnb\nlpw2bXjn888Z2K0bXTt0YNu2QiZ9+QVde/ak96BBfPfdd/Tt25eDDjoo6NDr7YiTzwk6BImwjPR0\nTjj6cF7570zSc9vTvnkqw/YeUP2OUaxtq7Zs+OlnmrWupq8YsGnFJgZWMzZgtBo9ejSXXHIJ9957\nb4XrL7nkkpjs6gfQuVtvVhVUfzN5/fY0WreL3VZ/QYqaSirn3GXAZUHHUZ3iklJKVEkVs76dPZv0\n1FQ+fuF5xl9ySdDhSBOxdt16Jk/9lHkLHFsKisgv3E5ZajPSczuQ0WkgzSNY0ZCW2Yy07nvteLx8\n62YefnkKZf9+hYzUJLLSU+nRLY9DR+5LXpdOEXvemrjm4mu46varKLNSsqrryF+B9Yt/om1ieyYe\ne1oDRFe1tm3bcsQRR7B9+3ZmzpzJrFmzSE9PJy8vj/T0dFq1aUNXMz6fN49he9a9O9vKNWtYvn49\nxx3mTT1dUlLCihUr+Omnn2jRogUHHXQQzZs3zqyJVRlx5CkMGjmWZx+4hdRVyxmZl0Ry0q6Ns7dt\nL+HDJWXk7DGAS2+5kpTYaUElIfYe0I9FH329o5KqpLiInOa1//xGm4wWGQw8YuAuM/uFGnjEQLrv\n153PZn8WU5VUAC1btmTMGG92sEWLFlFaVsaML7+ka4cOfDpvLoP235/SsjLGjh1Lku7yS5Q7fNRw\n3p70EfkrN3DTNVF/qVitK867gqvvuJrNJZtp3q7y7/P13/1Mq5JWnOFPShOLLr74Yj5/fxKfLVyw\ny/JLL72Uiy6KzfEMwZsJtede+7Nw5VR6t6v43GbOD0UM2HdsI0cWPyJRSRVb7aDrKX9bYcTGVZHG\nVbx9Oy8+8AAtunVj8Yyv+GnVKlp16BB0WFJ3UZ17pn8+k/emfMLCb+aQ3ak3yTntyGrVh3Vff0Kn\nQQfv2G7lrCkN+njdoi93ebxs5odsTu/AZ488R0pJAdlZGew/dDDHHDaqwVvlpKenc+ef7uSef9zD\nyhUraNu/LYlJ1fc6L8wvZN3snzh42MGcdORJDRpjdVJSUhg2bBgAa9eu5auvvmLLli20bduWvYYM\n4cvp05k2ezbD99qr1v/PJStXsuiHVYw7ZQLr169n+fLlJCYm0q9fPw466KCom8koK7sF5151F4vm\nfM6LT97HYXlFtGzm3Vlctr6Iz9Y259SLrqF957xgA5V66dK5A2VFn+14XJS/hbZtWlexR6yo2VdI\nWXR/1VTqhx9+YM6cOaxds4YVS5YwoLs3C+mQPn2YPH06rTt14s0336RHjx707dtXlVWxKTbfnHUw\nqH8fPpsxk+w4qCBPTk7m9qtu56//vIfl36ygbb82u6wvLSll9azVDO0zjLPGnxVQlJGzb8+ejEpL\n45EF80lISGDfLl05//zY7i4OcORpF/PobUvJWL+SLi13bVX17doi1qb24Ozxgc+DErNqUkn1FzPb\n4v+dgDf+wm1mttFfFvwt3Ua0avUaEvRFHnM2rlvHw9dex77FpcxLSODgtDQevvqPTLj8MnoMis1m\ntE1Ao+YeMxsBPAT0AhYClzvnptTlWC++OYl3p80kN68/KTmradkzemZmSUhIICu3DVm53klRSUkx\nb326ALf4O37364bvKpGakspVv7qKmfNn8vhzj5O2Rwo5nSuela60tJR189eRXZrNTZfeROuW0XVh\n3KZNGw4//HBKS0tZuHAh8+bNI7NFC5plZ/PG1Kkcus8QsjIzqj1OaWkpH8+aRVbLlvQfOoQ5c+bQ\nsWNHjjjiCNJiYCa8XnsN45KbHmTeB0/TrL33sUxZW8Rll53RFC584/4isXVuCyjZOY/N9sJttG4Z\n+93lk7YnVdqKCrzZ/RKK4LyromjEiRpYvXo1n3zyCdnZ2SxfvJjizVsYO3TojsHTm2VlcezIkbjv\nv+frL7+kRYsWvP7663Tq1GlHBbxEDV2D+fpZd6Z/+nnQYURMUlISvzv/994sfx+9Tvsh7UlMSqS4\nsJjVX6zmlxPPj9lZ/cI1b9WSnps38c8DvS7F7xYWVjkhTKxISEjg3D/cyUN//g1pyatpl+21qFr5\ncxGLivfgl1fdEnCEsa26SqqPgTb+T7mpQCugfBqhBOC/kQ8tOv28cTPb47wlVTydcRcXF/PGww+z\n5MsvGZWSSlZ6GvOAjORkjkpM5P2//Y3ULl2Z8LsraJYde9NMx7FGzT1mlg28BtwAPABMAF41s16h\n0z/XVG5ONpmJ29m4wtGm765jFIW2agr6ccn2Ijav/p6kgnW0b9u4lbWD+w5m0PWD+NeL/2LGZzNo\nu3cbklN2fiXlb9jKxnmbOPW40xi+9/BGja22EhMT6du3L3379mXLli188cUXdO/fnw/nzKZH27b0\n6175bFNr1//MJ3O/ppsZua1asddee9EhBlt4pmdmsc+xF+x4HEeXuk3+IjEnJ5uE0p2VVCXbttJt\nj8btKtwQJr/1YbXbzJ++gO5dYmu2uJ9//hmAnJwcVq1cycgBA3ab3S8hIYFunTqxcNkySktKSE1N\nZcOGDUGEK5XTNViIlOQ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PaF7Txwumvb1bBRXAumWOBdPervfxAVp06ctLL1Q/SHtDS0tN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\nBcwHjg4fv6kSpwHLnXOf+PfJLgUOx+uSF3rsnsAQ4Gr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+UEpd5DcqD6bX0poSu/i8RAy5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qFAHOZC8QbgQqAmZtGH1hVceasI2Ka1Hq61dmutl2Byz1lBDV6Uyuv18uLbI6jTzqyCFdM0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\n1OuRKGwnHJ5w7Dj71GpSBYHs/AK65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V0d9XV1bPzpCw7v/wtJXRnxXnquiVQilUiB5oOq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\nyNF7c8P9jxIaLdh9zo5gL98kCIIM2Az8Ioricx23tP0M6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mSsViu7t27lVEYmQkw0BpMJ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\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XC1KF9BbI3tXxv6sBJapWROR7CH8Z3wTgplRiMBmRNSN4L5P2jB0oPx8/rHZuli2XOr8E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avwrK6xHk+HrR3wK7CEIPxb/tU0EH+MwCfAJcAsPDHof6qqrtE07Ss/NbnJqi50RwBXAY9U224g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\nGsXBDXz0yswTG5M9iXO+B7J37uJwSSl7NvzY4piDe3dSZVPy9r/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xb948xo0bh9Ho/mxX\nT6LRarn3yTn867m7saPi/h4coDpOb11/HSe7HdlE+Tu3eMCS7mXJz0sISGjafVIXqWXFryv43eW+\nIWfQFcaPvgzlqp+J9vNjpdnMxf4BCMCPgoCmjcxyXyAhIeFEhYkkSVgslhNSLMOHD2fy5Ml8/vnn\nzZ47ZcoUhg8fTmlpKSUlJdjtdnQ6HaIoctFFF/WouZA3aHGVKoqiCtdi7RrgaqAA+AOwHAjFNZHK\nBl7sfjM9z678QvwSh7R7vN4UwJHdu9oe6IMoFApkWSYuPY649KYTGdku42fwjQWTw+Fg8+bNFBYW\nEhcX12Lm0eZ16xiQEN+pHcIR6Rn89MMPjD8tui8IAsnJydTW1rJ48WL8/f0ZMWLECQ2rzmAymTh6\n9GiHg1Q11VUYDZ0XdNXrtDQ01GMytX+HxuFwUFZWRuAxwVVP4UE/NArYJ0nS3GOv14iiuAQYA/hM\nkKquwYLVCY2NFnQ633+4A+g0unZ36ekovrYImf/WW4gtNDOwCQJSfBzJ0dHsVCjot78Yv2aaFYTp\ndSxbs5bRU6b43ATGbrez+PO32btzE8MirEQlta+dslKpYGKqgl27FzNnxk9kXTiGUVfd6HOf71Q8\nPQcSRfEC4Frga+i2n8w5jnHg0AGsegtBhtafaeYIfwo3dF+5f3eydOlSAgICUCua/zqZdXriYmJY\ntGgREydOdMvmmzfR6Q3c/ez7gOBzz4aO0NvXX8dpz/NB1wOCHG1RVV2FPr7pb88vyMjeonwvWeRe\ngqOjOXC8bFqWTzzclOq2S+V8BYVCQVJSEklJLnmc2tpa8vLy2L59O2lpaUycOJ4FCxY2OWfS73/P\nwIED2bFjB9HR0VxyySXnNKg6SGur+EdwZRMMkSTp+1OOPypJ0rBj70UDPV+xrhmiI0IpXNc0zfTA\n1pUtvm6oqcLf2HEha1/AarO0+DDQmDXsLpA8bFFTbDYba9asYf78+TidTrKysghtQUfh8IEDFOyS\nSIrtnFaNn0GPWa1m64YNzb5vNBrJzMwkODiY77//nqVLl1J7LHW1o4wdO5aAgACys7PJzc2lrq7t\n+nsAnc5AYxc68VmsVnTtyBAEqKioYPv27ezYsYO0tDSGDRvW6ft2Ek/5odXA74+/EEVRDaTiEiz1\nCZb//Ct2bQC6sCTe/eQLb5vTbnQ6HSqrCmuje7vVVB2qJirUNzpwy7LMDx99TM3OXKJP+W3ZBIGD\noSFs79OHXQMHEJ+SQlRgIOkpKRwYkMq2vkkUREfRcMpkTaNU0t9q5Z+znsTqIx03K46WMPeNZ/jH\nk7cTdHQNv+8PUYHtC1CdSkq4hkn97DRu/4Y3nriN+f9+lYa6zvlPD+CxOZAoimbgQ+AWXALJ5+hm\nlColraY9norc83rcl5aW4nQ6CQsLgxa6OAeYjFRVVjJw4EB++eUXD1vYPag0OlSaHh+46NXrr+P8\n+c9/dssYX0cQBOTTfIzD5kDT87/HAFSXlqJXnrl55+zBen9Go5EhQ4YwceJExo8fz/XX38jvJowj\nIjyUqIhwrpkwjuuuv54JEyYwYcIEsrKyzgWoOkFr9T5TgKckSdp82nEZQJKkjaIo/hl4EljWPeZ5\nj4fvuZXb7rqP6kN7MUf2aXVsbdkBVNXFPPPUYx6yzn04nU5qLLX40fyPxxRqIjs3m2uv8nzdsCzL\nZGdns2fPHhITExk8eHCr44sKClizYgXjRozo0n2HpKby85YtOBwOsoYPb3aM2WwmMzOT+vp6li9f\nTkBAACNHjuxwh8f09HTS09OprKxk69at5Oe7dk5CQ0MJDQ1t9npGs5kDhws6/sGOYXc4m9RUn4rV\nauXw4cNUHNOqCAsL44orrvDmDqtH/JAkSRVABYAoiinA+0AD8HZnr+lOKquqefvdD8jP24YgCBQP\nGMzlF13AgJS+3jatXTw57Smeeu1JIoZGoNJ0vQtqXXkdHIQHn3jIDdZ1jSOFRXz28ssk1tVzntnI\nwaBgKox+ODUalDo9IUGBpBqNKE7ZCFAplSTHuLT+qhsbKY4sp7GuDqxWTA0NhJcdxXzkCK/ecy9j\npkxm8GXeEYbdsXEVqxZ9idZazvnRMoH91EDXJs6CINA/Qkv/CAeHKtfz0ezNqEzhjLnuDuKSB7jH\ncPfgyTnQ28CnkiRtEkXxxD3O0X1EhkWilw3YbXZULWQ/AtSW19InpvU5oC9SV1eHXq+nqrISQwvZ\nJlEhoewoKCBJFM+KlvdnEb16/XWc0aNH86c//Yk333yz2fenTp3K6NGjPWyV+0npk8y2kh34h5+s\nbqg5VMOky6/1olXuQ8rO5oJmqj/sdbXYrFbUmo5vePkSKpWKIUOGsGnxp6RNvgYBV1xg0KBB3jat\nx9PaaiEZWHPasSLgVCGNn4C/udkmn0CpVPLJv/7JZ/9byKoN6wjoO5jozEuajIlMu5Dy3b8xMCma\nPz0206dLF1pi07aNKANaTqhTqpQcrT+KLMse/3zLli1Dr9eTldW62K7FYmHZwoVogatHjHCLEOiF\ngwaRW1DAFx9/zOVXX01QcPMaUAaDgYyMDCoqKvj666+57rrOdV0LCAjg4osvBlyBot27dyNJEjab\nDbVaTUREBIGBgQiCQNmRwwSYOx+RN+i0VFdVYfb3x+FwUFJSQllZGU6nE71eT3JyMiNHjvSVdHmP\n+SFRFHXA87jaP78OvCBJktdn7vUNDdz4x9spyNt+4ljO+pU8+GgpH/7zbWKiWpPp9g1CgkJ48v6n\nePHtFwjMCERn6nzQs+pAJeoyLbMfe9arPjd3wwaWzp2LuqKSATExlCcnk+9nICQoiH4GQ7v9kFmn\nw3yswYMsy9RYLByqqKC+pobU2lpyPvmUFV99xaALL+Ki66/rcCC8o9jtdn75bi7b1q8iVlfDVbFq\nVM3sgrqDyAAtVwdAo+0IP3/yLOVyICOvuIZBo8b4wvPUI75HFMUbgCRcWVTgKvXz+ofvDdxx4x28\n9eWbRGS27ENrpFr+/ETP07CPi4tj06ZNHC4qok8LDWRMRj+qKirYt28faWlpHrbwHK3Qq9dfp3L/\n/fezbNVa8rb91uR4n9RMpk2b5iWr3MuEy3/HujnrmwSp5CrI7O9besCdwW6301B2FLX+zCBVklNm\n9ddfc8mNN3rBMvcj4MRAIzqFjTq5+ezVc3SM1maejUCTb5UkSaf3cdUAPrGS7S6mXDueUUOzeGHO\nOxiTzj8hpO6w26jM+5X775hMxoCe1d72VJav+ZGAuIDWBxllduXvol9fz3UUs1gsVFVVtdpNz263\ns27VzxwsKmREWhoBZvd2weifmEhSTAy/LFmCSm/g4jFXYGghXTMwMJCgoCD27NnT5Q6AGo2GAQMG\nMGCAK6ugpqaGvLw8duzYQWFhIZVlh7n60vMB2JJXxKB+J3XE2vO6X98Evv9uAf0GpKNWq0lISCAr\nK6tL+lrdiEf80DENiMW4JoEDJUk60JXruQur1ca1N/2xSYDqOEXSDm6/+z4++/B9wkI6L6TvKaIj\novnrEy8x48UZhJ4f0qmMqprSGsy1/sx67EmvBDHsdjsr//tfslevIayhgYsMBtRGI5uCg4iNjiLU\nZGrVrvXZ2bz/1VcA/N/11zP0NF09QRBcQavISGqCgsgpKGCwvz+D7Hb2Ll3GnB9/JFJM5ur/+z/8\ng9zbLRFg7ZL/sX7FAtKDG7lG1CIInvEJOrWCi5K0OBy1bPv53/y06AuuvukuxMzmM1k9hKfmQJcD\ng4G6Y1lUakAWRfFGSZL6d/Ha52iFlMQUdA59i5twjTWNJEQmoO2BZTcKhYK4uDhWLFnCgPPPb3Gc\nCqisqCAurmd3SDvLOLf+OkZdXT0Rif3xT8oie5lL5iDjihsw6HSsWL2eS0cO9bKFXcdsNOOnMuJ0\nOlEoFFjqLUSGRPrCRk2X+eGjj0h1Np8Y3MdgYMlPq86aIJW9sRYBBwrZjt3iszIGPYrWVgnrgMnA\n1lbGXAmcuXo6y4iPjeKFWY8w86W3CExxafJUFmxj+p/upG9iz36w19TVYNS1npWjCdKxNXerR4NU\nWq2WsLAwli1bhtlsPsNZKx0O9uTmMSi5L4NOKe/bum9fs9fLTEho9nhb4zVqNZeddx5VNTUsmTcP\np1ZLbELCGfYkJiZSU1NDnz7uLwswmUycd955lB3ez7YNP3FBeAMFUi42pYG6Bme7stzsDif7j5RT\nVl6B0XmUgdpCDuRUctujL6Btpz6Vl/CUH/o9Lo2HNEmSfEMICLhn2qPk525r8f1CKYepD03nPx++\n2+0ZNu7AaDDy+L2P8fK/XybyvMgOnSs7Zep21TH7mb94fPJWUlzMwvc/oHL/flJkmSv1eoRTumEN\nzt/L4epqckwm0GrRGfwICQzAX68/YeuXixfzxeLFJ855+YMPuGHsWK4fOxaAequV0qoqaqqrwWLB\nr6GRrEOH0DidIAgk+RlIAsql3Xzy8MPIgYFcev31DGyhJLkjNNbX8cmcpwl1HODaVDXgnfJepVLB\noBgt6Q4rq/73OpvX/sh1dz/hraxOj/geSZLuxJW9CYAoih8CBZIktd7X+hxuYWC/gewszcEcduYm\nV1VRFbdcc6vnjXITkWFhKOTWBahT4uPZd8An9mTOcZJz6y9cm0KzXngNfUwqgUZ/osSME+85nU7m\nfr2IpPhY4mN9Q5uyKwwbPIw1+38hMCaIyoJK/nDNH71tUpeRZZmd6zdwlV/zes2CIBDRUM/2NWtJ\n62A3dl9j367thKjqOF6pb5RrKDlYSFhUvHcN6+G0Vo/wPDBNFMWHRFE8Y4YoiuJkXO2S3+gu43wJ\nrVbTRDxTkB3oe0h3rdaQaTslUa1VUVNf4wFrmnLRRRdhNBopKyujqqoKWZaxWa1sXL8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4EHH6Sqqoql335H5uHDXDpubIuu+PU9oG578slWx9994AD3/OUvHsny+86d9B04kBkzZ57m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\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rT61Wuorf3vcM/3rsNq4UJjy0Ha95VdYb2VcbxF1/ua+f1TkEZR5SUFBwCbIsT3C1Blex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CI+bz9H8vLoM+y0NrUjOjqarjFhaLdtp2v1QXbLVDSmCLqlxKOtjSpzuNzs3XcA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\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PiIT2K/mTJIllh+xMuO4etPqO1TLqSP9jMpludvWY5zN0QB82//szdH5ByBWNzxdOm3Y1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0lDVr1hAcFETGtm2sLyrCpNMxOC6OMfGKrQuXL2+2unH9uStTU7E7HOzJyGDTz7+gNxlJGjKE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\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yGcjt1ZhrqwmL6H61w75AFMVqYKogCOOB6UA2sArw4dcIng98KIpiXd9Z2X00GjXP/edJ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\n6tBC9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aNHcfbEifj1kIBuq6tj+7pl7Nq6nrqaMnRyDQMCnVzYx6e+AFlzkc6FElloPMYL\ngkBCqJaEUJDlCnJzlrP4pVXUKY34GAMYOuZsksad1yvXYN6mrbflX6q6zZJffiMgfoS3zWjAP2YA\n6zam9IhI9em3n7AtezsRyS27O3cmcajWV0vw6GCefvNp7rr+Lgb19+xEtyscSE3l29ffYKpOh0/9\npESpUDDVYGDdRx9TlJfHpGuu8bKVzdmXuoHVPy0CSzGjwhwk9/ehpUGxMwQa1ZzXH2TZQtb+n3n3\niRUYA6OYetU/CIuK7ZZ7dBUvCOWLcCcnDcSdlHQdsAn4vBv6bkRi/7489cBd1NXZ+OLbn9ia9ic+\nEQPRNcnFNOy82ScN9xt23uxm52xWC9WHdpDQN4qbH5mDn6lzu2g9Rfq+dBT+QsPiSGv04UjFUVwu\nV2+o9NYlnn/hBebefXebbdqTe6M3sPidF5geZ8fvJAmqWxKqjhERoGGEs4zlX73LtKvv6G4Tu42e\nFMhP4AlgNPBrN/fbjGsuuwBfwxp+WrEWbeQA9N3stWivtVKVk87AflHMual7f8/F5cXINleXx4eY\noTEMmzas1bxUw6YN63A+qpZw2p047A6qa6rx9/PsAstls0MbQpjT6UBvbz2vqEmAguxsfJO6noC5\nu8jZn8GKJZ9gLStgkNnGjDgNgtCxuZCvTsWk/uB0VbFv+2IWrv4R/9AYpsy8udfMeVqgR9ZhoigO\nB17Hnd/zT2Am8F9RFDfXV133Gseq9zUVqubOnXtSLytPUVtXy8JPFqAP1zcIVL0VrdGHoNFBPD//\nOV594rUu96dSqRgwehQRYn927dpFaWkptbW1FBcXk1laCrVWAlVKcrMPcnlcXLtCBG1WK/uyshjQ\nty9ZZeU4lQr0CQmYg4OJM5nQarX079+fPn36eHw++NbChcT6y2RLGQh1FeQVVXPTGb5owhWAmq9S\n65htPu4g8FVqNbOHuyOKHDJkHK4mLjIIpwxKofHnAIvTapg93EhMEEAd/03ZT4kzm/d//S+y1o/D\nlQrmvfjqacGqnra+XX+p6jZGnY4KqwWN3uBtUwB3+I1S4fndqtTdqaTsTyFseMueGl1JHKryUREx\nLpy3P3+b1554DXUvrPp3KCOD7157nWkGA8omg5sgCEw0GtmyYiW/K5Sce9WVXrKyMbWWGj5/40mM\n1hzOi1GjUSkBz+z4CYJAXIgPcSFQXXuIHxY+REj/0cy44Z+9YTe1x4RyURSHAMOBKfUhPNmiKB7z\nqPIYPj4abrr6Mq69/EKeeWUhFfY6DCd4VUWIwxgw/oIWE6cDDBh/QbN8VDZrDbac7bz48D8xB5wa\niW8P5R9CaWz8OpIVLhwOB5pT/GV9/tSp3HDddXz8ectapzd3gztKXXUpfsGNx3l3LrzW3fSPtzlO\nbKCGZVn7WmntfbzhSS6K4jjgCmAJ7cwX2lUuPv9szjtrLG++/wWZUiZ+fYeh7GK1Y1mWqcjZg0lR\nx7x7byY8tP05+NrLxNETUavVfP7t52gjfQiICeh0yN+xTbim86Ck6cMYOrVrYSQOm4PSrFKEMoEn\n737S4wIVgMtma1Ok4iTl4v1cMkezsxF7gUi1Z/t6Vnz3BWZKGR+tQh+qpKsbdUqFgoHhPgwMl6my\nZvLr2w9h0QQx/crbiE3wfCGlDtJT67DJwCpJktbVH38liuIbQALgVZEKYPqFM1j553ay9qW756xD\nkgmKivOaPRtTN6KJVBPRr/HG/4leTb3pWKPVYPOxU1hcSEhQ18bjsrIyvvjiC+Li4oiMjCQ8PJyt\nW7dy3tTjocxrVq+mYM8e1qWmcdMVV7AqNZXU1NSGz4cPH97o+ILzzyc7N4+ho0dzyTnnsH37dpKT\nj3uwbty4EY1GQ1paGhEREYwZ45mIqM0rlrBz40qShqu5KFaDICj5yqpsNd/vMewuBdlyDEddZlSK\nQuLj+rIhS0GEshiZPW1eq1QKDIrQMSgCZLmSDzZUsuCxmznnoitJmtDrakL1OG2JVH+p6jZ33/o3\nHn/hdexB8ZTnZLTYprUqWYfTVndre2tlGbV56Txy9+0nM7vLLP/jNwISWne137x4y0n72Lx4S6u7\niQqlAnwhryCPvtHdnxi1q3w9fwHnnSBQbSos5P297kHj1sQBjAkJIdlgYNlvvzHmwgsw+Hrf42TB\nU3cxOcZCYHj3eE21F6NWxfQEyCzewicvP8QND/yrR+/fAj0plI8FDgBviqI4C/eC9APcng0ex8dH\nw7wH53L7Q881EqkAEs90v6iaClUDxl9I4pnTaErV4X08efftp4xABRAfHcfKnSsanVPKylNeoDrG\nQ489Rp3Fyn+//abReW/uBncG2dE8BdPc82N58tu2c4fPPT+20bEgCC321YvoUU9yURRNwMfANUCP\n/kHotFoenHMz2YfyeOHNd/GNS0at7VwlUlmWKd27iSsvPo9JE8Z2s6WNOSPpDEYPHs2K9Sv4Y/Na\nqmxV6KJ0mMJMHd5tHzZtKAGR/u75kABjZo4hZmjnquE67U7Kc8uxFzkwGwO49rzrGDV4VM9t+nTx\nPg5ZRq3tWCqB7sbpdPLJyw/jaznIRbFqVErPzIWOeVfV2cv44/Nn2RQ5lCtvf9Qj9+okPbIOkyTp\n38C/6/tUApcBvrg9yb3Ojt37iBg0lsTzrgbAbqsldWcGsy/xfI63lhjUfxBf/7QYR6QDlU/v9qQC\nsFZaUVqV+Jm6PicsLy8H3BV+tVpti+Oa3mDgwUcfZe+uXVTk5JAQG9tIlDqR2dOmgUrFI0+2XoVT\noVDg6+tLRUUFVVVVXX6G1tixdT1zz9Tiqzv+Oz3RC+rYsdWlplg2UySb6TNEy3allvCwEJKMWoYP\nEgFIGtSfksooYpURbHLU4kMdwUIJlwxTAY4W+xcEgVvO9KO8xsbWjWtPi1S0LVKdsuXfW8Lfz8Rr\nzz7CW58sYn1aAQq9H2pt8woonsRaWUZtwT5iI0O4+5mH0es6NxHsCBeceyHPvPA0g2cNbpi4NY1j\nPhlOW+N44hOvr7PUQSXE9kJ36YJDhzBZLKjrhafFWZl8k5ODw+FApVLxr507uLJfP2b1i2MwAn98\n/Q3TbrzBqzZbaqoxKSwEGr23OI8L0pCxv+2KKj1ETwrlobg9qb4EgnHvIK4BioE3uqH/k/LLqnUo\nfVtOZp545nRMwZHsWOFeDw+bMpuI/i1X9NOaI1n846/88x9/95Sp3U5sdF8c1Y3HGbWy93lmdoUn\nn38OW0kxFYKAn9HIsy+8wJQpnau84y1kZx1Npw1nigFcPyGST9cdbvGa6ydEcqbYfKNEdtS10LrX\n0NOe5AuBzyVJShFFEVrNKuQ5+vaJ4on77uDpBZ9j7mRqhKriAs5KTvK4QHUMlUrFtLOnMe3saVit\nVn5a/RNpu9Korq1C9gX/GD98DO0TOGKGxnQ6tK+mtIaq3CpUdjX+Bj8uSL6Qs5LP8nj+qRbp4ryy\nQKngrFGjusmYzvHNey+SqMkhJrRnNup81ArOidew8/BOVn37EZMuv7FH7tsOenQdVu/N+QegAD6t\n79urVFXXUFFVTW1ZAaYI97qjpqQAMzKHcg/TJzqyx20KCQzhsTmP89bnCymyFWEeaMZH17Obyu3B\nUmGhYl8F4f7hPPR/D+Oj6bqNffv25c477yQzM5OsrCxqa2vRarXk5eUREhKCRqMhOTkZq9XKzu3b\nGRkfz8z4eOTaWr5atgygQbC6cvp0Lj//fH5ev54sSSIuIQGA0aNHU1paytGjR7HZbBgMBpxOJ5Mn\nT8Zg8Fw01OzbH+Xrd15AyM1jZDho9QbK8aMMf2pcPqBQ41Ko0Wh1+Pv50seoxaeVcE9BEAjy0xPk\nFwtArd1BRXUtGeWV2G21CE47gmzHoKglgAr8hXKqq6rZdkSBKqAPs/7xsMee81Si1TeoJEn7RVEc\nTOPqNsG4q9uk455Ueb26TUdQqVTMvflabrtuFh8tWkLG3gPYNCb8IuNRqloXBVrzmGpPe5fTQUV+\nNkpLMTE+1dzy2D2YfHuuIt5gcTAD4waSvyGf4BHB+OgbD1LtSRwal9SyW21FfgX2HDvP/9/zvSE0\nrBmbf/qZ/vWTxK9ycsjQ6wkNDeXw4cPo9Xri4+P5SZIAmNm3H6szdnnTXAB8tDqqZAN19jp81N7J\nxXO00obev/srtXSCnpygOYBCSZJerj/eLYriImAKPSBSrd2UwtJVGzAntJ6kN0Ic1iy0ryUM5lD2\n5+zh08U/cP2sS7rTTI/x27rf0IU0XlzVuuoorSj9S1T4O8YVt9zKTz98z9+vvfaUE6gArE41suxq\nNt5fN969UGgqVP19QiTXjm++iKizu3AIvVqE7DGBXBTF2UAccH39KYEeCvdryvotqSj1nQ9J8zGY\n2C217VXnKXQ6HTOnz2Tm9JnIsszeA3v5Ze0vFOwroFauRR+pxRTq1y1zFafDSfnhCuyFNvQqA/36\n9OOiv11ERFjLeT97kpgEkfzUHUToOy5WybKMRW8gIDjYA5Z1DI3Q4zotaoULhbL3eMb09DpMkqQN\noihqcOfFW4Lbq3NBd/R9MiwWKxnSATL2ZZJ9KBdrbR11die1DhdqczTBA8c1tPUP74ulppLn3lmE\nBhs+ahVajYqw0BAGJ8QxdIBIYKBn5w0RoRE8e/9zHD5ymI8Wf0hB+RF84wwYg7wfjVF+uBxrjpXY\niL48eM9DmHy7t4KrQqGgf//+9O/fHwC73c6hQ4fIzMyk8OhRig4fBrudCYMHYzK617uzpk2jT2Qk\n7y9e7PYYmjmT5PqqfBeOH0/Knj2kbNpEYFgYwWFhREVFMX78eEwmz1afBaisrCQ3N5eCggJCB5+F\n1WLh95xsasut+Op9SOwXycCQwC69O7RqFdoAI6EBx9f/siyTe6SIzVn5VNea0Ol1RA2PRavTs3nr\nNsLDw4mOjsa3F0T4eIt2/cR7a/l36HoJ1G07M/h26W+UVFpQmWMwBoV3yySmuqwQW2E2/noN0yef\nxVnjRntVyCmtKOXV91+lWlVFUGJQI1taS5wOtFjhz15rp2hHEUliEjfOvKnXVkhZ+MADJAkCm9Vq\n1u/ZQ2ZmJrW1x1MMKZVK+vXrh16v56zAQOzAPW+/7T2D6zmad5AvXnuESxKFHheqSqpsrDpsYO7T\n73QocZ+nSqD2VBl4URSvAN4Bgo9V8xNF8S0gSJKkWW1cF0sXSzBvSU3n/S9/wJwwplvHiPJDuzkr\nKZ6rLu3dzq479u7g3f++Q8S4iEbPX1tdS1V6FU/f90y3T7J6GlmW2b59O3l5efy5ahXJ48djNJmY\nNGkSanWvFmsakb5xJauXfMjU/kKLJd//lMr4Nc+fspIirhqmbtGDqqzGzvIsFdfcPY/wmO7JK9Ld\n448oiv1xC+SxQJsCuSR1TZURRfED4FqOe0+p6/99QJKkASe5NpZuKAHvcDh4/b3PyDxSiV/s4E73\nA1BdmIO+rpiH5t7Wa0KOKyor+Gn1T6TvSafaUYUp3oQhoOM78uX55dTm1uGn92PcqHFMGjepW7wT\nuhNbXR1v3n4703QtRwqsjonmnJzcFj9Lr64mdtZMxl5wgSdNPCl2m42FT89lXFAZ4f4941V+oMiG\nZIvklodf7nC4aE+UgPfkOkwUxaVAhiRJD51w7kugTJKkVqsedHX8efWdTyg4WoTV5sDmBEHrh9o3\nAJ2vP8oO5riVZZna6grqqspwWcpQyXa0GjW+Bh23XDeLqPDQDtvXEWosNXzy7SfsydyN30A/9H49\nG6kDUHm0gppMC2eMGMfsC2b3qCfnhl9+Yd2aNQg+WoaI/YkO6Xjuqzq7nQ3p6VSVlhEVGsrlt96C\nwUMiVVlZGX/++WdD3tOAgAACAgLw8Wk8npeVlpCeto2SokIUuBD7RtAnKhxFJ7/yLpeLrNx8Mg8V\n4EJJSFg4g4eNwM+/8VyptraWsrIyysrKsNvtaDSaVkW7nhh/vEWbD9ZadRvAk9VtOkR3TdLq6mx8\n8/NyNqXsoE5pwC9abNO7qiVcTicVhzNR15UyZIDItZdfiMHQ8wNVW6zetJpvfvka/8H+jQbRloSq\nlhKHlh0sQygW+Oct9xEe0rXyz56iqqqKbdu2sXHNGobGxfHcggWU1MdRt4QgCMT368e40f/P3nkG\nRlWlf/iZ3pNMMuk9IUMgjRY6CAgqoIiKKLrqWlZde1s7uGJbXbGuZXd17QWQphQF6TVISegMENJI\nSJ20ySTT7v9DIJDekwH+z7d7594zZ5KZc8/5nff9vckMGjGCAQMGEBDQtNF8T5F/KoOv35nN5DZU\n0uoqskrs7Dbruf/Fd5Er2udJ0d2DZHcL5UajUQOcAD4G/sG5dL87TCbT8hbui6AT44/NZueh517B\nq9/obqlaUnRkBy89djehQe75W12yegm/p6whYHBAk8bHNVU1FO8p4aE7HqJfnxbX626Jw+EgNTWV\nzMxMAgMD8fX15YuPPmLM5ZcTHB7OsWPH8PT0ZPjw4Wi1PRdh2xmK8nP49sNXiNWUEhfY+Bm5uTqe\nQEkJfWS59c4LgsCOTAelsiBue+xlVJqu2x3sjvGnpwTyJt73C+CkyWSa24ZrI+jk/Gf5mo0sX70e\nWYARjb5rTM7PVhdNjI3m/ttn9k7aWzOUV5Tz5aIvOZ55DE20Gq1v699D80kzrkIXyUnJ3DB5BvJO\nmst3NwvffQ/9/gMEqho/x5sTqQRBYJXTyd/+/WlPdLFVHA7RdS2gAAAgAElEQVQHP/xrLjKziZER\n0m7b5HW6XKw/4cIzchDX3f1Uh96nO+c/PbEOMxqN9wIvAlcCx4AxwE/AX0wm0+IW7ougE+PPa+99\nSmZ+GXLvEDTefl36P662lFOVn4ncUc4/Zv8NbQ+txSqrKvnwqw85VXoKvwRfJLLu38S3VdsoSism\nLrI/9866r1vH24qKCvLz88nPz8dsNiMIApnHj+O0Wkky9kWvUTcqUtUeBEHAYrNxMi+PYzk5JA4e\njFQqRalU4ufnR0BAAD4+Pp0Ojli0aBEREREcP368nkn7zp07mz222Wz8vvo3aixliEUuhg+IJf1U\nCQNjz6WJ7z2S1eRxSWkZO9NMWB0i4uPjiI1LRCaTtfh+5x8XFBSQn5/PtGnTGn2WS1KkalDdZgtN\nV7e5Huiy6jYdoatEqvNJO3SUL39YRLXUC4+QmDYNnBX5WYjKcpg5fSpjhw/ukn50F9XV1bz07ksQ\nLODhf06VzdqX1aJxaP7+AgZFDOLPM/7cwz1uGydOnGD//v1IpVLCw8M5un8/MquVNz79FHN5eYv3\neul0zJoxg6umTycrK4vKykoiIiJISkrqtUixqspyPv/nc8Rpiojx675JsSAIbM904PTpx00PvNih\nz9uNkVQ9JpQbjcZEase8oUA+8A+TydRiaF1nx591m3fw4/o0vNtQqSbXlEramgUAJE26qU2pfzVV\nFQSJzPztQbfx2ACgxlbDa/96jUp5OT4xhhavdTqcFKQVMKjPIO6aeXcP9bDjCIJAeno6hw8fxuFw\nEBgYiN+ZXcW0XbtwlJSQXVzM9FmzAKisrCQzMxOHw0FYWBgJCQkXRHTV2sVfkLl7NZNizk2IK1wq\n9jr7g8vJMPkBFKJanzGn08WyIwLjrruTpJGTurwv3bxI7NFI8p4SqaqqrLz01odUirV4BLdtntNe\nLCX52PNNPPnA3cREdszvqbtwOp3MmTcbZ5ATXQtCVcnxEoz6vtx3y3092LvOYaup4aP7/8oV6sYL\n8+ZEqhMWCx5TpjBu5o090cU2s2vDL+xY+T1T+4qRdrCCY3NU2538chSu/tPDGAeM6HA73Tj/6ZF1\n2Jkx7nXgTsAbOAm8bzKZPm7lvgg6uf4yl5bx8+oNbNnxB16xo9odQdUUpcd2ERPmz7VXTqBPVHin\n2+sIR08e5d/ffgre4B3t3S3jq8vpovBIEVqHhkfvegx/Q9dGixUXF3P48GFKS0txOp0IgoBMJsPD\nwwNPT0+0Wi0ikYhfFi4kNjCIIL+uSxN2OJ38vGkz1996Cxqtlurq6jrjdIvFgkgkQiwWo1AoiIiI\nIDo6ul1rF5vNxpYtWzh06BDjxo2r87pqq2hUVWVh09rfsNlsXDHyXDBHUyKVy16NxS5izIQr2L9/\nf5tFMYAtW7agVqsxGAyMHDmyybnhpSpSnQBeaKG6DWeq2zxlMpl6rRZod4hUZ1m+eiM/b0hp1US0\nLOsIg2MCuPuWG9zSm6kpnE4nz/3jWZRxyjYZ/hWfKGZ4xAhmTmk286nXcDgcrFixAq1WS3h4eN1A\n5XQ6+fHLLzGoNbz71ZcttjF+5EgeePRRdJ7n0hNOnz5NdnY2kyZNQq9vvkJidyIIAr98/T45h1KY\nGN10ik1nKCyvYUO2nLGTZzJkQmOFvq10UySD2wvlnR1/Dh49xvvf/IJ3VMulzo9sXdlEdb+pdZX/\nmqOyKJdhUXpum3FNu/vWXRSVFPHyey/jEadF7dX2lJuSjBI8q7144aEX3DLFuKioiN27d1NVVYVe\nryc4OLjejmaVxcKSH35g2pgxbN+3j+jExDqjUKj9rRcWFpKXl4dYLCYuLo6oqCi3fqb88NErhDuP\nYdOEUuD0Qqb2IDo8EIdD4ERmDiJ7JQHiYopzM/EfeQvDL+8ej7RuGn8u6kjy2+++D+8BV6LU1G5U\nnUpdX89Ps6uOnU4HZUe28p95r7Tz03U/B0wH+PfP/yYgofnFXe6OXOY9+w7KdkYX9zYfPv44E2z2\nRuebE6nWWCw8+N//uKVAfvJIKr9//Q+uMnZt35YddnLz429iCOhYJcezdKNI5dbrsK5af1ksVTw5\n5x9oYoYik3f+d1ZyNIW7b5rGsEEJnW6rs6xYv4JV61fiGefRrvlOa5SfLsOaXsNtM24jOSG5y9qF\n2pS077//noqKCnQ6HVJp/UjG80UUqF1rrfn5F8otlQQaDI2yAgZERDT5PqkZGU2e10mkHMzMYOLU\nqfj6nxubd+5sXI3e6XQSEhJCTk4OgwcPpu95c6q2UFNTw6JFixgxov0itcPhYNEPX3LtxJEtXrd0\nzTZuvPXODs1bt2/fzo033tjiuHwxi1QtxQT2dHUbt+PqKy5j47YdCILQ4kJB4Sjnnlsf6sGedR6J\nRMLf7n+al//9MoGDW09tExWLmPmA+wlUAEeOHMHb27vRQ1IikXD19dez9pdfuGny5LrKEg0ZmzyU\na6dPrydQAQQEBKDX69m+fTtTpvSOr49IJGLaHY9RdDqbHz5+gwBxEclhsk6F0wJU211sOulE4h3N\nA3Nno2zGv6KX6dEy8L1BXN8Y9AoX1RWlKHVNGxY3JVABdeeaE6rsthqchceZ9cScrutwJymvKGfO\nO3PwTTYgV7YvOtA7wpuKggpeemcOrzz1qtuINyaTiYMHD6JUKomIiEDZRPl2m83G0h/nM2lIMmKR\niJGJiazYuhWdlxd+ZyZhIpEIPz8//Pz8cDqdZGdnk5qaSlBQEMnJyb2aMuVyuSgvL6ewsJDCwkLM\nZjPWKgum01Uo+47G1+BNf50SyZn/iUwC8cYIHC4XJeVWLEIov2/exekKF3K5Ah8fH3x9ffH19a3b\njXUnGgjkP9C0QL7ZaDT2aiR5Z7A7HCjU3W/IKpFIEUTSVudRPc2+I/v49JtP8B/RcvSBR4wHL/7z\nBf52/9NdHqnQnWg9vbCePo2qjQsjsUrplgIVQGTsAOxKP8DcZW1abS40hrBOC1TdzCWxDlvx+ybE\nvtFdIlABeMUks2j5r24hUk0dP5WJIyfyxsdvUGYpwzO48z59JaYSInQRPDDnwW7ZsDu7SbZt2zbK\nyspQKBSoVCokEkmTY7hEIuGq66azYf16Tp48SYivbyNvpzYhCGTn5xMREcHNd97Z4vPC5XJRU1OD\n1WpFrVbj6elJVFTbq9YLgoDJZGL//v1ER7df3xUEgZ8X/8jIgf1bvXZoopGVy37i6utmtvsZGBER\nwc8//8yAAQM61M8LnZZmvT1Z/t0tOXL8JGWWGrxb+VJZXRK27tzLqKEDe6hnXYOvjy9KUesPherK\nasJC3CtU/3z69OnDsmXL0Ol0eDYQmjz1ekQyGTMnT+bgsWMcOH683uvxMTHE9jUSm9g4kqW6uppD\nhw412jXoDQwBoTw892P2bV/L4mXfEe9loV9A+1MAnS4XKVkOigQD0//yKMGRxm7obZdxSUzQ5j79\nCE/PfYsqVwxqT596r+Wa0poUqM5yeMsKPHyDG6X+2axVWNL/YO6zj7mNH4zNbmPOvDkYBvm0W6A6\ni85PR6mtlPc+f4/H73m8i3vYPk6ePMnu3bsxGAwkJiY26ylms9n46dtvGZuYgOZMxS2RSMRVI0aw\ncvlyJkyejH9Q/apgEomE8PBwwsPDKSoqYtmyZYSEhJCcnNwt3mVQu6NYUFBAQUEBJSUl1NTU4HK5\nEAQBQRBQqVRoNBoUCgV5WemUmYuYNmEoSkXz/0upWIyflwY/rygigg1s+mM3oRFR+Pn5kZOTw5Ej\nR7DZbIjFYkQiESKRCJVKhcFgwM/PD4PB0FsL54teIL/jz3exdNNevEJjgcZVjLvquKqsmOiwrilI\n0xUUlxbzyTcfk19VQOCowCa98M5H66PFprEx9+OXiY+M566b7nY7s/SmUKqU2F2uNotUIjeMTj0f\naRf3TxAEZHL3FOXO46Jeh7377rs45B4cyziFPnZEi9GZuaZU9qz8BolMUWd30Nz1YrGYCkHJ7Xff\nxxOPP0ZSXGyvjj8KhYKXHnuJW+6aRXFgcaPXoy5rWlxJ35je5HkvtRcPP/xIl/axIQMHDmTgwIHY\n7XaysrLIzs6msrISl8tFWloaKpUKT09P9Ho98jNFlsaNH49t1CgWfP0114wa1epvtmGE1ca9exk5\nZgxhkZF155xOJ5WVlZjN5roNQLFYjEwmIyAggKioqA5VAFy9ejUKhYJBgwZ16Lux4fdfiY8Oxten\n9Wq4Qf4Gqqpr2L55PSPHTmjX+5y1i8jIyCAzM5MJE9p3/4VOS6uXniz/7lY4nU7++91P7Dl4DC/j\nOYGiOU8Yr+hBfLNsDdv+2MMj99yGooVJu7uhkLU+2bKYLYyIbjmcsTdRKpXccMMNbN26lRMnThAQ\nEEBAQABisRin00mVxcKCVasaCVQAB44dQ6FUMmnaNHRnBrri4mJycnJQKBRMmjSpR0qgtpXEEZeT\nMHwC6xZ/wWtLfuahkco6Y/X5eyu5aeA58+WGx59vK0PrpWfyjfcRlzy2x/veAS7qCdpZFAo5815+\nltn/eJ+KGitav3MRgWlrWl//pq2ZX0+kqi43Y887xFsvPY2nh3uUrnU4HMx+ezbqvioUms4t8LxC\nvMg5nsXn8z/j7pvu6aIeth2Hw8Hq1auRSqUMHDiwRdFIEAR+nr+AsQkJ6BsI6FKJhKkjR/LLipVM\nn3UzmmaM0w0GAwaDgYKCAhYtWsTEiRO7JP24sLCQPXv21IlRUqkUrVaLTqcjLCysbuJ5FnNJEds3\nb6C6qpLkhBh8k9q+awmg9/Tg2okjyDx1mvW/LkPv48fwMePQaOp/7pqaGsrLyzl06BAWiwVBEBCL\nxahUqp40mL/oBfKrxo/it3Wbuv19bPnHeGLuM93+Pq1RXlHOh199SF5pLvpYPQG6tkdFyZVygoYF\nkVmYyZOvP0G8MYF7Zt7jNhsATSESiRAEofULLxCcNZYubU+tkFCZ33WRWd3ERbUOy8sv5MdlK8nN\nK6Cqxk5BTgbBQ67Eu1/LvlHnR5Pba6pJWfIf+o2eik6javYeXWg/ygpy+WTh74i+XYxaIcPLQ8Pk\nyy9jSFJcj4tWOXnZCF2ks9a4bNTYanpELJfJZERHR9eL4hEEAbPZzKlTp8jMzKS6uhqXy4VCocDf\n35/LJk5k795UkuNajzI6S43NRo3LhV9gIJmZmZSWliISiZBKpfj4+NCnTx8CAgK6bNMqKiqKAwcO\nUFlZicFgqCe2tQVzcSEj4oe0+fo+4cGs3Li7XX202WyUlJRQVFSE3W5n0KCWrYcuRpp9wppMpmNG\nozGe+tVtfKmtbrMf+Ihuqm7TWwiCwOKVa/l941akhii8+w6ve61hys3ZQTJ21BREIhFe0YPIKSvm\nkdlvMGxgArffOM2tJzBnkUvlCC4Bkbj5AdtpdREa6NYh0UilUi677DKcTidHjx7lwIEDCILAsYMH\nUYhEfNNMqh/A7v37+fiDD5h8zTW4BIHg4GCmTJnSrgGrJxGJRFx+w10cOlXB6uwDTAqtwKuVCoBH\n822YRd68+PpnF8T38gwX1QStJaRSKa+/8ASvv/dv9m39GfmZymdOe02r9zrtNZxKXQ+Ao6aaAF8f\n3pn7nNuI5acLT/PGv15HY1Sj8W7ekyErLYuUhbWeA8NmDiUssfnoTe8+Phw6cZBXP3iVZx54BlkX\nmK22lbVr11JcXMzo0aPrzjVnfrl7+3ZiAgPJNJvriVSpGRkMiIhAIpFwxdCh/LxkCbNuu63V9vR6\nPWvWrGHmzM6nXm/duhWoTWv28vJqMjxfEASOHTnIvtQ96FQyhiUY66LBOkp4cADhwQGUlJaxbuUS\nnIgZMmw0IWG1CxWFQlGXCghgtVoxm83k5OSwe/duLrvssk69fxu56AVyh8OBzW6nu5c5AiJKy8vx\nM/i0fnF3vL8gsGDFAjbt2oQ+zovAmI5XOtX56tD56sjIT+exuY9x63W3MmJgxw23uxOpTI6rPSKV\nm0S6NYfg7IalhrOxZ5c7cTGtwx599iVKKm14G4egCg1GB+j61M9SaCoasyW7g36jp7Z4f8jgy+sd\nl9tr+PeC1cx7930+fOcf+Pr03Jj0+fzP6XuNsV1R5M1FWFUUVvLFwi+4/9b7u6p77UIkEuHt7Y23\ntzcJCedSKs1mM0eOHOF0YSH55WVYq6tRNWF/0BRb0tKI7N+f9PR04uPjCQkJ6Vbv0ZiYGGJiYrBY\nLJw4cYLMzMx60eMajabOIL6puZFSpaaisgqdtm1WKSWlZXWBEA1pyRQ+KCiIAQMGoFJ1bt51odLi\natVkMtmBJUajcSk9VN3GaDRKgM3AbyaT6eXuep+GbNy2iwXLViB4BOHRd2Q9lb2tnjAqTx9UnqPY\nk53Lzude5crxo5k++XK3CXNvCh9vb/It+Sh1zQ8kzionQf5Bzb7uTkgkEvr370///v356vXX8bdW\nszYjg7CwMLKyshpdbzAYCA4OxlxWxpF163j6/ffd+v9VU1PDyZMnKSkpYdYtt1JttfDDv+dx3YhI\npl9Ra5pylvOPd5oyePHVFykpKeHQoUNEREQQEhLi1oLVxTRBawsikYgXHr+f2+66F6ddiUQmI6Lf\nQI6lbm/xvoh+tWnGgkvAZSnhzQ//gdxN0hh+2/wby35fht9gP2SK5r9raav2kbYqre54w2cbSZqc\nRNLk5g3lvaN9sBRX8sTcJ3ji3ieIDIls9tquRK1WU1PTungIkJOZycRBg0jLzGy+PZUSweVqU3tW\nq7XL0v2mT59ORUUFmZmZ5OTkYLVa61L7RCIRRQWnOZ2bTUSQL5PHDOzyCaO3lyeTRg/GZrfzx/4/\n2LxpPeERUWg9asPnz07UNBoNgYGBDB48uEm/r27iohbIbTY7z732NhK/7k/31kYkMfuN9/j7048Q\n6N911Z/ayoKVC1i6YilqvYq8vdZ6r7U3zebs9Tp/DzS+Wr5a9iUGvYGYiJiu7XQX4BsSQtnevejb\n6A0j6kGhvyOIFDq+35XNLUPORQe3Fj3e0nFZlR2VR+8Ip+2hN9Zh3cFrs5/hs+8WkZF9jJJTLsQ6\nX3S+wUhkzYs2HbU7OB+Xy0VlcT6OsjzkIgdBnlruvG9OjwpUAFZbNR7Krols13irOX00r0va6ioE\nQcBisVBaWoparWby9On8vGABEwcPRtdC9LMgCGxJSyMkOprgkBCysrIoKSkhKCioRwrkaDQaEhMT\nSTzP8sXpdFJSUkJeXh45OTl1kWJno8W8vLwYednlrFy6kGsnjmi1n3aHgw079nP1DTeTl5dHaWlp\nnc3B2Shxf39/+vTpg7e3d7dZOlyItLhCba66jdFo7M7qNnOAZODXLm63SWpqbLz01oeUOuV4xIxo\n9OXoyCCpNQQh+ASyek86G7fu4O9PP4KXp/ukjJ2Pr7cvmcVZLYpUQo0Lby/vHuxV11Bw/ARTVCo+\nTktDrNMxePBgjh8/TllZGXK5nLi4OIqKikhLS0MvlzNWpaYwLw+/IPcQ5Ox2O7m5uWRmZlJWVobL\n5arbwfD19aWiogLT4QP4h4Rj17e82FBozZzKycbTS4+vry8ZGRmkpdWKAhKJBIPBQEREBH5+fm41\nQF4sE7T28MYrLzP34+/xjk4iGJBqvZsdg86v8Fean82VUya4jUA177N5ZFdmETSiZT+ahgLVufO1\n51oSqjQ+WpRDVbz9xT+ZMmYKU8dd3fmOt8KYMWPQarXs3r2b8PBwDAZDI9+6s8cSiQSH09nIe6Hh\nsaLBb65he/Hx8aSlpaHVapk2reNVOBui0+mIj48nPj6+7tyxfTt5++23uLK/B32CDFRQztLfNhEb\nEYggloJExtH0U1w2fABeWiVSiZilqzcz/YoxdW00dTxlwkjMFdVUVFSQevgEseF+iFwORIKDglO5\njI6SkZOxnpM1Sm6+/1kCQur/jXqSi10gf/61d3Doo1F7dP9zXSZXojMOZ86bH/DuK8+h1fRskY7t\nf2xD6dn14qZYLMY3zpf5v8znxYdf7PL2O0tM8hB+/eUXItpwbZXDgdbfr7u71Cmuv/tJZj/9GA6n\nC2krPmKtYbU5WZUu4/7Zf+ui3nUfvbQO63K0GjWP3VsbLVxdXcOWnXvYuG0nJWWV2OU6PIP6NBKs\nOmJ3ALXCVEVBNkJZHjq1nNEJcVxx2VQMPr23jklOHML249vw7tN5caxgfwF3TLmjC3rVOaxWKwcP\nHiQvLw+Hw4GHhwdRUVF1WSg3/OlPLP/pJ6L8/IiNbLyJWF5Zyca9e0kePbqu0vFZa4OVK1cCoNVq\n6d+/P4GBHY+AbS8SiaReNPdZBEGgoqKC7OxscnNziTT2Z8nvKQyOjyEkwBtZA7HK5nCSlVvI3kMn\nMPZLoLCwkKCgIBISEtDp3MOKw91pVqTqjeo2RqNxJDADWAz0SDjLc6/Nw+ljxEvXdMWFjg6SIpEI\nz6AobNYAnn3lbT7558tuHaHTIiIRLpfLLcu+t4RPaAg52ae4N7Yfb+5Lo6SkhISEBDIyMjAajaSm\npuJwOAC4LSaGSrWq1wQql8vF6dOn66KkXGciKzw9PfHx8SEkJKTe96eyooI1K5chEexcNuzcd++r\nBb+w9NcNAFw3eTy331i7aJ8wYiBrVizB1z+IkZddTnj4OQ8AQRAoKyury88WiURIJJI6U0Jv7957\nsF8sE7T2kHXqNCL5udDesyJUQ6Gq3+iriR01ue5YplCTe7qgZzrZCtv2bCOrPAu/uJYjJ7L2ZTUp\nUJ0lbVUa+mCvFlP/JDIJQUODWLlhFZcNHYdW3f2eRQMHDiQxMZGdO3eyf/9+4uPjmxzfo/r2JTM3\njz5hzadLuwQBSQs+C9nZ2VRUVDBp0iQ0mq4rYd0QW00NP370CpQcJ9rTQXKAHcgD8jhcUckwaX5t\nf11wssKBkOfiuNMDu0iBQxCz63BmXXU/B1L2HsnC4XShVUpwOF1kHD2AQWwmBjNHSosZHpkNZ9aa\nJ6sqiZRpiQyFans5Sz96jsC+Q5l2x2O99ty8WAXy4uISSmtcGHpAoDqLVKZAYohixe+buOnaq3rs\nfQFunj6Lr5d/RdCQoDZ/l5qLsDofh91BYWohr/3t9c52sVsIDA+nvI3+LZlVVuJGuq/vKIB/cARz\n5r7BDx+/xtQ+DnQqab0oKaBNx8UVNtZkKbnrb2+g8+y8t193crFWGVUqFUwcO4KJY2tTZVMPHGHB\nspWY7XI8w2I71XZVWQm2nP1MnjSeqRPvcJtsgRmTbyT93ycpzCrAK6x1s+3mKDxcyLC+wxiSkNyF\nvWs7TqeTtLQ0srKyEIvFBAYGEhfXtMeXSqXixttuY/f27fy2YwcThgxBdub/cfjkSbKKi7n2llvq\npbKJRCL8/f3xP1P1uKqqigMHDrBjxw40Gg0jRozoNYFHJBLh4eFBXFwccXFxABScyuR/H75BUVAQ\nGi8/YiKDa20STp6iqrSAvPx8Hnx8Nj6+PSeyXUy09Ovt0eo2RqPRA/gCuBV4sLPttQWrtZpyqwND\nMwJVVyBXqalQenEsPRNjdES3vU9HyTyVhca/5Z1NsUpMTl4O4SEtmxu6G3fMmcN/XnyRUARujori\nx/R0Dhw4wKhRo0hLS6sTqKaHhVNuMPDwW2/1aP9cLhf79+8nIyMDl8uFTqfD19eXwMDmI09KigvZ\nsWUD9uoqRg6Kq5cPPfvNj9h/5ETd8eKV6ziWnskrzzyIUiHn6gnDOV1QzNL53+DjF8Dw0ZehVmtq\nPdW8vPDyOvfgdDqdlJWVsXPnTqxWK1KplPj4eCKb2A3pLi7WCVpLCILA90uW4xFVfwISO2oKHr7B\ndaJ50hU3ERRTXxjXePmw7/BWqqqsqDvpG9RZKizlOKsdrZadT1mws9W2UhbsbFGkArBX23HanVRU\nVvSISAW1u23JycksXrwYp9PZ5GTY18+PQ+nnUodS0tL478KFAPxl5kyGJSZSaalq1qsAID8/n6uu\nuqrbBCpBEFi/9Gv2bf+dMcE1+PVRAPUXt+cv9sQimJUoBXKIFEO1S4oosA9lNgdqRe190WHBAFRa\nq/GljFsj85GJz6Un3NzCYlIpkzC1L6QX7uDdZ+9iwjWzGDD6ii7+1K1zsQrkPj7e+HsoKTl1Ao+g\nqB4RAS3mAkSlGVw57oZuf6+GDB8wHIfDzvfLfkCfqEft0fmxsfx0GTUnbTz71+fw8XLPlDGRSIRE\nqwGHs9Vrc8Uirh41qgd61TlCovtx/5wP+c8bTzEuqBI/j/b5LmaW2Nlb7sujr85D3sY0yF7moq4y\n6nA42L4rlXWbU6iwWJH51C9mkDTpJlKW/KfFNpIm3VTvWKHRUS2Wsn3XXux2B1eOH4VO232bO+3h\n6fueZu57c6ksqkRraP88xZxhJj4wnj9Nv631i7sBq9XKL7/8QlhYGElJzadYNmTwiBGER0ezfNky\nrhk9mjSTCZmXF9ffckur96rVavr06QPUClZr1qwhNjaW/v3bbsrenfgFh/Pk3A/44u3n8KpK56Bt\nIIIgoCzcg1MWxJNzP3AbofRCpKW/XE9Xt/kI+MZkMu0yGo0A3V6WRKVS4qGSUV1VgVLdtDLbkUHy\nfBy2GiQ1ZcREuafAU2wuwiOi5VREdYCK9TvW8ecZd/ZQr7oGiUTC/a+/zgdPPslkqZQDZjMHzGbk\ncjnl5eUAxOv1eIaH8dg776DqmapRdSxbtgx/f38SEhJaXSgcObiPA2l78dQoas2LVfXTFxoKVGfZ\nf+QEs9/8iFeeqdV9A/x8uOZyH4rNpaxdvhinSMzQkWMJCq4f6SGRSOqMEaFWtEpPTycjI4Px48c3\nep9u4qKeoDVFyu592OV6tJLGQ3OQMalF7wUAeYCRrxYu46933NxdXWwTV465Co1ayw/LfkDuL0Mf\noUci7fpITFu1jZJjZqRVUuY+MRc/n55JWXE4HKSkpHD69Gmio6ObnIQIgsCmtWsZEVu7M7xg1Srm\nn1fA4a3PPuOmyZO54coryc/NxVJZ2WSFv4SEBNatWxL1LYoAACAASURBVIdCoWDkyJFdWm10z6ZV\nbFixgATvKm7oL4cWbLSdAlhcKkrxpETwokaQIYjlyJRqQkMNxDdTtbG4rIq9RUG4bFZEThtqSQ16\nyvASlaER1TTr1xzlKyfcx8budZ+z8ddFXD3rXqLjBnfBp26di10gf+35x1myci1rNm7FqfbFMzgK\nsbjrf58VRbk4ijKI7xvFfQ+/2GupyKOHjGFg/0G8/q/XMHua0Ud0LIJGEAQKDhQS7R3FQ7Mfdvvo\ncoVGi91sRtZKCr+gUKC8QIx5dZ56Hpn7CR+9/DATpWV11Y1bI7/MzgFrMA/O+adbWRq0wkVXZdTh\ncPD087MpKCzC5nCBTIlMpUMskRDcp75IFWRMot/oqS3aHTScE0mkMqw1NnJOF3MyYwXzFy5AJgad\nRsObr7/S69Yrzz30HE+9/mSHRCpXgcA99/2lG3rVNrKzs/H19a2LcmoPBj8/rpg2jd9XrUKp0zG1\nAwVQ1Go1CQkJHDx40G1EKgCZXM69z8/j2w/+Tk1pCWJsKAxx3Hlv71e1vdBpSaTqseo2RqPxJiAa\nOJtkK6KH0v1eeeYRnn3lbawBsaiaCH/vyCB5FluVBcvJXbz8zKNumeonCALVjmo8aHnQ1hl0HD9w\nvId61bWIRCKiYmP5Yf58Dphryw2fX5b5gNmMMi+vxwUqgMDAQE6dOoUgCPj7+zc54c3NyWLzht+J\nDPZlyrghiJv4Hn29cHmTAtVZ9h85wdcLl9el/gH46L2YNGYwNruDP/Zs549tm5lw5VR0Ho2jCm02\nG6dOnaKkpIQBAwZ08NN2iItugtYaJaVliDtRVliqUFFeUdSFPeo4owePZtSgUWxM2cjqzaspry5H\nHabCw9+jbjwcNnMoGz7b2GI7w2bW92dyOpyYM804i534ewXw8A0PY4zsfgPos+Tl5bFp0yaio6Ob\nLQmcn5fHxjVriAsLw8vDo5FAdZaz564eN56lP/7IgCFDiGvwG1MoFCQmJmK1WtmwYQP+/v4MGzas\n05/jtx8/peTQWmbEKhCJaiMSql1SygQdZXhS6VLhQIogliKIpIgkMlQaFVqNmhCNAqW8bbuDPp5q\nfDxrIz4FQcBqc1BuqeG4xUK11QouR503lUzkwENUhU5UhhflyMVihobJGeio5Lev3mTk9L+QNHJS\npz97G7joBfLrplzO9MkTWLs5hRWr12NBgWdYPySdNNAWBIHyU+mIqwoYOiiRWY895xY+eRq1htee\nfp33Pn+X3NO5eAa0P4K+8GAh00dNZ8KICd3Qw65HrVFjKy5uVaQSyy6snX6ZXM5fnnmTL19/iGlt\nzA7bmqfggblvXEgCFVwkVUadTicpe/exZsM28gqLMZdYkGl9UbRhXRQ7agrleSc5deJQvfPBffrX\nWSE0hUgsQq7WwpnI6nJbDU+//iF6rYLhgwdwxbiRaNQ9648HIJPK0DYTFNESNquNAL+AbuhR2+nT\npw8nT57k0KFDlJWVMWLEucqmzVUkbnicU1DAbVdf3ebrz5KSkkJwcDB5eXlMmOCe4++tD7/EP59/\nELEY7nzqjd7uzkVBS0+mnqxuMwkYBFjORFHJAMFoNN5sMpn6dUH7zaLRqJk39zlm/+M9LHYbGp/G\ng0BbPWHOx1peiiv/EG+//KzbhJo2hcPlaPUakUiEw9V6yLg7ciI1ldXLl7M6J6fZa3YdP8682XN4\n8pW5PdgzGD58OA6HA5PJxJEjR3A6nXWpdwaDgVNZGRzZ9wdTxyUjbWHHdsmq9a2+15JV6+uJVGeR\ny6SMGhKPxVrN0gXfcf2s23G5BIqLiykrK0MkEqFQKOjXr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l3odDpGxIVyT5IVkehUXTs1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\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pVIroC3ygJYrdL7sdCorLirnzsX9Rb7Ci0HYux4s6VsXX677ihXdewOWOjKR9dVVVvHLn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gCoUCs9mM2WwmNTWV2qoKfvz8Hfbv3UGEQWZAbE9y84sxGTW43TL5RcUMjNNSnbOKnDoVSSl9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\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rsdpbjgzsTP47dy62PXsZbTCgbaEmnlqWiamsIqayCgAPUGk2sz0qksjYWMrKy4moqaFX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\nQrR1c595hmEmc7MuOvQLD+enj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rrdgTeAuZKGN3kNpZ8UAAADF0lEQVTJx7Pj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\nRK5CYII=\n",
+ "text": [
+ ""
+ ]
+ }
+ ],
+ "prompt_number": 8
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "study.interactive_pca(sample_subsets=['all_samples'], \n",
+ " feature_subsets=['protein_coding'], \n",
+ " savefile='figure1_b.pdf', featurewise=True)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "savefile : figure1_b.pdf\n",
+ "y_pc : 2\n",
+ "data_type : expression\n",
+ "featurewise : True\n",
+ "show_point_labels : False\n",
+ "sample_subset : all_samples\n",
+ "feature_subset : protein_coding\n",
+ "x_pc : 1\n",
+ "list_link : \n"
+ ]
+ },
+ {
+ "ename": "ZeroDivisionError",
+ "evalue": "integer division or modulo by zero",
+ "output_type": "pyerr",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mZeroDivisionError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m/Users/lovci/Projects/flotilla_dev/flotilla/visualize/ipython_interact.py\u001b[0m in \u001b[0;36mdo_interact\u001b[0;34m(data_type, sample_subset, feature_subset, featurewise, list_link, x_pc, y_pc, show_point_labels, savefile)\u001b[0m\n\u001b[1;32m 129\u001b[0m \u001b[0mx_pc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx_pc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my_pc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 130\u001b[0m \u001b[0mshow_point_labels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshow_point_labels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m feature_subset=feature_subset)\n\u001b[0m\u001b[1;32m 132\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0msavefile\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 133\u001b[0m \u001b[0;31m# Make the directory if it's not already there\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/Users/lovci/Projects/flotilla_dev/flotilla/data_model/study.py\u001b[0m in \u001b[0;36mplot_pca\u001b[0;34m(self, data_type, x_pc, y_pc, sample_subset, feature_subset, title, featurewise, show_point_labels, reduce_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m 777\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 778\u001b[0m \u001b[0mfeaturewise\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeaturewise\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshow_point_labels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshow_point_labels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 779\u001b[0;31m title=title, reduce_kwargs=reduce_kwargs, **kwargs)\n\u001b[0m\u001b[1;32m 780\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0;34m\"splicing\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 781\u001b[0m reducer = self.splicing.plot_pca(\n",
+ "\u001b[0;32m/Users/lovci/Projects/flotilla_dev/flotilla/data_model/base.py\u001b[0m in \u001b[0;36mplot_pca\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 368\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mplot_pca\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 369\u001b[0m return self.plot_dimensionality_reduction(reducer=DataFramePCA,\n\u001b[0;32m--> 370\u001b[0;31m **kwargs)\n\u001b[0m\u001b[1;32m 371\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 372\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/Users/lovci/Projects/flotilla_dev/flotilla/data_model/base.py\u001b[0m in \u001b[0;36mplot_dimensionality_reduction\u001b[0;34m(self, x_pc, y_pc, sample_ids, feature_ids, featurewise, reducer, label_to_color, label_to_marker, groupby, order, color, reduce_kwargs, title, **plotting_kwargs)\u001b[0m\n\u001b[1;32m 364\u001b[0m \u001b[0;31m# pca(show_vectors=True,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 365\u001b[0m \u001b[0;31m# **plotting_kwargs)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 366\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mvisualized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mplotting_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 367\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 368\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mplot_pca\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/Users/lovci/Projects/flotilla_dev/flotilla/visualize/decomposition.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, ax, title, show_point_labels, show_vectors, show_vector_labels, markersize, legend)\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 149\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataModel\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_violins\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 151\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 152\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/Users/lovci/Projects/flotilla_dev/flotilla/visualize/decomposition.py\u001b[0m in \u001b[0;36mplot_violins\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 340\u001b[0m \u001b[0mphenotype_groupby\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 341\u001b[0m \u001b[0mphenotype_order\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 342\u001b[0;31m ax=ax, color=self.color)\n\u001b[0m\u001b[1;32m 343\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 344\u001b[0m \u001b[0;31m# Clear any unused axes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/Users/lovci/Projects/flotilla_dev/flotilla/data_model/base.py\u001b[0m in \u001b[0;36m_violinplot\u001b[0;34m(self, feature_id, sample_ids, phenotype_groupby, phenotype_order, ax, color, label_pooled)\u001b[0m\n\u001b[1;32m 693\u001b[0m \u001b[0mpooled_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpooled\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mphenotype_order\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 694\u001b[0m \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 695\u001b[0;31m label_pooled=label_pooled, outliers=outliers)\n\u001b[0m\u001b[1;32m 696\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 697\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mcached_property\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/Users/lovci/Projects/flotilla_dev/flotilla/visualize/generic.py\u001b[0m in \u001b[0;36mviolinplot\u001b[0;34m(data, groupby, color, ax, pooled_data, order, violinplot_kws, title, label_pooled, outliers, data_type)\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 47\u001b[0;31m \u001b[0mplot_pooled_dot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpooled_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0moutliers\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/Users/lovci/Projects/flotilla_dev/flotilla/visualize/generic.py\u001b[0m in \u001b[0;36mplot_pooled_dot\u001b[0;34m(ax, pooled, x_offset, label)\u001b[0m\n\u001b[1;32m 162\u001b[0m \u001b[0mxs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 163\u001b[0m \u001b[0mxs\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mx_offset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 164\u001b[0;31m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpooled\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'o'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'#262626'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 165\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 166\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/Users/lovci/venv/lib/python2.7/site-packages/matplotlib-1.3.1-py2.7-macosx-10.6-intel.egg/matplotlib/axes.pyc\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 4135\u001b[0m \u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4136\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4137\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4138\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4139\u001b[0m \u001b[0mlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/Users/lovci/venv/lib/python2.7/site-packages/matplotlib-1.3.1-py2.7-macosx-10.6-intel.egg/matplotlib/axes.pyc\u001b[0m in \u001b[0;36m_grab_next_args\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 315\u001b[0m \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 316\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mremaining\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 317\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mseg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mremaining\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 318\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mseg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 319\u001b[0m \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/Users/lovci/venv/lib/python2.7/site-packages/matplotlib-1.3.1-py2.7-macosx-10.6-intel.egg/matplotlib/axes.pyc\u001b[0m in \u001b[0;36m_plot_args\u001b[0;34m(self, tup, kwargs)\u001b[0m\n\u001b[1;32m 303\u001b[0m \u001b[0mncx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mncy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 304\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mxrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mncx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mncy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 305\u001b[0;31m \u001b[0mseg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mj\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mncx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mj\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mncy\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 306\u001b[0m \u001b[0mret\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 307\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mZeroDivisionError\u001b[0m: integer division or modulo by zero"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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yCwcffDDLly/n/vvvb/V+3/zmN3nVq17Fhz70ITbaaCM23HBD/vSnP/H0009z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KExUYZAoERkZiX7iQTQJ2cUtqxt9um5KakD79LT09Pdm2bZv5YGCcgGpVt3xc9PfDaDTGKzFp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\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95rZeQ0RERCTXcjWXai0zuwS4O83h1e5ekcvxiEj2KBgoIiIiIiIikbj7u0BRDq5zSntf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\nRkQjAfYzSYcA75P0EpkE2SgirivL7g8cW5ILzf5RKbV4j6SVyd6I44B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+ "text": [
+ ""
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+ },
+ {
+ "metadata": {},
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+ "text": [
+ ""
+ ]
+ }
+ ],
+ "prompt_number": 4
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "study.interactive_pca(sample_subsets=['all_samples'], \n",
+ " feature_subsets=['custom'], \n",
+ " list_link='https://www.dropbox.com/s/qddybszcses6pi6/DE_genes.male%20adult%20%2019.txt?dl=0',\n",
+ " savefile='figure1_c.pdf')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "savefile : figure1_c.pdf\n",
+ "y_pc : 2\n",
+ "data_type : expression\n",
+ "featurewise : False\n",
+ "show_point_labels : False\n",
+ "sample_subset : all_samples\n",
+ "feature_subset : custom\n",
+ "x_pc : 1\n",
+ "list_link : https://www.dropbox.com/s/qddybszcses6pi6/DE_genes.male%20adult%20%2019.txt?dl=0\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stderr",
+ "text": [
+ "WARNING, downloading things from the internet, potential danger from untrusted sources\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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Pz/Tp09PR0bHpwdBPN9xwQ/bYY49cfPHFufDCC3PBBRfk29/+9hpj\nRo0alQ9/+MM544wzVv/7d8ghh+SLX/xiVqxYkSR59NFH85//+Z9Jkuc973l5+OGH86Mf/Wj1MX7z\nm9+k1pr9998/c+fOzYIFC3LRRRcJAAEAAACAQdffdqDPT/KiWuuvSynHJPlDrfXfSyn3J3lPkuMG\nrUIYQYSAA6+rq8t6gAy4yy+/PIceeujq99OmTcsWW2yRm266aY1xpZQceOCBueKKK5IkRx55ZB5+\n+OG8/vWvz9Of/vSMGTMmRx99dO69996MGjUqn//85/ORj3wk559/fsaOHZtJkyblC1/4QsaNG5fr\nr78+CxYsyNFHH50kGTt2bL70pS8N3ZcGAAAAANpKf0PAjiTLe1/fkeSZSX6c5PokH44QEBgk3d3d\n1gNkQMyZM2f163PPPXed/XPnzk2SXHjhhet8ru9nTzjhhJxwwgnrPcfUqVNz3nnnrXff+eef/6Rr\nBgAAAAD4a/U3BLwhyb+UUv4hyW+SHFlKuTjJKwatMhjGvvKVr+QHP/hBxoxp/Cf03ve+N5dcckl+\n9LObsnLsxPSs/HNGj908uxz41mz+9K1yz29/lP/978vytMlT8/TNN8tR3xqf2bNn5+CDD85JJ52U\nRx55JIsXL85JJ52UfffdN/vtt1++/vWvZ+rUqeuc+8wzz8wrXvGK7LnnnkP9tZtCCAgAAAAAAE9e\nf0PAv09yQZLXJ/lCkn9Isqqn4ekDXhUMY11dXbnmmmty8cUXJ0luv/32vPvd786uu+6a1x55dG4f\nt0uS5P56c353+bnZ8y1nJkkmz9w9z559XI7aZ0b2nrV1kuSCCy7IrrvumhNOOCG11hx//PG5/vrr\n13ve22+/PR/84Adzyy235MADDxyCbzo8dHd354UvfGGzywAAAAAAgBFlVH8G1YYX1Fo/Wmt9IMlz\nkhyZZJ9a6xmDWiEMMz09PVmwYEF++tOf5k9/+lNmzpyZr33ta0mSbZ8+fvW4rcv/yYplS7P0oQXp\n6OhI0pMkKVMmrR4zc+bMHHTQQUmSSZMmZenSpRs878yZM3PRRRfl1a9+dXp6egbhmw1PXV1dZgLS\nb+303wYAAAAAwMZscCZgKeXCJJck+X6tdUXffb1B4KWDXBsMuAcXL0tduCi3LVyc2xY2JrPOmtKZ\nWVM6U6ZMyuTOcZs8xsyZM3Pqqadm7ty5Oe2009LZ2ZljjjkmSTJx/GY586DdV5/jlqdPztM6lmfy\nNhPz45/+Tx6++tP5h+s6kiSf/exns++++yZJbrvttpx88sl53/vet9Fzjx49ujdQbB/agdJfn/70\np/Ob3/wmX/7yl5tdCgAAAABA022sHej+Sd6Y5OFSypVphH7X1FqfGJLKYIA9uHhZTvnGLetun7cs\nP5/3QJLkzMN232QQ+L//+7+ZOXNmPvnJTyZptOk85phj8pznPCcdHR2Z3Dkue3dunb1nbZ0LVizK\n6W96cW644YaMfemL89GPfnSd45177rm58cYbc9ppp2W33XYbgG/aOh555JGsWLEiz3jGM5pdCsPc\nZZddlnPOOSc/+clPml0KAAAAAMCwsLF2oNsn2TvJ53t//leShaWUfy+lvLSU0l7TkRjx6sJFAzOm\n1nzsYx9b3XZw2223zcSJEzN69Og1WhF+97vfzY477phtttlmg8eaO3duFixYkIsuumidAFBbw2T+\n/PmZMWNG281+5Mn54Q9/mDlz5uSqq67KTjvt1OxyAAAAAACGhQ3OBKy19iS5sffXyaWUWUlek+TV\nSY5N8kApZW6Sy2qtPx6KYuGpWNX+c1Nj9p619UbHHHjggfnjH/+Yww47LJ2dnUmSE088Mddcc00+\n//nP5/LLL88TTzyRCRMm5JxzzkmSDYZY119/fRYsWJCjjz46STJ27Nh86UtfSpK84x3vyGabbZYk\n2WuvvdZoFdouoZj1ANmU3/3udzniiCP+f/buOzyqKnHj+DeJEAIZihEMhhY0V8CMqEsRZWEpSleQ\nBFTgJ8VGUVbAtSCsioCwBqQJAipFxSUBUWmKBUFYsLAiIHApE3qUhJIJJXV+f4RkgSSTAaakvJ/n\n4WHm3jP3vpMBxLycc/jkk09o2LChr+OIiIiIiIiIiIiIFBlX1SQYhlEV6A+8ApQ3TTPAramuLEsd\nwPbNN99Qo0YNX8WQYmBU7FaSUlKdjgkJDmRMtIqEouLtt99m//79TJ061ddRpAg6dOgQ9957LxMn\nTuThhx/2dRwRKeX8Ssu/0BEphvT/jCK+of82ioiUPvp7l4hvOPt7l7M9AfMwDKM20I3sGYH3AseB\nD64pnYhIAeLj4wkPD/d1DCmCTp48Sfv27fn73/+uAlBEREREREREREQkH4WWgIZhNCS79OsKNAQS\ngSXAq8D3F5YNFSnyIkItJO11PhMwItTipTTiCpvNRsuWLX0dQ4qY8+fP8+CDD9K+fXuGDRvm6zgi\nIiIiIiIiIiIiRVKBJaBhGJPJ3v+vDnAS+BT4B/CdaZoZXkkn4kYRoRY27U0sdExxZbfbSUlJoXr1\n6r6O4jbx8fHaE1AukZmZSe/evQkLC+Nf//qXr+OIiIiIiIiIiIiIFFnOZgL2B5YBQ4A1pmmmeyeS\niGcYoRXdMqaoWrVqFUuXLuWTTz7xdRS3cDgcKgHlEg6Hg7///e+cOHGCVatW4e/v7+tIIuJM8lH4\ncXr24yZDoOJNvs0jIiIiIiIiIlLKOCsBq5mm6XztRJFiJMQSyJiohpgJyexJsLMnwQ5kz/6LCLVg\nhFYkxBLo45RX79Zbb+W3337zdQy3OXXqFH5+flSpUsXXUaSImDBhAuvWrWPdunUEBhbf36siJV5G\nGmz7GLYvgsy07GPHfoHIR8D6KFxX1rf5RERERERERERKiQJLQBWAUhKFWAJpZqlKs4iqvo7idvXq\n1cNms5GamloiChKbzaZZgJJrwYIFzJo1i40bN1KpUiVfxxGRghzcmD37L+XYpccz02DrfNi/BhoP\nhlr3+CafiIiIiIiIiEgp4mwmoJQwSfbUEjsLrjS7+HMNvuEmnpn6Gc3vblTsP1ctBSo5vvzyS55/\n/nnWrl3LTTdpOUGRIm3LnLwF4MXsR7PHqAQUEREREREREfE4lYClRJI9lVFxW/Me35vKpr2JAIyJ\nalhsC6PS6vLPtfJNddm/ZyfX3VCn2H+uKgEF4JdffqF3794sW7aM+vXr+zqOiBSmdks4Fe98TJ2/\neSOJiIiIiIiIiEip51/QCcMw/moYRuCFxy1yHkvxZCYku2WMFC2Xf2bXh91M0uF9TscUFzabjfDw\ncF/HEB/av38/DzzwALNnz+bee+/1dRwRcYUrBV/tlh6PISIiIiIiIiIiTkpA4BugyYXHa4EbPZ5G\nPCZn+c9rHSNFy+Wf2fU1bubEkX1OxxQXmglYuh0/fpz27dvzyiuv0K1bN1/HERFXVakDlesUfL5y\nnewxIiIiIiIiIiLicc6WA10LfG8YRs7z+IseX8xhmmaAm3OJm6kELJku/8xCwm7hxGGVgFK8nTlz\nhs6dO9OjRw8GDhzo6zgicqWcLQmqpUBFRERERERERLzGWQnYGbgLKAd8CzwC/OGNUCJydSw3VCf1\njJ3Us3YCy1t8HeeqORwOlYClVEZGBj179qRBgwaMGTPG13FE5GrU+RtsnZ//OS0FKiIiIiIiIiLi\nNQWWgKZppgGbAAzDaAX858IxKYYiQi0k7U0tdIwUL5d/rn7+/lS5KZwTh/dR3bgjd0xxk5SURJky\nZahUqZKvo4gXORwOnn76aTIzM5k9ezZ+fn6+jiQiV6NKHej7na9TiIiIiIiIiIiUes72BATAMIw2\nwABgs2EYuw3DWG8YxpuGYdT1fDxxF1eKoOJYFpV2+X1m2fsC7nc6pqjTLMDS6bXXXmPr1q3ExsZS\npkwZX8cRERERERERERERKdacloCGYcwA1gCRwDogFjCBHsDvhmE84fGE4hZGaEW3jJGiJb/PLKTG\nzSQd3ut0TFGnErD0mT17Nh9++CErVqwgODjY13FEREREREREREREir0ClwM1DKMP0B/oYZpm3GXn\n/IDewEzDMPaapqk1n4q4EEsgY6IaYiYksyfBzp4EO5A9Sywi1IIRWpEQS6CPU0pBkuypBX52z3Wo\nR6I9NfdcnVvq8dOOH+jTPLzYfq42m43w8HBfxxAv+fzzz/nnP//J+vXrqVatmq/jiIiIiIiIiIiI\niJQIBZaAwBPAG5cXgACmaTqAhYZhhAIvACoBi4EQSyDNLFVpFlHV11HkCiTZUxkVtzXv8b2pbNqb\nCMCYqIa5n+ufLatz67QXuPuWG4rtnmrx8fHUq1fP1zHEC/7zn/8wYMAAVq5cyS233OLrOCIiIiIi\nIiIiIiIlhrPlQK3A54W8fhXQ1H1xRORyZkLyFY2pVq0aZcuW5ejRo56M5VFaDrR02L17N926dWPB\nggU0btzY13FEREREREREREREShRnJWBZIKuQ1zuAcu6LIyKXy1n+80rGWK1Wtm3b5qlIHhcfH6/l\nQEu4Y8eO0b59e8aPH0+HDh18HUdERERERERERESkxHFWAu4DCvvObHNgv/viiMjlrqYEjIyMLLYl\noMPhID4+ntq1a/s6inhIcnIyHTt2ZMCAAfTr18/XcURERERERERERERKJGcl4DxgtGEYLfI7aRiG\nFXgV+Nj9sUTkWhTnmYDHjx8nKCgIi8Xi6yjiAWlpaXTv3p27776bkSNH+jqOiIiIiIiIiIiISIl1\nnZNzU4GWwLeGYawC1gGJQEWyZwA+CPwIxHg6pEhpFhFqIWlvaqFjLma1WpkxY4YnY3mM9gMsubKy\nsujfvz/BwcFMnz4dPz8/X0cSERERERERERERKbEKnAlommYG8BDwDFAbeBN4D5gM3A6MBlqbpnne\nCzlFSq3LCz5Xxtx2223s2rWLjIwMT8XyGJvNpv0AS6iXXnoJm83Gxx9/TEBAgK/jiIiIiIiIiIiI\niJRozmYCYppmJjATmGkYRjBQGUg2TTPZG+FEBIzQilc8pkKFClSvXp29e/dSr149T0XzCM0ELJmm\nTp3K559/zg8//EBQUJCv44iIiIiIiIiIiIiUeE5LwIuZppkCpHgwi4jkI8QSyJiohpgJyexJsLMn\nwQ5kz/6LCLVghFYkxBKY53U5+wIWxxIwMjLS1zHEjWJjY5k4cSIbNmwgJCTE13FERERERERERERE\nSgWXS0AR8Z0QSyDNLFVpFlHV5ddYrVa2b99OdHS0B5O5X3x8PF26dPF1DHGT77//nsGDB7NmzRpq\n167t6zgiIiIiIiIiIiIipUaBewKKSPGWMxOwuLHZbFoOtITYtm0bPXr04JNPPqFhw4a+jiMiIiIi\nIiIiIiJSqqgEFCmhIiMji10J6HA4OHDggGaMlQCHDh2iU6dOvP3227Ru3drXcURERERERERERERK\nnSteDtQwjErAVKAp8AMw3DTN0+4OJiLXJiIigiNHjnDmzBkqVKjg6zgu+eOPP7BYLMUmr+Tv5MmT\ntG/fnqFDh/LII4/4Oo6IiIiIiIiIiIhIqXQ1MwHfARxANFAO+NCtiUTELcqUKYNhGOzcudPXUVxm\ns9kIDw/3dQy5BufPn+fBBx+kXbt2DB8+3NdxREREREREREREREqtAktAwzDKFXCqJTDONM1twHjg\nPk8EE5FrV9z2BYyPj9d+gMVYZmYmvXv35qabbuKtt97ydRwRERERERERERGRUs3ZTMBPDcN4xjCM\nspcd/y/wmGEY5YHHgJ89lk5Erklx2xdQJWDx5XA4+Pvf/86JEyeYP38+/v7aclZERERERERERETE\nl5ztCdgR6A58bhjGp8B7pmlmAE8CHwEngY3Aox5PKVKCJNlTMROS2ZNgZ0+CHYCIUAsRoRaM0IqE\nWALddi+r1crXX3/ttut5Wnx8PHfeeaevY8hVmDhxIt9//z3r168nMNB9v4ZFRERERERERERE5OoU\nWAKapukA4gzDWAo8DKwwDOMTYL5pmq29FVCkJEmypzIqbmve43tT2bQ3EYAxUQ3dVgRarVa2b9/u\nlmt5g81mo1u3br6OIVdo4cKFzJw5kw0bNlCpUiVfxxERERERERERERERnC8HCoBpmlmmaX4MdAAc\nwCrDMHoZhuHn8XQiJYyZkOyWMa6qUaMG586dIzEx0W3X9CQtB1r8fPXVV4wYMYJVq1YRFhbm6zgi\nIiIiIiIiIiIicoHTEtAwjBqGYXxjGMZ54EfgF6AzUAFYbRhGtBcyipQYOct/XusYV/n5+RWbfQGz\nsrI4ePAgtWvX9nUUcdGWLVvo3bs3S5YsoX79+r6OIyIiIiIiIiIiIiIXKWwm4HzABKoCM4Elpmmm\nm6Y5G3gAuNEwjNUezihSYni7BITsJUGLQwl47NgxqlSpQlBQkK+jiAv2799Ply5dePfdd2nevLmv\n44iIiIiIiIiIiIjIZQorAf8CzDFN0w68D9Q2DCMYwDTNVNM0pwNdPZxRRK5BcdkXUEuBFh/Hjx+n\nffv2jBw5Uns4ioiIiIiIiIiIiBRRhZWAa4CXDMNoALwKbDNNM+XiAaZpnvdQNpESJyLU4pYxV6K4\nzARUCVg8nDlzhs6dOxMdHc2gQYN8HUdEREREREREREREClBYCdgfOAEsBW4HNOVD5Br4ogSMjIxk\nx44dZGVlufW67hYfH094eLivY4gTGRkZPPzww9SvX5833njD13FERERERERERERExInrnJ28sAzo\nU17KIlLiGaEV3TLmSlSpUoWKFSty4MCBIl2y2Ww2mjRp4usYUgCHw8HAgQNJT09nzpw5+Pn5+TqS\niIiIiIiIiIiIiDjhtAQ0DKMhMBS4F6gBlAVSgH3AN8A7pmke8HRIkZIixBLImKiGmAnJ7EmwsyfB\nDmTP/osItWCEViTEEuj2++bsC1iUS8D4+Hh69Ojh6xhSgNdee43//ve/rF27ljJlyvg6joiIiIiI\niIiIiIgUosAS0DCMB4A44D8Xfj4MpAJBQBjQGhhsGMYDpml+64WsIiVCiCWQZpaqNIuo6rV75uwL\n2KVLF6/d80ppT8Cia86cOSxcuJCNGzcSHByc75hhw4Zx/Phxjhw5AkBYWBg2m43w8HAWLlyYO+7F\nF1+kadOmdOvWjf379zNu3DjOnDkDQOXKlXn55ZepWbMmAB999BGffvop5cqVIy0tjb59+9KxY0dM\n0+SFF17IzfL444/TsmVLT34JRERERERERERERIodZzMBxwHDTNOcXtAAwzBeB6YAVncHExH3iYyM\nZNWqVb6OUaDMzEwOHTpE7dq1fR1FLvPFF18wevRo1q1bx4033ljguEmTJgEwfXr2fzKGDBnCjz/+\nyDgI2G8AACAASURBVLRp0y4Z5+fnh5+fH2fOnOGpp55i/PjxNGrUCIAvv/ySAQMGsGLFClatWsVX\nX33FRx99RGBgICkpKfTs2ZNbb72V+Ph4evXqRVRUlIfetYiIiIiIiIiIiEjx5+/k3C3A14W8fhFg\nuC+OiHhCzkzAouro0aPccMMNBAa6fylUuXqbNm1iwIABfPbZZ0RERFzx6x0OR4HH16xZw+23355b\nAAK0a9eOatWq8dNPP7F48WIGDhyY+2siODiYFStWcPPNN3Pw4EGWLVvGww8/zEsvvcTZs2ev7g2K\niIiIiIiIiIiIlGDOSsDfgWGGYQTkd/LC8UFA0W0WRASA+vXrs2/fPtLS0nwdJV9aCrQQyUfh65ez\nfyQf9cotd+/eTdeuXZk3bx5NmjS56uvs2rWLPn365P5Yv349AMeOHaNWrVp5xlevXp0TJ05w/Phx\nwsLC8r3m4cOHuf/++/nkk0+oWbNmntmGIiIiIiIiIiIiIuJ8OdCngZVAF8MwvgcOAGeBQKAm0Aoo\nB3TydEgp3Ub+8KJL48Y2f9PDSYqvcuXKUadOHXbv3o3VWvRW71UJWICMNNj2MWxfBJkXCtxjv0Dk\nI2B9FK4r65HbJiQk0KFDB8aPH0/Hjh2v6Vr16tW7ZE/Al156CYDQ0FA2b96cZ/z+/fvp3bs31apV\nIyEhIXd/QIAJEybQokWL3GVBIXv24Lhx464po4iIiIiIiIiIiEhJVOBMQNM0fyR7qc+JQHmgI/AY\n8ABwPTANqGeaZt7v4opIkRMZGVlklwS12WyEh4f7OkbRcnAjLOsLW+f/rwCE7Mdb58Nn/bLHuFly\ncjIdO3akf//+9OvXz+3Xh+x9Adu2bcuPP/7Izp07c49/9dVXlClThoYNG9K1a1feffdd0tPTAdi3\nbx+rV6/GarXy6quvYpomAJs3byYyMtIjOUVERERERERERESKM2czATFN8wQw+cIPESnGrFYr27dv\n93WMfMXHx3PPPff4OkbRsmUOpBwr+Lz9aPaYWu77uqWlpdG9e3eaNGnCyJEj3XJNPz+/fI9bLBZm\nzpzJxIkTOXfuHAEBAVSvXp2ZM2cC0L17d06dOkXPnj2pUKECAJMmTSI4OJiRI0fyyiuvEBQUhMVi\nYeLEiW7JKiIiIiIiIiIiIlKSOC0BDcNoCAwF7gVqAGUBO7Af+AZ4xzTNA54OKSLXzmq18v777/s6\nRr7i4+N59NFHfR2jaKndEk7FOx9T529uu11WVhb9+/enQoUKzJgxo8DyrjBDhgzJfdy0aVOaNm16\nyfnx48fnPr711lt57733CrzWgAEDGDBgQJ7jt99+O3FxcVeVT0RERERERERERKS0KHA5UMMwHgB+\nAm4G4oARwFPAKOAroCWwwzCM1l7IKSLXyGq1ajnQ4sSVgq92S7fd7qWXXmL//v18/PHHBAQEuO26\nIiIiIiIiIiIiIuIbzmYCjgOGmaY5vaABhmG8DkwBrO4OJiLuFR4ezvHjx0lOTqZixYq+jpMrIyOD\no0ePUrNmTV9HKVqq1IHKdQqeDVi5TvYYN5g6dSqfffYZGzZsoHz58m65poiIiIiIiIiIiIj4lrMS\n8Bbg60Jevwh4wX1xRMRTAgICaNCgATt27KBZs2ZOx4784UWXrjm2+ZvXnOvIkSNUq1aNsmXLXvO1\nShxnS4K6aSnQ2NhYJk6cyA8//EBISEih43/99VcmTJjAokWL3HJ/EREREREREREREfGMApcDBX4H\nhhmGke+6cBeODwKK5vqCIpLHtS4JmppalqTE64m31WL7bw0YFbuVBev38589x0myp17VNePj46lT\np85VZyrRnBV9blgK9Pvvv2fw4MEsX77cpc9g69attG/fnqioqGu+t4iIiIiIiIiIiIh4lrOZgE8D\nK4EuhmF8DxwAzgKBQE2gFVAO6OTpkCLiHtdSAqamlmX7tgaXHEtKSSVpbyqb9iYCMCaqISGWwCu6\nrvYDdKJKHej7nUcuvX37dnr06MGiRYu44447Ch3/22+/0b59e6ZNm0b37t09kklERERERERERERE\n3KfAmYCmaf4IGMBEoDzQAfg/4AHgemAaUM80zc1eyCkibhAZGXnVJWCKPbjQMWZC8hVfVzMBve/Q\noUN07NiRt99+mzZt2hQ6fvv27bRr1463336b6OhoLyQUERERERERERERkWvlbCYgpmmeACZf+CHi\nE+7Yd06yWa1Wtm/fjsPhwM/P74pea3ehBNyTYKdZRNUrum58fDwtWrS4otfI1Tt58iQdOnRg6NCh\nPPLII4WO37FjB/fffz+TJk2iZ8+eHs/nzf0oRUREREREREREREoypyVgYQzDqAB0N01zgZvyiIgH\n3Xjjjfj5+ZGQkED16tWv6LWuzATck2C/4kw2m43HHnvsil8nV+78+fN07dqV++67j2HDhhU6/vff\nf+e+++7jX//6l0uFoZQOmzdvZsqUKQQEBHDu3DmaN2/O0KFD8fPz4/3336dKlSp069YNgJSUFMaP\nH8/evXspW7YsWVlZPPPMM9x9990MGzaM48ePc+TIEQDCwsKoWrUqPXv2pH///ixZsoR69eoBMG3a\nNGrUqEG3bt2oV68ejRs3zs1TsWJFZsyYwXfffcf06dNxOBxERUXx6KOPev+LIyIiIiIiIiIiUoRc\nUwkIVAXmASoBRYoBPz+/3H0Br7QE9BQtB+odmZmZ9OnTh9DQUGJiYgqdCbpr1y7uu+8+JkyYQK9e\nvbyUUoq6AwcOMHLkSObPn09YWBgOh4MRI0Ywd+5c1q9fz5YtW3jjjTdyxz///PM0b96csWPHAv8r\n/efNm8ekSZMAmD59OgBDhgwB4Mcff6RGjRq88sorLF68GH9/f/z8/C75Nbtw4cJLcp0/f54xY8YQ\nGxuLxWLhwQcfpGfPngQEBHj06yEiIiIiIiIiIlKUFbgnoIsOAXXdEUREvONq9wUMtqQUOiYi1HJF\n10xPTychIYEaNWpccR5xncPh4LnnniMxMZEFCxbg7+/8j/7du3fTtm1bxo0bR58+fbyUUoqDZcuW\n8cADDxAWFgZk/8OCmJgYnnjiCT744AOefPJJHA4HAMeOHWPXrl2XlMjh4eFERUXx+eefO73PXXfd\nRf369Zk3b55LuX777TciIyMJCQmhbNmyzJw58+reoIiIiIiIiIiISAlyTTMBTdPMBOLdEwUMw2gD\nTAJuBZKAqaZpTnDX9UUke1/AjRs3XvHrLJYUkpKudzrmSkvAw4cPExoaSpkyZa44j7juX//6F2vX\nrmXdunUEBgY6Hbtnzx7atm3LmDFjtEyr5JGYmMgdd9yR77mAgIBLCuYjR45w00035RlXvXp1fvvt\ntwLvkVMivvDCC3Tv3p377rsvz5iLy+lHHnkEh8NBamoqTz75JCkpKXTq1EkzWEVEREREREREpNS7\n1uVA3cYwjMrAMuAp4N/A3cBqwzB2mab5mU/DiZQgVquVd99994pf58pMQCO04hVd02azER4efsVZ\nxHULFy5kxowZbNy4kcqVKzsdu3fvXlq3bs2rr75Kv379vJRQipNq1apx7NixS46tXLmSw4cP8+ST\nT15y/MYbb8wzFmD//v0uLQEcHBzMCy+8wKhRo2jUqNEl5y5fDvTLL7/k9OnTLFy4EIfDQc+ePbn7\n7ru5+eabXXxnIiIiIiIiIiIiJU+RKQGBvwLxpml+fOH5BsMwVgPtAJWAIm5y2223sXPnTjIzMwvc\nL2ts8zfzPZ7UJBUzIZk9CXb2JNiB7Nl/EaEWjNCKhFiczzK7nPYD9Kw1a9YwYsQIvvvuu9zlGwuy\nb98+WrduzejRoxkwYICXEkpx06lTJ5588kl69OhBtWrVOHPmDHPmzGHUqFF5xtasWZNatWrx0Ucf\n0atXLzZt2sT8+fPZs2cP//73v126X+vWrVmxYgVffPEFAwcOLHCc1WolKCiIMmXK4HA4ch+LiIiI\niIiIiIiUZgWWgIZhfAc4Ljrkd9kQx4VjDtM0W7shyw/AQxfdvwzQAFjghmuLyAUWi4Vq1aqxb98+\nDMO4oteGWAJpZqlKs4iqbsmiEtBztmzZQq9evVi6dCkNGjRwOnb//v20bt2akSNH8sQTT3gpoRRH\ndevWZfTo0QwZMoSyZcuSlpZGr169uOuuu3LH+Pn9768Lb7/9NuPGjWPZsmWUL18ef39/qlWrRkJC\nAiEhIfne4+LXA4wcOZJOnToVeB7gpptu4v7776dHjx44HA7atWtHrVq1rvXtioiIiIiIiIiIFGvO\nZgL+G3geCAe+AE4WMM5RwPErYprmyZx7GIZxKzAHOAfMcMf1RUqLJHvhs/WsVivbt2+/4hLQ3Ww2\nG23btvVphpLIZrPRpUsXZs2aRfPmzZ2OjY+Pp3Xr1rzwwgs89dRTXkooxVmLFi1o0aJFvueGDBly\nyfPKlSszceLES46lpKSQmZlZ4GuaNGlCkyZNcp9ff/31/Oc//8l9vnPnznzv/cgjj/DII4+49iZE\nRERERERERERKgQJLQNM0ZxmG8SPwMzDaNM2tng5jGEY5YAzwODAFGGeaZpqn7ytSUiTZUxkVl/e3\natLeVDbtTQRgTFRDrFYr27Zt46GHHsoz1ps0E9D9EhMTad++PS+//HKhn++BAwdo1aoVI0aMYNCg\nQV5KKKVdcHCwryOIiIiIiIiIiIiUCk73BDRNc4thGIlAprNx7mAYxnXAKiAdiDRN84in7ylS0pgJ\nyS6NiYyMZMmSJV5I5JxKQPc6e/YsnTt3pnv37gwePNjp2IMHD9KqVSuee+65PDOxfKmg/SjFu44f\nP86xY8e4/fbbfR1FRERERERERERErpJ/YQNM06xmmuZ2L2R5CAgDuqgAFLk6Oct/FjYmZyagL6Wl\npfHnn38SFhbm0xwlRUZGBj179uTWW29l7NixTsceOnSIVq1a8cwzz/Dss896KaEUB7t37+bpp5/G\nMAxWrFjh6zgiIiIiIiIiIiJyDZzOBPSye4GbgZTL9imbZ5rmE76JJFK8uFoCPnJ3Aw4ePMi5c+cI\nCgryQrK8Dh48SFhYGNddV5T+GCqeHA4HgwYNIj09nblz5+Ln51fg2CNHjtC6dWsGDx7Mc88958WU\nUlQ5HA7WrVtHTEwMmzZt4umnn2bXrl3ceOONvo4mIiIiIiIiIiIi16DIfPfdNM2hwFBf5xApDcqU\nKUNERAQ7d+7krrvu8kkGLQXqPq+//jpbtmxh7dq1lClTpsBxR48epVWrVjz55JMMGzbMiwmlKMrI\nyCAuLo6YmBhOnz7NsGHD+OSTTyhfvryvo4mIiIiIiIiIiIgbFJkSUESuXUSohaS9qYWOAYiMjGTb\ntm0qAYu5uXPnsmDBAjZu3EhwcHCB444dO0arVq0YMGAAzz//vBcTSlGTnJzM3LlzmTJlCrVr1+aV\nV16hS5cu+PsXukJ4sfbrr78SExNDVlYWGRkZ1KpVi5deeomAgABeeeUVTp06RUZGBnXq1GH06NEE\nBQVhtVq54447ADh58iQdO3Zk0KBBbN68mcGDB1O/fn0ATpw4weOPP063bt34+eefmThxIv7+/gQF\nBfH2229TqVIlX751EREREREREREppVQCipQgEaEWNu1NLHQM4PN9AW02G+Hh4T67f0mwfPlyRo0a\nxbp165wu3ZiQkECrVq147LHHeOGFF7yYUIqSQ4cOMXXqVN5//33uu+8+4uLiaNy4sa9jecWxY8cY\nNmwYc+bM4eabbwZg3rx5PPPMMzRq1IgWLVoQHR0NwOTJk1m0aBH9+/enatWqLFy4EIDU1FT++te/\n0qdPHwDq16+fe+6PP/6gc+fOdOvWjXHjxjFt2jTCwsKYMGEC8+fP196bIiIiIiIiIiLiE1ddAhqG\ncaNpmn+4M4wUPyN/eNGlcWObv+nhJAJghFZ0eYzVamX69OmejlSg+Ph42rdv77P7F3ebNm2if//+\nLF++nIiIiALH/fHHH7Rq1YrevXvz8ssvezGhFBVbtmwhJiaGVatW8dhjj/HLL7+Uulm4S5cupUuX\nLrkFIEDfvn1ZsGABhw8f5siRI9x1113UrVuXwYMHk5GRkecap06donz58pQrVy7PuaSkJCpUqABA\nt27dCAsLA8BisWC3F75Xq4iIiIiIiIiIiCe4VAIahnE9MA1YDHwJbADuNAzDBnQxTfN3z0UUEVeF\nWAIZE9UQMyGZPQl29iRkf/M5ItRCRKgFI7QiIZZAwPczAbUc6NUzTZOuXbvywQcf0KRJkwLH/fnn\nn7Ru3ZqHH36YV155xYsJxdeysrJYuXIlMTEx7N27l2effZYZM2ZQuXJlX0fziWPHjnHnnXfmOR4a\nGkqfPn3YvHkzr732GgcPHqRBgwa8+OKL1KpVi8TExNyZf3v27OH+++/nuuuy/+q0a9cu+vTpg8Ph\nIC0tjXHjxgHkHouLi2P58uW899573nujIiIiIiIiIiIiF3F1JuAsoBqwDXgIqAE0BgYC7wB/80Q4\nkdIoyZ7qUolXkBBLIM0sVWkWUdXpuFq1amG32zlx4gTXX3+92/K7SiXg1UlISKB9+/aMGzeOTp06\nFTju+PHjtG7dmqioKP75z396MaH40vnz51m4cCGTJk0iKCiI4cOH06NHD8qUKePraD4VGhrK0aNH\nLzmWlZXFwYMHOXToEE888QRPPfUUWVlZfPTRR7z11ltMnTqVG264IXfJz6ysLJ555hk+/fRTatSo\nQb169XLPXeyPP/5gxIgR3HrrrSxevNjpXp0iIiIiIiIiIiKe5GoJ2A5obZrmfsMwRgPLTNP8xTCM\nKcCPnosnkte1lmRFWZI9lVFxW/Me35uau9ffmKiGbnmPfn5+REZGsm3bNlq2bHnN17sS58+fJykp\niZtuusmr9y3u7HY7HTt2pF+/fvTv37/AcYmJibRp04Zu3brx6quvei+g+Mzx48d55513mDlzJn/5\ny1+YMWMGrVq1ws/Pz9fRioQHH3yQvn370r1799w/d+bPn0+jRo344osvCAoKom3btvj7+1OzZk1+\n++23PNfw9/enfPnyZGZmOr3X8OHDGTRoEPfcc49H3ouIiIiIiIiIiIirXC0BA4CzFx63BEZeeOwP\nZLk7lEhBvFmS+YKZkOzSmGYW57P8XGW1Wtm+fbvXS8CDBw9So0YNAgICvHrf4iwtLY3u3bvTuHFj\np0t7JiUl0aZNGzp37szrr79eIkugM/9eDECFnj18nMT3du/ezaRJk1i8eDFRUVF8++23NGjQwNex\nipyaNWsyfvx4XnjhBRwOBwCGYTBhwgQSExP55z//yXvvvUdgYCAhISH84x//ALhkOdD09HRq165N\n165d2bJlS76/t06fPs327duZOXMmM2fOBKBNmzb07dvXO29URERERERERETkIq6WgN8A4w3DOAhU\nB74yDKMWMJrs/QFFvMLbJZm35cxsLGxMYUt9uspX+wJqKdAr43A4GDBgAEFBQcyYMaPAYu/EiRO0\nbduWDh06MHbs2BJbAJ6Njct9XhqLQIfDwbp164iJiWHTpk0MHDiQXbt2ceONN/o6WpHWpEmTfJfv\nDAsLY+7cufm+Jr8ZgQBNmzaladOmeY5XqlSJX3/99dqCioiIiIiIiIiIuIm/i+OeBhxAW2CgaZqJ\nwFtAFeApD2UTycPVkqy48vb7y1kO1NtsNhvh4eFev29x9dJLL7Fv3z4WLVrEddfl/283cgrAtm3b\nMn78+FJRAJ6NjcudFVgapKens2jRIho3bswTTzxBx44diY+P57XXXlMBKCIiIiIiIiIiInm4NBPQ\nNM1jQLfLjpW+6RficyW9BPS2nOVAHQ6HV0sjzQR03bRp01i2bBkbNmygfPny+Y45efIk999/P61a\ntWLixImlogDMkXOsJM8ITE5OZs6cOUyZMoXw8HBGjx5N586d8fd39d/xiIiIiIiIiIiISGnkUglo\nGEZZ4AkgEggE/MieGQiAaZr9PZJOpJSJCLWQtDe10DHuEhISQoUKFTh06BC1atVy23ULEx8fT+fO\nnb12v+IqLi6OCRMm8MMPPxASEpLvmFOnTtGuXTuaN2/OW2+9VaoKwBwltQg8ePAgU6dO5YMPPuD+\n++9n6dKlNGrUyNexREREREREREREpJhwdRrBPOBN4EayC0Au/Ox/0XMRj3OlAHNnSeZtvnh/vtgX\nUDMBC7du3ToGDRrE8uXLC/xanT59mnbt2nH33XczefLkUlkA5ihJS4P+8ssvPProo9xxxx1kZWWx\nZcsWFi1apAKwAA6Hg59++ol//OMf3HzzzSxeXDJ+HYiIiIiIiIiIiFwrl2YCAg8A3UzTXOPJMFL8\njG3+plfvFxFqYdPexELHuMPIH150aZw7vwZGaEW3jLkSOfsCdurUya3XdUZ7Ajq3Y8cOoqOjWbRo\nEXfccUe+Y5KTk2nfvj2NGzdmypQppboAzFGcZwRmZWWxcuVK3nrrLfbt28fQoUOZOXMmlSpV8nW0\nIsnhcLB582ZiY2OJi4ujXLlyREdHs3TpUm6//XZfxxMRERERERERESkSXC0Bk4Gjngwi4gpflGTe\nFGIJZExUQ8yEZPYk2HP3N4wItRARasEIrUiIJdCt97RaraxZ471+/9y5c5w6dYrQ0FCv3bM4OXTo\nEB06dGDy5Mm0adMm3zF2u50OHTpw5513Mm3atBJZAJYW586dY+HChUyaNIny5cszYsQIoqOjKVOm\njK+jFTlZWVls2rSJ2NhYlixZQnBwMNHR0SxfvpzIyEj9PhAREREREREREbmMqyXgDGCUYRi9TdPM\n8GQgEWe8WZKlppYlxR6M3R5Mij0YgGBLChZLCsGWFAID09xyn8uFWAJpZqlKs4iqHrn+5axWK5Mn\nT/bKvQAOHDhArVq18Pd3dTXi0uPUqVN06NCBZ599lkcffTTfMTkFoNVqZfr06SW6+MiZ0efqbMDy\n0VHFZhbgn3/+yTvvvMPMmTNp3LgxM2fO5G9/+1uJ/jyvRmZmJhs3bswt/qpUqUJ0dDSrV6+mQYMG\nvo4nIiIiIiIiIiJSpLlaAjYCOgCHDMPYA2RedM5hmmZrtycTKYA3SrIkeyo///fi5Sqzf8mfPhsE\nfwQBVal681quK3vOYxm8pUGDBpimSXp6uldmH9lsNu0HmI/z58/z4IMP0rZtW4YPH57vmJSUFDp1\n6kT9+vV55513SkWR6moRWFwKwF27djF58mQWL15MdHQ0a9eupX79+r6OVaRkZmayfv164uLiWLp0\nKVWrViU6Opqvv/5aXysREREREREREZEr4GoJuPXCj/w43JRFpMgwE5Kdnnc4ArAfN/DzczAqNvu3\nhieX7PSkoKAgatasiWma3HbbbR6/X3x8vPYDvExWVhZ9+vQhNDSUSZMm5Tsb7MyZM3Tq1ImIiAje\nfffdUlEA5iisCCzqBaDD4eD7778nJiaGzZs3M3DgQHbv3k21atV8Ha3IyMjIYN26dbnFX/Xq1XNL\nUsMwfB1PRERERERERESkWHKpBDRN81UP5xApUnKWGc2PwxFA+rnKZKYFcV1gCknXpQKQtDeVTXsT\nARgT1bBYFYFWq5Vt27Z5rQTUTMD/cTgcPPfccxw/fpzVq1fnW+6dPXuWzp07U7duXebMmVOqCsAc\nBRWB7ioAz/x78SX3cYf09HRiY2OJiYkhJSWFYcOGsXjxYoKCgtx2j+IsIyODtWvXEhsby6effkqt\nWrWIjo7mhx9+4JZbbvF1PBERERERERERkWKvwBLQMIwPgOOmaf7jwuP8Zvz5kb0caH9PBRTxBWcl\nYFZm9m+brKyCO3QzIZlmFu/s6ecOVquV7du3e+Ve8fHxdO3a1Sv3Kg7eeustvvvuO9atW0e5cuXy\nnD979ixdunShVq1azJ07t1QWgDkuLwLdWQBeXC5e6zVPnz7N3LlzmTJlCnXr1uXVV1+lU6dOpfqz\ny5Gens63335LXFwcy5YtIzw8nOjoaDZv3qwZwiIiIiIiIiIiIm7mbCag34Uf5POzSKnlyCp837w9\nCXaP7lnoblarlQULFnjlXjabTd/sv+DDDz9k+vTpbNy4kcqVK+c5f+7cOR588EFuuukm3n//fQIC\nAnyQsmi5uKDzRAGY8/hqrn3w4EGmTJnCvHnzuP/++1m6dCmNGjW65ozFXVpaGt988w2xsbF89tln\nGIZBVFQUP//8M7Vr1/Z1PBERERERERERkRKrwBLQNM2++T2+mGEYQUBDt6cScYORP7zo0rixzd/M\ncywi1ML6A/mPz8rMLgH9/TMKvKazmYRFUWRkJNu2bfPKvbQcaLY1a9YwfPhwvv32W8LCwvKcP3/+\nPF27dqVatWrMmzdPBeBF3LVk5+UFYI4rLQJ/+eUXYmJiWL16Nf369WPLli2lvtxKTU1lzZo1xMXF\n8cUXX1C/fn2ioqJ47bXXqFmzpq/jiYiIiIiIiIiIlAou7QkIYBhGayCMS2cD1gRGA8Vn8zMRF0SE\nWgod4xeQ7oUk3nHLLbeQkJBASkoKwcHBHrvPmTNnsNvt3HjjjR67R3Hw3//+l169erFkyZJ892E8\nf/483bp14/rrr2f+/PkqAD2goAIwR2FFYFZWFitWrCAmJob9+/fz7LPPMnPmTCpVquSRvMXB+fPn\n+fLLL4mLi2P58uVYrVaio6MZO3ZsvkW3iIiIiIiIiIiIeJZLJaBhGKPILvsSgJuAg8ANF06P80w0\nKa2uZQafuxihFSl3XVD+J8tARro/QWUD8PfPf4wrJWJREhAQQP369dmxYwdNmzb12H3i4+OpXbs2\nfn6ld2Vhm81G586dmTVrFn/961/znE9NTeWhhx6iYsWKLFy4kOuuc/nfaoiLCisAc+RXBJ47d44F\nCxYwefJkKlSowPDhw4mOjqZMmcKXCS6Jzp07x+rVq4mNjWXlypXceeedREVFMXHiRKpXr+7reCIi\nIiIiIiIiIqWaq99dHgD0BT4GdgEdgGPAEuBXjySTUifJnoqZkEy8rRYp9uzZaMGWFCyWFIItbbb8\nhAAAIABJREFUKQQGpnktS4glkEjr76TYg7Hbgy/J43D4kZh4Pf7+jgJf7+4S0BvFqNVqZdu2bR4v\nAUvzfoCJiYm0b9+el156iYceeijP+dTUVLp3706FChX48MMPVQB6gKsFYI6csWda/Y0ZM2Ywa9Ys\nmjRpwqxZs2jZsmWpLLTPnj3LypUriYuLY/Xq1fzlL38hOjqayZMnl/pZviIiIiIiIiIiIkWJq99h\nDgM2m6bpMAxjL9DQNM39hmGMA6YBn3ksoZQKSfZURsVtzX58+vrc46lJ15OUlP080vq7V4vAwMA0\nAgNPEHLDiUuOp6aW5cSJKk5fa4RW9GQ0j/DGvoCleT/As2fP0qVLFx566CGGDBmS53xaWhrR0dEE\nBgby8ccfl9qZZUXNnuRk5syezbKnnyI6Opq1a9dSv359X8fyujNnzrBixQpiY2P56quvaNq0KVFR\nUUydOpVq1ar5Op6IiIiIiIiIiIjkw9US8ChwJ7AX2H/h8afAGaD0fTdU3M5MSM5z7HzGuUue70mw\nU77ykdznObPjXJn9lppaNt9ZfRZLCkn2VEIsebe1dHbdpCbZsxb3JNjZk2AHsmf/RYRaMEIr5nu9\nos5qtbJq1SqP3qO0loAZGRk8/PDDGIbBuHF5V1BOS0ujR48eBAQEsGjRIhWAHpSztKez2YAOh4MN\nx48zc4/Jr+fOMujvf2f3oo9LXdllt9tzi7+vv/6aZs2aERUVxcyZM7nhhhsKv4CIiIiIiIiIiIj4\nlKsl4HTgQ8MwqgBfAHGGYQQBbYGtngonpUdOkeZM2tmQS0rAwuw/vR+AjLQgju/720VnMgE4fTYI\n/ghi1MmtjIlqeEXFXYglkGaWqjSLqOrya4q6nOVAPclms9G4cWOP3qOocTgcDBo0iNTUVObOnZtn\n+cj09HQefvhhHA4HixcvpmzZsj5KWnoUVASmZ2Xx+eHDzNyzm7MZmTz78MN8NnUKQUEF7A9aAiUn\nJ/PFF18QGxvLt99+S/PmzYmOjmbOnDlcf/31hV9AREREREREREREigyXSkDTNP9lGMZWINk0zU2G\nYUwAegOHgMGeDCilg2sl4NV9A9qV15kJyTSzlJxC72pUr16dzMxM/vjjD4/t61UaZwKOGTOGX375\nhbVr1+aZ4Zeens4jjzxCeno6S5YsUQHoRRcXgcnp6Sy07Wfu3j3UrhDMPxrcxgNDhmB55GEfp/SO\nU6dO5RZ/a9eupWXLlkRFRfHBBx9QpYrzpY9FRERERERERESk6HJ1JiCmaX510eOxwFiPJJISKcnu\nfPlMT0o7G1LomD0J9hI1q+9q+Pn55e4LqBLQPebOncv8+fPZsGEDFovlknMZGRn06tWLc+fOsXTp\nUhWAPpB4d1PeWrCAD9d8RevQUOY1u5eGVapQPjoqtyQsqU6ePMlnn31GbGws69evp1WrVvTo0YOF\nCxdSqVIlX8cTERERERERERERNyiwBDQM45+Aw5WLmKb5utsSSYmTZE9lVFzeVWOT9qayaW8iALfX\nrExSSqrT65Qtf+Kq7u/KTEBXZiKWBlarle3bt9O2bVu3X9tut3Pu3DmqVi0dZevy5csZNWoU69at\nIzQ09JJzGRkZ9O7dG7vdzqeffkpgYPHbQ7I4+/nnn4mJieGrr76iX79+/KdjR0K+WwtQogvApKQk\nli1bRlxcHBs3bqRNmzb06tWLRYsWUbGiZ/8xhoiIiIiIiIiIiHifs5mArSi8BPS7MEYloBTITEgu\ndEzZMv6FjymfdEX3rVupLgBnywST6tAsK1dYrVZ+/PFHj1w7Zxbg5XvilUSbN2+mf//+fPHFF0RE\nRFxyLiMjg//7v//LnYlVrlw5H6UsXbKysli+fDkxMTHYbDaGDh3KrFmzcme9nbnhBoASVwAeP348\nt/jbtGkT9913H3379mXx4sV5ZqeKiIiIiIiIiIhIyVJgCWia5t+8mENKMFdm2aWmZxU65mpnAgZb\nUkhNcj4bMCJU3wyH7BLwvffe88i1S8tSoKZp0rVrVz744AOaNm16ybnMzEz69u1LYmKiCkAvOXfu\nHAsWLGDSpElYLBaGDx9OVFRUnv0ZS1L59+eff7J06VLi4uL46aefaN++PY8//jhLly6lQoUKvo4n\nIiIiIiIiIiIiXuLynoCGYfwFGADUBc4DO4D3TdPc56FsUkK4UgIePXmO5zrUY/3uPyl70B/b8TOc\nSztFheAzVK58mhuqJmKxVL+q+1ssKSSpBHTJbbfdxu+//05WVhb+/oXPzrwSpaEETEhIoEOHDrzx\nxht06tTpknOZmZn069ePhIQEPv/8c4KCgnyUsnT4888/mTFjBrNmzaJJkybMnj2bFi1alNiZqAkJ\nCbnF35YtW+jQoQODBg2iffv2lC9f3tfxRERERERERERExAdcKgENw3gI+DewDvjxwutaAc8ZhvGg\naZprPBdRSoPU9Ewmr9qV+zysShCpp4+RleXPiRNVOHGiCpHW3wkMTLviawdbUgodY4QW7f2wxjZ/\n0yv3qVSpEiEhIdhsNm6++Wa3XttmsxEeHu7WaxYldrudTp068dhjjzFgwIBLzmVmZjJgwAAOHz7M\n8uXLVcp40M6dO5k0aRJxcXH06NGD77//nnr16vk6lkccPXqUpUuXEhsby2+//UanTp149tlnadeu\nnUpmERERERERERERcXkm4ARgtGma4y8+aBjGeGAyEOnuYFJyRIRaSNqb6nRMxaAypKRmOB2TYg8m\nMND5kqBJ9lTMhGT2JNjZvq0BkF0ChoUdBeD8+XKk2INzj1ssKYxp040QS6Crb6fEs1qtbNu2ze0l\nYHx8PPfcc49br1lUpKWlERUVRaNGjRg1atQl57KysnjiiSeIj49nxYoVKgA9wOFwsHbtWmJiYvjp\np58YNGgQpmlStWpVX0dzu8OHD7NkyRLi4uLYsWMHnTt3ZsSIEdx3331aXlZEREREREREREQu4WoJ\nWBNYks/x+cBz7osjJVFEqIVNexOdjnHkc6xupbqXPL87pAn/17xuPiOzJdlTGRW3Nfd5alrZ7J8v\nWgo0v9mEKgAvFRkZybZt2+jatatbr1tSlwN1OBw8/vjjlCtXjhkzZlyy3GRWVhZPPfUU+/btY+XK\nldqPzc3S09NZvHgxMTExnD17lmHDhhEbG1viZsEdPHiQuLg44uLi2L17Nw888AAvvvgibdu2JTBQ\nf36JiIiIiIiIiIhI/lwtAU2g2YWfL3Yn8LNbE0mJ48pSm/bzzmcBQuF7C5oJyZc8v7xEBOheqw3N\nIkre7CB3slqtfPbZZ26/rs1mK5El4Msvv8yePXv45ptvuO66//2RmpWVxcCBA9m1axerVq1SAehG\np0+fZvbs2UydOpVbbrmF119/nY4dO7p9H0tfio+PJy4ujtjYWPbt28eDDz7I6NGjad26NWXLlvV1\nPBERERERERERESkGXC0B44DphmE0A/4DpAJ3AU8DbxuG8X85A03TXOD2lFKshVgCGRPVMHeZzpwy\nLyLUQkSoBSO0Im+v3kXhO/c5V1hJmDNGJaBzVquVsWPHuvWap06dIiMjg5CQELde19emT5/O0qVL\n2bBhwyXLfDocDgYPHsz27dtZvXo1wcHBPkxZchw4cIApU6Ywb948OnTowLJly/jLX/7i61hus3//\n/tziLz4+nm7dujFmzBhatWpFmTJlfB1PREREREREREREihlXS8D+QBLQ7sKPHCeAPpeNVQkoeYRY\nAmlmqVpgAefKvoERoRan510tAcW5evXqYbPZSE1NddtSgwcOHKBOnTqXLJVZ3C1ZsoTx48ezYcMG\nbrjhhtzjDoeDIUOGsHXrVlavXo3F4vzXrRTup59+IiYmhjVr1tCvXz9+/fVXatWq5etYbrF3715i\nY2OJjY3lyJEjdOvWjTfffJOWLVteMrNURERERERERERE5Eq59B1G0zTreDiHlHKu7BtYWAlYkiXZ\nU53OpHTnvoZly5albt267Ny5kzvuuMMt1yxp+wGuX7+egQMH8uWXX17yvhwOB88++yy//PILX375\nJRUrFr4UruQvKyuL5cuXExMTQ3x8PEOHDmX27Nkl4mu6e/fu3Bl/CQkJdO/enZiYGFq0aEFAQICv\n44mIiIiIiIiIiEgJ4VIJaBjGB8Bzpmmeuux4HWC2aZr3eyCblCKu7BtY2Bh3zCYsipLsqYyK25r3\n+N7U3OJ0TFRDtxaBVquVbdu2ua0EtNlshIeHu+VavrZjxw6ioqL4+OOPufPOO3OPOxwOnnvuOTZv\n3syaNWuoVKmSD1MWX2fPnmXBggVMnjwZi8XC8OHDiYqKKvbLYe7cuTN3xl9SUhLdu3dn6tSp3Hvv\nvSr+RERERERERERExCNcXWusOfC7YRjPmKa5xDAMf2AoMAaI91Q4KT1c2TewsJKruMwmvNJZfWZC\ncqHXNBOSaWZx316HVquV7du3u+16JWUm4OHDh+nYsSOTJk2ibdu2uccdDgfDhw9nw4YNKgCv0h9/\n/MGMGTOYNWsWd999N7Nnz6ZFixacXRxL2tJPKdOzh68jXhGHw8GOHTtyZ/ydPn2aqKgoZs6cyT33\n3IO/v7+vI4qIiIiIiIiIiEgJ52oJaAVGAh8ZhtELCAMigTeAf3kom5Qyhe0bWBh3zCb0tKuZ1VfQ\nPobpmQ7OpWVwNi2TGWtMVv561G1LhFqtVmbNmnXVr79cfHw8LVq0cNv1fOHUqVN06NCBIUOG0KtX\nr9zjDoeD559/nu+//56vv/6aypUr+zBl8fP7778zadIklixZQs+ePVm/fj233norAGf+vZizsXG5\nYysU8SLQ4XDw22+/ERcXR1xcHGfOnCEqKoq5c+fStGlTFX8iIiIiIiIiIiLiVa7uCXjeMIzXyS7/\n+l44/IxpmjM8FUzkSrljNqGnXc2svvxKwPRMB7bjKbnPz6VlUDGojNuWCI2MjGTbtm1X9dr82Gy2\nYj0T8Pz583Tt2pU2bdowYsSI3OMOh4MXX3yRb775hm+++YYqVar4MGXx4XA4+O6774iJieHnn39m\n8ODBmKZJ1ar/+3V/eQGY87ioFYEOh4Nff/2V2NhY4uLiSEtLIzo6mnnz5tGkSRP8/Px8HVFEROT/\n2bvzuKir/Y/jrwEUVAZFBFFBUeNkBplLlluZLWppZQ6V3m6/um33ZveX15Zbt+v91bXFbuGSWqm5\nl6ag4lZZrte01MoMbTkukCggiLLLAMP8/hiYWIcBhk0/z8fDBzPne+b7/QwSjfOezzlCCCGEEEII\nIS5Tzu4JeCswF+gM/A3oBswqHp+stf6t/koUwnl17Sasb1V19ZWfU139F/MLqz1PXZYIDQkJIT09\nnQsXLtQ52LJarcTHxzfbPQGLiop46KGHCAgIYMaMGfZQx2q18o9//IOtW7eyfft22rdv38iVNn0F\nBQWsXr2ayMhI8vLymDJlCtHR0bRq1arMvPIBYImmEgRarVa+++47e8dfUVERERERrFy5kv79+19a\nwV9mIhyYa7s98Gnw6dy49QghhBBCCCGEEEIIIZzm7HKgXwCfA7drrU8BKKVWAR8CPwFt6qc8IS4t\nzoaApYUGGkk7bi4zlptvKXO/VYuK/yk7EyZWxc3NjauvvpqjR48ydOjQGj325a9eLHP/YlYeZkse\nbx+ZXiYceX3o9FrV1pCsVitTpkwhJSWFzz//3L6co9Vq5Z///Cdbtmxhx44d+Pn5NXKlTVt6ejoL\nFy7k3XffJTQ0lNdee43Ro0dXujxmVQFgicYKAq1WKwcPHrR3/Hl4eBAREcGaNWvo27fvpRX8ARTm\nQ+xKOLIKLPm2saTvIGwChE8Ej5aNW58QQgghhBBCCCGEEKJazoaAD2qtV5Ye0FofVEr1B15wfVlC\niBKhgUb7Ep8lLpYPAVu6V3icM4GjI+Hh4cTGxtY4BCwvIymDtoFtm2VI8s4777B9+3b27NmDl5eX\nffz//u//2LhxIzt27KBDhw6NWGHTFh8fz+zZs1m2bBmjR49mw4YN9OvXr8r51QWAJRoqCCwqKuLA\ngQP24M/Ly4uIiAhiYmK45pprmuXPtFNO7bN1/2UnlR235MPhZXDyS7huEnQd3Dj1CSGEEEIIIYQQ\nQgghnFJlCKiUuhHYr7U2lw8AS2kJyFKgQjipsq6+yuaUpgJ9qj1v60pCwLpy1b6A6UnptOvUzgUV\nNayPP/6YuXPnsnfvXtq1+73+V199lbVr17Jz584ye9iJ3x04cIDIyEi2bdvGn/70Jw4fPkxwcLDD\nxzgbAJaoryCwqKiIr7/+2r7Up9FoJCIigs2bNxMWFnbpBn+lfb+wYgBYWlaibY6EgEIIIYQQQggh\nhBBCNGmOOgF3ASHAqZIBpdRO4CGtdULxUACwHPi4nuoTolJpWWZ0cibHkrPsHW+hgUZCA42oQB/8\njJ6NXGHlKuvqq2xOaX5GT6aZ+pR5vpkXC8g1W2jV0p3WLd1p4VFxWcXy56mp8PBwoqOdD2WqkpGU\nQdtO1QeZTcm2bduYMmUKO3bsICgoyD4+bdo0Vq9ezc6dOwkICGjECpueoqIiNm3aRGRkJL/99huT\nJ09m4cKF+Pg0/b97i8XCvn37iIqKYu3atbRv3x6TycTWrVvp3bt3Y5fX8LrdBOnxjueEDG+ISoQQ\nQgghhBBCCCGEEHXg7HKgJW4AWpUbuwzaIkRTkpZlZmr04Yrjx832gG2aqU+TDAKd6eqrbI6f0ZNB\nRn/7Hn9fH0tlxVdxDs/jihDwyJEjWK3WOnU/ZSRn0q5z2zrV0pAOHTrExIkTWbt2LVdffbV9/PXX\nX2flypXs3LmTjh07NmKFTUtubi7Lli1j5syZtG3blmeffRaTyYSHR83+91LS0edsN2DrCFOdugAt\nFgt79uwhKiqKdevWERAQQEREBNu3b6dXr161Pu8lIWS4bdlPR7rd1CClCCGEEEIIIYQQQgghaq+m\nIaAQjU4nZzo1Z5Cx6S3VWFlXH9S8i7G2YWJN+Pv74+npyZkzZ8p0w9VUelI63fp1rVMtDSUuLo4x\nY8bw/vvvM2zYMPv49OnTWbFiBTt37iQwMLARK2w6zp49y7x58/jggw8YNGgQH374IcOGDatTYFwS\n6uWtX0Erv+MAXEy7gqLCsp89qW0AWFhYyO7du4mOjmbdunV06dIFk8nE7t27UUrVuu5Ljm8ItAup\nuhuwXYhtjhBCCCGEEEIIIYQQokmTEFA0OyXBWXVzSrrmmpryXX21PYcrwsTqlOwLWJcQMCM5g3ad\nm/6egOfOnWPUqFG89NJLjB8/3j7+n//8hyVLlrBz5046derUiBU2DT/99BMzZsxg7dq13H///ezZ\ns4crr7zSNScvzKeNysWz/ymKzqYBYOxygbyMrpgzuoLVrcYBYEFBAbt27SI6Opr169fTtWtXIiIi\n2Lt3L1dccYVr6r4UOVoSVJYCFUIIIYQQQgghhBCiWZAQUDQ7zoaAzXXfQGe5IkysTnh4OLGxsYwe\nPbpWj7darWQkZdK2o3NdiTmr1wDUaZnH2sjNzWXs2LGMGzeOp59+2j7+zjvvsHDhQnbt2kXnzp0b\ntKamxGq1smPHDiIjI/n+++956qmn0Frj7+/Cn71T++DAXMhOwsO/PRZrIZaUVDAU4dUunpbeZ7H2\neYxWTvxsFBQUsGPHDqKiooiJiaFnz56YTCb2799P9+7dXVfzpczRkqCyFKgQQgghhBBCCCGEEM1C\ndSFgUKkl0gzFf7oopfKKx7rUV2FC1IW5wNLk9g1MyzLz8vbZZGV5k53lDYC3MRujMRtvYzaenvn2\nua8PnV5vddQkaAsPD2fXrl21vtbFzDzc3A14Gb2cqqv0fnANFQQWFhYyYcIEQkNDefPNN+3jM2fO\n5IMPPmDXrl106XJ5/qorKChg9erVREZGYjabmTJlCuvWrcPLq/q/zxr7fiFkJ9nvugcEANiCQKBF\nZ2/cfU9W+fD8/Hy2bdtGdHQ0GzZsQClFREQEU6dOpVu3bq6v91LnGwIP72zsKoQQQgghhBBCCCGE\nEHVQXQj4VSVj2+ujECGcFRpoJO242eEcn1YtyDYXOpzTkPsGpmWZmRp9mPiMsnvjmdPak5bWHoCw\n8J/KBIH1oSZBW87qNVyRnMyc2NhaXy8jKZ22ndrWuK6S2/UdBFqtViZNmkReXh7R0dH2/exmz57N\n3Llz2bVrV52WQm2u0tPTWbBgAe+++y5KKV577TVGjx6Nm5tb/V20kuUnS4JA++1yy1CazWa+/PJL\noqKi2LRpE71798ZkMvHqq68SHBxcf7UKIYQQQgghhBBCCCFEM+AoBBzh5DmsrihECGeFBhrt3XxV\nceaHsiH3DdTJmdXOyc7yxtPzfL3VUJOgrWRu18JCfv3pJwoLC/HwcG714NJdjNHJ0aSHZTnsbCxf\nlzP1ucprr73Gt99+y65du2jRogUAc+bMYfbs2ezcufOyC5Li4+OZNWsWy5cv54477mDjxo3069ev\nYS5exfKTpYNAut1EXl4eW7duJTo6ms2bNxMeHk5ERARvvPHGZduxKYQQQgghhBBCCCGEEJWp8l19\nrfWuBqxDCKepwOr3l8vKc9wFCM7tLegqzlwrK8sbvw71EwLWJGgrPbeNhwcdW7Tg8Lvv0n/KlBpf\nNz4+npCQkBrXVVl9L3/1olPXdHYp1UWLFrF06VL27t2L0WgEYN68ecyYMYOdO3deVktIHjhwgMjI\nSLZt28ajjz7K4cOHGz4A9Q2BdiEVugEBLuZb+OyEB9Ff/INPP/2Uvn37YjKZ+M9//kOnTp0atk4h\nhBBCCCGEEEIIIYRoJqoMAZVSa4G3tNYHnDmRUup64O9a63tdVZwQlfEzejLN1AednMmx5Cx7wBYa\naCQ00IgK9GHW57+Q3ch1luZMCFiyT6Cr1SRoq2zuVW3b8sOaKHp1CapxV158fDyl9hWtUV3l63Pl\nDqRbtmzhn//8J7t37yYwMBCADz74gLfffpudO3c6DC4vFRaLhU2bNhEZGcmpU6eYPHkyCxcuxMen\n+pC93pRaEjTXbOHTH9OIOpjK50fOMyBcEfHIfcycOZOOHTs2Xo1CCCGEEEIIIYQQQgjRTDha328u\nsFAplQesAvYCJ4ALgAHwBUKBocB9xed6ql6rFZettCxzlaHfndd2wc/oWWa+M/sGhgYa663epqIm\nQZv5wAEsv52qcOwqn7b8lJFRq+U54+LiGDlyZK3rKl2fZaIR94C6L9964MABHnnkETZt2mQPKBcs\nWMCbb77Jzp076d69e52v0ZTl5uaydOlSZs6cia+vL88++yzjx493ernX+pTd4To+PfA2UQdT+eLo\nea7v4YNpgD9zHgwl4I8f27oFhRBCCCGEEEIIIYQQQjjF0XKgO5VS/YCJwP8CMyqZVgT8F5gJrNZa\nF9VLleKylpZlZmr04Yrjx832vQGnmfqUCQKd2TewIUNAZ0JJb6NrexdrErRZUlIoOHoUd39/3Erv\nwYatE3Bdgi0crGkQWN1yoA1Na83dd9/N4sWLuf766wH48MMPee2119i5cyc9evRo5ArrT3JyMvPm\nzeODDz5gyJAhLFq0iGHDhmEwGBq1rqysLDZv3kxUVBTbtm1j8ODBRPx5Cu/ffTcdOnRo1NqEEEII\nIYQQQgghhBCiOXPY+qG1tgArgBVKqQDgGiAAWydgIvCD1vpCvVcpmhxX78/miE7OdGrOIOPvXWLO\n7BvozBxXcSaUNLo4BHSWJSWFotTUKo9f5dOWnzMy7PedDQKtVivx8fGV7q1X8lhnQ8rWESbcA76v\n8rjRnMUNCcXHr0kEn84V5pw9e5bRo0czbdo0xowZA8DixYt59dVX2bFjBz179nSqlubm6NGjzJgx\ng3Xr1vHAAw+wd+/eKpdobSgZGRn24G/Hjh0MGzYMk8nEhx9+SPv27Ru1NiGEEEIIIYQQQgghhLhU\nOL3+m9Y6BdhWj7VcshwtZakCfSosZSnKcmY/vWPJWQwK/T0EdGbfwIb8vjsTOLq6E7CmQVtlXYAA\nPby9OZuXR05hIW1qsGRkamoqXl5eVe4x56g+S0qKraaAAFpHmGxzv6oYAroXWbgm+Sf6JP+Eu7W4\nEXnDIxA2AcIngkdLwNZtdscdd/DQQw/x2GOPAbB06VL+9a9/sWPHDkJDQ51+Xs2B1Wplx44dvPPO\nOxw6dIhJkyZx7NixRu2sS09PZ+PGjURHR7Nr1y5uuukmIiIiWLJkCb6+vo1WlxBCCCGEEEIIIYQQ\nQlyqGn8TqEtcbZayFGU5GwKW52f0ZJDRv0w42FhKQsmXt+8iK8ub7CxvwBb8GY3ZeBuz8fTMd/l1\nnQkC3QMCaHndgEr3AwTwcHOjp7cRnZlJ3/btfw/lqhEfH1/t/nqV1Ve6M7HldQOqvFbX9NPckPA9\nxvycsgcs+XB4GZz8Eq6bREGn6zCZTPTv359//etfACxfvpyXX36Z7du3N3pXnCvl5+ezevVqIiMj\nyc/P59lnn2X9+vV4eXk1Sj3nz59n48aNREVFsWfPHm6++Wbuu+8+VqxYQdu2bevvwpmJcGCu7fbA\npyvtDBVCCCGEEEIIIYQQQohLnYSA9aw2S1mKS5Of0ZMP7nmhwa9bXRBYEuo52kOwV1sffs7MYMiT\nT7h8P8DS9ZUOAN39/bH8doqc1WsqveaAM4crBoClZSVi/W4Bj8XMx9PTk/feew+DwcBHH33ESy+9\nxLZt2+jVq5dTz6WpS09PZ/78+cyZM4crr7ySN954g1GjRuHm5tbgtaSlpRETE0N0dDT79u3jlltu\n4cEHH2TVqlVVdoW6TGE+xK6EI6tsYTBA0ncVOkOFEEIIIYQQQgghhBDiciAhYD2rzVKWoqzQQCNp\nx83VzhFVqyoILN3V5ygsvMqnLcc7dnQ6AATnQ8CSa5sPHKDg6FGg7NKk9nq62L5YUmwhYZxvV3yT\njjg878ub09D6HNu3b8fDw4OVK1fywgsvsG3bNq666iqnn0tTFRcXx+zZs1m+fDl33nlKcWbcAAAg\nAElEQVQnmzZtom/fvg1eR2pqKjExMURFRbF//35uu+02Hn74YaKiovD29m6YIk7ts3X/ZSeVHS/X\nGUrXwQ1TjxBCCCGEEEIIIYQQQjQyCQHrWW2XsmwoL3/1olPzXh86vZ4rqVpooNG+dGplTmacpCht\nF79+dd7heRrzOTS0Sveh7DyA4NEedPlyA76FFytd1rOqILDvnXfy3nff1qiGuLg4wsLCnJqbs3oN\nlt9O4e5vC8PL702YGxWNZaIt6C3ZL/BEuzb0c3DOudtOs3a/hb1fH6B169asXr2aZ599li+//JLe\nvXvX6Lk0Nfv37ycyMpLt27fz6KOP8uOPPxIUFNSgNZw9e5b169cTHR3NwYMHGTVqFI8//jjr16+n\nTZs2DVoLAN8vrBgAlpaVaJsjIeAl7/Tp04wePZprr70WgAsXLjB27FiefPJJFixYwNatW2nTpg25\nubncddddPPTQQyxYsIA9e/aQlZXFmTNn6NWrFwaDgRkzZnDffffRpUuXMtf497//zebNmzEYDDz9\n9NP28REjRvDRRx/RubMsQSuEEEIIIYQQQgghGl+VIaBSyg14GXgC8Ae+A57XWu8rNScEOKG1dq/n\nOsVlTAVWv4SgtzG7ASqpvUpDuUAjoYFGVKCPS/eEdLgPpVsQlr738c+gXPzvj6j08eWDwNYRJq4b\nMpjYAQNqVEd8fDxjxoypdl7pZUjLh3+l/W3WCazY9jAs0W5Id1r4GirMXfdtKm9+lshX3x6hQ4cO\nrFmzhsmTJ/PFF184HUw2NRaLhY0bNxIZGcnp06eZPHkyixYtwmhsuC7Y5ORk1q1bR1RUFIcOHWL0\n6NE89dRTjBo1itatWzdYHZXqdhOkxzueEzK8ISoRTYC/vz8rVqwAwGw2M2zYMDw9PTl8+DCrVq2i\nZcuW5OTk8Nhjj9G6dWueeOIJnnjiCQ4cOMCcOXPsjy1R/j6AwVDxd48QQgghhBBCCCGEEE2Jo07A\n6cDjwGwgGbgf+FIpNUxr/X2pefIumAOylGXd+Rk9mWbqU2WItvbUZjw98xu5yqpVFsoVFBZxMiWb\nmG9Pc7GgkPBgX8KC2rokFKxuH0r3AH9OD+xOsIM5pTsE29x/H62tVvLy8khNTcXf37mla51ZDtTR\nPoSlFaWkYCneKxB+DwIvHs3FTeWUCQb36HT+vFyzdcE/6d69O9HR0fzv//4vW7duJTw83Knam5Lc\n3FyWLl3KzJkz8fX15bnnnuPee+/Fw6NhGrkTExNZu3Yt0dHR/Pjjj9x5550888wzjBw5klatWjVI\nDU4JGW5b9tORbjc1SCmiaUlPTwdsQd6iRYto2dK2N2SbNm145plnmD17NiaTCQCr1dpodQohhBBC\nCCGEEEII4WqO3kX+I/AnrfV6AKXUAmAd8LFS6hqtdUFDFNjcVbeUZckc4Zif0ZNBRv9K907cfLbp\nBoBQMZQrKCwi7lxOmbHEC7lk5xXYf1ammfrUOgh01T6UpYNAg8FAeHg4R44c4eabb672/Farlfj4\neLp161Z9wdUoHwCWVpDjjyUlHrAFgz+dycE07ygfP3kVfUc+xLp163j66af5/PPP6dOnT51raUjJ\nycnMnTuX+fPnM2TIEBYvXszQoUMbpPvo9OnTrF27lqioKI4ePcrYsWN57rnnuO222/Dy8qr369eK\nbwi0C6m6G7BdiG2OuCycO3eOP/7xj4AtSJ86dSovvvhihd9JnTp14vx5x0tJA/ZzAQQEBBAZGYnV\namX9+vXs37+/zHWFEEIIIYQQQgghhGgqHIWA7YCfSu5orYuUUo8Wj70MvFK/pV0anFnK0pk5ovkq\nH8rl5lsqzMnNt+DTqoX9vk7OZJDRuY676q5X2znlhYeHExsb61QIePbsWYxGI97e3g7nVbUHYYny\nAaCbv3+Zrr+igjZkxA+HeLgw4mZGL5hO5LzF3Pbgg8TExPCXv/yFzz77zL43WHNw9OhRZsyYwbp1\n65gwYQJ79+5FKVXv1z116hTR0dFER0fz66+/ctddd/HSSy9x66234unpuuVq65WjJUFlKdDLSocO\nHSos4Tlz5kzOnDlTZv/MkydP0rVr12rPV9VyoPfee2+FPQGFEEIIIYQQQgghhGgqHIWAR4FHgRdK\nBrTWaUqpSdi6Ab8CjtVzfc1edUtZuno/uIby+tDpjV1Cs1E+cLuYbyGv8GKZMXN2Lrkk2u/P/eZb\nNp891aS+z+Hh4Xz//ffVT8S5pUBLVBUEVhcAlpaRn889L73I42PG8OCDD7Jx40aefPJJPv30U/r1\n6+dUHY3JarWyfft2IiMjOXToEJMmTeLYsWN06NChXq8bFxdn7/g7ceIEd999N//6178YMWKEfcnE\nZsXRkqCyFOhl77777uPdd9/lzTffxN3dndzcXObPn89TTz3V2KUJIYQQQgghhBBCCFEvHIWAfwc2\nKaXuAP6rtX4KQGsdrZS6Fvi0+I+ohqOlLEXzl5ZldhjylnexoLDac2ZnOe6gc6S+9qEMCwtj2bJq\n9lwrVpMQEKrvCHQUAJotFv7n630M9Q9g8ti72Lx5M48//jhbtmyhf//+TtfQGPLz8/nkk0+YMWMG\nBQUFTJkyhfXr19frkpsnTpywd/zFx8czbtw4pk2bxs0330yLFi2qP0FT5hsCD+9s7CpEE1DZsrlP\nPvkk7733Hvfffz+tW7fGYrHw0EMPceONN5Z5XGWPLb0cKMCLL77o+qKFEEIIIYQQQgghhHCxKkNA\nrfV2pVQY8ADgW+7YP5VSe4CHgSP1WqEQTVhalpmp0Ycrjh832/f3Cw9uR1q241DO3aPiEqG1VV/7\nUIaFhXH06FGKiopwc3NzODcuLo7u3bvX6Pzlg0C34tDPClUGgEVWK09/ewA/T0/emTaN//oY+dPD\nD7N582YGDBhQo+s3pAsXLjB//nzmzJnDVVddxZtvvsnIkSOr/b7W1rFjx4iOjiYqKoozZ84wbtw4\npk+fzk033YSHh6PPggjR/AQFBbF9+/YK4waDgUmTJjFp0qQqHztw4ECWL19eZmzHjh2Vzr366qsr\njFU1VwghhBBCCCGEEEKIxuDw3V+t9UngjSqObQW2ltxXSkUC07XWqZXNF6Kpqa6Dz5llWnVyZrVz\nPFuUDXZatfAgq1wm6OFeNgT0NmZXe96q1Nc+lL6+vrRr147ffvut2oAvPj6+VvvwlQ8CvSc9VeZ+\naVarlX/9eJiUvDw2zZjJPt92PPzQQ2zatImBAwfW+NoNIS4ujlmzZrF8+XLGjBnDli1b6m2/wl9/\n/ZWoqCiioqI4e/Ys48ePJzIykhtvvBF3d/d6uaYQQgghhBBCCCGEEEKIpsOVLSBPAfMBCQFFk+dM\nB980U59qg8Dy+/1VJr+gqMz9Vi0rBjDu5UJAYx1CwPrchzI8PJzY2FinQsB77rmnVtcoCQLL3y4f\nBL53TPPflLN8OWMm3wb489Af/8iGDRu4/vrra3Xd+vTNN98QGRnJjh07eOyxx4iNjSUoKMjl1/np\np5/sHX9paWmMHz+eOXPmMGTIEAn+hBBCCCGEEEIIIYQQ4jIj68Bd5l4fOr2xS6iz2jwHZzr4dHIm\ng4yO93F0JgQ8c+FimVDuSEI6py6cx93Dgoe7BXd3Cwa3skFhXToBof72oQwLCyM2Npa77rrL4by4\nuLga7QlYXunwr/T9kiBw7alTfHj8GDveieTnrsH88Q9/YP369QwaNKjW13Q1i8XCxo0beeedd0hM\nTGTy5MksXrwYo7HmS7FWxWq1cvToUXvHX2ZmJiaTiffff5/BgwfX2/KiQjQUi8XC2bNnSUhI4PTp\n0yQkJJS5/cwzz3D//fc3dplCCCGEEEIIIYQQQjRJEgKKy5Iz4d2x5CyXhWjlQ7nntseQneVNVpY3\n2VnegC34Mxqz8TZm4+mZ75Lrulp4eDiffvqpwzlFRUWcOnWqTiFgZUqCwM/nzWPqjz/w6Wuvk3Cl\nYuIDD7Bu3TqGDBni0uvVVk5ODkuXLmXmzJn4+fnx3HPPMW7cOJftvWe1Wvnxxx/tHX+5ubmYTCYW\nLVrE9ddfL8GfaDaKioo4e/asPdCrLORLSkrC19eX4OBggoODCQoKIjg4mP79+9u/CiGEEEIIIYQQ\nQgghKichoLgsORsCVic00EjacXO1c8rz9MzH0/M8fh3OV3uNpiQ8PJy33nrL4Zzk5GTatWtHq1at\nXH79Y1cq/nz4Bz568UWy+/djwv33s3btWoYNG1Zhbs7qNUDFrsL6kpSUxNy5c1mwYAFDhw5l6dKl\nDBkyBIPBUOdzW61WfvjhB6KiooiOjiY/P5+IiAiWLVvGwIEDXXINIVypqKiI1NTUKsO906dPk5iY\nSNu2be3BXknId+2119pvd+nSBU/P2i1fLIQQQgghhBBCCCHE5U5CQCHqIDTQaN9D0NGcS0WvXr04\nceIE+fn5tGzZstI58fHxLu8CLDnvmDFjeH/RIrz8/TGZTERFRXHjjTdWmJuzek2ZPQTrMwg8cuQI\nM2bMYP369UycOJF9+/YRGhpa5/NarVa+++47oqOjiY6Oxmq1YjKZWLlyJf3795fgTzQaq9VKampq\nleFeQkICZ86cwWg0VujgC78iiOAsA8H+YXQZ9TxeAT0a++kIIYQQQgghhBBCCHHJkhBQXJZq28FX\nngr0ccmc5sLLy4uQkBB++eUXrrnmmkrnxMXF0b17d5deNy0tjVGjRvH3v/+dgIAATCYTt//zVr70\n+Jwvv/q8zFxLSiqWCylwq+3+C8VhoCuDQKvVyrZt24iMjOTw4cNMmjSJ48eP4+fnV+fzHjx40N7x\n5+HhQUREBGvWrKFv374S/Il6Z7VaSUtLc9jBd/r0adq0aVMm3AsKCmLkyJH220FBQWW7gQvzIXYl\nHFkFLfOhKBG2/gXCJkD4RPCo/EMFQgghhBBCCCGEEEKI2pMQUFyWXNXB52f0ZJqpDzo5k2PJWfYl\nREMDjYQGGlGBPvgZKy5l9/rQ6bUr3AVe/upFp+ZVVWN4eDhHjhypMgR0dSdgbm4uY8eO5Z577qFv\n376MGzeOTz75hB2eX1aYa0lJxZKSUvEcLgoC8/Pz+eSTT4iMjKSwsJBnn32WmJgYvLy8an3OoqIi\n9u/fb+/48/LyIiIigpiYGK655hoJ/oTLWK1Wzp8/77CD7/Tp07Rq1apMuBccHMytt95qv92lSxfa\ntGnj/IVP7YMDcyE7qey4JR8OL4OTX8J1k6DrYNc+YSGEEEIIIYQQQgghLnNOh4BKqVbAVVrr74vv\n9yu5XewJINnF9QlRL2rawedUcGaA1yMaL9xrKOHh4cTGxlZ5PD4+ngEDBrjkWoWFhUyYMIGePXty\n5513cu+997Jy5UpuueUWdnxVNgSsKgAsUZcg8MKFC8yfP585c+Zw1VVX8dZbbzFy5MhaB3RFRUV8\n/fXXREVFsXbtWoxGIxEREWzevJmwsLDGD/4yE22hDcDAp8Gnc+PWI6pltVpJT0932MGXkJBAy5Yt\nK3Tw3XzzzWU6+Ly9vV1b3PcLKwaApWUl2uZICCiEEEIIIYQQQgghhEs5FQIqpYYBMcBpoE/x8AGl\n1M/AXVrrOK31inqqUYh6cXf/IA6eSOOXpEyy8goxenlwZScfBvb0Y0B3v0o7+ASEhYWxaNGiKo/H\nxcVhMpnqfB2r1cqkSZO4ePEizz77LOPHj2fFihXcdtttFeZWFwCWqGkQePLkSWbNmsVHH33EmDFj\n2LJlC9dee23NnkhJjRYLe/fuJTo6mrVr19K+fXtMJhNbt26ld+/etTqny5VestGSbxtL+q75Ldl4\niYWYVquVjIyMajv43N3dK3Tw3XjjjWXGjMZG2KO0202QHu94TsjwhqhECCGEEEIIIYQQQojLirOd\ngDOBtcAzpcY6A4uA94DRLq5LiHqTlmVmavRh+32fVi3wadUCgKT0i2z47jQDutdtb7eGlpZlrvGS\npLXlTCegK/YEfO211zh48CDvvPMOJpOJZcuWMXLkyArznA0ASzgTBH7zzTdERkayc+dOHnvsMWJj\nY+nSpUuNn4PFYmHPnj1ERUWxbt06AgICiIiIYPv27fTq1avG56tXl8KSjc00xMzMzKwy3Cu5DZQJ\n94KDgxkyZEiZMR+fJrr/aMhw28+QI91uapBShBBCCCGEEEIIIYS4nDgbAoYDE7XWF0sGtNYpSqkX\ngYP1UpkQ9UQnZzo1Z5DRvwGqqbvyoaZ9/LjZvu/hNFMflwWBPXr0IC0tjczMzAqhg8ViISEhga5d\nu9bpGosXL2bJkiXMmzePBx54gKVLlzJ6dP1+1sBisbBhwwYiIyNJTExk8uTJLF68uMadU4WFheze\nvZvo6GjWrVtHly5dMJlM7N69G6VUPVXvAs19ycYmGmJmZWU57OBLSEjAYrHYg72SUO+GG27AZDLZ\nx3x8fBp/mdja8g2BdiFVdwO2C7HNEUIIIYQQQgghhBBCuJSzIeBZbMuA6nLjQUCOSysSop6VdMpV\nN2dQaMOEgHXt4mvoUNPNzY3evXtz5MgRBg8uG6gkJSXh5+eHl5dXrc+/ZcsWXn75ZebOncvDDz/M\n4sWLueOOO6qc7x5ge17OdgO2jjCV6QLMyclhyZIlzJo1iw4dOvDss88ybtw4PDyc3jKVgoICdu3a\nRVRUFDExMXTt2pWIiAj27t3LFVdc4fR5GlVzX7KxEULMnJycajv48vPzK3TwXXfdddx77732sXbt\n2jXfgM9Zjn6+mvLPlRBCCCGEEEIIIYQQzZiz73LPApYopQYAXwH5QD/gb8CSeqpNiHrhbAjYEFzR\nxdcYoWZYWBixsbEVQsC4uDhCQkJqfd4DBw7wyCOP8Pbbb/PUU0+xcOFCxowZU+3jnA0CSweASUlJ\nzJ07lwULFjBs2DCWLVvG4MGDnQ5j8vPz2bFjB9HR0cTExNCzZ09MJhP79+93yXKoDa65L9no4hAz\nNze32g6+vLy8MuFeUFAQ/fr146677rKP+fr6XvoBnzMc/Xw15Z8rIYQQQgghhBBCCCGaMadCQK31\nDKVUAfAc8HzxcDowF3ilfkoTwvVe/upFjqT2xpzveG8wP++rGqQeV3TxNUaoWX5fwJzVawCIzzfX\nOgA7duwYd999Ny+//DIvvPAC8+fP56677nL68dUFgSUB4JEjR4iMjCQmJoaJEyeyb98+QkNDnbpG\nfn4+27ZtIyoqio0bN6KUIiIigqlTp9KtW7dqH1/yfXK0H2Gjae5LNtYgxLx48WKFUK984JeTk2MP\n+Eq+9unThzFjxtjH/Pz8JOBzlm8IPLyzsasQQgjhYkqpIsAMdNRaZ5YaN2JbTcZLa+1W7jGLgYeB\nvlrrw6XGHwYWA5Zyl7ECgcBdDo531FpfcMFTEkLUwNexiXyw7kcA/nxvHwaFd2rkioQQovlTSo0D\nXsC2PZcB+AWYp7VerJRaCli11o+Ue8wrwE1a65uVUiHASSq+ZgKI0VpHlJ5f6hy3ABuASK31/xWf\nZz4wBCgoPvaU1jrXhU9XCOGkurzucnq9O631HGCOUqo94Akka62tNaxViEbnbczGnNbe4ZzQwJrt\nA1dbTW1pUmeFh4ezYcMGwBZs5UZFA6DdDISE1nz5y7NnzzJq1CieeOIJ3nzzTd5//33uuece+3Fn\nw7OqgsBWpvHsa+9L5KhRHD58mKeffprjx4/j5+dXbW1ms5kvvviC6OhoNm3aRO/evTGZTPz73/8m\nODjY6edY+vvkzHNpFM15ycbiEDMv9QRnLuSTcD6PhPNmTp83k3DeTEKWG6dnjyMhIYHs7Gy6dOlS\nJuQLCwtj9OjR9rEOHTpIwCeEEEI45yJwL7C01Ng92MLBMstZFIeDEcD32ILAv5U7129a60o/UVa8\nt3KVx4UQDWvVF7+ycusv9vtvLD3AxJG9mHD7lY1YlRBCNG9KqSeB14A/A5uxhW/9gUVKqc7YPvzk\nrJ5a61NOXvcOIAp4SWv9bvHwR9hes90DBGALAV+n4us3IUQ9q+vrLqdDQKXUZOAJoCeQBxxRSr2n\ntf64ZiUL0biMxmzSmlgIeDLjZJVzzsT+wq+GBQC8PnR6heOhgUbSjpsdXsfVz6ekEzD7k9VcjF5r\nHz9x4ACD27UrM/flr150eK783Hw++t9V3HP7PcyfP5+5c+dy77332o9XFZ5V9r0o/5j8oiLWB/oz\n7803sFgsTJkyhQ0bNuDpWfnyqiVho/vdd7F161aioqLYsmUL4eHhRERE8MYbb9ClSxeHz8dRPSVK\nbje5ILCJL9loNps5c+ZM1R188cfJyMqmcztPgtt7EuRr+9q7c2tu7z+a4Bv/h6CgIPz9/XFzc6v+\ngkIIIYRwxnpgImVDwAnAOuCRcnMnAAeB2cACpdTzWuvChihSCOE65d+IKlEyJkGgEELUXPGHpf4D\nPKS13lDq0EHgmuI5S6g8CKx1o05x5+FHwF+01stL1TIYuFtrfRH4TSm1EJhU2+sIIWrHFa+7nAoB\ni1uEJwOR2H7xuGNrBV6glOqotZ7hbNFCNDZvY3a1c1SgTwNU4hqhgUb7/oGO5pRwFJ45KyAgAENB\nAXErPiKwVSv7eEJuDoE//0LO6jVOBVyWQgvrpsbQNtCHmJgY3n33XUwmk/14bcMz82238l7MeuZv\n3cbV/fvz1ltvMXLkSIedXedWfMSm995j85nTbP/TI/QdOBCTycTbb79Np061X9am/HOo6XNpUI24\nZGN+fj5nzpypcnnOhIQELly4QOfOncvsw6eU4pZbbrGNGSFg3/O4uVXy93z3lKa9nKkQQgjRfMUA\nK5VSAVrrFKVUB2Ao8AcqhoCPATOBLcX3xxQ/XgjRTHwdm1TpG1ElVm79hZBOPrI0qBBC1NwQoAWw\nqZp5lb255exYGUqpB4AVwF9LAsBiF4EBWuu0UmPXAr9Vd04hhOu46nWXs52ATwGPaa1Lv5O9RSn1\nM/AWICGgaDY8PfMJC/+J7CxvsrK8yc7yBmzhoNGYjbcxGz/jA2Ue44rgrDLOdPFVF1o6E1jWNtRM\nyzKjkzM5lpxl71oMDTQSrH/gilZt+Ckjo0wIeConl+A2bZwKuKxWK5++9TkF+YWcPZ7Ch/M+5L77\nfp9fm/DsxIkTzJo1i48//pixY8fy2c6d9OnTp8oacnJy+Oyzz1gVOYMvvz3Itb7tGRsUxL+vuZaQ\nPz5Y54CuqufgzHO5lBQUFJCYmFhluHf69GnS0tIIDAwsswffFVdcwfDhw+1jHTt2xN3d3fHFeu1q\nkOckhBBCCLtMYCtwH7Y9403F98tsfq2UCgd6AOu01oVKqVXYlgQtHQJ2U0pdLHf+v5dalqq640KI\nevbBusNOzZEQUAghaswPOKe1LnIwxwA8WBzeleYB7Ck39qtSqnyH4Bit9fbi29cA7wM/Av+jlPqw\nZIWG4q/fAyilfIHpwN3ALTV8TkKIOnDV6y5nQ8A2QGwl4weB5tMy1QxUFbqEBhpRgT74GStfxlDU\njKdnPp6e5/HrcL5R63Cmi89YTQjoZ/RkmqlPrX5uHC3VaTa35EhsbwB6tO1hH0/Zd5qi1Ayyu4Rx\nKPsCI4rHC4uKSM67SJfiUNAeflWxeubuBf8l+VgyuRcucuvTI3jggd9fv9Q0PPv666+JjIxk165d\nPP7448TGxla5bGd2djZbtmwhOjqaL774ggHdQrjDowWvjbqDDqWWCa1rQFfdc3DVdRpbYWEhiYmJ\nDjv4zp07R8eOHct08HXv3p0bb7zRPhYYGFh9wCeEEEKIpsgKrAKexRYCTgDepeKnzx8HjMDp4v39\nPAEvpZS/1jq1eE51e/7JnoBCCCGEuFSlAr6VHVBK/R8wEvgVWK61/lMlx4eXf1g1ewIagNuAROAw\n8CbwfLnz/ql4fBtwjdY62dknI4RoOpwNAXdgW7rl+XLjE5HlW1wmLcvM1OiK6W7acbM9KJpm6iNB\n4CXEmQ49Z5Yv9TN6Msjoz6BQf1eUBWDvkCzNkpKK5cwZAHw7BPHdyTP2Y0kXL+Ln6YlnqSAnNyoa\ny0Qj7gFl6/p23fcc3fYzhfmF3PLUzVx9W2/7MWfDs6w1UcTs38+8b74mOTmZyZMns3TpUry9K9ad\nlZXF5s2biYqKYtu2bQwePJiIiAjeuX0krbd+UeU1ahvQOfsc6nqd+maxWEhKSqp6D77Tp0lJScHf\n398e7gUFBdG1a1cGDx5sHwsMDMTDw+ktaIUQQgjR/HwKLFJKDQX6AJuBQSUHlVKe2P7teC9wqHjY\ngG1Z0AexLREqhGgG/nxvH95YeqDaOUIIIWrsa8CglLpTa12ydDpKKXfgAWAN0JVaLv1ZiR+01t8W\nX+MxYJ1SalfJtZVSr2H7cNfdWutvanF+IUQduep1l7Pvyl4AnlFKjQG+AfKBvkB/ILp4U1IAa/lP\nIgjn6eRMp+YMMrou6BGNq6SL7+Xtu6pcmtTTM79RasuqJAS0ZmZCQQEAvu068kNGhv1YQm4uXVu3\nqfa8v+z+la+W7MXgZmDEX4YTNvJq+zFnwrOcwkI+iY/ng+OaDp6eTH7of5jwztsVusgyMjLYtGkT\nUVFR7Ny5k2HDhmEymfjwww9p3779ZdOpVxWLxUJycrLDDr6zZ8/SoUOHMkt0BgUFcf3119tvd+rU\niRYtWjT20xFCCCFEI9JaX1RKbQCWAxu11ubibr8S44Gs0m9oASilorEtCSohoBDNxKDwTkwc2avK\n/WkmjuwlS4EKIUQtaK2zlFJTgQ+VUo9i677zwbYUpz8wD/gPtlUYnFFdMGg/rrXeoJT6AFiqlLoW\nKAKeA8K11sdq9kyEEK7iqtddzoaARcDKUvdbAj8V/yn5xVObTxyIUkqWcaxujiu7vUT9c7TkZomM\nFifp0b1HtfMaUvlOQEtKKlZzHrRoAQUFtDP6k5SeisVqxd1g4FRuDsFtWpd5TOsIE+4B39vvJ/x4\nmk/f+hw3DzdufvImwkeFOV3P2YsX+fDEcT6KO8n1HfyZd91ABvp1oPUNN9gDwPuCnloAACAASURB\nVPT0dDZu3EhUVBS7d+/mpptuIiIigqVLl+Lr+/uKCvXdqVcyz9lrtI4wuTRkLCoq4uzZsw734EtK\nSqJ9+/ZlOviCg4MZMGCAfaxTp060bNnS8cUyE2H3XNvtgU+DT2eXPQ8hhBBCNCursHX1PV1qrOTf\nio8WHy9vHfCqUqpv8VxHb2pVd1wI0UAm3H4lQIU3pP4wqhcP3HZlY5QkhBCXBK31TKXUOeB1bK+T\ncoGdwBCtdUrxHn+VvR6qbPx4uQ9lAXyttb6xivnPAjdie802h+L3/8udI05rXeGkQoj644rXXU6F\ngFrrh2tWmqgNZ0NAIRqaJSWVotQUAAwtW2DF9kqgjZc3J3/7jdCQEE7l5JTpBLQHW1/ZQsDUuHNE\nvbQWN3cDNz95E9fcEV7hOpWFZz9lZPD+sV/5PDGR8cFd2XLzLfQoXvKzdYQJ8223smbJEqKjo9mz\nZw8333wz999/Px999BFt27atp+9I9ZwNAmsaABYVFZGSkuKwgy8pKYl27dpV6ODr16+f/XaXLl2q\nD/gcKcyH2JVwZBVYirtVk76DsAkQPhE86nBuIYQQQjQLWmu3Urc/B9xL3d9Vcl9rfUsVj/+p1GMO\nAcscXGuZo+NCiIY14fYrCenkwwfrDgMG/jL+Gm4Ikw5AIYSoK631CmBFFcceqWL81VK34wG3yuZV\nNr/UWB5Q+s26KCfKFUI0gLq+7pJNmsRl5fWh0xu7BKeczDhZ6Xj5rsL6fD7exmzMae0hv8AeAJYo\nCQK7erfjaFISPVu3JiE3h0EdbF2q5YOtrNQsVv1tNQA3PX4jfe68psrrtrn/PqxWK5/Om8f7WvNz\nZgaP9ryCA6PuwLc4tEozm9nRuRMblyxm3+OPceutt/Lggw+yatUqfHyq32exoTr1qrtO+fNarVZS\nU1MddvAlJibi4+NTJtwLDg6mT58+9tudO3fGy8urxvU67dQ+ODAXspPKjlvy4fAyOPklXDcJug6u\nvxqEEEIIIYQQjWpQeCdZ+lMIIYQQogHU5XWXhIBNSGigkbTj5mrniKaluuU+SwK9Hm2b1nKf1TEa\ns0lLMmItyKeyDxAZWrbgKqORX84ncmdqKqfS07mva0iFYCsvK4+Pn/mEAnMBN/95OH3vurbKa5rN\nZlatWsWMGTOwpKfzZHAwK4KH4OnuzjmzmeUnT7LpzGkOZWVy2x138MgjjxAdHY23d8X9C6tTX516\nVV0nZ00Uafn5JF3M5UzuRVJ7XUnqD4dI2LTRHvKdOXMGb2/vMuFecHAwYWFh9rGgoKD6Dfic8f3C\nigFgaVmJtjkSAgohhBBCCCGEEEIIIUSjkRCwCQkNNPLN8XPVzhGXnh5te5Tp6nNmH8H61sr8G9YC\nP4dzbujQlm3nEwFIyMklJPSKMoGZ2Wxm76vfUJBewIy3ZvDUU09Vep7z58/z5ptvMmfOHMLCwnj7\n7be5/fbbiVuwkFXz57Pp9Gl+uHCeEYGBPDpxAve+9hpt2rSp9Fw1UdNOvapYrVYSFi3mTFoaaVf3\nrrSD7/Rvv9HKYKBTq1Z07dGTbi1bEOztze23324P/Lp06ULr1q2rvV6j63YTpMc7nhMyvCEqEUII\nIYQQQgghhBBCCFEFp0JApVRX4IzW2lJu3B3oqLVOrI/iLjcqsPplDJ2Zc6lxNhBrLkt9NheeLfLo\n1e4Lsgs60DehE/Fe7QEIyTtPj7w0euSd56x3K+aZ87D4+ZHy68/0GDHC/viioiLGjx9PbGws06dP\nrzQAPHHiBLNmzeLjjz9m7NixfPbZZwQEBLBu3TqmT5/OoUOHuP3qMB7p2ZMRHYfQYcIDderKq0xV\nQWBJAGi1Wrlw4UKVy3MmJCRw+rffaGm10rlVa4J7dCdk4ECCg4MZMWJEmQ4+Nm8pc81mK2S4bdlP\nR7rd1CClCCGEEEIIIYQQQgghhKics52A8UAIcKrc+FXAQaCV60q6fPkZPZlm6oNOzuRYchbHkrMA\nW/dfaKARFeiDn9GzkasUlwv3AH9aAS1TThFxLrXSOd7eRs7k5vJb69Z09POj3R8m2o898cQTbN++\nnddff52//vWvZR63b98+IiMj2b17N48//jhffvkl+/bt469//SuxsbHceeedTJ48mdtvv51WrVqR\ns3oN4Nrw7OWvXsRqtWLONpOZl0V6r3Qy4pLJupBHdh5kv32AzBf+Qt65PFq0aFFmec6goCCGDx9O\nUFAQfrFH8P3vHrw9fv912nrELZXX6qD++niO9cY3BNqFVN0N2C7ENkcIIYQQQgghhBBCCCFEo3EY\nAiql4krd3auUKiw3pR3gYGMoUVN+Rk8GGf0ZFOrf2KU0upIOwJI99arS3Pbac5bZ3JLsLG+ysrzJ\nzrLteedtzMZozMbbmI2nZ36dr1FZ92Ralvn3INqShSHnFFEdDtu7/3wLL9rntnBzo7u3ke+6BdPd\n/Pv41KlTWb58Oa+88gpTpkwBwGKxEBMTQ2RkJMnJyTz88MMMHDiQTZs2MX/+fMaOHcvzzz/Pbbfd\nVmHPO/u+ejUMyjIyMqrs4Ptef0dmShYGgwGfACNGfyNGn5YYO3jT9ZqO+AT44BNg5J27Z2I0Vr4M\nb87qNeTu+xo8yv4qLekqdLbOnNVrynQiNosg0NGSoM1wKdBmFcIKIYQQQgghhBBCCCGEE6rrBHy1\n+OtiYBaQVu64GdjhqmKUUkOAD4BQ4FdgstZ6p6vOL0RzYTa35Ehs74rjae1JS7MtyxkW/pPLr5uW\nZWZq9OGyg+07cr57Xw6lpgDw/OldZYLA8PAwDlsshISEADBnzhymT5/OSy+9xD/+8Q+ys7NZsmQJ\ns2bNom3btoSHh1NUVMTs2bO56667+Mc//sEtt9yCp6fjLtfyQVnRHaOrXJ6z5LbVarV37pV8HTx4\nMEFBQaxLjcInwAfPNo6v6zAArGIvQXA+CCx/npoGiJWdry6Pd5qjJUGb2VKgzTKEFUIIIRqQUioC\nmAyEA27AceAjYJbWulAp1R6IBO7A9kHRZGAt8C+tdbZSKgQ4CZRsL2EAMoFo4Gmtdb5S6mFs/+4s\nPScdWA48r7W2FJ9nPjAEKAA2AE9prXPr79kLIb6OTeSDdT8C8Od7+zAovFMjVySEEM2HUmo7cGPx\nXbfir0XFX+O01qp43r+AV4BxWusNpR7/MLbXSC9prd8qNT4c2KG1dis1NgKYCvQvHvoFWKi1Xliu\nphHAS8AAwBP4Ddvrsula65ziOSHI6y7RxMlrFOc4DAG11ksBlFIAUSW/BOqDUsoH2y+TV4D3gPuB\nGKVUqNY6pb6uK0RTVNL5V9c5NaWTMysddw+wdaYWpaZw0qs9/bPPALZ98/rGnWTDhg2MHDmS1atX\n87e//Y3Jkyfz5z//mZdeeon58+fTpUsXPD09OXXqFP369eOVV15hxIgRtGzZsvLnlp1tD/Rm75hJ\nRlwyGafPkXUhz/Zny3qsVjB2tHXr+fgbuXvAOK6//nrGjx9vD/zatm2LwWCo9Bp7vtpV6+9TdQFg\nieoCvarOU9sgsEHDLN8QeLj5f0bD1SGsEEIIcalRSj0HvAj8Fdu/1/KBm7D9m20wcC+2oK4ACNNa\npyqlrgSWAkuAiFKn66m1PlV83quBrcBfgNnFx3/TWncvde0w4AtsAeJcbMHj98A9QEBxPa8Df3P1\n8xZC2Kz64ldWbv3Ffv+NpQeYOLIXE26/shGrEkKI5kNrfUvJbaXUEsCqtf5T6TlKKQPwCPAd8DC2\n1zilFQBTlVJrtNZxVKL4Q1vzgWewfTCrCNtrtg+UUu1LAkSl1H3AIuDvgAnbB7OuA94FblNKDdNa\nFyCvu0QTJ69RnOfsnoArgceVUuFA6XftDVTyi6uW7gQytNZzi++vUkpNBcYD77vg/MKFZOk855Us\nV1rZ0ptVyXIi4HNmTk2V7ENZmZIg8GS2H/2zz9A6wkSb++8jfMsW3n33XbKzs/nDH/7APffcQ1xc\nHD179rR30Q0aNIiIiAiGDx9OQUEBCQkJ/Pe//62yg89sNtuDvJSiU3h7QWC3doRe64WxXSuMvl54\ntm6BR8eO9rr+PfTfVdbuyp9XZwPAElWFSq7qJKzqfBJmVc/VIawQQghxqVFKdcb2Zs9orXXpFWC2\nK6VGAr8opUYBtwImrXUqgNb6V6XUZGBSVefWWh9VSv0XqPJf6VrrI0qpXcDVSilvbKHj3Vrri8Bv\nSqmFjq4hhKib8m+ulSgZkzfZhBCixir/pDrchi20exzYr5TqoLU+V+p4IrAT23vko8o/WCnlBcwD\n/qK1Xl3q0BdKqUeAB8vNe0ZrvbjUvAPFr+008KhS6mPkdZdowuQ1Ss04GwIuBcYCXwIZFId/VP2L\nqzb6AT+UGzsKXOXCawgXkKXz6l9jdQI6CgHBFgQmePSh9c3K/vceFhZGamoqM2bMoE2bNsTExNCi\nRQsGDBhAaGgoHh4eJCYm8txzz5GQkMDFixfLLM8ZHBxM3759GTt2rH2sffv2GAwGclav4ZULK6qs\nx5KSYq+rKk3x57XC0qbFz8MtIKDMvNouKVrTx1+OXB3CCiGEEJeo0UBiuQAQAK11vFJqNzAC2AfM\nVkr1Br4Cvtda7wf2l3uYAeyfdr8GGA48UdmFlVJuQB9sn2D/J5AHDNBal96i4lpsy1cJIVzs69ik\nSt9cK7Fy6y+EdPKRZbeEEKJmrFWMPwZ8qLX+QSn1E7bQbla5Oc8CPyulJmqtV5Y7NgjwxrakZxla\n693A7uK7QwAjUOHNNq11hlIqBttruw+R112iiZLXKDXnbAh4F7b1iL+sx1p8gfIJRC7Qqh6vKWqo\nqXUbFea3Iu1ce7KyvJmaZNvLLjTQSGigERXog5/Rtt/by1+96NT5atKtV19yVq+h90VfMtp3dDjP\nz9vxXnb1xaNrV1qND+fnn39m//797N27l8LCQsC2jKe7uzseHh78/PPPnDhxAi8vLzw9PfHy8uLK\nK6+kZcuWuLnZlitPTEwkKSmJb7/9FoPBgJubG25ubhgMBqxJyVgSEznR0bZMusHN9pkDg8GAwUDx\nHwMYwK1VKxJDztofW/LVcjKOopMnMQBuBgOGHw7RctkyWva6kn1JXwEGDG6G389V+rbBgMFgYNrO\naRXOW+jVksIfYzFgwM1Ama+GUvfdDAY8B/SnVU42hiVLcHNzI//AAQoOfmuviYxMrFmZuGHA/Vwq\n7m3b/V4v4PbuHLwOHaL18JsqfI/c3NzI27mLgt27oVwtbsW1uC1YSKu4ONqMHlXmOZT+WtlYbeZU\nNbeqJVkbS+5HH2D4ejZtOsLFtCsoKqz8fzON/ftNCCGEaAICgdMOjqcCPthWdXkSGAm8APgopfYC\nrxS/8VTiV6WUFXDH9m/Rg8CBUse7KaVKNp82YPsA6jJgqdbaim1JKpRSvsB04G7gFoQQLvfBusNO\nzZE32IQQom6UUh2wLd/5TPHQMmxLgpYJAbXWF5RSzwDvKqU+K3eaTsA5rXXJ3soopc4BbYrvegA9\nsb22Sy1e7rMy54AQrXUh8rpLNFHyGqXmnA0BM7G1HdenbKBzuTEjcKKeryuc1NS6jQrzW5F6YjhZ\nHrY38Nu2NQOQdtzMN8dtHfPTTH3sQWB9cWVwWPI9Du5wDee793XY4RYaaHTZdUufM+24ucL4xax0\nUk4e4ezJo+QlaeY+cQRfX19uuOEG8vLyKszv0aMH48aNY+jQobRp04aioiKKioqwWq2Vfs3IySch\nLZsz53M4cz6XwpSz+BX4EGAMxO2K87gb8sAKVitgtWK1gpXir0W2D1EN9PSi5XUD7Oe9eOAg5nNp\nFPn4YLVasQJFVivWM2fw8GtPm/ZtsBYBWLEWFZ/Lai0+n+1rUZEVs9lcse5OnTCnp5P/q/79vJV8\nde8eglthIdavvrI/Pv9kHAXJyVixUpSXhyXPbHsMVqyZGZByFmtLT9vx4udpyMnG7YdDFb6PhWdT\nKExNwWotfnyp70sR1t/r+e4ghvffg+LvRWV/B47+fmoyp/xcq9X299NYAWSZr4AhLQFD3gXcDFZb\naGo4ARYvrP/P3n2HR1Xlfxx/z0x6I4UEEloI5NB7SULvVQNKbwJiR2R31f2tBetaQFwsa1sXUBEU\ndNGAgEiRXQRCDYgIHFoASQIhISG9TOb3x50ZJslMMoEEopzX8/CQuffce8/cTMLlfu75nmJ3c/Ba\nJsj9aTsu81/HtUGDm9fPGtrf7XpsR+sqsnv3bqZPn87SpUuJiYkBIDc3lx49enDffffx6KOPkpiY\nyLBhw3j//fcZMGAAAKtXr+bNN98kIiLCuq8RI0YQERHB7NmzadXqWnGD7t27M2fOnMp/MSuKotxa\nqYDjC1LtZtJ6oFBK+Rbmm1VCiJbAX4ANQojGNu2FzZyAddHm+fsWrdwUlJkT0B4hxL3Aa8BmoL2U\nMqXK70pRFEVRFKX2uAfwAH4WQoB2v95PCNFJSplg21BKuVIIMQV4A23OPotMILBM27oAQggvtPvu\noIV8AUIIvZSyxE5fIoDzlhfquktR/hicDQHfQ5t8dKr5SYCacJjyNY3bAl/V0PGUKqiNpfMKcwMr\nbSNTrhLjW9F9i9rD9hxH5KeRkFpxqcuaCgF3HEsm7fwJLp7SQr+Lp38hP+sKweGtqdesLdPve5Dp\ndw0hJCSEwsJCGjfW7uv4+PiQnZ2Nr68vubm5vPrqqwB06dKFKVOmEBsba21rKy2rgHlfHwJDCARD\nYDAYA1IpCWxGClAcUEJz/x9wM+RW2Pd7AvpbP385K1eRm5EJzZrZb5xXQOZdERWGrBZ/7/V3h+sq\n+rmwzJnoaLvs997HmJpqd70hONhaGtTRfq4du00l78C5PtWk6ggXryeALPV38kFKDq+i+EIaxemF\nWlBqE54ai/XkXw2jKN/PGuRi/tu1Zw/ce/e6Of2spI3RaKS4uPiWHLu27e9Gjg2WUcU63nrrLbth\nXOPGjVm3bp01BPzxxx+pV+/aCO3Vq1czfPhw4uLirCGgTqejT58+vPbaa6X2tXv3blq1asWyZY7L\nGyuKotRSG4H3hBBR5vKeVkKItkA34EmgSAgRYpm7Rkp5TAjxF7TSVhHApbI7llJeFkJ8ihYCOkUI\n8XdgEtr8NPHX+6YURancQ3d34NVP9lTaRlEURblhs9Dm2ltrfq0DPkQbDZhgp/3DaFNonbdZthPQ\nCyFGSCnXl2k/yObrn4AiYCywyraRECIErbrDRPNrdd2l1ErqGqXqnA0Bu6LNB3FeCHECMNqsM0kp\nB1RDX/4DLBBCPAQsRisn4wXEVcO+lRtQWQBoUVNBYESdCLvLE9MbU+Di6XA9aHPcxUTW/hCw7DmO\nyE8HoKSCIFDU97vh45pMJs6ePcvu3buJj49nx854Eg4epE5IQ0Ii2tCgVRc6j5yOf2gT9HoDAPfa\njK5csmQJ/v7+pKamkpuby8KFC3nyyScpKChg4cKFpKSk8PHHH/PCCy/w1FNP0bRxQ+7qHMSoHoIW\nPt3ALQjZuX+5flner+X9ZxfVJdBw7lqDInPVAldXc/sQvO+2CQCd+LyWnU/QeCm11GtnWD7rluNZ\n5vbzmf1IpT8HjorAA9Zw0Jn9/B7odDoMBsOt7cS3/4TQQggNx3jpkvX7fY2JkqIMsi5Ellp6q4JT\npWZZRqmWlJTY/WzqdDo6d+7M4cOHKS4uxsXFhfXr1zN8+HAASkpK2LRpEytXrmTEiBFkZ2fj4+Nj\nDRgVRVH+KKSUZ4UQrwBfCSEeBrai3TjqCfwLmA/sQJvb/SMhxONoc8XUQysLehHtYU/LUxTWodhC\niGDgEa7NUVMhIUQo8ATQTkp54sbfnaIoFYlpF8rkoS0dzrkzeWhLVWZLURSl6nTY3BISQvQAmgDL\npJS5NstXAouEEE+U3YGU8oIQ4mngH5Z9mUuF/h34WAhxP9o1mwltwM1CIM/cLsd8vfahEMIF+A7I\nQZuH+QNgrZRyvbruUmozdY1Sdc6GgIfMf+ypljteUsoMIcQo4H1gEfAzcKftL0Dl5nM2ULG4mSMC\ns7N8Km1zIqXsNJO1j71zHFCcx5O/beO0RyCns4M479IBl8aN7c53WBUpn3zKgdOnOOTpaQ3+dDod\n0dHRREVFsWD+azRt0Y6k7BJOpGRZz5+94+bn5/PKK6/Qtm1bzp8/T2FhIZGRkSxevJhZs2bx3HPP\nMWbMGE6dOkX8ju18/I8X2LA9ga+unGPx2t1g/JRhDYPwG/AAxg7DMLi6leqrNYwrTiGnuC6BmEPA\noiJMhYWAduVkaNDA2rYqn9e/bvYEsvAaNxS49tk1NAnAvXt3pz/DlnaWkX364IpDREsfDeaRfiUO\nRgNW9ou1bABZmds+zGrSFzISAaznvmwQWJhT+nt325+zPzDLKEDL/KSO2vTo0YPt27fTtWtX0tLS\nGDRoEBcuXOCnn36iQ4cO+Pn50a9fPzZs2MC4ceMA2L59O9OmTbPu54MPPgDg2LFjpZa/9NJLNG1a\nYcU7RVGUWkFK+YL5QdB5wEq0y5QjwN+llMsAhBBD0OaK2QUEAenAJqC3lDLPXNoK4KT5axNaWarv\ngZnmdSYqvgSKAdyAX232B3BGSinsb6Ioyo2YNKQFQLmbbFOGtWTi4Ba3okuKoii/d2Wvd2YBa+zc\n//4O+Ddwh51tkFK+L4SYxLWS6kgpXxVCJAF/B1oDuWgPa41Fu9duabfE3O6vaCMOXYATwBLgHXMz\ndd2l1GrqGqVqnAoBpZQv1HA/LMf5CWh/M46l1H6VzbU3L/kQadnl56/7PakotAoozqNL9gW6ZF+A\nyz/j1XAs3r2dDySMRiNHjx61hn27fviBMxcu0Mbfn+joaCbfcw/vvPMOjRo1Kjc/VhOodATlv/71\nLzp16sTp06fx8/Pj4sWLxMXFsXjxYry9vZk0aRLbtm2je6d2rHqkNV9N8eFybBTLNp1iya7LZJSY\nOJefwYVvFnF6yZuEte1B0059aNyuBx4+dQAtCNSlppNTVFc7qE0ACNXzBELBnj0Yz2oBo/HSJYqO\nHKFo7z6gamG2CdAHB2MICXEYhpf9fjsKAivbj4WzQaAKs4DwfnDoU+tLe0FgkU0IqM6ZAtp8fp9+\n+ikZGRkMGnStgsrq1atJTExk2rRpZGdnk5iYaA0Be/fuXa4cKEDLli1VOVBFUX63pJTLgeUVrL+M\nVvrT0fpEwPGTF1qbT4FPK1i/urJ9KIpS/SYNaUF4qB8frj4E6Hh4THui26qn6xVFUa6HlHJmmdez\nHLTLQJsn0KLcNZKUsredZZ8An9jZ5YAy7b5HexjLUT/VdZdS66lrFOc5OxIQIcSf0J5OaIxWHvQv\nwP+klF/UUN+UWqA2jzaKrO9L2smKQ8CamDevulT3KMuLFy+ye/dua+i3b98+6tWrR1RUFJ1c3Rjf\nPJLWXbrhZh754mUCbztz9DnVl9xcXn/9dVavXk3//v1p2rQpKSkprFmzBqPRyPjx4/H29mbUqFF0\nDPdnwPObeenupjzQxoXZooRHIgPYf7GYpYfz2SPzaVvfl1xXT07t3cr2z9+gbuMWhHfqQ3jH3uDu\nis7kUS4AxM0Nnaurtayn7blx9rwamjQuFQBawjhjairZ771fap+OlB3ZZz1HTo6KLRsEWgJAZ1X2\nnlWYZRYQDv7h1tGAUDoILCnypqTIG1DnTMFa1rNdu3acOHGCixcvMn/+fHbv3k1GRgZHjhxh06ZN\n1vYDBgzgwoULt6q7iqIoiqIoNSqmXagqq6UoiqIoSq2jrlGc41QIKIT4M/A3tBrCf0d7EuAw2rwP\nflLKj2qui8qtdkOjja4mwZ5/al93fxT8wqqtX5H1fYk/ebnSNmVdz9xvtU1BQQEJCQnEx8dbQ7+M\njAyioqKIioriiSeeoHv37gQFBV0LGwMCS+3jRkq3vv/++/To0QODwYAQAr1ej4uLC97e3uzatYte\nvXoxcuRINm7cyLBhQ+gW7sPHm8/xwy4THw7xJdBTT9f6rnSt78r/Dfbi9WNNWHfoBJkZl2jR6w78\n6zfh8tnjHNywDHdvP6LbdqbfxUt0yM9Hr9OZgzLbORGzyClahfeE8U5/Xh0FgBbOBIGVBbllz7Gj\nvtmGfrZfOxtGOdqvCrPKsCkJamE53/lSBYDKNbajo/v37098fDxhYdq/X+vWrWP06NGl2g8bNoy4\nuDhCQ+1feFrKjyqKoiiKoiiKoiiKoijKzeTUHSkhxCngz1LKNUKIPKCDlFIKIaYAL0gpI2u0lxX3\nLRw4s2XLFho2bHirunFbqCjwKHfjvLgQDq+AX74Ao3n0lsEN2k6CdpPBxc3ufqoiLauAeV87mqpS\n8/LYDgT5uvPMT38DtADQMnLMEBJSLgisrARpdavonC4YlAdoI1IyL+dyMb2E5PPZJP2aROqZywQ2\nCiSsdShz7/oT0dHRREZGlpvfypnRhlUNPbKysmjevDlbt25l586d7NixgyNHjpCYmEhERAR9+vTh\njTfesLbfufFr+o8cT4dgA93qu/DdqUI+HelHjwau2v4M7rxR8hhpprpkXrnIidP7kQnb8AsOo0XP\nO/B188E7/hsOnT1OWl4eg4OCGN60GX3q1cPTYHD4Xip675UFgKXaBgfjM/uRSkt7VqTsOa6J70vZ\n/f7ewqyclauAGp5P9EoixM20uyrXMAaTW8jv6pwpilK76FTSqyi1lvo/o6LcGurfRkVRlNuPuu5S\nlFujousuZ8uBhgG/2lm+B236MOU24PRoo3M7tdF/2cmld2As1ObkOr0Jus2Gxj1KrU7LKkCmXOVE\nShYnUrIAbSRfZH1fRH0/gnzdS7UP8nXn5bEdnN7GNgDUXlvCwFs3ItDeOb1aVERCejq71l8kOfEK\nyWcyMLi50KBdQxq0DqNl//6EtqiPq4cWok3vNd3uvp0Nqao6IvDdd99l4MCBtGnThvfee4+OHTty\n7NgxQkNDKS4u5ttvv2XBggXWUS8dMkrYNq4+A75KJq/YxPy+3kxck8nDd9/iZwAAIABJREFUnTz5\na3cvXIrdSXPR5vyrE1CPrl1G0HXwNC5cPMGvm78m+dTP3BkWxsLOXQh2c+eHlGQ+PCl5ZO9uegaH\nMCwsjMH1Qwn28Cj1XhyOuqtCAAj2RwTeaCnXmirhabvN7ynMKns+a6zvAeEw40e7q7xq5oiKoiiK\noiiKoiiKoiiKoii3jLMh4GFgKHCyzPKRgKzWHim1Wtnwwm5YceDj8gGgrawkrY1NCOhoVF/ayQJr\nyU/LqD5bQb7uxPgGExMZXCpEXH8wifUkWQPBh852weObrwDPsp3Ba9zQWxaYFBcXc7JlC/4X4M+u\nTZs4kJ7O+dwc2gcEYOzkR9voRgyd05+AVuFV2u+NhlSWkZNl5WcX8MGCj7jn/Sk889PfOHjwIBMm\nTODrr7+madOmHDhwAJ1Ox9GjR2ndurV1uzbeDdkxNY/eyzN49qccNo6vw2Obc9h2LpMPezbgSdM2\nTnsEctojiESPQAzuOnqPvYtG7ZvituZLNp4+xtx9ezHodEwKb8q/ukfjotezJSWZ75OSeO7nQ7Tw\n82NoaBijL/xGZ5MJnU5n9/MKkHv2nFMBYEXn6EbVVAnP31P4B+U/q9V9nhVFURRFURRFURRFURRF\nUW5XzoaATwDfCSG6mbf5sxCiMTAEGFNTnVNqp0pHG9mZd6uc8H6lXsqUq5UeV6ZcJcbX/qi9ikLE\nHTuPUpKazZMungQU55VrczNDh+Tk5FLz+O3fv5+GDRtqc/kNGsR9iYm09KuDq17PgkF5dkuWVoWl\npGhl/rq5bDhq355Ve2ke04ygxkGUGEv4+eef6dChAwaDgdatW7Nx40buv/9+4uLirCGg94Tx5BZe\nou2hl9hzTwAxy65wx9dX2THFn8WH8+nz7THe6uzLoNA8umRfMIdgd2oH7B1Bjk8x4quveVS0YHda\nGisSz9Djh+/pGRzClPCmfNg9CqPJxM7LqWz28mT0okV4fvghsbGxxMbG0mPM3db+236Ps8yj+ypj\nCA5GbzNPn+1+rrccqKP9/N5KeN4oR2G1CgIVRVEURVEURVEURVEURVFunFMhoJTyf0KIHmhh4HGg\nH1p50AFSyu011z2ltqrw5nx4P63sZ0Wa9C310lLKsyInUrKIibQfiDkKEY2XUilJ1cp+nvYIpEv2\nBbvtaiJ0yMvL48CBA6VCv5ycHKKjo4mKiuLpp5+mW7duBAQEWLexDUVuJAD0njCe9GIdly7u4kph\nI3KKgikuccdFX4C3ayr+bucJcL+AmyEXcC58yruax/7VB5jx0TQArly4QkhICP7+/ri4uNCuXTsK\nCgoYOnQoL7/8Mk899ZR1W69pj5Krc6HprreJH+9PzKrzdPkkh80DBtGlew6P7N3NnamNePX558v1\nwzYoi65bl+i6dckq6si3v53nH8d+5YmE/Uxo3IR7H36Y8XMfw2QykZCQwJo1a/jTn/7EuXPnGDly\nJKNGjWJIdjY+Pj7WfWa9936l8wFaAsCy58jZILCyc/t7LeF5oyobraqCQEVRFEW5tYQQiUBDwGRe\nZAIOAXOklPFCCDfgcWAKEAEUAAeBt6SUceZ9nOTa9BEG8z5KzK+3SSkHCyFKzMssx7FIkFJ2F0LM\nAJ6XUjYt079+wFYppR5FUWrErsNJfLj6ZwAeursDMe1Cb3GPFEVR/pjMg20WAf0BbyARWA68CvQC\ntgJGm00uAJ8DL0opi8z72Ab05tq1lsV3Usq7hBAdgfeBjkA2sAx4UkpZYt72RynlixX00QNIAjpK\nKc9d/7tVlBujrk+qztmRgEgpfwFm1FxXlD+MgHDwD3c8GtA/XGtjw9kQsCrrbANAgNMeQQ5DQLix\n0MFkMnHixAlr2Ld7925rSczo6GhiY2N59dVXadasGRXNjW57bEPIAYftTmeetn5tr3RnQYEbhy+2\nJSerPybTtX/7i41u5Bt9ScuPwMslndYB3+MZ6oP33ZW/591f7KFF70gCGmihZYq8SMeOHQFwcXEh\nMDAQvV6Pp6cnJ06cICkpibCwMCguhMMr8DJ9g7GJnuaXsjkww5+oz67Qd/MPbOw/kC0DB/NEShKD\nF/2DL6O607x5c7vnxfI98nV1ZVrTCKY1jeDY1UxWuboy8JW/0+bbb5g1axZjxoyhc+fOvPDCC5w7\nd461a9fy0UcfMWPGDHr16sWoUaO48847qYPjILCiANBRv8pydmTf7RZ0VRQAlpjn6tSHhKggUFEU\nRVFuLRNwr5TyMwAhhBfwPPCtEKIh8DXQCHgY2IlWd38csEwIMU9K+baU0npRJ4T4Ee3m0kt2jjVA\nSvm/mn07iqJUxRc/HGfFxmPW169+sofJQ1syaUiLW9grRVGUP6z1wP+AcCnlVSFEZ+BLwBf4DkBK\n6QoghNABXYB/A03RHsgC7drtRXvXWkIIAxAHvAf0BVoAG4BzwNvmbcs+kGXZNgi4C5gE+FfDe1WU\n66auT66PUyGgECIQeApoB7jZrNIBJinlgBrom/J7VlFJ0DKlQKuDMyFiokdgtR3vypUr7Nmzh/j4\neOLj49mzZw8+Pj7WUX5TpkyhU6dOeHo6V2bTljXw+MlxCFiZ7CwfjEYD6HXoSvSlgkALo8mVXO/m\n+IQY7eyhtJwruSSsOci9i2dYl106eYnhnUYCYDAYMBqN1KlTh+3btzN8+HDWrl3Lg8PbwZ5/WueI\nNJhDtUaksn9GHaKWZTBo6yY2/f1V1vz1Sd577z1iYmJ4++23mTx5cqk+OArcOs+aRe8J43mzsJA1\na9awePFi5s6dy4QJE5g1axadO3dm9uzZzJ49m8zMTDZu3MiaNWt4+umniYiIYFjTCPrnZNMiJ9ca\n0DoTAFbWr9uttKezKgsAjTaBrAoCFUVRFKX2kFLmCiGWAE8CE9GeSo+UUqaZm2QDS4UQBcC/hRCf\nSikzblF3FUW5AWVvsFlYlqkbbYqiKNVHCBEKtAYmSimvAkgpDwghHkcL7EqRUpqAfUKICcCvQojX\nzIN3KtIaqCOlXGB+/YsQ4ktgKFoIWJG6aKFjstNvSlFqgLo+uX7OjgRcDrQFvgLK1l20+5SAcpuz\nUxLUMnrtP6nxZPx0tNS6xPzGpGVqIV1EnQi7u4ys71ulLlhKadqOBqyIo9CmqKiI7fH72bD1f+za\nFc+xwwlkXUklomVbunWLYvI997JkyRJCQ2vP0OOsLB+KjQbthYMgsETnSa6hAdpDPxWLXxFPq4Gt\nqFO/jnVZirxIxwnXRgIajUYaNmzI/v37mTZtGkuXLuXBevHWANDCEgSGksreewLo8UU2A196kU29\ne/Hoo4/Sq1cvJk6cyKZNm3j33Xfx8fGxblvRHHpubm6MHTuWsWPHcv78eT755BPGjh1LnTp1mDVr\nFlOmTCEwMJDx48czfvx4ioqK+Omnn1izZg2z/ruN4sxMhnh5M7xBQ3rUrYueqo/ku13n9qsOZQNA\ny9dl52NUFEVRFOWmspawEEL4AfcBZ9Hmho+zCQBtrQL+BcSgPWFepeMoinJr7TqcbPcGm8WKjccI\nD/VTpbcURVGqzyXgJPC5EGIxWoWFn6WUa4G15hLo5UgpjwshTqCVALWEgI6uqU4DPcss6wCcqqxz\nUsrjwMNCiCbA5MraK0pNUNcnN8bZELAf0F9KGV+DfVH+SALCYcaPpRYttlO20sLXN5u0tIpH6llC\nwLSsAmTKVU6kZFlHAGblF3E1rwhPNxdcDdf+vbMNAsPz0x3u2za0+e2330rN45eQkICbfz3qNW1D\nvWZtGdhjDAENItDrtZBtVz6M8Km+UYbVITvLB2Ox4dqCMkGgTqfHiBvZWT4O9mCzr8vZHFp3mPs/\nvde6zGQycfHEJTp16gRoIwGLi4tp3bo1Bw4cYNiwYdx3331cfXo+fnZGhFqCwDAg4ZsXiLpvEYMG\nDSIuLo7Bgwezb98+5syZQ9euXfnyyy+tZUfBuTn0GjVqxLx583jmmWf48ccfWbx4MfPmzWP48OHM\nmjWLAQMG4OrqSv/+/enfvz//+Mc/2Pvmm6z5No7Xfv2V08ePMrhbd+42lTAsIwN//8qrHdyuc/tV\nlb2Rk2UDQAtjaiqu3bqq86koiqIot4YO+FgI8aH5tQn4GRgDvA7Y/b+hlLJYCJEB1LG33oEfzHMD\n2potpVxi/rqJECKvzHo96oFURal2H64+5FQbdZNNURSlekgpjUKIGOAhtLKbfwdchRBbgGcq2fwS\n10p06oBnhRC2N2BNQAMp5RXgCIAQogHwT6AZVZv6Sz20pdwy6vrkxjgbAl4ECmuyI8rtzcc3u9I2\nor4faVkFzPu6/A99Zm4RF6/mA9A02MduEBhx+edy2+UUF3O8Y3sOnU1k95gxxMfHU1RUZC3r+cIL\nL1ASEM43h+w95HyNTLlKjG9wpe/hljIHgZavnb1nsnN5PO2GtcU3+NpIzOy0bEwmEw0aNACujQTs\n1q0b69atw8/Pj549e/L9CSPj9fb3awkC63a8g337ptCjRw9GjRrFsmXLGDNmDEuXLmX58uUMHjyY\n559/ntmzZ1vLdTobCun1egYOHMjAgQNJT09nxYoVPPnkk2RkZDBz5kxmzpxJo0aN0Ol0dH/iCbo/\n8QRPrVxFypUrbDXo+fzzz3nggQeIiooiNjaW2NhYmjRp4vB4Kqxyjm0Q6CgABNAHB2M8e46clavU\nub0FclauAtTnWlEU5TZmAu6zzAloSwhxGbA7XF8I4W1e91sVjjW4kjkBz0opm5Y5Tl/gRwftFUVR\nFEVRfk8ypJSvAK8ACCE6Ac8BG4FpFWxXH0gxf20CXnYw/zJCCD1aWfe/AZ8D0y3lRxVF+WNzNgR8\nBXhDCDFOSul4OJWiXCd390LatvuV7CwfooO6W0f4Rdb3JbK+L6K+H0G+7uw6YT8s8HK7Nuotr7AY\nV0/XUusNIcG0CotGLlvKgfR09qWncSA9ndP5ebRLSSI6OpqxY8eycOFCwsPDrWETwGfbT1fa/xMp\nWcREVm8I+Eqv1x2ue6aCUZWghaqZV/0oLirzI663CUddiisNX69eusqRH45w/7L7Si2/eOIS9SJD\nrOfJxcWF4uJievfuTU5ODvn5+YwePZq4Lf9j/Lhwx/ND+odDQDgBQHx8PL1792batGlcvXqVmTNn\nMmXKFLp3786kSZPYvHkzixcvJigoqMI+OxIYGMijjz7Ko48+yoEDB1i8eDEdO3akW7duzJo1i9jY\nWNzd3fGeMJ5maI9D3X///eTk5LBp0ybi4uJ46aWXaNCggTUQ7NKlS6nPiuI87wnjKdizh6IjR+yu\n1wcHW4NiNS/gzVd27kZ7516FhIqiKLe1dcCbQognpZRlJ+eejvZUek1XkVEXYYpSAx66uwOvfrKn\n0jaKoihK9RBCjAY+EUIESSmNAFLKBCHEPLQqDHZvhAkh2gARwGYnD/Up2tyAMVJKx3UVFaUWUtcn\nN8bZEPARoBWQIoRIAYw260xSSvuTuClKBQoKtHKUWVk+1rKUPr7ZRLbxZWTHBgT5upfbxhIOluXq\noqdpXW9yC43U9XXH18OV3KtXIO0UV879yplfD9ExYT/+bm50dnenc0AQM++9l5gnn8DdvfxxnDlm\n2TaVBXMWFYV71cXXNxsXg7F8CGjDYDDiW0kIuOOzXXS4owM+gd6lll88cZF6zevZ7EsrB9qmTRsA\njh49yp133slTTz1F0dwFuDoKAcP7Wb+sU6cOO3bsYMCAAcyZM4fMzEz+9Kc/ERkZyc6dO3nqqafo\n1KkTy5cvp3fv3hWfgEp07tyZzp07s3DhQlavXs0HH3zA7NmzmTJlCrNmzaJt27bWtt7e3owePZrR\no0djNBqJj48nLi6OqVOnkpWVRWxsLFeaXaZJ5ya4uFX8K/VmfO9vxM0MdXJWrsJ49hz64GBKyowE\ntA0ALVQQePOUDQDtnXtnQkJFURTlD+0L4F60eWr+gnaDyh24G+0B0ilSyuIy2+hwHNypQE9RaomY\ndqFMHtrS4bw7k4e2VKW2FEVRqtdmIAt4VwjxItrDVE2Ap4DDaBX6rIQQOqAr8AnwLynlefMqh9da\nQojuwB1AcwdzOuuAOkKIhmWWX5RSFl3Pm1KU6qSuT26MsyHg2xWsU/MwKFVWUODGL4dbl1+eFsiy\nn84A8PLYDuWCQEeBnLG4iCvnT3Dx9BEOHd1LftJxUnNz6datG9HR0dz157lERUUREhJyW4xe8fHN\nxmAwVtjGYDA6HAn4Sq/XSUxM5MPtH3P8+HHq1q1bav24t8dx1113WV9byoF6e3vj4eHB9u3beeyx\nx4iMjOS/FzwY5KgTTfqWeunr68u2bdsYPHgwzz33HBkZGTz//PO4ubnx5ptvMmDAAMaNG8cjjzzC\nM888g8FgcLBj53h6ejJlyhSmTJnCqVOnWLp0KcOGDaNBgwbMmjWLiRMn4ufnZ21vMBjo2bMnPXv2\nZMGCBRw/fpw1a9bwzrLv+PbFtYR3aYLoFUmzmGZ41fG8ob7dbLcq1LGEfZYg0F4AqNw8ZT8HFrZB\noDMhoaIoivLHJqUsEUKMQCsntQLtRlUesAMY7mAueROO/++4RQhRdl2ylLKxzbb2qP+LKkoNmDSk\nBUC5G21ThrVk4uAWt6JLiqIof1hSymwhRB9gPtq8fX5oJT7XAUOAlgBCCNswLgVYCrxos6yia62e\naPM1pwghbJdvk1IONm/3Z/Mf2/3FAHvKLFOUW0Jdn1y/Kj1xaa4dHAwUSCkzaqZLVSOECAfObNmy\nhYYNyz6soNQmtiPl0i4HkpjY2G67iDrawNJpvZqWK7E576tDXM7KJzsthYunf+Hi6SNcPPULaedP\nUCekIcFhzQj3q8vTulQ6zJyJ36SJN9zvz7afJv7k5QrbRDevy3Hdv5zaX3WMBnNm1GFBgRuDgx9i\nz6k0jidfJSu/GF8PF1qG+tGtWRBdmwaVClnLhqOzZs0iLCyMl19+udy+mzdvzpo1a2jdurW1bUxM\nDPfddx8RERFER0ezYsUKXnvtNZKSknj33Xer9P7y8vIYOXIkBw4cYMaMGSxatMhadjMpKYmpU6dS\nUlLC8uXLrfMSVhej0cjGjRtZvHgxW7ZsYfTo0cyaNYtevXo5LP35zE9/I+dKLqd2nULuOMnZ/YmE\nNK+H6NWcyF6RBDYMAGrpSMCrSRR+OpviY8fIS2tOSbEWXnqNG1vjoY5tmGS8dAnAYQB4M/pzu3MU\nANoyNGmM8ew5u+vU90i53elUfWhFqbXU/xkV5frsOpzMh6sPAToeHtOe6LZVe8Je/duoKIpy+1HX\nXUpNu9Hrkz+qiq67nBoJaB5m/BIwB+1pBIQQZ4A3pZTvV0cnldtLlrn8J4CpRI/RaKDYaMBYbOBM\nYQ6ebgY2/5KCqO+HG4Xs27eP+Ph41q/fytGfD6DT6agX0ZaQiDZEjXmYkPBW6K/mUJJ6iU7ZF2h5\nuZCC/6wmR6+/4ZvSkfV9KwwBT2eepiRtG5muFc8daAk3bxZ390KGtg9jaPuwStuWvfmf3KUza9as\nQUpZru3Vq1dJTk6mRYtrT1hY5gQEiIyM5Ih5nrdRo0YxbNgw3nnnnSrNnefp6cmGDRsYNWoUn332\nGVeuXGHx4sW4uLgQFhbGpk2beO211+jSpQv//ve/ueOOO5zed2UMBgMjRoxgxIgRXLp0iWXLlvHg\ngw9iNBq59957mT59OvXr1y+3nXeAF+1HtKP9iHYUFRRxdv855I4TxM9ejoevB6JXc3YZdtG9e/dK\nRzDelNGqxYVweAXFmxfBxWRcPMG3wRXyMxtTkNn4pozusuw796uvKxz9p8KlmudMAFhy6RJFR444\nHK2pRgQqiqIoiqL8scS0C1WltRRFURRFqVXU9UnVOVsO9G/AbOA14BDgBfQBFgghvKSUC2uof8of\nlGUOQFOJnpwcL+tyU4mRKxdOcfa3Y+xdcZS3HpPkpyfRsWNHrazn2Am0HjUHn8B6pUIl46VUSlK1\nkUQR+ddKW1fHTWlR36/SNj6+2WTmX/chqqw6R5TZK+0375//5LHHHiMgIKBc+0OHDtG2bdtSQZal\nHCho8+19+OGHALRq1Qp3d3cSEhLo3Llzlfrl7u7OmjVrGD9+POvWrWPcuHF8+eWXuLu7YzAYePbZ\nZ+nXrx9Tpkxh8+bNzJ8/v9L5HasqJCSExx9/nL/85S/Ex8ezePFiWrVqRZ8+fZg1axYjRozAxaX8\nr1FXd1ea92hG8x7NMD1uIvlYMnLHSR544AEuXbrEHXfcwahRoxg0aBBeXl6ltq32spxXk2DPP7Wv\nuz8KfmFwbifs+SfG04couWQzH5+uBA//RNx8LpKX1uymB4H2qACw5jkbABrNJVstpVtVEKgoiqIo\niqIoiqIoiqIotZuzIeBDwANSStu7hKuFEPHAq4AKAZXrkpuRRdqpw2T/dpys88fJTjqJm08AdRq1\nIqBJK6KGjeexCYPo00Yr+ZiWVcC8rw+V2odtAAgQkZ9e+hg3eFM6yNedl8d2QKZc5URKlnVewsj6\nvkTW9+U/577D3b0QbmIIWF3s3fyXV6+yee8e/nHvvXa3OXjwIJ06dSq1zGAwWEcCWubLKywsxM3N\njdGjRxMXF1flEBDAzc2Nr7/+msmTJ7Nt2zZGjhxJXFwc3t7eAPTq1YuEhARrOdKVK1cSGRlZ5eNA\nxaPvdDodMTExxMTE8NZbb/HVV1+xYMECHnroIe655x4y2qcT1DjQ7n51eh1hrcMIax3GK71e5/Tp\n06xdu5a33nqLqVOn0r9/f2JjY7njjjvw2fbf6ptrzTzSj1++AGOhtix5P7SdBGe2Yjx9CKNtAGhD\n75KHZ+AZsi7UddwHe+HidXIUBKoAsHawDQCdoYJARVEURVEURVEURVEURakdnA0B6wEH7SzfD6ji\nvopTnuv2IgkJCcTHx3Pxu6/49dB+8nOy8G3YgjqNWhPeZyJ1GrXA1asOAH4eroT4e5CYXkAf8z7K\nBnJH9xzBmHye8Px0IvLTiMhPJ6A4r9yxqyMIjPENLjdHIcB3Fwuva5+3mqPRP28cPcJDkQKXdevJ\n8fIqd84SEhLo3r17qWW2IwHbtWuHXq/n5MmTtG7dmlGjRjF79mxefPFFroeLiwtffPEF06dPZ8uW\nLQwcOJDvv/8ef39/AAIDA1m9ejUffPABPXr0YNGiRUydOrVKx6jK6DsfHx9mzpzJzJkzOXbsGEuW\nLOHzOSsIbBRAh5HtadmvBW6ebg63j4iIYO7cucydO5crV66wYcMG4uLiePyxx2ju4cHwsAYMDQ1D\n+Pqi0+mu77NrHulHdnLp5cZCOPQpxpQLlKSnAq4Od1GYc+2zXqoPFYWL7SaDi+P3XlFwWDYIVAHg\nzVPZaMyyHJUDVRRFURRFURRFURRFURSldnE2BPwFGAm8XWZ5f6DiidCU25LJZOLMmTPEx8eze/du\n4uPj+eWXX2jRogVRUVEMHjyEpgOmcUUfRLHJ/j483bRyk5aRdxaWQK79gR/J3eXcTWuo+dEpxYWe\nFOYGUpgbRGGuNirMzSsdN6803LzSK9nazCYkyU1pjMk10Kn+PvPT35zavaWMqKMA8EhmBjtTU1nU\npZvWBzvn7ODBgzz44IOltrOdE7BRo0aYTCb2799P69atiYmJITk5mTNnztC0aVOn+lmWwWDg008/\n5YEHHmDDhg306dOHTZs2Ua9ePUAbqffII4/Qs2dPJk6cyKZNm3jvvffw8fGpZM/2y6GWfc+OtGzZ\nkgULFqAfYeLkzlMcWvczm9/dSsv+Leg4sj2hrUIrnAsxICCAyZMnM8rgQkaRkR2pl9iYlMTEn/6H\nq17PsLAGDAsNo3tV5wg88HH5ALAMvWsexgLHIWBRTvnAu7JwkdOboNtsaNyj9Hong0Pb96cCwJur\noiBQbw78jKmpTgWAKsBVFEVRFEVRFEVRFEVRlNrB2RDw/4DvhBA9gP8CRUA0MBWYXkN9U35HMjMz\n2bt3b6nQz93dnejoaKKioli4cCFdunSxzn9mKeuZkZoDxhK7+/RyM9hdXhsVFLiReqpfueV5mQ3I\ny9RKmTbxO6OVDbWnTEhivHQJl4tp5Gc2JsdUjPfEydXW14rm/1rw6xEeFS3xsZnnzjYUKyws5Nix\nY7Rr167UdrblQPV6PUFBQezcuZNp06ZhMBi48847WbNmDXPnzr3ufhsMBj7++GNmz57N2rVr6dmz\nJ1u2bKFJkybWNh06dGDfvn089thjdOnShS+//LJc6VJnzkVVA2ODi4EWfQQt+giyLmdxeMMvxL20\nFhd3FzqMbE/bIW3w8veyu62lD256Pf3r1ad/vfq81rETv2Rm8H1SEs/9fJDfcnMZuH8fsfHxjHrp\nRXx9fSvuUL32cGar9rVvA3ApPVeioX4DinV6SLUTTuuM6PVGPANPk5fWnJJiz2uhzrczKw4Xs5K0\nANI2BKxicKjCo1unsiDQtVtXjGfPVbgPFQAqiqIoiqIoiqIoiqIoSu3hVAgopdwihIgB/gI8jFZD\n7hgwUkq5uQb7p9RCxcXFHDlyxBr27d69m7Nnz9K5c2eio6OZMWMGH3zwAQ0bOq4Uaynr+cFmyYHE\nK+QVaQGSp6sLnm4GvNwMuLroAW3uPXuqWsKuJm9OZ2dVPOKsxFTCiZQsvPwvlBu11zjjN6LPH8C3\nMIeIOhEYL13S5mrTgYd/IiXxT5GXdwrPmfNqpO8Wh65cISE9nQ+7Rztsc/ToUcLDw61hroVtOVCA\n8PBwDh68VkF41KhRLFq06IZCQNACxvfffx8PDw+++uorevXqxebNm2nRooW1jbe3N4sXL+aLL75g\nyJAhzJs3jzlz5pQbkVdRGArXP3LUt64vPabFEDM1mnMHz3No3c9sX7qDpt3C6fN//Rk0aBAGg6HC\nPuh0Otr5B9DOP4AnW7fhQm4uG5OTWPLFCh756EN69u1LbGwssbGxNGjQ4NqGljD56DdQcFVbVpAF\nPvXAux7YnAOXXvdh3LHUZl5AE3qXAvQu+ZQUe+DimYZvgyuYxN03oSXOAAAgAElEQVS4jxmtNWnS\nFzISKz4B4f1Kv65sVKK94FC5ZSqbn7GinxsVACqKoiiKotQeuw4n8eHqnwF46O4OxLQLvcU9UhRF\nURwRQmwB62xIevPfllETiUAzoL+U8r9ltksEnpdSfmp+3Qx4ERgIBACpwCbgJSllos12dYGXgTuB\nYCAd2Aw8I6U8Z26zFhhkczgT0ExKWXHpKUWphLpGufmcHQmIlPKgEOJJIALI1xbJnBrrmVJrJCcn\nlxrht3//fho2bEhUVBTR0dHMmTOHtm3b4urquLSgPUG+7gxsW5+kjPJz+NlyFAKC80FgTd+czsry\nwcPF0+H6/OI8fEoiCa/jXm5d1wuH8C3UfpSsAaANvUseJQeXkLOyRbW8B0fnbP6vvzC3RSs8DaVH\nYNqeu4SEBDp27Fhun7YjAUGbF3Dt2rXW14MHD2batGmkp6cTGBh4Q/3X6XT84x//wN3dneXLl9O3\nb182bNhQbsTfpEmT6N69OxMnTmTz5s0sWbKEunXrApUHgBbOBoGWMqvl9AbmaCNlv/jiC5599lnu\nv/9+ZsyYwcyZM3F2VrUGXl7c26w59zZrTvHIEfzk401cXBzPPvssTZs2JTY2llHdm9A+63t0OSmg\nN4CLBxTnAybIToG8dG1UoEcd8A+H1mMxnFgHQMnlJPSueeh02vWlqUT7WTbUC8LgfhTiZmqj9cL7\naaP3KtKkb/nXVQ0OlVuqovkZKwsJFUVRlNpJCHEX8FegHaBDe6DzPSnlEps2YcB5KaWhzLa+wDPA\nGLT54HOA3cB8KeX/zG2K0W4MARjQblpZXn8CLAe2Ah9JKR+22Xc42vQS4VLKc0KIbWhXUGVLhXwn\npbzL3Jf3gVHm5euBe6WUuVU+KYryB/bFD8dZsfGY9fWrn+xh8tCWTBrSooKtFEVRlFtFSjnQ8rUQ\nYilgklLea7PM9trKlsmyXAjRAtgBfAF0l1KeF0I0BZ4H9gghekgpTwoh/ICfgANALyllohAiCHgQ\n2C2EaCulTAME0FpKeaYm3rNye1LXKLeGUyGgEKIO8CkQa7O4WAjxKTBHSplfE51Tbr68vDwOHDhQ\nKvTLycmxBn5PP/003bp1IyAgoFqOJ+r73XCbyoLAm3FzurKRgBW1ORPQmIDkXzAVFpYLAC0Kc4Ip\nqMY5Dcues31paRzNzGRpdOnRWGXP3cGDB+2W13RxcSE/X/s1kLNyFZ0NLnySlkZxcTEuLi54enoy\nYMAA1q1bx7Rp0264/zqdjtdeew13d3cWL17M4MGD+fbbb+nVq1epds2aNWPHjh0888wzdOrUic8/\n/5yuKRedHj0K1TOXZJ06dXjooYd46KGHOHToEEuWLKF79+506NCBKa3bMOj8b3gYKi9/a/l+jAPG\njRtHUVERO3bsYM2aNdw9YyHFxYXEdqzLqE5B9Anzw63Y5lezsVAbkedRRwvdAsJhxo/aXbpF/TBe\n+LXUsQwhNnO/WUbrjV6qBYiOQj3/cDC4weantdfdH72+4FC55Sqan7GikFBRFEWpfYQQDwJ/Bx4C\nvkOb2qELsFgIEQp8hvYU+Cw723qghXcXgNFSyiNCiEDgXmCjEGKKlHK1lNLFZpszaE+kf2azrJ/5\ny3uEEMuklDsddNcEvCilfMnB+ncBb6AR4Ab8ADyO9iS7oiiUv7lmYVmmbrIpiqLUerrKm9j1JrBe\nSjnHssAc4M0QQmxEux6ciFbpL0dKOdmmXRrwqvkPQggDEAqcvc6+KEo56hrl1nF2JOAHaCMA+wJ7\n0Z7u7In2FOY/gftqpHdKjTKZTJw4caJUWc+jR4/SunVroqKiiI2N5ZVXXqF58+blSilWF0tZUJly\nlRMpWZxIyQK00X+R9X0R9f0I8i0/eq6s3/PolDMBjel09gCmoiKujfgvrSgnGKieQMrC9pzN//UI\nf27ZGnebIMreuUtISGDkyJHl9lXy66/k5eRaR9g1T0/HoNNx5swZIiMjAa0k6LffflstISDAszue\ngsHQLCmChG8TGDJyMLHP3UmzqIhS7V7p9TpvvPEGAwYMYOLEiczs2YvHTGCooc90ZTp06MDbb7/N\n/PnziYuLY/HixTwRH89dIfWYHN6Udv7+drez9/1wdXWlX79+9OvXjzentuPXDR8Ql3CZZ1ef4XhK\nLkOb6xnVypXhLVzw99SDh3nfNqHb0qVL+eFHA4bMACgqYm6rPL5O8WPPj0X8OPW0dU7B1YmBPN2y\nJVvffZiwjES+P5bHkt3ZuLvoyCsyMaqNJ9OGGLRRg8ZCXv4hk6HxO+g+cib4Nabds/F0bOAGwJXc\nEka09uSRnr5acBgQXhOnWrlBFf2eqSgkVBRFUWoP88i5BcA9Uso4m1V7gfbmNj3QRgj+BpR92utR\ntLDtbillCYCUMh1YKIRwA94WQnwjpbT3ZLo984GPhBCdpJTFlbYu/V4CgEloowYzzctGm/unKAqw\n63Cy3ZtrFis2HiM81E+V3VIURandHF1XObyRZX5wawgw2EGTT4C3zV8PA/5TSR+aAEZghxDCcp34\nkpRyRSXbKYpd6hrl1nI2BIwFhkkpf7JZ9oMQ4j5gDSoE/F24cuUKe/bsIT4+nvj4ePbs2YOPjw/R\n0dFERUUxZcoUOnXqhKen47KWNSHI150Y32BiIoNvaD+3anTKK71e5zPTaeJPXnbY5nTmaXx80+2u\nS8sqJL3EhQCK7K4vKfKmpMjb+rq6g8AdR49yZsN6JoWHW5fbO3cmk4lDhw7RsWNHclausm6fs3IV\nxsNHyL2STvaVDPQhITT39cVoNLLvo4+IXLgQgDvuuIO5c+eSl5fn1Ges7NyJZZ3OPA1Ar+k9cHE1\nsPerfax5+TuGPT6EVv1blms/fPhwDhw4wLRp0xiblMR74RGElZnb0J6a+hx5eHgwYcIEJkyYQGJi\nIv964kmmb1hPoJsbk8ObMqZRY+q4uZXrg+25t6Vr2p82DT6jTQNvnr6jCckZBXx3KI0VCWk8GJfB\nnuc603LKqlKB25kzZ9i8eTNffLoY4mZyUqbw56162gbl46pzYf+5PLrUOwY+9diQkEzjxo2hYQz7\n4lfy3k9ZLJtSF39PPUU5Gdy7Kp0I/wRCA714et0VDiUVMaylhzYKsDifYG8dy6Zo5VgLik30fjeF\naV288VWlQH+3VPinKIryu9ATbT73tY4amEfl7TSP1ruzzOpRwCpLAFjGJ2hPlLdAKy/qjNeA8Wil\nSV910MbRDa6uwBXgESHE/WjvayXa0+yKogAfrj7kVBt1g01RFOV36QdzWVBblpETQWj3+c872PYy\nUMf8dSBQ2bx+zdGqRzwB7ALuBj4XQlySUm6uascVRV2j3FrOhoAFQLad5VfQ5gdUapmioiIOHz5c\napRfUlISXbp0ITo6mgcffJAlS5YQGvrH+sG6VaNTIuv7VhgCAvj62vsR0px2DaKL8Te76wpzbiwc\nrYjJZOLVbT/y9D334Jqq9d9R6HXmzBl8fHzw2vqjNYgs2LMH49lz6HNzKM7NxZiqlTP1DQnBXa9n\n19q1xHbrjveE8dStW5eOHTuyZcsW7rjjjmp9H9GTo9C7GohfHs8PizZRkF1Axzs7lGsXGhrKxo0b\nWbBgAYPnz+fN1m0ZFhbmcL83K0gODw/n1a+/4m9ffMn377/PisQzvHrkFwbXD+XeGdMZNm4sUH4u\nw1J9CwgvVaoz1N+d+/uGcX/fMIwlJgyBTcuNuDOZTCQnJ7PzaBKd8aB5QDqfDStiwV4fhjctYP0Z\nd7rUKyI9I5P8i5L6AQ0w+TVgVVYvZvw5Cv8xYwBw/XYmy2YmWve7fGpdnlmfce3RNZMJSozW9Rl5\nJXi56fFw1alSoIqiKIpSs4KAyw5CPGfUw/FNIksde6fnCZBSFpkDvE1CiJVoT5jb0gHPCiFsnwYz\noc1FWA8IAXyBcKAusBktWPyzs31QFEVRFEX5nRpsmY/ZwlyGHbR79EYgGG2+5bIiuBYQpprblSKE\ncAcygFgp5Q9o110WXwshpgJ3oV1/KYryO+JsCPgRMF8IMUNKmQwghPAHXkKrN6zcYr/99lupefwS\nEhIIDw8nKiqKXr168fjjj9OmTRsMTsw79ntXHaFNWlZBlUqUOjO3oY+DENAQEkxioaDLafshYFGZ\nELBsMJWWVUDa5UCysnys8w76+Gbj65uNj2827u6FDvu0detWkpOTmbV5MwX/WQ04Pn8HDx6kfb16\n1hCq5NIlio4cAXd39NnZGE1a5GMJAsM8vTiYkVFq5OLo0aOJi4ur9hAQoPu4rri4Gti+dAf/W/IT\nBTkFRE3sXq6dwWDgqaeeom/fvkwaNZrtly7yXLv2pUqhwvUFgI5G6TnLb9JEBu3bR1+dnisdO7Mm\nMIC/ffstc5YvZ2q3bozJyiHUPIrS7ojQJn3tztdn0Ou0ufnKiIiI4LnnnuOrr77i+Z2J+Bq8md4m\nF4Do0EJe2+NDiQk2nvdjaHM9G08lAXD58mUaNGjg8LgGva70I/yunlwucGPaloYAnDhxgiHDx+Jy\n74twi8qyKoqiKMptIhUHIZ0Q4nlgqJSyh731Zpexc5PIzFKD/UJVOiSl3CWEWAJ8SPmKMibgZXtz\nAgohLOVD/2aek/43IcS/sDOXoaLcrh66uwOvfrKn0jaKoijKH4uUMlcIsQ2YDuy2XSeE0KHN57za\nvGgLMAatZLytMWhB4k4hRH3AKKVMtVnvDmRWe+eV24K6Rrm1nA0B70b7T95vQojzaCMDw83btxdC\nPGRuZ5JSRtjfhVJdcnJy2L9/f6nQr6ioyFrW84UXXqBr167UqVOn8p0p5aRlFTDv6/JDlNNOFlhH\n+708tkOpILCyuQ3/c+67CsO4qw0j+LebL4+vyKqwb/YCwHlfHyIxs3GpdgVpgaSlBQLQtt2vdo9t\nMpmYN28ezz//PC4uLrhUElzt/nw5rXLzAC0ANKamYioshJwcDMaSUo9xG1NTiXR341CW9n4sgdWo\nUaOYP38+RqOxRgLpzqM7YXA18ONH/2X/6gPkZ+dj6mmyO6dljx49OCiPM3PYcIb/uJV/RUXT3NcX\nuL4AMP3xJyjcuw9DiPag1PUEgTkrV2E8ew4TECIi+eubC3nSZGL7a6+x+N+L6fvbeboGBTE5vClD\nQsOgbBAY3k8rv2mPnRF3p06dIiIigkWLFsGOBpz+7nWmf+9Ph7pFGPTQtV4Ru5Jc2XjawMLRHmy8\noH3mQ0JCSElJubaj8H588vF7+HvqGd3OfonVunWDWLZsGQAlJSXMmTOHb775hrvvvrvK50lRFEVR\nFKftAnRCiJFSynWWhUIIAzARWFXJ9uuBSUKIhVJKo3nbqcDPwFQgQUp57jr69RTwKzCjCtucMv/t\nzrVqNC5AznUcX1H+kGLahTJ5aEuHc+5MHtpSldlSFEWp/XQ4nhewIo8D/xNCXEJ72Ooi0Ah4EfAB\nXja3ewuYKoT4N/A8kAL0AxYBb0kpc4QQTwB3medfPocWEPYzH0NRqkxdo9xazoaArzvZ7np+QSkV\nKCkp4fjx46XKekopadeuHVFRUYwdO5Y33niDpk2b2g06lKqTKVedahPjW/qh6IrmNvzuouMA0MIQ\nEozXuKGlyj3asgRTtqPNLH2NqOM4ex/TeKDdPn3//fdkZmYyYcKESvuWs3IVB3ftYmJ4eOkAsEib\nx9BQYqTYWLqaUwednk15eZSYTOh1OnK/+pp6QHBwMLt376ZHj4oeOr9+HUa2x+BiYMt7P3Jsm+Sx\nxx7j7bffRq/Xl2sbEBDAN/G7eOf+B7hz2We80L4DM5944roCwPz1G6yvr2feRttSn4aQEIxnz1m/\n160OHGRh5y681L4Day/8xscnT/DXhAOMa9yEKVev0tFyrIBwmPGj08eUUrJ+/XreeecddK3HUu/I\nWnw8L6P38obABowIgg93ZqPXQ11vA3j4A1qYu2DBAgYNGoRPyVUu//gOn++7yicT/O0fyD8c9Get\nL/V6PV5eXhiNZSuAKYqiKIpSnaSUWUKIecC/hRCz0Mo3+aH9/y4YeK+SXbwFTAb+I4R4Fi240wHx\naHPy9b/OfuUIIR7m2hPpFjoczAkopdwrhDgCLBBC/AmtRNUDaDesFEUxmzSkBUC5m2xThrVk4uAW\nt6JLiqIoStWYuI577FLKn4UQ0cALaA9s+aGVdf8G6CmlzDK3SxdC9EIrqX4QrdT6GfPrt827W4BW\nin2vef0xYJyU8tfrf1vK7U5do9w6ToWAUspPargfitnly5fZs2cP8fHxxMfHs3fvXgICAqyj/GbO\nnEnHjh1xd3evfGfKdbGM4qusjb1g7UZZQqOyQaBtAGi77kRY10r3aa+vJpOJ5557jhdffLHSEXmW\nY/6SmUGbYiPGjNRybQyAscSIqbAQnZsbAK09PNDrdJw7f57wxtdGKlpKgtZUCAjQdmgb9C56fnhr\nMzt27GD69OksXboUF5fyv/J0Oh1z//0x0S1aMP3tt9j93VreHzEcX/OowMqUDQBLzOVQqxIElv2+\nWmS99z46QG8eXejl4sKEJuFMaBLO6awsVpxNZMz2/9Jo/z5mbt3KPW8uxMfHx6l+Awz7f/buOzyK\nan3g+HfTAyQESEJASmgnQQhNilGKgEpTA0gNnQiC13vtiiJFI4qoXLFcQEUp0iEICkJ+IiIgRSUg\nRTggVSU0pYVQUn5/zG7YbLIlyYYk8n6eZx93Z86cObNBOJl3zvt27Mivv/5Kjx49jOs9ncqzbQP5\nZn8qJqBpVV9+O3OOkXeXMQJ5XmmYTCaio6MZNKA/g3p2oVTa31xPz2B0S6hy/QBcqgilKwJWd/DC\n7+HMmXcYMGAAYNQsrV69Ol27dnV5rEIIIYTIH631f5VSZ4AJGEG3y8A6jJtBp2yaZ9oce0kpdRcw\nBlgBVMZYebcG8APGKqUe0Fo7f+ItZ9+rlFJLMVYkWrdxdNOrCzAVOAtcAKZprZ0FMoW45fS9P4Lw\nSoFMS9gJmBj5cAPurC9P1wshREmgtR6Sy7acT7Yb22vYfN5H9rmVvXP8iZE61N7+VOAx80sIt5E5\nStFwaemYUkphPA1QDyP9irUiTQGqlAoHDq9du5YqVaoU1TDy5dq1a+zcuTPbKr9Tp07RrFmzrKBf\nixYtCA0Ndd6ZcJsxi3dy9tJVh20qlPElvmfh5Sm2DgrZCwACTI4ewPnyFfM81uXLlzN27FiSkpJy\nXSFnO46zV6/SYs3X7G8RTcaZM1n7LasBF6dcYtOVK7xbIRi8vTH5+HDk2jXaHf6NGbfX4/7IulnX\n8dNPP9GvXz/279/vcNyjN45yuP/QeaPOsaNVkPvW72fDlE3Url2b0NBQFixYgJ+fn/3rTUnhySef\nZP369cyfP5877rjD4RhsA4DWPEJC8AwNdZpa1F4AMP3UqayAomdISFYg0FZaRgZrk5OZe+QwWy6c\np3vv3sTFxREdHZ331cFJM+2nE200GBqZ54fHfoBtH8ClEzf2X0+Fs+afqacPBNwGfuaUxO0nwv7l\nxvvmj0Ng5byNSwghSgCTpGQQtyClVEut9caiHoczJfl3RiFKMvm3UQghbj0y7xKiaDiad7maDnQB\n8BfwFmCbv01SgLogMzOTY8eOZavjt3PnTmrXrk2LFi1o164dL774IpGRkYVSK02ULNZBI3sBQID0\n48dJT/PAMzT3VYll005z78kE+KZiVvAlIyODsWPH8uqrrzoMAFrbfe4c9cqWxbNiRUwmE+nm4JTJ\nx4dMjJWAaTZ/FVT19iYtM5O9QFerQNgdd9xBSkoK+/btIzIy0qXz51dkmwiGNIxjyJAhlCpVii5d\nujB3wEAC/P1zDcyVLl2ajz/+mIULF9KpUydeeuklnnjiiVyDaY4CgODaikB7P1dblu87t0Cgl4cH\nHSpXpkPlyly8716WnD/HkCFD8PT0ZOjQoQwcOND1BwlcrSm4/ePsAUAAb38Ia3Tjc1A4PDAdds2D\n78ZCunmBwImfoX5fiIoFLx/XxiWEEEKIYqkkBACFEEIIIYQQ4lbmahCwLnCH5P11XWZmJuvXr2fz\n5s1ZQT+TyZS1wu/111+nadOmeUrdVxw5W61lMaGlq2Uli16dsADOHnS8ErBOmGupIgvCEjRyFCgK\nv/IXSaf9AbIFAj0zr9MsZQ1NUxIp7wf8figr+LJ0vy8+Pj489NBDLo9h12sTiAoqB9wIRFkHAj29\nvEjPJGsVIIBPaCjBRw+z098vWwDMZDIRExPD8uXLCz0ICPDAAw/w+eef079/f5pWq0aXp59mfstW\n3Ib9VJ29e/emWbNm9O3bl2+++YbPPvuMkJAb36+zAKCFo0CgswCgp/l7tvThKBAIxorRkN69eB54\n7rnn2LRpEzNmzCAiIoK2bdsSFxdHhw4dck2JmsXVmoLV28C5I47bBNwGXwzOGSxMv2YEGg/9HzT7\nF1QrvLSwQgghhBBCCCGEEEIIcStzNQi4HWiIUQi+SJmXFK+zzXm8Zs0a2rZty+uvv87ly5cBKFeu\nHC+++GLW0uO5c+eybNky/Pz8uHbtGoMHD6Zz584kJCTwzjvvULNmTUwmE6mpqdSrV4/x48dn9R8f\nH0+HDh1o3rw5AJ988gmJiYl4eHgwfPhw2rVrl22cx44dIz4+nkaNGhEbG8t7771H1apV856eT9x0\ndcIC2HLwjNM2BZGycBHgvF6cs0BRzStnSSpzGxmnjXIunqEh1LzyC20uLqZsunEN/r5GkJD0a6Qn\nzWTcuCTeeW2sy38WS/fuxb7336OlVR1K20Cgl5c36T4+WQFAS/rKOjVqsP/cuRx9xsTEMG7cOF54\n4QWXxpAbSxpQVwLMHTp0YOaIkQx8cyItQyvSdf06FqWnUwP7P4OaNWuyYcMGxowZQ+PGjfn888+5\n5557SFm4iOs//uTyOC1BvPywDQTaY5ty1GQy0bJlS1q2bMmUKVNYuHAh8fHxDB8+nMGDBzN06FBq\n1aqV73E5XDFocWY/pDr4/+jin8aKQgkCCiGEEEIIIYQQQgghRKFwNQj4H+BbpVQ74HdupAA1YdQE\nfLUwBpcXqampjBw5krfeeosGDRoARmAwLi6OlStXsmrVKhITE5k7dy6+vr5cunSJ3r17ExERgclk\nonXr1rzxxhtZ/XXu3Jlff/0VX19fXnrpJXbu3EnHjh0B+O233/j+++9ZtGgRly9fplu3brRp0yZb\nGs/q1auzdu3am/slCLdQYYFuaWOPbWDPXhDKlVSRNa/8lfXeEgi82+OLrAAgQCmfG38uF2w9RZAv\ndAzclacx7/rrL54aPBi23Qh+WQcCvcqWJSMzA4+QEEzmfaV69qDpltv43//+R2ZmZrag4z333MO+\nfftITk4mLCws16CoO1ePpixcRPNf9zHzrrsZsvkH7q1UiQfXr2NxRgZ1sf8z8PHx4c0336Rdu3bE\nxsbyyCOP8Exk3RxBUGd8mjXNcQ7LZ2c/Y0sg0PK92nJWczAwMJBhw4YxbNgw9uzZw4wZM4iOjqZe\nvXrExcXx8MMP4+/v79J1ZPs5BYXbXw0YFG6sFnQWKAy/x/jvhT+NGoMgNQOFEEIIIYQQQgghhBDC\nTVwNAo43t70dqG213YQRECzyIOClS5do1KhRVgAQjNU/c+bM4ccff2TRokU8/vjj+JpXM5UpU4aV\nK1cC8Msvv2Tr68KFC1y9epXy5ctTsWJF5s6dy+jRo7P2b926lbZt2wJQqlQpatSowYEDB25KakNR\n+CoE+BLfoyE6+QIHki9yIPkiYKz+qxMWgAoLpEKAr5Necmcb2HNUL84V5dJSee737zjkV55DfhU4\nXimYEwHNqXF5Ff6+XpTy8cTb06j7l5aewSvLjzB1oMJUo63L57h8+TJHjhyhyZNPcn3ZF9nG7xEa\ninezpvhs3kLG338R8K/HsvaV7t2LRldS8fDw4I8//shWDNjHx4eOHTvy5ZdfEhtY1qWgaH5Zf+d3\nBocw+66WDNq8ic6VbyNm/ToWpqfT2Ml5O3TowPbt2xkwYAAPrFvHJ336Un7dd4DzQKBf506Uf+ft\nXPe5Ggi0fK+27ZwFAG3Vq1ePyZMnM3HiRFasWMGMGTN44okn6N27N3FxcTRp0sTuCtEcwWvlICVo\n+D2uBQGrREPSTNg9X2oGCiGEEEIIIYQQQgghhJu5GgRsB3TWWq8vzMG4KDO3jRkZGVSunHP1SKVK\nlThz5gynT5/mtttuy73DzEw2bNjAgAEDADh48CBdunShYsWKAHh6ema7MX7+/HlCrVbkBAYGkpKS\nkqPff2K9vFtFhQBfogNCiK4T4ryxi+yt7LMXCHQ1QFQuLZU7Lv1Bq07RlO79IPwdBcuz13U7dP4Q\nS7ZeonyZDMIr/8Vbp7dwbuOvOfrK7c/irl27iIyMxMfHBx+bMVmCUKVHvUjm5ZQc1xAREYG3tzd7\n9+7NFgQEIyXozEmT6FrzxnMFBQ2K2srtO29WoQJz72pJvx820rVKVbp//x1z09O4y8l5w8LCWLNm\nDZMmTaL1q68wZcBA2pv32QsEOgoAWjj7OdsG+my/+/zw8fGhR48e9OjRg+PHjzNz5kx69OhB2bJl\niYuLo1+/fpQvXz6rfW7Ba1NMa0rZO0H1NkZ9QUerBb1LwXfjpWagEEIIUQwopY4AVbjxu1YGcAL4\n1Drri1KqOnAIWKG17mbTR4b5uEyMh0VTgTXAI1rrc0qpwcA427IOVsf3BV4BqgF/AK9qrWe50ndB\nr1+If4LNu/5kWoLxgPOI7g2JjqpUxCMSQgghxK1O5ifFg6tBwENAWmEOJA9SgRzLsPz8/Dhx4sbN\nZEv6wUOHDjFgwABCQ0NJTk6matWqWW3efPNNWrdujclkolWrVlnpQC9cuEDHjh15+eWXcx1AQEAA\nFy5cyPp88eLFbDfMhbDlLLVnQQOB2QJCuQRfrqdn8kHied6KrcA5/7Kc8y/r8th37NhBo0aNcozJ\n+n3pdm3J/DlnnbyIiAiuXLnCnj17uP/++7Pta516hWG7dkAGAmgAACAASURBVHGpanXKeHtnbXdX\nINDRd964fHkWtmxN300b6FGtOrGbNvJxWhr3OTmvh4cHo0aNok2bNsTGxtIhIoLRFSrgQ85AoCsB\nQAt7P2fbQJ+99wVRtWpVxowZw+jRo1m3bh0zZsxgzJgxdOrUibi4OFqcPsOVpQk5jktZ/j2ZPUc6\nHkd1B6sFr16A65ftHys1A4UQQoibKRMYqrWebdmglGoOrFVK7dFaLzVvjgOSgM5KqWCttW0B4HZa\n6+/Nx4cDXwDjgKccnVwpFQl8DMQA3wEPAouUUju11jsK0rcQt4L5ifuZt2Zf1ufXZ24jtkMkfe+P\nKMJRCSHErUkp1Q14HojCeHhpH/Ch1vpTqzaVgeNaa0+bYwOA0cDDGA9opQBbgTet5kFp3Hhwy5Mb\nD0oBzATmAt8C07XWI636Dse4xx+utT6mlPoOaGU+3tpXWutu5rH8D2N+BrAKY77o4GaOEDfI/KT4\n8HCx3WvAVKVUF6VUTaVUNetXYQ7Qltb6FHBJKdXUvCkQoE2bNmzdujUrtee0adN45pln8Pb2pkGD\nBnTt2pXp06dz/fp1wKjrt3r1aho0aEBmZvbFhYGBgfj5+eU4t6Vds2bN+O677wA4d+4cycnJ1KiR\n6wOtQrhU2w+MAJCl5pq10r17UapnD7vH5boirHqbbB+XbE2herA3zWr5cbhc3v6XTUpKonHjxjnG\nZH1OLy8v0tPTcxxbrlw5fH19+fnnn7NtT1m4CO+Vq2havgLrTp7McZy978KdooKCWNyyNQnHj9Gn\nejjDtm7ha5tx2hMdHU1SUhJnAwLokvQzv/n54RlyY9VoXgKAFrY/Z3sr/Wy/e3fx8PCgffv2zJs3\nj0OHDnH33XfzzLBh1H8kjrd/3cvvl3PO8Zz+nCw1/3ITEWN/nyvHCyGEEKJQaa23Ab8ANQGUUh7A\nYOBpYC/Qz8nxR4CvAVd+y78PWKe1Xqu1TtdafwHsNG931LfUYxC3PNsbbBbz1uxjfuL+IhiREELc\nupRSjwIfAW8DFYAAYATwpFJqtFKqqlLqMeDLXI71wwjeRQJdtdb+gALWAWuUUt0BtNZeWmtvrbU3\ncBQjMOdtfg2z6nKgUsrRk9WZwCtWx1pelmwP7wOlgapALYw53TP5+2bErUbmJ8WLqysB55v/m+Mv\nKIy/MDxz2V6Y+gBTzE8+VACoWLEiU6dOZdKkSaSmpuLt7Y2/vz9lypTh3LlzPPzww5w7d47evXtT\nunRpACZPnkzp0qVzrYHl7+/Pnj17qFevXtY2S7uIiAiio6Pp1asXJpOJl156qfCvWJRIrgYALfK6\nItBuSsjwe7LqsV29nsGHief5YHAwQJ6DgDt27KBfP4f3ePD09CQtLffFwjVq1GDnzp1Zn62/k46V\nK/P1iT940CZVKBR8RaArqyjrli1LQqs29Nj4PYM7deLx2bO41qghsbGxTvsPCgpi0aJFfPzxxzz0\n7LOMVRH0Cg7Gp3mzPAcAbcds+/5mK1++PENCQul9RzN++ftv5h45TPu1/0ejcuXoF16DDpUq4+tp\n/LXv8OdULhwGr8u5HeDvI3BgpeOB2ASzhRBCCFGosn4pUkp5AncB9YAnzJs7Ape11t8rpWZiBASn\nOOijJvAQxhPkziwGVlgdWxaoDhxzQ99C/GNt3nUi1xtsFvPW7CO8UqCk3hJCiJvAvHJuEjBQa73c\natePQANzm7swVgj+DjS26eJxwAforrXOANBa/wW8rZTywbgXvkxrnWuprFy8CUxXSjXWWucpw59S\nqhzQF2PV4Hnztq7m8QnhkMxPih9Xg4A1C3UUeaS13o6xXNmylPkwQGRkJJ9++mm2tidOnKBsWSP1\nYVxcHHFxcTn669atG926ZStpwcqV2W9OW1KFWowYMYIRI0YU6DqEyAvroNbfXv78cV8Mxys34sBi\nI8BWJyyAOmEBqLBAKlgFXz7+4ANK1/2Dnx/ugWvr3G5IT09n165dNGzY0GE7eysBAaKiovjiiy/I\nzMzk8qLF2YJyHStV5s29e7iekYG3R86FyTcjEFgnMJCvX3+D1hPHUv/+eox48lFm/zyLO7rZzsUM\n1nUTTSYTw4cP56677qJXp05sql6dj8eNzddYbcdclKwDtQ3KlaNBuXKMb9CQlX/8zsxDvzFqRxIP\nV61GbHgN6pYtm7+fk7OagUHhRhshhBBC3Awm4GOl1DTzZy+MBz1na60tOd8fAWaY388DJimlGlml\n6wRINNfv8wC8MX5PW+3s5FrrZMt7cxrSGRg3zBYXtG8h/smmJex0qY3cZBNCiJvibow5Sm6LaADQ\nWv8A/KCUugcj/bm1GGCRJQBoYyZGpr4IjPSirngD6IWRmvR1O21yrowxNAX+Bh5TSg3DuK6FGBkh\nhHBI5ifFj0vpQLXWR8wpV8oDLYBGQIbV9mKrUqVKua70E+JmcJbK05bdlX1W/V3p1pN3GvdisUcV\nthw8w9lLVzl76SpbDp5hzsbDjFmyk7MXrwKQmprKG2+8Qeu4lvkav9aaSpUqERgY6LCdl5eX3ZWA\nDRs2JCMjg1OnTuXYV7lUKaqXLs2WM7blZNzHlXSqDZ98gv7v9WXvN3uJ6hjF1gXb+OHzLS6fo379\n+vy0fz9B9erRpEkTfvopZ33Eks7f05Me1aqT0PoeVt7TjlJeXvTZtIGO365l9qFDXMglXahTjlb6\nSSpQIYQQ4mbKBB7RWvubX94YD132U0rdo5SqCDwAvKSUOo2RDtQTGGLTz33m432BIGA58J05lahD\nSqkgpdSnGPVmPgIesLkJZq9v+WVPCCGEEMVBBeCMnSCeKyoCJ+zsO23+bzlXO9NaXweGYczfauXS\nxAS8rJRKtXpdVkqVN48lFCOdaTjQEGiHEVgUQpQwLq0EVEoFYaRnaWmzfQkwRGudUghjEyWA9aoo\nkTtXVqOB8wCgxe/N78Hz2mGHbXTyBaIDQpg6dSotWrQgLCLM9QFbSUpKolGjRk7bOUoHWrduXfz9\n/dm7dy9tc/kuOlaqzOo//6BVaGiOY139TpxxJZ1qUOUg+r0fy7wn5lO3XSS71+zm6qUr3PNoG5ce\nJChVqhTTp09n8eLFdO7cmVGjRvHkk0/ikcsKx+LO2Z/ZGmXK8GK9+jx/ez3WJSezIO0a8U89Sdf1\n3zF06FBatWrl2sMXVmlrc5BUoEIIIUSR0lpvUkr9CVQDmgMbgAFWTe4F3lFKPZNbiimt9QWl1MfA\nkxg3kexSSgUCm4AkoLbW+pyTsVn3XRFIdtReiH+qEd0b8vrMbU7bCCGEuClOYydIp5QaB3TQWjuq\n0XcGCLGzz5Kl74+8DEhrvdn8kNU0jKwO1jKBeK31q7mM1zK3G6W1vgL8rpT6CMiZYk8IGzI/KX5c\nvTv9DhAIRAOlgGCgO0Y+48mFMzQh/jlcWY2WW7Dr7MWrbD5wmtkbDjFm8U7GLN7Jwi1HuZB6nevp\n9lOAH0i+yKVLl5g0aRLjx4/P97h37NhB48a5p8W05igdaEREBOnp6ezduxfI+V10qnwbq0/8SWZm\n9utxVwDQwva8ufVftmIg/d+PZf/3mtp31ebI9mOsfieRjHTXH+Lq2bMnW7duZdGiRTzwwAOcPn3a\n+UHFkCurWD1NJh76z79ZvnUrWmuioqIYMWIEERERTJw4kRMn7D3AZmZJW5vbS1KBCiGEEMVBBsaK\nvzhgjtb6T8sLI1WnH0ZtPgvrun0BwFOAtkr36amUuk0pVcXqFQSMwLjxNcBBANBZ30LccqKjKhHb\nIdLu/tgOkZJqSwghbp7NgEkp1cV6o7nWch/g/5wcvwroa25vOba/UqoBxlwsSWt9zO7R9r0IKIx6\nzq76zfxfX6ttXoAsBBJOyfyk+HE1CNgVeFxrvVVrfUVr/ZfW+gtgOEYwUAjhhL2giqMA4JglO5mz\n8XC2tJ/7T1wg+fwVDp++ZDcQeCD5Ih988AFt2rShQYMG+R6zO1YC1qhRg8uXL/PLL79kbbP+LiID\nA/Ewmdh76SSlK+4yXl3bFUptPMt5HQUYA0IC6P9eXw7+cJBqjary17G/WBH/FelpuQc5c1OjRg02\nbNhAw4YNady4Md9++627LuGmykvwOjQ0lGeeeYY9e/Ywa9YsDh48yO23385DDz3E8uXLuX79OmDU\nG0xZuOimjF8IIYQQBXYe6A9UBZZa79Bap2LcrBpktXmtUuq6UuoaxpPqlYHO5n2ZQBXgOHDM6vUG\nRg2dlsA18/GW18su9i3ELavv/RG53mjr1zGSvvdHFMGIhBDi1qS1vgiMAT5RSnVWSvkopYKB6Rgr\n/D500sW7gA+wVClV35xO3QRswXj46Yl8jisFGIkRDLRmwk5NQK31j8AejBrQ/kqp6hhxADvpnITI\nTuYnxYtL6UAxov5/5bL9DEZuYCGEC2zTLDoKRunkC077S72Whre/d47tVy9fYvrkyaxfvz7fY83M\nzMzTSkB7QUBvb2/CwsLYvn17tu1Z38WSRTxYowzfXtzOXf6l8AwNwTNzGST5QVQsePnk+xpy40pw\nsUyFMvR7L5b5Ty+keuNq/P3H3yx9aRnd4mNcPo+3tzdvvPEG7dq1o3///gwdOpTx48fj5eXqX7vF\ngyupVK2ZTCaio6OJjo7m3XffZdGiRUyaNIkRI0bQt1lzel29Rq2AgGx9CyGEEKJoaa1r2Nnu8Gkw\nrXVvq/cOHzDVWs8inzeOnPUtxK2u7/0RhFcKZFrCTsDEyIcbcGd9ecJeCCFuNq31f5VSZ4AJQAJw\nGVgH3K21PmXTPNPm2EtKqbswAokrMB54SgHWYGRfGKuUekBrfc2Fodj2vUoptRRjRaJ1G/tpxqAL\nMBU4C1wApmmtnQUyhcgi85Piw6Ui6kqpL4FTwHCtdbrV9veBhlrr1oU0PlfGFg4cXrt2LVWqVCmq\nYQiRJ5aVUI6CILM3HGLLwTM5tiefu8KFK8aqqkB/b8LK+uVss3E+pa+dYc6cOfke4++//06TJk04\nefKk0/puBw4coHPnzhw4cCDX/R06dGDLli2cP38++45jP5C25HnWb9vNM9+m8POzCk/r2oABlaHZ\nv6Cao5Tp7jF646gc21IvpDL/6UVUrleZqxevcOHURXZ/v5vAwMA89X3y5EkGDhxISkoK8+bNo1q1\nau4adr648ucvt2NcCV7b8/PkyXw6bTqLjx2lZpkA+oXXoPe/Hyd00MA89SOEEMWVyaViqEKIoiC/\nMwpRNOTfRiGEcB+lVEut9caiHoczMu8Somg4mne5uiTlP8C3wFGl1A9AGtAMo9jpfQUeoRD/QGcv\nXkUnX+BA8kUOJF8EoE5YAHXCAlCdY6gQ4OvweMsxtvx9PLOCgKnXcqaovJpygW8TZvPTj1sLNH7L\nKkBXfm9zlA4UoEGDBqxfv56zZ89SoUKFGzu2f4xXeW9aNqzMH19pfvcIpLr1gRf/hO0f35QgYG78\nA/2J/W9vFj63mJBaIYTUCKZdu3asXr2a4OBgl/upWLEiX3/9NW+//TbNmjVj6tSpdO9eNJmUrYN5\n4Hog0LpdXgOAKQsXUe2HLYxv0JDR9aNIPHGCeUcOM3bYI3SdPZsRr0+gefPmLv1ZE0IIIYQQQggh\nhLjVlIQAoBCieHIprYrW+jBQHxiLsSLwAkYe47pa66TCG54QJZO9en5bDp5hzsbDjFmyk7MXr+ar\n71I+ng7371gzjwcefJDatWvnq38LV+sBgpEOND3dfs28yMhIAgIC+PXXX7PvqN4GAN9KFXmgcQgr\nduRc+Uj4Pa4OuVD4BfjR551enDlylutXr3PffffRunVrfv/99zz14+HhwfPPP8+KFSt49tlneeyx\nx0hNTS2kUefONgB4efGSPNXnK927V74CgNbn9PbwoMtttzH37pasv68DlU+eJDYmhqioKP773/9y\n5kwufwaEEEIIIYQQQgghhBBC5JnTIKBSqhEYRUS11p9qrR/HWBX4mdb6dGEPUIiSyJV6fs7a1AnL\nvdymt5cHNYJLUzHQj4hKAVQo40uFMr7cWTuYB28P5PCm5bz26vj8DDsbV+sBguOagAARERGYTCb2\n7t2bfYdVgC+mcQW+2J5LAMgcKCxKvqV96fNWTy6cvMDx48cZNGgQrVq14uDBg3nuq0WLFiQlJXH2\n7FlatGiRMzBaSGyDcRZ5DQS645wWlfz9eTKyLj/c3Yq3uj/M9u3bqV27Nj179mT16tUOA8tCCCGE\nEEIIIYQQQgghHLObDlQp5QnMAXoDtkuP5gOXlVIvaa3fL8TxCVEi2Uvladsmuk6I3f11wgJyrQkI\nRiCwrJcHve+snq2PF154gd69exEeHp7nMdtKSkpiwoQJLrV1lg40IiKCS5cusWfPnuw7yoVDUDic\nO8J99coz8ON9/J1ynXKlvY39QeFGm5tgQsuJTtu80vI1unbtyvbt23nhhRdo06YNq1evJioqKk/n\nKlu2LAsWLGDGjBm0bt2aiRMnMnTo0EJLh+ksGGfZl9dVfgU5pzUPk4mme3+ldc8evP/++8yfP5+X\nX36Z4cOHM3jwYIYMGUKNGjXcNjYhhBBCCCGEEEIIIYS4FThaCfgM0A5on8u+UOAt4B2lVNfCGJgQ\n9py9eJXNB04ze8MhxizeyZjFO5m94RCbD5zOd4pNd3M1COiICgt02od1m5MnT/LJJ58wevRo5wN0\n4ty5c5w6dYo6deq41N5ZOtDg4GA8PT3ZuXNnzp3mlX6lfT1pGxnEyp1nb+wr4lSgtkqVKsWKFStI\nSUkhMTGRSZMmce+997Jly5Y892UymXjkkUdYv3497777LrGxsZw/f97tY3Y1GFeYKwLzIigoiJEj\nR/LTTz/x5Zdfcv78eZo3b07bqCg++88TXLlypaiHKIQQQgghhBBCCCGEECWC3ZWAwGDgOa31d7Y7\ntNZngVeVUpkYwcIvCmV0Qtiw1NrLsf3g1axVc/E9GlIhwPdmD83tKgT4Et+jITr5AgeSL2YFDeuE\nBVAnLAAVFpjtOidOnEj//v2pUqVKgc+9c+dOoqKi8PR0XH+QC3/Ctg/wvHSFtLTrdpuZTCaUUjlX\nAoIR6Ns5C4CYxsEsTzpL/7vCjH3FIBWoLT8/PxISEujduzfz589n+vTpPPTQQ8ybN4977703z/3d\nfvvtbNu2jaeffpomTZqwYMECmjVr5pax5mU1Hrh3RaClD1fPX6pnjxznbdiwIVOmTGFs02YkfPAh\nsxYu5NmZnxE7aBBxcXEu16wUQgghRO6UUkeAKkCmeVMGcAL4VGv9qlJqJjAQsDztlQnsB/6jtV5n\npw+LD7TWT5nb9ASeBKIwHkQ9CHwOvKu1TlNK3Qb8AnyktX7RanyPA/FAQ631MaXUk+Z+woDfgGe1\n1l+759sQonjbvOtPpiX8AsCI7g2JjqpUxCMSQghRGFyYN5UH3gE6A0FAMrAUGKu1vqSUCgcOcWP+\nZgIuAEuAx7XW15RSg4FPbdqcA2ZjxAPSzf1MB+4GrgPLgce01pcL7+pFSSZzleLLURCwJuBseUsC\n8IL7hiOEY67W2osOsJ9m82aoExbA2YOOVyXaq/lnrUKAL9EBIQ7ThgL88ccfzJ49O/cgWz7Y1gMc\nvXFUtv2eGek0SN5Lw+S9eGZmkHI1g7QrlyBpJkTFgpdPjj6joqLYvXs358+fp2zZsjd2lAuHwesA\neLDLaZ6uU4crfb7Gz8/PLddSGHx8fFi0aBH9+/dn6tSpzJ07l9jYWKZPn063bt3y3J+/vz9Tp05l\n6dKldOnSheeff56nn34aDw+nZVuLNVcDgbkFAC1SFi4iY/kKulatSteqVTmWksLSE8nExMQQHBxM\nXFwcsbGxBAUFuX38QgghxC0gExiqtZ5t2aCUag6sVUrtMe+fqbUeat5XChgDLFRKhWmtM3Lrw5pS\n6llgFPBvjJtH14A2wP+Au4DuWus/lFLDzP1+qbX+QSmlgInAI+YA4L3AS8D9wG7gUWCJUqq21vqE\nu78YIYqT+Yn7mbdmX9bn12duI7ZDJH3vjyjCUQkhhHA3V+ZNGIG660B9rfVppVQEMBP4DOhp1V0t\nrfUxc7/1gDXASGCKef9RrXVW7RWlVH0gESOA+AFG4HE70BUjK+ByYALwlLuvW5R8Mlcp3hzdYU4F\nnEUpvDGeFBDipnBHms2bwZUAnyttXPX6668zdOhQwsLC3NJfUlKS3VVW1c79zsN7VtLkxG48MzMA\n8PIwkZaRaazoWz4Ejv2Q47jIyEjKlSvHr7/+ave8ISEhREVF8e2337rlOgqTt7c3c+fOJTQ0lDfe\neINly5bx2GOPMXt2rve/XPLwww/z448/kpCQQOfOnTl16lSBxli6dy9K9ezhcntHwbjCGoOzAKBt\nALFa6dI8hYldE9/kjTfeYP369YSHh9O/f3/WrVtHRkaGW8fvbikLFxWLtKtCCCGEPVrrbRir8mqZ\nN5ms9l3GuMkUbH45pJSqjHGzqJfWer7W+rLWOk1rvRboAHRWSnU0950AzAJmK6UCMW5wJWitF5i7\n6wgs1FrvMPfxIXAZaFngixaiGLO9qWYxb80+5ifuL4IRCSGEKAx5mDfdC8zQWp8G0Frvx1g5mGqv\nb631HuB7wG5ERmu9G/gOqKeUKoMRdHxFa52qtT4KfGwehxDZyFyl+HMUBNwKDHJyfC/gZ/cNRwjH\nSkoQMK/1/Ari6NGjLFiwgOeff94t/UHOlYDWmv6xk4BrKdm2eXhAuiX2cvFP2P5xjuMiIiLw8vJi\n7969Ds8dExPD8uXL8zXum83Ly4uZM2cSHh7OqFGj+PLLL3n55Zd5//33891n9erVWb9+PXfccQeN\nGzfmm2++KdAYXQ0EFkYA0NkY8hoAtHZ1aQJ3/32OhQsXcvDgQZo1a8YTTzxBnTp1mDBhAn/88Yfb\nxu8ulmsqLvUXhRBCCLOsIJ9SylMp1RqoB+R4Kst8Q2gY8LPW2vppJXsPhnYC/tRa5+hLa30EWI9R\nh97iPxgpSXcAIcBjVvveA163Gks4EAgcs39pQpRsm3edyPWmmsW8NfvYvEsWwgohxD+Eq/OmH4Ap\nSqnnlVJ3KaX8tNZbtdYDbQ4zASilTEqphsA9wKrcTqyU8lBKNcZYdbgNuAI0NZcEs2gEHC3IBYp/\nHpmrlAyOgoDxwEil1GvmJzGzKKXKKKVeBp4DXivMAQpRElnq+Q1oWYM7awdToYwvFcr4cmftYAa0\nrOHWuoWvvfYajz76KCEh7kmBevXqVfbv30/9+vVz3X+4XLUc2zxNRhAwM9NcCib8nhxtIiIiuHLl\nisOVgGAEAVesWFHsV3RZeHp68sknn1CvXj3+/e9/89VXX/Hee+8RHx9/4/vII29vbyZMmMDs2bMZ\nNGgQL730Etev26+56ExBVuO5i+0YChIAtLAE04KDg3niiSfYuXMnCxYs4Pjx40RFRdGlSxcSEhK4\ndu2a264jv2yvSQKBQgghigkT8LFSKlUplYpxw+c74Aut9U/m/QOs9p/HeNJ8mr0+zC9LgDAM+N3B\n+U9jBPKArJWGHwHhwCda60tW+45Z0n6an4JfD8zSWm/N/+ULUbxNS9jpljZCCCFKBFfnTV2A94G2\nwArgglJqnVKqjU37/eb521UgCTiOEeCzqG41x7sMrAbmYqSCT9NabwdQSpVTSk0HYgD3rUAQ/wgy\nVykZ7NYE1FpvUkr1Bj4BnldK7cMoEBoARGL8BTJEa514U0YqBO6rtXczuFrPryB+++03li1bhtba\nbX3u3buXWrVq4e/vn+v+w+Wq0eTE7mzbPDxMmEyQkWkEBKluO++AWrVqceHCBXbv3p1jn7U6depQ\nvnx5tm3bxp133pnv67iZPDw8mDp1Kk888QRDhw5lxYoV9OnTh3PnzvH2229jMuUva3L79u1JSkpi\n0KBBtGnThnnz5hEeHp6vvuzV57sZAUDbMdi+t+ZqANDC0rZ0716YTCaaNWtGs2bNmDx5MkuWLGHK\nlCmMHDmSAQMGEBcXR926dQt2Eflg75qsxy6EEEIUkUyMmnvWNQHvBr5TSs0y759tVRPQA/MNJ6XU\nMa31/+XWh5XTGCv67KkFrLQ6dw3gZWABMEoptUBrfdhq/23AVKAx8LzWen5+LloIIYQQohhyZd60\nCrimtX4XeBdAKRUJPA18rZSyfnJfWdUEDMao8/cFRppPsKkJmBul1FDgDeAboIHWOjnPVyWEKHKO\nVgKitV6G8RRmHLAW2G/+7yNAda3154U9QCGs3exae8Xdq6++yuOPP0758uXd1qejeoAA5/zL8rdf\nzlSmXh6QnpEJQeFQLjxr++iNoxi9cRSv/jgO//L+bPx5Y9Y265e1kpQS1MJkMjFlyhTatm1L3759\nWbJkCZs3b+aRRx4hPT093/2GhoaycuVKunfvTvPmzVmyxPUAma28rMYrLKV797op5yxVqhQDBw5k\n/fr1bNy4EW9vb9q3b89dd93FjBkzuHjx5qQNdhbUlBWBQgghihut9SbgT6CqeZN1TcAMc12aXcAd\nLnS3BqillGphu0MpVR9oBiwzf/YC5gFfa61jMW42zVVKeZr3VwV+BA4AdSQAKG4FI7o3dEsbIYQQ\nJYIr86Z1wHVzUA8ArfU+jCCgH1Azt4611mcwai+7Mn+znPM1YDQQo7XuJwFAkRuZq5QMDoOAAFrr\ni1rrOVrrp7TWw7TWz2qt52qtz9+MAQph7WbW2ivu9u3bx6pVq3jqqafc2q+jeoCHzh/i0PlDbPPz\nJzUtNdvL0wMO/H2IJddO5XosQEiNYC7/fZlrqY7TM3bt2pUvvviiQNdRFEwmE5MmTeKBBx6gW7du\nzJkzh2PHjtGnTx+uXnW8gtURDw8Pnn32Wb766iteeOEFRowYQWqq3XrPDlkCgUURAHSFqzUMLVy5\njjp16vDGG29w7NgxXnzxRb788kuqVatGXFwcP/zwQ77TtjqT17SmQgghRDGSAXjabjTXi+mMsRLv\ne2edaK2PAhOAxUqpLkopf6WUlzld1VLgTa31HnPzUVezlgAAIABJREFUV4EqwEjz5xEYT7yPNX9+\nEVirtX5Ga32lANcmRIkRHVWJ2A6RdvfHdogkOqrSTRyREEKIwuLKvAnYhFE7ebpSKtxc7y8MYx51\nEuNBLQvrus+WWsvrXRmLUqoS8CzQUWu9xQ2XJ/6hZK5SMthNBypEUTp78So6+QIHki9yINlYsVMn\nLIA6YQE81SmSMxev5rpPhQW6rdZecffKK6/w1FNPUbZsWbf2m5SURExMjMM2ewNCaH3mSLZtHh4m\n0jJyrxloUaFaeZIPnOTssb+oFBFmt13Tpk05f/48WmuUUnkaf1EzmUy89tpr+Pr60qVLF1atWsVz\nzz3HQw89REJCAqVLl853382bNycpKYlHH32U5s2bs2DBAurVq5fnfopj8M+avdSltvIayPTy8uLB\nBx/kwQcfJDk5mdmzZzNkyBA8PDyIi4tj4MCBhIaGFmjsFgVJayqEEEIUA+eBu83vByql+pvfpwMH\nMcpC/OBKR1rr8UqpA8AYYCFG+tA9wGta6zkASqm2GPXmO2mtz5mPO62UGgksUEqtMY+nnlKqj80p\nhkiGGvFP1vf+CADmrdmXbXu/jpH0uS+iKIYkhBCikLg4b7ofmAhsBioAfwH/B7TSWqda3Uc7aH6f\nCVzCqPk3xLwv0/yyJxrwAfba3Jc7rLUuWTfqRKGTuUrxl79CVcWIUiocOLx27VqqVKlS1MMRbnD2\n4lXGLHFcMDS+R8NbJtiXm927d9O+fXt+++03ypQp47Z+MzIyCAoK4vDhw1SoUCFruyVd56Hzh+we\nu7T3Fzz2v16UrlmFCS0n5jgWIGnFDjbP3UKrIS2J6lg/2/HWxwCMHDmSmjVr8txzzxXomiwrrIoi\nuDJx4kQ++eQTEhMTiY+PR2vNypUrCQoKKlC/mZmZfPbZZ7zwwgu8/vrrPPLII/muO1icOQqkuWsl\nY2ZmJps2bWLGjBksW7aMdu3aERcXR4cOHfDyyv9zMnkNAkLRpGcVQriP6Z/4F7EQ/xDyO6P4J9i8\n6wTTEnYCJkY+3IA76xf/p+rl30YhhLj1yLzr1lUS5yr/JI7mXbISUBQ7OvmCS22iAxzVyv1nGzdu\nHM8991xWANDRysm8rI48dOgQQUFB2QKArsi8dh2TB1w/eYr0MvbPVb5qeTLSMzlz5KzTPmNiYoiP\njy9QENA2EHOzAyyjRo3C19eX9u3b88033/Dee+/Rtm1bVq9eTcWKFfPdr8lkYujQoURHR9OnTx++\n+eYbPvroI7evCi1q9lYEujNYZjKZaNmyJS1btmTKlCksXLiQ+Ph4hg8fzuDBgxk6dCi1atVy29jt\nkQCgEEIIIYRwJDqqkqTTEkIIIUSxJXOV4stpTUAhbjZLEKugbf6pkpKS2Lx5M4899hhwY+XknI2H\n2XLwDGcvXeXspatsOXiGORsPM2bJTs5edK0enb16gBNaTmRCy4nULFszx6v61QCqnkzDy+RBRkYm\n6adO2a1vVqFaea5cvMKZo2ecjqVt27bs2bOHkydPujR2W7YBwKKqu/bUU0/x7LPP0q5dOx5//HFi\nYmJo1aoVx44dK3DfdevWZcuWLQQHB9O4cWO2bt3qhhEXL7Y1AgszWBYYGMiwYcPYsmULa9asITU1\nlejoaNq2bcvnn3/O5cuX89Sfq/UNJQAohBBCCCGEEEIIIYQoDHZXAiql1rnYR6bWup2bxiOEBAGd\nGDduHKNGjaJUqVKAe1dOJiUl0ahRI5fHkn7qNOmnTgFGTcDMDCOduG19s/RTp43PIcFkZmZy6rfT\nTvv29fWlQ4cOfPXVV8TFxbk8JrCfirGo6q7961//wsfHh7Zt2/LNN98QFBREq1atSExMJCKiYLmx\n/f39+fDDD1m2bBkPPfQQTz/9NM899xweHv+cZzysf14362dXv359Jk+ezMSJE1mxYgUzZszgP//5\nD7179yYuLo477rjDpRSszlYESgBQCCGEEEIIIYQQQghRWBylA13vYh+OiogKIdxo27ZtJCUlsWjR\njRVtrgZNo+s4DwLu2LGDYcOGuTQW6wAggMnDREbGjb8OLEGPdO/s7YKrV+DkwVOkXU3Dy9dxRuKY\nmBjmz5+fpyCgs1psRRUIHDZsGD4+PrRr147ExETKli1L27ZtWblyZa6rL/OqW7du3HHHHcTGxrJ2\n7VrmzJlToJSjxU1RBcp8fHzo0aMHPXr04Pjx48ycOZOePXsSGBhIXFwc/fv3p3z58g77uBlpTYUQ\nQgghhBBCCCGEEMKW3TvwWuvxzg5WSvkDDd05ICHqhAVw9qDj9JV1wgJu0miKl7FjxzJ69Gj8/Pyy\ntrlz5aTtSkBL+kzbQIVtABCyrwS0uPTh/0jr6YfJ29t83CnKh5bm4ml//jr+F6G1Qx2Op3PnzowY\nMYJLly5l1T90xFkA0KKoAoGDBg3Cx8eH++67j6+//poPPviAjh07kpCQwN13313g/qtVq8Z3333H\nK6+8QuPGjZk1axb33XefG0YuAKpWrcqYMWMYPXo069atY8aMGYwdO5aOHTsSFxdH+/bt7a7AtA0E\nSgBQCCGEEEIIIYQQQghR2Bwvw7GilGoH3AZY5z+rCowFfN08LnELqxMWwJaDjmvG3YpBwE2bNrF/\n/36GDh1aKP2fPHmS1NRUqlevDuQMqFmn9rQNAIIRBMxIvxEEzDh1ivTTp+FaRTIhKxAYVNaLE76e\nnD5yxmkQMCgoiBYtWpCYmEj37t0dtnU1AGhRVIHAvn374u3tTYcOHVi5ciVz5syha9euzJkzh44d\nOxa4fy8vL+Lj42nbti0DBw6kf//+xMfH423+/kXBeXh40L59e9q3b89ff/3FvHnzeP755/n7778Z\nMmQIQ4YMoVq1ajmOK4q0pkIIIYQQQgghhBBCiFuXS0FApdQYjGBfMlAZOAYEm3e/XjhDE7cqFRbo\nljb/NGPGjGHMmDH4+Phk2+6ulZM7duygUaNGmEymHAG1rPe32T/eOh1oVgAQePqTk3iEhOAZavzM\nVpwqx8QLfxK5y4cJ4yc6HVdMTAzLly93GgQsSXr06IG3tzedO3dm+fLlLF++nG7duvHBBx/Qs2dP\nt5yjXbt2JCUlMXjwYFq1asX8+fOpUaOGW/oWN5QvX57HH3+cxx9/nO3btzNjxgwaN25M06ZNiYuL\nIyYmBl/fG8/JSPBPCCFESaWUOgJU4UY5iAzgBPApcBT4xLzdBHgA6ebPmcC9wFBgoNX268AvwHit\n9WrzOcKBQ1ZtANKAA+Z2CTZj6g2M0Fq3dcc1ClEUNu/6k2kJvwAwontDoqMqFfGIhBBC5IdSaiww\nHuimtV6ulKoOHLRq4okxf7LMpV4BNgLfcmPuYwJSgC+Ax7TWKea+RwP/BoKA7cAgrfUBpdQBYInW\n+kWbsYwH4oBqWutM87YWwAKtdQ2rdoMx5nLWcy+Lp7TWH1i1zXG8uDXJ3KXkcXUlYBwwGJgH7AM6\nYfzCtxTYUSgjE7esCgG+xPdoiE6+wIHki1mpLOuEBVAnLAAVFkiFgJu7+HT0xlEutZvQ8kZQKz/H\n2LNu3TqOHTvGwIEDc+xz18rJHTt20LhxY7sr6i4vXsJL5hSGubVZedmTQZt9qHv1YlYAEDAHAG+s\n+KsVEEBKWhp7tm4hZeEip0GRmJgYxo8fT1paGl5e9v/Ksld3zZ6iTscYExODj48PMTExLF26lMTE\nRDp16sSFCxfyVAPRkZCQEL788kumTJlCixYt+PDDD90WZBQ5NWnShCZNmvD222+TkJDAtGnT+Ne/\n/kW/fv2Ii4sjKiqqqIcohBBCFEQmMFRrPduyQSnVHFiLcSPK27ytDbDO8tmq7RBgptZ6qPlzKaAP\nsFQp9bAlEGhWS2t9zNzOAxgGLFBKVdFan1JKNQXuA54A9hbS9QpR6OYn7mfemn1Zn1+fuY3YDpH0\nvT+iCEclhBAir5RSJmAI8DPGPfTlWuujgLdVmwygndb6e6tt9wBYz5vMD0V9iREkfNYcqBsEtMRY\nmDPN/GoPfIYxT8oWBMSYY83SWmcqpeoCHYHHuBGAtHbUUWBPKRWJEQuwd7y4hcjcpWTKvXhRTrcB\nW81PDhwEGmqtUzFWAY4vpLGJW1iFAF+i64QwsFVN4ns2JL5nQwa2qkl0nZCbHgAsapmZmYwZM4Zx\n48blGgRz18rJpKQk6l696jCIdnnxkqzAXamePbLt8zSZuH72rMMAIEDNMgGcvXaV/efPZ/XnSNWq\nValevTobN250eg25jSs3RR0AtOjUqRPz5s2je/funD17lu+++474+HgmT57stnN4eHjw1FNPsWrV\nKl566SWGDx/O5cuX3da/yMnf359+/frx7bffsmXLFsqUKUOnTp1o3rw506dP58KFC0U9RCGEEMIt\ntNbbMFbz1bLabLLTPNs+rfVlrfWnwBTgNQfnyMC4weUFVDdvrg9UA47nb+RCFD3bm2gW89bsY37i\n/iIYkRBCiAK4D2OV3zCgs1Iq2El7u7TWR4CvgHrmTf8GxmqtD2qtrwFPAy+Y980Bqiqloi3HK6Ua\nAwqYad5U2/w5v/OmOgU8XvxDyNyl5HI1CPgn0Nj8/pDV+xSgrrsHJYS4ITExkbNnzxIbG5vrfsvK\nyQEta3Bn7WAqlPGlQhlf7qwdzICWNYjv0dClwOn2DRtQ+qDTdvYCgV4eJtIzbzwQlFsAEMDf05OK\nfn4cTUnhWkaG0/PBjZSgrnAWCCwuAUCLe++9lyVLltCnTx8OHz7Mhg0b+Oijjxg7diyZme57wKpp\n06b8/PPPpKSk0KxZM3bv3u22voV9tWrV4rXXXuPo0aOMHz+exMREqlWrxqBBg/j+++/d+jMWQggh\nboKsIJ5SylMp1QrjBtW6AvT5JdBYKVXaznn8gEcxMtHsBtBaz9Raj8S4QeYo6ChEsbR514lcb6JZ\nzFuzj827TtzEEQkhhCigR4BPtNY7MLIU9M9PJ0opk1KqDvAAsE0p5Qs0BGorpQ4qpS5iZOo7DaC1\nPg58A/S16qYPsFFr4yaf1vpL87xpFvmYNxX0ePHPIHOXks3VdKAfAJ8rpcph/JK2RCnlj1HbYWdh\nDU6IW51lFeD48ePx9PS0265CgC/RASFE1wnJ13lOzprN7ydPUqdFtMN2k+5NNd78PQfPhDV43hZC\nemwA6adOcXJ7JnPuK0XlqqE8s/hKrgFAi9oBAVzPyODE3XdxmwsBua5duxITE8PkyZMxmZzPN+yl\nBi1uAUCLNm3asGzZMrp168ann37K999/T8eOHTl37hzvvvsuHh6uPq/hWGBgIJ9//jmzZs2ibdu2\nvPbaawwfPtyl71QUjKenJ507d6Zz586cOnWKOXPmMGLECNLS0hg6dCiDBg2iUiXJoS6EEKJYMwEf\nK6WmmT97YdS2ma21/qkA/Z4y922dumK/UsrypIyPeX+cORuN7ZiEKHGmJTi/jTItYafU2BFCiBLA\nvOqvM0aacjCCZYOBd/PQh2WOYwIuA8swMvCFYCziuRe4G7iEUcPvC24s0vkMeFcp9YQ5i18v4NVc\nTmNv3lTd6vwWmUBZrfV1F44XtwCZu5RsLt1Z1lq/BTwI/KK1TgTexHgi4TT5fLJBCOHcV199xZUr\nVwq9jtueY8dQAYF45yPY5BlqrPjz8DCRkZ6B5223EfCvxxweU7tMAOWCgzlSvZpL57DUUtu1a5fL\n47JdEVhcA4AWd999N1999RVxcXFs2rSJdevWkZSUxODBg0lLS3PbeUwmE4MHD2bDhg1MnTqVXr16\nce7cObf1L5wLDQ3lmWeeYc+ePcyaNYuDBw9y++2389BDD7F8+XKuX7/uvBMhhBDi5ssEHtFa+5tf\n3kAroJ9Sqm0B+q0IpAHWRa6V5TwYQcDngQ/ND6IKIYQQQhQnAwE/4Bel1GlgHBBlTsvpEqv5lZ/W\nurzWOk5rfRVjjgQwUWt9UmudgnFfvqFVytHlgC/Q3pwWNARwXHsnu6NW57e8StkEAIUQJZjLd/y1\n1ola6y3m9xO01nW11vdrrQ8U3vCEKP4OnT+U9Rq9cVTWy3r7ofOH8txvRkYGY8eO5ZVXXnHbSjB7\n9gdXoFHjRi639wwNxTM0xOpzCB5+vpjKBuEZGuI0JWfdNq3xDQ5m7969Lp3PZDLlKSWohWUcxT0A\naNG8eXO+/vprRo4cyerVq1mzZg2nT5+mZ8+eXLlyxa3nioyMZMuWLVSqVInGjRuzefNmt/YvnDOZ\nTERHR/PJJ59w/PhxunbtyqRJk6hWrRovvPAC+/dLPnUhhBDFm9Z6E0bpiCouHpJbHuxuwHp7N5q0\n1ulAAsbNtXzX1xGiOBnRvaFb2gghhCgW4oB/YaTtbIhRu3gVxmrAgjoFXMQI8ll4YdQfvAKgtb4C\nzAdigd7AYnOwUAi3kblLyeZyZEEp9aRSaq9S6qpS6rxSapNSql9hDk6IW9myZcvw8PCga9euhX6u\npKQkmj38sMPAnYVtANDCw9cXAm9kcbIXCCzVswcNYmNJTU3l119/dXmMXbt25YsvvnC5vfU4SkIA\n0KJJkyYkJiby5JNPsnTpUpYvX463tzddunTh0qVLbj2Xn58f7733Hu+++y5du3Zl4sSJZLhYp1G4\nV5kyZRg6dCibNm3i22+/JSMjg9atW9OqVStmzpxJSorM34UQQhRbGbhWZsJE9lp//kqpRzFumo1z\n4RxgpB8VosSLjqpEbIdIu/tjO0RKOi0hhCgBlFJ3AdWBOVrrP82vP4CFQKxSyrsg/WutM4A5wEtK\nqUpKqTLAi8AyrbX1TaLPgO4YqUA/K8g5hciNzF1KNpeCgEqp8cB4jKcKYjCeLFgPfKSUerqwBifE\nrSo9PZ1x48bx6quv3pR6bTt27KBRo0ZOV/DZCwACeHga6UCt2UvJGRERwZkzZ1xeCQjQsmVLjh49\nyvHjx10+pqRq0KABa9euZdSoUXz++efMnz+fmjVrcu+99/LXX3+5/XwxMTH89NNPrFq1ig4dOpCc\nnOz2cwjX1a1bl7feeovff/+dp59+miVLllClShWGDx/O1q1byczMbRGFEEIIUWTOY9SosZbbP1aZ\nwECl1HWl1HXgb2AIEKO1/sHJsRfN21vm0qf8wyhKpL73R+R6M61fx0j63h9RBCMSQgiRD3HACq31\nZZvtXwEBGOW0nHE2l3kW+BHYi5GBIdV83izm+szHgcta6+8dnMf2XJkYNQGv5/KKd+F4cQuRuUvJ\n5VJ0QSl1CnhMa73EZvsA+H/27jyuqmp9/PjnAAKiiCg4m2i6UNOc0jKHFEUcwnlCxbEyS+veq1nO\nmjcbvnW1MsvM2ZxTcUwE57TJpDK1pb80S80pcUBFGX5/7AMdDgc4wGF+3q8XLzn7rL3Ws0FxsZ+9\nnsXbWutKORGcPZRSfsCZyMhIqlSxtwqNEJkz6eBrab5nWeqzhlcNm8et37P0Rsu3Uh1bvXo1c+bM\n4fDhwzmeBIyLi8PLy4u//voLT09PAGLWrOXOuhT/3PHo05tZlX9Is581r6yjSY/G1Hzy4VTXFLPG\nKEWetCIvMTERT09P4uPjuXXrFi4u9jw8DkOGDKFZs2a8+OKLdl9fQaa1pn379kycOJGRI0cyfvx4\nvvzyS8LDw6lY0fFP18TFxTFz5kwWLFjA4sWLCQoKcvgYImvOnz/P0qVLWbRoEe7u7owYMYLQ0FB8\nfKQqmhD5hSk3ntoRQmSJ/M4o8rPDP1/kkw0/AiZG9XqUJ+oVnqfo5f9GIYQoemTeVfgV5rlLQZbe\nvMu+O+9QAvjZxvHvgFI2jgshsiguLo7p06fz4YcfZioBaJ1os9evv/5K5cqVkxOAln0kJQKT99Q7\nmHYS0MnZKdVKQOv+kphMJpRSXLp0iTNnzlCrVi27Yu3WrRsff/xxkUkCKqXYs2cP7dq14/79+7zz\nzjt4e3vTqlUrIiIi8PPzc+h4Li4uzJgxg7Zt2xIaGkpISAj//e9/cXV1deg4IvMqV67MxIkTee21\n19i/fz8LFy5kxowZBAYGMmLECAIDA3F2lgppQgghhBAFTfP6FaV8lhBCCCEKDJm7FDz2JgF3A88A\nr1gdHwBkfpMuIQoYW6v1kqS1StB65V96fVj29fOXx7jjHsMe9wj2Hoy0Kx7rlXuZSQQePXqURo0a\npTpu2Yc9/ZmcnTK1n1zt2rVJSEjg+PHjdicBO3TowNChQ4mOjqZ06dJ2j1WQPfzww+zbt4+AgABi\nY2OZOHEiXl5etGrVip07d1K3bl2Hj9mmTRuOHj3KsGHDaNmyJatXr6ZGDdsrWUXucnJyok2bNrRp\n04bo6GhWrVrF5MmTee655xg6dCjDhg2jevXqeR2mEEIIIYQQQgghhBAiH7A3CXgdeFkp9TTwNXAf\naAQ0AdYrpZI2HE3UWg93fJhCFA3xcfEcWPwVXV7rZPcqQOsEYNLn6SXuLBOXkdt2U7x0cZvJzDf6\nZZy4TGJrT8D0+Pv7c+bMGY4fP077e7EZxgxQsmRJnnrqKbZv386AAQPsHqugq1atWopE4OTJk/Hy\n8iIgIICtW7fy2GOPOXxMHx8fNm/ezAcffMDjjz/O3Llz6devn8PHEVlXunRpRo0axahRo/jxxx9Z\nuHAhTZs2pWHDhowYMYIePXrg7u6e12EKIYQQQgghhBBCCCHyiJOd7RKAlcA3GPsIumFsRrocSNr4\n1ISdewwKIWz7eccxSlf0olqjh+xqb2vvPjASgUnlQTPyl75MeVU+U3Ha4uTkRGK8/fsD+/v7ExcX\nx0/bd3Bn3Xq7Y+7WrRthYWHZCbVAqlKlCvv27WPlypVMnTqVgQMHMn/+fDp37sy+fftyZEyTycTL\nL7/Ml19+yeTJk3nmmWeIiYnJkbFE9jRo0IAPPviAP//8k2eeeYZFixZRpUoVxowZQ1RUVF6HJ4QQ\nQgghhBBCCCGEyAN2rQTUWg/N4TiEKPJiY2M5uPQQ3acF29U+rQRgEntWBCYmJnL59CXK1yxn15jp\nlTQ9V/E8HWp2ILRlqF19+fv7c+3cH9y/HwvlK9gdc3BwMOPGjSM2NhY3Nze7xiosKlasyN69e2nf\nvj3379/nzTffxNPTkz59+rB48WK6dOmSI+M2adKEH374gRdffJGmTZuyZs0a6tevnyNjiexxd3en\nf//+9O/fn7Nnz7J48WK6du2Kr68vI0aMYMCAAUWmlK4QQgghhBBCCCGEEEVdmklApdQ04LrW+gPz\n52ku8dFav54TwQlx7VYs+q+bnPrrFqf+ugVArQqe1KrgiapQirKehScJtHDhQnz8ylKlfpUM22aU\nAEySUVLt5uVbOLk4U7JsycwFa4OzszNxcXF2t6/8089cuHoFJyAhMREnc/nTjGIuX748jzzyCHv2\n7KFjx47ZjrugKVeuHHv27CEwMJDY2Fj+97//sWXLFrp168bs2bMJCQnJkXE9PT1ZtmwZy5YtIyAg\ngNdff53nn3/e7rK1Ivf5+fkxY8YMpk6dSkREBAsXLmTixIkEBwczd+5cvLy8UrT/888/6dSpEw0b\nNgQgISEBNzc33nvvPby9vfnqq68YMWIEYWFh+Pv7A/Daa69x8uRJPD09uXfvHu7u7sl9165dm5Mn\nT6YY4/Lly4wfP54HDx5w584dZs6cSb169ahfv37yuNevX6dz58688MILufBVEkIIIYQQQgghhBCi\n8EpvJWBb4CLwgflzW0lAk/m4JAGFw127FcuU9T+mPn46lq9PXwVgZu8GeZ4ITG91nL3u3r3LrFmz\nCJzWLsO28Zev2JUATJJeUu3SqUuUr2XfKsD0xKxZC+fOER8fb3d705atlHVzIy4hgT/u3KFaiRJ2\nxQz/lAQtiknAmDVrcQciIyPp2LEjo0eP5sMPPyQiIoKOHTty48YNnn/++Rwbf/DgwTzxxBP079+f\niIgIPvvsM7y9vXNsPJF9zs7OBAUFERQUxNWrVwkLC0szeevr68vy5cuTX0+dOpWwsDCGDh3Khg0b\n6NSpE2FhYYwfPx4wSsZOmjSJpk2bJrfftGkTQ4YMsdn/Z599Ro8ePejWrRt79uzhrbfeYsWKFSnG\njY2NpVWrVoSGhuLp6enIL4UQQggzpVQk0Nr8MmmLiKTNnc9orZW53VRgOtBDax1mcf5QYBEwQWv9\ntsXxNsBurbWTxbEAYArGfvIAJ4EFWusFVjEFABOAxzC2n/gdWA+8pbWOMbfxA+YDLYAHQBjwgtb6\nDkLkA4d/vsAnG34C4PmeDWhev2IeRySEECI/UEo9BMzGuMdeAjgLfA7M0lrHWbTbjTHPqay1vmpx\nfAkwGBiltZ5v1fcKYAAwVGu9TCm1F2Oe10lrvdOq7UHgSaCN1nq/Umoc8Ab/zAMx97NGKeUJLACC\ngdsY87fJ2fxSiAJA5jOFT5p7Amqt22itQyw+bwu001q3NX8eYHFcCIfTf910SJuCYP78+TRp0oRK\ndXL3h+qlU5eoUCt7+wEmrUpMPPs7MV9/Y3d7gIdLelLevTinbqb+Pqa3R2D37t3ZvHkzCQkJNt8v\nrJK+dnfWrcc1fBe7du0iKiqKkSNHUrduXfbt28c777zD22+/nXFn2aCU4vDhw1StWpVGjRrx1Vdf\n5eh4wnF8fHwYMWIEpUqVyrBtQkICV69epXz58ty8eROtNRMnTmTHjh0p2iUmJia3//vvv6lYMe2f\nYw0aNKB1a+Oes6enJ3fupL5nGx0djYeHB+7u7pm5NCGEEJmgtW6ntS6mtS4GLAOWJr22SACagGHA\nEWCojW4eAFOUUtXTGkcp1QcjkbcIKA/4ApOBCUqpVy3a9cVI6G0E/DBujg0BAoFIpVQxc9MVwK/m\nfhqaP97IytdACEdbFf4rs5Z8x983Y/n7ZiyzlnzLqvBf8zosIYQQ+cN24BLgp7V2A0KAQcCbSQ2U\nUg8DTYHTwEAbfdwwn4fFOcUxknTWN9ZstX0IqEPKhT61gOe11sUtPtaY33sPY85VGeMhrf5KqVF2\nX7EokGQ+UzilmQS0pJSqrJTaARy1OBytlPrz5AREAAAgAElEQVRIKSV36USOSCr/md02+V1MTAxv\nv/02r79u34Ja53K+ePTpbXf/Hn16p7mi7tKpy5TLxkpAy4Ses8nE3e+PpJm4s24PUNPTE89iLvx6\ny3YyN61EoFKKUqVK8f3332c59oLG+mt3Z916nHd8yc6dOzl16hTDhg3Dz8+PAwcOsGzZMiZMmJCc\nnMkJbm5uzJkzhw8++ICePXsya9Ysu1eCivzr6tWrhIaGEhoaSlBQEBcuXKBjx45s27aNDh064Ovr\ni5+fH4cOHQKMBOCsWbMIDQ0lODiYkydP0qBBgzT779KlC97e3hw5coTp06fz73//O9W43bp1o3Xr\n1ri42LVtsRBCiOwzmT+sBWI8Ff4s0Fkp5WP1/gVgLfCxrU7Nvyd+hPHE+nKt9V2tdazWOhwjuVjT\nqt3LWut5WusbWutErfW3QBBQHRhhfhr9SWCGua/fMZ5OD8rW1QvhAKvCf2XlzpOpjq/ceVJunAkh\nRBGnlKoI1AXmaa1vAmitfwDGknIONgJYB3xK6gewEoFwoJFSqrLF8WDge+C6VdtNQHellGX5tP7m\n45Zj1gS0jZiLYSQRp2uto7XWf6QRlyhEZD5TeNmVBAQ+AUoDltn+YRi/cM1xdFBCQNFJAn700Ue0\nbNky3Rvn1kr062tXIjC9BCBkbyWgdVLKxWQiPjEx3RV81mp6egImtI2VgBlJKglaFKS1B+Sddesx\nbdvO9u3buXDhAoMGDaJcuXLs37+fiIgIXnjhhRxfLdm1a1eOHDnCzp076dChAxcuXMjR8UTO8vHx\nYfny5Sxfvpzw8HA8PDw4duwYGzdu5MCBA4SGhnLp0qXkf3tJ5UCXL1/Otm3beOWVVxg3blya/d+/\nf5+pU6cyf/585s6dS6tWrVKNe+jQIa5du8bGjRtz5ZqFEEKkuff7M8BnWuso4DjG0+rWxgINlVID\nbLzXHCiJsRIwBa31Pq31s+aXLQBPYLmNdjcwblYFAHeBx7TW1yyaNMQoGypEnjn880WbN8ySrNx5\nksM/X8zFiIQQQuQzlzFW961QSo1RSjVRShXTWm/RWo8DUEo5Y5T7XIBRJrSuUqqhVT93gK0Yybwk\nIcBqG2OeBX4BnrY41t9G21rA60qpG0qpC0qpN8yxKIzKDFEWbY9jrCQUhZDMZwo3e5OAbTGe4DyU\ndEBrvQHjh1PaGQYhRLpu3brFu+++y/Tp0zN9rmUiMOHyZRIuX07xfkYJwLu37nH3xj28K2d+Pzdb\nSSknk4m4RCPhlFYi0Dp5WdPTk9txD9BprARM7xq6d+/Opk2bMh17QRGzZm3yR3p7QN5Zt57ELVvZ\nvHkz0dHR9O/fH09PTyIjIzlx4gSDBg3iwYMHORprlSpV2L17N61bt6ZJkyapykWKgslkMlGtWjV+\n+uknnJycWLt2LcuXL2f9+vUcOHCAu3fvAqRYcerp6ZnuitB58+ZRtmxZPv30U/z8/Gy2cXJywsPD\nQ1aWCiFEHjKv+usMLDEfWoqNJ7+11teBl4HZSinrSWVF4KrWOvkHulLqqlLqrvnjgbksVQXgitY6\nrQnLVcBLax1nfmoepZS3Umo+0A0Yn9XrFMIRPtmQeh/7rLQRQghROJnnQs0xVvn1AHYDN5RSW5RS\nj5qbdQGitdaHzXsB7sD2qrtVmMt8KqW8gHbYeODKRtvaQCUgMulN8yrBihiJwTIYc7+BGPtBe5tj\nt1x9cQcobv+Vi4JE5jOFm721tu5jZP+txWJs2i5Etkw6+FqqY2fvPcS1G2VSHKvhVSPF61oVPHM0\nrpz2/vvvExgYyCOPPALAGy3fytT5Jfr1Jfbbb3nwyy/Jx5zKlcswAQjGKsByNX0xOdmq/pS2tJJS\nLk5OxFskA5LaWMeR9PrOuvXULOnJpXv3iImLIzExEZPpn1gyuoZmzZrx999/c/r0aWrWrJmpa8jv\nkr7GCZcvkwg4l0u/ZOuddevxADZt2kTfvn3p3bs369atY8eOHfTt25cePXqwbt06ihfPubmas7Mz\n06ZNo23btgwcOJB+/foxa9YsXF1dc2xM4XiW/wYB3N3dmTlzJlOmTEk+5uHhQdOmTQkPDwdg1qxZ\neHoaP4sfPHjAtGnTktv26tUr+fPg4GD27NlD8eLFk0v5Vq5cmbfeeiu5HGhSH9WqVaN79+45c5FC\nCCHsMRhwB35SSoHxe2MppVQjrbXlFhFordcopQYC/4exZ1+SGxg3lCzb+gAopTyA2+bDVwFvpZST\n1tpWCYMawB9JL5RSwzH2z4kAHtVa/5XlqxRCCCGEyB3RWus3MO9lrJRqBEwFdiqlqmJUYHhYKXXF\n3L44cEcpNdacRDRhlGnfCSxVxgStBbBPa33dPF+zlIhRtv1Nc0n1EGCd1johqa3WOhYoZnFOlFJq\nDkYp+C/McRbXWt81v18SY34nhChg7E0CrgM+U0r9BziIkRRsDPwP44ePEA7n6Xmba9fKpNumICcB\no6OjmTNnTvLeWlkRs2Yt8b+fw8nXl4QrV4i/coViTR/LMAH4Rsu3mP3dbLxbl81U4jG9VWlJ5UAt\nZZQIrLx2HTcfPKCEiwsX796lkocHkHECEIzVQsHBwYSFhTF27Fi7ryG/s0wAxl+5knzc3kTgunXr\nGDBgAN27d2fDhg1s2LCBoUOH0qlTJzZv3kypUqVyNP7WrVsTFRXFsGHDaNGiBatWrSp0SdrCqkqV\nKkRGRqY4Nm3atBRJvSTvv/8+YJTlTcvJk6nLSAwdOtRm259++ikTkQohhMgFI4AXgS3m1yaMLSKG\nknKf+CSjMEpO/WFx7BDgpJTqrLXebtW+vcXnB4EHQG+Mm1XJlFLlMJ6M729+/V+Mm1jdtNZfZ/qq\nhMgBz/dswKwl32bYRgghRNGklOoOLFFKlU2qkKC1PqqUmgL8BPhgbLn1OEbpUDCq9x0FugLJe2Vo\nrR8opb7AmA89iVGtwSat9WWl1GGgF9APGG4VlydQxrzPchI3jETfrxj3/xsCh83v1QOOZPoLIAoE\nmc8UbvaWA/0PRt3fbRg/CO4CX2H8sjYyZ0ITRV1Jz9sZtlEVcjahkZNmz55NcHAwNp7WsYtlQs65\nXDmcfH1x8vUl/vdzdu3Jd/ToURo1apSlsW1xNpmIS0xrS5nUSvTrS8m+fahesiRVihfnV3NJUHsS\ngEkKW0nQtJKsCVeuEG9V7jUtrq6urF69Gm9vb4KDg7l//z7Lly+nbt26BAQEcPXqVUeHnUrZsmUJ\nCwtj8ODBNG/enFWrVuX4mEIIIYTIshTLwJVSTwLVgOVa6wvmj/PAGmCAUqqYdQfm9ycCEzDvMWgu\nFfpfYIFSqrNSyl0p5aaU6ga8h/E7JVrrGIy9BT9RSg1QSpVSSjkrpRpjJCG3aK23K6UqAuOAjpIA\nFPlJ8/oVGRBUO833BwTVpnn9irkYkRBCiHwmArgFfKiUKq+UMiml/DDmTT9jPHx1WGsdZTH3+hMI\nA4ZZ9JM0Z1tlPucJcxtrlnO7VRgrDt0tt/kyawwcU0q1VEo5mUuTvgQsNq/+WwXMUEp5KaXqAC8A\nH2f1iyDyN5nPFG52JQG11jFa615ATaAPxqbwjbXWrbXWV9I/W4iscXO7T736x/HzO0fZsn/j5nqf\nsiXdeKKmD6EtqzOzdwPKehbMarTXrl3jo48+YurUqVk631ayyLlcueTVYmntyWcpKiqKhg2t9xhO\nn/WefinGt7ESMKOEXol+ffGvUwcvV1f0zZuZSgACBAQE8NNPP3HZzgRZfmb9PXUqVw5nX9/k1xkl\nAi2/di4uLixfvpwqVarQuXNnYmJi+Oijj+jQoQOtW7fm/PnzOXchZiaTiTFjxhAeHs706dMZPnw4\nMTExOT6uEEIIITIt0fyRZASwWWt9x6rdVsATeNrGOWit5wHfWR2bBUzCSAZGAxcxyl31Br6xaLcI\nGGB+70+MG2WLgZUYv3uCsZeOK3DcvJ9g0ofO2mUL4TghHfxt3jgb2LE2IR388yAiIYQQ+YXW+jbQ\nGmPF3y8Y22vtB25irAAcijHnsfYFEGSujGA599oHOAM7zQ9TWbOco23E2Pcv1U1CrfU+YDLGasJ7\nGA9ffai1XmBu8jJwDTiPkcj8n9a68DyJL1KR+UzhZfdmYEqpukB9bOwBqLVe5sigMsP85MSZyMhI\nqlSpkldhiGyytSegLZndMy+/mjBhAn///Tfz58/P9LnpleS0llZS7d69e3h7e3P9+nXc3d2zFcM7\n7Y3S4Ie3nyLuQTytuhn/WRhJyX+SWGl976ZMmcK3W7dSqUxZFkdGZDqWPn360KlTJ4YPH55x43wq\nve+pdVlQJ1/fVKVBPfr0pkSnlvDtXONAs9FQqhIJCQmMHDmSX375hR07duDl5cU777zDxx9/zK5d\nu3KtTOft27cZPXo033zzDatXr6ZBAykfIIQofEzWm2oKIfIN+Z1R5JbDP1/kkw0/AiZG9XqUJ+oV\n7Sfm5f9GIYQoemTeVfDJfKZgSm/eZdeegEqp14BZwHWMpxSs5VkSUIiC5vLly3z66adERUVl+tzM\nJAAh7T35fvnlF2rVqpWlBKBlfylWrjmbSIg1HjayTgCmx9/fn6+++orT92OzFEu3bt1Yt25dgU0C\nZvQ9dTIn/JISgQnmP5MSgR69u1NC3YGwYRB/3zjp4hGoF4JT/QHMnz+fMWPGEBgYyM6dOxk/fjyl\nS5fmqaee4ssvv6R+/fo5eHWGkiVLsmTJElasWEH79u2ZPn06L7zwAnJPQAghhBBCFCbN61eUUllC\nCCGEKNBkPlP42JUExNij4d9a6/dzMhghioK3336bAQMGULVq1TyLwRH7ASYnFq8vB8DJyURiQmKm\nEoBgJAGvXLnC+fPnSUxMzHRiqHPnzrzwwgvcuXMHDw+PTJ1bUFgnApOU7FKf4i5b4ceLKU+Ivw8/\nLoXfduHU9EXmzp3L2LFjCQgIYNeuXTz33HOUKlWK9u3bs3nzZh5//PFcuY5Bgwbx+OOP079/fyIi\nIli4cCFlypTJlbGFEEIIIYQQQgghhBCiqLFrT0CMEqDbcjIQIYqCCxcusHjxYiZMmJCl89Pbk8+W\ntMqBRkVFUTeRDPcNtCeepBVpJicTiW7umUoAgpEE/O233zCZTFna269MmTI0bdqU8PDwTJ+bH9j7\nPU3aIzCpHKhHn94U9/4Nbl9M+6RbF+CHBZhMJt577z06duxI27ZtuXz5Mv3792fRokUEBwcTGRnp\nwCtKX61atTh06BB+fn40atSIgwcP5trYQgghhBBCCCGEEEIIUZTYmwTcCPTJyUCEKArefPNNhg4d\nSqVKlbLch71Jo7QSgABHdu3C//ffubNufbYTgc7ljKSUi1cpcMt8edFSpUrh5eVFzZo1OX78eJZi\n6NatG2FhYVk6Nz+w93ta8sUX8HzxhX++t9WeyrhzvzYAmEwmZs2aRa9evWjTpg0XL16kS5curF+/\nnpCQEDZtyr29nd3c3Jg9ezbz5s2jd+/ezJw5k/j4+FwbXwghhBBCCCGEEEIIIYoCe8uBngamKqUe\nA44BSXdrTUCi1vr1nAhOiMLk3LlzfP7555w8eTLbfdnak89SegnAW6tW8/Nvv/GIf50UfaTV3h7O\n5Xxx9ipFwtXMr+QDYzVgiRIlOH78OG3bts30+d26dWPmzJnExcXh4mLvj7X8JUvfU782RtnP9Fgk\nCk0mE9OnT8fV1ZWnnnqK3bt307p1a3bs2MHTTz/NrVu3CA0Nzc5lZEqXLl04cuQIgwYNYvfu3Xz+\n+efZSpALIYQQQgghhBBCCCGE+Ie9d8vbAd8AZYHWFsdNQCIgSUCRLW+0fCuvQ8hxb7zxBs899xzl\nzOUzsyutpFF6CcCYNWv5ZclSyrq6UtrVNfm4IxKBTs4mEuMTsnSuv78/V69ezfJKwGrVqlGlShUO\nHTpE69atMz4hn8r099TbD0r7QfRZ2x2W9jPaWJk4cWJyIjAyMpImTZqwe/dugoKCiI6OZsyYMdm5\njEypXLkyERERzJo1i8aNG7Nw4UK6dOmSa+MLIYQQQgghhBBCCCFEYWVXElBr3SaH40Ap9SYwFPAG\nfgJe1Fp/l9PjCpEbfvvtN9avX4/W2qH9WieNMkoA3lm3nmM3onmkdOlU72c3Eejk7ERCQmKWzvX3\n9+evv/7KchIQ/ikJWpCTgJC57ylgrPRLKwloLgVqy7hx43B1daVNmzZERkZSp04d9u/fT/v27blx\n4waTJk3CZDJl8Soyx9nZmSlTptCmTRsGDhxI7969efPNN3Fzc8uV8YUQQggBSqmzQBWMhzwx//kj\nMAZwB3ZrrZ2szvEDfgP8tNbnlFJ7gVaA9ZNhW7XWPZRSDYF5QEPgNrAceEVrnWDR5+PAaq11dYtj\n04Gp/FORBuB3YJLWek2WL1qIbDj88wU+2fATAM/3bEDz+hXzOCIhhBAFVXrzMK3110opV2AsMBCo\nAcQCUcAcrXWYuY/TQDXz+c7mPpLmWHu11oFKqQTzMesbeEe11s2UUkOBaZbzMHPfbbAxFxQFl8xj\nipY0k4BKqSHALa31BqXU4PQ60Vovy04QSqlngJ5AC+ACMB0IU0pV11rHZqdvIfKDmTNn8uKLL1K2\nbFmH922ZIMooAQjwc3Q09Ut722yXnUSgyclEQjZWAm7atClbpVK7d+9Or169ePfdd3MteZVT7Pme\nJkuvJGgGewa+9NJLuLm50aZNGyIiIvD39+fAgQPJKwL/7//+L1e/lq1atSIqKorhw4fz5JNPsnr1\namrVqpVr4wshhBBFXCIwPOl3O6WUBzAN2IRxw8nePmbY2i5CKeUMhAEfAU8B/sAO4BzwvlKqDtAR\neIHUN6bAuHkVYO6rGPAcsFQptVtrfcXuqxTCAVaF/8rKnf/87jJrybcMCKpNSAf/PIxKCCFEAZbm\nPEwpVQVYD1QFRgGHgOJAH2C5UmqK1vp9rXXNpM6UUnuAPWls4RWgtd6fs5cj8jOZxxQ96a0EnIHx\nC9kGjHKf6S3xyVYSEOOXvU+11r8BKKVmAuOBRwFZDSgKNK01W7du5dSpUzk2RnqJIssEIMCx6GiG\n1ng4zfZZTQQ6OTuRmI2VgGfPnuXevXtcu3YtS8nSBg0aEB8fzy+//EK9evWyFEd+YvfX39sPhu7J\n8jgjR47E1dWVgIAAdu3aRd26ddm7dy9dunTh2WefZf78+Tg7O2e5/8wqU6YMGzduZN68eTz55JPM\nnj2bQYMG5dr4QgghhDBore8opRYBrwC+DuiyLuCltX7H/PqYUmo1EAS8D9QEFPAH4Gfj/OQnk7TW\nD5RSnwEfAtUBSQKKXGN94yxJ0jG5gSaEECK7rOZh/YGWQC2t9TVzk9vAYqVULPCZUmqp1jo6j8IV\nBYjMY4qmNJOAWms/AKWUE8aTmue11nE5FMcE4JrF64YYS5PP59B4QuSaGTNm8PLLL1PaRgnOvPBz\n9HXqOzCWpP0c15xfw4ZfN2Rpf0c/Pz8uX75M/fr1OXHiBC1btsx0HyaTia5duxIWFlYokoC5adiw\nYRQrVox27dqxc+dOHn30UXbt2kX37t0JCQlhxYoVuFrsIZnTTCYTL774Ii1btqRfv35EREQwd+5c\nSpYsmWsxCCGEEEVUcqJNKVUKeAaj7OalrPRh5TeMyi+WGgD/D0BrvQXYYq5IMz29AZRSbhhPwv+B\nsZWEELni8M8Xbd44S7Jy50n8KpaSklpCCCGyIq15WAcgzCIBaGkt8CnQHKPCQqbGEUWLzGOKLnv2\nBDQBx4AmgGM3NDPTWicvkVJKDcR4EnSq1vpCTownRG755Zdf2LVrF5988kmOjzXp4Gu236gM8QM8\nib98mZgb94jbkkil4sXT7CfDPejS4OzsTFxc1p4TcHZ2pkaNGlSqVInjx49nKQkIRknQV199lUmT\nJmXp/KJs0KBBuLq60qFDB7Zv307jxo3ZunUrISEhdO3alS+++IISJUrkakwNGjTgyJEjjBkzhiZN\nmrBmzRoaNmyYqzEIIYQQRYgJWKCUSpq4JmIk2HoBpQCUUndtnGP9erJSynJimghU1lpfB34x91MZ\nmAs8jLEvfHp9JmltMb6rud0MrfW9jC9NCMf4ZMOPdrWRm2dCCCEyKb152FvA17ZO0lrHKaWiAa9M\njBVu3hvQ0ota60Xmz6vZmPM5kX6VQFEAyDym6MowCai1jldKfQq8DLyY1YHM+wouTOPtAOAqsAAo\nAwzQWodndSwh8osZM2Ywbtw4PD098zQO53JGBadLx45Rz6t0mvu8ZTUBCODi4kJ8fHyWY/T396dE\niRIcP348y320atWK3377jfPnz1O5cuUs91NU9e3bF1dXVzp16sTmzZt5/PHHWbduHSNGjCAoKIit\nW7fm+orWEiVKsGjRIlauXElgYCBTp05l9OjRBX7fRyGEECIfSgSesbXfu1KqDYDWurjV8WrAGas+\nZqax/0xSlZlXgNeAFcAQrfVNO+Pbl7QnoLmvJsBWpdRFrfWndvYhhBBCCJEfpTcPuwqUs3WSUqqE\n+b0/MzFWYAZ7Av6uta5uNc5TQNb3ohFC5CknO9vVBEYopc4ppfZYfey2pwOt9TKtdTFbHxh1jA9h\nbBRfTxKAojD48ccfOXDgAC++mOXcuUM5l/PlanQ89dJI4mQnAQjZWwkIULt2bYBsJQGLFSuWnMAS\nWdO9e3cWLVpEcHAwBw8exMXFhcWLF9O4cWPatm3L5cuX8ySuAQMGcPjwYZYuXUr37t25ds1WFQwh\nhBBC5LLMPpWzFOgLNNdaj8lEAjDVWFrrI8B+oHEmYxAiy57v2cAhbYQQQohM2AZ0V0rZWmEwBLhM\nGisFHUiexC4EZB5TdNmbBDyKsfR4EbDPxkd2vQHM1Vr/n9baejmysBKzZi0xa9bmdRgFxrVbsRw+\ndYVlB35jyrofmbLuR5Yd+I3Dp65w7VZsjo07bdo0Xn311VwvoZieyxdu81hQh1THs5sABMesBLx9\n+3a2koBgJLE2bdoENy9AxETj46ZUFs6MLl26sGLFCnr06MHevXtxcnLi/fffp2vXrrRq1Ypz587l\nSVw1a9bk0KFD1KpVi0aNGrF/f3oPrgkhhBAiD5hI4yaRUqoZ8DTQQWud9mYkdlBKmZRSTwDtMRKB\nQuSK5vUrMiCodprvDwiqLSW0hBBCONoqjJLqW5RSjZVSLkqpEkqpUIx76s9pra2fyk9zTpbOcVHI\nyTym6LJnT0C01tNzOI4WQAervSMAArTWB3J47AIlZs1a7qxbn/w6u4mbwu7arVimrE9d7/ja6Vi+\nPn0VgJm9G1DW082h437//fd8//33rFq1yqH9ZtflU5d5/MPn8Tj2S/LfI0ckAMFIAmZnJaC/vz9/\n/PEH0dHR3LhxAy+vzJQz/0dQu7YMHxrKjVWD8HIzlyu/eATqhUD9AeDimuUYi5IOHTqwZs0a+vTp\nk1yKc8aMGZQuXZpWrVqxa9culFK5Hperqyvvvvsu7dq1o1+/fjz//PNMnjwZZ2fnXI9FCCGEKGLS\n2gcm0erztNq1wNiv5i+rOcRerXVgBn0kAk8ppR6YXydglL2aqbVeaUfsQjhMSAd/AFbuTJnLHtix\nNv0D/fMiJCGEEIWY1jpBKdUZo5z6SqAacBf4Cuiktba1CjC9OVmkUsr6vYta64cszrVF9gQsBGQe\nUzSlmflXSrkAk4FugDOwE2Pj9du5FJtdlFJ+wJnIyEiqVKmS1+HkKOsEIDgugVNYHT51heUHz6Tb\nJrRldZrX8nXouJ07d6ZLly65Wgp00kHrHHpKsXdi+aDbR9y5dQcXF5fk1aSO+vuze/duZs6cyZ49\nWSsRfv36dapVq0atWrX46KOPeOKJJzLfyblD8O1cuszcSeiT5en/ePmU73tWgqYvwkNPZinGoujA\ngQP06tWLJUuW0LlzZwAWLVrE5MmT2b59Ow0bNsyz2C5cuEBoaCjx8fGsWLGi0P8fIITI30yyWakQ\n+VZR+p1R5J7DP1/kkw0/AiZG9XqUJ+rJk/PW5P9GIYQoemTeVTDIPKbwSW/eld5KwLeBZ4BlwH1g\nIPAoEOTQ6IRdbCUAgeRjkgi07dRft+xq48gk4OHDh/nll1/YuHGjw/p0hMunr+BT3QcXF+OfvaP/\nzmS3HKi3tzfu7u5Ur16d48ePZy0J+MMCuH2Rbo18CDt6LXUS8NYFo40kAe3WqlUrNm/eTNeuXVmw\nYAHdunVj+PDhlCpViqCgIDZs2ECLFi3yJLZKlSoRHh7OW2+9xWOPPcaCBQsIDg7Ok1iEEEIIIUTR\n0rx+RSmZJYQQQogCSeYxRUt6ewIOxqgpPEZrPRboCQQqpXxyJzSRJK0EYJI769bLHoFpsDcJ6EhT\npkxh8uTJuLk5tsRodl06dYnytcrlWP/ZLQcKRklQb2/vrO8LWO0pAIIbluXLn//mfpyNLUb92mQ9\nwCLqiSeeYMeOHYwcOZJ169YB0Lt3b5YtW0b37t3ZuXNnnsXm7OzMpEmT+OKLLxg9ejT/+te/iI3N\nub0+hRBCCCGEEEIIIYQQoqBILwlYFvjO4vW3QBzgnaMRiRQySgAmkURg/rBv3z7OnDnD0KFD8zqU\nVC6dukz5muUzbphFzs7ODkkCuri4ZD0JaE7wVSztRu2KHuw9GZ26jTlRKDKnSZMm7Ny5k5deeomV\nK42td4KCgti0aRODBw9m/fqMf07lpBYtWhAVFcW5c+do3rw5Wus8jUcIIYQQQgghhBBCCCHyWnpJ\nQDCSfoCxCSkQTzr7CArHsjcBmEQSganVquDpkDb2SExMZMqUKUydOpVixYo5pE9HunTqEhVUzq4E\nzE45UDCSgPfu3ePEiRNZ68DbD0r7AdCtUVnCjl5N+X5pP6ONyJIGDRqwa9cuxo0bx5IlSwAj+ZaU\nHFy0aFGexuft7c0XX3zBs88+S4sWLVi2bFmexiOEEEIIIYQQQgghhBB5Kb09AYUo8GpV8OTr01cz\nbOMIERERXLp0iYEDBzqkv8x6o+VbaZWYne0AACAASURBVL734MEDZv/xAR8N+iTHxnfUSsDdu3dz\n6dIlYmJiKFGiROY7qfYURJ+lWyMfAt/9kbmDapG8L6qUAs22evXqsXv3btq3b8/9+/d57rnnaNiw\nIXv37iUwMJAbN27w73//O8/iM5lMjBo1ihYtWtC/f38iIiL46KOP8PR0zL9zIYQQQgghhBBCCCGE\nKCgySgK+q5S6bf7cBBQD3lRK3bA4lqi1Hp5TARZlJfr1BbB7NaBHn97J5wiDqlDKIW0ykrQKcPr0\n6bi45L/c+okTJ6hWrVrWkmp2csRKwNq1a6O1RinFyZMnadKkSeY78WsDPy6ldkUPSrg5c+TsLR6r\nbv4eSylQh6hduzZ79+6lXbt23L9/n9GjR6OU4sCBAwQGBhIdHc306dP/Sb7mgUcffZTvvvuOl19+\nmSZNmrB69WoaN26cZ/EIIYQQQgghhBBCCCFEbksvW7Ef8DV/JDmIsVdgGfNrE5CYM6EJsD8RKAlA\n28p6ujGzdwP0Xzc59dctTv11CzBW/9Wq4ImqUIqynm7ZHmf79u3cvn2bfv36ZbuvnBAVFUXDhg1z\ndAwXF5dsrwSsXr0658+fp1u3bhw/fjxrSUBvPxi6BxPQ7cR4drh48tjQKSmafPPNN/znP/+hRo0a\nJCQkEB0dzfjx43nqKSNJ2L9/f3x9ffnwww+TzwkICGDFihVUqlQJgNmzZ7N+/Xr279+Ps7MzAKGh\nody6dQtPT09iY2Px8PDggw8+oESJEkyePJkzZ84QExPD8OHD6dGjB6GhoYwZM4ZmzZoljxMfH2+z\nbX5Ts2ZN9u3bR0BAAPfv3+c///kPDz30EAcOHCAoKIjr168zZ84cnJwyqjqdc0qUKMFnn33G6tWr\n6dixI5MmTeKll17K0+SkEEIIUVAopSKB1uaXSf+hJ5j/PAs8DLTVWu+zOu8sME1rvdT8+mFgBtAO\nY3/5K8Au4HWt9VmL83yAmUAwxu+gfwMRwCSt9Tlzmy1Ae4vhEoGHtdYXs33Bosg7/PMFPtnwEwDP\n92xA8/oV8zgiIYQQ4h9KqQQgFiivtb5pcdwTuAS4a62drM5ZBAwFGmmtf7Q4PhRYhLH1l6VEoALQ\nNZ33y2utrzvgkoQDyTxGpCfNJKDWuk0uxiHSkVEiUBKA6Svr6UZzT1+a1/LNuHEWJCYmMnXqVGbM\nmJGnCY/0HD16lEaNGuXoGI4oB1qsWDH8/PwoV64cx48fz3ZMEyZM4ObNm6mOm0wmWrduzZtvvgkY\nX58ZM2bw1FNPcebMGVxdXTl27Bg3btzAy8sr1fkJCQlERERQt25dDh48mJw8BJg0aRJNmzYFYMqU\nKWzcuJHKlStjMplYvXo1f//9Nx07dqRjx47JsVjas2cPTk5OKdp26tQJd3f3bH89HM3Pzy85ERgb\nG8uECRMoV64ce/bs4emnn2bYsGEsXLgwz1fH9u/fn2bNmiWXB128eDE+Pj55GpMQQgiR32mt2yV9\nrpRajFUFGPONKFsPhCYmHVdK+QNfAauAZlrrP5RS1YFpwLdKqSe11qeVUqUwHjj9AWiptT6rlCoL\njAS+UUrV01pfAxRQV2t9JieuWRRdq8J/ZeXOk8mvZy35lgFBtQnp4J+HUQkhhBCp3AV6AkssjnXH\nSA6mWOVgTg72wZhfDQWs9275XWtd3dYgSql03xf5i8xjREbyZ8ZCpFKiX188+vROdVwSgHlv06ZN\nJCQk5MvVWklyayVgdsuBgrEvoLu7u0OSgN7e3lSrVi3V8cTElPerrl27RoUKFQDYsGEDvXr1om3b\ntuzYscNmv1999RUNGzakV69ehIWF2ew7ISGB69evU6JECXx8fBgwYAAAHh4eJCYmppkw9fHxISQk\nxK62+UHVqlXZt28fy5YtY8aMGSQmJlK6dGnCw8O5dOkSffr04d69e3kdJjVq1ODgwYPUqVOHRo0a\nsW/fvoxPEkIIIUQSk/kjs94Dtmutx2it/wDQWp/RWg8FjgL/Nbf7DxCjtR6QtDpQa31Naz1La11R\na31NKeUMVAR+z+a1CJGC9Y2zJCt3nmRV+K95EJEQQgiRpo3AAKtjIcAGUs/VQoDvMCotDFBK5b/9\ni0S2yTxG2EP+8Rcg1isCJQGY9ybuf5XPxi2mzXOtmXJoYprt3mj5Vi5GlVJiYmKuJAEdsRIQjCTg\n/fv3HZIETM+BAwcIDQ0lLi6OEydOMGPGDOLj4wkPD2fUqFFUrlyZ9957j/79+6c6d8OGDfTv358G\nDRrw+uuvExMTk7zf4qxZs/D09OTu3bv4+/vTrVs3ihUrBsDFixeZOHEiQ4YMwdPT02ZcSd8ny7Yl\nS5bMoa+CY1SqVCl5j8DY2FjeeOMNPDw82Lx5M4MGDeLpp59m06ZNeX4drq6uvPPOOwQEBBASEsKz\nzz7LlClT8nylohBCCFEApLUFRJqJQaWUO9ABCEyjyRLgffPnHYEvMoihGkZJqq+UUvWBPzFKiq7M\n4Dwh0nT454s2b5wlWbnzJH4VS0lJLSGEEPnFJmClUqqc1vqyuZx6S2AgMMyq7TPAbGCb+fXT5vNF\nISHzGGEvufNZwFgm/SQBmPdO7D1JMTcXaj75cF6Hkqbff/8dDw8PypUrl6PjOHIl4N69e/njjz+4\ne/cuxYsXd0B0KZlMJlq1apVcDvTmzZt0796d27dvExMTw8iRIwE4efIkf/zxB1WrVk0+98aNG3z1\n1VdcvXoVMJKfX375Jb169QJSlgO1tHLlStavX8/YsWNp0aJFuvFlpm1+Ub58efbs2UNgYCCxsbG8\n++67uLq6smrVKkaOHElgYCDbtm2jTJkyGXeWwzp27MiRI0cIDQ0lICCAzz//PMX3WAghhBB2CzeX\nBbWUVIqqLMbvm3+kce5VIKnuehkgo339agIPgHHAYYxSWCuUUpe11hGZDVwIgE82/GhXG7l5JoQQ\nIp+4CewE+gJzgd7m1yn2wjE/MFUD2KC1jlNKrcIoCWqZBKymlLpr1f+rWusP7Hxf5DGZxwh7SRKw\nAJLkX/4QHx/PgUVfETgmINW+bvlJbqwCBCMJ6KiVgAsWLODhhx9Ga02DBg0cEF1K1uVAixcvjrOz\nM4cPH+btt99OTrx98MEHhIWFMXr06OTztm7dSkhICP/+t1FK/euvv2bevHnJSUDrvgEOHz5MREQE\nq1evxtXVNd1Y0mub3/n6+rJ7926CgoJ46aWXeP/993F2dmbBggWMGzeONm3aEB4enlx6NS9VrFiR\n8PBw3n77bR577DEWLFhA165d8zosIYQQoqAJ1FrvtzyglErar+86xso9X+A3G+fW4J8E4RVzuxSU\nUm5ANNBVax0OWD7Vtl4pNQjoAUgSUAghhBBFQSLGXstjMZKAIcAHpK7O8CzgCfxp3t/PDXBXSvlq\nra+Y22S055/sCShEISF7AgqRRatWraJ4KXeqN8vf/x8ePXqURo0a5fg4jiwH+uuvv1KnTh1OnDjh\ngMhSM5lMyeVAQ0ND6devH3379uX48eM8+eSTye06d+7Mli1bks8B2LhxI08//XRym6ZNm3L27Fku\nXryYop2l3bt389dffzFixAhCQ0MZPHgwN27cAGDq1Kn06tWLXr16MW7cuFRtQ0NDk9sWBGXKlCEi\nIoLvv/+eUaNGkZCQgMlk4t1336Vv3760atWKs2fP5nWYADg5OTFhwgQ2btzISy+9xEsvvZQv9i8U\nQgghCgOt9R1gLzDE+j2llAkYjrF/DUAk0MtGN70wEomHlFIVlFLWiUI3oOBMlES+83zPjB84tKeN\nEEIIkYu2A3WVUi2BBsBWyzfND1ENwKia0MD8UQc4DgzK3VBFTpJ5jLCXrAQUIgvi4uKYMWMGrUe3\nyterAMFYCRgaGprj4ziqHKiPjw8mkwk/P78c2xewWbNmHDx4MNXxZ599NsXrmjVrsnPnTgAiIyMB\nWL9+fYo2zs7O7N9vPAC/fPlym+NNmjTJ5vG02hd0Xl5ehIeH06VLF0aMGMFnn32Gs7MzkydPpnTp\n0rRu3ZqdO3dSp06dvA4VgCeffJKoqCieeeYZnnjiCdasWYO/v39ehyWEEELkJybS3hcwPWOB/Uqp\ny8AnwCWgKjADKAnMNLebAwxSSn0GTAP+Atpg7GMzR2sdo5QaB/RQSnUHzmEkCNuYxxAiS5rXr8iA\noNpp7qczIKi2lNASQgiRr2it7yqlwoBlwGatdax5tV+SXsAtrfU2y4NKqfUYJUFn51asImfJPEbY\nS1YCCpEFy5Yto0qVKvg1qZbXoWSooK0ENJlM1K5dm5IlS+ZYElDkPE9PT3bs2MG5c+cYPHhw8t+N\n0aNH89///peAgACOHDmSx1H+o3Tp0qxbt45Ro0bRsmVLlixZYrO0qxBCCFFEJZKFJKDW+ifgCaA2\n8BNwF9iHUSq0hdb6lrnd30BLoBgQBcRglLh6E5hi7u4d4BDwHXAHmAT00VrLhFFkS0gHfwYE1U51\nfGDH2oR0kAfDhBBC5EurgGrAaotjSXO1Eeb3rW0A6imlGpHx3C5Lcz+R+2QeI+yRv5cw2UEp5Qec\niYyMpEqVKnkdjshl33zzDSNGjGD9+vXUrm38wJs7dy6VKlVi2rRpKfbCM5lMzJs3j8WLF7Nx40Yq\nV66c/N6QIUM4ceIEJpMpef83gICAAFasWEGlSpWSj92/fx9/f3+WL1/OjpQr7tP0Rsu3snupWXLt\n2jVq1KjB9evXcXLK2Zz/vXv38PLyIjY2Ntt9DRs2jGrVqrF27VpJBBZwd+/epXv37nh5efH5559T\nrFgxADZt2sRzzz3H+vXrad26dR5HmdKxY8fo168fjRo14uOPP8bT0zOvQxJCFACm/F4aQIgiTH5n\nFPY4/PNFPtnwI2BiVK9HeaKePDmfXfJ/oxBCFD0y78obMo8R6c27pByoKNBMJhOVK1dm8uTJrF27\nNjnRZTKZ8PX1tVlu0WQy0bNnzxTJPoCTJ20vnbYWExPD4MGDadmyJTsO2pcEzCtRUVE0aNAgxxOA\n4LhyoGDsC3jp0iXOnDnD/fv3cXV1dUi/IvcVL16csLAwevfuTZ8+fVizZg1ubm50794dT09Pevfu\nzeLFi+nSpUteh5qsXr16fPfdd/zrX/+icePGrF69miZNmuR1WEIIIYQQIgc1r19RSmYJIYQQokCS\neYxIj5QDFQVe48aNqVOnDkuWLMmV8by9vZkxY0aujJVdUVFRKVZD5iRnZ2fi4+MdUkLR39+f06dP\nU7VqVU6fPu2A6ERecnd3Z8OGDTg5OdGzZ0/u3bsHQLt27diyZQvDhw9n9erVGfSSuzw8PPj00095\n44036NSpE//73/9ISEjI67CEEEIIIYQQQgghhBDCbrISUBRoSQmnV199lV69ehEYGJj83tWrVwkN\nDU1+XbduXSZMmEBiYiIbN27km2++AYyVSp9++mmq40l9FGRHjx6lXbt2uTKWyWTCycmJhIQEnJ2d\ns9WXv78/v/76K3Xr1uX48ePUrVvXQVGKvOLq6sqaNWsIDQ0lODiYsLAwPDw8ePzxx4mIiKBjx47c\nuHGDkSNH5nWoKfTt25emTZsSEhJCZGQkS5YswdfXN6/DEkIIIYQQQgghhBBCiAxJElAUCiVLluTV\nV19l8uTJNG3aFAAfH59MlQO1dTwgICDdcfNqrz97RUVFMXbs2Fwbz8XFhbi4uGwnAR9++GHOnTtH\njx49ZE/AQqRYsWKsWLGC4cOH06VLF7Zs2ULJkiWpX78++/bto0OHDty4cYPx48fndagpVK9enQMH\nDjBlyhQaNWrE8uXLadu2bV6HJYQQQgghhBBCCCGEEOmScqCi0AgICMDHx4ctW7bkdSj5wt27d/nt\nt9945JFHcm1MZ2dn4uList2Pm5sbVapUoWzZspIELGRcXFxYvHgxNWrUoGPHjty8eROAmjVrcuDA\nAZYsWZK8Yjc/KVasGG+99RaLFi1i4MCBTJkyxSF/14UQQgghhBBCCCGEECKnSBJQFGgmkynF60mT\nJnHr1i3gn3KgSR+DBw/m0qVLeRFmnjh27BhKKVxdXXNtTBcXF+Lj4x3Sl7+/Py4uLpIELIScnZ1Z\nsGAB9evXp0OHDkRHRwNQuXJl9u/fz65du3jhhRfy5R58HTp04IcffuCbb76hTZs2nDt3Lq9DEkII\nIYQQQgghhBBCCJukHKgo0Jo1a0azZs2SX5cpU4ZDhw4B0KNHD5vnWJcBTe/47t27HRBl3jh69CiN\nGjXK1TEdtRIQjCTgnTt3OH36NHFxcbi4yI+rwsTJyYl58+bxr3/9i3bt2hEeHk7ZsmXx8fFh9+7d\ndO3aldDQUJYsWUKxYsXyOtwUKlSowJdffsm7775L06ZN+eSTT9L8eSOEEEIUVEqph4DZQFugBHAW\n+ByYBbQEdgPfaK2bW53XHggH9mmt25qPeQBTgb5AJeA2cBCYrLU+ZnX+q0BtrfUwi2N7gVZA0hNC\nCcAPwEit9U+OumZRuB3++QKfbDD+ujzfswHN61fM44iEEEKIfyilEoA2Wuv9VsfPAlO11suUUkuA\nwcAorfV8q3YrgAHAMK31UvOxRsB04EmgJHAB2ArM1FpfVUo5AQcw5lattdaJFud9A/TWWm+2GCPV\nPE3kPJnDiOySlYBCFFJRUVE0bNgwV8d05ErA2rVrc+bMGSpUqMCZM2cc0qfIX0wmE3PmzKFdu3YE\nBARw+fJlAEqVKsWOHTu4ceMGPXv25O7du3kcaWpOTk6MHz+esLAwxo4dy8svv2yz3Z9//kn9+vWT\nVyQPHDiQ4cOHM3PmTEJDQ+nUqRMtW7ZMXq18/vz5FO179uzJ0qVLk/sLCAigX79+ye179+7NTz/9\nc+/zypUrjBgxIsevXwghRJGwHbgE+Gmt3YAQYBDwJpBUt1sppapbnRcC3Ehqo5RyAb4EGgNPa63d\nAT9gC3BAKeVvbtdGKfU6MMmi/ySJwAytdTGtdTGgPKCBpQhhh1XhvzJryXf8fTOWv2/GMmvJt6wK\n/zWvwxJCCCHsYT0vuoEx30qmlCoOBAM3+WcO1hLYDxwBHtFaFwe6AjWBr5VSZbTWCcBAoD4w3nye\nG7AcWJSUAMxgniZykMxhhCPI0hohCqmjR4/Sv3//XB3TxcXFoSsBly1bRt26dTl+/Di1atVySL8i\nfzGZTLz99tu4ubnRtm1bIiIiqFixIsWLF2fjxo0MGTKEIUOGsHbt2hTn/fnnn3Tq1Ck50X39+nWC\ng4M5e/YszZo1S7Eyr3bt2pw8eZINGzbw3nvvUaNGjeT3unTpQnBwMK+88grR0dHcunWLV155hdat\nWwNGUu21115j4cKFAHz44Yds374dHx8fHjx4QFxcHFu3bmXbtm0kJCTg5JT62RpfX1+WL1+e/Hrq\n1KlUrVqVKVOmsHHjRr799lvefPPN5OuybB8bG8uQIUOoWrUqAQEBAMyePZtKlSoBsG3bNj7++GM+\n/vhj5syZw7p163j44Yez900RQghR5CmlKgJ1gf5a65sAWusflFJjgf/P3p1HV1Wd/x9/30ASBoGi\nICAgCPIkQQa1tRUnQGQSJ+bJEW0Ri9WipbUoOFStVquiVdSqCBaoKIITgjLJ11Ktv4KgMXmggKWA\nAiIgg4Akvz/OTbxcbgYgyb1JPq+17iLZZ+999slawM559n52x4iqMwleQt0XbpcCXAa8BuQFBy8H\nWgInu/uecF87gefCnzw/BuoTrFAvlLtvM7MpBDsLRQo1dW42U+ZkHVKeVza4W1pZD0lERORI5RJk\nXOhhZo3dfX24/GLgY6BFRN2ngEfc/e68Anf/zMwuI8ioMBr4nbuvNbORwF/NbDbBTsMqwM0RfRV7\nniYlR3MYKSkKAopUQAcOHGDFihW0b9++TO9b0ulAs7KyGDZsGJmZmVx66aUl0q8knlAoxD333ENK\nSgqdOnVi/vz5NG7cmOTkZCZPnszatWtjtosOlp1zzjmcccYZh5wVGum8887LD7jlmThxIm3atOGG\nG27A3Rk+fDgLFiyIGVQLhUIMHz6cyy67DICnn36amTNn8vvf/75Yz5qTk8OWLVvo0CHInJabW/gC\nutTUVAYNGsRbb72VHwSMbPPll1/SsGFDAG666Sb69u1b7LGIiIgUYhOwCnjJzJ4D/gEsd/c3gDfM\nrFO43jTgz4SDgEBPIAv4gh+CgD2At/ICgAVx94cBzOwFINZ/5vllZnYscBVBOiuRAi1ZsTHmy7M8\nU+Zk0bxRbaXVEhGRRFHwC40f7CaYAw0CHg6XDSaYl/0ewMxaAqcAF0U3dvf94cVUvSPKXjKzC4HX\nCTIunOXu30VcL2qeJiVMcxgpSQoCilRAK1eupEGDBtSpU6dM71uS6UAbNGjA/v37adq0KR999FGJ\n9CmJ7Y477iA1NZWOHTsyf/58TjzxRKpUqVKsnW3btm0jFAqRnJxcZGAtWosWLWjevDkQpCLdvXs3\nUHBQLbL/r7/+mkaNCp9wbdmyhSuuuAIIgnY1atSgZ8+eAIUGLPM0aNCAbdu25X8/atQoUlJS2LVr\nF2vXruXNN9/M76s4/YmIiBTF3Q+YWQfgeoIXRH8Aks1sHkEaqDzzgPpmdoq7f0bwAmoKUC+izrHA\n4eQsCnFomqkQcLuZ/S78fSrwHdD3MPqVSmjCjE+KVUcv0EREJEHMDZ8NGCk1Rr2pBGf9PWxmdYAu\nwDDCQUCgYfjPdQXcZwsQ/dLwzwSBxXfdfWkB7WLN06QUaA4jJUlBQJEKKB7nAULJ7gQMhUKkpaVR\nrVo1MjMzS6RPSXyjR48mJSWFjh07Mm/evINSd0aLDK7t3r2bsWPH8sEHH/DMM88wY8aM/HqRgbHF\nixfntwF46qmn8lN/rly5kttuu43Ro0fnt4sOquXm5ub3v3PnTjZu3HjQmX2x1KtXL3/HYm5uLkOH\nDmXFihW0bdu2WAHLDRs20Lhx4/zvI9OBPvLII8yePVvnAIqISGnY5u73AvcCmNlpwFhgDnAFgLvn\nmNl0YKiZ3UuwE/BXwA0R/WwmSB91CDNbA9zn7s9GFMf6zzEXuCcvnZWZJRMEAGeZWXt3//zIH1NE\nREQkYXR19/cjC8LzpTwhIIdgPvaimRlwNrDI3b8JvgWC+RcEc7CvYtynBREBwvDcagLBmc0XmFk/\nd38lRjsFAEXKIQUBpcLa9ffgDLGaAyvfUSFLly7ltNNOK/P7luROQAhSgu7fv5+srKwCz1uTiufm\nm2/OTw06b968As+DjAyu5fnggw8OStcJwZmAec4999xD0oECPPbYY3z44YeMGzeOtm3bFji26HSg\nn376KSNGjGD+/PnFerZQKESzZs3Ys6fQjGj59uzZw5QpU7jttttiXm/evDnr16+PeU1ERORIhc+K\nmWhmx7n7AQB3X2pmdwDLgeMiqk8FXgJWAB+6+6aIF1AQ7Ba808x+7e57I+5xFnBi+Pphcff9wDQz\nexRoDygIKDFd36c9900sPKvI9X3K9ggFERGRoxVO6fkqQRaGs4AXo667ma0iSJ/+YOQ1M6sGDAX+\nGFF8P1AbOA+4DnjazJZEnDkoZUxzGClJCgJKhbTr7y+ze/oPC1YqWyBw2bJl3HjjjWV+36pVq5bY\nTkAIgoD//e9/OfbYY/niiy846aSTim4kFcINN9xASkoKnTt35t133yUjI6PYbQ83Hej06dPZuHEj\nf/vb34qVTjOy/1q1ahUZ+I7us1q1anz22Wf89Kc/jXm/6B2O/fv35yc/+UnMvvP6Kux+IiIiR+A9\n4FvgcTO7i+CMwGbAbQTBvsgV5UuAJIKUoffG6GsyMAKYbmajgNXAaQQvq6a6++qo+gWlA408EzAZ\nuJIgjdU/j+D5pJLo0LYRQ7qnF3imzpDu6UqjJSIi5U3enGgqwXyqDtAnRr2RwCtmtjtc9xugFUHa\nz9XA0wBm1p0gk8N57r4bGB9eEDbZzLq4e+S8TOlAy4jmMFKSFASUCic6AJj3dWUJBObm5sZtJ2BJ\npgOFIAg4ZcoUWrduTWZmpoKAlcx1111HSkoKXbp0Yc6cOYfs0Cso2BVdnvd9QfUXLFjAxo0bufLK\nKwFISUnhueeeK7C/yHSje/bsibmzME+TJk2YN+/gDQ7jxo3L/7p379707t37oPrLly8vsL/oHYc9\ne/bMP18wr/2kSZMKbC8iIlIc7r7TzM4DHgA+I1gZ/iXwFtANyCD8Asjdc81sGnAz8Gq4i9yI6/vN\n7ALgHmARwXmB6wl2D94T4/b5baPKxprZ7eHvvwc+BS5x97VH+7xSsQ3ulgZwyEu0oT3SGdQ1LR5D\nEhEROVKR86RFQBVgjrvviq7o7nPNrCtwB3A3UB34L8H5zQ+Ez4A+HpgY/j5yYdU1BNkffsPBOwlj\nzdOklGgOIyWl3G8XMLPmwJp58+bRpEmTeA9H4iw6ABipRv9+lSIQuGHDBtq3b8+mTZvKfEfQ6aef\nzl//+ldOP/30Eulv+fLlDBo0iO7du9O4cWNuvfXWEulXypdp06Zx8803M3v27LgEt0VEiiOkbbgi\nCUu/MwrAkhUbmTDjEyDEiL7tOLONVs+XNv3fKCJS+WjeVfI0h5HiKGzepZ2AUmEUFgCEyrMjMG8X\nYDx+3yrpnYCtWrVizZo1pKWl8dFHhefBlopr0KBBJCcn06NHD958803OOOOMeA9JRERERMqZDm0b\nKW2WiIiIlDuaw8jRUhBQKoSiAoB5KkMgcNmyZZx66qlxuXdJnwlYvXp1GjZsSN26dcnMzCyxfqX8\n6du3LykpKfTq1YuZM2dy1llnxXtIIiIiIiIiIiIiIgktKd4DEDlaxQ0A5tk9/RV2/f3lUhxRfMXr\nPEAIgoAHDhwo0T7T0tLIyckhMzOT3FylHa/MLr74YiZNmsSll17K+++/H+/hiIiIiIiIiIiIiCQ0\nBQFFKph47gQs6XSgEAQBN2zYQI0aNVi/fn2J9i3lT48ePZg2bRp9+/Zl3rx58R6OiIiIiIiIiIiI\nSMJSEFDKvZoDB1Cjf79i16/R2lHZRgAAIABJREFUv1+FTQe6fft2vvzyS8wsLvcvrZ2A2dnZtG7d\nWilBBYAuXbrwyiuvMGjQIN555514D0dEREREREREREQkISkIKBVCcQOBFTkACLB8+XLatGlDlSpV\n4nL/kj4TEBQElNg6duzIrFmzuPLKK3njjTfiPRwRERERERERERGRhFM13gMQKSl5wb2Czges6AFA\niO95gFB66UCzs7MZOHAgy5YtK9G+pXw766yzeOutt7jooot48skn6du3b7yHJCIiUqGYWQ7Qyd3f\njypfC4x190lmNhG4Ehjh7k9H1XsJGAJc4+4vhstOA+4EzgKOATYAbwL3uPsWM0sCFgM5wHnunhvR\n7kOgn7u/HnGP3wLp7n5NCT++lCNLVmxgwozlAFzfpz0d2jaK84hEREQOj5mdCDwCdAZqAmuBvwH3\nAecA84EP3b1DVLsLgLnAInfvHC6rAYwFBgAnADuB/wNud/dPo9ofMpcys4XAuQTzMcJ//hsY7u7L\nS+qZpWia40hJ0E5AqVAK2hFYGQKAEN/zAKF00oE2btyYnTt3cuKJJ2onoBzijDPO4J133uGXv/wl\n06ZNi/dwREREKovcqO+3A4MjC8ysOnAxsCOvvpmdA7wP/D/gFHevDlwCnAz808yOdfccYCjQFhgd\nbpcKTAaezwsAmlknM7sbGBNjPFKJTJ2bzX0T/8XWHXvZumMv9038iKlzs+M9LBERkcP1NvAV0Nzd\nUwnmVpcD9/PDXMfM7KSodoMJ5mJ5862qwDvA6cBF7l4NaA68ASw2s7RwvcLmUrnAXe6e7O7JQAPA\ngRdL7nGlKJrjSElREFAqnOhAYGUJAELF3AkYCoUwM6pWrUpmZia5uXrHIwc77bTTePfddxk1ahST\nJk2K93BEREQqm1yC1eenmVnjiPKLgY+BbyLKngIecfe73X0TgLt/BlwG7CEc9HP3tcBI4C4zawfc\nC1QBbo7o68dAfYKdhFJJTZ2bzZQ5WYeUT5mTpZdkIiJSbphZI6A18KS77wBw938DtwChiKoziVh4\nZWYpBPOo1yLqXQ60BC5198xwXzvd/Tl3r+vuef9BFnsu5e7bgClA+hE/pBwWzXGkJCkdqFRIkUG/\nyhIA3LdvH9nZ2bRp0yZuYyiNnYAA6enpbNq0iaSkJDZt2kSDBg1K/B5SvrVt25Z58+bRtWtX9u/f\nz7XXXhvvIYmIiFQUoaKrsJsgpecg4OFw2WBgGvB7ADNrCZwCXBTd2N33m9kUoHdE2UtmdiHwOsHq\n87Pc/buI6w+H+32hmGOUCmbJio0xX47lmTIni+aNaittloiIlAebgFXAS2b2HPAPYLm7vwG8YWad\nwvWmAX8mSBEK0BPIAr4A8nYI9gDecvc9hd2wGHOp/DIzOxa4imC+J6VMcxwpadoJKBVWzYEDKk0A\nECAzM5OTTjqJGjVqxG0MVatWLfGdgBCcC+jutG7dWilBpUAZGRnMnz+fu+66iyeffDLewxEREako\n5prZnsgP0CxGvamEV6abWR2gCxB5WHfD8J/rCrjPFqBOVNmfgROB9919aQHtFACspCbM+KRE6oiI\niMSbux8AOgDTCRZFzQe2m9kb4awIeeYB9c3slPD3gwl26EU6Fth4GLcvKAB4e8TcbwvBjsPnD6Nf\nOUKa40hJUxBQpIKI93mAUDrpQCEIAmZlZSkIKEUyMxYuXMif/vQnHn300XgPR0REpCLo6u7VIz8E\nq83zhAhSgs4BTjIzA/oAi9w9MhXo5vCf9Qu4TwsiAoRmlgxMIDi/5hwzO/Tg74ByxYuIiEhFsM3d\n73X38929DnA28D3BHKsqQPjs5OnAUDOrSbATcDoHB/I2U8B8y8zWmNnPo4pjzaVygXsi5n6pwDBg\nlpllHPETikhcKAgoUkHE+zxAKL10oGlpaWRnZ5ORkaEgoBSpRYsWLFy4kCeeeIIHHngg3sMRERGp\nFNx9P/AqwYr0QQQ7AyOvO0Gaq6ui25pZNWAoMCOi+H6gdri/24Cno84clEru+j7tS6SOiIhIvJnZ\nZcDXZlYlryycBeEOgrTox0VUn0ow17oE+DDvnOUI84CLzCw16h5nEWRYmHe443P3/e4+DdgK6D/X\nUqY5jpQ0BQFFKoiKtBNw199fZtffX87/3sxYtWoVaWlpCgJKsTRr1oxFixbx/PPPc88998R7OCIi\nIhVd3urzqcC1wJnArBj1RhKklhppZseZWZKZpREED1cDTwOYWXfgV8CV7r7b3ccDnwCTzSw6ZZXS\ngVZSHdo2Ykj39AKvD+merrNyRESkvHgP+BZ43MwamFnIzJoTLIRaAXwVUXcJwTv9PxCcERhtcrj+\ndDM7OTzf+jHwIjDV3VdH1S8oHWjkmYDJZnYtQer2fx7JA0rxaY4jJU1BQJEKICcnJyGCgCVxJuCu\nv7/M7umvsHv6K/mBwJo1a1KvXj3q1KmjIKAUW+PGjVm0aBHTpk3jjjvuIDdX2cJERERKQS4/pJFa\nBFQB5rj7ruiK7j4X6Ar0AFYCu4DXgY+AHu5+wMyOByYCD7h75Euma4AfA78p5P5SyQzulhbzJdnQ\nHukM7pYWhxGJiIgcPnffCZwH1AM+A/YC7wM7gG7harnhurkEwb/GBAup8q7lXd8PXACsJZib7SFI\nGTqVYD4VLdZcKhcYa2b7zWx/eBzXA5e4+9qjelgpFs1xpCSV+1WT4VURa+bNm0eTJk3iPRyRuFi9\nejUdO3Zk3bp1RVcuRSNHjiQ9PZ2RI0ceUfu8AGCkGv37UXPgAC644AJuueUWBg0axH/+8x/q1atX\nEkOWSmDz5s1ccMEFdO/enQceeIBQqNz/1yciCSikf1xEEpZ+Z6z4lqzYyIQZnwAhRvRtx5lttDo+\nEej/RhGRykfzrpKlOY4UV2HzrqplORARKR2JcB4gFJ0ONH9n38ABMa9FBwCB/LK0tDTcndatW/P5\n559z7rnnltCopaKrX78+8+fPp1u3bvz617/mkUceUSBQREREpALp0LaR0mKJiIhIhaM5jpQEpQMV\nqQASIRUoBOlADxw4EPNarDSf0dcKsnv6KzTftZvs7Gxat26tlKBy2I477jjmzZvHkiVLuOGGG8jJ\nyYn3kERERERERERERERKlYKAIhVAouwELOhMwOggX2QgsKgAYJ5m//kPme8vzt8JKHK4fvSjH/Hu\nu++yYsUKfv7znxcYsBYRERERERERERGpCBQEFKkAEmUnYKx0oIWl+dx6y63FCgACnHxMLXz1f2ix\neYt2AsoRq127Nu+88w6rV6/m6quvLjR9rYiIiIiIiIiIiEh5piCgSDm3efNmdu7cSfPmzeM9lEPS\ngRa1y2//vz7mwKZNxeq7cY0abNu3j6b16ikIKEflmGOO4a233uKrr77i8ssvZ//+/fEekoiIiIiI\niIiIiEiJUxBQpJzL2wUYCoXiPZSDdgIWJ81n0vHHE4JiBQKTQiFaNm5Mbsfz2LZtG9u3by+JIUsl\nVaNGDV5//XW+/fZbBg4cyL59++I9JBEREREREREREZESVTXeAxCRo5Mo5wFCsBNw3759xT7nD4JA\nYM6mTRzYtIkqxx9fYL0a/fuRkQQrV64kPT2dzz//nDPPPLOkhi6VULVq1ZgxYwYDBw6kb9++TJ8+\nnWrVqsV7WCIiImXCzE4EHgE6AzWBtcDfgPvc/fuIevOBs4HG7r4lonwicCUwwt2fjur7JWAIcLW7\nTzKzhcB5QE93nxNV9/+As4BO7v6+md0K3AvkRFS72t3/bma1gGeBi4GdwLPufvtR/igkQSxZsYEJ\nM5YDcH2f9nRo2yjOIxIREREpe5oTSUlTEFCknFu2bBndu3eP9zCAIAh4JGesJR1/PFWanciBL/4b\n83qN/v2oOXAAaZ+uIDs7m9atW5OZmakgoBy11NRUpk+fzpAhQ7j00kuZOXMm1atXj/ewREREysLb\nwPtAc3ffYWanA9OAWsBvAMysJXAGsAoYCjwW1cd2YDCQHwQ0s+oEQbodBdSdE1H3RCADyI2o1wq4\n3t1fiDHmh4H6QOPwOBeY2Xp3f6r4jy2JaOrcbKbMycr//r6JHzGkezqDu6XFcVQiIiIlw8zWAuPc\n/cWo8oXAAne/y8waAHcCFwINgG0Ec7U/uPvyiDY1gLHAAOAEgoVR/wfc7u6fRtyvCT/MsXKAjcDz\n7n531BhOANa5e5Wo8nHAjUAKMJtgfvbNUfwYpBg0J5LSoHSgIuVcIu0EzEsHWnPgAGr071fsdjX6\n9+PYhx+K2SYvAAiQlpZ2UBBQpCQkJyczdepU6tWrR69evdi1a1e8hyQiIlKqzKwR0Bp40t13ALj7\nv4FbgMgc89cC04FngKujuskF5gKnmVnjiPKLgY+Bb6LqzgQuM7PUiPJB4fLIe54MeIwxJxMEEe90\n923uvq6AcUk5E/2yK8+UOVlMnZsdhxGJiIiUuFwOXvR0ULmZ1QeWEGzY6eTu1YDTgDXAP8ysA4CZ\nVQXeAU4HLgrXaw68ASw2s7SIfoe5e3L4k0oQNPyNmfUN99XUzG4Itz2ImQ0BhgPnAI2AZGDC0f8Y\npDCaE0lpURBQpBzbtWsXX3zxBRkZGfEeChDsBDxw4ABAsQOBkUG+6DaR10BBQCk9VatWZdKkSTRr\n1oyePXvy7bffxntIIiIipWkTwe6+l8zsRjP7sZklu/sb7n4rgJlVIUj3+SxBmtDWZnZqVD+7gTcJ\ngnl5BhPsKIy2FvgMuCiibFCMuq2Au81su5ltMLN7w2MxgrSlyyLqZhLsJJRyasmKjTFfduWZMieL\nJSs2luGIREREylwIGAdkufvP3X0NgLtvdPffAlMIsiEAXA60BC5198xwvZ3u/py713X3AiNF7v4R\nsBxoES5qCrQF/sfBC7IArgKecvcsd98F/BHoY2bHlMDzSgyaE0lpUhBQpBz79NNPycjIIDk5Od5D\nAX7YCZinqEBgdJAvsk2sa2lpabg76enpCgJKiatSpQrPPfccGRkZdOvWje3bt8d7SCIiIqXC3Q8A\nHQh2+fUG5gPbzewNM2sXrtYL2ObuS8JnAc4m9q67qQSBP8ysDtAFKOhw6Mi66QQprOblXQzvEmxE\nEBg8liAd1lCC1Fh1w2OPXKmzG1Ae73JswoxPSqSOiIhIOXcJwaKrWCYCPzOzmkAP4C1331OMPvMD\ne2ZWxczOBU4BFgC4+z/cfQTBGdHRTuPQhVdVCBZrSSnQnEhKk4KAIuXY0qVLOfXU6AXZ8RO5EzBP\nQYHAWEG+yDaxrtWuXZs6deqQkpLCpk2blLZRSlxSUhITJkzgJz/5CRdccAFbt26N95BERERKyzZ3\nv9fdz3f3OsDZwPfAnHCqqeuAlma22cw2AxcAQ8K78iB4sZRLcMbfSWZmQB9gUQHnxeQCLwPdzawW\nQTBwurvn5FVw973hlFXPuvsBd18GPBrudyfknzmY5xiCswZFREREyqtcoCHBmX2xbCaYd9UhWCRV\nnO1gIeBZM9tjZnuA74BFwEx3/7gY7ety8PnOu8N/avGVSDmkIKBIObZs2bKEOQ8QgiBg5E7APEWl\n+TwcaWlprFq1CjMjK6vgbfIiRyoUCjF+/HjOO+88unTpwpYtW+I9JBERkRJlZpcBX0cE9HD3pcAd\nQAOgHtCdYLdg+/AnneCF0iWRfbn7fuBVgqDeIILdfjG5+yaC8276AgOj65pZLTNrFtUslSDQlw3s\nAyJXwLUB/l9xnlkS0/V92pdIHRERkQS3l+C8v2hVwte2APULaNuCYA60OfyJWc/M1pjZz8Pf5gLX\nuXv18CcZOBcYamadizHeXUCNiO/z0oBq8VUp0ZxISpOCgCLlWKLtBIxOBxqpsDSfhyMtLY2srCyd\nCyilKhQK8dBDD9GzZ086derEV199Fe8hiYiIlKT3gG+Bx82sgZmFzKw5cBuwArgWWOLuy9x9Q/jz\nP2AWcE1EP3lppqaG25wZrhMt8pyZqcBYoJq7/yOq3unAp2Z2jpklhVOT/gp4IZz2aipwl5nVMbMM\n4AbgqSP9IUj8dWjbiCHd0wu8PqR7Oh3aNirDEYmIiJSKNQSpOPOZWRLB+X6rgbeBK6Ku/zK8OOpa\nghSg+wnSqF8UTqEeWfcs4EQi0qxHc/cPgA1Ak2KMdwWHLrzaRbAoS0qB5kRSmmKtQBCRcuD777/n\n008/pX37xFkFEisdaKSjCf7lSUtLIzs7m4yMDAUBpVSFQiHuvfdeUlNT6dSpE/PmzeOEE06I97BE\nRESOmrvvNLPzgAeAz4DawJfAWwQ7ABcDf4rR9FVgppkdT7DCPDdcvohgJfscd4+Vrz034uvXgCeB\nx2OMa5GZ3Q68CDQlSHf1uLs/G65yE/A0sJ5gJfrD7j6zuM8tiWlwtzQApsw5OMvH0B7pDOqaFo8h\niYiIlLS/Ai+Y2bvAuwRpPe8AcggCgP8APjazZ4A/EgQNfwR8Hm6fF5CbDIwAppvZKIIA4mkEc6ep\n7r66iHHkULx4wF+Be81sOrAVuBt4zt1jr/yXEqE5kZQWBQFFyil354QTTqBWrVrxHkq+wnYClpS0\ntDRmz55Np06dmDRpUqneSyQUCjFu3DhSUlLo2LEj8+fPp2nTpvEeloiIyFFz9zVAQSu0WhXQZjZB\nek6I2BEYPtevcVTdkyK+7hzx9TaizpNx96SIrx8DHivg/jsI0o5KBTO4WxrNG9VmwoxPgBAj+rbj\nzDZa7S4iIhWDu78aPhP5AWAGQQrQxUCX8AKqXWb2E+BO4H2ClJ/fhOu2BMYAV7n7fjO7ALiHYBFW\nPYLFUS+Fy4qyneAc6BeiyiMXbOHuL4azRPyDYO43Hfjt4T21HAnNiaQ0KAgoUk4l2nmAUPCZgCUp\nbyeg0oFKWbrttttITU2lY8eOzJs3j5NOOqnoRiIiIiJSbB3aNlKaKxERqbDcfSIwsZDr64GfR5eH\nz3D+aUS97QTp0n9VSF8xX1q4+yFnCrn7QoKMDtHldwF3FXQPKT2aE0lJUxBQpJxKtPMAoeh0oCWh\nefPmbNq0iRNOOIF169axZ88eqlevXnRDkaM0atQoUlJS8lODnnzyyfEekoiIiIiIiIhUYO5+AFgS\n73GISPmVVHQVEUlEibgTsCzSgVapUoUWLVqwdu1aWrZsibuX6v1EIo0cOZIxY8bQuXNnsrKyim4g\nIiIiIiIiIiIiEifaCShSDuXm5lbanYBwaErQ9u3bl/o9RfL84he/IDk5mfPPP5+5c+fSpk2beA9J\nRERERERERERE5BAKAoqUQ+vXr6dq1ao0apRY+aHL4kxAgPT0dJ0LKHF1zTXXkJKSQteuXXnnnXcU\niBYREREREREREZGEo3SgIuVQIu4ChLJJBwoH7wT8/PPPS/1+IrEMHTqU8ePH061bNz7++ON4D0dE\nRERERERERETkINoJKFIOJeJ5gFC26UAff/xxfvvb32onoMRV//79SUlJ4cILL+T111/nzDPPjPeQ\nRERESpyZ9QZGA22BEJAF/MXdnzeziUCuu18T1eZOoKO7dzaz5sBqINZEcaa794+sH9FHF2AW8LC7\njwv38zRwNrA/fO0Gd99dgo8rcbBkxQYmzFgOwPV92tOhbWJlPBERESkpZrYWaALkhotygI3A8+5+\nd0S9ZgTzp9fdvXdUHznhdrkEc7M9wBzgOnffZmZXA+Pc/aQCxjAYuAs4EVgP3O3uLxan76N9/opO\ncxpJRNoJKFIOaSdgGu5Oq1atWL16Nfv27Sv1e4oU5NJLL2XixIlcfPHFLF68ON7DERERKVFmNhx4\nBngIOA6oBVwP3Gxmt/PDC6ziaOnuyVGf/gXc90LgdeD37j4uXPwSkA3UB04Nf+49kueSxDF1bjb3\nTfwXW3fsZeuOvdw38SOmzs2O97BERERKSy4wLGIulAoMAH5jZn0j6l0LLAUuNLN6Mfo5P9y+KsFC\nrZOBcTHqHcTM0oFngRFATeAW4Fkzi3zReER9V3aa00iiUhBQpByq7DsB69atS7Vq1di6dSsnnngi\nq1atKvV7ihTmwgsvZMqUKfTp04cFCxbEezgiIiIlwsxqAQ8SrPx+1d33unuOu//L3du5+x/CVWMF\nAg8nOBh9397AdGCEu4+PGMtZwF3uvsfdvyB4gdX9SO8j8Td1bjZT5mQdUj5lTpZemomISKXh7h8B\ny4EWAGaWBFwNjAIygaFFtF8LzAbSinG7rsACd5/n7gfcfSbwSbi8sL7Ti9F3paU5jSQyBQGFr7/d\ny5KVm5m0eDV3TP+EO6Z/wqTFq1mycjNff7s33sOTKNu2bWPz5s2cfPLJ8R7KIapWrVomOwHh4HMB\nlRJUEkHXrl2ZPn06AwYMYO7cufEejoiISEk4G0gG3iiiXugoyg5iZoOAl4Fb3H1SxKU9wE/c/euI\nslOBL4rqUxLTkhUbY74syzNlThZLVmwswxGJiIiUmfw5kZlVMbNzgVOAvFXFPYDd7v4+MJEgIFhY\nHy2AS4C3inHv6cDIiLZ1gGbAf4vo+81i9F0paU4jiU5nAlZyX3+7lzte+eTQ8lV7+eeqLQDc0689\nx9VKLeuhSQE++eQT2rVrR1JS4sXwyyodKCgIKImpU6dOvPbaa/Tp04cXXniBXr16xXtIIiIiR+M4\nYIu75xRSJwRcHg7eRaoKROfJzjaz6B2CF7n7vPDX7YCnCFbCX2Vmf3X37wHCf/4bwMzqAn8ELgW6\nHOYzSYKYMOPQ30Nj1dFZOiIiUsGECNJvTgh/XxWoAkxy94/DZdcBz4W/ngI8aGanuvuyiH7mhs/v\nSyJYtLUGeKeom7v7l3lfm9lPw/f5F0Fw8Kj6rqw0p5FEl3hRBClT/uWOEqkjZSdRzwOEsksHCgoC\nSuI655xzeOONN7jmmmt47bXX4j0cERGRo7EZqBvrgpmNM7N/EKT9nOTu1SM/wB84dOefRdeLCAAS\nrt8VuJjg7Jn7Y9x3GJAFHAO0c/cVR/uQIiIiImUolyDVet5cKBk4FxhqZp3MrAFwEfB7M9tMkA60\nCnBNVD9dw+1TgR8Bs4CF4VSihTKzH5nZ88DbBGc/XxS16KugvovM6iAiiUdBwEpu5ZfflkgdKTuJ\neh4gaCegSJ6f/exnzJ49mxEjRvDyyy/HezgiIiJHagkQMrODtrabWRVgEPBuuOiIUn/GsMzdP3b3\nDQQr4EdF3tvM/gCMAS5196GRK9ml/Lm+T/sSqSMiIlLeufsHwAbgROAqgmwKpwDtw59hwBAzi5nV\nz913EJyV3Bg4vrB7mVlt4AMgBTjZ3R939wLPco7qu8HhPVnloDmNJDoFASs5BQHLH+0EDKSnp5Od\nnU16ejorV64ss+CjSHH9+Mc/Zs6cOdx000289NJL8R6OiIjIYXP3b4E7gL+a2YVmlmJm9YCngfrA\nXzi8YF9RdfOvu/ssYAIw0cwam1kj4Fagh7v/83CeQxJTh7aNGNI9vcDrQ7qnK22WiIhUJjkEO/6u\nBSa7+4a8D0GqzmoEZ/PliTy3rxbwa8AjFklVCc+hmkR8fgRcD2wBrnD3bQWMpai+JYLmNJLodCag\nSDmyd+9eVq5cSZs2beI9lJiqVq1aZsG4k046ifXr15OUlESjRo1Ys2YNrVq1KpN7ixRX+/btee+9\n9+jWrRv79+/nmmuis3eIiIgkNnd/xMy2APcCM4DdwALgbHffFD7jL9bq8Vjlq8wsut4Sdz+vgPq3\nAOcBU4HHCVasZ0b1scbdD+lUyofB3dIAmDIn66DyoT3SGdQ1LR5DEhERiZftwOVAU+DVyAvuvsfM\n3ibYJTgjXDwvYh72HfA+cGH4Wi7QBFgXdY+ngROAc4B9UXOqu9z9D8XoW2LQnEYSmYKAlVyrhrX4\netXeIutIYvjss89o2bIl1apVi/dQYirLdKDJyck0b96cVatW5acEVRBQEtEpp5zC/PnzueCCC9i3\nbx/Dhw+P95BEREQOi7tPBiYXcC3mChd3vyvi67UUkYUmsn5E2XdA24ii6cUYrpQzg7ul0bxRbSbM\n+AQIMaJvO85so9XyIiJSMbn7SQWUF5r2y90HRnxd1LzqReDFIxyfMgceIc1pJFEpCFjJtWpYi3+u\n2lJkHUkMiXweIJRtOlA49FzASy+9tMzuLXI40tLSWLBgAV26dGHfvn3ceOON8R6SiIiISMLo0LaR\n0mSJiIhIuac5jSQiBQErOWtYu0TqSNlI5PMAoWzTgcIPQcCMjAzmz59fZvcVORInn3wyixYt4vzz\nz2fv3r3ceuut8R6SiIiIiIiIiIiIVGAKAlZyx9VK5Z5+7fEvd7Dyy29Z+eW3QLD7r1XDWljD2hxX\nKzXOo5Q8y5Yto0+fPvEeRoHKMh0oBEHA999/ny5duvDEE0+U2X1FjlTz5s1ZtGgRXbp0Ye/evYwZ\nMybeQxIREREREREREZEKSkFA4bhaqXSoVZ8OrerHeyhSiJycHD755BPat28f76EUKB7pQJ999lky\nMjLIysoiJyeHpCSlLpfE1rRp0/wdgfv27ePOO+8kFArFe1giIiIiIiIiIiJSwehtuUg5sXr1ao49\n9liOPfbYeA+lQPHYCZidnU2tWrU49thj+eKLL8rs3iJHo1GjRixcuJAZM2Zw2223kZubG+8hiYiI\niIiIiIiISAWjIKBIOZHo5wFC2e8ErFevHqFQiM2bN9O6dWsyMzPL7N4iR6tBgwYsWLCAOXPmcMst\ntygQKCIiIiIiIiIiIiVK6UBFyolly5aViyBgWe4EDIVCpKenk52dnR8E7NWrV5ndX+Ro1atXj/nz\n59O9e3duvPFGxo8fr5S2IiKSkMysNzAaaAuEgCzgL+7+fESdZsBq4HV37x3VPgfIAXLD7fcAc4Dr\n3H2bmXUC5gN5K8pCwC5gJnADUC98z2hVgY7u/o+SeVIpa0tWbGDCjOUAXN+nPR3aNorziERERA5f\nUXOdiHohYCVQC2js7t9HXLuTYF7TOarv+cDZ4fpbouqP5eD50ybgcXe/P1znVOBJ4FRgJzAZ+I27\n55jZQmCBu98V43mqAY81s/2eAAAgAElEQVQBfYEawD+BX7j7qsP/6ZRvmqtIeac3jSLlxNKlSznt\ntNPiPYxClXU6UPghJWjr1q35/PPPy/TeIiWhbt26vPvuu/z73/9m+PDh5OTkxHtIIiIiBzGz4cAz\nwEPAcQQvra4HbjazMRFVrwWWAheaWb0YXZ3v7snuXpUgmHgyMC6yQvh6Xp32wOnAXe7+hbtXj/wA\ntwOzFQAsv6bOzea+if9i6469bN2xl/smfsTUudnxHpaIiMiRKnKuA3QGkgmChUWuZDezlsAZwCpg\naIwqC6PmTwOBMWbWy8yqALMIFlXVAc4HBgA3htvmhj+x3A60Bk4BGgDrgelFjbei0VxFKgIFAUXK\nifKwE7BKlSrk5OSUaVrDtLQ0srKylA5UyrU6deowZ84c3J1hw4aVaVpdERGRwphZLeBBglXsr7r7\nXnfPcfd/uXs7d783XC8JuBoYBWQS+yVVPndfC8wG0oqo8ybQJsa42oXvdc0RPJYkgKlzs5ky59DN\nnVPmZOnlmoiIlHsRc530qEvXAc8BUwnmTkW5liD49kwB9UNR910MLCeYP7UG6rj7g+6+390/BaYB\n3Ytx3x7Ao+7+lbt/CzwAtDez+sVoWyForiIVhYKAIuXAV199xXfffceJJ54Y76EUKhQKkZSUVKYB\njLydgBkZGWRmZupcNSm3atWqxdtvv826deu44oorynxXrYiISAHOJlit/kYR9XoAu939fWAiRbyk\nMrMWwCXAW7E6M7OQmbUiWCH/YYwqE4A73P3rIsYlCWjJio0xX6rlmTIniyUrNpbhiEREREpErLnO\nmxFldcNlzxPMlwrKnpBXvwpwJfAs8DegdTi9Z4H1wynW2wEfEaRpPzuqWnvgi2I8y1UE6Uwj2+0A\ntsWuXrForiIViYKAIuVA3i7AUChUdOU4q1q1alyCgMceeyw1atRg/fr1ZXZvkZJWs2ZN3nzzTb75\n5hsGDRrEvn374j0kERGR44At7l5Uvuq8Ve0AU4j9kmqume0xs70EKa2qAe9EVghf30Nwjs6HwL+A\n+6LqXAbUJ3h5JuXQhBmflEgdERGRBFPUXOcKYJG7/8/dPyHInnB5If31Ara5+5LwWYCzOXSh1XkR\n86ddBGf+jXP3Be6+y90/AzCzxmb2GtASuKeoB3H3z9x9ZziweBPwFHCju+8v+sdQ/mmuIhVJ1XgP\nQESKVh7OA8xTtWpVvv/+e1JTU8vkfi1btuS///0v+/bty08J2qRJkzK5t0hpqF69OjNnzqR///70\n79+fl19+ucz+PomIiMSwGagb64KZjSNIJ3UZcBHQ2cxGhy9XIUjVeVNEk67hnYKYWW3gTmChmeWn\nuwif9VeU0cDjxQhMioiIiJSlAuc67p5LkNqzpZltDtc/hiCo92gB/V0XVb86sNvMbnX3vPRBi9z9\n/IIGFE7Z/hvgd8BLwFXuvqM4D2NmZxGkId1JcN7hx8VpJyKJRTsBRcqB8nAeYJ4qVaqUaRrD1NRU\nmjRpwurVq3UuoFQYqampvPLKK1StWpXevXuzZ8+eeA9JREQqryVAyMx6RRaG01MNAt4leHm1GDiF\nIFVUe2AYMMTMYi48Db98ehZoDDQo7mDMLAM4g2C3oZRT1/dpXyJ1REREElX0XMfMfgo05+D50unA\nKbFSfJpZI4LFVh0i6qcTpBy9OKJqUWnDXgQGAB3c/cbDCAD2BN4G/uTuZ1a2AKDmKlKRaCegSDmw\ndOlSbr/99ngPo1jKOh0o/JAStHXr1ixbtqxM7y1SWlJSUpg2bRpXXnklffr0Yfbs2YfU+d///kfP\nnj3zFwnk5OSQmprKqFGjuPrqq8nIyCA3N5etW7cybNgw+vXrx4wZM3j44Ydp0aJFfj+9evVi0KBB\nLFy4kKeeeoqUlBS+++472rZty+jRo0lOTmbIkCEMGDCAvn37AvCLX/yCTp06MWjQIMaMGcOaNWvY\ntWsXw4YNo3fv3mXzQxIRkVLn7t+a2R3AX83sWuA9oDbwR4KUnE8C7wP3u/uGvHZmNh34C8G5NzPC\nxZHn5NQCfh3cwr80s/RiDuky4ONwSiwppzq0bcSQ7ukFnrUzpHs6Hdo2KuNRiYiIHLXC5jp3A6+5\ne+R5fBvMbBHB4qlfRfV1DbDE3Q960WVms8LXXitqMOHA40XAyQWcoxwC6phZdEqtTcDDwC3u/mJR\n96mINFeRikRBQJEEt3PnTtatW0daWlq8h1IsZb0TEH4IAv70pz9lyhQtCpeKIzk5mZdeeonXXnuN\n3NzcmOeC1q9fn8mTJ+d/P3bsWD7++GNat27NpEmTANi0aRO9evWiX79+AJx33nncf//9B/WzfPly\nHnjgASZOnEiDBg3Izc3l/vvvZ+zYsTz44IM8+OCDXH755Zxzzjl88MEH5ObmMmTIEN577z2SkpKY\nNm0aW7dupUePHvTs2ZNq1aqV4k9GRETKkrs/YmZbgHsJAnq7gQXA2UAa0BR4NarNHjN7G7iKH4KA\n88wsF8gFviMIHl4Y0Sy3GMM5G/jHkT+NJIrB3YLfb6Jfrg3tkc6gruXjdx8REZEoMec6ZlYTGAj0\nj9HmVeAuM7sl3C5vPnQN8KcC6s80s+Oj6sdyNlAH+NLMIssXunvXcNtfhz95coFzCHYdTjCzCVHX\nWrr7ukLuWWForiIVhYKAIglu+fLlnHLKKSQnJ8d7KMWSdyZgWUpPT+fDDz/k6quvJjMzs8BgiUh5\nVKVKlfzgXVFycnLYsmULTZs2JTf3h98Dvv76axo2bFho22nTpnHNNdfQoEGQkS0UCnHLLbdw5pln\nsm/fPpo1a8YNN9zAqFGj2LRpU37AvV69egwePBiAGjVqkJubW+b/BoiISOlz98nA5BiXsoEaBbQZ\nGPF1oUdRuPtCgnMEixrHRUXVkfJjcLc0mjeqzYQZnwAhRvRtx5lttKpeRETKn6LmOgTBuFjtngKe\nCn97V0R5qwLqzwZSo+sXUPcR4JFCrncupLmOEUNzFakYFAQUSXDl6TxAiF860EmTJlG/fn2SkpL4\n6quvigx4iFQUW7Zs4YorrgDgyy+/pGbNmtx2220888wzXHHFFeTk5JCVlcV1112X32bx4sX5bUKh\nEE8++SQbN27kkksuOajv1NRUateuzfbt26lfvz59+vTh0Ucf5aKLLqJ+/foA+f8+bdy4kd///vdc\nddVVHHPMMWXx6CIiIlIBdGjbSOm0REREJGFpriLlnYKAIglu6dKlnHbaafEeRrHFKx1oVlYWoVCI\n1q1b8/nnnysIKJVGvXr18tOB5ubmMnToULZt20Z6enp++b59+xgyZAinn346oVCIc88995B0oA0b\nNmTDhg0Hle3YsYPdu3dz3HHHAfDoo4/Ss2dPFi1axIABA/LTFE+ZMoVXXnmFW265hbPPPru0H1lE\nRERERERERESKQdt6RRKcdgIWrUGDBuzfv5+vv/6a1q1bk5mZWab3F0kUoVCIZs2asWrVqoPKk5OT\nSU1NJScnp8C2/fr144UXXmD79u1AEFAcP348AwcOJCkpiX/+85988MEHjBkzhjFjxjB69Gj279/P\nkiVLeO+995g2bZoCgCIiIiIiIiIiIglEOwFFEtj+/fv57LPPaNeuXbyHUmzxOBMwFAqRlpZGdnY2\nGRkZCgJKpRJ9/mW1atX45ptvyMrKyk/5uWfPHs444ww6dOjAa6+9FrOfH//4x9x4440MHz6c5ORk\nDhw4wM9+9jN+9atfsWPHDu644w4ee+wxkpOT6dy5M2+//Tbjx4/nu+++48svv+Taa6/N7+uJJ56g\nTp2Yxx2IiIiIiIiIiIhIGVEQUCSBZWdn07Rp03J1vlY80oEC+UHA1q1bM3PmzDK/v0g8NGnShHnz\n5h1UN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rvt1ua+gJIkSZIkSZIk1SvLgUqqW8OHD+fKK6+s9TQkSZJ6jYiYAxxYfm1adLqq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\nAedERCNFNYYXKZKHh1ec1tiB6exHERNK6sEa2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+ "text": [
+ ""
+ ]
+ },
+ {
+ "metadata": {},
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W78S43lHV33DBGH9ZU/F2EPO930zgWN+lR3rHPsBNg8tnx52GD/S+/1iesrDj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7AAAg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\n34+ef54hD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P7ppJNOOmmCs0IXzXr/MxZ/9UWz42a8+CLXXj0RjcYfmzntp0e3HuzbvhdT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SLyMjg4svvpgVK1YQHh7e6H4yMjJ47bXXWLduHcXFxYSGhjJy5EgmT56Mm5sbP/30E4888gjL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FukoTfXNzkdWpdxuqVhMKVBSX8OYDD/L4xx+h9IB02ZjEFI4d+5VeAU1HcVVUOfDyj+4c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0qzAB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\nVM7uWctzTzxkB+vsyyeTPiH7v2wKTAUAaDyqVs5jL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bPemTJ1KnrfUP7bu48ClSe5GaeI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THlrxIiUtAiFpE/C4nNzz+PMs++uVpCb3LfAaiNx4xU3c/sjtuJ1uZIrOn09RFHGXeLn77rv9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TYDCmPQ0H8CswNN+M7AyOC2nDFmkVLq/9m77/Coqq2Bw7/JpCcktBBARJqLIiD27rUL6rXr\nvfZr+ezdq9gFG14rzYKoIIqKIjYEBUGKjSIdhA1IlV6SkN7m+2OfwWEySSbJTCYJ632ePCFnzuyz\nZsKsnLPP3mvf5lvQC1jmlzx+FpHN2Lmhp2BH/vgXByvDmYIxBFvoayJwq1PzwldBgET1g5NAbwOe\nq+w4SqlaU1G++RjbKQxV+1xvrOAEZ6Pz/aDyAnJWrGlO2dE+j2Fr9fQFXsDW9HnS77nB5CilVN1V\n6TlQiI7Tzvnuvyz6sT7/dmFHTL8MjBORjgGmwyul6gljzGpgNTAMQEQuw+aVR7Aja67E5pwV/s8V\nkV7GmAX+7YnIYmyh5l+w066+rCyGYJ5jjJnP3ze2vNtyRWQm0MPZlAE09ntqivN9R0VxqPDSTh4V\nSKCLqd+clbS8NXS8RcH+jT0h8uqG7ZU+g8rnYXoTWIsK93I4Bb++Ao4DrjbGfBTM8/yOd24Vn6OU\nCq9g8k1FqvS5NsZsFJFV2JX9ylve81Ln+3feDSJyCPYk7C1jzEtOHYy+IjLGGLPU2aemOUopFXk1\nzUnBOgvIAub7bgxw7Fki0hoYhC36HKrpYkqpWiAiRwKzsXX8/Kd5fyYi/wU6OEXYe2NvIn3ns5sb\nWyvwX9jaPv7GAdeJyADgeAKvxBWK53jFYUc4AizHTufydbDzPVDhZ1VLdLqWCqS8ooNZ/P1/5krg\nD2PMp8aYGd4vbNHRDIJbZes45/uySo7rdRVwGtCnkoun8to5DlgaRFxKqdoTTL6paL/qfK5fA3qI\nyG3+D4hIGnaEzqfGmJXONjfwHrCdv+fJP4idSjHcKUIPwecopVTdVVFOqvL08kCcGhj/B4wyxgSz\nOqD3gqq8m7OVnT8ppSJnCfYzfIX/AyKSjB3Vtxa4DPsZH+R7bWWM+RE746G8KVvjnDaextbDmRZE\nTBU9x+PENlhE1vmuquWMdD7NZ/+JwCnOuZPXJcBW7FRTFSE6kkcFUt60KQ92hZtG2Lvgr/nvYIwp\nEZHvgAtF5Bafh470qV8RhR3m9wQw3hiz0Oe4h4jIvQGO/SU2+S0A4kXkDL/H/zLGeJcYTRCR031e\nRwI2MZ7ktKGUqjsqyzcxzs+h/Fy/hS0QP1RETsDeIcvELnN+L7aIoG8H0L3YufOXeVfTMsbsEJFH\nnLbuxE7RCjZHKaXqropyEiISY4wpCvI5LqC1Tz6Iwt7l7gvsxK4QGAxvJ05cBcdRStVBxph853xh\nqDOl+1PsDfEO2HONWGAwts7gz+UsEvMNcK6IHGmMmevX/mJnhPIt2I7jSqd0VvIcbz4Zi51C9rWI\nvIftN3gIyAdedfZ5C1s/8SsReRE7vese4AGdWhpZ2smj/Hko/47QJuex27BFwfyXzPP6BjuS50wg\nx9n2ss/jpdi6GMPZd7qEB7uCxVEBYlqJPTHqDEwOcMyR2JW9PNgVcXz3ycf2ol9qjBlXTsxKqdoX\nTL65hhB/ro0xHhH5F3a1iJuwc+PjsDV43gNeMsbkAohIR+ww5vHGGP+cNxy4HnhWRL4kuByllKq7\ngslJVwMjgnyOB3sudJbPth3Y86RHjDG7/fat6Ng4x/bvGKroeUqpOsAY84aIbADux3aMJAAbsKUt\nXsQu9HAy8EA5TYx3vl8OzA3w+DjsCOMvfLZVlhsqfI4xZoaIXIAd3fwR9rxrBnCNMWaLs89u5wbc\n684+u4DHjTFDKjiuUkoppZRSSimllFJKKaWUUkoppZRSSimllFJKKaWUUkoppZRSSimllFJKKaWU\nUkoppZRSSimllFLB0yUXa5mIjASurWCXV4Cl2BVephljTgvQRinQ3xjT32fbLcDtgAAlwEJgmDFm\nVIDnHwk8il16OBXYBkwFBvgv8SsiJwKDgEOwK2K9Yox502+fx7FLCDcCZgH3GmMW+Tx+APAmcDqQ\nDXwCPGSMKfDZ50JgANAeu5JWf2PMWJ/HY4GXsCvtRAPTgDuMMRt89ukGvAEcC2zHrnzzjLOSjovy\nlx4FKDTGlIpIV2AgcILzPn4H3O27nKGI3IBd/rQdsBkYaox52XksqOP4vX/tsKsE3WGMeb+C5ypV\nLZp3wpd3fPY92XnvogI8drPz2lsAi4G+xphpPo+nAkOBC4Bi7CoadxtjMnz2uRe7TOkB2LwzEnja\nm0+cWPtjV99JA9YDrxpj3qrKe6JUqGjeaRB551ygn/Oe5ALfAvf47uOz71Rguu/vytl+GHa55WOw\nKwh977QRaJlopWpE806DyDsVnu/4HS8V+AP7u+jv81rKxObw7C/nPOW9ASq8tgBnlPM1jL+XuztF\nRC4qp429S+KJyBPAEOwH5WLsB/QPYKSIvOD7JBG5HPgVaAzcB5wHPIdNLr+LyAk++7YDJmKT06XA\n+8AQp5PDu89/sct5DsEum14MTBGRNOdxN/akoAt2+eBHgCuwHTDeNo4BxmKXBLwEmAKMcZbk83oV\nuBG75Pp/gNbADyIS77SRgl22OBH4NzZR9cVe9AD8A3uCUt7X1SLS2Dl2I+wfiJuxyXyCiET5vH/D\nscufXoDtVOrnJKSgjkNZw5y4dQlUFU6ad0Kcd3zaSgIeI8BnWEQuxS6XOg64DLtM+0SnQ9lrNHaZ\n5buBO4Djga982rgWe2I6BrjI2f8x7LKmXq85zx/k7PMj8IaIXBXse6JUGGjeqb9551jn5z+xyzY/\n7ryHnwU43tnY5Z89ftvTgB+wN5X/hV0e+jT+Xg5aqXDQvFN/804w5zu+XgRa+sUzifKvw5aX006D\nEx3pAPZTBcaYqeU96PTqAhjgRREZb4wpKmffWOAh7B3bx3we+kJESoB7RaS/MSbPSSYjgPeMMbf4\ntTMcmIm9QDjS2XwfkANcZIzJB8aLSCdsAnhPRGKAh4G3jDHPOe1MBzZhe5yfAs4HegJHGWN+d/bx\nAO+IyFPGmDXYHt+lxphrnON+69z5eRKbyFpgO1weMcYMddpYgE0eVziv6UagOXCEMWaLs08z4AHu\nKT8LAAAgAElEQVQReRH4HTvCx99NwNnYETvXAklAb2NMls/r2Qici+3YeRj4wBjzX+f534kIwCMi\nMjjI4/i+71cDXQPsr1Soad4Jcd5xTna+B3pRfkftE8C3xpj7nTYmAsc5r+E655jnAJcZYz539tni\nxHCKcwfsDuAjY8wjTpsTRKQl9gToaRFpBPwf9o7Za84+E0WkvfN7Gl3Je9LPGPNngNiVqinNO/U3\n79wDLDbG/MvnvcsCPhSRXsaYBSJyK/BfoEOAGMBeUDYBrndev/eidJiI9DDGLC7neUrVhOad+pt3\nKjzf8XtPT8Le2M/0i+M27A17X6nAp+xHN7Z0JE9kBDti4yHssLp7KtinObZjYnOAx94E3sZ+GMH2\nmuYFas8YUwxcD7widsoR2I6Nb53E4zURaCu2Z+MYoCn2Q+NtZw/wE3CWTxtrvInHpw0XcKaTwM7C\n9jDjt8/xTlI5C9sh6XucP4EV2N5g73F+8nbw+LSRCJxgjNljjJnt++W8F1cBVxtjtgHdgUXeDh7n\nOFuw7+0ZzqZDsEna1zzs9IhDgzwOACLSHNtzfidKhZ/mndDlHe9xirCdv09j74ztQ0QOBHr4tVGK\nPVHyjbUAnztZwAzsHSfvPt2An/2azwLczr87O/+e5LfPHOzdPe9xyntPzkCp8NC8U3/zTnfsKBxf\nc5zv3rxisO/7IwSW6nzf5rNth/PdjVLhoXmn/uadys53vMeLw3bYPAHsM33UGPNHgGux64HZxpjn\n/WNvqHQkT2REOf85y9RE8vugL8T+B35cREYaY3b474/9w7kFeFRE8oDxxphNTlsLsAnH6yxgku8x\nnA+/94OzFvDeaYnH3pl5i30Z71OxcyXBDln0tRK40vn3If6PG2O2iMge4GDnGHEB2jBOXO2dNnID\nzAtd6bThPc7n5cTaCZtk9hI7/eo9YJQxxttpsxto5bdfAjbBt3c2ZfjvAxzofG8PzA/iOF6vYTum\nvnZGAykVTpp3Qpx3jDGFwP+c2BOx8+F9dasg1nQRSXaOs9o5AfTGWiIiq32O08g5RhT2ZPI47NTP\noT5xn4q96+arB/aOXzDviVLhoHmnnuYd7Ahp/7o5PZzvm53nTMXWGkFEBlDWBOxF4UCxU14aYadd\nLMX+zpUKB8079TTvBHG+4/U4sMfZfh8VEJF/Ahf6xLhf0JE8kdEW29PrP08wx+lR9fJg/xOXAs8G\nasj5oFyG/Y/+FrBRRIyIjBCRC316iwEOwiYYXx/7xZCHLRTWzHl8l9/+3iFxKdjOj/L2SXH+3TzA\n4959UoM4TqrTxu4K2ijvOL5t+PsP9u6379DLz4D2IvK4iDQTkbbAO0AskODs8ylwp4icLCLJYouP\nPeE8lkBZgY6DiJyFHWKpo3hUbdG8E/q8U5mKYvU9TqBYswIc5xxn+/fO97cBjDFZxpgZZt8ii3dj\nawEM84mlovdEqXDQvFNP845zB3yd9wGxi1sMApZh775XypmOdSe2w2gTdmTAIcANxhitQ6jCRfNO\nPc07PgKe7wCISHfgfuBmE6AYsy+nk+014DVjzNpKX0UDop08kbEFW7fF/+s4bALYyxizE7uywY3O\nf+oyjDE/G2M6OW08hP0DfDG28NV3zvxngBhsIvP1sM/xLw/QfInfz1H+2wN8wKL8nuffRjD7+B+n\nsjY8wcQKez/wTwCvG2P2JhtjzFzsPM6+2NW51mKHSf7M37+Xh7EF1abxd/L50Hlsn99decdxesDf\nAh733g1QqhZo3gld3ikOsL0iNc1vXjOBE7F5Kg6Y6tQL2EtEWonIF9hVAt/GufNG4BxZ3nGUChXN\nO/U874hIlNjFJeZgV+65INgOGrG1T4YCH2Hv/F+C/Z1NdKZ3KBUOmnfqed6hnPMdZ4TPcGydovlU\n7j/YwsyvBLFvg6LTtSKjwJkfGFCAqTuvA7dg67ecVeYJDqfN2cDLzjDA/sCD2GJVY7HDbtv6PWfv\n8H6nU8LL2/Pa2O8w3p7jHTi9tiKSaozJ9NvHO+QxA1t0z593H+88yvKOs93Zx//xQMepKFZflwNt\nKDv0D2PM2yLyAXaY5C5jzAYRWYszrNgYkwNc5BQBa4mdHnEk9k7A+iCP0w/7/r4r+1atjxWReL+h\npEqFiuad0OedyvgeZ53P9hTsieBOZ59A06VSsKva7OW83l+AX0RkA3alj9Oxc+u9K1sMx16IXWSM\n8Z33Xtl7olQ4aN6px3nHGdH8MXA0MBh7cyovyDjAnhstM8bsXVlURH7BLmhxE7ZwrFKhpnmnHucd\nqPB8pz22TMbzPtdQLiBGROL8RjS7sIXhP/S92b6/0JE89YAxpgQ73/AMETnf9zER+a+IlIpdXcX3\nOfnYzgSAjs73X5w2yit2d4rP87Oxf4S7+O3j/XAu5e9l6ALts8T593LsdCXfmFsCyc4+f2KLeQVq\nIwc7d3U5kCK2+ntFxykv1iV+228GJhtj/vKL6yQRuc8Yk2eMWeh08LTFJuzZzj53icipxpgtxpgF\nzvt0MpAP+K8SEfA4wFHAodgLMe/wTbB33XNQqg7QvBNU3qlMRbGuNHY1j+VAR+fulDdWN9AOWCIi\nRzvv9VF+bax0vnvnr1+DXXL0S6CrXwePN5aK3hOlIk7zTt3IOz6x/4y90DzWGPNAFTt4wNYD2efc\nyBizFXsx7P8alYoIzTt1I+8Ecb6Tgr2Gao3tnPJeQ7XFlsXIc67bvE5xjv9ekK+hQdFOnsio8jxk\nY8wkbC/my34P/eJ8vyrA07wF8tY639/Ajj55yH9HETkIuNdv80TgAr/pAJcA84xddeoX7JQl3+U1\n07DD6751Nk0AOotIT782ivi7ONk07HxXbxsubK/4984QxUnYXuB/++xzCHa0jfc4E4FTnOP7Hmcr\nf68G4U18J2KHWPrrCLwgIik+2+7Azp8d7/x8MXCrT3sJ2Irt3zhFyYI5zu2UHT4K8IzPv5UKNc07\noc87FTLGrMaenPjGGoezoobP603C1s/x6u1s+xY7LLyAsitgnex8X+ScfA4B3jbGXO+cPPqbSAXv\nSTCvR6lq0LxTP/MOwHNOLCeYfVfuqYq1wNF+F3UtsB08y8t7klI1pHmnfuadys53FmKvlfyvobZg\na6ge6/zb63Lgr4pGdTVkOl0rMhJE5HQCVH1n3/+c/u7Hr0fVGPOLiIwFBolIF+wHuRg4HLgLWIS9\ns4sxZqaIvAQ85ySDr7DJoxc28UwBLvBp/kVsUhsrIsOxq7dcgq1QjjEmT0T+B/QXka3YD/fD2KF9\nI5w2xgKPAmNE5Els8nsOGGqM8Rb5ehqYLiLvAV9gP5S9sPMwcUbUvAs8IyKF2OF+TwNzjTHezpe3\nsBXuvxKRF7FLf94DPOA3l/Vs7Ps+PcD7+4Xzmj8VkTeww5MfAJ71ifUd4H0ReQzby34P9mSln19b\n5R7HGONfed47dHT1/pqIVK3QvBP6vBOMfsBoEXkBe8J2G/YO22s+7+Vk4E0RScX+XR4AfGGM8d5R\nH4Zd/aPUeW97Oa/vM2PMchG5GHuH60cRKbMcujHmhyDfE6VCTfNOPcw7zkXgJc5r6iVlp7cscS5C\nK/Mq9sJ5nIiMwC5Q8SD2BtzIKrwepapC8049zDtQ+fmOc5x9pnY5MW8McA3VmyCLxDdEERnJIyJ9\nnWTv/bmViHwnInkisl5E7opEXLXEA6QDk7E9p/5fTzr7lOmFduZ1Dgrw2BXAI9hhaR9jV4m62Nn3\nBN/5icaYvtje3AOwtRvGYXtdn3PaWeSz72rsB6Sl0+YFwPXGmK999hmATQR3YwvrZQJnOrVrcIbn\n9cYmpvec1/cWPr3cxpifgUuxnSpjsR/oC40x83xe413O858B3sUO/z3Pp43d2LmaBU4ct2Lnjg/x\ne6+OBjKMMSv9tnvnf/bBTn/4FLgB6G+M6eezz2gn9puxUyOSgd4BOm7KPY6KDP+84/fYSyLyY23H\nVIs074Qh7/gp7/37GJuPLnNeTxPgbL9pnJcDP2BH4wzE3tG61ufx/2JrBtyFPWm8GVsfw1vnwju8\n+xPK/m6/D/Y9UaGn5zuad6ifeScd23F8A4FzSrl1S/zi+A57J78Z9vc1DNgAnGL2rTGiQkjzjuYd\n6mfegcrPd8qLZx/OiMGDcMpt7I8C9XCGjYicApyG7c0ca4y5wdk+CdgG/B92ysx04GpjzMTajE8p\n1fCUl3d8Hj8NO4T0Z2PMabUfoVKqodHzHaVUbdO8o5Tyqu2RPEcAacDeZaNFpBV27t0jxha8XYId\nIfGfWo5NKdUwlck7XiLSFHtncTC13OmtlGrQ9HxHKVXbNO8opYBarsljjHkFwG/KxOHYaS0bfLYt\nww7PUkqpGvHLO/4dOW9jl7jPAI6s5dCUUg2Unu8opWqb5h2llFekVtfyvdBqgi1K5SsXW5xNKaVC\nZZ8OHhG5EUg1xgzyf0wppUJEz3eUUrVN845S+7lIra7lWyApB0j0ezwZW1iqUiLS+M4779x93XXX\nkZKSUvkTlFIh43K56lPnyN68IyIdgaeo5pL1mneUipz6mnfQ8x2l6i3NO5p3lKptNck7kRrJA3/3\nMi8GmjtzRr26A78H2U7joUOHkpXl30mtlFJleE98TsCuvrBKRPKw07ZOFpFcETkwiHY07yilgqXn\nO0qp2qZ5R6n9WKRG8rhwLraMMatEZDrwgojcgi0adjm2OrxSSoXK3t5wY8woYJT3ZxG5DviPMebU\nSASmlGqw9HxHKVXbNO8otZ+L1EgeD/sOJbwKaAHswl543W6MmReJwJRSDZZ/3gn0uFJKhZKe7yil\napvmHaX2cxEZyWOMud7v501An0jEopTaP/jnHb/H3gfer8VwlFL7AT3fUUrVNs07SqlI1uRRSiml\nlFJKKaWUUiGinTxKKaWUUkoppZRSDYB28iillFJKKaWUUko1ANrJo5RSSimllFJKKdUAaCePUkop\npZRSSimlVAOgnTxKKaWUUkoppZRSDYB28iillFJKKaWUUko1ANrJo5RSSimllFJKKdUAaCePUkop\npZRSSimlVAOgnTxKKaWUUkoppZRSDYB28iillFJKKaWUUko1ANGRDsCXiJwHvAwcBKwAHjDGTIls\nVEoppZRSSimllFJ1X50ZySMi7YBPgaeBRKAfME5EWkUwLKWUUkoppZRSSql6oS6N5LkAWG2M+cj5\n+UsRWQlcAgyNXFhKKaWUUkoppZTyV5CXR1RhIQDFxcUkNGsW4YhUXerkiQGK/LZFAQdHIBalVAMj\nIn2BLsaY652f2wHDgBOwuecr4HZjTG7EglRKKaWUUqoeeeuRRzmtoJAol4uludm0v+IKjjr77EiH\ntV+rM9O1gElAdxHpLSLRIvJvoCcQH+G4lFL1mIicIiJPA48BHp+HPsTW/koDejlfz9V+hErVTxsX\nzyRn9S/krP6FjQumUFxcHOmQlFJKKVWL1i1fTvzuXURFucAFXRISmfbFF5EOa79XZ0byGGMWicgN\nwOtAC2AGMBXYFNHAlFL13RHYjpy9uUREGgHHAxcYY/KAdSIyHLgjMiEqVb+sXDyHuZ8O5oR2bgDW\n7ipm8R+GPlfcFuHIlFJKKVVbxr3xJqckJO792R0VRZucXH4d/y3HnXduBCPbv9WZkTwikg4sM8Z0\nNMY0As4DugAzIxuZUvXDtvUr2bJqYaTDqHOMMa8YY24DfgVczuY84EhjzE6fXXsB62o7PqXqox++\neJ9jDorF5Y7B5Y6hfVoCZtHsSIellFJKqVqy5OefaZyZSazbvc/2HolJzPjyS0pKSiIUmaoznTxA\ne2CSiHQWkXjsKlu7jDFTIxyXUvXCmGH/Y+zbL1CQnx/pUOoqbwcPxphiY8w8ABFpIiLDsMXfH4pU\ncErVF4UFBXhydxETve8pRMvYHNaYJRGKSimllFK1paSkhPEjR3JEYmKZx1wuFz1KS/j6rbciEJmC\nOtTJY4z5DfgfMA3IwE6luDCSMSlVX8yd9g2t4/ZwVKtSvhzxSqTDqas8/hucKaLLgWSgpzFmca1H\npVQ9s37VMlol+K+TAB2awLI502o/IKWUUqqO2rRtE9uzt5f52rhtY6RDq5HPBw3m0JJS3FGBuxMO\nSkhk7ew5bNtQv19nfVVnavIAGGNeAl6KdBxK1ScZO7cxc/wnXHJIDC6Xi2Url2AW/Ir0Oi7SodVp\nIvIscAW2Ls9vkY5Hqfpi89oVNE0oLbO9WXIMi//aEIGIlFJeJSUlLBwzhgMSEtmR0ohDTj890iEp\nFZTXR46htMlBbF+3kjuuOIf0tPq9DHdJSQkD33uNDTkbaHxw4zKPZ67JormnGQ/d1peY6JgIRFh9\n86dMYffCRRySnFThfv9ISODd/v15YOgQYuN1LaXaVGdG8iilqq64qIh3XnyYcw4uxeWys5FO7RjF\n+A+HsGPrXxGOrs7ZO11LRFoB/wV6awePCqfpH3/C9AEvsP2vhvN5zM7cSWKsu8z2mOgoiovKjvBR\nStWeGZ99xvpJk9nxww989+FoCvLyIh2SUpXKyMxi4bKV/LUzm2x3Cq+/NzrSIdXIitUruP+Z+9ke\nt520rmnERMeU+Wp+cDNymmVz/9P3MW/pvEiHHLQFP07jx1EfcEJS2Wla/uLcbk7yeBh4333kZmfX\nQnTKSzt5lKrHxrz5LCe3yiEx7u9Bee6oKM4T+HDI03g8ZWYo7c88/D1l6zggFlgmIkU+XyZy4amG\nxuPxMGfqFOLWrOGb4cMjHU7IbN64jqbJge86lhRrTbBgiEhfEVknIoUisl5EHol0TKr+83g8zJky\nlU5OjYzDoqL4YujrEY5K1RV1Oe8MGPw2yQf1ACA2IYmtewqZu3BZhKOquoLCAl4c9iJDPxtCs6Oa\n0ii9UYX7JzVLpsWxLRgxYQTPDH6GnNycWoq0en74cDQ/jRzJWUlJe28uV6ZJfDwnFhcz8J572LKu\nfq1vsmntSrI3LCJv09IyXxtWLY10eBWqU9O1lFLBKy4uZtdGQ6tusWUeS4h1c2D0TsziOXTueXQE\noqt7jDHX+/x7HNrJrcLst/Hf0q6wmEbJSexet57CggJi4+IiHVaNlJSUkLVrG7EtAn98XPkZZO7e\nSWqT+j3MPpxE5EygH3AS8Du2BuEPIvK7MWZSJGNT9dv0z8bSqagIV6w9L0iPj2f+smUNIveomqnL\neeeDz75hj6sRKQl/T/1JbdeDt0d9gvR7iJRGyRGMLngzZs9gzPgxpHRpRMt2LYN+XpQ7ipY908nN\nzOWhFx7knFPP4dxTzwtjpFWXl53Nu/36k7ZzB6cmV/33kRobR++SEj568il6nnUmZ1x1VRiiDJ3t\nmzcw7t1XiMvbzD/alRIVoENrzkYXX5Y24fyr7+Ag6RGBKCumFzlK1VMZu3bQKKZsXQyvpvEetqxb\nVYsRKaV8zZkyhc7OcOaOnlJmTZgY4Yhq7rtP3qJHk9xyHz/hwFLGvPl8LUZUL2UAxYCbv8/DPMCW\niEWkGoTff/wR8VvppgcuvntvRIQiUnVIncw7P8+ez8x5y0hp3XGf7VFRbpI7HMHjz79KcXFxhKIL\n3tBRQxk7cyytjm9JcrPqdUolpibS6vhWTF40mRff+l+dGY0/a8IEBt91N4dlZNA9seIaPBWJc7s5\nOzmZXZOnMPCee9i5dWsIowyNTetX8/Zz9/HlkIc4JW0rpx8cS3RMPFHRcWW+jmkXyzlt9zDjg2d5\n/anbWbVkTqTD34d28ihVTzVv0ZKM0iRKSgN39CzdHcfRp51fy1Eppbw8uTl77/60T0hk2ezZEY6o\nZhb/NoWty2Yi6eWPCGicGEMrz19MGK1TRMpjjJkDvAL8ChQCM4H3jDGLIhqYqtd2bNlCUm5umSkU\nByQmsGZp3Z5WoMKvLuadRcsMIz/7hiadDg/4eGxCEq4WXXj0uVcpKSmp5eiCN3jkYNYVrCW9e4ug\npzCVx+VykdYljR3xOxnwRmRvmORmZ/P6gw+x8tPPODchgcYhGg14SFIiJ+TlM7JvXyZ/8EFI2qyp\nPZm7eWfAf/l+2GOckraV3hK9TymM8sRGR3FKx1h6H5jJnE9fZuhTt7NtU92YkqadPErVY+f8+2Ym\nryr7h2/BX4UcfPjJJCTVjyGuSjVEnqK/7z5GR0VRXFgYwWhqxiz8jWnj3uGMTpWf9PQ6IIZM8xPT\nvhpVC5HVPyJyEvAg0Ac7bf4C4CYRuSiige0nVqxewRPDHuONma/T//1+/Dzv50iHFBK/fvklndxl\nC6IDkL2HwoKC2g1I1Sl1Le8sXb6Kwe9+RBM5psKOkYTUpuQnH8DjAwZSWs5NzUgqKCzArF9B47Zl\nV8+qidRWKWzavYndmbtD2m6wFk6bxuC77uKw3bs5rAr1d4KVGBND76RkMqb+yMB77yU7MzOk7VfF\nivm/8M4zd3Bs6kbOODiGhAALS1QmNjqKEzvEctYBGYwb/DA/Tfg4DJFWjXbyKFWPyaHH0uHwM5m/\n8e8VbTZnFLE9ph1n/+vWCEamlMJ/qHUdGXpdVZvWGr4dNZB/dokK+kTvxPbRrJs7gd+njw9zdPXS\nZcAkY8z3xhiPMeYb4HvgzAjH1eBl52YzeMRgYtrEsjt3N+50N6O/HM22ndsiHVqNrTUraVnOEsUH\nlHpY/HPD6MxS1VZn8s7yVWt4bfgHNO1yHFHldUz6SGySTnZcOk++MKjOTGHyinZHExWuy+lSSIhP\nCE/bFZgyejS/jhjJeQmJpIa5lle3xESOzcll8H33s33TprAeqzwTPn2Xi7q6SE2s+TL2CbFuzu0S\nzayp4yM++kw7eZSq506/9Ea2RrdhZ3YRxSWlzNiUwLX3PxfpsEJCRJqLSGsRSYl0LEpVWZRfh0iI\n74TVhqLCQka//hznd3XjjqraKcOpHaKZ+c1odmytv8vHi0iqiFS8PErVlWJX9/NVAuwJ8XGUD4/H\nQ//X+tGkV2Pc0fbC0uVy0eKINJ4d9CxFxUWVtFB3eTweirOyyu2EbZ+YyLypU2s5KlUZETlRRMqu\nnhEedSLvrN+4mZffGBF0B49XUrOWZMY045lX3whjdFXndrs55tBj2b5se0jb3bVqJ9079SA+LnDH\nbbj8+vXXrJ00mZOSk0M+eqc8jWJj6R0by9uPPU5Bfu2v0JnSuCkZeaGr+5RXWII7LomoKp4zhVq1\njy4iuSIioQxGKVU9V97xBD9tdPPr+mIuuPo2oqPr78J5InKOiEwVkTxgG7ARyBCRHSIyRkSOiXCI\nSgXFFf33XaGi0lJi4uvf6ja//TCOw5vlEhtd9dMFl8vFWR1hwkdvhiGy0BKRf4nIlyLyuYhcIyJu\nEfkA2IXNP5+LSKjmv44DzhCRs0UkWkTOAs4APglR+yqAD74YBekeEhrte2c8Jj6GJElg6PtDIxRZ\nzS2aOZP0ovKng8a73WRt2VrnRkEoJgPTRaRtLRwr4nknNzeP5wa+SZPOxxLlrvp5alKz1mzNj+Xt\nDz4NQ3TVd9UFV3Fy15P4a/ZflBTVbPRGaUkpm+dvpkfLntx6Ze2OyC8uLmbGl19yTFL1iytXV5zb\nzYlRUYx56eVaP/aVdz7FjG3NWLG15lPqN2UU8s3qeK65u1+tdZKVp8JPmIg8ha287s8FxAB3ishO\nwGOMeToM8SmlgpDUKJXYlHR25mRxcD1eMl1EbgKGAmOAj7EdPAVAAnAAcBowU0SuMcaMiVigSgXB\nlZiIp7AQl8vF+pwcupx5RqRDqrI/5s/irFbVv9HcKCGa7M11eyqMiNwHvAxMweabd4BbgdbAlUAR\n8CzwKnBzTY9njJkhItcCrwEdgXXAjcaY+TVtW5Vv4fKFND2iacDHktMasXb2mlqOKHRmfPklJ1Wy\n6k2boiLmTZ7MEWedVUtRqSD9DswXkWeAIcaYsMzxqAt556U33iP+wJ64Y2rwN6VVe+YunUPvDZto\ne2DrEEZXMxf3vpRDOvfg9ZFDSeyYSKP0qg8AzdmZTdYfe7jxips4rNthYYiyYuuWL+eA4pKIdU40\nj49n4ebNtX7chKRk7ur/Ol+NfI2Za2ZzUvvqTdtatKmQ7XEduee5p+vEzfbKIrgc6AosBXb4bHdh\nRwEdA+RhO4K0k0epCGov3Zk/55dIh1FTjwD/McaUd2fpbRG5DXge2xGkVJ3VpmNHdsyfT1pCAutd\nLs44sx6WXHFF7V0hrLoiPWQ5CPcDNxljRoCdQgHMAC4zxnzubMsGRhOCTh4Ap5Nac1gtqqw+Qomn\n7hV1DUZ2VhaeXbuJqeTue5fERKZ9+6128tQ9Q4D3gWHAPSLyMvC+MSY71AeKZN7Zk53Dxu0ZNOvc\nucZtpbY/lGEfjOG5R+8LQWSh07l9Z157ciCDRw5mzcI/SeuRFtTfP4/Hw44/dpAem06/J58mtgad\nYDWxfcMGkiNc3NpTFJlpsy6Xiwuvv5+Z337C3HlfcOSBVfsdrNmeT3bTw/nPrY+EKcKqq+x/3uHA\ni0Bz4C1jzKnO1ynY5feuNcacYow5NRTBiEhfEVknIoUisl5E6s47pVQdl9w0HVx1/mKqMgcAiyvZ\nZwb2DrtSdVqbzsIu54SlODqaxEahLu0Sfk2aNSczr2Y3ll3RdX6aWhrgW5X2V2z9CuOzbS2gtcHq\nsfYHdCB7R+Dr5vw9+bRIbVHLEYXGzM8/J5jaCdFRUXgys3SVrbrH4yxvfhT2BtZDwFYR+UxEbhaR\nHpENLzSm/jyL6NRWIWnLHRNLZnZeSNoKNbfbzX033seVZ13F5l83U1pScaeJx+Nh8+wtnHvUeTxy\nx6MR6+ABaNSkCfkRnmLkqkKdpnDofPjxbKtG9+r2XJDuR4Q+oBqo8IrQGFNgjHkYu8zeEyLyhYik\n++wSssm9InIm0A+4BIgDrgCedOaMKqUqERUVRVRUZJNjCMwCnheRgGPqRaQx0N/ZT6k6beW8ebRw\nVqaILypmy7p1EY6o6g7s2JVtWdW/KMwvKiW+kmkkdcBc4FERSReRJOA57PnROT77nAMsj/JajBEA\nACAASURBVERwKjRuu+Y2clbkUlywb4HNkuISdi3cxf3/d3+EIquZP5cupU1iYlD7tiwpwcybF+aI\nVHUYY0qMMcOxU6muxZbFGAgsiGhgIbJnTw5RsaErIuyhbi9kcGyvY7nj6jvYOq/i6crbFmzjqn9e\nxZknRn6kb2lpKVFBvK8F0dGsOrANOw49FM+xx1T6tVIOZnPz5gQ3RigydcM8Hg+zp37D6Fce5rRO\nVb+WOrxNLLO+HcGkT9+J+KpaXkFNGDPGzBaRI7AXV4tF5KEwxJIBFANu/u588gBbwnAspRogF676\nP5LnJmA8sFlE5mPnjOcC8UAb4EhsnZ5zym1BqTogc+dOti5fQS9nCsWRiQl88upr3DtoYIQjq5ro\nmDhKSqt/0lVS6iEmuubLkobZ7cC3gLcYQBFwN/CSiJyEnaJ+NnBjZMJToRAbE8sjdz7CM0OfptVx\nrfZOo9j6+1bu/s89JCeGqq527SrNLwi6hkZ6TAx/LlhI9+OOC3NUqrqMMcXA58DnIhIHHBrhkELi\nwANaMnPZJmgamhFz0e663ckDcIh0Jz01naL8ImLiy/4dLCkuISU6leMPOz4C0ZW1+KefaBMbuGug\nBNjWvDnbG6cSk5zMgenpJPksrz5r4UKGf/YZAP93+eUc07Pn3sc6pqWxLTOLJdu3E5OXS5stW2lU\nzipaUXn57Ni8meatQjPqqzIF+XlM+nQ4f/4xn/ZJOVxySGy1ahJFu6M4t3MUK9ZM4vVHZ5B+UGfO\nufI2GjUOXAeuNgR9Regzquc84EHsaJuQcYYqvoIdJl0IzATeM8YsCuVxlGqoPB4Pnrr/N69CxpiV\nQHfg38BsIBE4CGgELAL+Axzi7KdUnTXi6ac5MfbvYdcJbjetM7P48eOPIxhV1a1dvoD01Or/uU+M\njSIrMyOEEYWec55xMHAucBUgxpih2M7kfGynz9XGmPcjF6UKhVYtWnHtxdexY6ld7njHyp2cedxZ\ndO5Y8zohkVJaGPxIu8axsWzZuDGM0agqmomtbRqQc+01uxbjCZujDu2OJ3dXSNoqLS0lMa7O3zwA\nILVRKkUFgevMlJZ4an2J9PJsWbeOLcv+oFn8vqsPZiYlsaRDe5Yd0o2o7ofQvWtXurRtu08Hz6cT\nJ/Liu++yOyuL3VlZvPjOO3w6ceLex10uF+mNU+lxcCc6dOvGtp49WNRZWNWmDYV+NYuOiovjgxf+\nF9aVAEtKSpg7YyJvPn0X7/a7keY7fuLizsUc1iauxkWnO6fHcVFXD1K8iI9fvIOhT93OzG8/pqiw\n5it3VVWVSz87o3oOw9bECNlfCudu2YNAH2AStjPpMxGZYoz5IlTHUeUrLS3l5oduZsCjA0hrmhbp\ncFQVuVwuXA1gdVRjTJGITAB+NcaUGcnnLG3c1hizPgLhKVWptcuW0Wh3Bkl+NXgOSUpk4o8/cuoV\nV0Qosqrbumk9x1RzpQmweakoN8MOA6/DBZiNMfnARL9tPwI/RiYiFS7H9jqWb6d8S2FuIe6MKC48\n88JIh1Rtmbt2EZNfCEHW8Yh1u8nPygpzVOGxfuUSJnw4iPN77Fsaa9qKPXT7x8X0OrF3hCKrPmNM\nwJIUTvH3340xdbPwTDUkJiYQF6KKArmZOziiU4fQNBZGBYUFrFxjaHl8y4CPx8RFs3nXFrKys0hJ\njlzJt79WrmLUc8/S26eDZ0uzZmxp2oSUJk3pktYcdzm1cj6dOJExEyeW2e7ddnmfPvtsj42OpkPL\nltCyJdkFBaxs0gSys2n/118kFhaSHBNDlz17eKNvX255/vmQrlK1eeMaJo8ZTsb2jXRqlEfvA2KJ\ndruxk4hCq3lKLH1SoNSTxeqlXzBs5rfEpaZz2oXX0LFrr5AfL5BKz7hEJE9EBorI3nfAGFNojFnr\nDCkMlcuAScaY740xHmPMN8D3QOQnKe4nZi2cRVRjFxOmTYh0KKoaXC6o41OUKyUiiSLyLpAFbBKR\nDSJyqd9uBwJVXuvWKew+wufnViLynZPj1ovIXTWLXinrj99m0dYd+M+ru6D27+ZU1+6d24gr2l3j\ndjok5bLgp8khiEip0PjXeZezfu4GzjjpjEiHUiOTRo2ic3TVLlBcWVlkbN8epohCLz83h4+H9uf7\nEc9xdttcSrK37fN1Yutclk8ewfABD5Cxs+L6J/XIZOy5ToOSEKLRN0XZmfTocnBI2gqX4uJinnr1\nSVK6VbzgQtPuTej3ylMUVGFEXijNGDuWT557lj4JicQ6HTmr2xxAgRxMzy5daN8yvdwOnlmLFgXs\n4PEaM3EisxaVPyEnOS6Obu3bcXDXLphOHdnj1BZrm5BA5x07efmOO9i+aVP1X5xjwU+TGPz4zUx6\n62GOTFzDRV089DggnuhyztNCKcrl4uAWcVzQxcUpzTcz5+PnGfTIDUz/5qOwjlaC4KZruYDjgFlO\nXZ5wKQX8b0WUAHvCeEzlY9yEz2lzWBvmLppbZ4pGqf3OEGzH7i3YqRJTgU+cwuy+gu7OEpFTRORp\n4DH2rej2PrADaOocq5+I9AnQhFJVEhsXS3mrkNZ0KHBtmjRmOEeGYB27bq1i+e3Hb2rekFIhcoh0\nJ397PicddXKkQ6m2woIC1i5eTMuEhMp39nFETAyfDxkapqhCp7CggC/fe4Vh/W6hc+lSekt0wIsy\nl8vFCe1jOaHxJj5+6V5GD36SnKy6PUUUQER+FJGpzvd9vrDXQ6O8+0Q61lAJ2d+/0mISE+vGNKdA\nsvZk8dBzD0IbSGxc8cIDcclxJHSO58Fn/8u2Wuyk9Hg8fDhgAOvHf0vvpGRifEbaZiQm0TYtrdLf\n1+ujR1d6nGD2iYmOpmObNvzVvPneba0SEjjTFcW7Dz/C8tnVm7G4bsUiBj32f6yZ+g7nd8jhtE6x\nNEoI3cigqoqPcXNChzgukkLyl37JwEduYPGvP4TteMG8Ug9wHdAbmCoi44EBxpglIY5lHDBZRM4G\npgCnAWcAz4T4OCqAT8Z/QkmTEtzRbpI6JDJwxEAeuOmBSIel9j8XApcbY6Y4P38nIvnACBHpaoyp\nTqfvEdglkvfeDhCRVtj8cpAzHHqJiIzB1vwp/7aEUkHIysikUVTgk6NoPGTt2kVK08gV4wuGx+Nh\n64ZVHN+55ndeo91RROXvImdPJkmNUkMQXWiJyBr+7gCu6KzWY4yp+3MEVKW805uT6v7Kb+X6fPBg\nDq/G8N3UuDjy1q1j87p1tDrooDBEVjP5ebmM/2AIm1Yv4Zj0Qo7oFkswZUAbJURzXhfYlW14//nb\nSUprx4XX309q0+aVPjdC/gSuB2Zgp4X6/jJPBOYAO4nUckNhUFRcUuZufnW4YhJY/9cWukmnELQW\nWr8v/Z13P3mX5r2aEZccXD27xCZJxBwRS79BT/Hv867g5KPD3/n83lP9aL5hA52SyubANrt2sXj1\najoeeCCJseX/xnLLKZ5clX08Hg9bMjLYsnkzPTds2OexeLebc5KS+O711ynMzaPnKf+o9Hhec6Z+\nxezvP+F8iSLaHdIywjXmcrno1jKOrulF/DjhXf5at4re/7415McJdpxSqTFmILYgKsACEZkhIneI\niIQiEGPMDOySga8BOcBQ4EZjzPxQtK/Kt3r9ambOm0Gzjs0AaJTeiHW71zJ91vQIR6b2Q/H8vcKN\n131AATCgOg0aY14xxtyGLerudTiQYYzx/YuyDOhanWMo5fXXylWsnDuHVuUsaXx4dAzD+/ePSBG+\nqlgyZwZt4nNC1t7h6cVM+uydkLUXYrdhR/W1A77DjvIr70s1EB7slIr6aM/u3WxZuoyW8dUbzXBs\nQgKfDRwU4qhqJj8vlzFvPsvwfv/HQbm/c1FXaN206l0CTZNj+GcXN4cn/MnHL93NiJf6krGz7k1P\nM8bciB1F3AFoCbxsjOlnjOmHXW14iPNz/wiGGTLFxcXk5AcuQFxVjdJa8+ucunV56PF4eGv0W4wc\nP4JWx7UMuoPHKyY+htbHt+bzGWN55Z1XwjqjIjsri7lLltDJ5zzlqx079v47fccO/pz5Exv++IMl\nf/5JZm4uX0/f95rw6+nTSQxiFKF3H//nfzV9Oht37mTRihW4li9n/W+ziPaZvuSNxx0VxRlJybzx\n9rCgX5/H4+Hn78ZyftfAo//qCpfLxWkdo1m7YDo5ezJD3n6VXrkxZoMx5ipsZ89CoB+wXER2VPjE\n4NsfY4zpZoyJM8aIMeazULSryufxeBj4zkDSD0/fZ3uLHi0YM34Me3J0tpyqVb8DfUVk7/ABY0wu\nduniW0TkOqp/V8v3LlkTbN0fX7lA1ca9K+XweDxMHDGSMc8+y1nx5f83So2L47DsXF6+/Q5WLVhQ\nixFWzY9fj+awNqFbvaRV4zjWrVhQJzu3jDHfAdc4P77pvdAK8NUgLrYUZGRm4E6IwqwxkQ6lWr58\n402OrkFB0gS3m5idO9m6LvLrFxQXF/PNqMEMe+pmOhUu5sKuUbRsXPM7740TYzi3s5ujk9fx0Ut3\nM3rwU+Tnhq7jOhSc3NMDOz1rqYgELMTcEIybMIXoJiGY/wu4Y2LZtisj7DVNgpWdm83DL/Rldf4q\nWvZqSVQ1OxZcLhcterRgW+xWHnzuQXZm7AxxpFbW9u1UdirtKi2ly7r1dF26jN3L/qDY42HTrl37\nvOd3XHllpcfy36eouJgVGzZQUlJC4pIlHGpW0mr7jgrHJOaXllJahU6vvNwcoilmzPzsfbbX1Z+T\nokvYufWvMq+jpqr1v9AYs9wYcxe25/kfwIshjUrVmu9mfEdM6xjcMfsW1XK5XKR2S2HUOL1xqWrV\n3dipodtEZG8RD2PMNOAO4B3g82q27fsXLQe7PLuvZCD0XemqQfN4PPzy9Te8ePPNFM2YwdnJ+85t\nDyQ9Po4+MTFMe20gg+67n01rqlxHPKy+/WAIBydlhfwO2Imtixj56qMhbTNUjDErgLnYJdNVA/fx\nNx+R3iudL76vn4u3blu3lsZxNesIOSw+nonvjwxNQNX0x9yfGPToTaRu+5mLu7lokRqKyTz7SkmI\n4Z9dounKH7zZ7xZ+nvBpyI9RE8aYTGdUzy3AOyIykmpen9VlP82aS3Jam5C150pO59spM0PWXnVl\n7cnikQEPE3twLI3bNA5JmynpKTTqmcyTLz/Blu1lFpmtsdYdO/LPc8/jtz1ZlDgFBC9ovu+0Ru/P\n0R4P7Tdt4uKsPbj/WM7CFSvYuWcP5//jHxzTsyf/6lN+Kct/9enDMT17AvDPk09m5caNmOXLabt0\nGZdk59AsM2tv5055x99TWMj3ebkMGDgw6NeXmJTMAZ0PZ+uecooj1iFmWyFFKe1o26lbyNsOtvBy\nQMaYEmPMTGOMdvLUU7Pnz6JJ28BJKblpMn9tqXlVc6WCZYxZAAi2Q2ey32NvA4c628dX8xDefLYY\naO7U5vHqjh1JpGqBx+PhmSHP8PqPQ3lzxhu8OeMNnnjt8Xo1fWLO95N4+dZb2Tjuc86NiaVjOVO0\nAomJiuK45GROzMvjq379eePBh9i2YWMYow3O5LHvsnvlL3RvFbpRPF7pqTG08Wzkw0FP1cni/saY\no40x9XNohwpa1p4slqxaStMDmrI5YxMbN2+o/El1yAZjaJJf85V4GsXEkLE59BeQwSjIz+O9F/sy\n7+shXNKliPbNQ9+54y8tJY5Lu7nYNW8sQ566nd076tZKXD6jeoqxNQTrzx/DSsxZsJSC6OSQLjzQ\nqFV7vp86I2TtVYfH4+Gp156iyeFNiG8U2kLQsQmxtDi6Bc8NeTYsfy/PufEGjrrhBr4rKWZVECPc\nXEDLnTvpZVayae1a8ors1LvL+/QJ2NHz73PO2Wf59LVbttBkzVq6/7mGxCBG9BaWlDAzO5sFzZtx\n56uv0qxVq0qf4+uiGx/kgkuvYNzSEjJzbaz/Oix5n30i+XNeYQnJCXHktjyB6x/8X2Uvp1oq7eQx\nxsTrSU/DlZiYRHFh4L8jHo+HKFeDu5nQYHk89qu+c+5qfWSMGey7XUSaAmuMMY8YY86vRtN7zy6M\nMauA6cALIhIvIicAlwPBT/pVNfLGh6+TGZ9JRmEGu/J2sStvF8VpRTz/+vORDq1SG8xKXrnjDlZ+\n9BFnR7k5JKn6J6/xbjcnJydzVFYWYx5/nPefeYaCvLwQR1y514cO4e3nH6DQTOIfHaLDNiy5e6sY\n2pcs5/br/82OLXXr4lpE4kQkvZzH3CLStrZjUqHl8Xh4ZvAzNO1hC5+n9UzjxTdforCo7k0jLM+P\nn35K12rW4vGXkJvLto2127m88JfJDHniZg6LX8vJHWJxVzLyMdR6tYnjzFa7+fDFe5k6bmStHrsy\nzvnPTcCRlK1PWG+NHvsVqW06h7RNl8tFUVwTpv1cvZWXQuGbqd8Qle4iLjE8hX2j46JJ7JjAqC9G\nhaX9nqecwoPDhpF42mlMKCpiYXb23pE95fEAHpdrn2lbl/fpw0M33USTlBSapqbS96abuKx3732e\n53K5KApiiumewkJ+zM5mZkICffo+xK0DBpDarFm1Xt+J5/yb6x8byqw9bflhZRHFJZEf2ePxePhl\nbSE/bG7KRXf9j39ee3fYVl2t9N0WkfbYooTHAi2wF0rbsXfCxxtjvg1LZKpWnH786YyaMpK0Li3K\nPJa1NZNecngEolLV4bGpN9Jh1JiI3Aj8E/tiJgKfYVff+wdQIiKjgVuMMVW9len/Bl0FvAvswp5M\n3W6MmVfD8FUQJv80GbPd0OKQffNOclojdu/ZxagvRnHtRddGKLryFRcXM+bll8n4YzmnJSQQm5xc\n+ZOClBgdzWnJyexYu47Xbr+dM6+8iiPOPCNk7ZenpKSEiR+9wZJZU7j1mDiaJof/jnrbprG0b5TF\n54MeIrmVcMlNDxEfwVWORCQRGAJcDcSIyF/AfcaYsT67HQisBtwBmlD1xKvDXyGqDcQ7RVGj46JJ\n7prEs4Of5ekHno5wdJXLz8lh259rOKKKy6aXp2dsLF++8SY3P/9cSNqrSF5ONh8N7U9S7nou7xaD\nyxX+XFOepPhoLuwGi5dPZPATv3H1XU/StEVo6sVURRjPd+qM76f9Qn50KvHu0C9dnXLAwYz5agIn\nHXsEbnftp+bFyxaR2j68K0amtExlzdLwTel2uVycec01nHH11Sz4cRpTxo0jKSuLIxITifd5T/Oj\no1nfuhV5iYm0a9OmzKpbx/TsuXdqViDtWrZkLbAwNYW0zExabt+xz0iTzXl5LPJ4SG3blituvaXK\nI3fKk9K4KTf2fZG1Kxbx2Tsvc8aBBTRPiUzuyc4vZuKqKE6/+EYOPT785bcq/MSJyOnAN9gOnZXY\noYMnAfOwSxKPFpGVwPnGmAbT47w/6dWtFyO+CNwxkLslnzP/eWYtR6SqzUO9H8ojIo8AjwEjgUKg\nP/BfoAS4BLuO6v+AZ4CHqtK2MeZ6v583AeVPJlZh4fF4+PqHr0k/tmzHMkCTDk2Z/dssLjn7kjq1\nvPGm1asZ9cL/OLy0hF4h7Nzx1zw+nvM8HuZ8OJr5M2Zw3ROPE1PBEqbV5fF4+GniGOZOn8gRafk8\nenqjfR4P97Dlq45KAWB71h+83f8W2nU9gnOuvovoGhSTrYEhwJnYmhhbgCuAT0SkjzHGd9poeG63\nqVrxwy8/sD5nAy0OSttne1LTJHZl7uajbz7iyn9WXkg0UjweD+/068/RIbzrmxIbS9SmTcz9fhJH\nnh2+i47Fv01l0tj3OL1dEc3SI9e5469H61g6FmQy+pX/0v24szn1wutq7dhBnu+8QDXOd+qKXbsz\n+Xz89zTpemJY2o+KiiI6rROvDXuf/95+Q1iOUZHUlMZsztlEYuPgp2pXVWF+IfE1rL8VDJfLxWGn\nncphp53KhhWGr98ZTlFWFq0PbEtpcjLRSYm0bdmywiXVK9OuZUs86els37OHpdu3Q34+RVu3sXnX\nTjr07MHtN99MfBWmvVfp2J17cs+zwxn21P9xYUpkrpVmrPVw46ODSG3avPKdQ6CyMZKvAC8ZY44x\nxlxtjDkNuAE40hhzKfbO1g5geJjjVGESFRVFfEzgYb/uoijSmqYFfEzVPR5PCaWl9X4K963AjcaY\nO40x9wPnAp2Ax40xXxhjPgHuBOrumbiqUEFBAaWxpRUOT3U3drNmQ90oRmxXzRrBmKef4Wy3mwMq\nWDkrVFwuF0cnJ9Fx02Zevu121ixZEtL2l86ezsBHbyRnwTgu6VJSK/UwypOWEsdFXV002/UbQx+7\ngWlffRCJMC4ErjfGjDTGfGeMuQ47ym+EiDSq5Lmqnpg4dSJpXQOfXDdt34RZ82fVckTBKyos5I2+\nfemwcyfNQjRVy+vYxER+++gjZo4LTxHqbz8Ywvzxb3PZIdCsFkYKVlViXDQXdnWTuWQC77/6WG0e\nOpjznbuop+c7OTm5PD7gNZI7HBG26SgASc1asmprNh+Pq/2JJZf2uZTMP4Nfr2P9wvV89vhYPnt8\nLOsXBbey3e5Vu7no7IurG2KVFBQUsHjxYhauNLQ+/njan3MO612QW1qCHHhgjTp4vFwuFy1SUmjX\nsiXrMjMp6tCezuedR3S7dvz0yy9s2LAhbKumbTCLiPJErh5gfIwLs+DnWjteZbfMugKX+W0bDbwn\nIgcaYzY4PdG/hiU6VSvKS77hTMoq9HZvXour/k/XSgfme38wxswTkRLgD599/nD2U/VQTEwMrqKK\nc0tJdgktW7SspYjKt3PLFkY++ywdcnI5K4yjd8rTIj6Oc0pLmfDSyzTt0Z3L77uvRkPSCwsK+Hho\nf6Kz/uSig924o8J/dzBYbZvG0raph8VLxzN03m9cc+/TpDap3jz8aoinbP2L+4AzgAHYjmVVz3mi\nKu5c9kRFvl5DIItnzGD8yPc5BhfpIZqm5cvlcnFqcjLzv/6ahb/8zHWPPUajJk1C0vbS2dPYvfJn\nTu0U+kLuoXZEm1iWbVnF92Pe4ux/3Vobh2yw5zubt26n/0tDiD+oF7Hx4Rvl4tX4oG5MX7iMHbs/\n5M4brqq165eWLVqS4k6huLCY6NiKL6kXTlzEwokL9/487Z3pHNrnUA7tU/4Up9KSUmLyYunaqWvI\nYvaXk5PD3LlzycjIwOVykZaWRpcuXfaea/Ts2ZPVK1bwzcyZnHvCCUSHYFrcxi1bWbRuLedddhmJ\nSX+P2M7Pz2flypXMnTsXt9tNp06d6NKlC1E1rNuVn5fLN6MGkbF2EedK5GrNntrBzU/TP2bpvN+4\n+KYHSWncNKzHq+yV/gUc77etk/O8LOfn5sCeEMelalGpJ/CJTXnbVd30p1lKHIX1anWiAFYA/+e3\nrRPgW/y9O7C11iJSIeV2u+nVpRcZGzICPp69I5s2TQ+keZPaGc5anskfjmbEww9zclExEqbhw8GI\njorilORkmixbzku33caGFSuq1Y7H4+GtZ+/lkJjVnNw+ptaLnQarR+tYTm+1i2HP3ktBfq0VoP4d\n6Csie69EjTG5wI3ALSJyHQ2h4Nl+LiUxlYLswKVNiguLiY8K7QiZmtqwYgWD7r2Xue++x7lxcaQn\nhDe+w5KSODJrD8Pvu5//Z++8w6Oq0j/+mZKZyaRPeichXAglCb0K0qSIKKhg3dW1r4UittW1oKI/\ndQURy9p2VVTAglhASgABpUMSIISbkAYhvSeTTKbc3x8DgUB6ZiYJy+d5eNY799xz3pudOffc97zv\n9/36jTdsIgB/8ngCfby7z1pS8FWSnZ7qqOEuy/XO408+wwtvfYBL1HA0Lu7kJGxrcN5ex57hfTlR\nYOS2u+4lr6CoQ/fQFm6deRvFqSXNtrnYwXP+80QSNyQ1eV1JVinTJthHVSAvL4/33nuP+Ph4vLy8\niImJoaamhoCAgHoHz759VlHrnr17M+m66/h1X0OR64TMzDYfV1RVkZiZwY23387RY8canE9KSiIi\nIoK4uDj69evHnj17+PHHH9myZUu7KowZ6+r4/uM3+PjF++lRc5ipvZUoFJ239pHJZFwV4cRg5wy+\nev0Rvlj6HDXV9nOhtHSnrwAfCoKwXBCEewRBeB7YAqwURbFcEISngC+Bz+1m4RUcQONr18ttRauv\nquK5u+9uuWE35NCODfjJShnsZ2Dtp292tjkdYQHWl6pkQRBWAoiimCWKoglAEIT/Az7BOu9coZty\nz5x7cCpRoS9rWLazrqaOmtQaFt23qJMss/LEgw9SsGUz01xc0SqVrCtquGBs7bEEVHl5sa5GT9UF\njqL29BfqrGGqQsk3ry7hjVfbLpK6fd0X9HEpwb+TBAfbgqtGyeTwOla/b38x2LM8BkwFCgRB+Pnc\nh6IobgcexjrnfO8oY65gH+b/bT6FCUVYGqkek38on0f+2jUCtnLS0nj38cdZv+Q1rqo1MMzV1WFO\nWTcnJ6a4uBBwQuTdvz/Mmrffps7Qfs3fMdPmsO2UhrJqow2ttA+1RjM/HpcYP2Ouo4a8rNY7ufkF\nPP7C65wuqkAXPQonleMjRV39Q5G5+PL8Wx/w6dff2y3t50IG9BmAvKbpyKHspOxGHTznSNyQ2GTq\nlqnYxMSREzts48XU1tayY8cOvL29GTBgAG5uLWcl+/n7I7dBFE92Xh4jxoxpMTpHLpfj4uLCwIED\n0el0xMfHt2mc/VvX8e5z9xJSdZAbomUEeHadyGVPFydm9FEQ65TGRy89xOZv7aN60+xfWBTFz4Dr\ngQjgKWAG8B7W3S2ASKyhzE/ZxborOAR5E1+Dpj7vriTv2YO5Wt/dI10u4c/fvmXf+i8YEa4kTKdC\nlpfItx+97pCHm60RRXEr0Av4AChtpMk0YBnwnCPtuoJtkclkvDD/BSqOVWGqs/4eLRYLhQeLeH7B\nC50lvgtYq02V5ecT59Kx9Cy9SkViZCQ1cbEQGEhOb4G0kBA6kg3uJJczxcWF9CNt1+g5k5VGuK77\nFIbycVdRW916rYOOIIpiAiBgdehsvujcR0Ds2c9/cYhBV7ALnu6e/PXGv1KQWNjgA5eWqAAAIABJ\nREFU88LkQqaPvZaw4PBOssxKfvYp3n18ET+9/AqjqvWMcXVF3QkVgwD8NRqmabXojh5j+UMP8e3S\nZe1aO+n8gnjkpffZUxHMbyeMfLm/osH51YerOv3YYLSw/WQdG095cOei/yNqwLAW7so2XC7rHUmS\n+ODz1bzw9scQ2J+IMbManA+OG+/Q47Ahk9D1Gcnh7EoefXox4snMVt9Le1HKm16z7F3Tcon3ptoo\nZQq7pJ5ZLBYkScJisbBv3776f3A+egdg2LCGvwWT2UxCZmb9P2gYrRPXo8clY13cvkivp7SkpNH+\nz41/sT3V1dU4ObU+5fNMpsiBjV9zU7SFIK+uu7Glc3Vidl8ZRUc2kbBrk837b3ElLYriJqDRkUVR\nfMDmFl3B4Xi4emKsrcNJc/4HZLFYcHayv8CoI9n5008McXbm92+/ZeKtt3a2OR2muqKMVR+8ilvt\nKTxkldyyIguAx6b0wLf0MMv/+SA337uIoB69OtnStiGKYj7wriAIMkEQfAAVUCWKYoUoik0nL1+h\nW6FWqVl4/0L+9flbBA4JpOh4EX+58S/oPOybo9wSCoWCcE9PDGZz/QvW9T4NU8eaO65xciJq6BCy\nPDzoGxSEk1LJzLFjASj38eGomysxFRWYc87U1+JuS//5dXWMiO7T5vsKDIsiLy2FSN/u4ejRG0yo\nNI6rriaKYjnwdRPzTjLwjMOMuYLdGBE3gqTjSaSeFvEM8aSqqIpA50BmjJ/RaTZJksS6Dz4gc98+\nxmicce4E/a+mCHR2JhDISTrCmw8+yI1//zvCoEFt6sPZxZX7nvkXRfk5vPzPp1mXItHbw0CvTqyy\nJUkS2cUG0ksk4vN9mPaX+wnr1c/hdnT39U5eQRFLln2IxT0EXe/hnW1OA1z9QjDr/Hnr428Y0i+K\n+++8WGLWNphMJmpN9qlwb5SZKK8sx8PNtmXatVot1113Hf/9738B8PDwaHFzLTkxEVcbVPnydHPj\nWEICQr9+OLegMabX66mqqiI8PJyrrrqq1WOkHPqDU8U1rEmou+TcxZU+z3GxA9iR7Xv7yDh66A/i\nxti2wmGL7kFBEMZgDWUeAfidvaYISMK6q/Wfs7nr7UYQhOewlhG8EDmQKYpi7xau7QFkxMfHExIS\n0hEz/mfJOJXB21//i4C480KnRWlFXDtwBpNGTepEy2zH5i+/pDh+GwNctKyvquLOlxcTEBbW2Wa1\nC7PZzIav3yfjyG6u7mHh50MFfL4zp0Gbv14VzJzhgWzPsKDyi+Lm+57CuYORCY0hs8MWgyAI07GW\nER2JtYToOUqAeOBtURQ7tQzKlXnHdjz31nOoop2oTKrizWe6RqphwanTfPLCC4yVyfBqQyWbjOAg\nanx86BkcjKqZBVOZXk/GqVP0zDmDR1XjD/7GSNbrKQ4I4L5XXm6zALOhtpZPXryHmX26R4TmznQD\no+94nnCh/yXnLod5RxCEAKypGBOAWuAb4BFRFJsMwbwy79gGSZJY8PICfIf7kLc3n6X/WNqp0YOf\nvvACfqdOE2kHUWVbYrJY2FpZwdTHHiO6kR34VvdjMrE3/kcSdm9FaSgj1tdEsM4xqRTFVUYO5YJe\n7k6v/kMYN/MONM6t01y7Mu80nHfKyit4cvGbuPUa0SmpWW2h/Ew6ffydmXffnTbve+WPX5JUkohH\nsGej57OTstn+ye/N9nH1veMIi7n0naSquJIgYwiP/vVRm9jaGGVlZezZs4fa2loiIiJwd3e/pM2J\nI0cQjxxh/GDbVEqrqq5m84ED3HjHHZc4eiwWC6dPn6awsJDw8HAGDRrU5vWOJEk8dNdc/DW1uGka\nXtvVnDwTeqrYkq3msZc/bHQu6si80+xTTRCEW4AvgLVn/zcImANsAMqA+cCTgiBMEUUxpb1GiKL4\nClb9n3PjqoA/gDfa2+cVWk9EaAQukmu9OrwkSZgLLXbJA+0Mfvn4Ywr+/JPhZxXcJzo7859/Ps8t\nix4nYsCATraubRzeuYFtP69isF8tN/RV8eWuvEscPED9Z3eOCaaoQuTfLz1A77gxTL31wS5dNU0Q\nhHuBFcBqrIuP04ABcAaCsS5MdgqCcKcoiqs7zdAr2Iyw4FAyyjNwcWDURkv4hYawcMW7/Ofll9Hl\nF9C/FS9fElDq4cHA8JZTPjy1WmJ69eKIycTAVjh5TBYLW2v09J84kZvvbN8iVa3RoPEM5Kv9qSiV\nlzp6utLCR5Ik1G6+jTp47EEnzTurgGOANxAA7AT20E30N7ozMpkML3cvLGYLbhq3TnXwnMnI4FDS\nEf4eFFT/2bqiogbRe13lWCmXM9HNnaVvvcVHa9a0+56VSiWjp9zE6Ck3UV1Zzu8/reRASiJaSwVD\ng8FTa9tKXPo6MwdPmyg2uxEQ1pcb5t2Fzi/QpmO0h+4+77yx4lNcI4d2eQcPgEdQJEdSD3EiLYPe\nURE269dsNrM3YR8BI5sugBYWE4Z/lD/5aY3rZ/tH+Tfq4AFw9XbjxJ4TGOoMqO30d/b09GTq1KkY\nDAb27NlDWloa0dHR9c6XPTt2UJaXZzMHD4CriwuThwzhh5VfMeX6mfj4+QGQm5tLbm4u/fv3Z+zY\nse0eTyaT8d5n3/DdR69TdSqJqyOVqBpZ91xIU2sUe7U3my3syTZxSB/GvFcWo9bY3snf0pNtMbBA\nFMX3zn0gCMJq4D9ACFYtnk+Bj4CxNrTrZeCoKIrf2rDPKzTD/bfdz9JvlhIQ50/xyRJmTp7ZpZ0B\nraG2uppPX3wR38LiegcPgFqhYLpWy89v/YueV43h2nvv7UQrW4ckSaz5cAnkJ3FTtBMymYo/xNJG\nHTzn+HxnDpF+WkYLXtzYF1Kyt/P+4mTue/otVDYIubQTzwB3iaK4qonzHwmC8BCwBOvC6ArdnLyC\nPDQRGvRZDquk1Co0Wi0PvfYa3y9fzvHDCUS3UGFLBniXlnEsI5OeIcFomskfL6msIivnNFG5ea2y\nZYu+mtmPLyIipmNO6am3PMAbLywitPENxy5Dmd7ClMnjW25oOxw67wiCMAAYCFwjimIdkCEIwrmd\n9Ss4AIOhFq1ci8nUuYLAOn9/zN1orSUBChs6xVzcPJh++8MAFOaeZtO3H1OckongUUPfAFWH1qFZ\nxQYSClU4ewUz6Y6/Osxp3Aa69byjrzW0OgqqK6B08+V4arpNnTyrflmFJrT59XR2UnaTDh6A/LR8\nspOym3T0uPV05ZPVn/DwnQ93yNaWUKvVjBs3Dr1ez8aNGwkODmbv77/j7+rKVXFxNh/P1cWFGaNH\nsXn9egYMGYKkVOLt7c3s2bNt8v6pUCiY+9CzZKceY+3nywlXlzIopGNziq04kV9HUomWKTc/SL8h\nrU9DaystzdThwMYLPxBFcaMgCL5AqCiKWYIgvAkcspVBgiD0A+4G2i46cIV2ExkWibPFGUmSkEos\nTBzVvaN4TiYksPqd5YxVKPByufQhpJTLmejqyok//mR5cjL3Ll6MtgvlwV/M+q/eQ1eeRJ8e53PY\nl2/MbPG65RszGS14AdAnQIVPRQGfv/0P7nvmX/YytaMEA0daaLMDeNsBtlzBzuQV5lFQUUCgOhC9\nrJqUtBT6RHWtqX/6Pffw0cMPE92KtuG5udQWFpJeWYHk7k5USEiDtK3S6mqyT+fgVVFBXG5uq6Xt\nlR6eHXbwAAT36EWv8CCmhVW0uKt1jkt2nyQokHzI1fqj0zZcQlTXSQyPKSJEfgZlK9dRje2GfZcs\nY/R0h1W4AcfPOyOANGC5IAhzsO7efwI8b6P+r9AMJzJOUGGuxEXmgkFt4I8DfzB6yOhOsUWj1TLh\nqjEcSzpCv7OO5LZodDny2GyxsElfzRNPP93k/XQE38AQbn/sJSRJ4o8Na/ju9w3E6Gro3UbtnjOl\ndezOU9FrwFgeeOyBNgm2OphuPe8E+vlwpqIUZ3cvG5lnXyzlZ7hq+KyWG7aBX9f9Sr+5feuP039P\nJ3JcZIPjg5tbfkXeu2YfYTFhjV4fOS6SlD0pSJLkEAeFVqtlwtixvLNsGaP6RBPg4223sZRKJVNH\njuSPpCSMMjnTnrB9ZdWwXv2Y98q/ObDtF77f+D193PX0C3TqFGdPelEdhwo1xAyfxoJZf7W7DS2t\n8k4CN1z4gSAIw7A688/VeO0NFGI7XgVWiKJYYsM+r9AK+vTsTUVhBToP7y7h6WwvSb//zrq3l3Kt\nRoNXCxErvbVahlRU8s78+VRXVjrIwraTkXqc3v4dX6j4uKuorSy2gUV2Yy+wRBCERtV3BUHwBF46\n2+4K3ZgqfRWvvvsKPrG+APgO8OWd/75DbkFuJ1t2nuP79vHO/AXEKlr/29OYTPTNzKLn8RRSUlIo\nKCtDkiRSsrMpSzlBzIkThLfBwQPgU1XNh888Q2VpYwVY2sbMOx9hZ0brq+TUWpScMvtzyNSHPaY4\n9jOCCt0QNIH9MOqiG/xT+EdjCRjEQflI9pgHcsDYlwxLCNUWDa0t9pecZ2Dg6MltzsHvII6ed/yx\n7qinAb7AROABrPqHV7AjexJ2885/l+EXY513fPr68PWGr/h1+6+dZtOcBQtgQH8O66s7zYaWqDOb\nWa+vZtb8+UTG2FcPWCaTMWb6XBa8/h9qAkezPb3181VCjpEUKYqHX/6Ua+94pCs7eKCbzzuP/O02\nak4dwWLpSM1Ix1BVlEO/nmF462wXxpqakYpFabFZf82h8Jaz+9CfDhmrvKiIFU8+SYSvn10dPOeQ\nyWSMHDAAeXU1K197zW7jDBk/g/mvfYb7wBtZm6pmX5YBk9n+//9JksTRMwa+S1FQEzKeR1/9lImz\n73LIe3ZLkTxPAt8JgjAWSMTqdb4JWCqKYrUgCMuBv2GdhDqMIAi9gSlAl82fSc88RWhwIE5OnZfD\nbS/CgsI4dOAQoV7dU5AYrD+mnz/7jOtdXVv9A/JQqxlfV8fnL7/M39/omjJQw66azME/v2FI2Pkd\nrcem9OCF71Obve6xKT0aHOeWGfEJ6tLVtu7FKuieKwjCYSAL0AMarCmiQ7DmrU/vNAuv0GFyC3J5\n9d1X8IrzwkltnUsVSgUBw/x5efliHr3rMaKjWhM7Yx+O/PEnW9esxrWsgulaZxTytosVa4xGavbs\n5TPlAcwWC1d5eDKunS8ccc4aKoqK+WThQjzCw7nhoYfQ+TetAdAcYb36IffuRV55KgEe5+cTkwRl\nFg+K0VFh0WKROSEpVDipNXh6eRDuokbdwnNPIZPh5+WKn5c1OsdotlBRbSC1vIJavR6ZuQ4sRlzl\ntXhTjE5Wjkp+fpFVU2cmucKD+TPvaNe9dQBHzzsmoEAUxbfOHicLgrAKuAZ4x0ZjXOEC9DV6ln66\nlHx9HoEjApGf/U3L5XKChgWxOWEzew/uYeF9j+Pp7vh8xjkLF7Llq6/YsXkLV2m1XWqjrcpoJN5k\n5G+LFxPQCs0xWyGTybjuL4/xy0o5Gbm/E+HbvAh+lcFEriyE+xe+6iALO0y3nndcXLT8/e7beP+r\ndeh6DbaRibbHWFuDsuI0jz3xD5v2u3bTWnpNbbievjAK59yx0kvZovDy8DnDmrwewCvci407NjFq\nsH0jDo11dbz/9NOMdPfA4G1/B885FHI53t46VIcO8/3y5dz4mH32O2QyGaOnzWH0tDkc27+D9b+s\nQmsqYUSoDFeNbd/rDUYL+04ZKbJ4MGzcbOZPmuXweb3ZOxJF8RdBEAYBDwHDsYot33eBAFgRcJso\nij/ZyJ6/AZtEUSxqsWUn8cqby3j0wXsYOKBvy427GdlnsnHxdqGkpPsGUemrq3G1tD2k0U2lwlDd\noSJxdmXYpBv4JiWJQ6ePMSikfWVHs0uMHCz14sHnnrKxdbZDFMVUQRD6AzOA8UAE1h2nGqxhze8B\nP5zNJ79CNyTpRBIffPkBAcP8UaobPoKUaiUBIwN4d+VybppyMxNGTnCobbkZGXz5+usE1dZytdYF\npWv7xaDXpJ9kVXo6Go2G4cOH887v28mPjGROZM929eeuUnGNSkVFzhm+fvIpNKGh3PXiC+0SjZ3z\n0LMse3E+fZ2DqJM5IylUyJRq3D1ccXdzIcBZhULe8cWIk0KOt7sz3u7nBQUlSaK61kh5VQ2nKisx\n1xmQWepQSbWcyDjFnY/8w+ELoU6Yd9IApSAIsguq2iiBrhvK0U2RJInVv65m54GdeEZ74O/ZuHPU\nN9qH2ioDzy79BwP7DOLum+52dDQZk26/HQ+dju2rVnO1i0uXcPRUGY1stZh5bOlSXD1sW8a5tYyY\nfCPr391BhG/z7bKLDcSNHOcYo2zA5TDvxPXvQ7/I/ZwsyUera9/Gg72pzDjMq08/YtPfkyRJ5OTn\n4NejhS8lVuHl2GmxJG5IbPR87LTYJvV4zqFUKcmrzMNkMtlVKH7VW28xTAKFRoPcBmuAtiBhza7Y\ndvAQp1NTCell3w3pfkPH0m/oWPJOZ/Lbqg/RZ2YzOtSCzqVj0X/VBhM7s0By8Wfy7fcQ0bvzCvy0\n5puiAxaJomi4+IQoiottbM8NdHGtDaVaw4HE5MvSySOmi7jFuVGc3X2dPC6ursh8fSivrMSjDUr0\niVXVDLq2aweH3PrI82xa/REbErYyOUrRak2eUb082ZttpMYtiodfXOzwhWtbEUXRCKwVBOFHwAdQ\nAVWiKJZ3rmVX6CjHxKN88PX7BI0KQq5oPDpGoVQQNCKIH7Z9D+AwR09Jfj7/WLiQnk5OZMvkZNee\nf+RdrFFxjnVFje9HGCrKWZWeDljLBZ+LGliVnk6KXk9sQMPKLm3t/3ofH/LO5LJs4UIWLV/e/I1d\nQFZWFomJiZjNZvqOnMbxo4e4bnxfh75MymQyXJ1VuDqrCPY9/9K468AxQvqPYe+BBDiQQGRkJP37\n93eYbQ6edzZg3VX/pyAIr2NNe58L/NUOY/3PcuLkCd7/8n2cgpQEjWy5mpLGVU3QiCDE3BPMXzyP\nO2bfyfDY4Q6w9DxDp03DZDKx7/sfGhSM6AyMFgvxRiOPLes8Bw/Asb3xhLq1nFYR6qXmYOJeho2/\nzgFW2YbLYd55+O5beeTZ1yjNTm70fHBc4yL6OQnb7N7eVGdgcExffL0bzYhrN6t/XY1TQOudLbHT\nrCmOFzt64qbHEjO1demPLj20fLL6Ex68/cHWG9pG/khIJEwmQ6qqwoyEpFTiZDYzc5zVeZqQmUlc\njx717X/YtQul2Zqul5WTQ7XZzNEjR3j0jjsYHhPTbHsAk0KB0mxmYN++eFRUADBYrWbn2rXc+uST\ndrvPCwkI6cFdi16nuqKM7z99i9qck1wTJW9XBPfODBPV6kBmPbYAn4BQO1jbNlpzB5uB7YIg2DWH\n56yYcxTgmKTDdpBfWIykciX5RPMpMt2RiqoKqkxVyGQyzBoTx9OOd7ZJ7ebexYvZIZdTZrjEL9ko\nyXo9Ut8+jJtzs50t6zjXzL2fyX99hu9SFJgtLYtcSBL8kGwhbPRc/rLw1S7v4AEQBGG6IAhbsYYt\n5wOngFJBEAoFQVgtCIJNV92CIDwlCEKWIAh1giBkC4LwjC37v4IVk8nEe1+8R9Dwph0855DJZAQM\nCuDbDWsor3CMb8/T1xdXd3dKTCYkqf152tllZfUOHoCwsDAqKirqy5Em5OWRXVbWIVtrzGaSTEau\nuuaaVl9z5MgRkpOT6du3L3FxcQyIjWXg4GHs2JfUIVtswdETGWjddYwYNYaYmBgGDBhAWVkZ27Y1\nvqi3B46cd0RRrMaaIjEJqMCasvGcKIq/2GqM/3W+XPsl765ajvdQHV7hbROG9Qj0wG+EH19tXMmK\nL1bYycKmGXnddbjGxJCm79zo4q16PXc89WSnOngAjhzYRU+/5lO1ANyclZTln0JqrQBYF+BymHeU\nSiU+nm5IDtA3aSsmfTl33jzTpn3mFeax88DONs8rsdNiuPrecTi7O+Ps4czV917dagcPgJu/O0fS\nj5Cend5y43YindVXkgGKzEwkiwWzTNbibyrx+HG279mDyWSipraWNz75hDUbNrQ8HmCSyZCnpxN+\nxqrHqMSaleFoXNw9+cuCV7h67jw2p7X9u7wny0j48Ou57x9vdwkHD7QukgfgIHBYEISXgXdFUbS5\nypYoioVAl34D/fSr73AJ7k117kkysk8TERbS2SbZjA3bN+AcYn0J0UXpWLf5x07VxOgIWldX5i9d\nyvLHFzHSYEDXjPjyEb0e+YAB3L5wgQMt7BgRfWKZ98rHFFffy9r4fc22HTpA4C9PvY3OJ8BB1nUM\nQRDuBVZgLRf6DdZ8dAPgjFUTbAKwUxCEOy9IG+3IeJOBF4GrsM5zo4AtgiAcFEVxU0f7twVn8grJ\nKq4Co4ERsb27RAh/e9i2ZxuaEE2LDp5zyGQyPPp4sPrX1dx/6/12ts6qzfHu55/z508/sT8+Hnl5\nOX3lCgKcnZu8prEInHuSjwGg0Wjo2bMn1dXVJCQkMGDAAEpLSzl16hQJZ3J4NCqqRZsu7N9ssZCq\n15OpVHJIp+OmhQsIFYRW35+Hhwfbt28nICAAT0+r5khpRTX+oRHs2n+EMUMHcDglm4F9zu/nOOJY\npbBQbpCYOGUi+/btY9iwYdTU1FBSUkK4g/Q/HD3vAIiimASMtUVfV2jItj3bOJC+j8AhLUfvNIVc\nLsc/1p/0kyf5dv0abp4+x4YWtszNC+azYtEiPMvK8dG07OCQgCJPT/J8vOnr54/8gudEqaGW/KIi\nws/koq1rXebP3qoqRt44m7A+nVvt0Gg0oqirQCZr3euBn5OeUydTCOsG69fLad4ZMWQQxRYRj8Ae\nrb6mqQgcW7avTNuLv2/jkbLtwWQy8dqKJfgOal+fYTFhLaZmNYf/QD/e/vhfvPXsv9C0Yl5oK8P6\n9UfIyTn/3lRWToGXF4knThAaHNIgKgdg9pgxrNmwgYRkaxTX4cOH68+t3rCBudDgmtljxgBgsVjI\nyMujtqSEqFOn0ZSd38zbV1PD9Xc4XJevHrVGSyv20C/BbAG1tnOjLy+mtU6ed4HPgX8D8wRBeAv4\nXBTFKrtZ1sUwGOrIOlOAV5+eKMKi+fSr73jlmfmdbZbNSElLwV1wB8BJ40R5ZffOjNFotcxftpS3\nH32MiSYT2kZyWNP0eujfjzndyMFzDpVazevvf4n5sfv5aWPjgm5XD4/hnf+u6W5OgWeAu0RRXNXE\n+Y8EQXgIWIJ1YdRRyrCGLys4H9koAXk26LvDlJSW8+Kby3HtNZLasgJ27NrFU490WV36ZqmoLEeh\naZsfX+2spqKowk4WNc6omTMZNXMmVRUVbPrv5yQeT8a9Wk+MRoNLM8LJElDh7ExYr16EKhTU1dWR\nlpaG4WxEYUJCAjqdjn79+uGsUnEqwB/fwiI05ub3TM7o9SRLFmSengybcS03TJ3aroi8sLAwIiIi\nqKmpITs7G4vFQkVFBcOHD0et0rBxx34qakwNnDAns3PsdixJEnmFxfj4+jFq7Biys7MpKioiMTER\nNzc3JkyYgJubW5vvs504et65gh35ZcvP+A7xs0lf3j292bX3D4c7eWQyGQ8sWcKy+QsYZTA0Wim0\nTi6jyNubEjc3LGo1Om9v+np5NXDwAHgBWl9fsnU6aqurUdXW4l9cjEdVdaPh/If11fiNHs3ImbaN\ngGgP5aXFuDqZae0esE5jJu9Uerdw8nAZzTtTx4/i583boQ1OHntTU1lGj+D2O3obY8l7S3DprcVJ\n0zlV2xROCjwGePDishd5/enXbd7/HU8/xTsLH2+wQe5XWopPaSnZVVXkeHoihIejObsW2puUxOpm\nInZWb9hAeHAwwy+oyJdXWkp+bi4RuXl4XBSxc1Cvp8fYq+yux9MY2anH2PjdZ1gqcrkmqu2pWqN6\nKNkW/zX7ft/EhOtuoc8g+4pkt4bWOnkkURT3C4IwFKs48nPAG4IgrMeazrVbFMUj9jKyK7Bl5x7k\nHkEAOKk0FJVfXv4thUKOxWKpf47KZW3/gnc1VGo1D7zyMl889RSTLnLymC0WRGcNixYu7CTrbMOb\nyz9C+48nWPV9Q+3z6RNGsfSD/3SSVR0iGKvgYHPswEbaXWfntX8Bu7G+p8uA98/udnUqaenZ/N+K\nT3CPGoaTSoXKL4TsvEyef/0dXnjikW6Rench4cE92HlyZ5uu0Vfo6RvYz04WNY+ruzuzH3sUgFOi\nyIYvvqD21GlGaTRolEpqnJwo9fSk3MUFs5MTMrUKVzc3Jvn68K/PPmu0z5KSEkpKSnjinntw6dWL\n7NJSDHo9GI1o6urwLK/As6ICJ0kit6aWQzLoPXQo9955By42cHjMmDGj/r/NZjN5eXlkZGRgkTvh\nGxxJ1sEDHEnNJiTQD3etGiUNSxd39FiBiUp9HfnFZSQeP0lgUAhevoHk5+cTHh7OqFGjOqvksUPn\nnSvYFyNGm25umORGjEajw7+bKrWaeW//i2ULFjDSYEDp6UmplyeVGg2oVCg1Gny8dES7ulzi2LkY\ntZMTvUKs0ed1RiMFFRXklJcj1dWhqKvDs6oaXVkZx8srcB06lBn33+eIW2wRfWUZGkXrUyecneTU\nVJba0SKbctnMO0qlkvGjhrIjMQX30PZHf50RE0jcvAaA2MlzCRJi29WP2VRHbXYSDy1+ut22XMzu\nw7spthTh520bB3J70XpoKXYv5uetP3PdBNvqT6mdnZm/bCkfPfssnoVFxJ0VgJcDPc7kYszL51it\ngdDwMHRubny8Zk2LfX68Zg3DY2KQJAnx9Gm0eXnE5TbcR9WbTOw01BI7aRITHRjFk5OVxh/rV1Nw\nJhNPyhkXqsQ5sH1ra5lMxoSeSoymEg79vIzN33+Gp28Qo66ZTWR0XKdsuLdJovtsmtbHgiD8B7ge\nuBNYBqjp4qlWHSW/qBilRlt/3J5Qrq5MTHQMv2f8ji5MR21FLT66lhXjuwNefn5Irm5gabhIyKup\noc+okd0tyqVRXlryJl5uar5avRaFHCaOGcary7ulgwdgL7BEEIS7RVG8RAF1bflnAAAgAElEQVRc\nEARP4KWz7TqMIAhXAU8A04BNWKtcfCsIQrwoimttMUZ7OJqSxrKPv8QrehQKxflp2i2gB2WlBTy1\n+E3efPGpbvX99fX2xVLXtjxnU62x0xdUkiSh9fZm8KxZZKan81tSEn7uHvj7+uDl4YG/Wt3A4Rbu\n68vpadOa3N2aO20aI2KtC1cv7flnSq3RSJleT1p5ORk5OTg5OxMbF0d4eDhyO1TTUCgUBAcHExwc\nXP/ZjTfdzJcrXiU/8wSBAf5EhfpxVMxE5+WFj5crN1xzVYM+Wjq+btJoCkqrKCouwWKsRQjyJPHA\nn1TVmvj7w0+j8+0y1VgcOu9cwb7YWpNFppRRa6h1mJNHkiRKS0vJzMwkNzcXYcoUth86THRYKCF+\nfoSp1R2a+1VOToR4exNytkSyyWymrKaGP9PSkPUIxyMslC1bthAWFkZ4eDjqZlLe7c3+bT8T7tX6\nTUc/DxXbkw4wbmbnpXu0gctq3rnlhmmUlH5DYnoSnj0GtPk7mvLHeo7v+rX+eO/aj4gecy19Rret\nKEptdQX6zMM8O//vuLpoW76glfy0aR0+A2yX+tURdJE6duz53eZOHrA6lx956y12//wLv/7wA0Nk\nsvq0dSeLhdj0dBLlMryi2xYtV1hZiWvOGUIKCuo/M1ssJOn1FHp48NcXX8A70LaRVxcjSRJpxw7y\nx2/fUV1agKe8igEBckb2dMLqyug4Tko5w8M1QB1VtWkcXrOEDQYtajdvho2/lgHDx9cX47A37Vo5\niqJoAr4HvhcEQQ20z9XajRg5KIY9x37Gxcvq/NAoLy+f1jVXTWHT7k0QBmWZZdw19+7ONskmFJ45\ng1RZCRdVqfB3dua3wwlMN5u7XUREY8x/5hVkFbk4SbXc/c93O9ucjnAvVjHAXEEQDgNZWAUJNUAI\nMARr3rqtSqHdDGwSRXHj2eOfBUHYCEwGOs3J89GXq9H1GYW8ke+m1suPSkMN3/2yiZuvm9IJ1rWP\nXQd2ofFs20NU6+XCwSMHGT+ybbn4HUGSJPLz8zl+/DiVlZWYzWa0Wi06nY6YuDgGDh7MutVrUMsV\n6LSNLyCPpTYtzn8sNRWmTbvkc42TEwEeHqRmZBAdG0u/uDhqa2spKioiLS0Ns9mMXC4nMDCQ6Oho\nXOxQeUfj7Mx9T7zCyaMH+PHL9xjqqyfMW0VBkTdpBYEU1Trh5nKpRtGFKVnnKC6v5nR2FiHKQgbK\n8imvrmX7KRWjpsxi+KTZNre9gzh63rmCHZGwsZNHJsNiZ1HZyspKkpOTycvLQ5IkNBoN3t7e9O7d\nG4VCQXR0NN9++SVRQUE2d+4rFQoqysvRurkx4ezcZDAYKCgoICUlBYvFgpOTEz179iQqKsquJZzP\nIUkS679+j6qswwT2VLX6OmeVAj9ZASvfeZ5bHn7eIbZ2gMtu3vn73bfy++4DrPzuZ7ThMTi7tk60\n+2IHzznOfdYaR48kSZSfFvGU1/Dq4mdwsaGD51z/MgeXFW8KmUxm83nuYkZeN4PB10zmh+Xvknjs\nGMOVSjzVamSAs9FIncnEfXPm8MYnnzTbz31zrKmupRUVRBQXA9a/paivIU3txOS//oWBE+xbRbXw\nTDa/rFxBVVkBgepqRgY6ofVRYCvHTlO4apQM76EELNSZ8kmO/zc7f/ovKlcfJt94FxF97Os+aY0r\naSdQ09RJURQNoig2r/56GRAt9ERlrESSJKpLCujXu2dnm2RTNGoNLk7WlwZlnZKeYd3//o7v28en\nz/yDsY2IkynlcgbW1bF03jwqO1jppqsQEBqBUeGCs4trZ5vSbkRRTAX6A7cA+wAtEAa4AUnAXUC/\ns+1sgQVrydILMQOVNuq/XUgyRaMOnnOo3L3JPp3rQIs6RnJaMn8e/gOPYM82Xaf10JJVnMWug21L\n82oPkiSxd+9e1q5dy5EjR/Dz82PAgAHExcUhCAI+Pj4olUpkMhkz59zMkaxM8oqKL+ln5U8/cTQt\nrclxjqalsfKnnxo9tzspicCICPrFxQFW8ebg4GD69etHTEwM/fr1Q5Iktm3bxtq1a8nOzrbNzV9E\nz/5DmL/kE0p0w/k1xYTeJMcsydr0cimXyZCQUWdRsiOjjsS6SB566cOu6ODpjHnnCnbE1unmkkmq\nr45na4xGI+vXr2f79u04OTkxYMAAYmNj6d27Nz4+PvWbUGq1molTp3LwuH0qnyZnZjF+6tT6Y7Va\nTXBwcL09giBQVFTEjz/+2EBc1dbUGQxsWvMxy5+9D9XpnUzo2fZNuMHBCnpJJ1jx7D18/8mbVHdR\njcnLdd4ZN3IISxc/ha4uj5LUg5hNxmbbnxETG3XwnOP4rl85IyY2eR6guqSAspRd3DA2jtf/ucjm\nDh6Aa8ZNoSilyOb9toeS9BJGDhpp93FUajW3PLGI+99ZRkpIMPFVVVQajdSo1aidnBgeE8PcRjat\nzjF32rR6PZ4AHx9yAgLI0Ov5tc6A17SpPPHhh3Z38Dw170G+fecpRnmc4gbBTGaJCa3q/Lyy+nBD\n+RV7HauUcuJC1NTqq5joX8D2L19j5TvPYzI2//voCC26uEVRrK/RKgiCD9YXoipRFB2rhtkFGDY4\nlr3puZhKsrnr0Sc72xybo1FprDtIKtsrtjuSWr2eL5YsgVOnma7VomgiLC5Yo8HdUMe/5y8gdsIE\nJv/lTgdbaltCevYjMyOjs83oMKIoGrFG0TgikuYHYLMgCFOAeKzVLCYBLztg7CZxVimtu0ZNvFTr\ni3IYe33XL8wjSRKrfv6GXQm78B/avtQc/4F+rNmyhsTkJB649QG77cyWlJSwZ88eZl4gNnqu2tPF\nx3K5nBtuuYXv16whpLiYuN7WKlcJmZn8GB9f337gwIENXojOHf8YH88dM2eSkJlJXI8e1BmNxO/f\nj4uvL7FDhzY5/v79+xk2bBg+Pj7U1dXxww8/MH++bQsA1NbWkp6eTnZ2NjLvnngPDGRXyjFGxPoz\nwL/1oepe7loUEWFs+rOUHlFTcPf0In7bdoKCgoiKinKkqHKrcPC8cwU7opLbdndWaVGiUrU+mqQt\nGAwG0tLSmDp1an06WFPzTmBICLu3b6+fN87R0eODJ0/i4eVZ/7xpavzg4GDc3d35/fffGThwoG3+\nAFgdXYd3/kbC7niMlUUM8DYwW9Bw6f5L6wnyVHGDu5mPt/3M6+99QVWtETcXFyZfM4XHFy1Cp9O1\n2EefPn2Qy+Xs2rXrkvaCIMzHqpXzuSiKd5/9LPzsZ+OxOmxSgA9EUfz3xX0LgnAD1vXHu6IozqMN\n887Zd7EVwFSsUhnxwAOiKOZf1G7q2fEjWtu3rXB10fLCoodJSc3gg/98RZ2zL+5BkY2uaRI3t6wp\nnbh5daP6PMbaGiqzEomODOXR+f+0a+TWhJETOJKSRJaYhbfgbbdxWqI0oxRvszc3Tr3JYWO6enhw\nz4svUpyXx+fvvYeiuhqT2YxSoWDOWSfPxSnqt0yfzs0XOI7NBgMJ+mriRo1i0d/udkgWhSRJlBTl\n88jkrrX5rVLKmdRLzg/HRGr0Vbh5eNllnBZ/DYIgTAcWASO5IK5JEIQSrBPL26Iodot80Y5y07WT\n2bl4GZ4uatRq+zzwOxOzxYITVkHO7kpxbi4f/fOfjJFA59ryj9pNpWKaSsWxbdv4OFXkby++2G3T\ntxRqjV20Oy5nRFHcIQjCX4ClQE+s4dL3iKJov63KFpAkCWNdXfPLW5mcyqrq5lp0KpIksXHnb6zf\nugF1qIqg4UHt7ksmk+Ef58+p/CwWvDKf0YNHc9O0m22+mPP29sbb25vExETkcjl+fn7NansolUoi\noqJwVir5edcuRg8Y0K5xj2dkcDIvj8kzZpCWnt5sW7PZTFZWFqWlpSiVSpuVGDcYDOzZs4fS0lLk\ncjk+Pj5ERkbWv3QOGTKUTb/+SEVlNdFRrRszv7CEvUdSuXHO7fVREGazmZKSEnbs2IHRaMTZ2Znh\nw4fXl3S/whVsQXRUNKlFIm4+HXckGvQGAnzspxPh6upKcHAwJ06cwGKxEBgY2Oy8Y49kERk0m45m\nsVjIzMyktLQUnU5HRETHfQa1NXp2b/yO44l7obaMnm4GJgeoUQbLsWYrdZzPd55md2oZz86MIMBT\nxaHMKr7esp5tW37jmUfuYuzMO/DwbN7ZI5fLiY+P5+abb7741A1Yo34lAEEQNMBW4Hfgaqyl0K8B\nlgmC4CWK4sWlkG7BWtlzNjCvjbe2EnDHmlYO8CGwCqtzibP29MKq59OpCqJ9ekXwzpLnWLs+ng1b\nd+IaMRCVs21SjSvOpOFiquClhQ8Q6O8YHdF5d89n1S+r2Ll/J/5xfiicHPe+YDFbKEgsIDYyjnvn\ndk6VVe+AAKKGDkXn4cH6LVvo36MHkSEhzJk2jfDgYD5eY63oe9/NNzPsbASP2WJhZ0ICCo2GkePG\nEREZ6bD3LJlMxgP3/o21m76nh7aaAUEq5g5s+G7o6OM5cS6k5NZyvFxD/xGT7ebggRaeF4Ig3IvV\nW7wa2IU1N9QAOGNVhZ+AdYK6UxTFTinvJwhCDyAjPj6ekLOVA+yFJEk8+PQSdG4aXnvucbuO1Rks\neGUBPkO9yd2Xx9Knl3ZWlZMOsWLRE4yoqsK5HRNIQlkZfe75G4PtHDpoL/Zu38COjet44rUPHTam\nrDsp/9oQe807GdmnWfHpSgwaf1z9Q5tsZzGbKEs7xOB+Udw194Yu43SurK7kix++4ETGCZS+CnQR\nOpvrR5SeLsVw2kB4YDh33Xw3Pl62F0Ksra3lxIkT5OTkYDQakSQJT09PfH190Taiw2MwGIj/dT1y\no5GM7CzWXRDN0xizJk3iuvHj2ZmYSJ8BMcQOHXJJG4vFQllZGUVFRdTU1CCXy9FqtURGRhIeHm7T\nRdKmTZtwcnKixwW7+42x4ecfiAz0Ijy4+aisispqtu07yk23/qVZO0tKSkhLS2Pu3LltsvfKvGP/\n9U53Jj07naVrlhIwoOPC3oUni5g1eBbjho+zgWXNYzAYOHbsGKdOnQIgJCSkQQRJRXk5f/62kasG\nxtl87I379jH7gqo2JpOJM2fOUFxcjLOzM3379iUsLKxD87kkSez8dRVH9u9EUVdGtJeRHr7qFiuD\ntZeb3jnE/Kk9GNP7/N8wLb+ahz47xnM39KRW4UGtwp0evfszZc4DOF0UrdWnTx+GDBmCq6srH374\nYf28IwiCN5AH/AFkiKJ499kN8ZWAjyiK9R4zQRCWAdNFURQu+MwFyAf+DSwAxoii+Gdr7kkQhCCs\n72KDz21GCYIwFtgORImimC4IwlasjiaATFEUI1v7N2tizB7YYN4pr6jklbc/oEbjh6vf+X7OiIns\nXftRs9cOn3V/fSSPxWymNHU/E0cN5pYbmk4Vsicns0/yzqfv4NxTg7u/u93HqyqqojKlkvvveICY\n3jEtX2BHtmzZQmBgIM7OzuyKj6emtJRRMTGNzg1V1Xo2H9jPxKnTCAwN4ejRo1x99dUOj+SVJImk\n3fH8sflHqCkl3LWO3v4q1E6OEUA2miycLDRwskKFycmdgaMmMnzSrFZtVnZkvdNS788Ad4miuKqJ\n8x8JgvAQsASrI+iy5lBSMmjcqagubzaVojuSmpGKSWMtd6sOUPHbjg1cN3FmC1d1PQJCQ8hMSCBa\n27adArPFwhmZjHGhTb9Yd3XMZhOSpftGYQEIgpDB+Z2n5n5gUkcXLl2F0rJyfvxtK4ePJFOLGrfg\nfrhqmtd/kCuU6HoPIykvl8eefwNvDxemTxzLiMGxnSI0efTEEVb/vJrSmlJce7riP9x+FbG8Qrwg\nBEorynjxgxdxU7gy85qZjIizXbU8jUZDbGwssWerYJlMJnJycsjMzKSsrAyLxYJOpyM4OBiFQoFa\nrWb67Fmcyc6mqKKc6MhIjjcRldM/KophsbHsS03l+ttuQ3OBZpher+fUqVPU1taiVCoJCAhgyJAh\n6HS2d5ZdyMSJE9m3bx9JSUlIkoS7uzu+vr64nC2feo6pM2axZuVnhAb5NftitmP/EW64+fZLHDw1\nNTUUFhZSVlZWP86F6XGdxf/ivHM5s27zOlwDbBMt4Obnyvbd2xzi5FGr1QwaNIhBgwZhMBg4dOgQ\nBw8epG/fvjg7O5Owdy/RPWwTvXcxTnI5er0erVZLXl4eZ86cITY2lrFjx9pk7tm3ZS1/bFlHXw89\n1/VQIZMpsHdR3lqjheKqhnoXUf4uvHFrHyL9nPHQKgE92fk7ef+f+xBiRzLttr83aD9p0iSWLl2K\nXq+/8OMZQDJwYX68K9ZsBx1woXDLG1gL1VzIdVjfv27Cqgu4XhCE5uq+XzjvnHPyHL3g/LlyRb5A\nOvAA1s34h7CmdHUJPNzdePPFJ3nl7Q/IKzhd7+gJEmKJHnNtk7o80WOuPe/gsZgpObGb+ffeTv8+\nvRxm+8X0DOvJ0ueXsuKLFaQeTsU/xg+5wvYOA4vFQuHRQoJcg3np+cWonDp/Q2/06NH89NNPxMbG\nctWkSaQmJ7Pt4EEmDGm4WVVTa2DzwQPcePvtOGu15OTk4Orq2imp2jKZjNhRk4gdNQmTycSxfdvZ\nuWsz+ooitFIV0d4Wgrw6VrXwYgorDSQXQLnFBZWLFwOGjeXuMVNRN6ITay9aehsIBo600GYH1hzU\nyxpJkvhizVrcwwdTXXiK73/dzE0zrmn5wm7Cus3r8Iy0hsx7Bnuy59CebunkuWnePH768EPi/9zN\nGK0WdSt2uwtra9ltMXPTwgWE9Oq8h0ZHKcvNoosI/3eEh4DFWKtK/BvrbldjdGoIckdJSUvnxw3x\n5OYXUWOWo9KF4BI5DG0bHzBuPoHgE4jRWMfKjftZufY33JxVxPbrw8wp43F3s28e8uFjh/ny+y8w\nu5rQ9fYmUGXf8pcXonV3RjvYGbPJzOpdq/lm3SpmTZlll0pc51KjzqVHmc1mMjIyOHbsGE5OTgiC\ngEKhICgsjBtvvx1Jsob5H7tIgLl/r17cd/McMkpLuOGWW+o/r66uJjU1FRcXF4YMGYKPj2PLtCoU\nCkaOHFl/b2fOnCEzM5PMzEwsFgsymQxPT0/8/PyIGTiEEyezm0zbKquowlNnFYzNzc2lpKQEk8mE\nQqHAxcWF8PBwRo0a1dUiRf8n5p3LneKyYpZ9spRqVTXeIbbRzNC4aSjTlPLka0/y6F8fITTo0kpy\n9kCtVjNy5EgOHz5MUVERoaGhFBYUkJOV1Wj7mePOO6H2Jiby8bffAhDXty9hwcHNtgfoHRZG0oED\njBg7lpycHKZNm2bTCn77t/2MSV/GkRoZR/IaOl4uTmc4x8UCpm1tH+rrxorNWexPL2dopAcDQt2I\n8HVmYA/3Rtob+Wn9Jqbc8mCD8sZ9+/ZFp9Oxc2cD8f/rgXXAhbuC8VgrYx0TBOE7rKlbu0RRPAOc\nucjUW7BG3izDGv3jDXxDK+YdURQPYBVnvpBbz46dfLZNKoAgCE3116k8u+BBFr34fxgN3jiprRta\n56pnXezoiR4zgz6jz0frlGce477bbuxUB885FAoF8+6ex+Hjh/n4q4/wHeqLSmM7J4ypzkT+/nxu\nu/42xgy+ymb9dhRnZ2euvfZafvvtN8LCwujVty+lxcWcyMqi9wUp5FsPHeT6OXNQazQkJyfj4eHB\n+PGOq5TaFEqlst7hA5CdkUb8hp/ZdqoEJIgI8qR/VDjKNlbRtlgspGae5nh2EZIEXh6ujJszhT79\nYzstKKQlJ89eYIkgCHeLolhy8UlBEDyx5nxe9po8n379PSaXQJyVTrgHRrLx9z8YPXSgw/JA7U1x\nWRGu4dYHp1whp8ZY28kWtZ+ZDz7ImcmT+WbpUsKr9UQ3UebYZLGwq0aPa8+eLHziCYd6V+1BZvoJ\nVHJTt44yE0Xxt7O76sexCgYmdbZNtuJk5ik+X72W4rJKjEoXXAMicI6MwBY1WxROKjxDooAoJEli\nT3oeO5esQOsko09UBHffMguVyrYv1W99/BbZ5Vn4DPZB0caHoS1RKBX4Cr5YLBZ+2v8jW//cysuP\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tMwkS+vDqwjcpLCxsc1e/q6++GmAy4IJZNPncpc/89x3MzWqWCILQTxCEWEEQxgKfA3+c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wuo7CMwUU7Ctk6MBh3HTfTarXnRc5t7n0vY3PU3hrvbMExZvnGSHEKyjhWrcAqnWyibjxyhsp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Ui8wDfaF4XaeTtAFrOF0iNGLEUWdEod3YVujB81AT8f94QwN8eiRYvo3bv3aefW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\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FDjj52pM55qJjyOidzpFnDWF35m6e+m/zn+4cExfHzU89yTcBP+WBQKUEj9PpJDExEYCJ\nmzbx4aaNAGwsLaNU9eLiO+s30qq50SsX8eUHLzG8YwiVGmLLslks+LLu+kxCCHG4qSvJ8x3wqFIq\n3nr8BVB1KZBrgeUNHVjz1ayu/RpMkMqrTYWcoToLLrYEPp+PUFFRte1p0dFk/vJLDa9oWXoPPoYd\n/hRKvftH7YRCIb7LjGLMxTfYGNkh+xm4VilVcaVwA6a9OiZsn2OALQ1wronAViAVOApzR/+31Bpr\nVFERrb78UKvTfdAgiuIT2FFaSr9hQ+t94Sts8QXwolKqu/X4Q+D2ioSvUioCM2pwnk3xNYmXXnmN\nPbt3M2rUKIYNG8ZZZ51F//79+fCjD1m3ofoI37i4OLp160a3bt3o2rXrvsRCfUVFRVV6fWpqaqXn\nQ6EQ48aN4z//+Q8TJkzglVdeITm5fnm2jIwMSopLiE3dnzAqKSjBgYOex/Sk3yl9adutLV2O2F/Q\nPrlDMqFQiKzsrIN6H3aJS0zkzD/8gc8yt+1L8ERHRzNo0CB27dpF165dcTgcTNy0iR9272aT28nl\n995rc9QHLxQKMXPif5k76RnO6+vA5TSXLyN7ushc9DHvP/9Aq+izCiFEQ6nrquHPwJfANqXULGA3\ncLNS6gRAA4OAnpg6GaIFc1RNXoXMnaCWbv2PP9KuhmlLDoeDYFHrKC592U3j+fCZuzi7j3m8+Fcf\np55zJdGeWHsDOzS3AZ8DNymlyjAJmKeAZ5RSR2CyrVdh6oT9ZkqpgcAQYJTW2gtsVkqdCpQdynEb\nWghw1jAlTzR/HXv2YOXixfz5oovsDkUc2A3AZEArpZZjEr9nAacqpTYBvQE/MMK2CBtJXn4BX377\nPStWryUjOZbBgwdXej45OZnTTzuNd977kNjEVE476ViGDh7QIOeuq2jwpEmT+O677/jggw/o1avX\nQR37yCOPJESInb/sIqO3WbV+1y+7SOmYTGRMJG26tGHDgg0EA8F9I5nzdxYQGR2JK8qFz+cjIqJ6\nzbvmJBQKkdSpEz+Ul9OvXz9cLhder5d169ZRXl5OSkoKAwaYn9WXAT/DExMpLS0lJibG5sjrL3dP\nFu8+9yB94vI4Q0WwO9SGxMRkHA4HeQV7ObZrFtvz1vLM3/7I2D/dSedeDfN/UwghWrIDJnm01put\ni6DfA2cAQzHDlVOB7sDXmDnsmxs7UNG43E53tcetIcmzde060moZAeH0eltEJ64ubdp3ol2PwWzL\nWUqbeDe7Qm34/Uln2h3WIdFaf6uUUsB5QBIwV2u9QCm1EpMAcgP/1Fof6pj6YzBTUp9TSo3FFHn+\nH4eYPGoMreH38XDU99hjWbJwEfEHOcJBNC2tdQ5wilLqOEx/pw8wHzNNdBcwCXhPa51vX5QNKzev\ngEeeeZlCL7iT0olLH8iRA6sX6QVISEhgyPGns+iX3bwx7XtenziVrN176NChwyHFEF54uSZTpkzh\nnHPOISYmhszMzH3bY2NjDziix+/3M33udNJ7prN0ylIiLz2W4rxi1s39mWMuMgNCO/TrQHR8DPPf\n/Z6BIwfgLfWy7LNl9B3Rh+j2UTz7+jPcfPUtRFUpbNzUgsEghYWFZGdnk52dTV5eHn6/n2AwSCgU\nojAvD18gwNb166vV4svNzSU3NxeAdmlp+I4YzDfffIPf78fpdOJ0OomOjiYlJYW0tDTS0tKIjo6u\nKQxbzP38PVbOm86o7iGKItqywNuJ+OQ0YtqZ/6d7XQVsyGpH54RdnBe3nZlvPExqjyM5/5o7D3rV\nMSGEaE3qHP9v3d1+3/oSrVTVP4bVRva0UKV795JYS6HsSKC0uJiIVlDr5Nyrx/Hy+KtIyvdy7tW3\n2B1Og9BaZwH/qbLtXeDdBjxNO8xIng8w9X16A98C2cCzDXieQ+JwOOq8GBLNU0bPnpSHgnXvKGxn\njVJerLVeYHcsDc3r9bJo2Sp+XLGG7OwcSsp8lPhDxHfqT0qMZ99+uSU+kmOr3/go9wXYsKeEiKgY\nkjspQLFq8TwWLl3FuAlPEBsdTfduXTjl+KF06Vi/Mo0Oh6POC/ENGzawcuVK3n+/chf0/PPP59FH\nH622f2ZWJlO/nsrPm9YR3SmGEX86mUWTFvHV81/hjnQzaPQguh/dDQCny8npfzmNHz78gS+emkFE\ndAS9ju3JEWOOACBnTzbjHhtHt4xunDfyPLp37t5oiYNQKERhYSFZWVlkZWVRXGxqeVUkcqKjo4mN\njSU+Pp5evXpVmvo55YMPGDtqFE/VsfT7leeeS0xMDNEREfTo33/f9vLycvbu3cvGjRtZuXLlvgSQ\nw+HA5XKRkpJCeno67du3JyqqaRJe3vJyXn/yXuIcJXTqNZCVTg9JScn0a5eM2+mkojZm+9QE2qbE\nsysnnaXZnUjuVkIw/1eevu96rhn3EEmpNScthRCitaszyWN1em7B3PFui5kmsQdYDUwD3tBalzRm\nkKLxhapchARbSXHpKE8MvmDNF1g+INrjqfG5lsbtdhMR14b80r107N6iCy7vo5Q6DTgb05ubAczF\nLHP8e0yB1FeBB7TWh/Kf1Q/s1lo/aT1eq5SaCIyiGSV5RMuVkJSEVIpoMWYBK5RSF2mtf7U7mIY0\n4fHn+HndGhI7KKKS0/Gkp5AS7amWtFi2rYhkTwQpYYkeXyDIxuwyCssqjxIZceXdBAN+yooK2FmQ\nw8Zvv+ezKR9x11//yjFHHVFnTDUlaapatmxZrc+FQiHW/bKOOYvmkJm1jRJvKcGoIHEdY2l/TPt9\n+5145Ym1HsOT6OGUP51S43NxafHEpcWTW5DDsx8/A8UOPJExtEtrzynDRzCg90Dc7kOrlVZUVMSs\nWbPYvn07PXv2JCEhgbZt27J69WqGDx++b7/FixfTu3fvSo+HDRtG1vbtRDoceJKTuWjMGCbNmAHA\nkCFDWL58f7nMi88/n+GDBuEPBJg+bz7ZeXn7jh8VFcXKlSsZNmxYteMHAgGKior46quvaN++PYFA\ngMTERE45pebP7FAVFRWxeNFC5n/3DV3TO9I+vR1tU+KJcNdez8zpcJDeJpH0NokEgkGy87vg27Wb\n55/9NwMHDeHk00bVu46TEEK0Fgf866SUuhh4G5hifc8AxmLq9ORjpk3cpZQarbX+uZFjFY3IH6x8\nGRII+gmFQi1+uKuv3IurllomLsDn9RIZGdm0QTWSDl168PPaVXaH0SCUUtcA/wVmAl7gE2AF0A0z\nlSoKuAtTO+exQzjVL4BbKeUISxa5gdZRsEnYzuVyEWrh7ehhZimwXCn1T+B5rXX1om4t0CP334nP\n52PLth2s1RvRm7aQt3MjpV4fJWU+YjL6EB2fRLk/yKx1ufRLjyXR4yYQDLElp5Tt+ftXcSzMXI+z\nrICY6EhioqLomZFOv+OG07fnxaSkmFop48eP57PPPqs1ns8//5wuXboc1Ht4+eWXOeakY5izaA67\n9uzku+nzyMvOA4c12jEQwuHa/7s2+NQj6P+7/SNWNs3dRPeTux/0Y0+iB0+ih01zN9FuWFty9+bw\n5jdvsvOpXXRWnWiTnMbJw09m2cJl3HDDwS144PF4SElJISsri/Lycvbs2UNRURFFRUXk5ubi8Xhq\nHT3j9/uZPWMGZx9/PKu3bWPsmDEA+xI9FS4+80x6dTOjl9wuF0f16sWarVsrJZHCBYNBfD4fu3bt\norS0lNLSUrxeL6FQCJfLRadOnQ7qPdbH5s2bWbFiBYRCrF7xI2efMozo3zBqyOV00i4lnnYp8Qzq\n3Y1pcxbjDQRxR0TRs2dPBg0a1OCxCyFEc1TXLYgHgdu11i9WbFBKTQLewCxxfDfwGvAKcFJjBSka\nX7WpIA6zMlVLT4Ds2r6dbu6aa+6kAhuWLGHwiBFNGlNjScnoSmDNCrvDaCj3ALdqrV+CfaN6ZgFX\naq3fsbb9immjDiXJMwMzmud+pdRjmOlaFwFXHsIxhaikpSfLDzPPA29hksy3KqWeBN7SWldfprGF\ncTqdREVGEBfrIS42lr2FRbidAZwOCAX357J8wRArtx/g7QYDOJ0OXA4H0dGRJMR5iImOwu127/u/\nfuutt3LttdfWeoiMjPpN6QLw+rw89tJjrFu5lp/L1xLfMYH4DvH02NWdjCH7j7Nt8TY6DdufgNj9\n0+56n+NgRMdHE907mr0795I4JJGi0r1MnP8+Wct2kVmUyb033kucJ67uA2F+JieffDInn3wyYBI3\nhYWF9OjRg/z8fLKysigtLSU2NpY1a9bsm77l8Xj47JNP6N+zJ95AgCOshNnYMWPo0qEDr374ISmJ\nifzpwgsZFpbYCASDJCYl4tqeyaKFC/HEmgUaYmJiWLNmzb4pWl27diU2NpaOHTuSlJTEmDFjGq0d\n8/l8LFiwgGOPPZYvpk7mjBOO/E0JnqqcTidnjhjKjO+WcuGlV7Fy5UrS0tJIT09vgKiFEKJ5qyvJ\n0wVzJ30frfVMpVQa0ElrvVUp9S+g9vG0okWoWnjZGXK1+ILEfr+fkj17iKxlFQnl8fD9tGmtJsnj\ndLpxOVv2zyxMF0xSp8JsIIAZzVNhMdCZQ6C1LlZKjQJeAP6GKbA6Xms97VCOK4RosUJa6x+VUkOB\na4DxwBNKqS8wbdJCrfVqWyM8CPMWLWPKF19R7g/hC4RwRHogKpbo+CSiUnvjcrk42JLgCZ377ft3\nrreM7RtymLNqC6HyIpxBH1ERLuJiIvnnPbcdcsH4UCjEzffeRMqRKQy4qPKqSX1G96n0uP9Z/Ss9\nTmibUOlx+CidhnwcFRNFVK802vRKo2xvGTffczMvP/nybyrY7Ha7SUlJISUl5YD7bVqzhi2fTyO+\nuISsXbspdbsgIoJQRAQZGRn894EHcLlclHi96MzteEtLcPh8uPx+PF4vRxQVsXj+99z1wvO2F/WP\niIigZ8+eLF++nHJ/AIerAeNxOAjhZPny5evldGEAACAASURBVCQmJtKuXbuGO7YQQjRjdSV5NmJW\nt6moV4FSahimRka2tak3pkaPaMFio2Lx+/y4I9yEQiGiXFEt/u7zjNdfp/8BitVGulwE92STu3s3\nKW1bfnE+hwNoPSswbQXOwSybjtY6pJQ6HTO9qkJXIO9QT6S1XoWMRBSNqmW3pYcja5rWq0qpN4Bz\ngSuAZzBTRWsvENLM9OjakTiPh2BxCb5AkMiENPK3rSU5Y39Nle0r5tBh8G977I6MpjDzZzKOGEHe\nljU4gj4i3C66de3SIMkDh8PBNZdfy8TPJlKWU0Zy92RcB6jPYpdgMEje1nz8O/1cdtGljb4i14w3\n3+J4p5OonBzIydm3PQTkxcezvH07unXsRNbmTfTcvoMYn6/aMToXF7Fq7lwGN1J9nYMxfPhwhg4d\nyoplPzJ96sf06pJObHwi7dskExdzcCPKS71+duUUUJifx+ZtWRw59BhOHX3WIddPEkKIlqSuFu8u\nYLJS6iRgJdABU/T0aesO+HOYO10PNG6YorGdM/Jc3pn9Fml92lK4s4Ah/YfYHdIh2/LTT5xaR2Hl\nfi4X30+ZwtnXX99EUTUek85qNReT9wPvWtO0lmmtx2ut51Y8qZS6AbgT+NSuAJtSa6iPdViTH12L\npbX2Ax8DHyulooC6KwrbpKSklC3bMtmxcw/bd2eze082hYWFeL1+HE4nbocfgtUv9huMrwy320Uo\nBBs3beXBJ18kNTWF9mltyGjXhk4d0klvl1ZpZaj6GD54OMMHD+fbH77l63lfU1CSjTvVSVKXZNwR\n9l24B/wB8rfl49vjIy4qntOGnsaZ15/Z6G11KBTCm59fY60ev9NJXmIisR4PnugofBERFMbHEZ2b\nV60Z6hvjYfGsWc0iyQNmetWRRw+nXWoyE1+YwOm9PWQVdWJzKA5XVCwd2rclMbbm5FlxmY/MrD14\ny/YSEyqlA9vYvKGAMedewxHHjWzidyKEEPY74F9HrfU0pdSRwA2Y1bXygD9prSdZu2QDl2qta6+u\nJ1qEowYexTtT3gag5Ncyfn/p722O6ND5y8vBdeAOYEJkJFt27GiiiBqXo9UsfA9a60lKqXWY2jg1\nXVQ9DnwI3NGkgdkoWMsqcaIFaB2LFR4O5gGltT2ptS7HTBNtVu6a8CibN27Ek9yOqOT2OCJiiIjx\nEBmTSkRaRxwOJ1GYIUgAsWkdK70+fJTOoTxOUUP3bQsBe31ecgpLWLM7i+CyjQTL9lKcvZ3yvXnc\ndecdDK/HClzhRgwfwYjhIwgEAixYvoBZ381iT9FOojOiSeqY2GSJ8MKdBRT/WkpidCJjjj2TU445\npUlHiZSXlxPh9YKV5AkCu1NT2Z2chMvjoVN6OvHWc4P79GFnfj6rs7Nxl5bROSuLuPJywIxm9pXU\n+t/dNh26KS677WHee+Y+/q+/xuVy4vW7+OXXrmwJJtKxQ0dSk8wNvMKScrZszSQ2VEhf12Y8LlMg\n/Iv1fk6+8Ab6Hy2DdIUQh6c6/ypprdcqpf4KJGmtd1Z57kGllEsp1bm1LTd6OOrZpReZOdtIiUtp\n9KHGTSGhXXsKdmwn8QDvZXVxCSeed14TRtV4HA5a1YgBaxrVuPBtSqloIAnTHh02WQ8HEAgcNm9X\nCFtorUdV/Fsp1QaIBIq01oX2RVW3e279C0tX/sSa9RvYk51Dua8Mb+4eivwhHJ5EEtp3wxXRtIso\nFOfnUJ67Hae/lOhIN5FuFzGJ0XQ74hQG9e3FEf17132QWrhcLk48+kROPPpEvD4vn309lYVLFrFz\nZxbRCdHV9q9aS6fCprmbatxe2/7rpv9Mm9Q2HDnwSMb+dSzR0dXP1RS2rVtHeLWh9V06E9+pMwOS\nk6oluhwOB+nJyaQnJ+P1+1kT66HXps0klJrkTnlpSbMcKdq+UzfOvvwm5n78HKf2dBLpCNDPtZGg\nE9buKKSopAseTzTZmZsZFrEWd1j4q3Z4UcPPlASPEOKwVtcS6h7MShOXAxFKqe2Y1bYmh+3WCVO7\np/lNkhYH5bxR5zH+mfFcfvZldofSIC6+cxzP3norZwaDRNRQGyCrrAx/l06oIS1/alpro5SKASYA\nx2mtT7Taov8BF2Lamjyl1NPAw2FLn7deDgdef6tYyVmIZkspdSZmGuix7B/4glIqF/gG+LfW+geb\nwqtVSnIiI0ccx8gRx1Xanrk9i6f/8z/2bMijfb9jmiyegM/LrhVfc+7ZZzH2vDMbdRGHyIhIfj/m\nQn4/5kJuvuMmcvNyiU5u+ORL/uY8PEEPT//9aVsTIj6vlymv/o+RYVPRO+/cxSa3m4LCAjLatSOp\nymITgWCQXQUFZGdnE19cTFzp/tE7ncu9fPXuu4y+4oomew/1pYYcx3dffkJR2Xbios3litMBA9y/\n8ENuNDk5bk6MXEv4jyMUCqGL4rn9PFkgUwhxeKtrJM/zwEjgemAncAkwUSk1RmsdvvJN87oF0Kha\n7/Vkx/SOePPLGXbEcLtDaRCx8fFcee/feOehhzjT48EVlujJLStjRXQ0t02YYF+A4kBeBkYDT1iP\nHwdOAe4G1gL9gL9i2rAJNsTXpJwuN9m5eXTMaG93KEK0SkqpP2JW2ZsEfABkAuVADKYe4anAPKXU\nFWFT1psNv9/PD8tW8dabb1JSWoYvECCIC1dMPBFRtSc9tq+YU+N2hyeZlbM+BOCIkReRoY444P7h\nU7hcEZF0OfH/mLd+G59e82ciXE7cLifJiYn89c47Gq0de+6p57n1oVtoN6x+KyjVNmKnJqU5ZTz7\n5LO2JXhCoRDffvghi2d+xdEOB5Fho4hiy8sZuHETPqeTHXv2sDU+nvT0dNrEx/PLjh14CwpIz85h\nUEFBtc56H4+H5V9/w78XL2bsLbfQsVevpn1jdTjlvCtYOukhju1a+XIl1ZFLVjCFqj+OX3PKGXjU\nqU0YoRBCNE91JXnOA8Zqrb+xHn+plCoD3lBK9dVa723c8JqfUIhmN6y1ITlCDhLiE+resYXo0Ksn\nF95+G58//Qwj4+IAKPX7me9yMu7fT8lqC83XucAFWuvZ1uMLgWu11tOtx18qpdYAb3IYJHlcEZHo\njVsZPKCv3aEI0VrdC1yltZ5Yy/OvWAXfH8EkgpqVx579L8uW/ojD4cAZGYMzMga3OxKX++BH0Wz/\nZS3bN67d9/iHKa/Q94Tf0ef4M+t9DKfTRVybdPK3JeL3+fD5fRRmbue2O+9h/N/uYfCAPnUf5CAt\nWbMER0LjrDAZ3S6KmfNmct7Ipp3evTszk0cmTKCT00U3r4+zYj1Mzc7m3LAkz9TsbM5t04aIYJAu\nWTtZvnoNoaFHU9yuHckbN7Fw40YGtmlTbX8w/dltZWWcERPDtIcepiwhgT1uF/947DEiayjs3NS6\n9xnEzPLqSUonAZyh6qNbNxc4GXPCmKYITQghmrW6rnCjgawq224HTgceBW5qjKCauwOsyt3iOWg1\nS3Dv02PwYAaNHs2aWV8xwBPLt+VlXNdMOjCiVm4gvA5GCKhaITsTSG6yiGzi9/txRnlY8dM6xp57\nht3hCNFadQBW17HPd8C/myCWgzZ+3I2EQiFy8/L5dXsWWzOz+HVHFjk5eZR7fZRvXkJefgFxnfoR\n1yZ93+uqFlH++fsvKiV4Kqybb/LrB0r07NHLcJTlkZycSoTbSWJcLP1GnEDXjhl07phOx/R2jVrH\n5vNZn5OqUhrl2Ekdk1m4ZEGTJHmKCguZ9fbbbFm7jpjiYhKLizmjXTs4wLS3EFDqdpPTJpU8B6xY\nuJCdWVmcN3o0gc6d2BMZRVJ+PrUdIcrl4sS4OEKBAO/u2M5Lf74BV3ISw0eO5KhRow56RbSG4nA4\nICKG6vXQHdQ0sr7QF0lqWxnxKoQQdSV5lgJ3K6X+qLX2AWitS5RS1wKzlFI/At82cozNisNh5je3\nWq10kNKpl17C0/Pn06akhK6DB5PaXjoBzdwXwItKqUu01pswK2ndrpS6UmsdUkpFAPdgVsNp1d6c\nNJXINt3I2bOZ4uISYmM9db+ohdj460bWZK0mLimOstJyMiIyGNJfamQJW/wAPKKUulprnVv1SaVU\nEvCAtV+z4/f7WfnTz6zbsJltO7LYu7cIry+ALxDEFwjiD4SISutCVHxSrcfYoVfuS+bUZN386SSk\nddg3dasqT1onyvMiKCwtIcLlotTrp6BwL5u3/kpamzZ069yRowb1I6N920N+v1UVlRSRV5RH+4j6\nTdU6WA6Hg2J/MXty9pCWmtbgx/f7/SyYOpVlc+fiKiykn9PFqJgYiIszXxYfcFKXzmTGxVEUHY3f\n5aa7UqyJcBMdE8Oyn37i81n7qym88v77XDRmDL6jj+aXwkICXi/dfT7W+P1E+/3EFZdwWlQkgXIv\nLut9XtHeJAH9pWVsmDiJeZM/Jr59O06/5BK6DxjQ4O+9Lgmp7Sko0SR6DjwqzR8IQnTTrbImhBDN\nWV1JnluAmcBupdR8rfXZAFrrb5VSN2IKoa5s5BibHX8rXuUm1IqHKXXp05ulCxfy5yulIF8LcAMw\nGdBKqeXAVuAs4FSl1CagN+AHRtgWYRPIzStg8Yo1pPQ9npKISJ78z+v8486WP4AyFArx3qfvsnD1\nQtod2Q5XvotgMMiXK2bQe0kfrrv0OiKbeDWgxrJr1y5CkREUFBSQmJhodziidn8EpgFZYW1OCWZE\nc0fgaMzowfrPWWoie3JyuP7GW3HFJBLwldNh8ClEp3XC7Y5g14o5lUbrbD/A45Wz6p6FtuyLt8lQ\nT9X4+tjkNPK3rql0/Mzls2nX7xjW7Mhi0cpv+N//XmXIUUOZcPdth/y+K5R7yxn/xHgSBzTuVPPU\nQak88MwEHrnr0Qad1r7jl428+egj9AsEODXGgys2Dq/LxfzkZLwRboJOJzid4HDgdLnolZFBbEwM\nbSMjiQibcv7RnDnozEyGVFlMYtKMGQCMHbN/GlMoFGLppk3sjo3F50vCHwgQCgQgFKJ9ZCQxXh9J\nBfn0djrpC5Tk5DL78X8xq3NHrn3ggSad6v67y2/io6duZYwK31q9r7o008cpZ1/aZHEJIURzdsBW\nWmu9QimlgN8Bbao894pSaj5wBdWnUbRaDqeLgsLWW4ooGAoSCARsG5rbmNp27caK+d+TkNzqZ/i0\neFrrHOAUpdRxwBlAH2A+EAR2Y2pivKe1zrcvysbl8/n5xxPPEt/tSAA8CSnszMzm/U+mc+kFv7M5\nut/G7/cz+cvJLFiygKhOkWQMz9j3nNPppP2R7dm2+1fuePgOhvQdzGXnXm7bMsUNYe3atWzZsoVQ\ndDRffvklo0ePJimp9pEUwj5a6w1KqQGYZPIpQDdMv6cUWIUpyjxFa+21L8qapaWmMvHt19mamcXr\nr79GhxQH2Tkb8fp8hIr24N26jEAwRCAYpDxvJznrf8DhjsLhjqK8qICivN1EeeLqNxc9BGVFhZSX\n7CVYXkJ5QQ45G5biCPpwOZ34CnZRvmUZLqvYsqs0h1TvTgb3zaDnmGH06NKJtLTUBnvvXp+Xvz1+\nL7F9PcTEx9T9gkMQGR1JypEp3PfEfTxyzyPEx8Yf8jH3ZGbyxoQJ/C4+ft8qoD937UIoMQlneRkJ\nbnelRSMAOqZW//x+WLWK9Vu21HqeSTNm0KVDB4YPGgSYETtulwu3y0VMZOWEer8uXSj1+cgrKiIz\nJ5f2u3fTLieHYxPi2bVzF8/dcQd3PPfcIb7z+ktObYsvMoVQqGDfKJ2axupkeeP4/VEnNFlcQgjR\nnNWZitdaFwDvK6UcSqk2QCRQpLUu1FqvxRQrPGy4I6NYu2ETA/v1tjuUBlfuLccV5eSXrb/Qu3vr\ne38Fu3cT7XJSXlZGVAu+cDycaK0XKKUWYi62KtqeApvDanTl5V7u/ue/cLbtQ0T0/guXxI6K75av\nwulycXELqs+zbfuvvPvpu+zI3kFMp2jaHpNW65D6hLYJJLRNQO/S3PnEONrEp3HRORfRt0fLKTpd\nWFjIvHnziIqKom/fvkz/9FMGDx7M7NmzSU9PZ9iwYa0ykd7SWdPSp1hf+yilTgSWNMcET4XIyEh6\nde/Cow89WOe+gUCA/IJC9uTkkbV7GL/u2MXOXbsZMuxYFs6ZecDXDj/xVLp7SujYswOd0tuR1uYc\n2iQnERvrsWWazJxFc9iRlcWAYf33bds0d1OllbMa8nGUJ4q8wlw+mv4R14y95pDjT27fnqjIyH0J\nHgB3IEDQ7WJoxx6469lOvPj++xSXlNS5T0WSB2Bw16617uuJjCQYG8ue7ByivPv/28e6XMTFH3py\n66AF/XX//woGCAaDOJ2tr7akEEIcrDqTPEqpM4E7gWOBqLDt/8/eecbHUV19+Jmt0q5WvVfLkkdy\nt9x7wcbYxmBjCIQWIJiE0Fvob0ILCaEEQgkQSoBQHYxpBhv3hrvlIpexrWLL6r1t33k/rFyEmiWt\ndiV7nt/PH2bnzp2zlnTnzLnn/E8FsBJ4SZIkj9Soi6L4EHAbEIO7Zfu/JEn6qyfm9gRWqw1B60/m\n3v1cNe/cU+9fuuZ7QsQQlq7+7pwM8hw7fJg+Gi2Htm1jyKRJvjZHoR3aWHvKgVV4aO0RRfFV4Baa\n5n9PkyRpc1fn7gxWq40/PvkcQmR//AObZ30EJw9hzY59OOwOrrtirg8sPDucTidL1y5l7c9rsGqs\nhIghRKeevRZWYFQggVGB2K0O/rXkDTQNGkYOHcmvZl+Jtg0BUl+Sn5/Prl27kGWZ1NRU/P1PB+h0\nOh0ZGRmUlpby9ddfExISwujRozEajT60WOEsWQ4MBSRPTehLf0etVhMWGoLLJXMoO5fj+QXU1tYR\nGB5LUv9h5B3IbPG65P5DCYqIoai4BJVKRWJsFLFREeh0viutHDtsLO+8/Q4NVQ0Ygrtfr8xSZ8Va\naWPq2KkemU+j0TBkymT2rFnDEIN7LUg9nk9NWTmHKiuR/f2pstsZnZqKrrFEKjM3t0mAJjM3lwbz\naWHijIwMdu3a1ez45JiWrh/Wpw+yLFNvtbLj6FFCNVoMDfUMyj+BtjHDy+lysdpq5ZY7vFcyLMsy\nH738J/oaqqFV2Wg3IyOtvP7knfz+0RfR6ZWNPAUFhfObNoM8oiguxJ2i/DnwKe56dCvgj7sTxQXA\nelEUr5ckqUstRUVRvBB3K+RJuAWfxwMrRFHcIUnS8q7M7Sne++wr9JEplJfmUFVdQ3DQudNqXJZl\n1m1eT9ToKLK35GK1WdHrzq3uU5bKSkYbjWxZtkwJ8vRwvLn2ACIwW5Kk1V2cp8vYbHb++MTfUEUP\nxM/Uun5LcJ9BbNiTBXzXIwM9P234ia9/+hp9nI6QESFd2uHX6jVEDXKLqe46sYtNf9nEpFGTueri\nqzxlbpewWCxs376dkpISTCYTaWlpbQahIiIiiIiIoLa2lpUrVyLLMqIokpaWpuxA+xBRFFfjDvS2\n9MuqAz4URdEMyJIkXdDFe/nU3/lk8fds2p6JDS3qgHCMofFogvwwAMP7jsQQtrSZAHP/iXNJn+De\n3HLKModrq9j79TrkT78myF/HnQuvJzE+poW7dS9BpiDee/M9/vXff3HsUB7+8f4kT05uMubMrJzO\nHCdPTqamuIb6Y/VEBUbz5j/fJCzYcyVnM2+4gXezcyg5fpzIxsBwoNnMoOwcXMCWkGByrFbsOh0q\nf38arFZkWW6yrhr8/Kg3/7IDVVMMLWQw2xwOahoa2HfkCFitBFgsGGtqGVLbXJbgZ7OZy+68k7AY\n7/ycy4pP8NErTzAitIbk6PYDifEhWvw1Zbzy2O+44rf3kjxAEfFXUFA4f2kvk+cR4EZJkj5r5fzb\noij+AXgW98tYV6jCLaSqhlN9vGXcO1w+Z/+ho+zMkghLH4fWz8if//5PXnrqkXMm5f5/PyxCFSUg\nCAIBqUZe+/A17l94v6/N8hjHJYkgiwU/k4na0jJfm6PQPt5ce1Lx4A59V3jl3x8iR4htBnhOEtxn\nIGu3bWHK+BEkxHr/5ao1vl35Lct2LiN6XJTHyzeC44IJjgtmS/ZmKj+u5NZrb/Xo/B2hpqaGDRs2\nYLfbSUpKIi4urkPXm0wmBg8ejMvloqCggP379xMbG6uUcvmObOAm3G3SV9M02DMR2AaU05Lia8fx\nur9TVFLGj6s3sHN3FnZDJEGpY1odmz5hDoERcaeEmIfOvIrYfqc7agmCgL8pBH+TW9/Oabfxl1ff\nJzIkgOmTxjJ+VAY6nfey7UxGEw/+/kHqG+r5fvX37Nq9kzpbHdpwLcGJwag1Hf97cjldVJ2owlpk\nw6g1MkAcyLw75xEc1D2aWr+6+y4euuUWEurrm52bB1Dplp9zAKVhYexz7OdIeTkqWUYARg8dyurN\n7uTTM7N4zjwePXQo36xdC0BeXh5OQLDZmC4IBDaYT/3CnwyRfV122leSZZmA6GjSRo7wzBduh72b\nV7Lqy3e4uJ/QTDOoLcJMOq7o72Tlf58jecRMpl/e9ZI6BQUFhd5Ie0GeOGBvO2PWAS911RBJkraJ\novgi8DOnd9PekCRpT1fn7ir7Dh7mlX9/REj6eAB0BiMNIX155JkXefrhe9Dre3cXmJz8HNZsW0Ps\nWLcIakB4AHm7c9mwYz0TR5wbGS/bl/9E38ZUZ73FTH1NDcbAcycT6xzEK2tPYyv2BOA/oiiOxf0S\n97IkSS93Zd7OIuXkEzZgwlmPD0oextsffsHTD9/djVZ1jPzCfAxhft2qz+EXpKes3HfB2uLiYtau\nXcvgwYO7LAytUqmIj48nPj6e0tJSvvrqKy677DIl0ONlJEm6WRTFRcDbwAHgj5Ik1cGp0qpXJUny\nSDDYm/6O1Wrj5tvvRfAPwpQ4AFPKaAxn8bcZKw5ttVX6L1FrdYSkjcZss/Lx8i28+e4HjB49ivtu\nvbGL1ncMo8HIlRdfyZUXX4nD4WDt1rWs3rSa6oZSdLF6QuKD21yXZFmmpriGhmMNmHQmpo6axswb\nZnolqzkwNBSXVofscrVpowaIKS8npryc7NpanNFRoNUSH5/AsAE1ZO7f3+J1V82ejd5gwKlSue/R\n0IC6qBhBlgkKD2/xmjOpcTgYN35cZ79eh9i/fT2blvybywZoOvUc0ahVXCSq+HnfclapVFxw2Y2e\nN1JBQUGhh9NekGcL8KwoijdJklTxy5OiKAYDTzaO6xKNwoZ/BGbjrn+fCywSRXGlJElftXlxN/Lp\nkqWs/jmTkPQJqM5wug2hkZg1Wu55/C88cvetPklT9gQWi4UX33qBqDFRTT6PHBLJJ19/QmpiP6Ij\nzl5HoyciyzLHiotI6dsXWRAIKS1l744djJ02rf2LFXyFt9aeZNybo68CM4EpwGJRFGslSXq3i3N3\nGFUH29KqtTpk2dVN1nSOW6+9lX+8+xLZ27IJHRCK3ui5FyS71UF5VikRhkgevv1hj83bUfLz84mN\njfV456/w8HDy8vJwOBxKkMcHSJL0oyiKg3EHj7NEUbylO8qnvOnv6PU6HrjnTrbs3MPxEwVY84uw\nO5zYnC7sLlD5BaI1hWIMDm/yQl0gZbL7py8AGHrhVU0CPg67lbryIlwN1WA3o9Oo0WlUaDVq+gQb\nSRs5jwsmtp4p5A00Gg3Tx09n+vjpWG1Wlq5ZyuoNqzCKBgIimgsHN1Q1ULWvmvEjx7PgygVN9LS8\nxW+vv57Mzz9nQkDAWQU35ptMUN+AAyiOiCB10iTS+/Xjpw0bKC8vB9xB5CsvuYT0hAS0FgvRZWUE\n19W7s3Za6NJ1JvMagz/1djvLbTYmLVjQxW/YPjarlR++eIfL09Vd3igYl6Tl683LGTbxIkIjeqeP\nrqCgoNBZ2nujWAh8BxSKorgLyAMaAD8gHhiJWytjjgds+RWwXJKkk60dvhVFcRlwIb/odOEN8o4X\n8I+33semjyA0bXSLY/wDQ9D5j+GZ196nf3Icd958LZoOvqT5mmdefZqgwUGotU1fKARBIHJkFH99\n7Vle+tM/eu0LR2VlJatWrUIODMQ+YAB6nQ5bVhbHS0upX7mSKVOm9Lqf2XmCV9aexp35M9U614ii\n+CGwAPB6kCfYoMdmrkfnf3ZivNW5WVxz8eRutqpjCILAfQvvp6y8jLc+fYuiikL8YvUEtbOL3hY1\nxdXU55oJCwjlgesfJCk+ycNWd4yMjAzWr1/P7t276du3L6YudpuRZZnCwkIKCgoYPnw4ev25pYfW\nm2js3nezKIqzgHdEUVzF6ZIqT+FVf2fYoHSGDUpv9rnFYuXQ0Ry2797P5h0bCBvoztw9uLGpJs+W\nr96m/8SLSZ8wh9ryIvTVecybNonB/UXiYjxflulp9Do9l828jEsuuITn/vU3qu3VBMWeLomtK6uD\n4/DS/73kUy3CMRfPQaVW8cOnnzFVr8dwlgLzGiCutJS40lIGAOGjRrGjspLi4mJmjB7NGKuNkIOH\nOmXTsYYG9vrpufPFF9B2s8C2xdzAG0/dzQXxFtQqz9zrwhR4928PctODfyM8qmMltQoKCgq9mTbf\nbiVJOiyK4iDcu0zTcO96RwBm3KUUrwOLPdRW1IVb3PBMnEBz9bdupMFs5pW3PyKnsJyg5Az8tG0/\naNRaHaHiaLIrSrjjkWeYN3sGsy+Y6CVru8aKTSuo09cRERTR4nmtXoO+j573Fr3HLb++xcvWdQ27\n3c769eupq6tj0KBB6FUqjhcWkpqQQHl1NRPnzqW2tpYlS5aQnp7OoEGDfG2ywhm0svaE41579uAW\nZf6qq2uPKIqhgJ8kSQVnfKwHfNKm/ZG7f8cDTz5PaP+JTTIHW6KhsoS4EB2Tx3pHI6GjhIeF89gd\nj+FwOPh6xdf8vGMTFo2VsLRQdP7tO/AOm4NyqRxNg4ahA4Zy1UO/xq+HdExRqVRMmTKFhoYGtm7d\nypEjRwgNDSU+Pr5DAfGGhgZyc3Ox2WykpqYybtw4RXy5h3BGVs+LQAHujD9P0SP8HT8/PUMHpjN0\nYDphocH8uGoDOUePkLuveYLkgQ3fWtHu2QAAIABJREFU01BRwICBQ3jqkXsJMHZ/JytPo9FoePi2\nR7j32XuaBHlqc2p57r6/94hmE6NmzaLPkCF88fLLqIpLGOPvj74Da4oauMDhZHR4BDuGDGFyTm6L\nSuLtUWS2sBMXqSNHcN/vf9+tm2FlxSdY+t/XqSk5xvREB6FGzwWTDDo189Kc/O/lB1AFRDPryoUk\n9hvosfkVFBQUeirtrtqSJNmBr0RRXIL7JUsH1DXudnmSxcBPoihehLs1+wXADOBpD9+nRWRZ5oMv\nvubnHXvwix1AqNi3/YvOwBgaiRwSwTcb9vLDijXcesPVDEhL6SZrPcOytcsIz2i7FjsoNoisrfu8\nZJFn2L9/P/v37yc1NZXkZLeEYGr//vywaBEp8fHIKhVqtZrg4GCGDx9Ofn4+X375JdOmTSM0NNTH\n1iucwRjg+24u17wEeEYUxdlAFu5yrWuBy7vxnq0SHBTIfb+/kZf+/V9C01t/4TfXVKKrOcajf/qj\nly3sOBqNhstnXc7lsy7nWH4e73/5PoU1hYQObLmUy2F1ULqvlCBNEL+/7FYG9mCH3GAwMHXqVGRZ\nJjs7m6ysLACSk5MJCAho8RpZlikuLqawsBCTycS4ceOUdaeH0ujnLGwsD3V6cGqf+jstMX/WBRjV\nTu74+uNWx+Tt38WDty/slQGekzhdTly/0M4WVAI2uw0jZ5dB2d1ExMZy+9//Tv7hwyx69TWiamoY\nYjB0KGMqwGrF5XR2OMBjcTpZbzETNWAgd999F7puyiosOZHH+qWfU5yfjd5exdgECOyvpb026Z3B\nX6fm4jQw24pZ9+GTVBNESEQs42f9ij6issGnoKBwbtJukEcUxTnAA8A43DvcJz8vB1YBL0mS1GVN\nHkmS1omi+BvgH0AK7vKMmyVJ2tX2lV1nv3SUN977GDko4ZS4cmcQBIGg+H44ncm88sGXJEYG8cAf\nbuqxwsx2p/2snAa7bMfpdPaKkq0NGzZgtVoZMaJpdoNer8cJFJaWEp90utRDEAQSEhKIjo5m1apV\njBkzhoSEBC9brdAKPwGZoiheJUnSsW66x0dAP2AZ7iB2LnC3JEk/ddP92mVAWgo3XnkJH329ipCU\nYc3OO+027AVZPP/MY73ib/JMEuOT+PPdT1BRXcFLb79IhbGC0JTTAY7q/CpchTIP3fQQCXGJPrS0\nYwiCQEpKCikpKTQ0NLBp0yaOHj1Kenp6k9KrsrIy8vLySE1NZd68eb3u53euI4riVcDVuAM6S4BP\ngP8A1zSeXwLccFKQubP40t9piyeffLLdMU888QQzZszwgjWep6qmimf++TSBfZuWVwb1C+LPL/2Z\nh297mNioWB9Z15z4fv2495+vsPGrJXz/zddcoNNj6MaMmmyzhcMGf657+hkiE+I9Pv+Lz/+dKF0d\nVSX5BAn15JZbuHFMICcDO5/vquOqjNPB8faOD5+oZEIqZz3+myxz47GFGrPEq88+Sp/YUIxBkUy+\n+NekDhru8e+soKCg4CvafFqIorgQd1nE58CnuDUwrIA/7u43FwDrRVG8XpKkrrYxpnGOLs/TET5e\n/B1rtu4lOGU0arVnHp5qtYaQ1OEUV1dy16PP8H8P3E58TFT7F3oZP40el8vVbnmAXqXvFS8jTqeT\noqKiZgGek4RHRrLnyBFmtiAeqNVqGTZsGNu3b++1QR5ZxjPNfXsWO4Bdoig+jbu7jSd305EkyQU8\n3vivxzBx9HC27drL0cpSAkKallPWZGfyf/fc6tUWxZ4mNCiUZ/74F97+9G0OH5MITgymtrSOYHMo\njz7+qK/N6xIGg4EZM2ZQW1vLsmXLSElxZ3QePnwYvV7PggULlJKsHogoivcCL+DOrLEC7wC3ArG4\ngzx24Bncosy/6+r9fOHvnM/s2Ludd794l/Dh4egNTbNT/AP90Y7U8swbT7Ng5uXMmNCzglgTLpvP\nwIkT+Ncf/8gclQFNN6wfRRYLJ6IiuO+vf/X43Jt+/IIdG5Zz/HgpF43xJ6ifFtDxea0nlB46R6C/\nhpggNfNFJ2ZbPjv/9zd++MRIcv8M5l5/l8/sUlBQUPAU7T0pHgFulCTpBkmS/i1J0g+SJK2SJOl7\nSZLeliTp18DdwLPdb6rn+c/nS1i/O5swcZTHAjxnYggKIVAcyxMvvE5+QZHH5+8qc6ZfTNnB8jbH\nVOVXMXRA82yCnoharUaj0VBTU9Pi+WRRpLSyElMrrdOPHz9OfLznd6+8hSzLyD1b/7IzvArMAn4D\nHBFF8XZRFFuugznHWHjtFdhKc5p85nI6CTLqiI/t3R3vTvK7q3+Hrcjt6Nfm1HL/7+73sUWew2Qy\ncdlll3H48GE0Wi0Gg4EpU6YoAZ6ey33AQkmSZkqSdAkwHXcG8wOSJH0uSdJi4B7gMl8a2Z088cQT\nHhnT01i65nve//Z9YsbHNAvwnESj0xA7LpZvN3/LR0s+8rKF7RMcEcH8225jV339WY23CwJ0YK3Z\nqRJY+LTnqwWl3Zs5sG4x81Ot3D8tkCDD6c2JM7NsOnosI9AvLqTT15957K9TM66PnvmiA0feJtYs\n+eBsvpqCgoJCj6a9J0AcboHltliHe6erV+FwONi8cy/BSQO69T5qrY6g1FG88/H/uvU+nWHyqMmE\nCqE01JhbPO+wOXCecPKby37jZcs6z9y5czlx4gSHDx/G5WraWjoiKgqz1drsGovFQmZmJkFBQYwa\nNcpbpnocQaBTAos9HFmSpG3AKNzB5AeBYlEUF4mi+LtGYdRzElOAEa266U/U5bQTHNS1Tk49iazD\nWTi17r9TXZCGNVvW+NYgD6NWqxkxYgT+RiPjx3e+FFjBK0QAG884/hm3QLJ0xme5QMu7BOcAM2bM\n4M4772z1/J133tkrS7WKy0owJhrbDbAKgkBIajAnCvO9ZFnHSB81isqz6Lhl1mrZl5xMbFgYR+Pi\n2hWTcsoyAaGh3SKuHBAUQpXTn7wKu8fn9jRlNTaOVmuJiPNt90YFBQUFT9BekGcL8GxjB5pmNIoR\nPtk4rleRe/wELp13EgK0en8qq73aNOOsue+W+6jKqmrxXNm+Mu646c4e3x71TDQaDXPmzCE1NZWd\nO3dSXn46U8nPzw/nGYGfk2Kphw8fZsaMGQwf3rvrsWXZnelxLiJJklOSpH/j1q/4De4i/peBTJ8a\n1o04HA4czqaBSrVWT01Nl+RAegSyLLN42WLe+Ph1ooZEAhAmhvPdhm9574v3cJ5Dv8eJiYmoVCol\ng6fnsx14VBTFKFEUjcBfcPtIc84YMwc46AvjvMUdd9zRYqDnrrvu4o477vCBRV3n0hmXYjlsob6i\n7bXTXGOmdHsZ186/1kuWdQxBEFCpW15HnMCJyEj2pKaQN3AAA9LTEOPiCElPJ2tAf/Yn96GqFcFs\nARBU3VOSH9snjXuefYeKsDEskbT8nGvBYne1f2F7yOCJ1GWH00VmvpUlh1QcoD+3P/UWA0dN7fK8\nCgoKCr6mvbD9QuA7oFAUxV24xQEbAD8gHhiJW6dnTqsz9FD6JiWAxTsvS067DaO/71tztoTJaCIi\nKALZJSOomj4wjSojfRM61mWsp9C3b1+SkpJYt24dFRUV9OvXzx2sagxY2e12du/ezbBhw+jXr5+P\nrfUMLpcd2eXJLr89D0mSHMCXwJeiKOqBoT42qdtYuWELgimyyWeCIFDbYPGRRV3HYrWwaOkiduzd\ngTpSIHbc6SRQQRCIHh7NwYL93Pv0PQzoN5Br5l1DYEDvTpzQnsXOu0KP4Dbge6Cw8dgO3AU8L4ri\nJNzvwhcBN/vGPO9xxx13kJ6ezj333kdwUGCvFlsGCAsO4/nHX+D/nv8/6oV6jCHNu2hZ66zUZdXz\n90f+jtHQM7pstYSrhWBMYWQEJeHhxEZFMSggoMnGXIjRQEhqKg6nkxNRUeSUlzMgJxe947SvUGWz\nERwa0mxeT6FWq7n0hnsBOLJvJ6u//Rh7dSETElyEGju5PgogdyFw3mB1sPGYjEUXzoQZlzJ3/IW9\nakNTQUFBoT3aDPJIknRYFMVBwFxgGpCMuwONGdiDW5T5K0mSfKee1klUKhWD0pKRyk4QEB7Xrfeq\nOrqDx+9c2K336ApGo5E6Sy06Q9MuYCqh54stt4VarWbatGn8/PPPFBcXExV1Wvz6wIEDTJ8+/Zxq\nXVyYfRBtLxDI7gDrca81LSJJkhXY6j1zvMuGzdsxRYjNPrer/cnLLyApvndUydbU1fDDmqVkZu2m\n1laLIdGfiDHhrY4Pig0mKBZyy3N49OVHMGoC6J+aztzplxAe0vp1CgpdQZKkPaIo9sPt6wQDmyRJ\nyhNFMQu4Hbe/dJ0kSZ/50k5vMWPGDFauXElEeNg5kYWm0+p46oGn+OOzD2Ac1zyIU7mvkr/c/2yP\nDvAAxKeJ/HfjRq6LOq3LtrmkhNTISNSNP6dv1q7l0ilTTp3/Zu1aLp40CbVajQz8UF7O/KAgwJ1V\n+VF5OX/7jXfK8lMHDSd10HBqqir45j8vo8o9yqQ+Hf/9MgsGXJ3M5MkscFBIDJf+4S6i4/t0ag4F\nBQWFnk67BbiSJNmBrxr/nVPcefO1PPT0C9SXqzGGeV7IVJZlqo7u4uILJpKUEOPx+T1FUUkhoUnN\ngx21ltpe0zq9LVJSUsjMzGwS5HG5XISEdN/OlS84cSwbtcuMw+Holtp6byNJ0kxf2+BL6hos6COb\n73JqAiPYuHVXjw3yOBwOtu3expotqymvrsDisuAf60fQ0CAChLN/gTKFmTCFmZBlmQNlB9jx5g50\nso7ggGDGj5zAxJET0et6ZoakQu9EkiQL8MMvPlsNrPaNRb4lKjKi/UG9CL1OT2hQKC6nq1nZU6Ah\nkEBTz88avOKee1izaxc5ZjPJ/v4AqMrKSc/aT2FZGfkBATgFAbvTiVatpsZsxoHAwf37iSkvJ6O6\nhuN2tz6OLMusrqsjLj2N0CjvdoANDA7l6jv/zCuPLgQ6loFsldVUyMHo/HXUuAwEqho6dP2RShW3\nP/NXdHrl+aGgoHDu0vvfBLuAIAj87fH7efqlf1Fc2EBgjOdKk5xOB5WHtnDtZbOZNmG0x+b1NNv2\nbsNhbPkB6xen54uln3P1Jdd42SrPUVNTw+rVqxk6tGlVT0xMDD/++CMzZ87s9UEsgMN7thEuVJAQ\nAT988gaX/EZpAdrbaS11XBCEZqWVvkSWZfYf3s9PG5ZTWFJIg70BdaiG4PggQlKCuzy/IAgERgQS\nGOF+AXPYHHy35zu+WrUYf7WB8JBwLhg3jeGDRpwTf8sKCgrdR3xsAkerjxIQejrg7LA6MBp6R9NG\nQRB47YMPePvxx1EXFpPo78e88HCQZRKLikmkmL56PfsOHSIwJARXURHzqqvRVFefmmNeuDsjck19\nPeOvu5YRM72/n7J382qWL/6AkZFmQNfueACXDHmuePJd0fTv1weNRkXWYQh2lNBPnY3mLB+Lk+Pt\nvP74QoZPns3kuVcrZVoKCgrnJOd1kAfcZVt/fuB23v10MVv27iC4b0aXU5Mt9TWYczN5+PaFpPZN\n9JCl3cPipV8SPrjlEojg+GC2bt3aK4M8siyzbds28vPzGTp0KHq9HlmW3erEQFRUFBqNhi+//JLx\n48f36tbpBbkS33zwDxYM0KJRq/jp0M9sX5PMyKmX+No0hS6g12pwOB2o1U2XaUdtBQP6jfSRVW5s\ndhsrN65k4/aN1JirEUwCgfGBBMYFEtjNzYc0Og3hyWHu4mGg3lzHx+s+5sNvPiJAZ2TEkBHMnjKn\nx5RduFyuU1pgCgoKvmXGhBns/m9mkyBPRXYl10ztPX6OIAj87plneOHOO4l0OPH7RXA7wGplUE4O\nmxwOph5vuVNYZn09w+bN82qAx2a1svLLd5H2bSdeX8fl6VrUqrYDPLIMZa4gjrnisKoMREVFMSzk\ntO7Q4LQ+VNRGsa0gHLW9gXhVMVGqUtRtLLmRQXquCIL9+5bwz43LiE1OZ/avbyUg6NzK7lZQUDi/\naTPII4piDm4Ne2i7O7MsSVLvVOht5OarF9A/dRf/+fJHQsXOt9G2WRqwH9/DS089jLGVTgY9CbPD\nTIC69R0su2DHarP2qrKIwsJCNm7cSGxsLBkZGac+r6utRXOGMxQWFkZwcDB79+5lz549TJ8+HX0v\nS9/N2rqWZV+8xfz+AprG9PMZqWpW/PQJ1WUlTL+id2qEnk9rT2tcNG0in67YQUhCU2Fwja2GoQPT\nfWJTXUMdb33yFrknctDFaAnqH0yUxrtp/r9E768nQnSXlbhcLjYXbWbNC2uJCY3mlqt/R2RYZDsz\ndC9lZWXILg90k1HoVpQ15/ygT3wfgjTB2K0OtHoNLqcLTZ2GMcPG+Nq0DiEIAjN/9SsOvPseg02m\nZuc1Tpd7Y6sVirVarl1wWXea2IS1337MznVLGRVlZ4GoB1r3tayymnxXHKWuIGS1H4HBwSRHBKPT\ntJypGWryJzStLw6ni5KKBPIqKsBuJlhVR5KQj0HVsmzogBg9A2JclNbs4oNnbyc2bQSX3nCPkhGq\noKBwTtBeJs8fgKdwd9F6CyhuZVzrT5JexPhRGRwrKGbd3mwCYzvnw9XnZPLc4/f1igAPgEB7WUsC\nKqH3iC7u3LmTgoIChg0b1uxBnXf0KCaDoYnOkFqtJj09nfr6er7++mumTp1KZKRvXwrPBofDwaI3\nn8VRvJ8FA9SnBBfB7fxd2E/DXmk5bzy1mxvvewZD7+tQdF6tPS0xZfwoFn27DFlOPbVrWVdezJAB\nzcWYvcWtd/yexIsSiR7r1jDLXptN3ymn10pfH+euz3Ufx4K13spd99/Ff9/5r081qrZt24bNaiU7\nO5u+fZXYQA/mvF9zzhdu+fUtvPDJ80QPjab8aDlXzrnK1yZ1iprycvxayBKUgaPx8RgNBqoDAgiq\na95JVnA6vabf9+0HL7NuzSqiAtVsPg6bj9tPnbsqw73J6JDhqCuZClcQGj8jB44eQ6CiMdpawK7G\n8fNnTmrxHkuWr29yLAMyKhrEcdjMtQTItaSps9EJTgA+3/XL/xM7uzavpay4iFseeb7L31lBQUHB\n17TXXevHxt2tA8C/JEna4x2zfMfoYQNZveNQp683+PsRHNR7XqiNfkacdidqbcs7F36Cvte0AK6t\nreX48eMMGTKkxfMHs7IY2b8/mVu3MWLc2CbnjEYjGRkZbNiwgQULFnjD3E5TdDyHj197mrFR9SSm\ntJ7qPDhWR0JDCf964jYuunIhg0ZP9Z6RXeR8XHt+iSAIjBkxlC05hZjC3SLL9rIcbrzrQd/ZpFLh\ncvSOrBSn0+XT13GHw8HKlSspLCzE0tDA7t27aWhoYNCgQb4zSqFVlDXn/CEhNgGX1b2OuWwuEmN6\ndll9a2xduYoLDac3FK0aDfnRUdQaDMTGxtLXZOKowUBeVRWRVdVElpef2tbrK8us+fxzZlx7bbfb\nGZMsYl2xApdLhaoFPTmHDBtsGfTtk8hgk1tM+tDR3C7dUwAEXKT1iQFiqG2w8XNOMMPV+zCpmjft\nlGUZmxNCwpUOjgoKCucGZ9Nd65AoitsBixfs8SkOh4PX3/sY/7DUTs9hEXR8+d0KLp87w4OWdR/z\nL5zPR6s+JLJ/8+yV6sIqMgZltHBVz0Sr1eJ0Ols8d/TQIUINBlISEvhm/XoGDc9osTSrpwvwZW74\ngfXffMC8fmr02vbFCoMNWq4Y4GL9d2+Sc3A3l/zmbi9Y6Rm8ufaIoqjG3bJ9mSRJT3b3/c6WKy+5\niI1PvAjhsbicDkJM/uj1ZydS2R288/o7vPPFvzm4+RDqcDWJ45u+HJ2ZVeOL46QJSVTklWMrtJMQ\nncibr77p9SyesrIyMjMzqampoW/fvlRVVQEwbNgw8vLy+PLLL0lKSmLw4MG9rjz0XOd88nfOZ/Yf\n3o/G5F4XNCYtew/vJT6md+ny7V69hsi6OtQBAdT6+5MdF4suIID4yEj6+vmdGtcvLg45NpaSmhr2\nlZair68nNe8YqQYD361axfRrrul2v2fk5Dn4+wWw8puPiVBVMiJeg7/u9MaiAGgFJ3X1ZgKNejQq\nVasZO63R1niXLNNgsYDLgU7jziI6mUFkd7jYW2gjp97EJXNmM/6iKzr+BRUUFBR6IGfl/UqS1HPb\nQ3mI6ppa/vS3V5DDUzGYOt8RJihpED9t2UNVTTU3X3O5By3sHkYOGcl/v/6oxXMNx81cfnXveeD5\n+fkRGxvLsWPHSEw8/fJZU1XF1g0buGTiRARBYGpGBt988QVXXHfdKefG5XKxe/duJk+e7Cvz2yX3\n4B53gKe/pkNOmUqlYkpfFZuyf2bddxFMntt7BCa9uPb8CRgF/Oil+50Vfn569I06BHarhcQI37Y0\n1uv13H79HTidTjZs38DqTasprS/F5eciIC4AQ4jB64FSc42ZmvwahDqBQP9AZoyayfSbpqM7iyCo\nJ7BYLBw5coRjx45hs9nw8/MjISGBlJQUAEaPHs26lSsBSEpKIjExkbKyMpYtW4YsywQEBJCamkp8\nfLyiBdEDOB/8nfOdYwV5aIzuvzW/QD3HThzzsUUdZ++mjYiNwZyCiHCCIyKIDw9H1cL6KwgCUUFB\nBBsM7MvLw6zXE2C1Emh3UFdbiymw+7PPB46ezMDRkzm6fxdrvvkYc3UxqSYL6dE61CoVE3R7KKoq\nQCqPxqH2JzAomKiwQPx1ncsktzmclFbWUlFRieAwE6MuZ7LuBILgztrJKbOSVa5DZQxn3PRLuHTc\n9B6/yaegoKDQETq0xSmKYjjuXod1kiTVdI9J3mfVxq189tVSApIz0Pl3vSNLcPIQdubmkvWnv/Gn\n+2/r8eVbQcYgnA4n6l+I2hm1Rvz0fq1c1TMZO3Ysq1atorS0lIiICBrq6/lm0SJmjxt36gEeZDIx\nODGJpYsXc/Hl7kDcgQMHGD16dI/W41n+v/eYm6butCMyvo+Wbzav6lVBnpN059ojiuJ44ApgMW0L\nrnodp9OJ3e7eeVSp1dS2oK3gC9RqNVPGTGHKmCkA5Obnsmz9MnJ351Bva0AwQWBCIH4Bnl8/7GY7\nVcercFY5MegMxEXFc+3c60hPTe92J93pdFJYWEhubi6VlZW4XC5UKhXh4eH069fvrLKGBEEgIiKC\niMaAndlsJicnh127dqFSqdBoNMTExJCcnExQUJDy4uEjzlV/RwFGDRnN0p+XQiLUFdQxfs54X5vU\nYUKjYyg5kk2ARkPaseOU1tayPzQUwd9AbHQUIY1lXC6XixMVFVRWVKA3mxlYUIh/4zOlXhC8/sBL\nGZBByoAMHA4HO9Z+z5KV35LkV0NGvJZoVRnRqjJkGcqrAjleGYMVAxo/IzFREQQZ2858rLfYOVFc\nhrWhFp3LQpyqmFRVOeoz4kQHi2zsq/Rn0KhpLLzrOnRKNqWCgsI5SrseqSiKc4AHgHGcIYcvimIF\nsBJ4SZKkLd1mYTfzv2+Xs/zn3YT0n+BRZ9oU3QebOZKHnnqBpx6+m6iIMI/N7Wn6Jaeyt2IfgZGn\ng1EOuwOTsWcHp1pj6tSpLFmyhNDQUL7+4gtmjhqNn67prn5CTDT1FjObVq9m7JQpuFyuJtk/PRGn\n036qg1ancTk8Y4wX8MbaI4piIPA+cC1we1fm6g6++2ktmNyBR63en4LcMqxWm09LtlqiT3wffn/1\n7wH3LmmWlMVPG5ZTeLiIBns9+igdQXHBzQLJZ4PL5aK6qBrLCQv+an/CQyK4eMpcMgZkdHspVnl5\nOUePHqWkpASXy92txmQyER4eTmxsrEeeGf7+/iQlJZGUlAS4y4YrKyvZvHkzFosFtVqNVqs9lR3k\n59e7Au+9iXPd31FwExEWQYguBEu9BV2DjsHpg31tUoeZ/dubeH77NqLsdoxaLRGVVURUVuEA8svL\nOR4STFxkFMfzj5NUXEJCbW2TgM6B+nr6T5xIgBeyeFpCo9EwZvo8xkyfR+aG5fz4/X+4OM19ThAg\nXF1DOO7YqsWmJTcvgVw5iJDQcBKiQ5usvcUVtRQVFWKijjTVMYxqC7TwqNmQbSNi6CzuWXCjEjxX\nUFA452mvhfpC4DXgc+BTIB+wAv5AHHABsF4UxeslSfq8m231OLuzDvLjhh2Ep3W+ZXpb6PwNCP3G\n8OQLr/HGc3/ulnt4gpGDR7Htu+1Ngjy1pbWMFyf40KrOU1lZiUajYdOaNWT0TcFo8G9xXHpyMj9t\n3UpNdTVWq/XUrnxPpW/6EHKOryQ5vHM7T2abE53Jt+2uzxYvrj2vAx9JkrRdFEXoQZ1z8guK+G7F\nOkL7n/471McM4InnX+Ovj9/nQ8vaRhAEBqUNYlCaW2DYarOyatMqNm3fSJm5DG2UlpDEkDadbFmW\nqSmqxnzMgsnPxNjBY5l91RyMhq5nWraF0+lk//795OTk4HK58Pf3JyIiggEDBnjtpUCj0TTJ9AGw\n2+2UlZWxfPlyZFlGp9ORkZFBdHS0V2w6HzjX/R2Fptx4xU089fpT/PZXv/W1KZ1CEARufeYZXnvw\nQS4EDI0NMjRAn8JCaisr2GmxMCk7p1kP1cMNDdSnpHDFLQu9bXaLpGWMY/lXH7Z63k9lJ12VDUBB\ndSRZtX2xu9zfymTQYqzPZbw6j/aW6DKzwAylLEtBQeE8ob1t0EeAGyVJ+qyV82+LovgH4FncjlGv\nInO/hD4iqVvvodXpqetYVZzXSe3TD2dt04451jIro2f1PmmCuro6VqxYwfDhw1mSmcnwMWPaHD8y\nLY1dmzczZPRofvzxR2bPnt1jHYALLruJt/60nuRONn/YetzBrBtv9qxR3Ue3rz2iKF4FpAA3NH4k\n0EPKtQqLS3jqxdcJFsc1+X30DwymxlLDs6+8xSN3/a7H/q6eiV6nZ/bU2cyeOhuHw8HSNd+zbss6\n7EYH4WlhTQKrsixTkVOBq1Rm1NBRXHHNFeh13kmnt1gsvPXWW5hMJgwGt7aQ1WqlqqqK0aNbXgu3\nbt3a4ufdPT49PZ3du3eTmZmpUHinAAAgAElEQVTJrFmzWhyj0GHOaX9HoSkpfVKwVdqYPKrn6vC1\nR2BYGLc/9xxvPPwwU20ygWdkLJssVlx2R7MAz/6GBiz9Urnh0Ue9a2wrFB3P5qNX/szsFAfQvv5O\nlFDCEWssgtZdjlZf30B/VX67AR6A2aKaj156mDnX3Eb68IldtFxBQUGhZ9Ne2kIcsLedMeuAWM+Y\n413GjxyGregIstx9m/fmumqM+p6bHQLunWN/TdMSAJVV3eu6TbhcLn788UeGDh2KRqNBfRZZOX5+\nfthsNsLCwggJCWHjxo1esLRzaHU6DKGxmG2da2FdJQeTkNLfw1Z1G95Yey4EhgP1oiiageuAx0VR\nPNCFObtMdl4+f3ruVYLEcahbEA82RSZSYNHzxN9f7da1qzvQaDRcOmMeLzz2IldMvJyijUVYG6wA\nOGwOCjYVMlWcyst/eplr513rtQAPgM1m6xVBM3AHw07+U/AY57S/o9ActbrzGnc9haDwcO5++WXW\nqdWUWqxNzo0sKmpyvKOhHlXGMK7vIQGenWu/Z9Grj7Eg3UWwoe0Aj8WlZb8jhZ+dI0hNSSYjPZGM\n9ETSUpLYIY8g05FGravlrO2T6LUqFgwQ+PnL11n22Zue/CoKCgoKPY72Uky2AM+KoniTJEkVvzwp\nimIw8GTjuF5Hv+RErrtiDh/9bykWs5n4EdNPnTuRuZq4YdO6dBycNAi5ROJvTz7Uzd+k6+i1fr84\n7n1idNXV1QQEBJxuSywIyLLcphNXVFpKTLw7mBUdHc2ePXu8YWqX0LZUa36ogleX5wFw10V9mCCG\nNBuj6YQeig/p9rVHkqSFwKl8dVEU3wdyJEl6qrNzdpVDR3N4/vX3CUkfj1rTutMbEBFPeXkhj/7l\nJZ555J5e2ZVp0sjJDE0bxiPPPULk2AiKt5Xw2G2PERcd5xN7AgMDueOOO8jKyiIvLw+Hw4Feryc8\nPByn09ni/3FrGTit0dnxVquVsrIyKioqcLlc5OXlMXToUGJiYjo0n0KbnNP+jkJzvC873D0YAgK4\n95WXeeOhh0mvqSHB3x3sMDUKLAOsr68nZcZ0LrzuOl+Z2Yyta5cxr3/LgTaLS0OJHEaJKwyHSo9G\nbyQmOoJEQ1Pf1E+nYUh6X+otdg4XR2NrqEXlshKuqiZaKMGgsjUZr1apmNFPxXf7t3frd1NQUFDw\nNe0FeRYC3wGFoijuAvKABsAPiAdG4q5bn9OdRnYnU8aOJD46ikce/z8aqsowBHeyFuYMZFnGWlNO\ntLqah/7yWLeLg3qCXz5ke6PzYzKZqKmpwWKx4OfnR1RsLMUVFUSHtS56nVNUxIxx4wAoKioi0Eci\nhGdDRVkR1aUFaCKbZih9tOEEH6w/cer4z18e5oZJcVw/senLss5RzaFdm0jL6BWdRM75teeX1NU3\n8MLr7xHSfwJqdftrRkBYDHUVKp577R0evfv3XrDQ8wSaAvntVb/l9S9eZ/b42T4L8JxEpVIxePBg\nBg92C7FWVVWRk5ODJEnY7fZTLc/DwsK6rfOV3W6noqKCiooKbDYbarUaf39/EhMTGTVq1OkgtoKn\nOe/WnPOe3ufmtIpOr+eul17k5bvvJsxmx3CG37mnvp602bOYdtVVPrSwOQOHj+Gb9d8zNlGN1S+a\nclcINkGLrNKj1fsREhxEiskf7VlsUBn9tIhJMUAMTpeLyloLh6qqsVrMCE4rGtlOqLoGk62IbXlm\nYsWh3f8FFRQUFHxIm28SkiQdFkVxEDAXmAYkAxGAGXda8+vAYkmSbK3P0vNJ6ZPAJ/95h2de+hcl\nJ6oIjEttkpUDnPWx02GnStrGb2+4nlnTeo9wscVuwe90MxEsNnO7WTA9DY1Gw5w5c1ixYgXBwcH4\nGQzYzJY2r3E4nWg0Gvbt20dgYCBTp071jrEdpKKkgHf//jDzRBdnto34ZYDnJCc/OzPQc0Ffga8/\neRWVWk2/IW1rFfkaX6w9kiTd5Km5OsNzr76NMTnjrAI8JzGGRpF3tIjtu/czcuiAbrSu+xgxeATm\nfzZw6fRLfW1KM4KDg8nIyCAjIwNwl4SWlpaSk5PD/v37cTqdCIJAaGgoERER6HQd73pWV1dHSUkJ\ntbW1qFQqdDodcXFxDBw4EJPJ5OmvpNAK54u/o3DuolKpuOGxx/jkoYe4IOD02nHCYODqHhLgqamp\nITc3l4KCAuxyIFEjL2P1UQmq7QxNj6VfdET7k7SDWqUiPMhAeJDh1GdlVTVs33sEq70PSYNTEQJM\nfP/990RHR9OnTx9CQ0N7lb+roKCg0B7tvk1IkmQHvhJFcQkQDuiAOkmSqj1tjCiK0cA7uLtYWHB3\nuLhDkqRuFx7QaDQ88eCdvPvJYnYcPUJgXGqH55BlmWppC4/csZC+fXqPnk1xWTEW2QwEnfpMCILN\nmZsZlzHOd4Z1goCAAObPn8++ffvYsn49I/v1a3WsLMv4+/mzaf16Lr700ibdbHoS21Z9w8YfPuNS\nEfx1pwM8G6XKFgM8J/lg/Qn6RhpOlW6pVSrmpcPKz//B/p1juPSGe3q0U+PNtcfXyLJMeXVDkw53\nZ4spMZ3vf1rda4M8AGpB7VX9nc6iUqmIiooiKup0lzqbzUZubi7Z2dlYLBa0Wi1JSUkYjS13ApNl\nmZKSEoqKihAEgZCQEPr37090dHSP7u53PnC++DsKjZyD/9OGwMBmCUp6bfuCxt3JokWLMBgM2O12\ndDodxcXFTJw48VSWu8vlYsiQIezYspHMrM0EmAKZNvr082zXwWNkpCd26riopJyNu48QHRnO9FmX\nYgoMZOvWrQwePBin00lVVRWLFi0iKioKtVpNeno6/fr169G+kYKCgsLZ0K5HKYriHFEUV+FOWy4G\njgOVoiiWiqL4uSiKnkwJ+Ax3inQYMAKYh1sM1WvcfM0C9LYKnA57+4N/QU1BNnNnTu1VAR6AD778\ngOB+wU0+C+0bxjfLv/aRRV0nPioKbXY21XnHOHLiRDOBUrvDwZ4jR0ipraV8564eG+D59sNXyF73\nMQsGqDDomqYs/3NZbrvX/3KMWq1iZj8txuLN/Puv9/do4VYvrz0+xWq1Ynd1zqnUaPXUm80etsi7\nCELvDW7odDpEUWTWrFnMnz+fSZMmUVxczM6dO6muPh0bkGWZ3NxcMjMz0ev1XHzxxcybN4/JkycT\nGxurBHh6AOebv3O+45I718SgJ/PNm2+Srv5FUKeqiuJjx31iT2ZmJqWlpYiiyNChQ+nfvz8Gg6GZ\njIGfnx8TpkznimtvwiFo+Hblzyz+cW2TMUuWr+/Q8cqNO8kpriU1bQDTZ12C6Rfl+Gq1+lTTjWHD\nhjFw4EAKCgpYsWJFV7+2goKCgs9p06sURXEhsBi3o3MXcDEwA7gEeAz3Psj6xlbEXUIUxcFABnCv\nJElmSZJycO9wrW37Ss8T2MkUeaeljpGDe033IgCcTifHi4/jZ2oqvKzWqqlz1lJeVe4jy7rGB3/5\nCxO1OtKOHSMoJ5eV27ax8PHHWfj442zevZu9R47Q//AR4ioqSGqo56f/fuxrk5ux4n/vYMv9mfF9\ndB7fVUqL0tFff4L/vPCIR+f1FN5ce3oCfn5+qITOvXDIsoxW1fuEl89VAgMDmT59OvPmzaO0tJRj\nx44B7pedqKgoFixYQEZGBlof764rNOV89XfOV6w2Ky7BSUVVM43tXsuJw0co3LOHGP+m/tw4Pz8+\nfO5vuFzeD2r16dOH2NhYDhw4QFlZGU6ns5kA/ZnHgiBwyaXzSU5Nx9zYLezMLJ2zPXY4nchqHdNm\nzmHMmKax2V/ef9SoUVRXV3Pw4EHKy8sZMKD3ZsUqKCgonKS9cq1HgBslSfqslfNvi6L4B+BZ4PMu\n2jIWOAL8UxTFKwEr7lTmP3Vx3g7hdDopLa8kMLzjDrg+JIYlP67itpuu7gbLuodNOzahCW/518DY\nJ4DFPy7mll/f4mWrusb6xYuJqqo+VS6xZvs2DphMVNbUAPCfb77hgpRURgQEANDfYOSHFSsYe/Ec\nTCHNu1L5iqMH9zEnsfXfw7su6sOfvzzc5hx3XdSn1XN9wnRkZZd11rzuxptrT49A38nuZ7aGOqLD\nQz1sjZc5BzPjNRoNF154Id9//z1+RiMZGRkkJyf72iyF1jnv/J3zmc++/ZSooZG898V7PPC7B3xt\nTpepq67mg78+yxx/Q7Nz/hoNg81mPnzmGW78k3d/xYKDg5k/fz4Wi4WsrCwOHTqEw+EA3AHx0NBQ\nTCZTk40sh8PBYekgl100uclc82dOOutjlSBQU11FQ0M9BsPp0llZlmloaKC8vJzq6mpkWUatVhMe\nHs6kSZN6dPMNBQUFhY7QXpAnDrfgYFusA17ygC1RuHe2PsUtdpgGrAHKgFc8MP9Z8eaHn6MK69Op\na42hkew68DOFxaXERPXM8p9fsnbLWoL7BLV4zhRmInv3US9b1DUcDgc/f/c9FzcGeL7IPsr/jh07\n1S0H3MJ/++vr+KKkmCv7pgAwUavlk+ef5/fPPusTu1siKTWdXUfWkBHfPTv+xyps6AJ6bAtmb649\nPQJdJ4M85toq+o3q3cGDczDGc4oxY8awYd06JcDT8znv/J3zFYfDwfa924keF03u5hzqG+oxGlrW\n0OoNmOvqePWBP3KBRou2lbLPBH9/anNy+eKll7jyvvu8bKE7W3XEiBGnjh0OB4WFheTn53P8+HGc\nTiculwuj0ciRQ1mMHipi+EVGUkdQqVTMnDSCpd98xfDR46mtrUUQBNRqNSaTiYSEBMaNG9cpsXwF\nBQWF3kB7IgBbgGdFUWxxm1gUxWDgycZxXcUBlEiS9IIkSU5Jkvbjrlmf6YG5z4rs3Hx2H8wlIDy2\n03ME9s3guVf/7UGrupea+ho0utZjfWZb292peho/vv8+gxt1ZraUlPBdSQlDhw4lKyvr1Bir1UpJ\nSQlZRiObytyZLCadDuuJAipKSn1id0vM+vWtGNIv5LlVNTicp9OsP99VB5y9Js/J8Sf5bGctW/Js\nZKvTuOH+v3rUZg/izbWnR6BRd06TRbZbiI4I97A13qXnKkN1ndDQUARFb6c3cF75O+czew7uQR3m\n/pvUxen5acNPPraoa7z/9NNMkWVM7ZSADjAYqNuVyZ7169sc5w00Gs2pQMvcuXOZN28e8+fPZ/jw\n4dQU5VJdWsC+A4fIOpxHQWk1doez3TmdLhcllXXsP3qcfQck8nKO4qovIzU1lUsuuYR58+Yxd+5c\npkyZQt++fZUAj4KCwjlNe5k8C4HvgEJRFHfhFglsAPyAeGAkkA/M8YAtRwCNKIrCGd0lNEC9B+Y+\nK954/2OCUoZ1aQ6tzo96bQjL12xi5tTxHrKse3A4HJjtDQThTk89tvsYWxZtBWDMlaNJHJKIU+eg\noLiA2KjOB768hSzLHNi6lTkGA3ZBYDky8fHx7Nq1q1ktenFxMTU1NQQMGUJSZCSxJSWM1GlZ8sbr\n/PaJJ3zzBVpgxhU3s1MqZMmRXPoH1jIwpmtOSUGljSMVKqZcehWjZyzwkJXdgjfXnh6BRqPGLssd\n1l+S7WaiI3t3kOdcRqVSQQ8WOFc4xXnl75zPqNVq5MaYgdBYrtNbMTc0YCkqIijg7LQkRwQEsOp/\n/2PIpEntD/YygiAQHh5OQlQwiQ2ZRAdpcbigtCyMoyVR2FT+hIVFEBMRhOqM52RpVT2FRcVonA1E\nqcrIEErQql3U2R0Ua0JJSkry4bdSUFBQ8A1tbi9KknQYGAT8GtgKGIAkIBB3WvNNwMDGcV3lB9y7\nW/8niqKuUZjwKuBDD8zdLlXVNdRYXag1XY/sB8b1ZfmaDR6wqnt5b9F7GBLdKcq7f9jDmnfXYq4x\nY64xs+adtez+YQ/BKcG8+/m7Prb07Fj2wQeIDgeFEeHsSxMpLi7m4MGDrYoNms1mDh44gCo9jczU\nFDSmQGpy8ygvKvKy5W3z4KOPc8+z/8Z/4Fy+zJKZne5uNd2W3s5J7rqoD1dlBGB3uFh6yMFxYwav\nvfdZTw/weHvt6RFERYZjqavq+IV2M1ERYZ43yJsoMRAFH3M++TvnO0PSh0CVe2PIVmBn9pTZvjap\n0/j5++PQ+511p8xSq434Hl46etUfHmdjaTBlNTY0AsSoyxmp3c841Q4CKnZy4NARBFsdgq2OI9k5\nuAp3M0bYymjtPpLURWhVLsw2J98e0fKbe5/x9ddRUFBQ8AntZfIgSZId+EoUxSVAOKAD6iRJqm77\nyo4hSVK9KIozgdeAR3G3L31ckqTvPHmf1sg9fgLBzzOCayqVGquzZ7+1HMo5xJ7Du4kZE8PuH/aw\n+4fdzcac/CwmLpo1W9YwdcxUL1t59tRWVrJ37VrSRBF7QgLDIiIYM3gwXxUXt3nd1NGjiQ4OJsJk\nIsvPj8FHs/nvc89x9z/+4SXLzw5BEJhy6fWMmHoJ77/4GGmGciaIIdwwKY4P1p9o8ZobJsUxQQyh\nuNrOmhN6fn3rI8T3Tfey5Z3HW2tPT2HO9Mk8/Oe/kDLlV6c+O5G5mrhh09o8Dgsy9fpOTfI5HuU5\nlzWHziXOF3/nfEcQBKaMncKKfSsZkjqkWTvv3oQgCMy69lpWvvceFxgM7szBVqi2WtmmVnH/H/7g\nRQs7jkar5bb/e4V/Pn4LV6Q7UTeWMgsCxAklxFECue6xwwBaSMRafhR++8e/YgrqOc00FBQUFLxJ\nu082URTnAA8A4wD9GZ+XA6uAlyRJ8oguhiRJe4DJ7Q7sBqw2G3hQN8HVg9Pzs49l8/K7LxMzLppj\ne461GOA5ye4fdhN8czCLln2BTqdj/P+zd97hVZTZH//M7S03vRdIG0JL6CBFBBUpCiq2XRTdXVax\ngL3CWhY7rrii/lQs2FZsoCJ2RTpBgQSQMvQESAjp5Sa59ffHpSSQ5Kbcmzqf5/F5nJl33jlzwz33\nnTPnfE//9lmC9s4TT3C+WsMBf3/6hrpFr3/btMnjeb9t2sT1kyejVCrpGx/PjspKIrfv4OePPuKi\nadN8bXaTMZkDuOPxV3h/wb/wL97LDSOjAc4J9Nw0KprrR0ZTaXWwOtePO598FXUHqz9vTd/THkjs\nHofSZcPpdKBoZEt0a0UZw8aO9LFlvsfVBq19ZWTOpqusd2RgysWX8+nSz5h+2/S2NqXFpI4+H4VS\nwQ+LFnGxwYiyjrVsYXU161VK7nrhBTRabR2ztC80Wi1Dx17K/sxPECPO7RjWEOVVdvwjkwgJj/aR\ndTIyMjLtnwajGqIozgCWAtnAbGAScBFwGTAHd5L9GlEUr/WxnT7HZDDgcti9Np+inb663bxjM/Pf\nep7IYREoVUrSP/UcCNn02SYiBkfwv28/4puV7e9F44blywktKsGkVuOqqqK8uhoAq93z37PmmCP5\n+QRaLPQ2GMj4+Rcqysp8ZnNLEASBabMfZ32OHoAbRkbzxNRkgk1qgk1qnpiazPUngz8rD8L0ux7v\niAGeLuN7ajJ71mxKs3af3q6ZtXP2ttPpJCg4mGunjG81+3yBzWbDpXRx/ETDWXcyMr6kq/qcropS\nqUSFEoOhaQGE9kqfkSOZdPvt/FhRgeOsoHlhdTUb1Gru/u9/0ZtMbWRh0xFTh3Hc0nS9pJxiK0m9\nB/jAIhkZGZmOg6dMnoeBmyRJWlLP8TdFUbwVeBr4xKuWtTKZOyVUxgCvzVdlteN0OhtMnW1tpIMS\nb322iKjzoppsl0KhIHJQJN9v+h6dVsdFwy/ykZVN572PPuKOILcmSZ/9B/gqJ4e0Pn0aJ3bqcvHV\nqlUkxMQSWFBAbG4uX+Xnc57RyPfvvMvUO2f72PrmoVKpCAiJoNqWjVatYIQYyAjx3LRkp9a/o77N\nahXfI4riP3CXS8Tgfrh7TpKkNmuPN3xwP7789ids1irUmobbx5Zm72ba1MuaLNTc3vjkmyUE9wrh\ng2Xvcd/ND7S1OTJdly6z3pFxIwjtZ33mDVKGDKH8+mK2f/Q/+tUI5mxwObnrpQUdIoOnJkZzAFX2\npv++VdkhxCyXacnIyHRtPP3CReMWHGyI1UD7b73UABZLJavWp2MKDPPanJrgWF5+60OvzecNFr67\nkMghkbUCPEOvGeLxvJpjItLCWfr9F5Rbyhs4o3VxOZ2nH3SVgDIrm8Dde0hNTWXEiBG1xvbv3//0\n/wuCQFJSEg67nR579hBbQ3DZX6Oh8EReq9jfXKK7JZBfZm1wjFKjbyVrvI7PfY8oiv2Bl4CbAD3u\nt/Wvi6KY2tw5vcEdf59GWdauBsc4nU50jgpGDe3Ybys3b/+DjTs2EtEjnOySI6xoh5mCMl2GLrHe\nkalBx46P18mgceM4bjjzu3+ispLuvXt3uAAPgMnPTLmz6XYfrVARn5LmA4tkZGRkOg6egjzpwNOi\nKAbVdVAUxQDgiZPjOiQVFRYemjcffbd+KDy00TwmZfDdq4/w3auPcEyqX8cGwBgSzZ6jxbz36Vfe\nNLdFKDQCSlXte4xLjSNtQv0/hmkT0ohLjTu9LQgCCr0C2pGExvkDBpBbWXV6e0pICEGlpYyqrEKp\nVJKWlnaOsKK/vz8DBgxgrJ+ZqRUWtDXKtqaEhPB7ZSUX/eUvrXYPzSEoPJbSqvpL0qptTrTahrNB\n2jGt4XsuAn6RJGmNJElOSZI+AU4APVowZ4uJi43CqHI0OKbsRA4jhw5sJYu8j9Vm5eXF/2XxN4sJ\nHxQOQFhqGD9t/YmnFj6JxWJpYwtluiCdfr0jU5tOGOMBQK06I8TvAAwdqETrbCLje5JdUN3o8WWV\nduyGcFlwWUZGpsvjqVxrBvANkCOK4lbgMGABdLjLGwYBR4CJvjTSV+w7mMX8Vxah794fnbHhzlq7\n133LrrUrTm+nL3uTniMnkTKi/lv379aLdGkfB59fyNx7bm3zDg56lZ7Kskr0frWzO9ImuBMXzhZg\n7jcxjdTxtZMa7FY7WARM7WjRcPXdd/PirFmcX12Nf423VcPCwsg6sJ/0o0dJS0sjMzOTrVu3Ehwc\nTEREBElFRYxKSDxnvl0WC+bevYjv3bs1b6PJGPz8yXHUH5issjsxGI2taJFX8bnvkSRpPjAfQBRF\nJXAl4AdsbJHlXkCn0TTYb8pZXUrP5PYpgt4Q5RXlLP5iMbsP7saUaCBiQESt46G9Q6koruCBFx4g\nNiyWGdfNIDigg7eHl+kodOr1jkzXwWW3n24kYlSpyC0sbGOLms8V/7if1/49G6WykKiAhrtIllhs\nfHdQy8y5T7SSdTIyMjLtlwYzeSRJ2gv0Aa4DNgEGoBtgxp3W/Deg98lxHYrMnRLPvvI25pQRTQ7w\nnGLX2hXsXvdtg+eao5IoUIbwwBPPY7XaWmRzS/nX7EcpySyhuvLctyJpE1K5YMZo9GY9en89F8y4\n4JwAj8PmIDc9lzmz5rSWyY1Co9Vy54svskalpLCqqtaxaxISuSI6mj///JPk5GQA4uLi6FtZxbV1\nBHh2WCzYevXkLw+0f22Q8pICtDXihuUOHcccoae31QqorKxsA8taTmv6HlEUhwPVuHU2PsX9INem\n2B0NZ/IISg2FxaWtZE3L2bx9M3Pmz+GhFx/kqCqbyGER+IXW7XeNAUYih0ZQGlTCY689xkPPPsjK\njb/iascdC2U6Pp15vSPTtXA6z/x+KBUKrNWNz4Rpb6hUKm579GW2WaLZdbz+NfSxYis/Zftx+2ML\n5SweGRkZGRrRQl2SJBuwTBTFL4EQQAOUS5JU4mvjfEVpWTkvL/qA4N4jPbYqPiZl1hngOcWutSsw\nh0YTJdZf8mQMDKVSqeTf/3mVJx++q9l2txSjwci8+59k7vy5BA0IRGuoXesclxpXqzSrJnabndyN\nuTww80EiwyJbw9wmoTeZuPu//+Xle+9jQGUl4bozZUrXJCQSZ/LjN42aIH9/hiQmcknxuf98N1sq\n8Bs0mCtuv601TW82f6z5mUui3H9Duwsy7CJ2VAQoSjAIVgxaFUWHj+FyuTqkOG9r+R5JktaLoqgB\nBuPurnM78Io3r9FULFVW/Bo4rg8MZ8MfGYwa1n5Ltqqt1Sz+YjG79u7CZXYS3DOYSHXjfYfB34Bh\noAGnw8nXm79i2Y9fEh8dz4zrZuBnbOjTkZFpHp1xvSPT9VCbzdjLK1ApFOyzWBh88cVtbVKLUKlU\n/POR//DFoufJPLqFtOjaGT3ZhVYyLVHMmje/zTPmZWRkZNoLHlsLiKI4URTFX3GnLR/H3YGmSBTF\nE6IofiKK4lBfG+ltVm/YjDq0u8cAD0DmT56baDRmjN4cREFpRaPs8yUB5gCefvBpirYVNem8E9tO\n8NDMh4iPifeRZS1Ho9Vy10sL2KzVUmqtLUgcPGAAf59yOYvmzSM2rhtF5tpZBDstFvwHD+kwAZ41\nKz4m2HmcQiGEzbZebHIOJjExidSeiWxXDCLd1ofDjkhSAy38b+HjbW1us/C17xFFcbkois8CnNTk\nScctrNqrpba3hB279mJXNxzE0BpMHMlpvy3H31/2Pvc9cy+HHAcIHRpCWM8wlOqmt8IFUCgVhCSF\nEj40jHzTCR5+8WEWvveynNkj43U643pHpusxavJkdpzUNTuq1dD7vPPa2CLvMPWfD3BMiKbEciaj\nx+VysS7XyM0PvyAHeGRkZGRq0KBHFEVxBu432p8AH+MuY6jG3YkmGhgLrBFF8YaToqUdgpDgAFzV\nbRBwcdQvktuamE1mgvyCsVvtqDRn/glkZWaR/tkmwN1Rq2ZWjx493WK6t7apTUalUnH788/x8uzZ\nXOpSU2oycTAinMjoaEL93YGdlG5x7FUqOF5YSMKRo5RXVFAQEcHM225tY+vrx2azkZOTw9GjR9m1\nI4OC3KPEx59PSYA/8YF+aGoIavdOjsPudFJYYqFQk0BRbh4vv/gcaQOHER0dTUxMDDpd+xZkbiXf\nsxyYK4rie8BeYBQwDvhnC81vEV9+/wvGiO4ex1W5VOTlFxAW0r40a95f9j4//PQ9ugAdlp0W8jhx\n+ljC6IQ6zzmw6kCd+yGKG70AACAASURBVM8ebwgwYBhiIH3FJmy2Bdwz4x7vGS7Tpems6x2Zrkfq\nqFH8+v4HWB0OzKFhHTKTtz7EPv0p2H0If4M7m8dqdxESFo7SQ+MUGRkZma6Gp7D3w8BNkiQtqef4\nm6Io3go8jXth1CEYNjCNj5d+g9NhR6Fs+CNIu/ha0pe96XGMJ8pyD3P+8MFNstOXlFnKCNacaSKS\n+d22WsLLv721irQJaadFmatsVTgcjg7xQ6rSaEgeeyE/799HQvfu9AkLQ1mjbbwgCIixsVSGh7PX\n7I90+DBTb7i+XZU1VVZWsm3bNvLy8nA63a3MzGYzOdmHsVUUc+mFDb+ZUykUhAWaCAs00Ssxll37\nsti+dROCMJTdu3fjcDgQBAF/f3/69u1LUFCdDWXaktbwPYuAeGAlEAQcBP4lSdLSZs7nFY7nF2FM\nTPI4Thfanc+X/8htf2tfXeAEaFA02ivXEGrrTsjIeIFOud6R6ZoISiVWlwu9wdDWpngNh8PBH+t+\n5crkMy+pNCqB4twsKkqLMZoD2tA6GRkZmfaFpyBPNG7BwYZYDbzoHXNaj79Pu5rX/recwIT6tXQA\nosQ0eo6cVK8uT8+RkxrU44GTDyMlR7huyt+bba83+W7Vd7j8zzyGnR3gObPfvS9tQir6bnoWLVnE\nzGkzW83OpmCxWNixYwc5OTkAxPUQ2btvL93Dw1HUE7jRazQEmozEJsRzPC+P3Xv2oNFoSEpKIikp\nqU0DWh999BEpKSn07t37dOBpy+8bsJad4PwhqR7OPpeeSXFos47xZ8bvjL/sytP7i4qK+OSTT7jh\nhhvaVcc0WsH3SJLkwv1g93Bz5/A2+QWFVLuUNKYnmsE/iP2Ht/jcpqZy/eU34HJB+raNmJP9MIV4\n1s+pL8PnbCwlFor3lDC4/xBuv+H2lpoqI1OTTrvekelaFB7PQ1VZidFoJD83t63N8RrvvvAwIyIq\nUKs0p/cJgsC4RDtvPHM/s+f9n1yyJSMjI3MST94wHXhaFMW/SZJ0Tg9GURQDgCdOjutQpPUS0dK4\nblen2qSfHejpOfJSUkZM8Hi+w1pFt7jYdpEl4nQ6WfzuYvpe1weArG1ZdQZ4TpH5XSbWomoG/3Uw\n2zdtI78on5DAkNYyt0HKysrIyMigoKAApVJJZGQkffv2Pf05905NZV9WFmK3bvXOsW3/fi697jo0\nGg3dunXDbrdz/Phxdu7ciVKppHv37vTs2RO1uuHWnd5m6tSpbNu2jR07duByuSg8kUd5ST4jBzVf\nLiY2KpziimqWf/kFsd0TUSgU6PV6pkyZ0t4CPNCJfU9DLP9pFeqgmEaPL6+0Ybfb29XCVhAEpl85\nnanjp/L+0vfZtXEnimAlQfGBKFVND5w6nU6Ksoqx5VrpHtWd+2bfT6DcPUXG+3RJnyPT+fjxg/dJ\nVasRBAFNWRkl+fn4h7SPdVtz2fTrckKsB4kJP7fU3KxXMyK8lGVvv8DVtzzUBtbJyMjItD88PRnM\nAL4BckRR3Aocxi1IqANigEG469Yn+tJIX1BeYaHaZqOxiawpIyZiDo0+LbKcNu5aopIbzuA5haBQ\nUXiiaULHvuJP6U8UOsXpQEj6p5s8nrM/Yz+D/zoYQ4yeX9f/yjWTrvG1mQ2SnZ3Nli1bUCqVxMbG\nEhNT90NxSt++LP/44waDPCiVaDRn3gqpVCqio6OJjo7G4XBw4sQJli9fjslkYvjw4a0WDAkMDGT0\n6NEAHJa28+X6zxiUHM6RvTuodmlwKTUoNTqCgwIJNutrlaOBW4ywuKKaEwVFWKssCA4rKpeVWGUZ\nrtJjBKsjOf/Saa1yL82k0/qehti5Zx+mmH6NHi8Yg1n/ewbnnzfIh1Y1D6PByK3X34rL5WL176v5\n7tdvKXeWE5QShNao9Xi+rdpOwe58tDYdY88bw/h/TGhXwSyZTkeX9DkynY/cQ4foq3X7WFEQWLNs\nGZf+s02l5lrM5nU/MyFKU+/xqAANm/btb0WLZGRkZNo3Da6YJUnaK4piH+BSYAxu/YpQoBJ3WvOr\nwFJJkqz1z9L+cLlcPDH/FfSxfZp0XpSY5rE0qy5UGi1FVhXfr1zH+DEjmny+N9Hr9IQkNE2oVXlS\nnNlR5cTP1Latiw8ePMi2bdvo1auXxwc+lUoFivobyDmcTlQNZOgolUoiIiKIiIjAYrGwYsUKrrji\nilpBIV+Tuf4nflv2DpNTFKiUecSRd/qY1argWE4Eu48G4VKbSOgWjVql5EBWDtaKEoKVpaQIxzAo\nrLW+6fHdYNWmb/i2tISJf22f3cQ6q+9pCLvdTqmlmsAmZPz5hcfx06p17TLIcwpBEBg9ZDSjh4wm\nJy+Hdz59h5ySY4SmhtYSfj+F0+Ek788TBCgCuOvau0nsltgGVst0Nbqiz5HpnKhUKrC5G31YXU5M\n/h1fq6bPgGHs2baMXpF1vyDIL7MSHC7/VsjIyMicwuNrUUmSbMAyURS/BEIADVAuSVKJr43zBQ6H\ng8eee5kqQzhGQ9MCFsekDDJ/+hRwiy03JeAT0L0XS39YhUIhMG708CZd15skdU/C5DJRVV6FzqRj\n6DVD+O2tVQ2eM/SaIdhtdmw5dsbPHN9KltaN3W5HoVA0qvSttKQEVQPjFIJAVWVlo6576noOR+uI\nvVZXVbL07flU5+ziil7KOu9Xo3DSnWN0Vx7D6lKyUarEJajpr9qJWWNpcP7RCSoyDq7m9XkSV9/y\nIMFhkb66lWbT2XyPJ75fuQ6FuWl/B6VaQ0FxWbsSDW+IyLBI5twxh4NHDjL/9fmEDw2rFehxOpwc\n25DDzX+9mf49+7ehpTJdka7mc2Q6JxHxCeRmbiNCr+MAAtMuuKCtTWoxIydex8KNq4mtLMZPX/vR\nxe5w8nOWltnzHmwj62RkZGTaHx6DPKIoTgTuA84DtDX2FwC/Ai9KktRhatSfXPA6+w9lodLlU3xE\nqnUsut+YOs85mrGSo/t2cnT/ztP70pe9Sc+Rk07r9Zw9vi6i0i7gs29/IyjAn0FpvVtwFy3jkTvm\n8NAzDxE2JJS41DjSJqTVq8uTNiGNmN4xHNtwjEdue6TNHySTk5PRarVs3boVhUJBXFwcZrP5nHGV\nlZV88/kXXDig/gdFQRDoHhbOyu+/54JLLjnn3pxOJ3l5eeTm5mI0Gpk4cSJ6vd7r91QTu93OD5+8\nzr7MjYyMthKW5LmsBUAjOIgTcjhkD/MY4DlFv2g1SVXH+WzBPZgikrnyH/dhMJ37WbYVnc33eGLN\nht/xi+rb5PMcukD+yNjB4P5NP7etiI+JZ/rU6Xy89mPCxNDT+0vzShk5aESnC/D4utuYjHfoaj5H\npnMyeeYtLLz1Vs6321FHRhIUFur5pHaOIAhMm/UvvvzvPYxLrn1s+zErF035J1qdb9dnMjIyMh2J\n+mtZAFEUZwBLgWxgNjAJuAi4DJiDe+26RhRFzz3E2wE/rlpPTpkDla5pLSXPDvCcYtfaFexe922j\n5xEEgcDkgSz64BPsdnuTbPAmfkY/5s6eS+7vx3E6naRNSCVtwrlZSf0muluo527OZea0W4mNjGsD\na88lLi6OKVOmcMEFF1BaWkpmZiY7duygqMite7R/zx6WfvgRFw7oj8nYcJ+i3okJBGs0fP7hhxQX\nFeFwODh69CgZGRn8+eefGAwGLrvsMsaNG4efn+9K1SrKSvjktad4dc7f8MtZw5W9BML8GxfgOYUg\nOFHgbNI5Jp2KST1U9Bb2sPjJmbzz/AMcP3q4SXP4gs7mexpDeZUVhaLpwsTG8Fh+XrPBBxb5DqvN\nyhfffoEpvPb30xhkYuMfGymrKGsjy2S6Kl3R58h0TjRaLYFxcWwsL2fSP9pHV1dv4LTbcTrPfdFo\ndQpt2g1VRkZGpj3iKZPnYeAmSZKW1HP8TVEUbwWeBj7xqmU+IPPP3RjCuxHQrfEdio5JmXUGeE6x\na+0KzKHRtUq36ssIAlAolDhVBhwOR5uKiEaGRTLj2hm8991iwlPDSZuQSmB0gFuIWYChVw8lLjWW\nwoMFXDBwDGkpTdci8jV+fn6MGjUKgPLycr5dupQ9koS/0ciFQwZjamTWTUJ0NGqtlh+//hoXcP7Y\nsUyePLlVFg2lxYUsfWs+lvzDDI20M7ynFneFQNNpSZJViFnDZWYor8pmxWsPUKUJZcr02UTHi82f\ntGV0Kt/TGKz2pgXoTqHRGigpKPeyNb7B4XCw9IelrEr/DXOKGYN/7YC7WqsioF8gD7/wEP1TBnDD\nlTegUbeeBpZMl6bL+RyZzsuIKVN459nniE1O9jy4A7B32yaWvbeQKT3OXegMilWzbOnbVFWUMeSi\ny9vAOhkZGZn2h6coQzRuwcGGWA286B1zfEtqrxT2/7YVTVyPRp9zqpuWpzGN1edxOp0Itgq02qZl\nafiCgX0GsuTrJaf1POJS44hLrZ2t48hzctUtV7WRhZ7JP3aM7xYvJu/QIaKtNqbo9ThKS8lC4KDR\nSEx0NEGmurN5XC4Xh/PyKC0oIKykhIkn8qm22/nj7XdI/+RTeg0exJi//AWND/5W1uoqlr39AvlZ\nOzk/zklAihpoWVDJG4V0Jp2Ki5Oh2lbED289isMYzdSb7ycoJMILszeJTuV7GoPQzKIeh8OOWtG+\n9XgKigv4YOn77M8+gC5KQ+Tw+rWHdCYtkcMikXL3cO8z9xAVEs30K6cTHRHdihZ7D6ezecE7mVan\ny/kcmc5LfO/eWFwd3/eUFhfy2RvPoi7L4upeSlR1vHxTKhRM7eVi8/qPeWXNT0z9xz1ExskizDIy\nMl0bT0GedOBpURT/JklS4dkHRVEMAJ44Oa7dc8kFw9mwaTOFRScwBLZNjXLRvi38c1rbtiCviclg\ncieh1/OMaNAb21yH52wqy8v5dckS9mZkoCsrp69azQCtFk6+8Vc6nCQdOYITOFhRTllUNN0iwmvN\n4XK52LZvPzE5OcQXF5/er1epGGUy4XK5yP5tNa+uWo06KJDzJk6k/5gxKBro1tVYXC4Xd95yE5Fa\nCwadkh/2AFQDcG3/ulu0f7K17kwNX4+fGHOMt5+5n1lPvoFO37QyxxbSqXxPYwgw6XHYbShV9Xd8\nq4uyI3u5afKFPrKqZaz5fTXf/PwNFU4L5iQ/IoaFez7pJOYIM+YIM5YKC88ufgatQ8cF541m0phL\n251Paoj8/HxcLlmVpwPQ5XyOTOdFo9GA0PL1Sluy4sNXOLR9PaO7OfEPb/h3URAEBsVp6G0tYsXr\nc9CFi/zljsfkMi4ZGZkui6cgzwzgGyBHFMWtwGHAAuiAGGAQcAQ4V324nTL3nluZ+8xLVDgdGIM9\nZyekXXwt6cve9DjGEy6Xi6J9m5k89jyGDGg/AqkVlnL8Ff71HrdUWtpF5x673c66r74iY/VqhJJS\neiBwkUGPYKo7aAFuwanEI0fJ0OvPCfJUWK0Yy8sIqRHgqYkgCMQZDcQBNksl0nsfsOrjJRhDQxl7\n7TUk9+vX7HtZ9/1nmBXuAE97x0+v4uJ4Kx+/Oo+/3fdMa1660/keT8yYdhXzX/+A4J7nNfqcqopS\njFQwbGCqDy1rOj+v+5lvfl4OQRCcFoxZ2XxBb51RS0T/CFwuFyullfy45kdGDR7F1RPbT7C8IbZs\n2YLVaiU7O5vY2Ni2Nkemfrqcz5Hp5HSgYPjZfL14AcLRdCb3bFqWs16j5BJRyeFCiXfnP8SMh+b7\nzkgZGRmZdkyDQR5JkvaKotgHuBQYA8QDoUAl7rTmV4GlkiRZfW2ot1CpVDwz916emP8K+Xk2TGEN\nL7qjxDR6jpzErrUr6jzec+Qkj6VaTqeTImkT100ex4UjhzTbdm+TsTODSnUV/tQf5FGGCSz9YSlT\nx09tRcvOcDw7mxVvv01RdjaJDidjDQYUHsSUa5IVGUFAUNA5+40aDRUmEyUGA/6WhrtRqRUKevuZ\n6A1UFxWx7sWX+NqgRxzQn3HTp6PV6Zp0T32HjmXLyi+5olfjFy71ZeC0xvgdeQJDJrfuc01n9D2e\nEBO7c/n4C/h6ZTqBCZ6DiDZrFZWHM1jw74dbwbrG88b/Xmdn7i5Ch4Z4NTgsCAJB8UEQDxv3b2Tf\n/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GVl9QqkFAAAIABJREFUJfaqanonJrB540bAnel08OBBtm7dSlVVFePHj2fy5MlygKft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\nRERERKSR+H9z94xlx957dgAAAABJRU5ErkJggg==\n",
+ "text": [
+ ""
+ ]
+ }
+ ],
+ "prompt_number": 3
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "from flotilla.external import link_to_list"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 6
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "link_to_list('https://www.dropbox.com/s/qddybszcses6pi6/DE_genes.male%20adult%20%2019.txt?dl=0')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stderr",
+ "text": [
+ "WARNING, downloading things from the internet, potential danger from untrusted sources\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 7,
+ "text": [
+ "['ENSG00000141076',\n",
+ " 'ENSG00000188747',\n",
+ " 'ENSG00000119636',\n",
+ " 'ENSG00000189298',\n",
+ " 'ENSG00000124205',\n",
+ " 'ENSG00000135365',\n",
+ " 'ENSG00000138796',\n",
+ " 'ENSG00000243477',\n",
+ " 'ENSG00000088179',\n",
+ " 'ENSG00000136574',\n",
+ " 'ENSG00000204849',\n",
+ " 'ENSG00000091136',\n",
+ " 'ENSG00000118961',\n",
+ " 'ENSG00000180745',\n",
+ " 'ENSG00000255242',\n",
+ " 'ENSG00000188672',\n",
+ " 'ENSG00000011295',\n",
+ " 'ENSG00000174948',\n",
+ " 'ENSG00000147224',\n",
+ " 'ENSG00000186288',\n",
+ " 'ENSG00000005810',\n",
+ " 'ENSG00000091483',\n",
+ " 'ENSG00000196104',\n",
+ " 'ENSG00000172889',\n",
+ " 'ENSG00000106070',\n",
+ " 'ENSG00000157617',\n",
+ " 'ENSG00000163006',\n",
+ " 'ENSG00000160505',\n",
+ " 'ENSG00000171533',\n",
+ " 'ENSG00000113328',\n",
+ " 'ENSG00000131620',\n",
+ " 'ENSG00000177646',\n",
+ " 'ENSG00000158373',\n",
+ " 'ENSG00000130202',\n",
+ " 'ENSG00000225748',\n",
+ " 'ENSG00000130208',\n",
+ " 'ENSG00000181804',\n",
+ " 'ENSG00000113327',\n",
+ " 'ENSG00000100796',\n",
+ " 'ENSG00000183558',\n",
+ " 'ENSG00000135686',\n",
+ " 'ENSG00000067191',\n",
+ " 'ENSG00000175426',\n",
+ " 'ENSG00000175063',\n",
+ " 'ENSG00000121680',\n",
+ " 'ENSG00000135378',\n",
+ " 'ENSG00000147896',\n",
+ " 'ENSG00000152620',\n",
+ " 'ENSG00000147894',\n",
+ " 'ENSG00000008118',\n",
+ " 'ENSG00000251369',\n",
+ " 'ENSG00000154330',\n",
+ " 'ENSG00000106397',\n",
+ " 'ENSG00000181896',\n",
+ " 'ENSG00000160799',\n",
+ " 'ENSG00000184650',\n",
+ " 'ENSG00000173457',\n",
+ " 'ENSG00000140931',\n",
+ " 'ENSG00000144802',\n",
+ " 'ENSG00000166225',\n",
+ " 'ENSG00000170144',\n",
+ " 'ENSG00000166220',\n",
+ " 'ENSG00000132591',\n",
+ " 'ENSG00000104218',\n",
+ " 'ENSG00000113552',\n",
+ " 'ENSG00000188483',\n",
+ " 'ENSG00000213920',\n",
+ " 'ENSG00000147571',\n",
+ " 'ENSG00000175581',\n",
+ " 'ENSG00000122863',\n",
+ " 'ENSG00000204848',\n",
+ " 'ENSG00000102678',\n",
+ " 'ENSG00000243509',\n",
+ " 'ENSG00000213073',\n",
+ " 'ENSG00000258315',\n",
+ " 'ENSG00000116729',\n",
+ " 'ENSG00000149809',\n",
+ " 'ENSG00000179051',\n",
+ " 'ENSG00000120645',\n",
+ " 'ENSG00000187664',\n",
+ " 'ENSG00000255398',\n",
+ " 'ENSG00000213672',\n",
+ " 'ENSG00000187961',\n",
+ " 'ENSG00000165238',\n",
+ " 'ENSG00000167565',\n",
+ " 'ENSG00000165233',\n",
+ " 'ENSG00000069399',\n",
+ " 'ENSG00000203812',\n",
+ " 'ENSG00000089225',\n",
+ " 'ENSG00000100314',\n",
+ " 'ENSG00000117748',\n",
+ " 'ENSG00000101654',\n",
+ " 'ENSG00000131981',\n",
+ " 'ENSG00000203818',\n",
+ " 'ENSG00000178878',\n",
+ " 'ENSG00000171033',\n",
+ " 'ENSG00000197782',\n",
+ " 'ENSG00000170367',\n",
+ " 'ENSG00000115514',\n",
+ " 'ENSG00000175203',\n",
+ " 'ENSG00000114988',\n",
+ " 'ENSG00000132824',\n",
+ " 'ENSG00000151893',\n",
+ " 'ENSG00000231370',\n",
+ " 'ENSG00000184709',\n",
+ " 'ENSG00000167011',\n",
+ " 'ENSG00000244405',\n",
+ " 'ENSG00000108641',\n",
+ " 'ENSG00000140326',\n",
+ " 'ENSG00000144381',\n",
+ " 'ENSG00000169217',\n",
+ " 'ENSG00000152926',\n",
+ " 'ENSG00000242028',\n",
+ " 'ENSG00000042429',\n",
+ " 'ENSG00000119227',\n",
+ " 'ENSG00000049249',\n",
+ " 'ENSG00000125445',\n",
+ " 'ENSG00000125351',\n",
+ " 'ENSG00000184792',\n",
+ " 'ENSG00000173566',\n",
+ " 'ENSG00000107521',\n",
+ " 'ENSG00000115602',\n",
+ " 'ENSG00000114737',\n",
+ " 'ENSG00000056487',\n",
+ " 'ENSG00000132612',\n",
+ " 'ENSG00000160124',\n",
+ " 'ENSG00000068354',\n",
+ " 'ENSG00000146425',\n",
+ " 'ENSG00000079974',\n",
+ " 'ENSG00000185614',\n",
+ " 'ENSG00000185164',\n",
+ " 'ENSG00000108395',\n",
+ " 'ENSG00000125848',\n",
+ " 'ENSG00000185008',\n",
+ " 'ENSG00000255526',\n",
+ " 'ENSG00000053372',\n",
+ " 'ENSG00000163513',\n",
+ " 'ENSG00000101276',\n",
+ " 'ENSG00000171962',\n",
+ " 'ENSG00000127540',\n",
+ " 'ENSG00000161791',\n",
+ " 'ENSG00000105993',\n",
+ " 'ENSG00000107738',\n",
+ " 'ENSG00000134283',\n",
+ " 'ENSG00000138741',\n",
+ " 'ENSG00000127423',\n",
+ " 'ENSG00000137133',\n",
+ " 'ENSG00000162711',\n",
+ " 'ENSG00000152192',\n",
+ " 'ENSG00000203965',\n",
+ " 'ENSG00000168090',\n",
+ " 'ENSG00000109016',\n",
+ " 'ENSG00000143801',\n",
+ " 'ENSG00000034533',\n",
+ " 'ENSG00000055070',\n",
+ " 'ENSG00000106462',\n",
+ " 'ENSG00000173418',\n",
+ " 'ENSG00000109861',\n",
+ " 'ENSG00000125945',\n",
+ " 'ENSG00000111305',\n",
+ " 'ENSG00000155719',\n",
+ " 'ENSG00000161939',\n",
+ " 'ENSG00000139278',\n",
+ " 'ENSG00000268657',\n",
+ " 'ENSG00000103496',\n",
+ " 'ENSG00000103490',\n",
+ " 'ENSG00000213516',\n",
+ " 'ENSG00000008394',\n",
+ " 'ENSG00000137075',\n",
+ " 'ENSG00000131386',\n",
+ " 'ENSG00000197826',\n",
+ " 'ENSG00000140157',\n",
+ " 'ENSG00000168003',\n",
+ " 'ENSG00000197548',\n",
+ " 'ENSG00000180424',\n",
+ " 'ENSG00000213453',\n",
+ " 'ENSG00000113916',\n",
+ " 'ENSG00000110090',\n",
+ " 'ENSG00000137869',\n",
+ " 'ENSG00000169064',\n",
+ " 'ENSG00000134030',\n",
+ " 'ENSG00000262742',\n",
+ " 'ENSG00000158552',\n",
+ " 'ENSG00000241838',\n",
+ " 'ENSG00000132141',\n",
+ " 'ENSG00000183019',\n",
+ " 'ENSG00000010292',\n",
+ " 'ENSG00000129028',\n",
+ " 'ENSG00000159685',\n",
+ " 'ENSG00000123737',\n",
+ " 'ENSG00000092978',\n",
+ " 'ENSG00000156127',\n",
+ " 'ENSG00000183765',\n",
+ " 'ENSG00000174842',\n",
+ " 'ENSG00000129187',\n",
+ " 'ENSG00000177599',\n",
+ " 'ENSG00000164715',\n",
+ " 'ENSG00000139880',\n",
+ " 'ENSG00000177103',\n",
+ " 'ENSG00000137509',\n",
+ " 'ENSG00000144152',\n",
+ " 'ENSG00000118454',\n",
+ " 'ENSG00000149743',\n",
+ " 'ENSG00000198805',\n",
+ " 'ENSG00000198807',\n",
+ " 'ENSG00000122584',\n",
+ " 'ENSG00000126602',\n",
+ " 'ENSG00000116791',\n",
+ " 'ENSG00000163121',\n",
+ " 'ENSG00000199886',\n",
+ " 'ENSG00000126767',\n",
+ " 'ENSG00000156564',\n",
+ " 'ENSG00000162654',\n",
+ " 'ENSG00000170616',\n",
+ " 'ENSG00000132744',\n",
+ " 'ENSG00000166199',\n",
+ " 'ENSG00000168539',\n",
+ " 'ENSG00000178425',\n",
+ " 'ENSG00000150967',\n",
+ " 'ENSG00000117519',\n",
+ " 'ENSG00000114353',\n",
+ " 'ENSG00000128833',\n",
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+ " 'ENSG00000187837',\n",
+ " 'ENSG00000163421',\n",
+ " 'ENSG00000167700',\n",
+ " 'ENSG00000137285',\n",
+ " 'ENSG00000143643',\n",
+ " 'ENSG00000147262',\n",
+ " 'ENSG00000189308',\n",
+ " 'ENSG00000156983',\n",
+ " 'ENSG00000077150',\n",
+ " 'ENSG00000163607',\n",
+ " 'ENSG00000081320',\n",
+ " 'ENSG00000104731',\n",
+ " 'ENSG00000163576',\n",
+ " 'ENSG00000156110',\n",
+ " 'ENSG00000010539',\n",
+ " 'ENSG00000112210',\n",
+ " 'ENSG00000175193',\n",
+ " 'ENSG00000167232',\n",
+ " 'ENSG00000013563',\n",
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+ " 'ENSG00000138964',\n",
+ " 'ENSG00000183597',\n",
+ " 'ENSG00000197620',\n",
+ " 'ENSG00000143546',\n",
+ " 'ENSG00000149679',\n",
+ " 'ENSG00000135482',\n",
+ " 'ENSG00000174059',\n",
+ " 'ENSG00000105443',\n",
+ " 'ENSG00000147419',\n",
+ " 'ENSG00000153815',\n",
+ " 'ENSG00000003147',\n",
+ " 'ENSG00000120694',\n",
+ " 'ENSG00000214944',\n",
+ " 'ENSG00000174238',\n",
+ " 'ENSG00000026297',\n",
+ " 'ENSG00000170892',\n",
+ " 'ENSG00000182307',\n",
+ " 'ENSG00000255701',\n",
+ " 'ENSG00000141959',\n",
+ " 'ENSG00000170185',\n",
+ " 'ENSG00000244295',\n",
+ " 'ENSG00000197724',\n",
+ " 'ENSG00000128298',\n",
+ " 'ENSG00000125388',\n",
+ " 'ENSG00000100949',\n",
+ " 'ENSG00000182575',\n",
+ " 'ENSG00000182903',\n",
+ " 'ENSG00000216966',\n",
+ " 'ENSG00000182901',\n",
+ " 'ENSG00000152763',\n",
+ " 'ENSG00000077935',\n",
+ " 'ENSG00000128595',\n",
+ " 'ENSG00000183527',\n",
+ " 'ENSG00000115363',\n",
+ " 'ENSG00000271867',\n",
+ " 'ENSG00000171049',\n",
+ " 'ENSG00000196139',\n",
+ " 'ENSG00000197405',\n",
+ " 'ENSG00000165507',\n",
+ " 'ENSG00000135655',\n",
+ " 'ENSG00000100218',\n",
+ " 'ENSG00000183688',\n",
+ " 'ENSG00000197140',\n",
+ " 'ENSG00000102287',\n",
+ " 'ENSG00000187193',\n",
+ " 'ENSG00000114416',\n",
+ " 'ENSG00000170324',\n",
+ " 'ENSG00000132002',\n",
+ " 'ENSG00000205426',\n",
+ " 'ENSG00000169851',\n",
+ " 'ENSG00000188986',\n",
+ " 'ENSG00000100985',\n",
+ " 'ENSG00000179477',\n",
+ " 'ENSG00000198218',\n",
+ " 'ENSG00000225164',\n",
+ " 'ENSG00000129810',\n",
+ " 'ENSG00000144355',\n",
+ " 'ENSG00000186188',\n",
+ " 'ENSG00000122133',\n",
+ " 'ENSG00000100453',\n",
+ " 'ENSG00000153048',\n",
+ " 'ENSG00000164796',\n",
+ " 'ENSG00000137100',\n",
+ " 'ENSG00000162086',\n",
+ " 'ENSG00000141499',\n",
+ " 'ENSG00000162946',\n",
+ " 'ENSG00000187003',\n",
+ " 'ENSG00000066651',\n",
+ " '']"
+ ]
+ }
+ ],
+ "prompt_number": 7
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "study.interactive_graph(feature_subsets=['protein_coding'], \n",
+ " savefile='figure2_a.pdf')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "savefile : figure2_a.pdf\n",
+ "data_type : expression\n",
+ "featurewise : True\n",
+ "weight_fun : no_weight\n",
+ "use_pc_4 : True\n",
+ "use_pc_2 : True\n",
+ "use_pc_3 : True\n",
+ "sample_subset : all_samples\n",
+ "use_pc_1 : True\n",
+ "cov_std_cut : 1.8\n",
+ "feature_subset : protein_coding\n",
+ "degree_cut : 1\n",
+ "feature_of_interest : RBFOX2\n",
+ "draw_labels : False\n",
+ "n_pcs : 5\n",
+ "u'RBFOX2'"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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EL+atr6ummXoSUWtI/FR1EbAIuFVELgAeBH4FvBrCtZcCl/pfi8hzwGpVvT2U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+ "text": [
+ ""
+ ]
+ }
+ ],
+ "prompt_number": 5
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "%pdb"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Automatic pdb calling has been turned ON\n"
+ ]
+ }
+ ],
+ "prompt_number": 9
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "study.interactive_classifier(feature_subsets=['protein_coding'], \n",
+ " savefile='figure2_a.pdf')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "savefile : figure2_a.pdf\n",
+ "predictor_type : ExtraTreesClassifier\n",
+ "data_type : expression\n",
+ "score_coefficient : 2.0\n",
+ "sample_subset : all_samples\n",
+ "feature_subset : protein_coding\n",
+ "categorical_variable : M_cell\n",
+ "Fitting a predictor for X:all_samples_protein_coding, y:M_cell, method:ExtraTreesClassifier... please wait.\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stderr",
+ "text": [
+ "configuring predictor type: ExtraTreesClassifier with 384 features[Parallel(n_jobs=1)]: Done 1 jobs | elapsed: 0.0s\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "\tFinished.\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stderr",
+ "text": [
+ "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s finished\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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f21S8eYokSZI0HhkQStXnYeCjIYSLgCeAU4AbgMOA6ZUsTGNnsKYdAG3DCAhH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Af2auY5oL7x2xCI0YqDmjU/yrIsxEGBQCAQCASCV0he73BraVxvQP1HnxkQIEmStrP7y9jmqgAk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99ls2bNgQdCjGGGOMMcaYkLMahMaET0dcDcLvRGQtbkZhHdz/r6eJyC1eu5iqnhhMiMbA66+/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+ "text": [
+ ""
+ ]
+ },
+ {
+ "metadata": {},
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CmdgZiYnkLVzIZ2+/HcPITJQF2bFk63AgBZglIoW+L41feKYuCObm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vDhhx/idDp59NF+M2l/CFiEq+vEP4DSdsb1+4jAvJ/M5K1PV+EX037k3lZRyD0LnupFq7qfkMBQ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PC8np2dz6yCt8WxTM3lLP6YAeqbSy4rAf19z3PENHn9eXl3K7b/ElPl66ErO/AZXav/OD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\n3DimnzeV2tIcxngWUlGQyYEjGdTUG7FYO7dYrjeZqayp5+Dho/g5yvC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1Mw2pVMpfHn6Y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WGiEoaIq+kQ3KaWMlMPlQ25vPnYrcy/7WGiEtL6fa8z2ed4OP2QSqVMmDuXCXPn0lBXx88ffcSu\nzCyiTWYSe4nuaVTI7fptb7ZY2GE00qzzZvwVl3Px9Om9dhJ0B6QyGX/562OsensBsxPFHCo1EZ02\nhYj45ME2rUf0er0P8DhwJ7AJGGkwGDrXTfDgwYnc9dRT/GfBY0QYjaSo1ajs/M43y2QcjI8jLS7O\nbvFDIZWSHh/PYaUKi1RCiB3pW9AqPOc0NWNA4NKbbuyTwNOGRCJh6tSp5OXlsW3bNoKDgwkP77nO\nYEBgEJdcfjVFhfn88MdaAnw0jE5L7NUvmswWtmQeoKHZzKRzziMgqPcUM5vNRl5eHrW1tYwfP57g\n4P6XOXDISu50clDD01LYkbUHqVcgUvng5Dabjc3UHtnBtfPnOm0x/eFXH7Lj6HbCxthflXzs/DG9\npmuNnT/G7vFkShlh40NZ/PliLp85n8ljp9h9raMRiUSIrDYAvJuayDBkc8BkIjAqmiCfkykkFquV\nvUeOEFN4DN/6egA0UimldafHJq5IJOKiG//Oyg9e5VDJJhKD5aee0OV1x6pbKFclce29T7jASvs5\nnXyLIxCJRNx67XySt+zk42/W4hvXfc2vupwdPPXQXwkNdu+aFFKpdMDt4bvDZrWhVHQutOkoVi3+\nD0NragjuYWcqsriYTI0Gb32C3UXdx6an96lVehsWq5Wc3DwyCo91+b5IJCJDo6GmqYnlb7zBbc88\nY9e4zY31fLliJf839WQk2NJdDR267K3IrOXy4VZiJEVQ3vl9R76OAjbtaWB4D+f/km1iXrqUlR+9\nxd1PLrbrOe3hTPM5Hk5ftN7eXHzXXQiCwObvv+fn774nqqmZlG42D0sDA9FpNBilUpQWS6f3LTYb\n65ubEYcEc+EttxAeP7COj4NBaFQ8E+ZcjVZeQ0CImYwZ1w22Sd2i1+slwB20dtmqB/5iMBi+HFSj\nPJyx+IeG8n9L3iU7M5N1y5dTX1qGT0sLcVIpAUpltyJIblgYybGxfY5uEYlEDAkPY3d9XY8iT73Z\nzFGjkRKpBJmvL6POn8OFM2YMeP0cHR1NVFQU+/btY+fOnXaJPWERUVx65XUU5B3l29/Woo8OJXlI\n545XgiCQuf8whWXVnH3udIJDel+LW61WCgoKqKqqIj093SF1YAf0L3Q6OqhHHnqApuZmXl70Xwor\nG9DFZiDu4YPZXcROf84XBIHagoPoxEaefvhuggN7zwfsDx9+9SG7CncSlNq3NKmo9CgyZmV0WXgZ\nWjts9ZaqdSpiiZiwsaEs+3kZEomEiaMm9el6R2Jt5xAkQGpuHtlWG7qoSBTH38uvqCAx5whqk+nE\nueWmFoKjTq82rGdNm8cvizdyajUdQRBA1Hmnr7LByshZXed7Dgano29xJJPGjmDpyh96PMfHS+X2\nAg8cL+Aqdk56bHNdE9FOrJU1+dJ5LFv4BOcpFMi62SEXAym5uexFQB8fj8ZJhVGbzWYOHs4hOTeX\nnqZSFpuN7Y2NTL/oFvvHbmxA7CwlzomIAIudnTp640z3OR5OX0QiEWfNmcNZc+bw/ZL3WfvH75yt\nVneI6jGLxdR7eZEYHs7h+gZSj3YsRVVnMrHWYuEvDz5AtBunZNlD6sQ5AAxaMRs70Ov1s4CXgGjg\nOeBFg8HQMrhWeTjTEYlE6IcPRz98OIIgcOzwYbb++CN7co5gaWhAe1z0CW4n+vg0NlJZV0eYX/dN\nfbrDaLEgP+U3vNZk4oixhXKZBIlWi29YFCPPO4/LRo50eGCESCQiNTWVoUOHcuDAAXbt2oWvry9R\nUVE9RulERscyPyqGrJ1b+fbXzUyfNBL58TS3pmYjP/2xgxFjxnHWeb1HGVssFo4ePUpDQwPp6emc\nc07fdIee6Pe/1unsoNQqFY/dfyd79ht4472P0caN7LV9+kCxWszUGLYyb855zDxngtPus+KnFezI\n3U7Q0P7VwWnrnnWq0DNsdgbpM/uXciUSiQgdFcqn332Kt1YbUQOEAAAgAElEQVRHetLgpG7FpqdT\nmLmLiHY1HBIKCqDgZBHCrvat9ksk3D//MhdY6BisVitfvf9vhvt2XpRaRQqsts7HI3yl/Pz1RySk\nje5X+KMjOZ19i6PYcyAbs7jnv0NdQzNNTc2onZjy6SicVg1BJMJmszlrdKISE7ng7/ex+qOPUdXW\nMEqlRtnFD7/SYiHjcA6HLBZkAQHEhYYidlANCEEQyC0ppaminLTcPGTd5Fy2WK3saGqiQefNtL/+\nleQx9kdd+gWFMnXKRLbkbWdsdGv0X/uoGXd8fXGamm8OWpl5xcA7OXp8joc/C7NvvpEdMVFkffgx\nI7xavzMWkYi9cbEkxcSglMnwDQ3hiMlE3LGTEYHrbVbuff01NF7uWdftz4Rer18NzAD+AG4FCoFg\nfRfdFQ0GQ75rrfPgoRWRSEREQgIRCQknjpXk5bFt9WrWGrKxNtSjNbYQ09xMvUpFntlCdLD9a8/q\nhkby8nKJzjnCrqZGyqUyJFotAUPiGT1jBgnp6b3WTnUUIpGIlJQUUlJSyMnJYffu3Xh5eREX13Ov\nhYwRY4iKjSc35wBJCa1NT3Jzyzj/kivQaLWtG+s9cOjQIUwmEyNHjnRKcfP+dtdq76D+QauD6vJJ\nnO2gBlr9vbaunoeefg3fROfWtKjKyeRv115MSqLzwl93H9rN0y88Tfr8k8rhkXVHiJsc1+fX+bvz\n2bJsK1aTlfFXjycqPXJA40FrasXepft4+/W38dP1XfEdKBaLhRfvuJPzxGLUdqrBmxsbSL30Msae\nP8fJ1jmGY0cNLH33JcYF1BLp17l+yCZTGoJIwnhZZqf3yutMrClUc8n1dxOfMsKhdvWhu5bb+Jbu\ncHbHiYbGJu7/53PoksYj7qHTVnN9Dcq6XJ5bcL9bFZU8la2ZW/lk7ScEpTg+6sjcYsF00MQzD9mX\nljQQCgzZfLPkXbJycrjEW0fE8WKnKysqmBsQcOK8FcZmotPSiIyIIMDLi1Xr1nHh5JM1g/ryurqx\nkbXbtzMF8K+t6/J+n5QUE+TljdTfjzk33jigwqgbf/iSbWu+Ynq8gFbpXp1z2lNQZWJjiYqr7nyU\nsJiEbs+zs9ON2/sc8HS68dA3XrnjDqaLJTTJ5RyMjiJpyBBU7QqqFlVWUld4jMS8PGpaWijU67ny\noQcH0eI/B3b6HHt3JgSDwTBoxZA8PsdDbxTn5rL1+9UcPXAAi1iEV3gEw5OTUMu7a/7S2sF3T04O\npQWFiOrrCIqIYPTMmSQMG+YyUcceGhsbkffwHI7AbDajtrNwfnc4o7vWjOP/n0TrxKg7BOgxsnzQ\n0Xl7IZe6oK2ixYg+3nkpP41NjSz+eDHqQMe0uo1KjyIqPYoj646cEHgGilgiRhWk4unXn+alBS+5\nfGEqlUq5/el/sfihh5ktFvfaIeZAUxP+o8ecFgJPWVEeX//3VaRNxVwQK0bZRYHYCpsPCq0vcrmM\nY3UhhItLOrwf6C3nEr2JTZ89zw+SQC685i4iXV+w8E/jW/qDIAj887lXUcUM61HgAVB5+VDfHMAb\nSz7hnlvcr1MDQGlFKf/78r+Ejre/AHBfkCmk1CpqWPbdUubPudwp92gjUp/AnS+8wKK33sKiVPLT\njp1oGxqwCR3n6+KGRoZlHya/sZGygIB+JUEJgoChoAB5RQWSnCP4txN1oHWStKOpiWq1CmtICLc9\n/zwqOws/98T4mZeSPOpsPn3zSWLkFaSHDV4Xyq6wWm38nGPDJ3oYf3vmQUeFbp/RPsfDnxOFVkup\nSkVpaChp0dGd6mWE+fujVavJlMmQ7dlD0pjRg2TpGck52LfJfvrl0Ho4owiNiTnRlKapvp5fly3j\np81bCPDRMT4trVNE88H8fA7k5RHp5cU199xNUHj4YJhtF5peuoQ5AntaqA+E/kbyTLbzWsFgMPRc\nyXeADFRp3pa5jyXLf8Y3NtXhtrWnofwYo2J0XH/5RU4Z//F/P44lwoxa5xiRx5nUFNaQ5JXMTfNv\nGpT75+3bxxcvvMBMjbZboSm/2UhxVCQ3LnzcxdbZj9VqZduvq9j+x0+orVWMjxKjUXS96Cm2BXGE\nWFITYxAD+3MKCLbkESPuuoBri9nGpnwL1YKOtFETmXT+VQNaUPUhksdtfEt3OHN367Ovv2f9/mK8\nQuwXhCsN23nsrmuJirS/yLor2H1oN29/9DbBY4KRdfO5dBQV+8uJ90vgzmvudOlOUE5mJstef4NJ\nYjG+XaQ51mq15MdEk3pKyO+WrCze/eILAG6ZP79TEeYDubmE5OXjV9u54HutqYV1FisX3X4byWPH\nOvBpOvLtR29gyt3A6Ej3EHqsVhsrD9o4/7r7iU8dZdc1du6qu73PAc+uuoe+8c6//41WqSQtLq7H\nDTWzxcKP27Zx2dVXEz9kiAst/HNip8/5FbjCYDCUtTs2FdhkMBiajr8OB9YaDIbOOVwuwuNzPPSX\nVUuXcnj/fmaln2wesi3nMM0yOTfcdafTI2TOJJwRyfM4djooYNAclD18/OVKdNEjnX4fbWA4m7Zv\n4Lr5cx0ewZJ5IJMqWxUhuv63WTuV/Kx8tnyxFWjtqNXXgss94RPhw64tO6mrvwxvL2+HjWsv0UOH\nct5117Plgw8Y18Xud5PZzD61ivsf/6fLbbOHipJj/LjsHSqLjqLXGTk/RoZE3LXDrLB6k22NQesb\nSHpYwInP3tAhURSWatlQHsAQaSHB4soO1ylkYqbEyxGEJg4bvmXxgp/RBoQz7bKbCI/uPj3CAfxp\nfEt/yNyzH6/wYX26RhkUw4/rNnLL1Zc6yaq+YTKbeON/b5BbdZSw8aGIJc4XXQJSAskvyuPvT97H\nX6+/myExrlmsxA8bxn1vvsGjN97EDXJ5J9+ua2jA1tKxtMuy1atZunr1idcvLFnC5bNmMX/WrBPH\nzEYjvl0IPAAr6ut5/J130Ho713eef83dvPn4fqCh13NdQVmdibi0SXYLPH3gjPY5Hv58lJWV4RsR\nQWlOTq/zTYlYjCDArsxM4uLj3Tr190/EFODUtpDfAhmA4fhrGeBR3Tycllx4eefIapfnBXjot8gz\nhT+Bg2pqaqbZAqo+tn3rL4ImgF179jMi3bGdCz79+hMC0wN6P9FOslbv7lB4+bcl68iYlXGiKLMj\n0CX7sPjTt3notocdNmZfGHbuOWxd8wvVpWWdduDXm0zc8NSTbjXZsVqtbPzhCzI3r0Vjq2V0OPgk\nyYDO0QONNiVHbFE0oEGr8yMpxB9ZFwvtiGA/QgN9OFYWTE5VBWqaiBXnoxM3nThHJBKREKQgIQga\njHn8umQBNYI3ScPGMeXCa5A5Xo2fwp/At/SX2Ogo9pWWoPEPsfsaU1URo6fNdqJV9iEIAl98t4zf\nt/2Bl15LSJT9z+AIdGE6rIFWXvvsVQJUAdx9wz1Or/1lamnh/SeeZJpa3clfmEUiDsZEExR6MlXt\nVIGnjbZjbUJPaFgYuy1WkvNykVs7poPNVGtZ8vhC7nj2GRRK57WPb26sx9zsHgIPgJdKyo68HGcM\nPQUX+hy9Xj8BWAwkAIeAvxkMhrWOGNuDB4CioiIio6Iozc+nurYWX52u23O37NvHuEkTqamvx2q1\nOrx7jYfBx+NzPHg4M3GfCkeDQHVtHUicN0k+FbFcTXFphUPHNJvNNFmakEgdI1SdKvCcPJ5F1urd\nDrkHgNpbRWlFqcPG6w8X33UX+9u1SofW9sOKoCD8gx0XFTUQTC0tfPPh67zx6I0Ydy9nblwj0xJk\n+KhPplCYbSIKrCFsMw9lk3Ukh1RjCYpLJS0lkdjwwC4FnjYkYjFRIX6kp+gJj08lVzOOTbZRbDWn\nkmsNxySc/FxplVKmxMuZG9+MPPdHFj12E1/85zmaG91nIXi6c8tf5kHVEYxN9Xad31h+jNhgL4al\nJTnZsu4RBIHv1n7HVTdexdairYSOD0EboOXIuiMdznPFa4lMQsiIECyRVu687w6eX/wcdQ11/Xqu\n7hAEgb0bNrLowQd56/Y70FdUEKk62eGsTqVkb2wMh1KHEpeYSIiPDwBbdu/uUuBpY+nq1WzZ3epj\nA7290SfqyR46lL1xsdS2K8wXolKSUVfPojvu5I2/30/mb7/12sGhr1itVv7z7AOcF+OY9uSOQKuU\nEiMt5ZuP3hhsU/qNXq/3BlYC/wHUtHby+lqv1/evHaYHD12QnJxMXl4e58yaxbqsLEzmrr/HuUVF\n2ORyvHx8UCqVHoHnT4jH53jwcOZyRnv03IIiRHLXtR+WqzTkFRY5dMzCkkLEGsdodfm787sUeNrI\nWp2Fb7iPw1K3TFZT7yc5kcCwMBplHb8C1S0thA6gO40j2fD9Urb99g2jg00MS1LQttlssokpEYIo\ns/lhESsRy5T4+/mSoNMgHUBqjEohJT4yCAjCarNRVWckq6oSa0szYlsLQeIaQsUlKERW4gIUxAVA\nSc1Oljx5G/rhk5hxxe2OefAzGIlEwrMLHuDBhc8jjhmJXNV9ja3GyhJ0tmoe/uu9LrSwI9uytvHx\nio+QBEtQhSjxjfYdNFvao9Qq0IRoqPOr4x///j+SYpK5/arbB7SIaaitZdV//kNx9mFCTC2cpVIj\nPy6+mCRijoWEUK/RoPHxITEwsFOh03eXLev1Hu8uW3aiPo9SJiMlJgaL1UphYCC51TVomxqJKC4h\nSKlgOmBububg+/9lzccfExgby9zbb0fn79/vZ2xj+bvPM9K3toOY7A6khspYc2gDhsxR6IedNdjm\n9Ic5QK3BYHjz+OvP9Hr9Y8A84O3BM8vDnwmFQsG0adP4+eefOW/OHH78/nvmTJzYoQhqdW0tBwoL\nmTB1KiUlJcyePfjRoB6cgsfnePBwhnLGijwtLSY+Xb4K7/gxLrunytuXzH3rKa+sItDfMWkEESER\nWBvt7cbYM1uWbbXrHEeJPDLJ4C4gjM3NSC2WDse85XIOlw5uhJEgCHzw7wV4NR5mXooci6CgwBpM\nqc0fi1iJRK7C38+XeG91j1E6A0EiFhPooybQp3URa7HZqK43sqeyGktLE2KbkWBxNWHeJVzkY2Pv\n0bUs/tdBbnroBWekcJ1RaDVqnnvsAR564kXE+rFIZZ1T8pprq1A1FfHkYw8MSlqhxWLhqdefokao\nJmBMAGKJGP+4juJC3OQ4t3itHqumsCyfvz35N26+4maGpfSt5lHBIQPfvLcEc1k5wyUSMpRKOP4Z\nr1cqyQ0PQ6LREh4STIwT0qekEgkxwcEQHEyD0cgRf3/MDQ3EFJfg3dREmpcXaUD10Vw+vP9+xAEB\nzLr+euJS+99MoL62mmG+7hno66+y0VDv2OgsFzICyDzl2D485Qo8OBg/Pz/mzp3LDz/8QPLw4WzM\nymLisFbfZ7PZWJuVxbAxYxCJRMyZM8et0tM9AI7rrOXxOR48nKE4U+Rx29Z/6zZu59OvvkEZkYZE\n6jqhQSQS4RU3ikefe5NzJoziirmzBvzDKpPJ8FH6YKw3ovRyXerZQKktqmWIcwv49squX9YQfkrG\nokIioaGqapAsamXf9j/Q1h8iMCqBTWY/RDI1AcEBxOucJ+r0hlQsJlCnJlB3UvSpqmtmZ0UltpYm\n/IOqiS87yIbVS5ky9xpnm+Mw3+Kuueo6by8WPnQ3j73wBn7JExCLT0aFmJqbsJUe5Omn/uHSTlLt\neendF2kJMhIUeHpEfHsFeaMJ0PLOZ//hmQefxcfbx+5rP3zheWbI5KhOaae5PzYGqZ8fSSEhnaJ2\numLKmDGs+OWXXs/pCa1SSVJ0NFabjVw/P/IqK0k9chQR4KtQcK5CQUtDI5+89BKP/e9/vdrUHbOv\nvJ1l/3mWKGU9w8OlSAbpc9aeJpOV9Xk28Irm/InTXX17R/kcX+DUXMwmwHUhxR7OGJRKJXPnzmXd\nunWovbxQxMQAremYiXV1pGdkEHP8mAeX85Jer2/LdRfRWvfrWb1e31Zx38tB9/H4HA8ezlAGIvK4\nykE5BKvVyorv1/Dbxi2YFb7okiYMygJJplThlzye9Qfy+WPzv8hISeT6yy9Coeh/9MMjdz7CQ888\nRNCYQOTK/o8zdv4YflvSc4fYsfMHHvnUWNWItdDKHQvuGPBYA2HH2l+ZpO78OydvaKC6vBzfwMBB\nsAq2bfwNIWAMgcFRpPp03+Z9MJGKxQT5aAjyaV34VtY1cdTiT/WevUyZO+DhXeJb2uWqLwQWAZfT\nmque0L7TzmARGhzInddfxeJPVuCnHw2AzWqh/sg2Xnr8IWSywQvEbGxqQuLvmoL1jkIsFoMEmo3N\nfRJ5/AICOFZSSqxafULsqNDpUIeEEh1of8H737b2Hin529atXH3hhb2eJxGLiQ8NpUippKSuntCK\n1lpvNkGgqNmIzm9gkaKhUfHc+/QSMtf/xLc/LkduqSPFz0JUgMKl/shksXGoxMTRBgVyXQgX3HEP\nIRExzriVq+YzDUDYKce8AKdUlPbgQSQSMWXKFKqqqlAdrx1ms9kIDQtD10NBZg9O5Xcg8Ph/bawH\n/IE25y0Cep6Q24fH53jwcIbS31WCKx3UgKirb2DJJ1+yecPvBCRPwGvIOEQiEccy1xI+7JwT57n6\ndV1xDuHDzmFvWSn3/PN5TNXFPP3kQsJC+r4z7qXx4l8P/ounX38aWbQE75D+/XBHpUeRMSuj27o8\nGbMyBpyqVZVThbdJx8JHnhhU8UIQBMy1tUiVnUUevVjM+hUruODWWwfBMghJGMnuHZsZN9w9BZ6u\n8PdWs6O2gdAhfUuF6QJX+ha3z1UfkZbEhJEpbDEcxTs0lpojWdxz87XovAdXQ3/0rkd5/NXHKRGK\n8dH7odR2TilzF2xWG1VHq7BWWLl42iWEBoX2flE7bnrySTZ9+y2//fEHttpahggCIRIJBeVleKtV\n+J4S4eMq6pqbKS0pIbmmhrzGJrIRQOdN6ozp3HbJxQ65x7CJ0xk2cTqN9bVs/PFLvt23E6G5hhit\nicRgOQqZ4zdKaprM7C+1UWZSofQOYuTk6Uw/a6ozi8K60ufsAWaeciwV+MIBY3vw0C1+AxR+PTgO\ng8EwxYW38/gcDx7OUPo1a3Kxg+oXgiDw/mcr2JK5D1VYEgrfULxDogfbrE5o/ILR+AWTv/1nFr76\nPvHhAdx323XI5X1LI/P38efFR19k0ceLOLT1IL4pvii1fU/famuTfqrQM2x2Bukz+99CvaG8gfrD\n9UweM5nLZs/v9ziOIu/gIfxMls6Nc4EQlYrfDh5yvVHHmT5jJhWlRaz4eTNnjRhKiL+X24o9giBQ\nVdfE+u178fcP4KJL5g1oPBf7ltMiV/26y+ay/f+eoqlOR0yID2nJg5vmCK1pAM8/8jxFpUV8uOJD\nivYdAy3oonRukTZqNVupLaqhpcyEWqLhgikXcs64c/r1PZIrFEyeN4/J8+Zhamlhw9dfs2nzFsz7\nD1BRXIzG35/Q0FAiAgJ6TNu6Zf58XliypMd73TK/Z99otVoprKqiuKiYxspKLJWVVGs1JE89h9vm\nzUOp7r5Q90DQeOmYdulNTLv0JiwWC3u3/MrvG36hua4CjdBIerBAoHf/hD6rzcbRchOGGilWmTd+\nwRGcdfUlRA1JcYnfc7HPWQ68oNfrbwfeA26jtePNShfa4MGDhzMHj8/x4OEMxT1Xjn1Ar9fHAEfX\nrFlDRETEieOLP1zK7oI6vENjB822/tBUU4mPqYQnH+l/x5yq2ire/OBNyupL8Rvqh0LV98l3/u78\n1kLMIhh72Vii0iP7ZUtjTSN1h+rQRyVy21W3oZC7x47/78uXU79yFTFeXUdErAHueevNLt9zFYW5\n2Xy05C38fH3x9fNDp/Mh0NcblWJw66W3mC1U1DRQXVVDXW01JWUVXHbl1ejTRnV7jcgNVSq9Xr8E\nkBoMhuvbHfsAMBkMhlt6uC6GLnyOMzl0OI8D+aVMHTMUL+3gRI70hCAIHMo5xOp1qykuK6LJ2ow8\nQIouwgepC9LKBEGgobKBxmONSE1SvNXejBt1FueMPQelwnmiU0l+Ptt//JHs7GyMUhkSuYyI4GCS\noqKQdxF5smz16m7bqF8+axbzZ83qdNxssXCosJCC4mLMZjNKs4UhsbGMmjWTMDeop1FReozfv/2U\nolwDWlsdw0JF+Gt73qQQBIHcihb2VckQFL6kjpzA6KlzUakd+9l2U78zkdb00ARgN3C7wWDYZcd1\nMbjY73jw4KFveHyOBw8eXElPPudP211LKpViNTZis9kGrThpf7A01yFTDqwYtJ/Oj3/e80+OlRzj\n/WXvUVxbjHeCNxpf+yfQUelRA0rNqi2uoTnPSFxEPI/8/f/w1nr3eyxnUFtejran8H+r1XXGdENE\nTAKPPPUKa756n92b1+IXoaaoKopmVAgSBTpvHQF+XqgVzi0ebjSZqahppKamBixGFEIL2pZCigtq\niUkZxXV/fcSZqRTO5LTJVU8cEk3iEPeLRGxDJBKRNCSJpCFJALSYWli/7Q827NhIVUMVJqEFRYgC\nXagPYgcVD2+qbaK+sB4aRWgUGhLjEplx7UzCQk79kzqPkKgozr/lpB5YVVrKz199xY+bNyMgwt9H\nR0Z8PNrjtTDaRJxThZ4rZs/mspknI+objUZ25+RQXlODSBCIDgri+ltvJTDMdc9mLwHB4Vxy04NA\nq+Czc81yYob0XKuovsmIxUfMDTPno1Q5J/rIXTEYDOuB/ofFevDgwUMf8PgcDx7OTE7LlZk93HzV\nPPSbd7D06+8wS73QhsYhV7nfDjiAxdRCfclRRE2VTJkwlssvnOGQccNDwnnsnn9S31jP+8ve5/Ch\nbFSRSnTh9hcd7Qs2m42qI1XYKmyMSh/FlY9e5baLf5FYjGMazzsXkUjEefNuYuLsK1n5v1coy97P\n+DAzgd4KKmu8Ka4JoVFQIUiU+Oh8CPL3QjHAyAmTxUp5VQPVNdVgMaLESKiohARxDfUtZv4oEKEN\njufGfzyNVufroCcdFDy56k5CIVcwdcJ5TJ1wHgB1DXWs2biGHVnbqTPWIfYR4xvji1Ru/2dVEATq\nSmtpKjSiFCmJDo/m+otu4P/Zu+8wqcqzj+PfXWAXliKIgi2CorfdWGNX7MZobLHFbkysGDXGEkss\nscXYsUdFY9fXLiio2HsXUW6xN6QJCCy77O68fzxnYBhmtjEzZ3bm97muvXbn1PvM7NzzzHOestIK\nKxVNd8bF+/dn36PDgPKNjY28MmoUL7/1NnX1dazcty+rLb88+2y1FQOWWIKbH3mEiooK/rzbbvxm\njTVIzJrFZ999zycTf6Kqupr11lmHQ3faiS5dCjcD5KJaov+y7PDH41vcrjfQvrahIiIiItKS4igZ\nL4LWNCf87IuvePCJkfzw02TqEp3p3HtpeizeP7YWPolEgtnTp1I35Tu6NM1hiT692HWHrVn/12vk\n9ctKQ0MDD454gNffewP6JOg7qG9O7qo31Dcw+dMpVNdXs/M2O7d77ItCeuOpp/nxrrtZOcsAts91\nqmTI1VcXOKqW1c6aySPDrmDyN+PYeOl6luodur8lEjCpqTffJZaijhqquvVgxQHL0rlz62ZAampK\n8MU33zNn5i9UJWazTOVE+lVOoVP0Mk6dOZdXvquk+5ID2eNPJ9Ord9sGcSzSJsy9Ca12zmB+X/XT\nAHP32c3sNxA1YW63pqYm3vzgTUa9NJJf/fpX1PRsXUuO8W+PZ0D/gey67a5F1zKwJYlEgteHD+eV\nJ56k/+zZrJPWbXVMfR3fd+vKb7bbji322qvo82dHUYx5p72Ud0SKn3KOiBRSczmnqJKRmW0G3EDo\nNzoOOMHdR7ewz0DakIQmT5nK08+/ykdjxzGzto76iiqqF1+Wmt5L5LVgXTtzGrWTvqPT3Jn0qKlm\npRUG8tutN2P55eJpfv/y2y/z8FMP09hjLn1tiXZV9jTUNzBp7GR6VfTk4L0OYdVBq+Yh0vyYMuEn\nHjjlVLbo2WOhdfWNjbzZpzdHXnRRDJG1zpza2Tzxv6v54fOP+U3/epZdvGqB9bObqulc1ZXOrXxZ\nmxIwp66eHpW1CyyfOL2O137oQp9lV2K3Q05od8udYi34tKevugo+sijGv/suS1Z2WuDz5qf6elbe\nMPuYVtI+xZp32kN5R6T4KeeISCF1iDF5zKwXYbT3cwhfuvYFHjGzld19Yq7Os0TfxTlgr11gr10A\n+OHHnxj+3Mv45x/wS20djZ1rqOk3kOruizZF8dz6Ocyc8DUVc36mR9cqVlx2aXY65PfYoBWK4i7t\n5htszuYbbM7r773OA0/cT7eBXem5VOuveeoXP9NpemdO3O9EBg0YlMdI86PvUv2p7b7w9OkA42bP\nZsN9458BrDldu9Xwh7+cRn1dHSPuvpY3PnmPDfvV8au+oYVATWUdNNS1+niVQI+UCqGfptfx2g9V\nLDlgbf509gl0675wZVgpUF91KbSV1ltvoWUdq12SiIiIiBSzoqnkAX4HTHf35JRG95jZWcBewPX5\nOukyS/fniAPCtM+JRILxX3zNI08/x3dffkptQyXVSw6ge+/mB5FMmjNrBrMnfEF1op5+fXvzxz0G\ns+5aqxX1wM8br7sxG6+7MYlEok2VT23dvhjZ+uvz9auvMaDb/MqeRCLBN9VV7Dt4cHyBtUFVdTW7\nHXYSc+vreeqe63jr47fZfNl6+i3WvlnMfp41lxe/6US/FdbhiLOPL9nKHRERERERkVJUTJU86wHv\npy37GFitUAFUVFSw8qCB/P2YwwH4edp07nl4OGPGvUpTzZL0WmZQxgqbXyZ+S+PP3zJwuaU58NgD\nWW6ZpQoVcs60tcKmo1fwAOx46KFc/trrpM5Z5LWz2WDHHTvc9XWpqmLXQ06gbk4tD/33Ut745BO2\nXRFqWjndet3cJkZ/2USXxVfgsDNOpXuv/AzOLSIiIiIiIvlTTJU8fYBf0pbNBjL3qSmAPr0X45jD\n9ieRSPDsS2/wwGMjqF52TWqicUnqa2fzy5fvstUmG65djhUAACAASURBVLDfbgcV7UxSklnnzp3Z\nYLttGff0SFbp3p3GpiY+61LFKfvuG3do7VbdtRv7H3c2Uyf9yJ1Xn8fK3aay5jJVze4zflI9703p\nwX5HncYyA1cuUKQiIiIiIiKSa8VUKzETSB+FuCdh9ptYVVRUsN2WG7PFRutxxoWXUVsxiM5da6j9\n6h0uOeMkFu+zWNwhSjttvd9+XDZ6NKsAn86ezVb77NPhWvFksviSS3P8+dcz+uFhNHabRs9umbtv\nzW1oZE4XOPHkY0viukVERERERMpZMVXyfATslLZsTeCBGGLJqLq6igvPOJkb7n0COsG+pxyvCp4O\nrqKignW33JLacU5djx5stPNv4w4pp7be49AWtymtKxYRERERESlfxVTJ83/Av83sKOAW4EighjDj\nVtGoqurC8QfvEXcYkkPbHnggAJrAWERERERERDqyopn2yd2nAbsBxwAzgIOAXd19dqyBiYiIiIiI\niIh0AMXUkgd3fxlYO+44REREREREREQ6mqJpySMiIiIiIiIiIu2nSh4RERERERERkRKgSh4RERER\nERERkRKgSh4RERERERERkRKgSh4RERERERERkRKgSh4RERERERERkRKgSh4RERERERERkRKgSh4R\nERERERERkRKgSh4RERERERERkRLQOe4ARETiYmYXAYcCfYAPgWPd/a1YgxKRkqWcIyKFpJwjUp7U\nkkdEypKZHQHsCWwG9AaeAx41s+pYAxORkqScIyKFpJwjUr5UySMi5Won4CZ3/8Ld5wDnA0sBa8cb\nloiUKOUcESkk5RyRMqXuWiJSrk4HpqQ8XgdoAr6PJxwRKXHKOSJSSMo5ImWqZCp5JkyYEHcIIpKF\nmfV292lxx5HK3T9L/m1mBwBXAWe7+w+t2V85R6S4FVveWdScA8o7IsVMOUdECqm5nFMKlTzTgBcO\nOOCAreIORESyOgE4p9AnNbODgVuyrN4GmAzcDCwO/NHdR7bisMo5Ih1DwfNOnnIOKO+IdATKOSJS\nSFlzTkVh48gPM+tNGFBMRIrTtGK6uwVgZusSBiG8ELjM3ZvasK9yjkjxK6q8syg5J9pfeUekuCnn\niEghFVXOERGJnZkNN7Pz445DRMqDco6IFJJyjkj5KomWPCIibWVm04HuQCJt1Tbu/lIMIYlICVPO\nEZFCUs4RERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERER\nERGR+GkK9TYws6+A5Zg/FWEC+AAY4u6vxxVXrplZEzAGWM/dG1KWfwX8091vjyu2XImusQ7o7+4z\nUpb3BH4Curp7ZVzx5ZqZLQ9cAWxNmE7zK+Au4MLU11iKi3JO6eQcUN5BeafoKeco53RkyjkdT7nk\nHCiPvKOcUzw5p2Se5AJJAIe7exd37wL0Bp4DHjGzUnsuVwZOTluWYH4SLgW1wJ5py3YnJKdSuk6A\n4YTkOtDdq4H9gQOBi2KNSlqinFN670XlHeWdYqacU3rvQ+Uc5ZxiVk45B8oj7yjnFEHOKcU3T8G4\n+2zgVqAfsGTM4eTaJcCZZrZi3IHk0cPAH9OW7Q88RAm1cjOzpYHVgeuSteru/i7wN0roOsuBck5J\nUN4poessdco5JUE5p4Sus9SVeM6B8sg7yjlFcJ2d4w6gA5r3oplZL+AI4Gt3/ym+kPJiNLAscAOw\nQ8yx5MsjwN1m1s/dJ5rZEsDmwAHAYfGGllMTgfHAnWZ2C/Aq8KG7Pw48Hmtk0hrKOaVFeUd5p9gp\n55QW5RzlnGJXLjkHyiPvKOcUQc5RS562qQBuNrNaM6sFJgBbAHvFG1ZeJAjNCdc0swPiDiZPZgBP\nA/tEj/8QPZ6RdY8OyN0bgU2AB4A9CM1gp5vZ42a2dqzBSUuUc0qP8o7yTjFTzik9yjnKOcWsnHIO\nlEfeUc4pgpyjSp62SQBHuHu36KfG3TeOmmaVHHefDhwHXG5mfeKOJw8SwD3Mb1K4P3AvRdDELg+m\nufsF7r6Nuy8GbAY0AE+bWaeYY5PslHNKj/KO8k4xU84pPco5yjnFrKxyDpRF3lHOKYKco0oeaZa7\nPwS8Alwedyx5MhxY3cw2B34NPBFzPDlnZrsDU1KTjbu/B5wF9Af6xhWbSLoyyDmgvKO8I0VDOac0\nKOdIR1IGeUc5J+aco0oeaY1jgd2ApeMOJNfcvRZ4FLgDeMzd62IOKR+eAX4BrjGz/mZWYWYDgdOB\nj9x9YqzRiSysZHMOKO+gvCPFRzmn41POkY6mZPOOck78OUeVPNIid/8ROBXoEncseXIPMIDQlDCp\nZKb4c/eZwJbAEsDHhCkMXyT0jS3VQd+kAyuDnAPKOyJFQzmn41POkY6mDPKOco6IiIiIiIiIiIiI\niIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiCy6irgD6MjM7AzgaGBJwIEz3f3RaN2u\nwCXAisAk4EZ3/1dcseaCmV0PTHD3c1OWrQbcCqwLfEN4Du6PKcRFZmaPA9ulLEoAg9z9RzPrCVxH\nmO4QYDhwuLvPLnCYOWFm+wPnAssD3wPnufvtaducCqzq7ofFEKKkMbNVgZuA3wDTgaHufn60bh3C\n/+c6wEzgf8Df3b0ppnBzInrfvU/K/6eZHQr8AxgI/AzcCZzq7g0xhdluLeSckvkcaS7fRHnmGMI0\nshOA6939orhilQWVS97RdYKZbUy4ztUIZbp/uvu92Y5V7Foop5dU+bWU6L1YWtcJLZZ1DqV0ynTN\nlXW2BS4HVgGmAFe7+yX5iqVzvg5c6sxsN+A4wj/sOOAE4F4z+xXQCbgPOBB4BNgKeNLMPnT3x2IK\nud2iLxqDgT8B/0pZXgk8DDxKmEJuU2C4mX3i7h/FEGouGLC6u3+ZYd01QHfgV0AVMBL4G3B+4cLL\njeiD5WZChdXzwK7A/dH/6HtmNhjYhvB//WBcccp8ZtYFeJxQKN0GWBN42cyeB14lvA+vJeSbVYAR\nhILrVTGEm0tDCR+WCQAzM8JzsCuhonV14DlCAf7GmGJcFBlzjpn1p0Q+R5rLN4SpR88BtgDeIXyO\nPGNm77j7yFgClnnKJe/oOu15QmX648B/gEuBTQhluk/d/f1YAl4ELZTTp1J65deSoPeiPU8JXWeK\nbGWdkinTNVPW+QD4ilCWO5JQttsYeCrKr4/mI56yr+Qxs4GED7bTCbWIfYA73f2oFnbdAbjP3T+O\njnMt8G9ghejH3f2haNvRUWF2ldxfQesswnVC+KCvIdxJTrURocLjbHefC7xgZi8QvpScmqPQ26y9\n12pmnQh3kr/OsK4PsD8w0N2nR8t2J1T2xGYRXtftgdHu/mz0+JEoCW0HvAesT7jz9UM+4i5ni/Ca\n7QQ0prRweN/MNgV+InwoLubu/47WjTGze4EdibFAsIh5BzPbBxhAKPAkW57WArOByugn6bvcRN12\n+cg5hIJdqXyOZMs32wOjgQbCzZHk65kgtOiRHCmXvKPrXKTr3AaoTrmz/IqZjSKU6WKr5MlTOX1l\nirD8Wkr0Xiyt64S8lXVKpkxH9rLODsBY4Ct3vzta94qZPUV4TfNSyVPZ8iZloRewIaHw/Gvgj9Eb\nLSt3P9bdTwAwsypCzdxPwFh3v9/d14nWVZjZVsAawAt5vIbWaPN1Arj7P9z9aEKtaqr1gHHuXpey\n7GNCM9+4tedaBwCNhDfeTDP71Mz+GK3bgNCE8Bgzm2BmUwgFgW/zE36btOdaHyDc4QLAzBYjXP83\nAO5+WfSav4a6deZDe16zjYEvzOx+M5tuZl8DW7n7T8AXwGZp2/+azB+ohdauvBPdbb0EOBhoImrJ\n4+7fAkMIH4r1wEfAG4Q7QHHKac4psc+RbPnma3d/C7iMkGvqgZeAW939wzzEXu7KJe/oOrNr7jqr\ngLlp21cS7sDHLafldIq7/FpK9F7MriNeJ+S+rFNKZbqsZR3gFWCvlHVdCJV5eXtNy74lT4q/RWOr\nfB7Vuq1kZs9m2fZ8d78Q5vW9u5PwRfh8d5+V3MjMliW8eJXAKKAYCq3tus4s+gAz0pbVAt1yEGcu\ntOlagbcIBZyTCV849gTuNLOJQH+gH9CT0Gd0CeAZ4CLgxHxeRCu1+3U1s98AtxCu/4G0bSuIvlxL\nzrXlNfsX4X9wB+AgYF9C8/JnzeybqKln8m7lsoQuToOAQ/N7Ca3W1vfixYT+52e6+zehNW9gZisR\nmjAfBtxBKCg9QWiKf0X+LqFVcpZz3P0ZKL3PkfR8Y2ZbAH8HfkvoArtLtPxZd384r1dRnsol7+g6\nF9bsdRIqkKvN7M/AbYTWhNsTWlIWg5yV0y20zi7m8msp0XtxYR35OiG336++okTKdM2VdaKxlH6O\n1q1C6NZVS7j2vFAlT8Tdf0552BAtazHZu/s9ZvYAoZnrQ2b2lrs/Ea37HuhsZmsA9xLe1CfnPPg2\naO91ZjGL0I0rVQ9gWjuPl1PtvNZ+KX8/aGYHAnsAL0bLTnP3OcB3ZnYTYZyi2LXnWs2sN2EAsN8T\nBgkb6u7pFTqq4MmTtr5mZnYD8La73xMtesXMRhIKCY9aGCPr78BphALtIe6eXoiNRTuu9VRgorvf\nlbI42aLs94Q7sMlBwl8zszsJX0ZiLRDkOOc8E+1fEp8j2fKNme0NjHT3p6NNHzezpwmvpyp5cqxc\n8o6uM7PmrtPdHzWzPQnv0ysIXbdHEMp6sctlOZ0wkG3Rll9Lid6LmXXU64Scl3U+D7uXRpmuue9W\nZtaVUOl1BKHb3YXuXp+H0AFV8jSn2S4qZvYRcK273+Bh9O+RFsZLWMPMdgD6uft+AO7+sZk9QRgh\nvdgsSlecD4Fzzawq5Z90TcIYC8Wopde0P9Dk7qljD1UTRrz/POXxnOjvzhRJ4SeDlq61F6Hp4HvA\nSu6ugk38WnovjieMg5Uq9X/wdkLTz03c/dMcx5ZrLV3r9sDmZlYbPa4CNoua945g4bGwGoFfchti\nTrQ358wws6spkc+RFvJNEx3n9SxF5ZJ3dJ1B1uuMuhbMdvc1kyvM7BXC3ehi1N5y+uqEQd5X7UDl\n11Ki92JQKtcJi/b9qgnokrZLsZYB2l3WMbPOhPLrXGDN6AZeXqmSJ7sBZpbeNznpPMKI6Eea2ZOE\nPr67EqZhHAL0JdRSbgK8SejLty+huV2xae46z/UFp+utYMF/8OcJg2P+08zOBXYmJKxinW67pde0\nAtjTwoDK3xD6Tg4mNNcba2YfA/82sxMINdJ/If6mhNm0dK11wGTgoAytd1Kpu1bhNPteJDRjPcfM\nDid8+G9B+P88PWoWugvhQ2VKIYJdRC3lndRpNjGz0cBt7n6HmQ0CLjazwwhdutYlDIp+ZF4jbp92\n5xxgOUrjc6SlfPMQMMrMdgSeJdxt344OOGthB1UueUfX2cJ1Erqjj7Qwze9bhJbKAwkzwRSjRSmn\nv0/HKr+WEr0XS+s6YdHKOnWUTpmuubLOnsCywFq+4FhgeaNKniDTl9iv3D29ZnGeqMnVEoQPih7A\nJ4QX9Z1o/T8JH4xLET5IbnH3y3MdeBu1+Toz7D/vGO7eaGGKyluAkwitXf5QiNrJVmjPa9qN8Hq9\nRSjsfArs7e5jo01+B1wPTCH05b7B3fPWl7IN2nOtjwKbA/VmC4ypmF6xt8BrLjnTrveime1CaAZ6\nPfAlIed8YGYnAosBE9Jez+fdffscxdxei5p3FuDun5vZ7wiDMt8ATAQu8PinFc91zhlbKp8jLeUb\nMzuYUGE+iDD+0J/c/b0cxixBueQdXWczsl1ntO5I4G7Cl5H3gV08ZazJGOWjnF6s5ddSovdiMzrg\ndUIevl+VUJkuW1nnPEIuGgTMTFs3zN3/vOjhioiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiI\niIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIFBkz+8rMDo7+HmZmt8Ud\nk4iULuUcESkk5RwRKTTlHUlVGXcAUpYSaX8nAMxssJk1xROSiJQw5RwRKSTlHBEpNOUdmadz3AFI\n2auIOwARKSvKOSJSSMo5IlJoyjtlTpU80m5mthIwFNgSmAXcA/yN0ELsEmB/oDvwHPA3d/+smWNt\nFW2HmTUCuwIPACe5+43R8grgG+B64AfgNOAh4EigGngMONrdp0fbrwVcCWwCTAWGAee4e0OungMR\nKRzlHBEpJOUcESk05R3JBXXXknYxsx7As0AtsCGwHyHpnATcCqxHSCQbAZOA0WZW08whX4/2BxgY\nHfsxYPeUbTYEliUkO4AVgfWBbYGdgDWB26P4lgJGAy8B6wAHA3sD/2nfFYtInJRzRKSQlHNEpNCU\ndyRXVMkj7bU3sBRwqLt/7O7PAhcAqxES0sHu/qa7fwwcBdQQEkVG7l4H/BT9/W30+F5gGzPrGW22\nJ/CWu38ZPe4Unf99d38ZOBb4vZn1j845xt3P8eA54EzgsFw+CSJSMMo5IlJIyjkiUmjKO5IT6q4l\n7bUe4U0+PbnA3a80s70ItbqfmFnq9l2AAW08x1PAbOB3hIS0B3Bjyvpv3f3HlMdvRb9XBDYANjez\n2pT1FUAXM+vj7j+3MRYRiZdyjogUknKOiBSa8o7khCp5pL2qgbkZlneJfm+Qtr4CmNiWE7h7nZk9\nDOxhZh8CKwH3pWxSl7ZLp+j3nOjv4cDJadtUANMRkY5GOUdECkk5R0QKTXlHckLdtaS9xgKrmlnX\n5AIzuxr4c/SwJmrG58D3wM3AClmOlciyHEIN828JTRRfdPfvU9YNNLPeKY83AxqAcVF8gzwFsAZw\nibtrGkGRjkc5R0QKSTlHRApNeUdyQi15pL3uBM4CrjGzywmDcv2Z0JSwEbjWzI4D6oFzgcWB97Mc\nKznNXx2AmW0MvO/uc5g/+NjfgOPT9usM3G5mZwN9gOuA2919tpndABxlZhcRRn1fGbgWuGYRr1tE\n4qGcIyKFpJwjIoWmvCM5oZY80i7uPhnYEVibkFwuBU539weAPwAfA6OAlwlJaacsNbwJ5tc0vwt8\nALwA/Do6TyPwYLT+/rR9vwFejc7zGPA8MCTa7zNge2Ab4EPgBuBad79oES5bRGKinCMihaScIyKF\nprwjImXDzG4yszvSlh1qZl9m20dEpL2Uc0SkkJRzRKTQlHdKm7prSdEys18Bg4D9ge1iDkdESpxy\njogUknKOiBSa8k55UHctKWYHEab5u83d30hbl9oMUUQkF5RzRKSQlHNEpNCUd0RERERERERERERE\nRERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERE\nREREREREREREREREOpqKuAOQ4mVmw4CD0xY3AROBJ4FT3X1qlu3qgLHAme4+IuWYTc2c8gV33zpl\n20OAI4G1gE7At8Bw4GJ3/ynapjPwPrAMsKq7T0zZvxswBpgLrO3u9Wa2CnA5sBnQGXgdONHdP2rx\nCRGRvDKzrYGzgA2AKuAL4F7gP+4+28wGRsua08PdZ0fH6wqcCBwArAg0EnLCTe5+W5YYbgUq3P2w\nZuLcGHgF2NrdX4yWnQOc7e6VLVzjjsDFwOrAVOAe4BR3b2jhukQkBrnMS1nKS43AOOAsd3/YzAYD\nz7VwvK/dfQUzqwEuA/YCegGfAJe4+72tvT4RKT65yjtAv1ZsN9DdvzGz7oR8sjdQQ/h+dZK7vxbF\nNAzYyt1XaNdFSUF1jjsAKXoTgANTHlcAqwD/AlYCts6wXQWwJHAs8KiZrenunnKMO6KfdD8n/zCz\nW4DDgKeAowhfhtYE/gocYGZbu/vH7t5gZn8ifOG6gvBlLulMYCAhIdWbWW9CwWkCofIoAZwBjDKz\n1dz9Z0QkFlGBZhTwIHAIMAfYCDgZ2MHMtkzZ/N/AyCyHmhMdr3u0zVrAVcBrhM+8LYHrzGwjdz8q\nLYZ1gH2A+5uJswtwc5bViWYuETPbEHgCuBP4B7AecA4wGbiwuX1FpPBynZci6eWq3sBfgAfMbAvC\nF6vtUtYfAhyUtqw2+j0U2I3wZfDbaLu7zOxHd3+hlZcpIkUkx3nnRxbMHUlLArcD3xNu3gPcTSiX\nnAxMA04DHjOzFd39l2ibZss5UjxUySMtqXP39DtKz0YtaK6MapIzbmdmzwM/AL8FUit5vshwzNT9\nDiBU8Fzk7mekrBphZrcTWt/cZ2Zru3uTu79hZtcCQ8xsmLuPMrNVCUnqJnd/Odp/X6AvsL67T4jO\n9RrwJXAooZJIROJxGvC2u++XsmyEmY0D7gIGM/9u1NjmckjkfGBdYFN3fz9l+WNm9hZwt5nd4e6v\nmtnuwAXAaq2I81RgMTK3hG2pdez5wNMprYRGRDl0O1TJI1KMcp2XIHN56QngO+AId/8TKS15kl/o\nMuyzGOHG1jHufku0+HEz+wzYHVAlj0jHlMu8U0eGloFm9ihQCRzg7nOiFsq7Ahu5+1vRNmMIN9E3\nI9x0B/UC6jCabVYu0oxPot9LNbNN8s5VW7shnAqMJ7TEWUDUHesUQleH1JrpfwDfEO7QdwWuJ9RM\nn5KyzZrAR8kKnuh43xLuqq3SxhhFJLdWINxxSvcYcC0wu7UHilrx/AW4Ma2CBwB3v8/dO7n7q9Gi\nbwl3tE4n3L3KdtxVCIWvE1obS8q+PYFtgFujx5VRLH92923aejwRKYic5aXmuHs98BmwbBt2W5lw\ns/aVtOUzCV3cRaRjymveMbOjCBU657r769HiPYAxKRU8le7+mbv3c/ensh1Lipda8khLsjXLWy76\n/U30u8LMqgk1vBVAf+CfhG4ID6bt2yWqiFngPO5eZ2b9CZUxl7l7tnM/Sag42oyoiaK7z4qS1nDg\neeA3wO4pzQsBriGtm0V0vn5kTqYiUjjvA3uY2fnAPe4+FsJ7GxgCkNJysCpDDgGY6+6NhD7sNcCI\nDNssxN3fAd6JznFUpm3MrAK4Kfp5p5XXlGotwmduNzN7A1jfzKYSCmznu3tz45WJSDxymZeSspVt\nlgFebG1g7v42UWVO1Lq6F7A/Idec2NrjiEjRyUfeIdpvNcLYpC+6+wUpq9YF3MwuIgxp0cvMXgGG\nuPuHObouKSBV8khLKlMqbyAUKDYAzgYecfcfzAxgeeb3EU/1j+QgySnOiH5SNRAGFksO5jU+W0Du\nXmtmkwgVSanLnzKz+wjdsh5x98fS1qd2GSMao+cewsDMmcYIEpHCOR5Ygig/RO/xVwgVuf+LCjdJ\nycqWdP8htN5bJnr8dQ7j+zMwgND9tF879k/eob8WuBT4G2FMs3OALmRouSgisctlXkpKL1f1JrQO\nHEAok7THxcBJ0d+Ps3DrHhHpOPKRdzCzKsK4O7UsOC4YhDLKpsCvCN1AOwHnAc+Z2eqpE9tIx6BK\nHmlJtsqbaYQklDSB0Ac8qRdhMMALzaxTWm3xf6OfVOl3tlrq4tWF0M90nmhA1DWjh6uaWVXUBHoh\n0Qw3N0Vx7u3uX7ZwPhHJo6gb5TZmNojQFXNTQvem3YFTowFJk84ntOhL90P0O5k/5uYiNjNbGrgE\nODia1aI9h0neabspJR++bGbrEgapVyWPSJHJcV5KylSuagAuWIRuEVcRKncGE7qd3kQY21BEOpg8\n5R0IY//9GviDu3+Xtq4roeJ5Z3efAmBm3xBaFR2Axi3tcFTJIy1Jr7ypBIzwZr8HSCaaOnd/M23f\nZ8xsOeBowqCmSd9l2Hbeuuj3gGwBmdkShBru9NY+ZxDG6jmVcFfrTEKLo9R9uxK6bf2J0JXjqGhc\nHhEpAu7+OfA5cCOAme1NuPN0OmEWCYDPm8khML/75XJkmDo0yiETgePc/bpWhHUV8BIwMsoh1dHy\najOrdve67LvOk/xS93za8tHA7mbWP0OrRxEpAjnKS0np5aoGwoQUWccDa0V83xLGFnshGpPsJDM7\nLu2Ov4h0ILnMO2a2HaEb53/d/aEMm9QCHycreKLzfxh1K2/XnS2Jlyp5pCWZKm9ej6YaHtKK/ccB\nv2vtydz9OzMbT2gFdHaWzf4Q/Z53x8vM1iAkvRvc/VIzW51Q232fu38cbVMJPApsAhzo7ne3Ni4R\nyR8z2wB4kzCOVno3ywfM7GRgxTYc8h3CwO+/I/MYF7tEv99o5fE2JFQ8p999fxr4qpWxJbuOVaUt\nT3bZyMkAriKSG3nIS0mZylXtie8U4Gx375G26jPCDbnugCp5RDqQfOQdM+tLGJZiHPDXLJt9Reiq\nla4SlU86JM2uJS3JNkDgDBb8/8m23SbAx2085xXAWmZ2dPoKM1uS0ELnfnf/LFrWiTBjzSTCzDcA\nfyfMMHFzNGAqhOaG2wC/VQWPSFEZA/xCGDR0AWbWAxhIKIC0irvXAsOAY6IZsVKP15OQQ8ZEAy63\nxh7Axik/e0bLj4nWtcYHwFQWvIMPsBPwftog8SISv5zmpRTZyktt9S5QY2abpC3fEpigMTREOqR8\n5J3/An2A/aPyUSajgTWiLmLJ8/2GMGbYC208nxQBteSRllRkWZ4gzKjVJXrczcy2Tdm+G7APoTvX\n3m085w2EAUmHmtlmhL6m0wnTnJ9A+KKUWgF0AuFO+97JL0ruPtnMTo+OdRyhi9behL6lXaNmi6m+\nd/dPEJGCc/c50ft1aNQd6n7CuF8rEt7rVYQuU8n8skaG93DSq+4+m1DhuznwipldQXjv9yMMeLw0\noXIlk4VyXvo07CmzWox19w/S1v01wzHc3Yeb2XnAldEgiqOBHYAdCVOZikgRyVNeguzlqrZ6lpDX\n7opyyyRCLjmAMM6XiHQwuc47hMqi3YAHgCWzbPsSYfbh44DHo3xSQRjv53XCeF9JvbKUc95295fb\ndLGSV6rkkeYkyH7H6Ydo3UHR7/7AqJT1cwi10X/I0vczK3dPmNm+wKHAEYS+qNWEMXhuBS5NFpai\nGudzgSfc/f/SDnUzYeDBf5nZI8DKhIqiUSxsHfVgFQAAIABJREFUGHB4W+IUkdxx9+vM7FvCDDE3\nECqKvyV0ifq3u3+VUrny9+gnXYIwffBYd59hZpsSWu0cThifZzrhjtQfm5kStLV32dO3Sz7ONDjh\ng8Bwd7/azGoJM14MARzY192Ht/KcIlJAuc5LNF+uyibjPlFZ6XfAlYS80w34FDjU3TVjqEgHlcO8\nszbhZleCcKM70033BLCCu38TDeh8JaHlTz2hcudEd0+kbNuHhcs5CeAyQJU8IiIiIiIiIiIiIiIi\nIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiJSdHI1wr+IiIiIiIiISFEws85kmGzK3efEEE7BaHatImBm\nw4CDm9nkMuBjwsxSz7v7NhmO0QSc6+7npiw7EjgGMKAR+AC4MdOsC2a2AfAPwpTniwETgeeAi9Kn\nFjezzQnT960BfAdc5u7Xp21zJmEqvp7AG8AJqbPZmNmywPXAtsBM4F7gFHevS9lmd+AiYAXgs+j6\nHkxZXwVcSpjhqzPwPHCsu3+b4fq2jJ67ygzr/hJdez/gI+BUd38+Zf1iwFDCFIQNwBPA8e4+LWWb\nE4DjgWWBHwmzdZ3n7k0psZ4LHAgsCXwDXO7uN7TlORHJFeWd/OUdM9sYuARYD2iKrumv7v5Nhufg\nVqDC3Q9LX5d2vFeArd39xSzbXAf81t1XSFm2TPScbQ90AV4k5K7PMuy/PPAVMDBTnCKLQvmm45dz\nUrY9OIpzhQzrdgQuBlYHpgL3RNfcYGZdgE7p+ySV+hcuKTzlnY6fd1p6TlpZznke2DQtvATN5KNS\nsNALIrGZAGyX5edG5k+fOdjM9shyjHlTbJrZWcA1hDfMnoQ36ifAMDO7OHUnM9sHeA3oDZwI7AJc\nQHhDvWNmm6VsOxAYQUhSfwBuB64xs8NTtjkZ+Gd0/n0Jb9xnzWzJaH0n4ElgVcLUxqcD+xOmPE8e\nYyPCtMNvA3sBzwL3mdm2KaFfDvwJOJsw3foywDNm1jXt+roDZ5BhClIz+wNhesKHCFMLjgdGmNlq\nKZvdRUgexwPHEhLFoynHOJjwQXEfsEe0/RmEqZuTroj2vyraZjRwnZkd0NrnRCQPlHdynHfMbAVg\nFDA3uv4hwLqEvLLAjRUzWwfYh2amVI6+GDWbB6Ln6igWfC06R8/ZGsDRhArm6ug56Zm2f7ISuq1T\nO4u0hfJNBy3npBxrCcK0zpnOsyHhtXgf2J3w5W0IcEq0yc3A7GZ+RPJBeaeD5p2WnpM2lHOWj86x\nccrPJukxlxq15Ckede7+XLaVUU0mgAP/NrMn3H1ulm2rCB+ql7v7GSmrHjazRuAEMzvX3WujN9Bt\nwK3ufmTacW4GXiJUTGwQLT4RmAXsEd11ecLMViIkglujLySnATe4+wXRcV4AfiDUPP8T+D2wNrCh\nu78TbZMA/mtm/3T3Lwk1vx+7+0HReZ80s3Wj8zxrZv2AvwCnu/vQ6BjvE5LI/sBtUfJ5GlgHqCHz\nF5izgCfd/aToGCMIb/zTgEOic+4M7O3u/xdtMyGKYXBUI30scLe7nx4dc7iZLUWozDkvSjR/JtRg\nXxFtMyL6MngKIck195yc4+5fZIhdZFEp7+Q470TrZwK7JO9Mm9l44GVg1+j52J1Q0Est7GRzKuHu\nX8bu1dHzfhPhDleqnYE1gZWia0vmt++Aw4Cro2VPAJtF51Alj+ST8k3HK+ds7e6jo/LKvcCvgSpC\nq7905wNPp7RKHBE999sBFwLnAdel7VMF/I9QMS6SD8o7HTTvtPSc0IpyTvS8LQM85e7jM8RZstSS\np3i0tnB9CqF53V+b2WYJoDuh21C66wlfCGqix8cDtZmO5+4NhDfJZWaW/ILxO8KbNrVZ7QhgeTMz\nYCNgceD+lOP8QviCs0PKMb5MJqCUY1QA20dvyB0INc2kbbNplFx2IFRSpp7nC2BcynnmAo8TChbP\npl+fmf0KWCvtGE2ExJUaax0L3tF6kXDXKbnN6oSuFKlmML8Z4CrR3yPTtnmLUNuePE+252S79NhF\nckR5J3d5Z/to0ZrAy2mxvhX9XiX6/S3hjtTpwELdIZLMbBVCgeiEbNsQCmyzCV1EUyuC1gR+TBZ8\noljnEO44puaUUYRm2w+icfokv5RvOl45J5nXZhLuyJ8NvJvhPD2BbQhfvDCzyuhcf052gXH3L9z9\nzdQfwpe0XwhfUkXyQXmn4+ad5p6TVWhdOWe56PfXZlaR8nyXPLXkKR6VZlZNhkJ22j/3B4Rmd2ea\n2TB3n5zhWBMJzRP/YWa1wBPu/kN0rPcJiSdpB2Bk6jlswX7TXwHJ2tGuwIqE5ncLhJjclTAmDYQ3\nWKrPgD9Gf6+Rvt7dJ5jZL8DK0TmqMxzDo7hWiI4xO0P/0M+iY+Du9YRxMTCzGkL/1FSrNxNrfzPr\nEZ3n8yghJ2NtNLPPU87TMzpHJSG5b0JoMjg0Je6tCbXgqdYi1MC35jkRyQflndzlHYv+PgNIH1ti\nrej3j9F53wGSd9mOIoOoIHJT9PNOlm1WA/4ObE7o055qGtDHzKqiXJg85q8IrXaIYrkqWncooTm0\nSL4o33Tccs6klPOsDmyVdry1CN8pupnZG8D6ZjYVuBY4P/qCt4DoTv7fgK2SOUokD5R3OmDeiV6z\n5p6TlYGfabmcM4BQSf0wofIoYWZPA0O8xMcfVEue4rE8ocY3vY/yrKhmNSlBGOulCfhXpgNFb5i9\nCXdHbgC+MzM3s9vMbPe0WswBLNzs9p60GGoJA4b1jdZPTdt+evS7F6GWO9s2vaK/l8iwPrnNYq04\nz2LRMX5u5hit0VysqefJFOuMDOfZOVr+dPT7JgB3n+HuL/qCg54dT+ibe2NKLM09JyL5oLyT47zj\n7h+6e7IQkhwEcRihcPhwhn2z+TPheTqTDIXT6Pm8mdB0+70M+z8a7XelmfU3s/7AfwiFn25tiEMk\nV5RvOn45J5vkF9BrgceAwYRxQ84m3O3P5ErgQXd/vZXnEGkP5Z2OmXda85w8RsvlnAHMf352JpSt\n1gNesLTxCUuNKnmKxwQWHBAqdWCoBQakc/cpwDnAn8xszUwHc/dX3H2l6BinAGMJA4Q9BDxlYXAu\nCCORp99hOS3l/PtkOHxj2uPK9OUZ7tpUpu2XfozWbJN+nmzHaMiwvDntPU/68pcId9SPJtSUP2eh\n/+48Zra0mT1MKNzcRFQTTvhwae15RHJFeSePecfCwOofEAoru7r7jAz7LsTMlibkhiHunm1A0qMJ\nzZDPyrTS3b8H9iMUSH+MfrYgVP5okFOJg/JNxy/nZJMckPUmd7/A3V929/MJ+ebY9I3NbHtCeSnj\nl2mRHFLe6dh5J+sxWlnOqSbMZra7uz/r7v8jDAw/gDCodMlSd63iURf1T84odMdcwLXAkYQR0HdY\naIdIdMw3gf9EzQHPJTTv34PQJ/MnQi136j7zuhVFTQuTkrWnvdNOk6whnUxUe2tmi7n79LRtkk0f\npwF9MoSb3CY5RkW280yKtklfn36elqSe5+u0YzQBU6JtMnWX6gUsMBhydL2vAq+a2beEkfe3JfQf\nTY40fzOh2eAe7p7aD7Wl50QkH5R38pB3zKwPYcydXQiDip7o7pnuWGVzFaHSeGT0/FVHy6ujiuPF\nCePoHAE0Rdt0BiqiJs4N7t7o7o9ZmF50VaDW3ceb2fNASTdRlqKlfNPByznNqI1+P5+2fDSwu5n1\nd/efUpafAozytCmkRfJAeadj5p3WPCe0VM5x92S393nc/R0zm8T8bmUlSS15Oih3bySMOr6dmf0+\ndZ2ZnWxmTenN0KJ+oedEDwdFv1+NjtGJzAan7D+TMGL5qmnbJN+kHwOfRn9n2mZM9PenzB+ANBnz\nUkCPaJsvCIN6ZTrGLEIf1k+BXhZGgc92npY0F+tnHkbX/xQYFI23k4y1EzAQGGNmv4me6w3TjvFZ\n9Ds5Xs9BhCnWHwFWS6vgScbS3HMiEjvlnZbzTtT8ezSwIbCjux/Sxgoeon13YX4T8+QXoacJgx+u\nQsgt9zG/2fc/mN8s/QwzW9PMziFU+HwUFXy6RcfOWuAVKRbKN8VRzmnleZJf5KrSlie7r8xrMWFm\nK5IySLNIMVHeiT3vrACMcfdZtPCcmNlai1DOqSJ0uytZquQpHm2eutbdRxJai/wnbdWr0e8DMuyW\nHAD0q+j3dcBShLsqCzCzASw8q8sIYLe0bkh7Ae+6+4To3DOAfVOOsyShWe6T0aLhwCpmtnbaMeYy\nf5Cy5wnN75LHqCDUjj8dNVUcSagN3i9lmzUIg5M9SSu4++eEypjUWKuJRnNPud7uhC9cSTtFy54k\nNNOsY+EZsLaMfn8YfRhcQ2jGfFiUzNONoJnnpDXXI9IOyju5zzsnEb4cbenu7Z0WeA8WbFa+Z7T8\nGEIz43dZuOn5Lcxvln4L4a7f2SxYQDqU0E/9/9oZl8iiUL7pmOWcTNJfyw8IY2fsnrZ8J+B9D7MA\nJe1N6PbxRGuuQWQRKe90rLxTk7ZNc89JX1oo55jZh2Z2Z2p8ZrYjYdyf51tzPR2VumsVj25mti2Z\np7Cd0Mx+J5FWs+rur5rZg8BVZrYq4Z+4gTDQ1BDgQ0KLEtz9JTO7FLggSgqPEpLIOoQE9CwLztry\nb0Jye9DMbibMGrUX0Qe7u9ea2SXAuWb2E+FNfhqhWd1t0TEeJNx1vs/MziYkwQuAoe6eHOzrPMKg\nWLcSBivdJ4rp6Og835rZLcD5ZlZPaPZ3HvC2u7el4HAOcJeZXUxIoEcTaryvSHkuRwHXm9lihPfM\nRcDD7p68c38jYTT+pui5XSe6vgfc/VMz25PQvHC0mS00Hbq7P9PK50Qk15R3cp939iZMaTogKsil\n+txTpvqMZJrx4/3Ux2Y2MPpzrLt/EP39Zto2O5PSLN3MfiS0+rndzC4i3F08D7jD3cemn1OkAJRv\nOmg5J4MFXkN3rzez8wgDoE4itGbcAdgR2DVt352ia9DYYFIIyjsdN+80+5wQplxvqZzzAHBOlJee\nIYxleB7wirsPb8P1dDixtOQxs1PN7LYs6y41s9GFjilmCaA/MIpQg5r+c3a0zUK10VH/zqsyrNsf\nOJ3QHPAewj/5ntG2m3nKTE/ufirhi8myhDFjHiLUvl4QHefDlG0/J3xALxUdczfgMHd/LGWbiwhv\noOOBuwn9KrePmt4RNdPbiZCgbo2u7wZSarvd/RXCdL6/ISStdQiDZr2bco1Dov3PJ9y5/ogFa4RT\nZXv+7gGOiq7/AUJf1h09DOaVtA8hMVxDGDD5SeDglPUnE/rwDiEk8b8AVxOmUYf5TQvvZeHX9unW\nPieSW2Z2ppnVpv3Umdm4uGMrEOWd/OSdlYHfZnlOD0p/LjM8h9m0tN0Cr1V0R+53hFky7iIUBm8m\n5Kf2nkPaIL2sY2YDzexpM5tpZj+b2TAL08+WA+Wbjl3Oac15riaMZbI74fndBtg39YuUha4Z66Mu\no3mRIecsbWZPReWbb8xsSJzxxUB5pwPnnZaek1aWc/4FnBpt9zCh8umhZq6nZGSq1cwbMxtMSPon\nEKZNPDxt/TaEplmvuPs2hYxNRMpb1Bz0FeDf7v5A3PGISMeUraxjZi8TutqdCvQj3BQY7e4nxhSq\niJSAZnLOSGAiYdroQcALwIHuPiKmUEWkQArdkmd9YEngh/QVZrY4cCOhBURBK59ERAh3LMaogkdE\nFtFCZR0LY7NtCpzr7rXu/jXhjuOO8YQoIiUkU85ZmjBe5OlRzhlDGKz/0FgiFJGCKuiYPO5+GUDU\nlDC9IucmYCih798GhYxLRMpbNKjcYSw8ir+ISJtkKevUAhu4+5SUTddhwellRUTaLC3nJK0HTHP3\nb1OWjaX5LrsiUiLiGni5gpT+e2b2J2Axd7/KzA6NKSYRKV/JgenaOtW1iEg288o67t5A6KqFmfUB\nLiaML7BtbNGJSKlJ/X7VhzDQb6rZhJmHRKTExVXJk1rBMwj4J7BJew5kZr2PO+64nw855BB69eqV\nq/hEJIcqKiqKtgumma1C6DJxRCu3V84R6QCKIO8sNBilmR1OmEHkGWDtaBrYFinviBS/Iss5swjT\nUafqQRist0XKOSLFr7mcE8vsWpFkItqMMPL5eDOrJXTb2tLMZpvZr1pxnN5Dhw5lxoz0ymoRkVY5\nHBjp7pNbub1yjoi0mZn9CzgD2M3dD2htBU9EeUdEWiP5pe8jYIlobJ6kNYF3Wnkc5Rxpt+mTJ3P3\nX09g/Hnn89zfT+GtERrru9DiquSpiH5w9zvcvdrdu7l7N8II8C+6e01aP1IRkXzYHRje4lYiIm0z\n7w5b9EXrZGAnd389vpBEpITNyznRFOAvABebWVcz24wwZfWNcQUn5ePOiy9hUG0tdT/9RL9Zs3j2\nwQeZW18fd1hlJa5KngQZmjGnrRcRySszWxJYCXg17lhEpOSklnU2AaqAsWY2N+XH4wtPREpM+ver\nA4B+wFTgDuAYd383jsCkfIx76226TppIjy5dAKioqGCDJnh46NCYIysvsYzJ4+6HNbPuduD2AoYj\nImXK3ScBneKOQ0RKT2pZx90fIt4u8iJS4tK/X7n7D8BvYwpHytRT/7uDwTXdF1i2dE033v9oDI2N\njXTqpGJ3IajAISIiIiIiIiLt1tTURNPMmXSuXLiKYZmGBsa9/XYMUZWnuGbXEhERERGRApg7Zw6V\nVJCorKBzVVXc4YhICaqoqCDbdE+VFRUQ+wR05UOVPCIiIiIiJWrWL78w7PjjGdyzF6MrKznuyivi\nDklESlBFRQVNXbuRSCRIn937x8pKdl9rrZgiKz/qriUiIiIiUqJG3nEHq1RUQmMj/Pwz0ydPjjsk\nESlRW+z2ez6cNXuBZdPq6ug1cABdu3WLKaryo0oeEREREZES9cWHH7F09OXq11268OStt8YckYiU\nqg133JEfe9RQ19g4b9mrDQ3se9JJMUZVflTJIyIiIiJSgr4YM4bFa2vnPV68upofx38eY0QiUur2\nO/FE3ojyzpezZ7P65pvTo1evmKMqL6rkEREREREpQS88+CBrdO26wLI+c+bw9bhxMUUkIqVu2ZVW\nYm6fPtQ3NvJpp0p2PPSQuEMqO6rkEREREREpQb9Mnkz3Ll0WWLZyVRVvDR8eU0QiUg7W3XJLvpk1\nm5q+fencWXM9FZoqeURERERESkwikaBx9uyFlvepquKn776LISIRKRcrrb8eX8+dy1LLLRd3KGVJ\n1WoiIiIiIiVm2tSpdGtoXGh5RUUFTfX1MUSUX+M/fI0lGifMm7p5wqxKVtns9zFHJVKeZk+fQfdO\nldT+8kvcoZQlVfKIiIiIiJSY6RMnUZNtZYbKn45szuxZPH7ntey1WmLesve/bqC+U3fW2njbGCMT\nKU/vPPsMK1ZV8cGECXGHUpbUXUtEREREpMQ0zp1LpyzrEommgsaSb48Mu5ytlqunoqJy3s9Gy3fm\nmUfvijs0kbKTSCT4+pNP6FdTQ+fpM5j0ww9xh1R2VMkjIiIiIlJianovRl2WdRWdslX/dDyJRIKJ\nXztL9qpeYHmnykqWq/4F//DNmCITKU/Db72VVeeG1oIbde3Kvf+5LOaIyo8qeURERERESszi/fsz\nK8u6is5dsqzpeKZOmkjvLpnHGBrUt5JP3n6xwBGJlK8ZU6Yw9qWXWbGmGwA1nTvTc+pU3nv2uZgj\nKy+xjMljZqcCq7r7YdHjgcCNwGbAXOBR4Bh3X3hKABGRHDGzpYD/AtsAc4B7gOPcPdHsjiLSav8+\n66+ccv5VcYchUnaqq6tpqqpaaHkikaCyujrDHh1T125dmdtUkXFdfUMTXWt6FjgikfJ116WXskVa\n3tmgpoYn7r6btQdvRacSakVYzAraksfMBpvZecAZQOqXqDuBccCSwDrRzwWFjE2klDU0NDB21O1M\ne+cBpr3zAP7sMGpnzYw7rGJwL/A10BdYH9gNODDWiERKSCKR4HtN1SwSm8oMlTyzGxvptVivGKLJ\nj+49F2NmIvMQ059O7sQ6m+9U4IhEytOk77+n8ccJ9OyyYEvBiooKVmtsZPS998YUWfkpdHet9QkV\nOfNGXzKznsCmwLnuXuvuXwM3AzsWODaRkjXqgZuZ9MFwZnz4KDM+fJSZHw/nkWFXxB1WrMxsLWBd\n4MQo93xJaNHzQryRiZSGUaNGsekmG/P8258ycuTIuMMRKU+VCxf16xsb6dqtewzB5M/aGw9m7I8L\njkBUW9/I7Ool6b/cgJiiEikvo+68i3W7ZO4KulL37nz8+hsFjqh8FbSSx90vc/ejgdeAZLvKWmAD\nd5+Ssuk6hLvrIpID/sEbrLhEFRUVFVRUVLBkr2omfzuOhoaGuEOL08bAeOBqM5tqZj8CBwHfxhuW\nSMc3dOhQjjvuOKb+PI059Q0MGTKEoUOHxh1WQZnZqWZ2W8rjpc3sKTOrNbNvzGxInPFJeWisX3jo\n5R5dujBl8sQYosmfwb8/iI+n96Chcf6sYc99meAPR5wcY1Qi5WXSd9/Su5muoE2zZpb7d4+CiWvg\n5XkdZ929wd3fBTCzPmZ2I6HLxCkxxSZSUsaPeZdluy489OKqi9Xx9nOPxRBR0ehPaMkzntDCcFvg\nSOD4OIMS6eiGDh3KNddcs9Dya665hgvPPzeGiAqrma7ptwOTgcWBnYFzzOy3MYQoZWLMa6/Rd87C\nlTxdKiv5ZeJEGhsbY4gqPyoqKtj1j0fx2tdzAZg0o45ey6xCv2XUikekEObOnUvil+aHgli2oZEx\nL79coIjKW1yVPAsNampmhwOfAj2Atd39o4JHJVKCPnnnRVZYfOEBCQcuUYWPeTuGiIpGAzDR3f/j\n7o3uPpYwRs8OMccl0mFdd9kFGSt4km6/827+dsQ+fPfFpwWMquAydU1fGtgOOD3qHjoGuA84NJYI\npeQ1NjbyxLBhrNU9c7esVZsSPHbDjQWOKr9WXvs3TKEvjY1NvPZDFXscrlY8IoVy/+WXs2aG7qGp\nVq6pYdT995NIaH6TfCuKKdTN7F+EO167ufsB7j4h7phESsXEn36gb4+FB17s2qWSObXZJlctC+OB\nzmaWWgPWGbLOOCsiWcz4eQpXn3UUNw27q8Vtn39zDE/fcg73Xnt+ASIrvLSu6UnrAdPcPbU76Fhg\ntYIGJ2XjfxdcwHoNDXTJ8qVrhW7d+PHNN/nk9dcLHFl+bTR4Zz76vo6qXv3o1r1H3OGIlIW3nnqK\nXz75lKW7dWt2u6pOnViltpY7L75YFT15Fnt3reju1snATu5eWp800iH88OWXPHX66Txy+ZVxh5IX\njXPn0qky89SiiRJqqt0OIwitec4ys6poIOZ9gTviDUuk43npyXtYb7HJzJnbck6ZVd/IDit3YdrX\nHzH95yktbt+BpSbePsCMtPWzgeZLxCLt8NDQoXT78iuW7dr8v9fmNTU8cf31fDVmTIEiy791t9iJ\n9yc0svr6m8YdikjJSyQSPHjllXx4731sXpN5hrt0g/6fvfMOj7Lo+vC9NdnNpvcECHXoTZqgKCog\nRcWKvvp+KlZQQREBeUVFVFBREEVF7KAiIKhgoQiIhY70NgQILY0kpCe72fL9sQkGSM828LmvK5c+\nbeYs2cwzc+ac3zEGEHzoEO88PQZzUZGbLfz34s10rTL3XU9AD+wTQpSU+5Fesk3hX8ayjz4iPDOL\npF07yc89fw5+8WMvqXwArerapY6UsgBnalZfnIuvH4GJUsofvWqYgsJFyBUDh7I5MwSDrvppRYBe\nw7rDFkwNWmMKCvGAdV6j/DZlAXD+DNgE5HjOHIV/A8vmzKFw2zba1WDBpVGr6W8MYMG0Nzlx4NJI\nodTpdORZVLRo38PbpvgEpQLwx4QQllLB9wnetknh0uDo7j1MGzECv927uTwgAJWq4g3limhmMNIl\nL4+3H3+cP5Z850Yr/71ovdGplHJYuf9fgo+kjSn8+8jPzaUoOQVjQABdbDZ+eO997pnwrLfNchnJ\nSYfQl2QDFZczDNPksX/rn7TueqVnDfMRpJS7gKu8bYeCwsVOSHgUT77yISrT88yeu7jKe6/q2oYe\ndzxDiw7dPGSdVymb9e4GIoQQsVLKlNJz7YBt3jFL4VLkx48+InvDRi4z1rw8ulat5nqjka+mvsY9\nE56lYatWbrTQM1hsEB4d520zvI4Qoh8wCeiNc6zpBfwqhNgmpVzpTdsULl4K8vL4+o03sJ04QX+D\nEV01EYOVEernx2CHgz1Ll/LWmtUMHfUkDUULF1v778UrTh4FBV9h3cJFtCn1PIf5+7MlKcm7BrkQ\nq9XKNx++zo1NK/ehdm+o49sFH9G0XRf86jhIKygoKABoNBpGPzcFXXBcpeLLdw+9hRdffs3DlnkN\nFaXRPFLKRCHEOuA1IcSjOMWZhwLXetE+hUuIH97/gNwtW7ishikT5dGp1QwwGvlq6lTuGjuWxu3a\nucFCz+FAhUaj8bYZvkA2zrR0Df9sqDsARftUodbYbDaWzZlD4qbN9NRqCXGB5pVKpaJ9QAAtrTaW\nvvoquoYNuHvcOExBQS6w2L0kHdhF4aHfiIkIPOf8sZQc4rrfTHSDxt4xrBQlgkbhX03SwYPElRMJ\n0xYXYbFYvGiRa3A4HHz6xniuiMrDX1f5REerUdMvoZjZr4zGarV60EIFBYVLlSeeeIKRI0decH7k\nyJH/JgcPnJuaDnAPEAVk4dT+ekxK+bc3DFO4tFg4fQYFdXTwlKFTqxloDGDhtDc5uGWLC63zPCq4\npMrD1xUp5RbgLZwi8BbgD+DT0ihmBYUas2PNWqY9Ohz9lq0MDAggxM/Ppe3rNRr6mEy0TU1j9qgn\nWTb7Q5/+G169+FNWfD4VQ/omCg6uOecnMHMLC2dOYPOv3k1DU5w8Cv9urCXn5JAGOODM6dNeNKj+\nOBwOPn59HEJ/krjQC6tqnU+4SU/PiDO8P3kU1pISD1iooKBwqfPEE0/w3nvvERoagr9ey7vvvssT\nTzzhbbM8ipRymJTygXLHyVLKgVJKo5TR/L4HAAAgAElEQVSymZTya2/ap3DxY7PZmDPxebS7d9Ox\nHg6eMrSlET2/zHqPDcuWucBC76DTODiTkeZtM7yOEKI3MBYYiDN7YwjwkBDiFq8apnDRcPrkSWY+\nNZpdX3zBIJ2OhGqqZ9WXED8/BgYEoNq0iWnDh7Pnr7/c2l9tsZjNzJkyhsIDKxnUSotWc6ErxU+n\n5ua2WpL+XMC8t1/w2ia64uRRUCiHCrBf5BEt82Y8T0vtcZpFVO/gKSM2RE+v8CzmTB2jlDRUUFBw\nCX379mX9+g306dqK/v37e9scBYVLCnNRETNHjyYhOZmWLnDwlKFRq+lnMrFn8WKWfTjHZe16CovZ\nTJAeDu7Y4G1TfIE7gJVSyhVSSoeUchmwAujnZbsUfBxrSQnzp03j64kTuaKoiC4mExq159wGTYxG\nBml1bJnzEbOeeYZsH9iAt5iLmTXpcbqaTtIpvmKt0/L0bKyjueMgH3opW6LGvy0hRIQQIk4I4ftJ\ncgoKNUV17p9ACeBvqn+OqbdYt+xLggoP0aQWDp4yooN1tPRPY+kXl2YpeQUFBc+jVqtRqXx7P0kI\n0dDbNigo1Ia87GzeGjmK7oVFNHDTznqvABNFGzcy95VX3dK+u/jrlwVc0UjNzo1rvW2KL2DHWcG4\nPDYgzwu2KFwk7PlrPdOGDydy/0H6BpgwaL0j4atRq+luMtEtN4+Px47l508/9epG9NfvvsS1cXlE\nBtV8jdUgVM9lwel898mbbrSsYqr8rQkhBgHP4Cxz7lfufBawGpgupdzkVgsVFNyIzuCPpbAQfalA\nX6FGQ0hYmJetqju7Nv7GzS1q7+ApQ0Tp+f7gDhwOR61KISooKChUjs+PJVII8TMwTEqZ621jFBSq\nwmI28974Z7lOrcakq343uT60NRo5eOQw386cye1PPunWvlxBUUEe2/5cydC2/iQfzWD3prW073GN\nt826ACHEI8ACKWVO6fEY4Emcml17gDeklAtd0NUSYJUQ4nqc67Zrgb7Ayy5oW6EG2Gw2Zn89m5IQ\nC8ERlcdJHN91ght63UiX9l08aN25OBwO5r76KiWHEhlsNHo0cqcqTDodA3U65B9/8ta2vxnx2lQC\nAgOrf9DF5J1JJ7wOa6wGoXq2HUlyvUHVUKmTRwjxEDALWADMB04CZsAAxOMcKP4QQvyflHKBB2xV\nUHA5HXr14ujChbQ0OQcLdYDxonVuOBwO1NZ8KiuXXlNCtMVkpqcSER3rGsPqgBBiLecKlp7/S3GU\nnnNIKZXqNAoKvozvD6kqnJHNe4QQo6SU33vbIAWFyvhu1iy6W62Y/P090l9Lg5E1W7dxJiOD0IgI\nj/RZFwrzc5n96hj6NbGiUum4srGG7779CGNAIM3adfW2eeczE/gNyBFCjASmAu8B+4CuwJdCiAAp\n5Wf16URK+bsQ4l5gBtAMOAY8KKXcXp92FWrG0ZNHefvjt/FvoidIH0xBbkGl96obqvli+eesWb+G\n0Q+ORuvh6BmbzcbsCRNokpFJgo9mNAijkViLhZlPPcVjr71GSGSkR/uPTRAcy9xGQnjtRKf3phTT\nutN1brKqcqr6Bk0A7pdSflPJ9TlCiBHAFJyOIAWFi47O113H5kWLaAkUlpQQHB/vbZPqjEqlAlX9\nS4ZabGAI8LyH/DwW4BQLbAIsA85Ucp8iIKSgoFBfHMCzQGtglhDiGWCqlPIn75qloHAh6SdP0c5D\nDp4yGqDiwObN9Bw0yKP91pTE3Zv57ot3Gdi0hGCjc6NLo1ZzSys7v8x7i+Zdr6Pv7Q/66ibeSOBZ\nKeX00uOPhBA7gDFAvZw8AKUb8co6zcMs/GkB6/5eR1TXKLS66h02ao2a6A7RZJ4+zdOTR/PM8Gdo\nFJfgAUudbF21isiUVBJ8vHR5oF5Pv5ISFsyYwaNTpni075sfGMMnb4zHnH4SEVWziJ4dp0rIDWzD\n3bfc52brLqSqb108sLua538Hpldzj4KCz+Ln749d4/wzKLLZCA4L9bJF9UOt88dZJbPumB16Arzs\nxZdSzhZCbAa2Ai9IKXd61SAFBYVLHYeU8nshxGqcDp/5panpi4BVwEYllUvBF2h1WWcOrl5LywDX\niS1XxxGNmmt79PBYfzUl63QK3855A2NxMre31qLVnBvJrNGouaG1mgOHVzJjwnoG3DGMNl16e8na\nSokCVp53bjWgCCRepMz9bi7bj20jrntcrZ8NjAzCEGJk6vuvMeGxZz3m6Enas5cYfd3lHjyJQaul\nINfzslJarZZHJrzJd59MY03i31zdVFNpSpvVZmdlop3Gnfpwz53DPWypk6qS7TYBU4QQFQqUCCFC\ngJdK71NQuCjZu2EDIaWK58F6PUnykJctqh9qbe1CCCtsQ1f/NlyBlPJvIAOnSKCCgsLFykUUbyel\nzJNSPgc0Bt7FWYVmOZDlTbsUFMro+9//ciwokExzsUf6215QQIdr+hAcHu6R/mqC3LmJDyY/weLp\no+kdnsrVzfQVljIuo1WMH7e2MLNn2bvM/N9DrP1hrtfKGpejLGRiJ9DhvGudgUzPmqPgCswWM5t2\nbSKidd1TibQ6LTGXR/PR1x+50LKqGTjsfjbabBdFhd2NBQVcd9ttXulbpVJx60Pj6HbzEyzZB4WW\nC5coOYUlfLtfxcAHnuN6Lzl4oOpInoeAH4EUIcR2nHmchYA/0ABnzuhJwDdjNxUUqqHEYmHpJ58w\nuLT0qFatJjIvjw3LfqTnjTd42bq64YrB2ZeGdylllLdtUFBQqC++NKrUDCllFvAW8FZp9a2eXjZJ\nQQFwLjIen/YG7zw9hq7FZiL93bcxs62wgNCel9PvPs+nGpxPfs4ZVi/5jGOJe4nR5tG/oQ69tuYa\nhBqNmp6N/XA4ikk8+CPvb/iVoIh4rrv1Pho2beVGyyvkGLBRCJEBFAHThRDfSiktQogXgdE4dVEV\nLjKyc7JRuUAPXa1RU2Quqn9DNSQoPJzBDz7A8o8/pq8xAJ2PiC6fz5aCAiJ7dKfjNX28akebrlcS\n27gFH78xnsFNzAQanC6VjFwLa1OCeHzSWxhN3k19q9TJI6U8JIRoB9wAXINTGyMSp6NnF87B5zsp\nZf1yQxQUvMRXr73O5Q7VOaF2nYxGfvr2W9pffRUmH89LPR+Hw4HNXFj/hiyFWMxm9H6+EdGjoKBw\ncePwfSfPcaDSbX0p5QnghOfMUVCoGr2fH0+9PYM3H3uc6222sxVCXUlSYRF+7dpx46OPurztmlJi\nsbBx1WJ2bf4DfckZOkVZuayFP+UK/tYalUpFiyg/WkQ5yDcn8ftnL5BlDyK+aUuuu2UYwWHuF5eW\nUrYSQvgDzYGWpT9lgkG341xjveh2QxRcTnRkNGF+YeRn5GOKqLv0QdqudG7ve7sLLauedr17ExQZ\nyZevvUZ/fwMGN4wrdcXhcLC2oIBON95A79s9++9SGaER0Tw28W3enzySO1rbKbE7WH3SyKiXZ/nE\nGqpKJSgpZUlpWdENUsrU868LITRCiEZSyuNus1BBwQ2Yi4s5c/QI3YwB55xXqVRcrtXw45w53PXM\nM16yrm7s/3s9sf5F1GfyAyBCS9j06xJ6D/6Pawyrqx1CXAmMAi4HoktPZ+B0Mv8IfCaldIFXS0FB\nwV3k5pxB4/DtjEspZcvzz5UuwEKANCmlz3upFP59aHU67vvfBJZMmkQfk+uLJezVaxn79NMub7c6\nHA4HuzetZf2qH7AVZNIq1MwNjf1Qq7RUs2ypNSY/LVc10wJm0nO2sOjNvynWBNOyYzeuuuFu/PwN\nLu2vPFLKYpzl0vecd7692zpV8AgvPPkiL709ieyCM4Qk1E7r01ZiI/XvVAb1HkyfHn3cY2AVNGrV\nikenTuXDCf9jgL8/fj7g6HE4HKzJz+eq+++n07XXeNuccwgICuHGe0aw8fuZFFhV3P34cz7h4IGq\nS6gbceaj/xfQCSFOAaOllN+Wu60hcBio1TdACDEeaCWlHFZ6HItTQf5q4DQwTUr5bm3aVFCoDXk5\nOQTa7BVei/A3sDf9tIctqj+//7yQfvH1jxFtEeXH0o3rvOrkEULcBcwFviv9bxwwFPgFyAaeAsYJ\nIa6XUh6oRz/vAg9zbj7JNVLKjXVtU0FB4R9WLfyIhFAVJ48cpEHTC3wpXkcIYQAmAb2klL1L5z4f\nA3fgnNucEULMAF5VnD0KvkZAaGiV4pr1waDXe7QaVUFeDr/Mn82pI/tpHFBAv1g9eq0ap0qE+4kK\n9uP6YHA48kk6upyPJ63BPySW64c+RAMXp3Mpm1iXNlqtlsljXmbe9/PYtGkTUZ0j0eqrd1DmpedS\neKiIp4aNRjQRHrC0YsJjY3n4lZf5auJE+gV4v5z6/oICOt54g885eMpo1bkXqxd/hlqnIy6hubfN\nOUtV37gywcFHgVTgP8A3QoiBUspV5e6r8RtACNEHuBbnAq28s+gLIB0IA5oB64QQiVLKX2ratoJC\nbdD7+1cam+9wOMA3y2xWSmF+Lo6C0+i19fe4q1QqAuxnSDt1jOh4z5VvPI/JOJ3K75WdEEIswOkM\nbgCMBz4B5gBX1aMfAQyUUq6tRxsKCgoVkLh7MxlHtnNDGwPzP5jCE5NmYQhwfcRBPZkNXA+8UXr8\nOs4U9fHAPqANMBbnfGmSF+xTcDH5hfm8/fnbJHRpVOH1Y38f58l7nyTQ976r51BcUMCcic/TVeue\nijgh+YX88P4HDHlshFvaL6OoII8FH0yhOPMY3WKs9Gjlh6ccOxWhUqloEulPk0gotCSz9rMXOeMI\n4eb7RtGoRdt6t++pTSwF76JSqbj3lnvp0+Nqpn80A30jHcFxwRXea7fZSd+dTtPwpox8YRRarWsj\n1upCZIMG6KOiMefmej2a57ifH7f7SIpWpegMPifzUdUGwM3AMCnl51LK5VLK+3Auqj4TQtT1zdcF\np65PctmJ0iievsAEKWWRlHIPsAC4v459KChUy+Gt2wiqwpFTlJ/vQWvqz6pvP+GyaNdViujeQMWK\nhZ5T9a+ABGBF+RNSyhU4x4+GUkobMA2ob03X5oCsZxsKCgrncXjvVn6aO53rW6jRa9UMbGLmvZdG\nUVzkc5vTQ4C7pZTTS4/vAB6SUk4vnftMB+4DHvGahQouY1/iPsZNGUdxVBHHs49X+GOOLGb81PHs\nlXuqb9BLHPr7b2Y8+SQ9i4vdJrx8mdGAefMWPpgwgfycHLf0sWH5Iua8NILO/kcZ3FJDVLBvpDmU\nYdRr6NNMz+DG+fz6+ct89c6LrihwUbaJdaeUcqKU8gHgNpyFbMYBrYC1ODexFC5yGsUlMOOFGTRQ\nNeT0vguzBCzFFpI3pHD/DcMY/dDTPuHgKaNJq5YsPp1+zrkfMjI8fuwfFOjRqMK6YHeAn5/3nNMV\nUZWTxx9IOe/caMAMTK1LZ1LKt6SUI4AN5U5fBmSXChuWsQ9oXZc+FBSq4+DWbSyfN492pVW1zkel\nUtGwqJC5r07BZvNtLQkAm83G0QM7iQt13eQoyKAjOzUJc7FnSrRWwGGcjuazCCG640yrKnsDtMSZ\n3lknhBA6nCmnnwsh8oQQSUKIp+ranoJCbcjOyiI9Obn6Gy9CrFYriz+ZzpBWmrPC9sFGHf0Sipg3\n4zkvW3cBWiC33LGDchtRpZwEaiesoOBzLPxpAe998x4xPaMxBFauteIf6E9srxg+WPQB3yz72oMW\nVk/KsWO8M/pp3nnlVQbp9ISUaj+4a6HVNsBIh9MZjHngAb55800sZrNLPgfAgb//ZN8fi7m1jYow\nkwvKEbkRvVZNvxY6GlgOsPDDOi2ByuOpTSwFH0GlUjHq/lFc16Ev6TvTzp63mq1kbMlg8lOT6dK2\nixctrJjQ2FhK7N7NUnY4HKh8yPFVGRqVlewzWd424xyqcvJsA8aXLoQAKM0PfRB4VAhxH3Wvi1re\nHRfKuRMscFbwcp/amcK/kvycHD6b9BJr3n2XwQbDOVW1zqetwUjMkcNMGz6CnevWedDK2vPrt5/Q\nPtT1u+OXx5Xw3SdvurzdGjIOeEUIsVQI8bIQ4lPgV2CGlLJACPEOzjDnmfXoownOijrvAkE4owdf\nEEI8WD/TFRSqZ/WiRSz79FNvm+EWUo4fJs5YgkZz7hgbFqCjMNe3JkHAz8B7QoimpccLgdFCCBWc\ndQY/C/zhJfsUXMCc+XPYkLiBuG6xaGqQ1qzWqIntGsumpM3M/mq2ByysmuSkJN4fO5YlL7xIr6Ii\nYnU6tB4qcRzi50eCVkfkvv28M+IxFs542yXOnl+XzOW6Zt4Xda0NTSP0ZB7ZSVFhveZcbt/EUvBN\nbrzuRi5r3pXsY2cAOL39NBNHPU9keKSXLasYQ2Ag3UznavIMiYjw6PENERHotb7tBM7LOQNFORTn\nnnapI7y+VPWGGAUMANKFEMvKTkopfwMexylMuLiO/ZZ3DhUA54dUmAD3xIYq1IjC4kIKLf/8FJu9\nFtFRb/Jzcvji5Zf56MmnaHHyJFeZTFU6eMqI9zcwSKtl16ef8eZjj7HrD9+b48udG0nasRYR7foQ\n59hgPSWpe9i6dln1N7sYKeWPOKP8juHczTIBD0spx5fekoEzxWJaPfqQUkqjlHKplNJROrbNBW6t\nn/UKCjXAbsdRifj7xU7Dpi3JJIzswpJzzu9LsdCkdWcvWVUpI3BuLEkhxBYgHqc+xgkhxO84o3iu\nwznvUbgI2bJzC7tP7iaiVe1LY0eICPam7GXjDu9o8aefOMm7Y8bww4uT6J6bRx+TCYNG4/GF1pCI\nCGIMBgYaDITt3s07I0awYPp0SiyWOn0uALW9uEZzMV+jgcnGMbm7Pk14YhNLwUe595Z7KUmxkpOS\nw2VtuhAbFettkyrFYDJRUv1tbsVss+HnppRUV2AtKeGTac/SJ8HGlfEWPp32LHa7b8ztKh1dpZQ7\ncIqSPg6sOu/aHKBj6fkf69h3WTTPbiCiVJunjHY4I4kUvEDisUTGvTGWt9dO5+2105mx5i2eeWUM\neQV53jatVuTn5PDJpEl89ORTND12nP4BAYTWsqydRq3mMpOJfg7Y+fHHvDliBNvXrHGTxbXjj5/m\ns2r+O1zfwn2TpKubatm1ej6/zP/AbX1UhpRyn5RypJSyv5RyqJRyAZytSjFNSrm0Pu0LIcKEEHHn\nnfZDcTAreABbSQm2eiyQfJ1HJrzFiiQDuUXOKeLBNAunA1px032+lREppcyUUl6DU8D9F8AO/Ikz\nbfwYTv2MtlLKQ96zUqE+fL/ye6La1n2nPKptJEtX1ut1U2ssZjNfTp3KN89PpGdBIVeZTBh8JGUh\n1mBgoMFI+J59TB8xgo0//VSndvaePFf7cMH2i+S4ntIg521iXY4bNrEUfBeVSkXblm3Jkme4+6a7\nvW1OlWg0Guxe1sKxA2ofdQafPHKAt597mCsicwg06IgM0tPGP5mZzz1CRuqJ6htwM1W+MaSUOcAF\nCclCiDDgqJRyQh37VVEazSOlTBRCrANeE0I8ilOceSjOKlwKHiYjK4O3PnqL2J4x/4RbqVSEdgrj\n+WnPM23iNHQ+HjZnLSlhybvvcnLXbnpotYQEBNS7Ta1azWWmQGx2Ozu/mMuaJUu4/YknSGjl2rKa\nNeFMZjoTxz1F38Z2bmrl/F0s2J7PnZ3/Cal01bFKpaJfcw3vr/6FxP27ueORccQ0aOzGT1cjVuF0\nMtdXMPlGnLtpA4G9wNXAPTgFEBUU3Erq0aOUWLy9R+Y+DAEmHnthJu9PepxrE8wkWhswfNRL3jar\nUqSU64H17u5HCDEeeAyIxVm59AMpZb1FPhQqR6VS1U8s1wFqDy50cjMzmTVuHD0cKjr7QPniyog1\n+DPY4WDnokXs3byFB1+aVLsGfFxItTJK7Gr8jfWbV0op9wEjS9NCIwCdECJISpkrpZzsCjsVfJfe\nXXvz1/q/8Pcxod7zyTmdgcHLmjxGjYb8/AKv2nA+cudmfv1+LgZLJrcI9TnpZE0i9MQEFbJk5jgI\niGbAnQ+7pCpfXajSyVOqTXEjTofML8AiYAnOxZBNCPEV8KiUsrYJaA7OTdm6B2flriycYs+PSSn/\nrmWbCvXE4XDw6qxXieoWeUHOur/JD5so4fUPXmfiyIlesrB6DmzZwnezP6SL3UFbFzh3zqcsssdi\ntfHT1NcIbtWSO8eO9YgafmF+Lks+eZO8lETi/Qvo2tBzpfrCAzRc3yCbZe9PQB3cgNsfHk9wWO1D\n32uKEGItzjGiolmgHpgnhCgEHFLKujqE5wEtcAogRgBJwJNSylVVPaSgUF9WfP4FYdk56IEf3n+f\nIY895m2T3ILRFMQV19/C0sXzGP+Wsm4RQvTDWYa9N85o5V7Ar0KIbVLKld607VLmpn438eXqeUS3\nja7T8xkyg7v63uViqyrGYjbz3vhn6avREqDz7Q01cDrQOgWYOHTiBPPfeIP/jBtX42fbNwrFWcvF\nSfmNJl8+LrKqCAgKoz4IIQYBzwA9cUYQl53PAlYD06WUm+rViYLPkhDfGKvF9wu7ZJ46SWANNMzc\niUalwlbi/ajn5BNHWbf0SzKSjxGtzeX6hjr0laz9DHoNA1tCcclpfp87mWxHMLEJzekz5L+ER52f\nQOA+Kl2ZCiEmAM8BnwMW4CWcA5IN5063H/A68DLO/NIaI6Ucdt5xMjCwNm0ouJ7l65ajjgK9v77C\n6wHhJlKOpXIi5QQNYxt62LqqsZaU8OXU17AcOcxgg9Hted56jYarTSZSDh1m2qPDufPJUTTt0MEt\nfRUV5PH9ZzPIPHGQK+KtRLTS49QJ/gdPTW4GCMgpPMHXb4zCEJHALQ8+Q3BoeK0/Uw04AgwDfsdZ\nSrS8s+dKYDOQSd3F35FS2oGJpT8KCm7H4XAwb8oUSEykS+lO8K4tW/j4+RcYNulFNJqLS4S0JnS5\n+gYWfP0lBh+OSPAg2TjF3jX8ky7vwBnRo+AmunfsztJVSynOK8Y/sHY758X5ZkwlJnp27uUm687l\nxEFJI4uFgMDAOj1vVakoiozEVIeFWW5BIcF1LJXewmBg9ZGjtXrGodHjcBT7fGnk88mxqAmPiqnz\n80KIh4BZwAJgPk7dLzPOgjPxODMZ/hBC/F9ZmrrCpYW/v389Zq+ew+5weN1Om92OwwvRRDabjQM7\nNrDtt5/JO5NGkCqfTjEqQlroKOeXrRJ/nZqrm+mBIk7nbmXZzG3kqwIJjYyj+3VDaN72MreOf1WF\nHwwHHiyng/ElsBW4Q0r5Xem5AuADaunkUfBNDp84jCGi4rLiZWiCNCSnJvuUk6cseqc7EOPhhUSs\nwZ9Bdju/vDWdkDatuXPMGJdF9dhsNn6c9w7H92/livgSIlv74Qxi8S7BRh03tILswqN8/fpIQhq2\n5o5HnkXrwl1HKeWDQohFwBxgPzBWSpkPZ9Md3pVS1jddS0HBYzgcDt4bN56mmZkklAv172AM4NSp\nU7zz9BhGvvWmR6ICPYler8cvtIG3zagUIcRR/pnGVjXbckgpm1ZxvVqklFuEEG8BG/gnUvF9KeWu\n+rSrUD3jho9jwrRnietVu13UrN1ZTHl6ipusuhCLuRjqkFpmB07ExpIdEkyzxo2hlvqDAAWZmRxJ\nSaFJWjohebXXYLRarbW6v1233uzf9R1tYn1XVPV8iiw2NKao+mqETADul1J+U8n1OUKIEcAUnI4g\nhUsMlUqFqr7iTh7g8sGD+eLXX/HmG/xIQQGtel/pkb5ysrPYuHwRhw/uwlGcQwOjmR5RWozhWuq7\n/ooM8uO6IAAzeUWH2LX4dVZ85Y/GEEyrDl3p0fdWjKa6Ofcro6rZZDSwvexASvm3EMKGc8FVxv7S\n+xQuAa7pcQ2zf5iNoUPl1ettmVbaiDYetKpyzEVFzJs6Ffux4ww2uj96pzK0ajV9TCZSDkimPTqc\nmx9+iNaXX16vNk8e3s+COdPoGlnAZa311NRr7ElCSp09ydl7ePu5h7jpnhGIjvX73OWRUi4XQrQH\npgN7hRAPKykNChcrR/fvJyA1lYSgC9Ms4w0GMrOy2L95M+17eSZiwFOkpqai1huw2+2+Kp44Aqe4\nclfgQyCtkvvqvZUohOgNjMUZubwSuAFYJIRYXbZ5puAegkxBtGvRjmNZxwkMq9lmUMGZAlo3aU1w\nULCbrfuHVl27sszfn44OR413eNPDwjgVEU7D+HgalUUA1aG6S2xoKNEhISSFhXEiI4NmJ05iLKmZ\nblhmcTFRzZvVqr8rB93F+1s3EJCVTkKY76emFVpsLD2oYtiYZ+rbVDzOojNV8TvOuY+CgtcIDg+n\nUY/L2bl1Kx2NVQcBuIPU4mKSwsN4YuhQt/WRcvIov/3wJVmpJ/Cz5dIm3MYNjfxKx1/3aCYFGrR0\nT9ACDuyOMyQdWsaXm5dj1QYR3bAp1wy5lzAXVF2ryslzEHgY54SkjObAqXLH7ah8QqRwkdFWtCXW\nGENuWi6B0Rd6E7MSM+nZsReBAa71NNaFPX/8wU+ffkZPjZpwk2+kAcQaDQy22/nrg9lsWL6c+55/\nvk7pF4X5uXzzwavc2sqBTuv9yJ3qiAvRc3uQle/nzSQ8phHh0a7LNy0Vf39QCDEA+FgIsYYqqgIq\nKPgqdqsVbRWLNq3KWXHrUiIlJYWff/oJq7mIpUuX0r9/f4xemChWRakz+SjOTasP3BxVcwewUkq5\novR4mRBiBdAPUJw8bub6qwbw9rczauzkyU8pYMDN17vZqnNRqVRcMWgQe374gfbV6AoW6XTIhg0J\njY6iY0SES8L+1SoVTWNiKImI4JDJhF/WGZqePFntS3eT3cbjI0fWqi+VSsXwiTP4auYLHD+aSK9G\nWjQa33y9J542sz3TxEMTXiM0Iqq+zW0Cpgghhkkps86/KIQIwSmRoWjyKHidmx9/jGUffsja9Ru4\n0mhE54HNGofDwa7CQnKio3ni1VfcEuF8YPt6Vn//JQHWTLrEqwhppgPKfjyHWqWiaaSBppEAhWTk\n/c3it3dgM0QxYOhDNG7Zvs5tV62DMEsAACAASURBVPWvNhr4XggxGPhbSvlfKeWxsotCiNeBB4GP\n6ty7gs8xfvizjJ86nkJDEcagfyJ6cpNziNbGcs+Qe7xonZMda9Ywe9YsnoiJPTup+SEjgyER/wgB\n1/TYAZyMj2NbSgq9hCDw8BEMJSV1bk+jVtPTZOLrPXt4b+w4Rr71Zq0nXos/nsb1Ta0+X8WsPBq1\nmkHCweKPp/HIczNc3n65qJ63gGScuhYKlyDjXxrP6y++7m0zXI7Dbq+m9K4Ku833hRirwuFwkJaW\nRmJiIhkZGWi1GpJPHKFPj/YkHT/MqlWrUKlUNGrUiGbNmhFYR90RVyOlPCiE2AoUu7krOxfGfNuA\n2ufGKNSaQ8cOoTXUfLGgNWiQxw7RvEkLN1p1IVfccjNvrf6VxPR01KoLF1RDIiJICw8nNSaa1gkJ\n6EoXQEvXrauwvZuuvrrC81Xdr9NqadO4MdlRUeww+NM66RgrU1IqvL+NwUCHa67BWIcNN41Gw71P\nv8r+rX+yeOEndI4ooEWU70QuZ+aX8McJDc06Xs3oZx5zlX7GQ8CPQIoQYjvOUuqFOMMGGuCMKjwJ\nDHJFZwq+ykUgylPKjY8+yrHevZn/9tt0MttoaKg846O+5Fks/F5SwhVDhnDFLTe7pY9tv//Mjp8+\nYWALf5/bTI8I1DMgEMwlGSyb8xI3PDyRJq061amtSt92Uso1QogWOMuZiwpuGQi8DSilPy8hNBoN\nL495mbFTnsHvcj0arYaivCJUaWrGjh9bfQMeYP2KFTTU6ur9sj0TGEhSdBRxcfGo8vMJbdaMA1ot\nxuxsHJmZ9Wo7QKPFlpmJoxYh12UUFxcRFHzx6XIY9BocdvctUkujeh4qTXmoeLapcNFzKvkUNpvt\nkhIhTj58mEVvz+R6Q+VRLC0CAvhl7lzCo6NJaOudcpu1obi4mLS0NJKTk8nMzMRut2Oz2TCZTERF\nRVGYl8OWjX/St2cnQoJM2Gx2DuzZzjX9BmI2m/njjz+wWCxoNBp0Oh1RUVHExcURERHhFV0iKWV3\nD3SzBFglhLgeZwWda4G+OAtYKLiR/MJ8lq1cSnSvmisMhDUO46c1P9G7W2+PRzDfMnw4M154gVjd\nhQuQkzExmBs2oH1MjNtFi0OMRtoLwW6NBkdWJirzuVVuHA4HUqdj7L3/V69+Wne9EtG5J7999wXf\nbllHp/AimnvR2ZNVUMKfJ9QExwoemDiGgEDXpexJKQ8JIdrhTNe8BmgCRAJFONO43gOWSCm9X1JI\nwS1YrdaLyMXjJKFNG8Z+8AELp0/n6J699DIa0bo4qmdvYSGpoSE89uKLmILdlya77a81dIrVotP6\nZuQggJ9OTZsoNZvW/FhnJ0+N3g5CCBUQjnMHKl9KmVun3tyAEKIxcHT16tU0aOC74o4XGwcSD/D+\nkveI7hhNysZUpj4z1SfStAC2rVzJ+q++4mpjQJ10eDKCgzkZFUlwWBiNIiMv0InILSri2KlT+Ofn\n0+TESXR1EEE8VVzE/qAgnpxR+6iWP39ZQNGOb2kV655cUHeRcqaYlJCeDBn29AXXVC6ciQohioCO\n3hJeVsYc93Lv4/fy6nOv0jDOd8Td60Pijh18O2MG1xuM6KtxXFntdlYWFDDo0Udoe8UVHrKwcmw2\nG6dPnyYtLY3Tp09TXFyM3W7Hbrej1WoxmUyEhoYSGBh4drF5JPEgWzauJyEmnA5tmqEu96dfbLaw\n4e99lDjUXHVtf0JCnWWIrVYr2dnZ5Obmkp+fDzhTOdRqNYGBgURHRxMTE0NwcHCtFrV1GXeEEBG4\nca4jhLgTeBFohnMH/zkp5aIaPNcYZdypE+mZ6bz89mRCOoXgb6p9da0zO84wcdREYiLrXlGpLrw7\n5hl65OWdU0o912gkuWVLWjb07HfAZrOxS0o6HUo8J3VrZ0EBTW67jZ433uDSvlYv/pS9W/+ga7SZ\nxuGe22nPKSzh9+MqTDHNuXXY05iCQ2v1fG3HnNL1Vfkxp27lzdyAMua4j+S0ZJ59eTxzZ83ztil1\nQv79N0tmvsMgF+qh/lVQQJO+fen3X/dnjFitVr5463/45SdxeSMdfjrfcvYUmK1sOG7DP7Yddz3+\nfJXznqrGnCq3y4QQg3CWTe9JOeVXIUQWzl2o6VJKJWf0EqRV81b42wyUFJcQGx7jMw4egC79+2Mw\nBbL0ozn01mgJqUUVif0JCRjjYulQRf56kMFA++bNyTeb2W0wII4dx1Rcsyh+h8PBlsICaNyYJ55/\nvsZ2lefyfrfx/m8/0qr+mlseZVuajnsfetAlbQkh1vJP9Znz0QNzS509DinltS7pVMHrFBQWoA/S\n8/NvP/Po3Y962xyXsOLLrxhUQ4e0Vq1mQEAAq7/91itOnoKCAnbv3k16ejoOhwOHw4HJZCIoKIgG\nDRrgV8VYm5ebw/Jl3xEXGcKN13SrUGTZ30/PNT07UVhUzPo1v6DxM9J34E1otVoiIiKIKJcCC87x\ntLCwkKysLJKSkigqKkKtVqPRaGjatClCCJdE/XhyrlNasVSpmOMhvl/5PSvXrySyawR6/9o7C/xN\nfkR0DWfye5Ppe/m13DrgdjdYWTH3TniW2U+PYaBGc3b8OBYbQ5t41+ne1RSNRkNcXBxp2dnEns4A\nIMdiJj0kmLtd6OAp66v/0Ie59tZhrFgwmyW7NtE73kxkkPsie8wldn47akcb2ph7xj1DcFhE9Q/V\ngyrGnExgDS4cc4QQMcDHOCMHi3GWbX9CSnmxBZNcMmzftx2Nn7ZO0f6+gLjsMm5/6kmWz3yHa6rR\nDqsJB4uKiO3V0yMOHgCtVsuD49/gmNzDikWfYM9Po3us1a1jTE1IPmNmW5oO/9CGDHh0OHGN6lXQ\ns3InjxDiIWAWzsnIfJz5oWbAgFMZ/lrgDyHE/5WVWVe4tPDT67GarUSHeXb3qia06dWTxh3a8/nk\nycSdzqBFDcU8C/U6moeF1WhQNfn5ERYWRn5mVo2cPBabjRXFxQy49z46XXtNjeypCK1WS2Qjwecb\nt2Lwu3ChdGfnivPeF2zPr/C8J+4vNFvRh8S7MqT5CDAMZ4WJtZzr7LkS2AJkcjElNStUy7tfvEtU\nx0h2H9hFfmE+JqNviKrXhw49erDxp5+4ooYToW1FRbTs4YnMoQuZN28enTp1on372gv9/fDtN9xw\nbQ/8/apfSBsN/lzbqzPJaRms+PE7Bg2peOGsUqkICAggICCA2Nh/vN42m43ExET279/PbbfdVmtb\ny6PMdS5NTqacYOanM7GF2IjvWT+niM5fR3zPOP48/BfrX9nIkw+MomFcIxdZWjnBERHcNGI4P86e\nTX9jAFq1GpXez2tV6iIDAzlgMhF7OoPTxWY263WMfOUVt/Wn1WoZfM8TFN/6AAs/mIL1UCIZucWo\n1RfO3+ozz9mdbOFQYQh3PDKGuMYVqVO4Fi+MOd8Ae3FmZMQAfwAbgYszjOQSYP3WvwhqaGLzzs30\n6NTD2+bUieadO4PJBHXIdjif4zYbox56yAVW1Y4E0Y5HnptBXs4Zln8zmz8PHKRZYAHtYvUeG2et\nNjs7TpVwvMhEk5aXMeyRhzEEuGbuW9UW2ATgfinlN5VcnyOEGAFMQdmVuuSw2WzkFuQSHhBO0q4k\nb5tTIUaTicfeeIM5E58nMDmZmBoIgbVJOsZ+ux1jaCgJ0dFoK0mfyCoo4GRyMuFZZ4jJyKiRPauL\ni7n/pUnEJCTU6nNUxE33jmLsY/fR1Hf0B6tk/XEHg0c84bL2pJQPCiEWAXNwVr0ZK6XMBxBCjAfe\n9Va6loJ7+PWvX0kpTCaqaRTqdmpenvkyrz372kW5y1We3nfcjtbfj5+/Xcw1/v4YKok8MdtsrC0u\nosvAgfS5804PW+lkyJAh7Nq1i7S0NBwOBwaDgeDgYIKCgjBUMb46HA789NoaOXjKEx0Zxo4Dx6q8\nx+FwkJ+fT05ODrm5uVitVlQqFWFhYVx55ZW16q8SlLlOBeQXFLIj8RThJj2tm9X/neYpbDYbH87/\nkL1Je4nqEInWz3X6TuHNwrE2sPL6p6/TOqENw+8e7nbtsNY9e2IMCuLr6TPo4nDUUGThQjbt3MlH\ni5xZgQ8PHUqPDh1q3YZKpcLucLClIJ/CqGhGv/IyOr37U6n8DUbuffoVjh7YydSXJ5EQbHdJeoXV\nZmfFIRstuvbnydtdE4VcQzw25pQWrOgM9C/V+DkqhCiL6FHwAgcSD5BjyyWqTSTzf5hP947dL8p5\nTkpSEiUFBeCCipmRKhXrly7lypvdI7RcHYHBodzx6ARnNsbaZSxevpgeMUU0CnPvIkymW9iRaaT/\nrQ9zS48+Lm+/qrdfPE4BsKr4HZjuOnMUfIUPvnwfv4Z+aLQaLAYzP6/7mUFX+6bQv1qjwVBDj6uh\npIQOh4+QazBwICsL/7AwmsbEnPXYniko4PjJU4Tm5tIuJYXaTN9UajWmkJA6fIILCQgMpmeXDrRT\nHyQiqGaTqPN3sortWlKIZlNJGE1jQzD5n1utq3+0jYMnzxCpziZWlYJRXXX55sp2yvKLrVj8o4lp\n0LhGdtaUchW1pgN7hRAPSylXurQTBZ/gyPEjLFm1hLjLndEahkADlugcZn42k6ceeMrL1tWfnjfe\niOjWjTnPTeRahwOT7ty/xUKrlZUWMw9OnkxMI/dHCFRGbGzs2YgZh8PBmTNnSE5OJjU1lcLCQux2\n+9nw8sDAQIKCgggKCkKr1WI0BVFUbMbgX/NJ0e4DR+jU1Rm1ZDabyc7OJi8vj4KCgrOaPCqVipCQ\nEGJjY+nSpYs7SrArc50KmLvwB/ZmgSbrKO+8+j9vm1MjTqWe4vUPXsfQxI+4bu7Jd9b6aYntFsvx\ntGOMnvwUY4ePpWGse/9mE9q2ZeyHs1nyzjscTkmhSXwDjIaaawst/OUXFvzyy9njNz7+mDsHDmTo\nwIG1siPx1CkSU1O5oZ7RynWlSauOzJwzjzmvjeXy8DPEhVRfgbSyeYu5xMb3B1QMHT6RRi3audrU\n6vDkmHM5kAi8I4QYijNi6GPgBRe0rVBL0jPTeefzmcRcHoNGq8E/Qc/rH7zGs49N8LZpteJUYiJf\nvPIKA/1dU2WrU0AAa7/7DrVaQ6+bbnRJm3VBpVLR/dqbuOyqQXw9azIlpw/SLNI9juztJ804GlzB\n02NHuc3JV2mrQoh1QDYwTEqZVcH1EJwDRYSUso9brKsBijCY6/lkwcfsSdtDRMt/cpJTt6Zy09VD\n6HdlPy9adi6JO3fy/Ycf0rCwiHZ1zAnNMRpJbBBP22bNSM7IwJ6SQtOTp6jLHlGOxcyGEitNLruM\nG4c/Wu8drqKCfGZPepTb2qiqHAAcDih0+JHpCCXTEYLFoceh0aPzMxAWGkpYkAFNBeHNzmcdZOUV\nk3kmG0tRISq7BZ3DQpg6h3BVFiZVMdWNPUv3W/nPmLcIi6o8JL6+wstCiAE4o3rWAP8B2ivCy5cG\nVquV0ZNHE9k9Ao3uXLdq2q40hl4zlCu79vaSda4lLzubz54azXXnjVfr8vO4Y8oUIuPjvWRZ7bBY\nLKSlpZGWlkZGRgYlJSUkHZYE+atp3Cie0EADOk3lo2iRpYSs7EL+3itpItrg72/AYDCcFVgOCwtz\nSYRETcYdZa5TMU9OnIKhaXeyDu/gueH/IaGhb383129fz5ffzyO6azRavWeqs1ktVtK2pnH3jXd7\nbIz6deVKdmzZQoBGy+Xt250tn14Z5zt4ylNTR09mdjYb9+3DPyiIYQ8/TFBQUJ1sdxVWq5UPpzxN\n18A04kJrP88yl9j57oCKYWOnEhHjWoF/XxtzhBATgcnAOGAG0BL4DXhVSjmzmmcbo8x1XMa+xH3M\n+vxdorpFoSu36Zp9PJuA/AD+98Rz6CuopudLmIuK+ObNN8lLPMwVBkO1xSRqg8PhYEdhIZkhwfzn\nmWeI8vJ3zmazMWvC/dzaxj3tLz6g5anXPqt3O3UVXn4I+BFIEUJsx1kFohDwBxoAXXHmkfpmeIdC\nrbFarUx9bwo5+uxzHDwA0V2i+XHjMo6dOsZDd3o+b7IMq9XKn4sXs23dOkILCuhrDEBXD9Gv4MJC\nzBs2Mmv7dgpycxmg0dI8Kqpuben9GKD349SOHbwzfAShDRty4yMP13nhZggw0XvQXWxY9xW9Gjtf\nCEV2HRmOMLIcIRQ79KDW4VDr8DcaCQo00cjkh5+u5hNclUpFeJCB8KB/vPEWq42cAjNHcvMpKipE\nZSsBuwW9qoQwVQ4RZGFUm1GpYFeyhZbd+lfp4HEF5aJ63gKSAatbO/RBXpzyJi/97xlvm+Fy3v/y\nfQJaGC5w8ABEtY/im6Xf0L1jD5+f/NSEgKAgrBU4XC0qNSF1HHe8gV6vp2HDhjRs6Fwg7fjzF46l\n/E2vlnrSUlJIPBVGidpAREQEsRHOilglNjtJp9Ipzs/BQBExqjSuD0ln1ZZjDBvzKuHRnheTLUWZ\n65xHTm4eRTY1BsAQ3YQlv/zK6Efu87ZZlbJ19xa+XPYlcT3jPJr2oNVriesZx/yf56PT6ejR8XK3\n99nn2mvJys6mYXw8K1f9SuOoSNo2a1bhvZt27arUwQOw4JdfSIiPrzR1q8Rq5Y/tO/ALCqTvTTdx\n+vRprzt4wKnV8+j/pjPrxce5Tp9DSED1ET1l2Ox2lh60c+9o1zt4aoEnxxwrkC6lfLP0eJ8Q4hug\nP1Clk0fBNTgcDuZ+N5ct+zYT09MZwVOekEYhFGQVMOblp3ni/pG0bNrSS5ZWjs1mY8Vnn7N3/V90\nV6mJMLleL1GlUtE5IICiomK+mTiRoKZNuWP0aAICvVP459TRg4T5WXDWenE9BlUxZzLSCY1w39yv\nyrehEEIH3ABcAzQBAoAinAPS78CS0hxPr6F4ml1Dbl4uL05/Af9mfpgiK/+DOpN0hoACE8+Pet4l\nVU1qw4zp09m7YQNhNjvBWu3ZydyQiIqrIPxQiZZO+fsXHjnMN0eOcNVVV7FlyxaKioroFBNDx5jY\nCu+vTft5JSVsKi6i+VVXMbgOgmI2m42kpCSWLZ6PSe8gKDgEP38DwUGBBAX4YdDXfGLjCswlVnIK\nzOTk5lFcVEhhfj7pOUXcdPvdNG/evMrvgytLqHsbb4059zwwnK8+ne2x/jxBTm4Oz739P2J7VJ5a\nkXc6l8Y0Yfh/R3jQMvfwy6efUvLHnzQ7L+XoZFEReZ06cvuoUV6yrG4c2r2FFYs+JVJ9hp4J2nMW\n2A4HnHDEcdweT9PGDUg8fIQOukRC1AXntFFksbH6CBgjGzPk/tGEhEe6zL6ajjvKXOdc/ty0jXkr\ntxIaX+o8SN7JaxPHuLXPupKbl8uzb4wntles1wSJ7XY7KetTmDJ2KiFBrknZroqjR4+yfPlyBgwY\nwI7Nmzm4dy8x0dF0bd787D07kpKY9fHHnMnNBaBz585s37797PWy49CgID5+5RV2JCXRqXHjs9d/\n37OH7Jwc+g0eTHBoKNu3b+eWW25B7wENnppSVJDPrBcf4/bWNrRVRA6WZ9WhEq64bRQtL+vlFpt8\nbcwRQtwOzAYiy6ppCSHexxklNLSaZxujrK/qRerpVN78cBpEqwhtVPXYYLPaOL3zNK0btWHEPSN8\nRqfnrx9+4M9ly2hrs9PE9enSlZJlNrPZZqVh+/bc9uSTbtc/K4/NZmPG/x7mlhZm9Fr3vFfyi62s\nOhnMqFfqN6+vcwl1KWUJ8J0Q4nsgAqc7K19KmVMvixR8iuLiYiZOe46QziH4GavWUwhtHEre6Txe\nevslJo+Z7NFBaPeGjTRwgL/ONc6NMgcPOCdper2eoqIidqSmApzj6KkLgTodfXU6Fq36lXZXXklC\nq1Y1es5isbBmzRoKCwsJDw/n+htv48clC2ktIggL8d4ump9OS1SIlqiQAPILili1fjs333EPGRkZ\n7N+/H51OR58+fTC5wcOvcGmy6s+VGBpVndMdGBnE4e1HPGSR+9i6YiWHf/+jwnKjDQwG/ty+nT+/\n+54rb/GO8GBNyUpPYfV3X5B6XBKlzWdQIx067YVjskoFjVTJ6B1mdh60cZ1xJ9oKXhcGvYYbWkF2\n4REWvTkSsy6Etpf1oteAofj511x3pD4oc51zMVtKQPXPxNYFxVPcxoxPZxDeMdxrDh4AtVpNeKcI\n3v5kBpNGv+T2/po0aUJgYCD79u2jY7dutGjThsULF9K5adOzpdbrQ3pWFjmFhdx1//1YLBa2b9/O\n9ddf71MOHnBGO9/56HhWfPYKA0T1n/vwaQvhLS53m4OnNnhwzPkFZzTP80KI13Cma90J+G5o3iXC\nT2t/5Kd1PxPVOfKc9KzK0Gg1xHSJISn5KE9PHs2EJ/5HVLj3InxtNhvzXp2C9sgRBhuNHnc6hfn5\nMQA/Tu7Zy/SRIxkxZYrLdE+rY8PKb8nKzEDf+p+KwQu255+j8VXf45/2F9M41MaB7Rto1bmnWz5H\nlaOiEGKQEGINzjDCNOAEcEYIcVoIsUAI4dK6b0KI8UKIY0IIixDiuBDi4lKiukiZNfddTG1M1Tp4\nygiMDCTfmMfPv/3sZsvO5Z2PPyIoOopYPz/6hYQwJCKi0igb4Oz1838ANqWnn3XwxMfHk5qaek40\nyo7UVGLs9jq373A4OJZfwE/5+Vx359AaO3gAfv/9d9LT0+ncuTONGjXC39+fBk0F67bsJTvXWQ50\n+4Hj5zzjyeOiYjM//7mDm++4G4PBQHx8PBaLhSZNmrBmzZoaf05fQgihEUKsF0K86G1b/k1ERERi\nza1a8NtqsaKpk0qWb5CVfpr3x41j1/z59KliF+xKYwBHli7l3afHcPrkSQ9aWDWzZ8/m4M7NzJ0+\nkfcmPsRLzwynmflvbm5RQq8mfizZXXjO/eeXLP5t9ymsNttZB8/518uOQ4w6BrTUUZSdCnIZn730\nAO+/OJxxox4hPfnc8cjVeHqu4+s0S2iAo9j5e7FZLbUS0/YkJ5KPk1GYjn+gZ5yBVeFv8iPDnEnS\nySSP9Pff//6Xli1bsn37dvR+flx33XVs2rPn7PVOjRvz8NB/AjXKR/GUPy67p3wUz8a9+7j9rrvI\nyspiz549DB48mBAPLa5qS6MWbQmIbUV6jrnK+xwOB39nGrnh/3wjWtJTY46UsgBnalZfIBdnmthE\nKeWPrmhfoWIWL/+WFdtWEt8zrkYOnvIExwUT3CmYSTMmkZ6Z7iYLq+fr119H7trJ8aIilmZm8kNG\nxtmfyih/j6vub2AwcJXVxgf/e85ln606juzfRZCf+51aCSFwaPdmt7VfaSSPEOIhYBbO8n3zceaH\nmgEDTmX4a4E/hBD/J6Wsd1lRIUQ/YBLQG9gG9AJ+FUJs86WKOolHjrP0zx34GQNpFR3Adb27e9uk\nepOakUpoQmitnglrEsaGresZfM1gN1l1IabgYJ557z2O7NnDmgULyElJJc5SQitj7cW/Pjp4gNjY\nWKKjo8nMzCQxMRF/f3/atm1LcXExx48fZ86B/fSopU5GSmEh++0O7MFBtO3fj6duvw29X+0myD16\n9GDu3Lns3buX2NhYQkNDUavV3Hrnf/l+0dd0a1dx/r0nyMrO5bfNu2nWog0GgxGHw0Fubi5ZWVlI\nKbnqqqtc0o8Q4ihQtn9c1UjrkFI2dUGXLwDdgOUuaMstOHx5O72O9Oneh3XrfyMnOYfguOALrlst\nVlI3pfLsiIvL3+9wONjx22/8/v0PaHKy6a73I6AG2mFdjEYKCwpY+NxESoKD6DVwIN0GDPD4Dlp2\n5mk2LF/I0cR97DmcTFTaarrHaAkI07IgT01kUM3GNIcDzAENaRoWxElLNA3UadU+o1KpaB7lT/Mo\ngAI+S8tlxQdjybUb0RlD6NCtN5ddPbjW42pleHquczGQ0DAeldXpvMs/k0G3Vs2recI7zF0yj7DW\n4d424ywRrcP58rsvmThyokf6a968OeHh4fz666+EhYZeEMXTo0MH7hw4sErh5Yr0ePz89Bw9ehSV\nSsUtt9zi0TSJunDTvaOY/9pjDLjwFXKWpAwzHbtf5xOfxdNjjpRyF+CayZlCtVitVlZvWE18r7qL\n1ev8dUR1i2TWF7OY/PRkF1pXc4oLC2tcudjdmHQ69HhuDtyr382sSU88W00ULqzUV9/j2zsa+WGf\nlbvucF/0dlXpWhOA+6WU31RyfY4QYgQwBedAVV+ycYYUavgnwsgBpLqgbZew7+Bhpn/4BaGte6Eu\nsbJzyyrsDjv9rnK/2J47Udcwl7k8KpUKlZf++P+fvfMOj6rMGvhvWqYmM5n0HpJwEyB0AUURQaQq\nNsAuWNbG4qprw7X3rqsg1rWighUbIiCi9A4hQIaQhIT03qaX74+BkEB6pgQ/fs+zz3rnvve95w6Z\n955z3lOS0tNJSk/H5XKRsW4d6374EUtlJUlOJyltGFI2kYhqrZZqbRDWgACSqirJP3KEXbt2NY0x\nm83s3r0bhUJBXFwcwVote/v0IchkRF9djdpibdXbUG+zsctiwaTRkDRiBDdcew2aHhQnDAwMZO7c\nuTQ0NLBv3z4yMzNRKBQYDAbOHDOe3Ts2k5eTy5DUuKbF53D+YYamHW/j6o3jALGD3OIqzj5vIg0N\nDezevRuxWExYWBhXXXWVp3f6bsfdEeIM4B3cO12t0eNVXxCE0cAM4Fs6qFPmL5atWINLqiA7N5+U\nPv5rse0NHv3XY7z87kuUZBejTzlusJkbLFTtrOThfz5CTGTv7uxzjNzMTFZ/uYTa4mKibTbOU6mQ\nqruWvqiSSjlPo8Fhs5P1xRL++vobAiPCGTdrFn2HDPGS5GC1WPjh49cpyc9G6axnQJiDAfFyLkro\nuiJjcUkocMZQ4tAz9uxYQrRqco5IOVIXzphBJThc5UhEnZvvhjOPraV27I4ysrd/wfu/f41LoWPY\nWedz1qTLe/rovtZ1ej0ikaipgL/TVE9qSu/czKppqEGnbMey9zEyhYzqhhqf3jM4OJjLLruMl598\nkoTIqBZGCcCsKVPIPHiQD98CEAAAIABJREFUvdnZLa5L79u31c5aNrsdkUiEubyC6ddc7XX5PYFG\nG4xdpsbtJ2mdw7Uipo3x3aZkB5xec/7GVFRXIFb03D6SyqXUmHy7njRn6uw5fPzcs5wnlqDt5KZK\ne9kP3R1vczr5vbGRMy+7rEtz94SUgSOomXgNy379nEkpYpQBnnUO15vs/JoNU6+5k4iYBI/O3Zz2\nnDwxQEYH1/8JvOoJQQwGw1ZBEF4BNuI23ETAW0c90H5n+e/r+Hb5GvT9RiOWuL+2YGEkX69cT97h\nAv5x3Uw/S9h9AiTdaEHZYCHcixXBO4NIJGLQmDEMGjMGu93O2qVL+Wn1agZIpCgjIqgJ1GCTyUAq\nRapQoNNqSVSpCJDJaLTZeHHHjlbnNZvNHDx4kPtvvpl+6QOoN5spq62lsbERkc0GNhuBFguqqmoy\nKyqQREcxY948QqM92x1Go9EwcuRx5dpisVBYWIhUKqWyqo5vf9tAYkwYWq0OJ2JsDme7bYu7gguo\nqjdTU1tPXV0tFTVGghscjBw9lpiYGGJiYlAq26+l0hOOdtPKBfYDi7y1DgiCEAR8CFwDzPXGPXqC\n0+nktXc/4WBhNbEjp/DCoo+ZOm40l04539+ieQyRSMR9t97PB0s/IPNQJiHJeuxWOzW7qnl+/gsE\nqv3TWaGzVJWWseLjjynOOYTOZGKwXIFKLoceRppIxGL6B2roD5iqqln/6mv8qFAQnpjA5DlzPLre\nVJeX8NbT/+aCJDujUgIA2dH/dYzDBbWuQCpcIdQ4NRTWOoiN0BMeFsrgQAW7sgoI1WlIjovA7ghj\nU4YcnTIZ7GZkLish4hpCRVWoRWY6CliSSsSkRSlIiwKXq47dW5ewYNNa/vnYGz15fJ/qOqcKp0Lk\nYG/0yIv8INW3b7yJkF+AzuViLy4G9OmD+OiPaeny5Sc5eAD2HjzI0uXLWzh6Gi0WDIcOcW55BX8e\nzCZnYDpJbXTeOhVx+jASoANOrzl/YyLDIgkUB2GsNaLSdr9Qcfm+Mqae6z/HZEzfFO564w3ee/gR\nwqprGKT2fV2eQpOJ7WIR1zw0n/gulL3wBGeMn05c2mCWvPUciYoqhsQE9Pj5HU4nW/JtVEoiuek/\nj6LVd80p1lXac/JsBp4VBOEGg8FQdeJJQRB0wBNHx/UYQRDGAPcBU4DfcFed/0oQhNUGg+E7T9yj\nu/y2diPfrdpISL+WhZFEIhHBfQazMy+bdz5Zyq3Xt1uo/jQexuVyUV1dTV5eHiUlJdg1GvpOm4Yh\nO5sgmYzhgoCsjY5PowYNIj0lpVXlByA9JaUpjFmrVKJt5tBwuVyU1daxNmMPKRdMQBMYyMbt2wk6\neJCEhARiYmKQeag4dHPkcjlJSUkkJSVxzjnn0FhXwxcLn8FyZCej46I5dGAvdrEckVTJeaNHtLj2\nkoljOjyuN1opKC7FaTWSFqPDUrCNqiNF1Im0/OehB73eJv1EDAZDliAI2wCzF2+zEPjUYDBsEwQB\nPBAZ5EkOFxSSmVNEeJrb2ReSdhY//bbmb+XkOcZNs27i30//G5KhfF8F9956b6918LhcLnb+/jt/\nLvsBWW0tg2QyBsnloPGOvEqJhBFH24jW5OTy1fz5mIPc6Vwjp0zpseIRpA8jJi6BvaXZ4DITqZWf\nNKfJKaPaFUQtOuodClxiKS6xDJEkAI1GjTZQTYRajiWrgH7JrbcmlkrEqBUBpKe6o9Fsdgc1DRYO\n1ddjMpnAYUPktCHFjlbSgJZadNQhE7f8WdodTnLKrRyqkTBi/Nk9enZ8rOucCjgcDqx2B2pAogoi\nMyuHwQN8q2B3hrTkfuwv3I8upndE89SW1Pq8/fF3Cxdi27OH/ioVVFahMJnJcDoZlJzMloyMTrdQ\nN1mtHDyYzeCcHCTA+SoVX7/yCrPuu4/E9HTfPVA3MBkbEdsaac+kiQ9ykrF+BeMuud53grXN6TXn\nb87jdz/OIy8/Qm1UDdrorkW5Ox1OSneVcs6gMUwaM9lLEnYOlUbDv15/jQ3LlvHzsh8YJRIT5oMa\nbWaHg79MJqIHD+a+O+f5vJvzMSKiE7jz6bfZ9Ns3fLXye86NtRGp7Z59l1dpY0upnAmXzGHw6As8\nLGnrtPet3Yy7QFexIAg7cbf1MwIKIBZ3GsURYKqHZJkJ/GYwGFYcPf5REIQVwAWAX508P/32O8HJ\nbYcrB8WksGPvBh9K5DkqayqpaaxBSdeiMgLUARj2GLDb7T798RmNRvbs2UNpaSlOpxOlUklISAiC\nIDTlWg8dOpT1v68hMyeHIW7D/SQ279nTpoMHYG92Npv37Gk1X91ktrBhz25mXHUVqmbpYY2NjeTl\n5bF7925cLhdKpZJ+/foRH++d9Bp1kI6b57/Eob3bWfbZQgYF55IaEYDNIWLroVQyCUQlP3kxap6G\ndYw6o5WsrCzOVh0gQOLgcI2VLaUKLrhsNgPPHO8V+TuDwWDwWp6AIAhXAMkc7zIhopdtDsfHRpMW\nH05OXiZBcalUH9zGpPFjOr7wFKXJKetwoQvsnYU+s7ZtZ9n77xNrMjFOpULq425yOrmcc+VyHHYH\nWUuW8sf33zNt9mzSz+6+s0MikTDn3mepKC1k/a/fsiEvF4tTglIhRxukQanSIFcqCNRo0CkDiFYE\nIBG3/lM5cX1p71gmlRCmUxGma7nbaXM4aDDZqGg0kdfYiM1ioaG+jpp6Iw67BVWAmMFDRnDruGko\nVR3XOuoAX+s6vZ7MAwdB4U6T0wSHkXngAL3x8a+/9Hruf/Z+DJkGhInH3/U5a3NIGpvk0+PIoVHY\n8+zM+c8cjz1fR+z5808qN2/hrMDjzuUgo5G4omLy1GoWLl7c4RwLFy9m1KBBZOXmMjA3l2NJCVKx\nmEkqNV+88ir3vvM2sl7WWas5v375NkPCHbRn0iSEylm2bV1vcfKcXnP+5sgD5Dz/4PO8vXgR+7bv\nI3xwOBJpxyk/jVWN1O6r48Yrb2T4gOE+kLRzjL74Ys6YPJn3H32U6LJy+nqxlXqV2cw6kYibn3ma\nsNhYr92nK5w58XKGn3cRny94giMFhzgjrvOOHpfLxV95dgKiB3PXXff7tC5Ym7kdBoPhIJAOXAls\nAVRAPBAI7AHmAAOOjvMETtwtBJvjAOo9NH+3kclkHe6U+svL2BNy8nN49JVH0Q/Wd/lasViMRtDw\nwHMPUFdf5wXpTuZ///sfK1euJCAggIEDB2K1WklNTSU0NBSJRMKWLccrlJ89fhzlFgubMjKaws53\n5eU1nV/4+ecMHTq0xfwnHv+xp2WG0K68PCqqa1ixdQuXXXUVezMzW5zPzMwkISGBQYMGMXjwYCor\nKzl48CBff/01RmPLDjSeJDl9OHc+/S7VEWNYnBPBRks6Sl0EioDO/01qlDJCwiLYbE3ni7xIcpRn\ncMeT7/jVwQMgCIJcEISINs5JBEHoiQftAmAY0CgIggm4FnhYEIT9PZjTo0gkEu7/581MGNGPgq2/\ncs8/rmbWRZP8LZZXKCjMp9HVCIA2Rcvbny/ys0Qn8+RDD7Hiv/9lkkjEYI2Gn6tabsKe2BXCm8cS\nsZiDZjNTJFL+eucd1n37bdce5gRsNhvrN20DXTzDxl3CtJmzGXHuJERKPYXlNRzKPcLh/AKcDlub\nDh5PIRWLMdbXcTAnj7wjpZRUN6KLTuL8C2cy8dLr6HfmZMotUrZu297je/lB1+n1rNu6C4XOveyK\nJVIaze13LvIXUqmUZ+9/Fnu1nfpS/6mKVqMV034jz9z/rE91wQULFjKy2UbTsfUhpKaGhpoaGk2m\nDucwms3Umc0E19fzc3l5i3M/V1UxAhE/LHrbs4J7EFNjPfkHdhId3H50gUQsJkpay+4N/u/jcnrN\n+f+BWCzmjuvm8s9Z8yjfXE5DedtrlMvlomxvGeoKNa8+8mqvcvAcI0Au544XXqAhNZU8L9k0FoeD\ndVIJ/17wZq9x8BxDFhDA7HuewRk1nMMVnU8wyCiyEDP8ImbeOt/nhd/bfRsZDAYb7igaX0TSfAus\nFARhErAad3X5CcBTPrh3u4TpdZSZjQQo2vZcquSnjpOn0djI25+/TW5JDhGjwpHIuvdHpwnTYFFb\neOiV+ZwxcATXXnytVxUci8VCYGAgGo2mU+kJ0XFxBGs0/LhuHecOHtzinLETyo/dbm/6b6fLRWll\nJVW1tVwxe3an0rHEYjEhISFUVFRQV1eHyoOeb7PZTHZ2Nvn5+dhsNlwuF6EJA0jsP5Lff/uJ9OBg\nhvfrfDEvsUiEQiZmd0kN4yddgsVqZ8UKd1CdRCIhNjaWvn37dqpDkCcQBEEFvInb8SITBKEQuNtg\nMHzdbFgccAjo1h+wwWC4GfeO2rF7fgjkGgwG/7QyaIcZF03km2+/oV9fTzQS650sWryI0AHu/GSV\nVsURwxHKKssID/Fv7a/mFOXkcple7/O89PaQiMWcGxjEb6tWc04PChNKpVLUajX1R1OnTCYTIWER\nREYfV7TKS0vI2L2dqor9FBYVEx6qO+m7ODEV9Bjf//ZXq583H280mdmwIxObQ0RsfAJjJ12Mulnh\napPJRE1NDSaTCbFYTHBw17pCtoWPdZ1eT2FhMYrI4yk6Zqu9ndH+RaFQ8Ml7n/LSuy9ScqCEsLSw\nFlE2gFePK7Iq6Ne3H/ff+oDPN/vEIk7qqNV0zuEgQCbDarO1O4dKqcRotaJpaGz1vFwixuFw9FhW\nb/Hxa49wXoKdztQQOzNBxtJvPyElfSTqIP9Gip5ec/7/kJaSxmuPvs7L771EWU0ZIX1bdgS02+yU\nbill1rQrOG/Uef4RsgvMuvsuFt52O4lemDvLaGTaP/5BgELhhdk9w7BzJvHXZ1tI6GQ5nfw6CVec\n5Z8N817jmTAYDH8KgnA98BruFIrDwE0Gg2GnP+WqqKwmJ+8wwQP6tDuursFE1qE8UpMTfSNYNygu\nK+aTbz8hvzQfbWoQUSOiejynXCUn6qwo9hVncs8zd9MvuR/XXHItQZrud5dqi9tvv51Dhw5hMBiw\nWCyoVCoKCwsJDQ1FLpe3KFIMNB3H9unDmuXLcVks2GJjkUmlqBQKdu5s+ad14nHWfndAR15hEXty\nchg15hySmqV/nXi/ESNGUF1dTUVFBY2NjSiVSkwmE9OnT/dYkeLGxkZWrlzZ1NWqb9++JymWM6+e\nw5qVv2DMyiU9tf2/22PkFhRzqLCSWdfMafI0H0szczgcVFZWsmbNGqxWK2PHjiUkxOtta9/EHWlz\nK+4Oe1cBXwqCMMVgMKxsNq73WNteRtyLHAuexul0Um+pRy0/7kTU9NHw85qfuWHGDX6UrCWzLp7O\n1hW/Ne2en9gZoivHDuDCHlzf/HinsZHUHqRrgbvG3IQJEwCorKwkPz+f3NxcrFYrLperKT02LX0o\nWq2WLz//nEOHj5AQG95m7bOukJNfxP7cYsZdMAWJVEZtbS2HD7ud2GKxGLFYjFKpJCoqiiFDhnjU\naX6aljSaLSiarTdOiZyi4lKio1oNqvQ7EomEB2+fz0+//8gvfy0nYng4Upl31VuHzUHp9lImnj2J\niydc7NV7tcXEs8/GsHMXwtHfQvP1wSWRcNfs2bz4/vvtzjH36qtRBgRQp1KdtL5cpNezoqGBqy/x\nz/N1xDfvvkCyrAS9unOpEyKRiCnJNt557l7++fhCAnpYHP80p+ksUqmUB2+fzxc/fs7mfZsJ6x8G\nHF1HNpXy4B0PEh/tvS5LnsRiMiHxUmF+mUhMQ021V+b2BPkHM/nqvRe5vF/n01fHJ4t5/8UHmX3X\nk17tpNUap7zVIAhCIpC7evVqYj0Y2pVfWMzir38kr7AUTeIQZIr2jXSHw07toZ1E6NRccfEU0vv1\n9ZgsPcHhcLDirxWs2bAGs8iIrm8wikDveUjrK+tpyGlAK9dy8cRLGDnYe61XzWYzhw8f5vDhw5jN\nZpxOJ3K5nPDwcIKDg0/aYS4vLeX3X38lOTKSuvr6DpWfO6+9FqPdTkxiImeOHXvSfGazmdLSUmpr\nawG3ohkeHk6fPn0IDQ31ym5/UVERf/75J9HR0URFRbW7c7jylx+IDw8kIaZ9xby8qpod+/OZfvmV\nbcrscDgoKyujsLCQoUOHkpKS0iW5RV38MgRBqARmGQyG1c0+eweYBvQzGAz1R3/7OQaDwTMtxTov\nWyJeWHM64pobb2Px/3pv2HxPcDqdzHtmHtEjjzuezQ1mwhvCmTf7Tj9KdjJrly5l58+/cJ5KhayN\nXfTOkBMWikUqo19xcbfncDid/Gk0Iowfx8TZszu+oAc4nU7q6uooKSmhtLSU+vp6LGYz+/bsYICQ\nSLA2CK1GgbKVOmCt4XK5qDfaqK1vpKyygvziagYOPQOJRIJeryc8PJzIyMgeOXO6uu70Zny17mTn\n5PPS+0sITjmevmxqqCGSKubfeYvX7usp8osO8+Kil9AOCEQV7J3IU2NNI7V767j31vtIjE30yj06\ny+Lnn0dyIIuBzaJsHcD+9AEMSEpi6fLlbRZfvmLKFGZNmYLL5SJz3z4GHsppOmdzOlnV2MjkW/5B\n+jnnePsxuoTL5WLJomdQVe9laEzXi6CW11n5oziQ2x9+DaWHi/ufXnNO0xFvL36bHHM22lgdxZuL\n+feN99IntnMbsr2Bd/7zH/qXlKL3QrSNw+nkZ5uVfy/sXU7Y6vISvv3fK0jqCxiTKCFA2jXdz2R1\nsDbPhTI8hUtuuAeN1jNRyND+mtOmhXi0ffExV117i5bLYDCc8jkELpeLvQcO8vOqtRSXVmByyVBH\npRCc1rlHk0ik6IURmKwW3vj8ZwIcjYQF67jgvNGMHDrQ52G8RaVFfPzNRxRVFBMQIUM3VIdO7P0O\nFIEhgQSGBOKwOfh87WI+/2ExyfEpzJkxx+OdchQKBampqaSmHu9mUVNTw8GDB8nMzMRutxMWFkZU\nVJQ78iUigitmz2br+vVYa2u5YsqUNpWfi8aNo8Zq5eJZs1A3K6x6rLiyw+FArVaTkpJCbGysz/59\no6OjmTlzJjk5OWRnZ2O1WnE6nWg0GoKDg9HpdE2ROOMnTeOrxR8RHx3ersNp864sps+8tmmMw+Gg\nrq6Oqqoq6uvrEYvFSKVS+vTpw4gRI7zSOawVFMCJ1u/duFM4nwP+6QshTuMbxGIxYlHLl6bNYvNK\nRGBPGTtrFonp6Xz1wotM6UHRZadEirOH0QZrjEam/OtOhGHDejRPZxCLxeh0OnQ6HWnNWpkOSIpk\nw7eLiOkTQXFpMEaUuCRy9Ho9ESFBSJs5wsxWO0dKKjEb6xE5zASJjeid5ezOqueeJ99G0fMiyl3G\n17qOIAiRwPu4U9LNwBfAPw0Gg987+1VV1/LSwvcISh3d4nOlRsfhQ3ms+GM9k87rcTczrxIfncAr\nj7zCE689Tq2xFq2HO2/VFdchKhHzyiOvIg/wvxFyzYMPsvLTz1i+ejVjAgLQyGTUBAY2pTIea5F+\noq5z5dSpzJzs7twjEomgmUGVazSRGSDlygcfIKF/fx89SedwOp189PJ84l15pMZ0rxh0WFAAE6UN\nLHj8n/xj/svo9GEelrJ9/r/ZV6dpya1X38pdT/yLenkd6ckDTykHz5evvEJoUTF6L0XSSsRizkHE\nG//+N3e99hpS39gbbXLYsJflS95DYizlrDjQRnRPHmWAhMkCVDYY+Oz5uUiDopl6zR1Ex3v3592e\nhnk78CTuKu/vAKVtjPO7YtJdGo1Gfv19PZt37KbeaMYp16IOi0eZ1KeLvaaOIw2Qo+/jzmWvt5r5\nZPkmPv76ZwKVAQxI68slk8cTrPOes6WqtooFHy+grL4UfX89kcn+Ca+WyCSEpbpfnMXVRcx/ZT5C\nfF9uvfo2rypGOp2OESPc7cOdTicHDhxgx44dJCQkEBbmlmfE2Wezdf16oo6mHLWm/Mg1GmZef32T\n88Zms7F//37UajVjx44lMNB/rZ0lEgl9+/alb193tJjT6WxKrzAY3B3PnE4ngYGBxMQnUFxWQXRE\n60pMfaMRtTaY4uJiampqEIvFSCQSwsLCSE9PJzw8HHEPohV6wHbgAUEQbj6au47BYDAKgnAT7tpd\nW4E//CHYaTxPdW01LknLV0mAKoCSkhI/SdQ+Cf37ExQbg6WyCnk3Cuk5gXqVEqdYjIPuFZVyuVyI\nw8J84uBpj7Rh57B78x+4ajMYpC8/KhuUVIaxrzQSfXgUUWHBGHKOIDZXkSI5jFZsbGr78HuujUuu\nv9svDp6j+FrX+RLIBEKASOAvYBPwqYfm7xbbdu/j3U++JDB5JBLpyYqsNmkw367ayKHcfG6f03bU\nZ29AHiDnmfuf5fm3nqfqcAW6BM/smtbk1xBsCWb+Aw/1que/4LprGTV1Cp889xyaigr00dFENDPC\nZk2ZQkJMDO8tXYpIJOIfM2cy8oTOodKAAGrtNjZZLCSNHMn9t9/eq57xGB88fx/9FIUk6HvW7Uur\nkjE9xcp7z9zDbY++QaAHd9Y7wd/evjpN24hEIlJT0ti5fweP/udxf4vTKawWC+8/9hjhpWWkdcHB\ns6msjPcOuEtf3JLWj1HhHddY1MvlnGGy8NIdc7n5sUf9UoA5/2AmP3y2EK2zkgnxUuQecjaFaAKY\nlgpGSyG/vvMQNmUEF8+5m0gvRYS2u4ILgpAK7AeGGAyGPe2N9RfdCSfcnZnFx0u+o8HiRKqLRhMW\nhVjs3YrXLpeLhupybJX5KEQOLpo8jgvOHd3xhV3AarNy1+N3ETJMj0LT+4pWNVQ24Mhz8uJDL/r0\nvk6nk3Xr1lFbW0taWhpSqZTqqiq2rFrFOUOGsHnPnhbKzxnp6azcvp3LrrkGgLKyMgoKChg/fjx6\nfdc7kfkDp9NJcXExezMy2L11HUJSPPExESiPdtyy2OzkF5WTk1dAfN8BnDHyTGJiYrxW+b0b6VpD\ngBW4O+6tMxgMFzU7dwuwENgNDDudrnXq89oHr1KuKkOtbxkZU7KxhNcefr1Xdi/88PEnGFhSgrIL\nvxmzVEpRRDi1ajV94uIRS8QcOnyYoEYjUaWlqDooktocu9PJBp2W2597rjviexS7zcZbj9zEpWkn\n2yQZ9r7USELp79pHiLi2xTmLzcmqklBue+S/XpGrs+uOr3QdQRAGAuuAMIPBYD36mQCYDQZDfgfX\nJuKFdcdqtfHKog/JLatF12dQh7pQQ0UhVB3m7ttuIDkxzmNyeIvn33qeKkVljyN66orq0Bl1zJ/7\nkIck8w571q5l2U8/MTA1jZS4zv2dOJ1Oft+5E3FVNdfdfz/B4b6NbOksfyz7hMbMnxkU7bl27nUm\nG2vLw7nj0Tc8Ml9vW3N6wul0Le+xN2svLy98iY/e+NjfonTIoV27WPrmAs5CRJii85v0S3MO8WVO\nTovPrkxKYlZScqeutzgc/G42ccbUaZw3a2aXZO4J33/4KjXZmzknUYpc5l3zwmh1sCbHQd8Rkzj/\n8hu7NUd7a0670hsMhixgG+6Q4r8FX//4G2988h2SuCHoU0cSFBHrdQcPuD23gfpw9H3PQJE0gm//\n2MULb7ZfE6arbN+7naryqhYOnpy1LX9gnT3O353PVw9/zZf3f0n+nvwuX9/asSZEQ62phtr6loq+\ntxGLxZx77rmceeaZZGRkUFBQwJ8rVzIw2b3QjBo0iPeffpr3nnqKkYMGIRaLcTkclBYXNxVjvvzy\ny08ZBw+4nzkmJoZJkyejtpaRatlCrmE/hWVVlFfXk5WVRZJpK7L6w1w2Yxbx8fE+b+3XHgaDYRcg\nAHOBlSecexcYfPTzn3wv3Wk8yZHiAg4VHTrJwQOgTlGx4JMFfpCqY0ZPncJqs4k6q/Wkcy7AJJVS\nFqzDEB/H3uQkMtJSOTxoIKEDBjAkLQ2tWkWgQsGQ1FQiBvTnyMCBZPRLIyMlmayEeEr1wZik0la3\nchttNlaajIyeNMnrz9kZpDIZGn0kZpvzpHP9JAeprm04ycEDsK/EytkTu98RzFP4UNc5E8gG3hAE\noUoQhGLgOqDAy/dtlX2GQ8x76ClKnFr0yUM7pQtpQmNQJo3ghbc/473FX3c43t88cPsDSCtkGGta\n7x7VGYy1RsSlYh68Y74HJfMOg8aO5dLZsykxGlm9ZWuLLqGtUVlTww/r1qENj2DeSy/2WgcPQNae\nbR518AAEKWVg8n2h17+jfXWaztMnrg92W+/tWgfu6J2PnnyKla+/ztSAgB47eAC+zMlhac6hTs0h\nl0iYotZQ+styXpt3JxU9qF/YWdb+8Cmugk2c3zfA6w4eAFWAhGlpARTtXE7G5jUen7/D7VGDweC9\nyrl+ID4mCpfdjNNuQyLxz+6wy+nAYTESE+XZXbCzhp7Fe+L3qM6rJjix+6Gnu5fvYffy3U3Hf7y/\nlsFTBjN4yqB2rmofp9NJ+b4KRg4chTbQ+7WBWiMsLIxLL72Ut55/npLycnKDg6GyEoCcI0dIOrpT\nMSQxEaPRyIqffmL2nDkkNuuodSoS3zedmop1jNRnsrEMbE4JY+QZ1JpsaCN6R4Hw1jAYDLXA54Ig\niARBCMUd1dNgMBjqDAbDPqD3a9ynaRez2cwLb71I2IjWDYvAsCBydufwx+Y/el1r0dSRI7klKYnF\nr/+XxsZGwvV6VBo1SKUglSJXKAjUaIhXKjsM9VXL5fSNjWk6ttps1JjNFDY0YDaZwG4Hux1TYyOl\nVVUotEHMmTeP0Kied0j0FCPGTubAykUMiW0ZRSoVAc6TnT8ABY1ypg7vHTVefKTrRABDcdfhCQNS\ncaedVgDeCWdqg50ZB1j48VL0qaMRd1EXkkhl6IUR7MrL5eW3PuTeO3pPB7wTEYlEPHbXY/z7qXtQ\nnKVELOma4u50OKnJqOWV/7zSK9OXWqNv377k5OQQER7OTz/+yLmDB6PXnqx37T54kCqzmRnXXUdW\nVlavf75AnZ6yulLCgzzn6LE7nFhcnnUcdZa/m311ms6jUqrcedu9lO0rV7Hyiy8YKRIToe5a7cHN\nZWWtOniO8WVODgknM5n6AAAgAElEQVSawE6lbgGkq1UkW618On8+iSNGcskd3kslNdbXEa72fZZk\niMKBydj9jYi26PSb/UQjy+OS+IiRwwYSFxPB6+9+QqXJiTZhAFKZb4rnOR12aguyUDobuPP6WaSn\net7A/mDRByz5eQl/bfwL/UA9SWNbFnXq6Lje2NDCwXOMY5+d6OjpzPwNFQ3UZzVwxfRZjDnj3K49\nkAcpLyrio6efJtnhojK0/agciVRKiiaQb595loEXXMAF117jIyk9z8RZt/DOY5uJ1YOOOmpFGkQi\n2FAgYta98/wtXpsIgjAVuBc4C5A3+7wKWA28ajAYNvtJvNP0kCPFBby46EWCBgYilbf9KgofFMY3\nq7+mpLyEKy+80ocSnkxNTQ379++nvLwcp9OJSCSi//hxKJVK9u3eTXlVFWOHDeuxAhIgkxEukxHe\nrPbX+t27EWm1nHv++ZjNZjZs3YrT6UQsFqPX6+nXrx8hR+uM+YP+Z4xlzXcfMaTV2KOTPzPbnMgC\nw3tdKp6XdR07UGYwGF4+erxPEIQvgYn42Mmz+Otl6FNHdtnB05zAqD5kHdiIyWRGqex9KeLHkAfI\nueWaW3l/2XtEDOlancLyzHJuvvJmFF7oJOMt5HI5LpeL8IgIZs2ezTeLF3Nmv/6EBeuaxmzfvx+Z\nVsuF06ZRW1vbVLOwNzPjlvn895FbuTDJgkbR83XD6XTy8wE7l970gAek6z5/F/vqNJ1HJBIh6oUN\nrq0WCx88/jjK4mIuVKm7pcss2JfZqTGddfIAKKVSJko15G7bxou33cYNDz9CeCfTUbvC+Mtv5M3H\ntqNTGd1Rfj6gvM5KtiWSiWMme3zudlfJv6uRFRURzguP3Et2bj4LP/gUizYedYh3d0TNjXWYD+/i\npmtmMnLoQK/dRyQSceWFVzJ17FReeudFymXlhKV17uWdvye/VQfPMXYv301wjI74QfGdms/pcFK2\np4xEfQJPPPIkATL/7JY4HA6WvbWI/O3bGBQVTWVcLNMTE5E2S08akpjY4pqLzz2XkupqXDIpVatX\n8crGjVx9/31EJST4WPqeI1coiEsdRkHlZkSBLnA5qWq0oQxLJjik84usLxEE4WZgAbAE9673EcAC\nKIEY3J1p/hIE4TqDwbDEb4L6gJUrV/LEE09QU1PLqlWrmDBhgr9F6hGNxkbe/eJdDhVlEzYiDGlA\n+8q6SCQicngkWw9tYdsz25h9+WwGpnlvDW2P77//nkGDBpGenn6S8mMzm/nt559pNJrQqD3becJs\ntZJfUsL4SZOIjIw86XxtbS3ff/89N954o9924yUSCckDRpBd/Bcp4R1vnPye4+Ci2+b6QLKO8aGu\nkw1IBUEQNeumJQU8v4XXAZERYRyuq0Id3P13gMNhR+Kyo+hCGL+/GJQ2CO3POqxmKwGKzukiNrMN\nrUjL0AFDOx7cy1AoFFitVgICAphx7bUs+ehjpo85B7FIRFFZGTaZjHPPOw9w1x0cOrT3P2OAXM6t\n81/hnefvIy42lvDoeKJDux4Z3mi2YsgtoChnH5OvuJXEtO5HqXeXv6t9dZpTl5LDh/nwqacZ7XIR\n2sXoneYYO0gR7eyY1uijUhHtcPDRww8z/uqrOWPSxG7N0xZyhZLbH36N956/jxEhtcT3sMB7RxjK\nrGSZI7jt4Ze8suHVXgv1v72RldInnpcef4Bb7nnY606e2pydvPTw3YSG+KauS1BgEE/d+zRf/bKU\n9fvXE9avY0fP5qVbOjWms06eku2l3HjZjQwfMLxT472B1WJhwX33kVrfyDi1hoy4WAYnJXXKEIoM\nDkalUFBmdzAuL4/PHnmUC+bMZsj48T6Q3LNceO08Fj6ym/QgEOHir3wxNzx8v7/Fao/5wByDwfBl\nG+ffFQThduBZ3GvU35IFCxbw5ptvNh3PnTuXefPm8c9/nnod5ItKi/j4248pLD9CkBBE1Miurbn6\nZD0Om4P3fnoX+Tdyppw/lXGjxvnUqTFhwgR27NhBfn4+Oq2WmooKfl2xAqfDiSJARoRez+/btgIw\nfezYVuf4Ye1aDhcWsnnXLgDOHDKE+JiYdscDqORyfvjuO0wWCyKJhMmTJhEcFkZNTQ0ikYgJEyb4\nPd1iyjVzWfjEAYLqKtpNqdhaYCVxyHiiE1J8KF3r+FjXWY47mucRQRCex52udQUwu4fzdpm7brme\nR57/L3VWM4ERnXunN8dqaqQ+Zzv333GT3//uOssNM2/gtSWvETmoc9E8lQeruPPyO70slXfQarWY\nTCYCAgKQSqWMOuds9hgMDElNZVd2Npdee23TWJPJdMrUHKxtMBI3dCL5hwwER4lxybrhULeLyS6q\nIXnwRBqdvo/Q+v9gX52mM/Se5mnGhgb+9/gTTJbLu9UxtDlqqZSGDpw46h44NOQSCVPValYvXkxQ\nWKjHO4yqA7XMe3IRn772CA0lh+gf6R1Hz7YCG/bwodx+3wNee4e29y3/7Y2svQcMvPfpUtRx6V6/\nlzZ5GA8/9zqzLrmQ8ef4Lg135tRZbNm91Wf3O4bNbCNKF+VXBw/AZ88/z4hGI3qVEhcgMpupM5rQ\ndmKn3el0UlBaSnRdrbsAmEbD9x99dEo6eWQBAQiDRlFZcgi7NIDgmL6o/VQbqZPEABkdjPkTeNUH\nsviFEx08xzj22ang6HG5XKzdvJZf1vyCCSM6IZiopO471CUyCREDI3A6nPy4fRnLfvue1D5pXHvp\ntQRpgjwo+cmYGhsp2LmL4k2bMNXUYpdIUIeGEhUWhkQsRuRydSr4evf+/ezat6/peM2mTQzp379N\nJ88xRCIR4Xo9LpEYh9NB6YED5Kxbh9hqQ6nVonc6CQ0ORh3k3e+hPcRiMbf95zXeeupfnENVq46e\nbQU2ZAlnM+mKW/0gYav4TNcxGAyNgiBMxG3gPYS7dfLDBoPB5wXkpVIpz/7nHj74/Bs2796MLmUo\nEmnnlNm6wmzUjjpeevRedFr//b11laT4JGTWzhsXUouEvn16b9269rBYLAQ2S/dMTk0lY6tbF5TJ\n5S12jaVSKRaLBVUXWiP7g71795Kbm8uIESMYOXIky3/4BofDSZ+4zr9TTGYLy9du49KZ16DWaDhy\n5Ai//vorkyd7PlWiHf729tVp2sdsNuPqRb7xT55+hvMkkh47eADm9h/AC3vazgo5NqYniEQixqnV\nfLNgIfP/90GP5moNiUTCnHuf5YuFT5FVmklqhGdTt7YfsRGQNIZLr/FuNHN7b7u/rZH116btfPfL\nShqcMrQJw5FIvZ93p1AFEpB2DktXb+W7n1cwfsxZTJ80zusdjcoqyzBZjOjo2KAfNWskf7y/tsMx\nnUEql1JaWoLFakEe4L9Q7pDwCEpy89ADImDQoRwO2uzUREaSENn2bp7JZuNA9iH6FhYSaDQCR8ML\ne1n9iK5w/uU38eYzD4DUxE1z/+1vcTpiM/CsIAg3GAyGqhNPCoKgA544Ou5vx6pVq1p18BzjzTff\nJC0trdembtU11PHxNx+Tffgg4hAR+sEhaCWeMwbFEjGhKWGQAvlV+Tz0+ny0ch0zp81kSP8hHruP\n3W5n4w8/sG3NH0jq6ohzwUiVkgCJxF1MuKwMF1Cp1VKiD8GpVBAcHEyETtfqfEuXL2/h4DnGrn37\nWLp8ObOmTDnp3NRzzqGsro6qqiowmYioria0rh5xXf3RL0OMrbaW/O++58Pvl2HVaBg65hzGzJjh\nl3o3soAAbn/4dd585BZm9W9ZWbKy3kqtMpk51/eq6Aif6jpH2yX7rzhdM0QiETdfM4PzxxzhpQUf\nII0UUOnajvp12G3UZG/j/LNHcOXFPjWKPYasC/qerJNOr95IXV0dcXHHm3uIRKI24waCg4PJy8uj\nf//+vhGuGzQ2NmIwGFqklU2ZfjnfLV2MNlCNXtfx+8XpdPLrn1u5ZObVqDXudJTY2FgKCwvZtWsX\nQ4Z47t3RAX9b++o0neNI6REkPuje1Fks1VVo5Z6x1UaFh5MeHMze6ta71qUHB3epHk9bSMRiQux2\nKkpKCW3HnusJV97xMIue+hdhDWXoNZ7xFRRVWalVpTDbyw4eaL+F+jEjq9UYzlPRyNq2O5Mrr7uB\nxb9uQho3FH3SYEr2rmsxpnDXGq8di8ViGiuLUKWcycpdh5n74FP89Nsf3X2cDtm6eyuPv/YYoUND\nOzU+flA8g6cMbvP84CmDO52qJRKJCEoP5P5n7iPvSF6nrvEGF99+G6pzzmFlfT1Gmw0xkJqfT115\nGXZH2+0LswsKGJidTaDRiMvlIqvRyFqZlH++8ILvhPcwcoUCpHLsSAgO9c6C6EFuBtKAYkEQNgmC\nsEQQhA8FQfhCEIS/gGLcbdT/4VcpvcTjjz/ukTG+JvNgJg+//DAPvT6fYnkR4aPCCU0J63JHm64Q\nqNcQNSIKeb8A/rfif9z91N18/sPiDlsHd4YPn3iSimU/cr7dzvkaDUKgxu3gaYYICK2tJT03l4H7\n9qPek0H2vn1kZBkwHCmk3mIBYPOePSxZvrzNey1ZvpzNe/YA0GCxcLCwkL0GAwf37UO+J4MBmfsY\nmJNLeHXNSS9umVhMcmAg4zQaJrpc1P+6grcfeqjHz99dXC4njla2KW0OJ+Le5yj/2+k6XaVPfCxv\nPPsfAk0lGCuLWh3jsFmpObCB+XfccMo6eACsdluL4+3LdvDJvz7lk399yvYfdrQ4Z7NbfSmax3C5\nXNjt9hYpAEajEfnR357rhG53YWFh5Obm+lTGrlJaWnpSSplIJOLCS2fx19a9uFwdp75s3r2f0eeO\nb3LwHCMiIoKiotb/7r3E//s15/87G7evR6qSYrFa/C0KAGYX2NvogtlVNpeVtengAdhbXc3msjKP\n3MsoEiERe0+/FIlE3Hjvc/yWJ8Nm7/n3Y7I6WFeq5po7n/CAdB3TnrZ1M/ATbiNrJ3AYMAIKIBY4\nA3ce6VRvC+kJFn/zE3/s2I9UG4UuPtWvsohEIoIiE3BFxPPz5v3szcrmwXk3e2z+gsJ8Fi1eRIOk\nkajRUV0ysI51zzqxAPOQqYMZNLlrxelUwWoCzpDz8icvE62NZu51c9EG+T5FaNrNN1E+eRJLX38d\nWXkFcUl9kAYGtSi8fCJxEZFkGo3oD2Sxx+Vk1LQpXDpjxilTe6At5EoVNpOt44F+xmAwHBQEIR24\nEBgH9MHdctiEewdsIfCtwWA4NbXwvxlrNq7h599/wqqwEZKmJ0rm+9beEpmEiP7u3aGdhTvZ+Owm\nUmKTufXq27rdGSf9jOFsLCuj2tjIALkCTQet0EWAvr4efb07ysYslVIcEU6eSsUvGzZ0eL8f//oL\nVUAAaqORuNIylLau/VYbbTb2mS1UKeQM93CeemewWiz8sewTMratY2K8GXfDmONE6hRUFGfxxiO3\nMW7aLNJ9XFOpDf5Wuk53kUqlPP3QXdzzyHPY1MHIFMoW52tzdvLw3beTEBftJwl7TsaBDBzK45s7\nK974jdLs0qbjzFWZVORVMOlOdzFPh8rBzr07GJru+99ST6isrEStVrf4LC87m+hQ94af0+HA5XI1\n/fakUqlHnOLeJD4+nq1btxITE9MiQjEgIIC0AYMw5BSQmtz2JqTZYqWmwUJCn5PrgGVlZfkyigdO\nrzn/r3G5XOzI2Ik+LZgvf/qS2Zf5vCTbSVzyj3/ww39fZ4Im8KSNrK7y7oH9nRrTk2gel8vFrsZG\n4oYPIzjcu50BFSo1V899mCULH+fSfiDt5qalxebkhywxNz7wtM+irNvVrgRBkNHSyFLjNrIO4w4l\n9LuRJQhCIpC7evVqYmPbbqd2xwNPEJR6ts/k6goVe//k3ZcfR9xDb2RBYT7vfPEOtbYaQvqHIlN0\nP7Qsf0++uxCzCEbNHEX8oLiOL2oHY62R2qxaYkJimXvdXIIC/ZPH/8Wnn5KdlcUFw4ahOUEJOpGN\ne/dSbbEw+6abiIjo9ZEvneL9ha9SXlHF/Mee9ul9RT2w5ARBEAHNW4zWekyw7smTSCfWnJ6watUq\n5s5tP5Rz4cKFfk/XOpR/iEWfvoUj0IG+r77Ha5inaaxqoDarntFDz+Kai6/t+II2OHzgAGuWLKWu\nsgJHYyNam50YsZhIpRJpJ57ZBTxfXcWh3Fx32lUrBAUF0b9fP+4NDGo3xPYYDqeTEpOJQqeTGqkU\niVqFOljP2BmXkzLId91iGutr2bJ6GVkZ27A3VjIoxEqfsJZOtR+qBjBdf7ytqt3hZHeRjQKTGrUu\njOHnTGTAyPM8rvh0dt35O+k6PaWwqIQn3vwYfd/j9fRM9TXESuu4b+6NXruvL7jnqXvQDdUikUlO\ncvA0JyIlgkl3TsRhd1C9vYZXH3m1NzgjO43BYOCvv/5C0yxiJS87m8jgYIYnJ7MlM5P00aMJa6bX\n/PLLLx2+c/xNVVUVq1atIi0trUW9IZfLxdLP/sf0889s89/pj027GXnuBPQhxw1Cm81GZmYmgiAw\nYEDPaoQc4/Sac5qOeHvx2xwyZ6OL1VG4sYj5tzxIXHTXi997mgLDQb568w0Kioq4KSKy6be0rKKC\ni0OPZ4R0dHzNH2swdeA0Dg4I4INzx3Zr/sWlJWh1OsbPmMEIH9bSKsjOZMnbzzMpyYZO1TX7uqzO\nypojSubc/RShUT2zp0+kvTWnXY3KYDDYgO8EQfgeHxhZgiBEAu/jrixvxl11/p/NWo12mzNHDGND\nxj50Cb0r57i+JI++feJ7ZBxZbVYWfLyAnNJDhA4MI1LugV10V/P/73kFeJVWhWqkisa6Bua/Mp/R\nQ0dz9fSrfa84SaVcdtVVLP/6ayafeWabw0orK5GoVIwZPZrc3Ny/jZNHKpEiFp8aymo7LUYrgd/x\nQItRQRBuwl0ANRYoAF4wGAzv9WROTzBhwgTmzZvXZl2eefPm+d3B8+ufv/Lhhx8y4PL+SGTunZ+c\ntTkkjU1qGuPv49KMMpLGJrEzbydZr2TxyL8e7VJNjmMkpKUx57FHAbdBUXz4MBlr/2TDvn3YGxtw\nGI0E2x3ESqWEKxSIRCLqlQqqg4OpUypBrqB82fdtOnjAXUOjsKiIzMvOBLMZjdmMvrqaIKMJXC7K\nzWYK7XYqJGLEKjXSwED6jDiDC8aOJaaTHQM9gcVsJmPzGjK2rMVUV4nM3oCgtzE5Rn40bLqlg8fl\nAk6QTSoRMzxOznDsWGyFZP3+Dut++AiRIoiQ8BiGjZ1Ccr8hPnMa+lrX6c3EREeikblaRHqYSrK5\n4d5eUyi7Wyz5aQmiMHfE3/YfdrTp4AEozS5l+w87GD59GJIIMV/+9AVXXXS1D6XtGWq1GucJqRcO\nh6OpBmSoVktpUVELJ8+pgF6v59JLL+X333/nyJEjCIKARCJBJBIxYNAQDhzKp19KwknXmS1WLA5a\nOHgKCwspLS3lvPPOIyQkxJePAfhnzREEQQL8BawwGAy+yRc5TRMul4vXPniVI8YCQlLdTouI4eE8\nt+g55sy4gZGDfdeUpzXihL7c8+abPPHQQ6xubERcW0s/UdffwWfGxrEmr/30z1vS+nVpzjqrlQyL\nhUaNGntEBP/+7399XnMwLmUAc594iw9feYh4SRmDYzqu2eZyudicb6NOkcCdTz1FgIfqHnWWdr8h\nXxhZJ/AlkAmEAJG4F6NNwKc9nfj6GReh1azhp9/XEyyMQCz2bsHjzlCTm8GgpEjuuOGqHs3z+GuP\n4YpxEXWGZ1Ikdi/f0yJd64/31zJ4yuCmVK6eoAxSojxLyY7c7VR9XMm8Ob4twDl69Gi2bNmCowOD\nqKC0lAC9nsbGRsafgt202kQkOsng6o34osWoIAhDgddxh0SvB2YCnwuCsPlocVS/cqx71omOnjvv\nvNPvO64bd23kp3U/oolSNzl4ejPBicE0VDTw9BtP8cQ9T/ZoLpFIRHRiItGJiYD7JV5eXs6OLVs5\nYMhiS10dTpcLjVxOakIChzIzuXjsWGrbyVE/RkNtLTlHjnDRuedSXFnJdrGYepUJkUhEoEZDn759\nGT9iBFFRUT51kB/JO8jGX7+ivOgwYls9iYFWzgmTowgT41Yj2lYljjgjUMjlWF0SAkQn10GTy8QM\nilXgfrvUU9OYwe6vd7HCJEes1NG3/xDOvOAyAnXea/HsB12nV9NPSGFPWSVqndsQUUpchIYE+1mq\n7lPfWM+f2/4k+iy3jpS5OrODK9xjhk8fRnBiMOs3rWfauAu93sHPU0RGRqLX6xnWLGWzKDubIUfX\nLJVSSUVDQ9M5i8VCnz59fC1mt5DJZEyaNImioiI2bNhAVFQU0dHRDBg0jG8+/6hVJ8/2jCxGj3Hr\ncfX19RgMBpKTk7nsssv8FqHlpzXnUWAE8KuH5z1NB2zetZnPv/8cRaK8ycEDIA2QEjU6is9WfMqv\na3/lzjl3ogtqvXGDr3js2WcBaKirY/Vni1FlZvJHfT1pUimRSmWLqBrgpON5KSlEiEV8mZPT6vxX\nJiW1SNVqa746m439ZjMKrRZDXBwXXndtk97lL5TqQO549E3+WPYJy9YvZ3JfMfI2Cmg3mu0sPyRi\nzNRrOWPcRT6W1E2bmpkvjKwT7jcQGApMPBqimCsIwrGIHo9w8eRxJMZFs2DxMkJS/JtjXVuUw7lD\nBa6+bFqP5qmqqaLaVE1sqGdCKU908Bz/3P2ZJxw9APo+evavO+CRubpCQkICMTExvLJhI7uyskhJ\nTETTzLPqcrnILipCZDIjqqhgwmz/58p6nt7v5ME3LUYnAKsNBsNfR4+XCILwXyAV8LuTB2Du3Lns\nycpl41+/I5bISB4wHF3UyUqsr/nml2+IGBpxUqRF8yia3nasCdVQXFRC3pE8EmMT6SmVlZUsWbKE\nqKgo1Go1YVGR1DTUM/XSSwGoKCvjz7VryS0s5GB+Pv+YNYuV27ezc+fOpjmGDh3a4njGxRdTXlrG\nT+vXEx4ZiSY8nAvHjQPcO/EbNmwgMzOTzZs3I5VKGTVqFOEe6FLRFobdm3j1lVcYkyAiPVLMmUky\nluw0MSD1eBrIkp0NXDH05GObU0S2M4nN+VamjUtk00EXKZIj/LknhytbGX+MFQZL07HLVcMHK5eR\ns30VYl0ss255EK2+c40EOouvdZ1TgWnnj2Hrgs9Q60KxW82EBvu+lp4nefuzRej6d/8ZtP11LPrs\nLR647UEPSuU9JBIJMTExlJSUEBkZ2eH4AwcOMHbsWB9I5jmio6O5/PLL2blzJ9u3b6dfv34E6YJp\nNJpQq1rWk6prtKAPDWXfvn1IpVKmT59OQID/Oqf5Y80RBGE0MAP4llNECTzVcblc/Ln1T35a+SNW\npZXQkaGIJWLyd+ez+astgLtjcfygeCIGR2CuN/Pw6w8THRLNDTNvICrc97UNm6MJCuLiO24HoKa8\nnLVffc2q/ftx1deT7HKRoFK1WfR4VlIywEmOniuTkpmVlNTaJQCUmExkORxYVSpCEhKYMONy4lP9\nW0e3Nc67+HrSho3h0zefYGK8Cb2m5XpSXGNlfWkQNzz4DLoQ7+loHdFeJI8vjKzmnAlkA28IgjAL\n94L3Pm7Ps0dwuVxsz9iHVOa/lt7HkEgk5B4uwG639yjkTK/To1PoaKxqRK1vv8ZMR+TvyW/VwXOM\n3ct3Exyj63SHrfaoPVKDkCj0eJ7uUFZQQGhlJYOsVnbb7Azsl9ZUhNlQUEDEoRz61tXxh1LZwUyn\n8SJebzFqMBheAl6CpjDmy4BA3NGDvYKdGftxaCKYdudLTZ+t+mM1V0yf7Jf22MeQiqWYa02ognu2\n5vgSp9OJo8F+UhpDd9m6dSsSiYT09PSmf4vmu8Kh4eHEJiRw6YwZbN+4EVF9PamJiS2cOs25YsoU\npFIp6UMGc9bYsUgkErZs2dJ0XiKRIJfLSUlJweFwcPjwYTZt2sT06dM98jyt8f0nC0jS2jg3ObDD\nsQ4X1LgCsap0bLLFg0xFdEwE0iPbUMplDBnQl6LyMMxaGZttwYSI64gSleKioc05RSIRgUopU9Nk\n1BiPsOTt57jloVc8+Yjge12n1xMdFYHY4d5fM9ZWMap/sp8l6hkFpQVEJB5PTRpw/gAyV7UfzTPg\n/OM1WlRBSo7sL2yRwtbbGTVqFN999x1arRalUtkigtdusyMLcKet5ufnExcXR3DwqRepJRKJGDZs\nGP369WPlypUo1UFUVNWc5ORxiSTs2LGDs88+m5iYGD9J2wKfrjmCIAQBHwLXAL278NLfgPrGer74\n4QsyD2Yi1ovQD9U3NcBpL1NCEaggalQkpgYTz77/NGqxhqnjpzFmxBi/rzu6sLAmh4/ZZGLTTz+x\nZt16qK2hv0hMtEp10jWzkpJJ0ATy7oH9iEQibklNY2Qrm1J1Vit7rBaMag2Jw4Zy1axZBHtx88pT\nRMb14c4nF/HmY3OZmmgkUOleUyvqrGyqCmHeU2/4VU+H9p08XjeyTiACdyTPF7i76KQCfwAVwH97\nMrHZbOHz735mZ8Y+nJpItAmeKbDWEzQRCZRUlTHvP88iJCUw54pLCNZ1b6fp8buf4IVFL7B3bSbK\nYMVJi8GJu9vHyFnb0sO65ectrY5rzobPNmCf1rKgVmfnB3A6XGh0KgbED+TWq/2T4x8eF0e1WIw5\nIACXVIK42felkMupU6sxl5URKfjHCeV9Tgkl9ViL0RsMBsNJhUw82WL06A7Xn4AY+Bj3rlqvoE98\nDCLT8RR9p8OBQibG4XDiz3fHE/c8wbMLnqU4pxh9Pz1ylf8d523hdDqpyq3GWeZgzmU3kBTf9i5S\nV5g8eTL5+flkZGQ0tSuOjY3FarU27RKPHPl/7J13fFRV+oefO71lJmUmvScMJEBoErqAggKiiIAN\ny+6qq66FXf25dlfZ1dVd29p2V11ddV17AexKE6VJr2EgIQkhbdJnkulzf3+EBAJJyKRMJrjP58Mf\nd+bec88wmXPP+Z73fb/NOfZyhQKvz8ei6dMRnc5WK/UWwefyOXNYNGsWP+3bh1KpbK2d0XK9x+Oh\nqqoKtVrNruuLHywAACAASURBVF27kEqlDB06lNQ+Dl0eM3E6ezatpqzORVx483e8cKSOGp+OOsKp\n8+tIHSZno08BMgVhOh0zZ4ShUx/f1br4vCkASASBxOgIFs6agk8Uqbe7sNTWkTKyiY0+D4LPg0bq\nYmJOA3Z/PVrBiSDAwhEa9pS6yGvQcfHVfVIXJdhznQFByzzC7/ehUHTfxKG/KTpaBCetP8ZcNJqq\nwqpOCy+PuahttLegg8NHDvfa+NHXCILAnDlzWL58OaNGjWpTWlEileDz+aiursbhcAy4KJ6TUavV\nXHTRRfzj2cexS9vOo+sbnXjcLubPn4/8NO6IQSTYY86LwFsWi2WLuXle2/NCm//jFDbv3Myn33xK\ng6sBXaqW6HFtXZ+6mimh0imJHROHz+vjk80f89FXH5ISn8K1C39BVHjw60edjEqtZtqiRUxbtKg1\npevr3buJdjgYqdG0WYOOi47u0EWryNHEPqkUU3IK8665htiU/i8+HShKlZrr/u8xPnzmd9Q3Nm9Y\nqVQabnzkqX4XeKBzkSdoi6xjeIFKi8Xy5LHjfWaz+V3gPLop8pRXVvHyW+9xtLIWuTEVXWbHhXb7\nA21kNERGU9hQw92Pv0ikTskvL7+EwZmB5UYrFUoeWvIQ9z98P/mF+cgNMhTa/gtF7QhHvRMccNf1\nd5E9qP8KYDc1NaHOGc5Wn48Jgwa1STlJiYmhTCbjR4eD+dOmDqiduzOMoFmMWiyW9WazWUFzrvrH\nNO90vdDTdgPB7/dTWl7J/oMF7D9UQEWFlUaHiyavgDp5eOt5EqkUwZjBbQ/9lTC1HK1aRXJiPEMy\n08galE5kRHhQ/l6VCiWP3PEIZZVlvPb+a5TVlKFNUaOPDZ20DrfTTc2BGpQeJXPPmcs548/p9f+b\n5ORkkpObJyZut5uCggIKCwtxOp3NtsROJwf27iU+PIKJxxyvLp09m5SEBF55/30EQeCGRYvIPfbe\n2Oxs9uTn88GbbzIoKwulTofb7UalUpGcnMyYMWNQBqlwn81mI3HYZDAks2nXNmxFNuRSCdFR4USb\nogjTaUnVKFDIAq/JJBUEIsNURIYdTyURRRGH24vN4eZgfQNlFVXU1NtBkBAdE8u4aSOR6Iy4XK7e\n/j8I9lwn5HE4nHjF5t+KUhtGUUlpP/eo++zYtwNF5KnzofNvP69dh63YQbGcd9vMU85XRCjYvm/7\ngBF5AFQqFeeccw7ff/89fv/xeljhYWEcys9HqlYz/1h66ZmAq/IQ+rB4yqoMxBkNON1eDhfkE+k8\nREONlaiY+P7uYgvB3MS6DMgAWmoPCAyQnb6BgMvt4u1lb7Nr/y4w+InMikIrPzVFsjuZElKZFOOg\n5vTk6vpq/vDSH9BJdVwy65J+L9LcwokpXT99/Q2fvfcuEwQJRpWqw2scPh/rHA5Sx57F726+uXVT\na6ASYYrFI1HRUl1GqtKhVIVGJkhnIk/QFlnHOATIzGazcIKblgxo7FZjh4u5+74HUYbHIJXL4ch+\n6o/sByBh5PR2rzm6Y3W7rwfjfLU+F5/HzRMvv8OC8yZxwYzAd1YeffhRvF4vr7z7MnsL9hIxNAKV\nruMf2skROLIIGWteXdvpPSZeNbHL6Vot7dur7NgsNi6YN5f5M+f3q2jyww8/UF1dzfRZs1j5+eeU\nWatIij0exu3z+9mybx9z5l1EbX09H330ETNnzsRgCJ3F688Bi8Vy0Gw2D6OtxaiJZovR3TTvTPXI\nYtRsNq8A9loslnssFosf2GQ2m78H+lSB9Hi8fL9hC1t376W6uhaHx4vT7QW5BolKj1IfgSpmGCpB\noL1frz4mGWKaf4MOn5ddlfX8lL8FcfkqJD43KoUMtUJGQnwcE8bkMGbEsD77LHHRcdx/6/243C7e\nXfEO2zZuQ5EgJzyp/0L/XY0uavbWEK2P4Y4r7iQtKTgFRRUKBUOGDCExLo5P//FPimpriDKZmJWb\n2yZaEGBcTg7jOrA5H5aRwdD0dDbl5VFRUUG8VsclN92Iwdi7tWhOprq6mu3bt9PY2IgoiigUCvR6\nPTGxcaSlL2p2DGtoYO/Orew/VAx+H/ExEQxOT0al7PmmQl2Dnf2Hiqi3O5DKFWRkDmZ69nDkcjk+\nnw+73c7hw4fZuXMnfr8fiUSCyWRizJgxPa2vEey5TsiTd6gAQd1cZFilNVBeXtzPPeo+efl56OJ0\n7b53/u3nsXX5ttZCzMNmDGP0haPaPVcXFcbBw5Y+62dfYTQaiYuOpnDfvtbX1EolNfX1/GL27KA5\n2PU1m777mFSNjSzpYX4s02OKDOPg4SOMVezHnQQf/etJfn1fyATjBXPMmQmMBhqPRfHIAdFsNl9u\nsVgCszj6H62IosgnX3/Mqg2r0GZoMI3r/Pm86f3TZ0psen9zh+srjUGDZowGn9fH26v+w4eff8jN\n19xMWmLoFEwfe/55jJg2ladvu43Oygyvczq56tE/YUrsnVqy/U3+3i2ECQ4uPFZL8EuLjYqjhcQk\npPZvx+hE5AnGIuskvqQ5mudBs9n8OM3pWpdxXH0OiH2WAgSpLCRctLqKIJWhUKrJLzzS7TZkMhk3\nX/Ub6hrqeO715yh3lmMaauqSA05yTjIjZo/oUG0eMXtEQPV4XA4X1btqGJxs5pEHlqKQ9290UXl5\nOQ0NDeQcW1xdsGAB77/5JpF6fWsO99pt2zh75kyMx8ILY2NjWbNmDfPmzeu3fv9cCYLF6ArgAbPZ\n/AZwEJhCc+TgDb3UfrvY7I3868230CUNIzxhOBqJ9ORsgi4jlcrQGKLQGNqG8Ho9brZa9rBnzx7G\nPP1Yzzt9GpQKJdcu+AXXXHIt733+Hj+s/4GwwTp0Ue0vrvoCn8eHda8Vo8rEw7c8grGXC/S2h8vl\noqKigsrKSqqqqjhaVERZ8RFS42I52zyIiHby1LuCIAiMz8qiLiWF4vJyXvzb3zDGxpKckYHRaMRk\nMhETE4Oqk92yQLDb7axYsYLc3Nzm+h0dEKbXM35K8yaG3++nqDCf9Tt34GxqItKgZfQwMwp510OU\n6xrsbN1tweUViYgyMmL8dKKMplPOk0qlGAyGNmK7KIrU1dXxwQcfsHjx4gA+bVv6Ya4T8hSXliNV\nNNfbEgQBj7d36lj1B3X1tejSOh6Hxlw0+pTUrPaQK2XUNfSZu3Wfsu/b74hSq3B7PCjkckqqq4mo\nq+NoXh6DRvevCUlv4GxqZP3Xn7AguzkdK1N2hCNl0Si8NpRyL0q1DF35UfZsXsOw3Gn921mCO+ZY\nLJbraRaVADCbza8Dhy0WS89sJn/m3HjbjeiGaomb2FwcuWBtQZuN85OPfW7vKW10RkftSWVSorOj\nObQqn8f/8Wfu+OWdDM4IncLECqWS2KQk6ouPYOhg80XUas4Ygaeq/AifvP4sC7KOr7HPSRN489k/\ncNMDzxJm6N9aZ53OxoKwyDrxXo1ms/k8mtMk7gMqgAcsFstn3WnvovOnkZWZxsv/eZ8GhxupPg5d\ndEKnok9HETh9eb4oitiry3FXH0Erh6suPJ/JuT1/6Ibrw3loyUMcKDjAy//9J0RBZPrpLWhbckJP\nFnpGzhlBzqyuOWv5fX6q9lvRE87DtzyMKerUSXt/EBUVhd1ub90FFgSBCxcu5JtPPmFmbi51DQ0o\ndDrik48LWVarNVQK9f3sCILF6Cs0T65WA5HAYeBBi8XycQ/aPC2REQZe+dtf+XzlOrbu3E2d0094\nRu9MtH1eDw0HNxMdFc5VF5zN1Aln9Uq7XUUQBC6feznzz5vPC2++wOGCAqKGRaFQ953AK4oiNfnV\nUCNw6+LbGJze+xOepqYmjhw5QllZWesY4vf7kUql6HQ69Ho96enpbFy7ljnjxqNR9474Eq7REJ6e\nzuDERD79fh3TzzsPm81GQUFBay2glrEsLCyM2NhYkpKS0GoDK4it0+nIzs5m1apVjB07ttWxa/Pm\nza11gU4+lkgkWKtqmTNvEQDlpSV8tmolGfGRjMhqLtS7Pa+YUUOOj6ctx6Io8u0PW3GJcubMvRit\nVtfa/okiT0f39/l8FBYW0tDQwJQpUwL6rO0RzLnOQEAuk7VJ7xmoGcter5dGdyM6ekdsbnI39tgs\nI9hYS0tpLCxkYkQEBVFRDE5KosZqZbLbw/JX/8WdLw18kefdl/5Eo60BQWiOPouW1PDFHgvnJx9P\nBDha46L0g9cZMnpySHx//xtzBi5NTU3U2etISe/6pnd0cjQllqOdnpOe2/VUUIlUIGFCAq+88wpP\nPvDk6S8IEvaGBsrzCzirkw0unb2Rves3MHTihCD2rPex7NjAZ/95nosH02reA6CUS5ib4eYfS2/j\nit/cT2JG/wXMdTrSBWGR1QaLxbILOLu32huUkcJf/3AXLpebr1b/wPqfttPQ5MQr06IxJaHS9U8K\njtvZRGPlEQRnHTq1grHZg7nk5tsJ0/W+U83g9ME89cDTfPrNp3z747dE5nSewgXNQk9EQnhzeKEA\n4xaNIzknqUv3s1ltNB5s4ur5V5M7MjRyRluQy+Wkp6dTXV2NydS8kNBotYjHZrBFZeVkn5RCUVlZ\nySWXXBL0vvYVA6XaXjAsRo+lhd577F9Q0Wo1XHrR+cw7fzq33fMwPq8HqaznRSGdDTXEGSN4+O7b\ne6GX3UepUHLn9XdSVlnG3//zd8q9FUQPM7U6TPQWDeX1OA47ueDcuZw/5fxebRvAYrGwd+9eZDIZ\nERERmEwmkpOTO0w5XXjllSz/4APMCQkMTknpldTUQ8VH2FtUyMIrr2jtx8lOOKIo4nA4qKmp4dCh\nQ3i9XsxmM9nZXc88zM3NpaqqCq/XS35+PhkZgTkqxcYnkmnOovDgvlaRpyPKrdVERseh0BhaBZ5A\n2L59O7m5uaSkpAR8bXsEe64T6pjTUxDXbgPA5/Oi7oV0vP7gizWfI4/pvWK78lg5y1cu55LzB86c\n4INnn2W8Uona48Fjs1Ftt2Oqb0AmkRBjt7Ptu+8YPWNGf3ez25QU5CHWHkalPP5sEYTmuU6kUHf8\nNYnApDgnK954lvnX/V8/9LQt/TXmWCyWX/Z2mz83NBoNyZnJOBocqPXNka8nl784+bi6/JTSS6dQ\nsLmgNarwdO2lT02nMq+Ss88KnaLpbpeLl+6+h6lyeadzn/EaDSv++U8iYqKJD3CeESqsePM5qg5s\n4JJsSbs28jqVjAVZfla8spQh42cx/eJuJSX1mA5FnmAssoKFUqlg3qxzmDfrHAAOFhTy2XdrOVJ4\niEa3F4nWiC46CWkfpRP5fV5sVaX46ivQKASioyJYPH8ao4ZnBS0f+uLzLubcSefy5xceoy6ijvCU\n8E7PT85JDtgq3brPSrwmgT89+GhI7JScTH19PevXr2f27Nmtr23evBlRbJY+tGo1e/ftI+kExxqr\n1cqWLVs466zgRkT0HQNF5vl52Bqv+GYNElNmrwg8ANrIGEosxVRV12CMOn3kXl8TFx3H0juWsn3/\ndl57518YhhtaJ0U9pWJHJUPiB3Pjgzf1WeG+yMhISkpKkEqlWK1W1Gp165h9YoRJC1qdjoyhQ6mo\nqGD/+vUkxcS0joUjO3DC2lFY2O7rWfHxfPfTT2QMGcIVv/pV66TpRFv1Exk9enRr37xeb8CWyKIo\nkpmZya5du1oFnpM/4+mOdSo5RsPxzYoTo3hOPI4xRrJxx/rWKKCutt9ybDKZ2L17N1qtFmMPaxWd\nSXOd3iI9JQlczVEQLns95oSQKVjbZURRZOUPKzGN771I4oikCNZsWMP88/q3tmBXaaipwVdRgVYX\nBoDe4eCotYqhVisAORoNKz/9dECLPMvfeoHzUqUo5W3FYmN4GErheIrMZcfqZWzetx2X04myl1Jd\nu8P/xpyBz8N3PMzjLz1OlcJKlNkY1PHA7XBTtauKqWOmsmDWgqDdtzNEUeS5O+9kgseD/pgxgkOh\nQKlUtlb5dvi8qJscSCUSZmk0vPHHP/LrP/+ZqLi4/ut4gPh8Pt546n4SfIeZMahzzUAukzB3iIQt\nu7/kvfKjXHbTfUHq5XE6W4mfsYusQemp/O7XqUCzNe0Pm7az+seNVNfZ8Mo0aGMzUKi7WyWjGa/H\nha2sCImzhnCdmlljRzFjyuVou1mjoTcI04bx2N1/5pl/PUNpUQnhKb2XK2jdU8nZw6aF7A7XoUOH\n2LlzJyaTqY0A5fV6URxbICbHxZK3s22aWnh4OE1NTXz++efMmjVrwFeB94kDRuY5o22NXS43H6z4\nhrXrNxGRPbnTc0stO9j57fsAjJh5GfHmEZ2er0sYzIOPP8PCiy7gnMnjQmIxMiprFI/+/jHue+Ze\n1Lk9F3nsVTayErK4+aqbe6F3HWM0Ghk0aBAej4f6+nrq6+vx+/2IosiuXbvQ6XSEh4ej1+vbjCvG\nmBgMkZEcyssjIyEBIVAxXxT5Yv0GLly0EEM7UTtutxuXy4Xb3VyyQRAEDh06REJCAtOnTw84Xcvt\ndrN8+XKMRiOjRo3q1ubDkaLDHLbs5dxJp0//kEgkzJk2js8+eZ/Lrr4u4E2BlJQU3G43mzdvRqlU\ncu655wbc3xM4Y+c63cXpctFiwCMIAh6Pp3871A3e/exd5PGd7ygHiiAIKJMUvL3sP1x18dW91m5f\nsWHFZ2QKx3/Lhvp6SrVa5Mc2tqQSCRJ7I26XC0WQXPt6k6ryElTuKpTtWqOL7aYZ5sZ6+faDV5h7\n9W193r9O+N+YM8DRaXT86f/+xHc/fsuK71Ygj5UTntKxu+m4S3NPa2wz7tLOsx+8bi9Ve6uIUETy\nwE0PEhcdOuLIsr//ncGNDmRRERwymmhSKVEbDGQkJLTm+9ZXV3OwshKFy4WpppZz/H7+88QTLHn2\n2X7ufdf57M3nSBMKyIzr+nh5VpKc7SU7+P6zdzh77hV92LtT6WxmdUYvslqQy+VMn5zL9MnNP668\ng/l89Pm3FBdZUURnNNucB4DDXo/jaB7R4VoWXTSd3FE5IbHIOpHfXfc77n78bryxXmTKnkfc2K12\nUsPTQlbgqaqqYvfu3YwaNeqU7yIhJgZpXXNIr0IuR3nS+y07x7W1tXz33Xecf37vp4QEC7vdTqPD\ng4hATU0NkZH9H+nRCWeUrXFdfQNfr1nPnv0HsDU5OXrYgmnYVCKHno0gCBzdsbpNza6W47wfv2D/\nD5+3vr7pk5fJmnwBYVp1u+cDqLR6qhw+Plyzkw8/X4lGJSc8TMfYUTmcM3kcCkXvpS8EQmNTI1VF\n1TgcjlPeOzkUuYWCtQXtvh43Kg5bTUOv9q8jZs2a1e7rXq8Xq9VKaWlpa4qUz+dDq9USFRWF0Wgk\nLTmZfZs2MaEDJy1oP8JnW14eE6dPQxsWRkVFBdXV1Xg8ntYCxCaTibi4OKKjo5G3u8AJHKlUit1u\np7q6msjIyIAF7a2b1jNzYucC5IkoFXJyzKnk7d3FsBGB1QVxu91YrVbcbnenhaK7yM9irhMIr73z\nMfKo5hRtVVgEu/f/iMfjRR5AUe3+xOV28eOWH1qLovYm4YnhbNywkUVzLkWpCG1hpM5qJfGE8UHj\ncOL1+tqcoxD9eDyeASnyrP70TcZ09BV3sJuVGKlg28E9fdanLvK/MecMYcakmZw7cQaffPsJq39s\ndtoKi9Gfcl5PjG38Pj/WvCo0bg2/W3wH6Uldr93TVzidTsrLyyktLaWmpoa9pWVkjD0LiT6MOIMB\nTUvRZVFs/gfERkQQGxGBx+vF2mCjoqEe19FSPvzgA4zH5jRxcXGEhYWF3Lq5heJDu7l4UOBj5cgE\nBcs2rQkpkeeMWmR1lSGDMrj/txm4XG6ee/UtDpcVoI/r2g/KUV+Nou4wj9x3G+GGU3/kocQtV/2G\nv/znr8SNiu1xW/Z8Ow/f+0gv9KpvyMvLIyMjo91BQ6VW4zy2Gy6KYmvq1slERERQXDwwbWStVivb\nt2/HbrcTGRGOz+Ni3bp1yOVyRo4cSVxcXCgOqGeErXGFtZqHHnsSURGGNDIBXdRgVNEyVHUN6E2d\np0CcLPC0sP+Hz0nIyO608LsgETAkZADNaTf1bhfLNuznw8+/JVyr4q+P3BP07/yfb/8TVUTvhMir\n9WqK9xdja7QRpg3rlTYDRSaTtU5KTqSxsZHDhw9jsVjwer3UNDXhcDpRdzE9wO3xUFJTQ4TLRV5e\nHikpKQwbNgydru+cyhQKBfPnz6exsZEDBw609r2lSH1YWFhrgemO7MoNEZFU1zUQHdX1CNHy6jpG\nZrYvDImiiNPppL6+HpvNRlNTE4IgIJFIUCgUpKWlMX78+N5IDf5ZznU64o33l7G3sAJDarMwKQgC\nirhsfv/IEyy957d9Ujuwt/nkm09Qp/ZOWmh7qFM1fPjlhyye131Xt2CQkJmBddcuwo/9ZhV+Pz5/\nW5HHIZGiCTDyL1SorjhKZEpHIncnMctue5/0JwD+N+acQQiCwCXnXcJF51zEq++9wp6te4gZFXNK\nRGx3jG2a6puo313PLy79BWcNH9s3H6ADfD4fVVVVlJeXY7VacTqdraYTMpkMnU5HREQEsbGxWLZt\nZ1hG19bKcpmM+MgI4iMjcDgcJMbHExYejtVqpaCgAJfL1WooIZfLMRqNREdHExMTg7KfxWiVLpIG\nRwl6dWCba9YGD5Gmnq+3A6Wz2dEZscjqLkqlggUXnM+fX/o3dFHkcdaWM/fcqSEv8AAkJ6ZgkBpw\nOVwo1d3/0dgqbQxOHRzSO1qDBg1iw4YN5OScGlUVHRfH98cieY6Ul7epx3MidXV1vWZX3Jf4/X6s\nViuHDx+mqqoKj8eDRqMhKSkJtVrN+h/WIgA5OTm4XC727t3Lpk2bWgu6pqWlERsb2+9paWeKrXG0\nMZJL5s3l69U/0FhzhAaHHY0x4RSB5uRjQRPJ/q9f7rDdo/n7KLXsbE3d6qg9URRpqq/BVVuG1NOI\nKdLAFfPn9ouo1+iyM2hGZkDXdBThAyANl1FQXMCIrK5HjwQDrVZLeno6jY2NFBcXc/5FF7Hi/fc5\nL3ccWk3nC0+H08VXGzcy55L5HC4qIj4+nvT09D4VeE7u++iTLJXdbjeVlZVUVlZSWFiI2+1uFcRl\nMhkGg4Hw8HAmTT2XD95+nQvPHY+iC9FFpRVVeEQpkUYTdXV11NXVYbPZEEWxVczRarWYTCYyMzO7\nFV3URfplrmM2m6XAOuBri8XS77skh4tKeP7Vt3Cqja0CTwtqQyROeTZ3PvxXZp8zhYtnnxOKGwOt\n7DuwD8PQvjPWMMTqyduT12ft9xa5s2fzwrLlDDp2LEAb7cPt86GIjAjp77JTxMBsqVuQ4sXtdnco\nWAeBn/X66kREUWT77v1s2nOIhDRzm/dKCg4wccRgRg4b0k+9CwyZTMZNi29m6+6t/PvT14nNPXVR\nH4ixjavRhSPPyZP3P9Xn6w9RFCkpKSE/Px+bzYbf7wea5wR6vZ7ExMTOBZZuDiF2hxOdwYBGo0HT\nTjkTr9fb6ii6Y8eO1vmBUqkkNTWVjIyMoNaAXXzbQ7z48G3MzXSjU3XtvjV2D2vLw7j14RCqyXOm\nLLK6g9fr5dX/fsS23RbCB3W94G54SjYffr2OLTt2s+TXV6PpeRh5n3LHDXfwwJMPED8xrluuN26n\nm6aDDm56qG/rYvSUmJgYcnJy2Lp1KwkJCcTGxrZOagRBwBAVRV1DA3sLC7l4cdudOYfDwaFDh1Cp\nVMycObM/ut8hoii2LrysVis+nw9RFFsXRtnZ2adM3oQTIpWUSiWZmZmtbdntdvLy8vjpp5+QSCRI\nJBIiIyNJSUkhLi4uaEXCWzgTLEYFQeD8aZM4f9okvF4vO/dZWP3DRo4e3k+jR0QRlYwu6tSJwM5v\nT5+Gv/Pb99qtz+P3+7CVH8FvKydMrWBYRhozFywgLTmxXyfzscY4rBWVhMX0PPLG7/PjqXRjTjWf\n/uQg0GLpfejQIZxOJ4IgkJiYyJgxY5p3+RYv5rOPPiIzLo7BHThCFZQcZU9RIfMuv4wwvZ5Io5G6\nujrWrFmDz+dDqVSSkZFBWlpaUCc1CoWCxMREEhMTT3mvqamJ0tJSSktLaWhoIDN7BMtWbiZ3ZBaJ\n0eHtuk44PV72Hyqh8GglQ0eM5sCBAxiNRjIzM4mNje219LOu0o9znYeAscBXvdxuQJRXVvHia29T\nUe9En5qDXt7+RF6lCUOZNYlvtuWz8vsfWXTRHKZODE1DgqSEJPKtBwmL7psNN3uVndS41D5puzdR\nKJUYkpJoKCtD3ypoHJ8DbHc4OP/GX/dP53oBiVyD12dHFuD81SdR96fA87NeX/n9fg7mF7Ju83Ys\n+QXYHW58Cj1h8ekU7S9pc67XLWPHB98gf/sjdBolQzLTmZQ7msy05KDPRwNhzPAxrFy/EputAVXY\nqeJMV41tag/Ucu+N9wVlg/m9997DaDQSGxvbLddKfzfv6/P7Ov0uO3IUbUnZ3rhxIwsWLAjaRphG\np+emB57hlSd+z+QYG3HhnY8jBVVudtmM/ObBv/RLSmyXZvxms1kgRBdZZrM5FTi8cuXKdiehgeD1\nennrwxVs3rYLmSkDbTuLr67gaKjFWZrHkIxkbrzm0pAWe/Yd3MsLb71A7LhYpLKu75K6mlzUbKvh\nkTuXEhUe1Yc97D38fj+7d++moKAAjUZDamoqCoWChvp6Vi9fgVStYu7ChUCzdXppaSkajYbx48dj\nMPTdrmAgiKLIzp07KSoqahV0jEYjBoOhSwv49evWUFFWyvxLr+zSvWw2G1VVVdhsNgRBIC4ujjFj\nxgS8oy50Q13oyGIU6Fdb494ac2z2Rj787Bu27dwL4UloTQmt73354n047Z0Psyqdgdm3PNbmtYbi\nPNRiI+dOmcDMqROCvmDuDJ/Px31P3IskRYLO2P0Hst/np3RTGTdfcTM5QzquddPXiKLI3r17yc/P\nx+/3t4Ytd7TbJYoiWzdsoPDgQc4ZMwblsUWGx+tl9datxCQlMWHq1A5/x263m4qKCmpqahAEgdTU\nVIYP6r+BqAAAIABJREFUHx5yk92qyjJe/dvjRJui0EcYSU9JQK2QUdPQxJGSo1RVVqBUKfnVkgf7\nPGIw0HEnWHMds9k8EXgF2APs60okT2/OdQAaG5t4+h//5oi1Dl3SsIDMJvx+Hw0lh1C4a7nhqksZ\nnh0aYmsLHq+HO5b+DpvTjlR26u8j0BpgJ57vcXqo2lLN0w89jaKPHFl7k5pKK//9/V1M1zaPuauT\nk5hefARRFPlagDtffLGfe9h9LDs3svmjZ5mafupzbnlNNhdF7jvl9bI6D/mybK649aE+6VOojjnd\noadjTm1dPdv37mfnngNUVFbh9HhxuL2gDEOhN6EJ77orlSiKNNZV4a6vRHDbUSvkqORSYmOjGZU9\nhBHDzISHyDwd4NEXHsWb7OlR3dPyneXce819xMf2vbvhoUOH2LdvHz6fD7VajcFgIDIysktiaEVZ\nGdu/X8eUUSMDvu/RykrKnU7O7oLDn9frpaGhgdraWux2O4IgEBMTw/jx4wO+b0/xer28/Of/Y4S2\nnOTI9ufZeRUeSuWZXPO7P/bpBmtnY06nf30dLbLMZnO/LrL6ggP5hTzzj9eRGdMxDJnU4XldcbpR\n6yNQ6ydwuL6GJQ88ztWLLuLs8WP6rO89IXvQUO68/v948uUnicmNQd6FAamprgnbPjuP/f7P6MNC\nPzWtBYlEwogRIxgxYgTl5eVs2bIFALPZTEVtDVPHzKC8vJyjR4+SkpLChRdeGHJW8EVFRezbt4/c\n3NxuLZD8Ph9+sWuauyAI6PV69Prm79jn87Fr1y6USiUjRvRtisyZbjEqiiLFR8uoq29AxI8ga/sg\nHTHzMjZ90nG6Vss5JyNIpUhFCWWVVVira4mPDaxwfF8ilUqbHbaeuJcmeRMaQ+BOg6IoUvZTObcu\nvoWh5mF90MuuUV1dzcqVK4mPj2f48OFdeoALgsBZEyeSmZXFZx9+yMyzzkIqlfLVpk3Mvvhiokyd\nWz0rFAqSkpJISkpCFEUqKir48MMPmTp1KjExMb310XqMMTqOu5Y+zXt//xOew+vJc47EFB1LY2UB\nlfl5jD9vEbkz5vd3N9sQzLmO2WzWA68Di4FbeqPNQFm3aRtvfbAcddIwIs2BCzQSiZTw5MH4fV5e\n+O9yMuMiuOuW6/qgp91DLpPzh98+zG133Io8Uo5c1Ttid1NdEw17bTz024cGhMADEBltwqc3IHq9\nbcap4sYmcmYOXOt0APOI8WxclUlxzUGSI0//fTg9PtaWKPjtY/cEoXedcyatr7xeLzv35rF5x16K\njxzF6fHidHvxCjKkmgjUBiOK+HhUgkB3Y1IEQUAXYYKI489JURQparKRt2obb3+2GileVAoZGoWc\n5KQEckcOY3i2OejzeK/XS3lVGbGDelaDRZ9m4K1P3+Tum/r+7zUzM5PMzExEUaShoYEjR45QVFSE\ny+VCFEX8fj9yuZywsDD0ej06nQ6pVEr+gQNsXb+e88eN69Z9E6KjKdmzhx9WrmTyuec2lxhoaqK+\nvh673Y7D4WhN35bJZERHRzN8+HCMRmO/lpWQyWTceN9T/O2Bm0gwNCE9KZrQ4fZxwGHk1rv/1E89\nbKbDv/wzfZF1Mk+99BqGIRORSjseDDpyuhkyqf20WY0hErV+Mv9+bxmjhw1BF6IFCzOSM3jo9of4\n4wtLiZ8Y3+mCxevxYttn46/3PxnSdXhOR2xsLHPnzqWqqopVq1bh9HhocrmIkEhYsGBByOaop6am\n4vV6ycvLw+v1IpPJiIyMJCoqqkuKe6O9AdHfNZHH4/FQU1NDTU0NbrcbqVTKkCFDyM7O7unH6Apn\nnMWo1+vlnvsfotxahdvjQ5TKkal1yORKEga1XeDHm0eQNfmCdgsvA2RNvqBdgbnBWkK9KFJ8dB1f\nffUVUnwoFXKuv/4GpoSA0CyTyVh65x+564m70IwPXOSpKazl/Inn9avAA7Bx40ZycnK6VQQwPCKC\nBVddxbJ33kEikTD/iivQBhhqLAgCsbGxREVFsXHjRubNmxdwP/oSqVTKlbf+ge8/+y8FW77ioEPE\ncSSPK5f8kbjkjP7uXhv6Ya7zIvCWxWLZYm4WWDqpENv7VNfU8e/3lmEcOrnHzzmJVEZE+kgKywt5\n9e0PuX7xwl7qZc8xRhr598tv8Ojzf6JJ00hE+undJDurAVZbWIvKpubJBwbe3CfZbKZq2zZMJ0SV\n5yNy/fyL+7FXvcPi2x/hpaVLUMqsxOg7ngO5vX6W5cEvf/8Y8n5M1YIzY31lb2zi9Xc/Ib/oCA6X\nD1EdjspgQh03HKUgEIxfiCAIKLV6lNq2m81eUWRvdS3bPl4N//0EtVyKOSOFaxfNQ6sNfN4RKF+v\n+xpFfM+FZY1ezdG8o611aIKBIAgYDAYMBgPDhrWdZ9nt9tb6fMVFRRzYvRu8XtITEqm02QjXalHL\n5V3uq9vrpa7JQUR4OGWVlfz39dfJzM4mNi6OmJgYhg4dil6vD7lo5RakUikZ5mzKa9eTENlWvjxs\ndTFy3NR+6tlxOpM3z7hFVkf4/X5EmnenOqIzpxugQ6FHEASkCiW2xqaQFXkA4qLjODt3KltKNxOR\n0PFkqCqvitt/uWTATXI6wmg0Mm/ePLb+9BMmk4lx3VSjg0mL4g7NTj5FRUWtintL1fuIiAiMRuMp\n6TqOpkZkklMHYK/XS3V1dRtBR6FQEB8fz9ChQwkLC7qD0RlhMer3+9m+ez9frVrHkbJKamvsyDVG\nlO18ByczZNIcGsoOczS/bch5QmZ2h+MNNI85cqUGubJ5MuPz+fnP15t499MvGDIojQtnTiM1uefp\nHt1FpVJh0HQvrNptdXHB9XN7uUeBM2HCBL777jsSExMpLi5uM25s3ryZ3Nzc0x6HR0XR1NjI3mOR\neYFeX1FRQVFREdOmTeujT9lzzp57JcUFB3BYS7jwmttDTuA5RtDmOmaz+TKaLe+uPfaSQLdLVnYP\npVJOfUUxpmHHb3t0x+o2xdsDPa4u3EujqW3B7lBAqVCy9M4/8toHr7Fr305M2Z1Hy3WEdb+VYXHD\nuf7G63u5h8EhY+QIDmza1Ebk8SsUqAeoq9aJSKVSbrr/aV5aejuTxDpiDKcurt1eP5/uF7ny9qUY\nY/rv2XcCA3599beX3+RwrQdj6lndjs7pKwRBQGOIRGM4vpbZenA3jf9+JygRhzv37SQssXeyHPxy\nPzW1NURF9n9ZDJ1Oh1ar5eD36ziwdg3jJVIiVSq81irqw3SUG8JpUipAqUQfHk58RASyE6JtRFHE\narNhrapCdLlQuN2E22yk1jcw2OfD5fOxvugrmtLTmHDPPf1SwyYQig/u5dDeLYwaemo/B8eqeH/l\ncswjcomOD7zGUW/RmchzRiyyuoJEIuGyeXN4/6vvicw8daJSatnZ4Y46NAs9elNC+zvrJQcZlzOE\nuJjuTS6CSYQ+An9J51Eefo9IuD48SD0KDkqlEqlEcopqPRDQarVkZ2e3ia5pEX4KCgpwOp1IpVLS\n0tKQy2SIoh+lXE5DQz0KhZKCggI8Hk9rcdXs7OzW9Kx+ZkBajHo8Hn7asYd1m7ZirarB7nAjqiPQ\nRicRPiSTQH85uQtvodSys7UQ84jzLiN+UMepcp3ZqgMcstXy2MvvI/c50KoVJCcmMn3iWWQPzgxq\n9JrQBZGrgytDIoUyMjKShQsXsmfPHqqrq9m5cycmk4no6K6nx6k1GjyerrvD+Hw+rFYrVVVV7Nq1\ni6SkJBYuXNjvTninY84Vv+HRe27GPCL4efNdJJhznZnAaKDxWBSPHBDNZvPlFoslqxfaPy06rZa0\npHiq9m8gPGM0sh5s2IiiSH1pPgpfEzdfe3kv9rJ3+dWiX/Hqe6+SV7KfiMSI019wAvVH6xgaM5Tr\nLxuYAg+AKTGRrSeNuUII1WvrKXKFglv+8DzPP3wr06V1ROmOR+r4fH6W5YmhFkU44NdXd91yHW+8\nv4z9B7fS6PIiqsJRR8ag0natPmRfIooiTnsdztpKcNSiVcmZNGwQ1yy6KCj3nzx2Mh+t/4DorJ6l\nUfv9fmgiJAQeAGtpKW88+hgZTU3M0RwXiGVAlM1OlM0ONIem1ul05EWb0ERFkRYbi7W+nrLSUmLq\n68myVtHerEUplTJdp6Oq+AhP3fwbLrzuVwyb1HH5lP7CWnaEFW+9gFBfxMVDpO3+vUulEi42+/j0\n+XvQxJq54KpbiYgKvg7Q2Wx5QC6yusuMs8dzpLSMr75bQdrEC1tfP7pjNbt+/Pa017c43Zy4w+Vs\nbMCk8nL94gV91u/eQhRFvlz1BVG5nQ8mkeYIXnrzJf7w2z8EqWfBo7a29pQK7gORk4WfxsZGPB4P\n/3zmMT5a/iWCABWlxdz18F9JTEzsjyidrjDgLEZt9kZ+d/8fkUaloo2KQ5GcGrCo0x7x5hHtCsjd\nQR0WgTqs+W9cFEUO2urY+fbn6IUmnloaPHtHiSDpVghyKCVRSiQScnJyyMnJwePxcPDgQSwWC0ql\nkvz8fJKSklAoFG2icIDjx4KAIAgdv0+zaGgymdixYwdyuZyUlBRuuOGGfnWGCZRIUwxef2iGWx8j\naHMdi8VyPc1jW0vbrwOHLRbL0p62HQh/ffxRio+U8uLrb1PrlhA7bHKb908Wi9s7tlWW4K8p5IIZ\n07jwwZv6vM895RcLfsGSR5cQEWAgR+MRB7+8/1d906kgoYuMxHlSUqDQDUfVUEYml/ObB5/luYdu\nZv4gT+vrXx3yM/9Xd4WSwANnwPpKoZBzw1XN6Zler5fd+yys+2kbJUcLaXK5cfkkSA3R6IzxnZbB\n6A18Xg92ayleWyUqGWgUcjIT4pgy4xyGDR4U9I2hs3PPZv3WH6kuqSY8sXuzQL/PT9lPZVx3aWiI\ny26Xi3/ccy+z1WrU7Vidn4gARNjtRNjtVNTXk+fzIVRXM7KouEv3MqpUXOD3891Lf0euUDB47Nhe\n+AQ9w1ZXw6plb3Dk4H50Yj25CQKG6M6FcrVCyuzBUG3P4+OnbsMhMzAoezRnX7gYtTY4bmCd/eUP\nuEVWT1k4dyZffN5xxE6gOGoqWDQr9FTI9nj2tWdRpCpPa6Wu0qmwClUsW7mMeeeGVh2I7pKXl4fo\n9/P9mjUkJyeHRKRAb6LVannhhRd47d1lra+999lqopJfY8mSJf3Ys44ZiBajSoWCs8aMYm/eIexH\nahDUETSUHSJ57KxWMaOnaRG9cRw7fApN9TV4bdWIznrClDKmTp7YS/8LXcNkNFLeUI7aEJjzoFwa\nmrvPcrm8jbBaUlLCli1biImJITa2/eKLot+P2EkRdKvVSklJCWeddRZJSUn9vjvaE9SakE4L+dnN\ndQCSk+J54qG72L3fwr/f+ZgGUYUhJfu09Q8aayrwVB5i0tjRXHnX1SEfSdbCwcKDSDSB/4akWgl5\n+XkMGzzwIn1bUGs0eE56TeikPMFARalSc9WtD7L87w+ACQ5UuEnJmU5adsilEp5RY45MJmNUTjaj\nco5HlNtsdtas/4mNW3dQVW9HkzAUpa53o8SdtjocR/djighj6thRTJt0OboQSUG8+6Z7eOk/L5G3\nYz/ROdEB1ZVx2pxU76zhhit+zajsUX3Yy67z3X/eZoxEijrA9VFMdQ37wvRMLj4S0HVSiYRpej1f\n/fe//SLy+Hw+8nZsYuvaz7HXVqLw2ciJ9jE6U0WzEV7XidIpOM8MotjIkaMrefNPa/DJ9YQbYxl7\nzkVkDh3dZ/O7Dr+tgbjI6imfffc90cOmtHktYeR0ausaOLjpm06vTRo6rvX8FrTGBFb9uIlJ40Lu\nAdOGH7aso7C2gOjhXQstNA0x8vW6rzlr6FkkxCac/oIQpaSkhG3btiGRSNArVdhra/n0009JTExk\n9OjRA2q3vDNeeOEFnn/++VNef+mll5BKpdx666390KvTY7FYPMAnZrP5U0LUYvREFAo5N13T7Hjl\ncDjZnWfhjX8fRFq+G6fbi8vrw1lbTm3RfiQaPdpwY6/de91/n6XqyCFAhK8/AkCp0RFhjEFtMIG7\nEaVMit9WibrawvCMVM4akYs5PRWZTMY999zD729vuxtvMBi48MILufvuu1trO61YsYKXXnqJkpIS\nYmJiuPnmm1mwoGuRina7nYceeohVq1bhF/0MnmpmxJwRrQ83r9vLpg82U7yzebcnITuBCVeMR648\nLuy4RTf5xflkhNau7CkkJiZSXl5OQ0NDh+fYbTZcTleH7zudTmJiYkhOTu6LLgaVx55/vb+70CH9\nOdexWCy/7O02A2V4lpmnlt7Dhi07efP9T5DHZqEJPzWi1+f1UJ+/jeHmVG7+7f0DajOkrLKM5//9\nHLETAne7MWYZeemtF3ngtgeJj+l7K+O+QCaT4T9pESGEaDHTnhKXnIHKmEa938+eOg1Lfn9jf3fp\nFH4O66uwMB0Xnj+dqRPH8vKb75JvLet1kcdRc5SsjGRuuGpRUAoqB4IgCNxy9S1s3LGRtz56k/Bh\nBjThpxegqixVaF06/nLvX9BpghPt0RVGTp/GG9+vJcHvRxbo2OH3IRcD9xfY1OTgrPPOC/i67lJV\nfpSN33xEccEBRGc9CRoX42JkaCJlNGdW92yTURAEkqOUJEcBNGFzWNj10RN8/R8lErWBjCE55M6Y\n36tpXZ0+pftjkWU2m6XAOuBri8XySF/dpz0Ki0tQ6k4VOo7sPX3E5JG9mxg2rW1ki0ypoqHK1mv9\n6ys++PxDTOMCs1qOGR3NS2+9xKN3PdpHvep9PB4P+fn5FBQU4Ha70Wq1ZGVl8c3y5UwZkcOGPXvI\nGjIEh9PJF198gSAImEwmhgwZQmTk6Z05QpHvvvuuXYGnheeff54hQ4YwY0boWakGw2LUbDafS3Pe\n+2CgGnjOYrE80ZM2AdRqFbmjcsgd1Tal3u12c6iwmH0HCti930KYWklDaQH6+GZXl66kSbR7LEDC\n4JEMO+cSaov2ovS7wOtk/ZpvuTJJy/XX3XbaHfeRI0fy9NPN/fX5fOTl5XH//fcTFhbGkiVL2LZt\nG/fccw/33XcfkyZNYs2aNTzwwAMkJSWdknLUHkuXLmXnrp3kTM5BFi1j67ItKLVKsqY1lyLZ+N4m\n6srqmPGbc0GEH99ez47PdzD2kuO7OKYRJp5582myUrK5/rLrQ7YA/MaNG3E6naSlpbX7fk1NDU67\nHZlEirWiAlM7FuhJSUmUlJSwdu1apk7tf5eGnhCq7hgtDDRBuS+YcNYIxo4cyn2PPYMDEfUJIrTf\n56M2bwP33HY9mWkDS3TcsGMDb338JjG5MUhlgUevSGVSYsbF8KcX/8jii69i0uiBEZ19CifvFA/g\nyMDTMfPSG3jtX68xOGVQyEZAnqljTlmFlSeffgZNRCwNjU04PGCrtZIybnbrOb0VoRyRMpT8Wiu/\nuLm5qH94mJac7CFMGTeKmOjQqIM6fuR4Rg4ZyZ9feoya6hoiM9pfS/g8Piq2VnDepPOZNyP0siTi\n09O58u67eefpp8ny+sjsw4ipSqeTbX4fY+bMYlIfOwDm79vO95+/R1NdBWFCE1lGPzkpymPjRtdL\niv9woIbnvykC4PbzU5lkPn3pjzC1jNyUZhlGFOsoKfmGT55eiQMdYcY4zpl3NYnpg7v1uVroVOQJ\nxiKrHR4CxgJf9XK7p+XaSy9m6ZMv4ksYitrQs0W929GE7fBWfn3Vpb3Uu77hQP4BxDB/wA9CmVKG\n1WHF5/OFdLh2eXk5+/btw2ZrFtuioqLIzMxsjUzYs307akEgXK/n7JGjWPb++1x6zTVERTXvZNbX\n17Np0yacTidyuZzU1FQGDx58imtVqPLwww936ZxQE3mCYTF6LO/9U+DGY/cZD3xlNpvzLBbLsk4v\n7iYKhYJscyYJsTHstxzChwSFund2a6QKJRp9JB5jEjSUkpM9CpnPya6dO7r0G5XL5cTHH9+pTkpK\nYtOmT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1JSZsOu8MEnqB16k7lWHUqWZUrys7Fkn0QtW9Bp1Jj9TQzr34W+iV0JCQ5sFfpX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axl+Kuim+TLgL9fO4NJsTWnWfpx7c/XNC9+L4shYRzW4fd7wdCfPnxOxLmDk/Pk2N70n0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od5mIycjHntTw0uYN9cAJ1ONdzkr2bV3HvtU/k9W5E9f89UzyumW36DVb6rflv+GMbvnwo6a4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\nhBAiiP4fFicFZRLrhK8AAAAASUVORK5CYII=\n",
+ "text": [
+ ""
+ ]
+ }
+ ],
+ "prompt_number": 6
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+}
\ No newline at end of file
diff --git a/flotilla/test/test_util.py b/flotilla/test/test_util.py
new file mode 100644
index 00000000..2dd1bafb
--- /dev/null
+++ b/flotilla/test/test_util.py
@@ -0,0 +1,23 @@
+"""Test utilities interfacing with external-facing modules, e.g. links to
+gene lists"""
+
+
+def test_link_to_list():
+ pass
+ # test_list = link_to_list(genelist_link)
+ #
+ # if genelist_link.startswith("http"):
+ # sys.stderr.write(
+ # "WARNING, downloading things from the internet, potential"
+ # " danger from untrusted sources\n")
+ # filename = tempfile.NamedTemporaryFile(mode='w+')
+ # filename.write(subprocess.check_output(
+ # ["curl", "-k", '--location-trusted', genelist_link]))
+ # filename.seek(0)
+ # elif genelist_link.startswith("/"):
+ # assert os.path.exists(os.path.abspath(genelist_link))
+ # filename = os.path.abspath(genelist_link)
+ # true_list = pd.read_table(filename, squeeze=True, header=None).values \
+ # .tolist()
+ #
+ # assert true_list == test_list
diff --git a/flotilla/test/visualize/test_decompositionviz.py b/flotilla/test/visualize/test_decompositionviz.py
new file mode 100644
index 00000000..2eb53c35
--- /dev/null
+++ b/flotilla/test/visualize/test_decompositionviz.py
@@ -0,0 +1,261 @@
+from collections import defaultdict
+from itertools import cycle
+
+import matplotlib.pyplot as plt
+import numpy as np
+import numpy.testing as npt
+import pandas as pd
+import pandas.util.testing as pdt
+import pytest
+import seaborn as sns
+
+
+@pytest.fixture
+def pca(df_norm):
+ from flotilla.compute.decomposition import DataFramePCA
+
+ return DataFramePCA(df_norm)
+
+
+@pytest.fixture
+def kwargs():
+ return dict(feature_renamer=None, groupby=None,
+ singles=None, pooled=None, outliers=None,
+ featurewise=False,
+ order=None, violinplot_kws=None,
+ data_type='expression', label_to_color=None,
+ label_to_marker=None,
+ scale_by_variance=True, x_pc='pc_1',
+ y_pc='pc_2', n_vectors=20, distance='L1',
+ n_top_pc_features=50, max_char_width=30)
+
+
+@pytest.fixture
+def large_dataframe():
+ nrow = 100
+ ncol = 1000
+ index = ['sample_{}'.format(i) for i in np.arange(nrow)]
+ columns = ['feature_{}'.format(i) for i in np.arange(ncol)]
+ df = pd.DataFrame(data=np.random.randn(nrow, ncol), index=index,
+ columns=columns)
+
+ return df
+
+
+@pytest.fixture
+def pca_large_dataframe(large_dataframe):
+ from flotilla.compute.decomposition import DataFramePCA
+
+ return DataFramePCA(large_dataframe)
+
+
+def test_init(pca, kwargs):
+ from flotilla.visualize.decomposition import DecompositionViz
+
+ dv = DecompositionViz(pca.reduced_space, pca.components_,
+ pca.explained_variance_ratio_, **kwargs)
+ x_pc = kwargs['x_pc']
+ y_pc = kwargs['y_pc']
+ pcs = [x_pc, y_pc]
+
+ true_groupby = dict.fromkeys(pca.reduced_space.index, 'all')
+ true_grouped = pca.reduced_space.groupby(true_groupby, axis=0)
+
+ colors = iter(sns.color_palette('husl',
+ n_colors=len(true_grouped)))
+
+ def color_factory():
+ return colors.next()
+
+ true_label_to_color = defaultdict(color_factory)
+
+ markers = cycle(['o', '^', 's', 'v', '*', 'D', 'h'])
+
+ def marker_factory():
+ return markers.next()
+
+ true_label_to_marker = defaultdict(marker_factory)
+ for group in true_grouped.groups:
+ true_label_to_marker[group]
+ for group in dv.grouped.groups:
+ dv.label_to_marker[group]
+
+ true_color_ordered = [true_label_to_color[x] for x in
+ true_grouped.groups]
+
+ true_vars = pca.explained_variance_ratio_[[x_pc, y_pc]]
+
+ true_loadings = pca.components_.ix[[x_pc, y_pc]]
+ true_loadings = true_loadings.multiply(true_vars, axis=0)
+
+ reduced_space = pca.reduced_space[[x_pc, y_pc]]
+ farthest_sample = reduced_space.apply(np.linalg.norm, axis=0).max()
+ whole_space = true_loadings.apply(np.linalg.norm).max()
+ scale = .25 * farthest_sample / whole_space
+ true_loadings *= scale
+
+ ord = 2 if kwargs['distance'] == 'L2' else 1
+ true_magnitudes = true_loadings.apply(np.linalg.norm, ord=ord)
+ true_magnitudes.sort(ascending=False)
+
+ true_top_features = set([])
+ true_pc_loadings_labels = {}
+ true_pc_loadings = {}
+
+ for pc in pcs:
+ x = pca.components_.ix[pc].copy()
+ x.sort(ascending=True)
+ half_features = int(kwargs['n_top_pc_features'] / 2)
+ if len(x) > kwargs['n_top_pc_features']:
+ a = x[:half_features]
+ b = x[-half_features:]
+ labels = np.r_[a.index, b.index]
+ true_pc_loadings[pc] = np.r_[a, b]
+ else:
+ labels = x.index
+ true_pc_loadings[pc] = x
+
+ true_pc_loadings_labels[pc] = labels
+ true_top_features.update(labels)
+
+ pdt.assert_frame_equal(dv.reduced_space, pca.reduced_space)
+ pdt.assert_frame_equal(dv.components_, pca.components_)
+ pdt.assert_series_equal(dv.explained_variance_ratio_,
+ pca.explained_variance_ratio_)
+ pdt.assert_dict_equal(dv.label_to_marker, true_label_to_marker)
+ pdt.assert_dict_equal(dv.groupby, true_groupby)
+ pdt.assert_series_equal(dv.vars, true_vars)
+ pdt.assert_frame_equal(dv.loadings, true_loadings)
+ npt.assert_array_equal(dv.color_ordered, true_color_ordered)
+ pdt.assert_series_equal(dv.magnitudes, true_magnitudes)
+ npt.assert_array_equal(dv.top_features, true_top_features)
+ pdt.assert_dict_equal(dv.pc_loadings_labels, true_pc_loadings_labels)
+ pdt.assert_dict_equal(dv.pc_loadings, true_pc_loadings)
+
+
+def test_large_dataframe(pca_large_dataframe, kwargs):
+ from flotilla.visualize.decomposition import DecompositionViz
+
+ dv = DecompositionViz(pca_large_dataframe.reduced_space,
+ pca_large_dataframe.components_,
+ pca_large_dataframe.explained_variance_ratio_,
+ **kwargs)
+ x_pc = kwargs['x_pc']
+ y_pc = kwargs['y_pc']
+ pcs = [x_pc, y_pc]
+
+ true_top_features = set([])
+ true_pc_loadings_labels = {}
+ true_pc_loadings = {}
+
+ for pc in pcs:
+ x = pca_large_dataframe.components_.ix[pc].copy()
+ x.sort(ascending=True)
+ half_features = int(kwargs['n_top_pc_features'] / 2)
+ if len(x) > kwargs['n_top_pc_features']:
+ a = x[:half_features]
+ b = x[-half_features:]
+ labels = np.r_[a.index, b.index]
+ true_pc_loadings[pc] = np.r_[a, b]
+ else:
+ labels = x.index
+ true_pc_loadings[pc] = x
+
+ true_pc_loadings_labels[pc] = labels
+ true_top_features.update(labels)
+ pdt.assert_array_equal(dv.top_features, true_top_features)
+ pdt.assert_dict_equal(dv.pc_loadings_labels, true_pc_loadings_labels)
+ pdt.assert_dict_equal(dv.pc_loadings, true_pc_loadings)
+
+
+def test_order(pca, kwargs):
+ from flotilla.visualize.decomposition import DecompositionViz
+
+ kw = kwargs.copy()
+ kw.pop('order')
+ kw.pop('groupby')
+ groups = ['group1', 'group2', 'group3']
+
+ groupby = pd.Series([np.random.choice(groups)
+ for i in pca.reduced_space.index],
+ index=pca.reduced_space.index)
+ order = ['group3', 'group1', 'group2']
+
+ dv = DecompositionViz(pca.reduced_space, pca.components_,
+ pca.explained_variance_ratio_, order=order,
+ groupby=groupby, **kw)
+
+ color_ordered = [dv.label_to_color[x] for x in order]
+
+ pdt.assert_series_equal(dv.groupby, groupby)
+ pdt.assert_array_equal(dv.order, order)
+ pdt.assert_array_equal(dv.color_ordered, color_ordered)
+
+
+def test_explained_variance_none(pca, kwargs):
+ from flotilla.visualize.decomposition import DecompositionViz
+
+ dv = DecompositionViz(pca.reduced_space, pca.components_,
+ None)
+ true_vars = pd.Series([1., 1.], index=[kwargs['x_pc'], kwargs['y_pc']])
+ pdt.assert_series_equal(dv.vars, true_vars)
+
+
+def test_plot_samples(pca, kwargs):
+ from flotilla.visualize.decomposition import DecompositionViz
+
+ dv = DecompositionViz(pca.reduced_space, pca.components_,
+ pca.explained_variance_ratio_, **kwargs)
+ dv.plot_samples()
+ ax = plt.gca()
+ pdt.assert_equal(len(ax.lines), kwargs['n_vectors'] + 1)
+ plt.close('all')
+
+
+def test_plot_loadings(pca, kwargs):
+ from flotilla.visualize.decomposition import DecompositionViz
+
+ dv = DecompositionViz(pca.reduced_space, pca.components_,
+ pca.explained_variance_ratio_, **kwargs)
+ dv.plot_loadings()
+ ax = plt.gca()
+ pdt.assert_equal(len(ax.collections), 1)
+ plt.close('all')
+
+
+def test_plot(pca, kwargs):
+ from flotilla.visualize.decomposition import DecompositionViz
+
+ dv = DecompositionViz(pca.reduced_space, pca.components_,
+ pca.explained_variance_ratio_, **kwargs)
+ dv.plot()
+
+ pdt.assert_equal(len(dv.fig_reduced.axes), 3)
+ plt.close('all')
+
+
+def test_plot_violins(pca, kwargs, df_norm):
+ from flotilla.visualize.decomposition import DecompositionViz
+
+ kw = kwargs.copy()
+ kw.pop('singles')
+
+ dv = DecompositionViz(pca.reduced_space, pca.components_,
+ pca.explained_variance_ratio_,
+ singles=df_norm, **kw)
+ dv.plot(plot_violins=True)
+
+ ncols = 4
+ nrows = 1
+ top_features = pd.Index(dv.top_features)
+ vector_labels = list(set(dv.magnitudes[:dv.n_vectors].index.union(
+ top_features)))
+ while ncols * nrows < len(vector_labels):
+ nrows += 1
+
+ pdt.assert_equal(len(dv.fig_violins.axes), nrows * ncols)
+
+ for i in np.arange(len(top_features)):
+ ax = dv.fig_violins.axes[i]
+ pdt.assert_equal(len(ax.collections), len(dv.grouped.groups))
+ plt.close('all')
diff --git a/flotilla/test/visualize/test_genericviz.py b/flotilla/test/visualize/test_genericviz.py
new file mode 100644
index 00000000..e69de29b
diff --git a/flotilla/util.py b/flotilla/util.py
new file mode 100644
index 00000000..2485e037
--- /dev/null
+++ b/flotilla/util.py
@@ -0,0 +1,320 @@
+"""
+General use utilities
+"""
+
+import datetime
+from functools import wraps
+import errno
+import os
+import re
+import signal
+import sys
+import subprocess
+import functools
+import time
+import cPickle
+import gzip
+import tempfile
+
+import pandas as pd
+
+
+class TimeoutError(Exception):
+ """
+ From:
+ http://stackoverflow.com/questions/2281850/timeout-function-if-it-takes-too-long-to-finish
+ """
+ pass
+
+
+def timeout(seconds=10, error_message=os.strerror(errno.ETIME)):
+ def decorator(func):
+ def _handle_timeout(signum, frame):
+ raise TimeoutError(error_message)
+
+ def wrapper(*args, **kwargs):
+ signal.signal(signal.SIGALRM, _handle_timeout)
+ signal.alarm(seconds)
+ try:
+ result = func(*args, **kwargs)
+ finally:
+ signal.alarm(0)
+ return result
+
+ return wraps(func)(wrapper)
+
+ return decorator
+
+
+def serve_ipython():
+ try:
+
+ assert len(sys.argv) == 2
+ path = sys.argv[1]
+ assert os.path.exists(sys.argv[1])
+
+ except:
+ raise ValueError("specify a notebook directory as the first and only "
+ "argument")
+
+ c = subprocess.Popen(['ipython', 'notebook', '--script', '--notebook-dir',
+ path])
+ try:
+ c.wait()
+ except KeyboardInterrupt:
+ c.terminate()
+
+
+def dict_to_str(dic):
+ """join dictionary study_data into a string with that study_data"""
+ return "_".join([k + ":" + str(v) for (k, v) in dic.items()])
+
+
+def install_development_package(package_location):
+ original_location = os.getcwd()
+ os.chdir(package_location)
+ subprocess.call(['pip install -e %s' % package_location], shell=True)
+ os.chdir(original_location)
+
+
+def memoize(obj):
+ """'Memoize' aka remember the output from a function and return that,
+ rather than recalculating
+
+ Stolen from:
+ https://wiki.python.org/moin/PythonDecoratorLibrary#CA-237e205c0d5bd1459c3663a3feb7f78236085e0a_1
+
+ do_not_memoize : bool
+ IF this is a keyword argument (kwarg) in the function, and it is true,
+ then just evaluate the function and don't memoize it.
+ """
+ cache = obj.cache = {}
+
+ @functools.wraps(obj)
+ def memoizer(*args, **kwargs):
+ if 'do_not_memoize' in kwargs and kwargs['do_not_memoize']:
+ return obj(*args, **kwargs)
+ key = str(args) + str(kwargs)
+ if key not in cache:
+ cache[key] = obj(*args, **kwargs)
+ return cache[key]
+
+ return memoizer
+
+
+class cached_property(object):
+ '''Decorator for read-only properties evaluated only once within TTL period.
+
+ It can be used to created a cached property like this::
+
+ import random
+
+ # the class containing the property must be a new-style class
+ class MyClass(object):
+ # create property whose value is cached for ten minutes
+ @cached_property(ttl=600)
+ def randint(self):
+ # will only be evaluated every 10 min. at maximum.
+ return random.randint(0, 100)
+
+ The value is cached in the '_cache' attribute of the object instance that
+ has the property getter method wrapped by this decorator. The '_cache'
+ attribute value is a dictionary which has a key for every property of the
+ object which is wrapped by this decorator. Each entry in the cache is
+ created only when the property is accessed for the first time and is a
+ two-element tuple with the last computed property value and the last time
+ it was updated in seconds since the epoch.
+
+ The default time-to-live (TTL) is 300 seconds (5 minutes). Set the TTL to
+ zero for the cached value to never expire.
+
+ To expire a cached property value manually just do::
+
+ del instance._cache[]
+
+ Stolen from:
+ https://wiki.python.org/moin/PythonDecoratorLibrary#Cached_Properties
+
+ '''
+
+ def __init__(self, ttl=0):
+ self.ttl = ttl
+
+ def __call__(self, fget, doc=None):
+ self.fget = fget
+ self.__doc__ = doc or fget.__doc__
+ self.__name__ = fget.__name__
+ self.__module__ = fget.__module__
+ return self
+
+ def __get__(self, inst, owner):
+ now = time.time()
+ try:
+ value, last_update = inst._cache[self.__name__]
+ if self.ttl > 0 and now - last_update > self.ttl:
+ raise AttributeError
+ except (KeyError, AttributeError):
+ value = self.fget(inst)
+ try:
+ cache = inst._cache
+ except AttributeError:
+ cache = inst._cache = {}
+ cache[self.__name__] = (value, now)
+ return value
+
+
+def as_numpy(x):
+ """Given either a pandas dataframe or a numpy array, always return a
+ numpy array.
+ """
+ try:
+ # Pandas DataFrame
+ return x.values
+ except AttributeError:
+ # Numpy array
+ return x
+
+
+def natural_sort(l):
+ """
+ From
+ http://stackoverflow.com/a/4836734
+ """
+ def convert(text):
+ return int(text) if text.isdigit() else text.lower()
+
+ def alphanum_key(key):
+ return [convert(c) for c in re.split('([0-9]+)', key)]
+
+ return sorted(l, key=alphanum_key)
+
+
+def to_base_file_tuple(tup):
+ """for making new packages, auto-loadable data!"""
+ assert len(tup) == 2
+ return "os.path.join(study_data_dir, %s)" % os.path.basename(tup[0]), \
+ tup[1]
+
+
+def add_package_data_resource(self, file_name, data_df,
+ toplevel_package_dir,
+ file_write_mode="tsv"):
+ writer = getattr(self, "_write_" + file_write_mode)
+ file_base = os.path.basename(file_name)
+ rsc_file = os.path.join(toplevel_package_dir, "study_data",
+ file_base + "." + file_write_mode)
+ writer(data_df, rsc_file)
+ return (rsc_file, file_write_mode)
+
+
+def validate_params(self):
+ """make sure that all necessary attributes are present"""
+ for param in self.minimal_study_parameters:
+ try:
+ getattr(self, param)
+ except KeyError:
+ raise AssertionError("Missing minimal parameter %s" % param)
+
+
+def load_pickle_df(file_name):
+ return pd.read_pickle(file_name)
+
+
+def write_pickle_df(df, file_name):
+ df.to_pickle(file_name)
+
+
+def load_gzip_pickle_df(file_name):
+ with gzip.open(file_name, 'r') as f:
+ return cPickle.load(f)
+
+
+def write_gzip_pickle_df(df, file_name):
+ tmpfile_h, tmpfile = tempfile.mkstemp()
+ df.to_pickle(tmpfile)
+ subprocess.call(['gzip -f %s' % tempfile])
+ subprocess.call(['mv %s %s' % (tempfile, file_name)])
+
+
+def load_tsv(file_name, **kwargs):
+ return pd.read_table(file_name, **kwargs)
+
+
+def load_json(filename, **kwargs):
+ """
+ Parameters
+ ----------
+ filename : str
+ Name of the json file toread
+ compression : str
+ Not used, only for compatibility with other load functions
+
+ Returns
+ -------
+
+
+ Raises
+ ------
+ """
+ kwargs.pop('compression')
+ return pd.read_json(filename)
+
+
+def write_tsv(df, file_name):
+ df.to_csv(file_name, sep='\t')
+
+
+def load_csv(file_name, **kwargs):
+ return pd.read_csv(file_name, **kwargs)
+
+
+def write_csv(df, file_name):
+ df.to_csv(file_name)
+
+
+def get_loading_method(self, file_name):
+ """loading_methods for loading from file"""
+ return getattr(self, "_load_" + file_name)
+
+
+# def load(self, file_name, file_type='pickle_df'):
+# return self._get_loading_method(file_type)(file_name)
+
+
+def timestamp():
+ return str(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))
+
+
+class AssertionError(StandardError):
+ """ Assertion failed. """
+
+ def __init__(self, *args, **kwargs): # real signature unknown
+ pass
+
+ @staticmethod # known case of __new__
+ def __new__(S, *more): # real signature unknown; restored from __doc__
+ """ T.__new__(S, ...) -> a new object with type S, a subtype of T """
+ pass
+
+
+def link_to_list(link):
+ print 'link', link
+ try:
+ assert link.startswith("http") or os.path.exists(os.path.abspath(link))
+ except AssertionError:
+ raise ValueError("use a link that starts with http or a file path")
+
+ if link.startswith("http"):
+ sys.stderr.write(
+ "WARNING, downloading things from the internet, potential danger "
+ "from untrusted sources\n")
+ filename = tempfile.NamedTemporaryFile(mode='w+')
+ filename.write(subprocess.check_output(
+ ["curl", "-k", '--location-trusted', link]))
+ filename.seek(0)
+ elif link.startswith("/"):
+ assert os.path.exists(os.path.abspath(link))
+ filename = os.path.abspath(link)
+ gene_list = pd.read_table(filename, squeeze=True, header=None).values \
+ .tolist()
+ return gene_list
diff --git a/flotilla/visualize/__init__.py b/flotilla/visualize/__init__.py
new file mode 100644
index 00000000..d6957118
--- /dev/null
+++ b/flotilla/visualize/__init__.py
@@ -0,0 +1,34 @@
+"""plotting tools"""
+
+import seaborn
+
+from .ipython_interact import Interactive
+from .predict import PredictorBaseViz
+
+seaborn.set_style({'axes.axisbelow': True,
+ 'axes.edgecolor': '.15',
+ 'axes.facecolor': 'white',
+ 'axes.grid': False,
+ 'axes.labelcolor': '.15',
+ 'axes.linewidth': 1.25,
+ 'font.family': ['Helvetica', 'Arial'],
+ 'grid.color': '.8',
+ 'grid.linestyle': '-',
+ 'image.cmap': 'Greys',
+ 'legend.frameon': False,
+ 'legend.numpoints': 1,
+ 'legend.scatterpoints': 1,
+ 'lines.solid_capstyle': 'round',
+ 'text.color': '.15',
+ 'xtick.color': '.15',
+ 'xtick.direction': 'out',
+ 'xtick.major.size': 0,
+ 'xtick.minor.size': 0,
+ 'ytick.color': '.15',
+ 'ytick.direction': 'out',
+ 'ytick.major.size': 0,
+ 'ytick.minor.size': 0})
+
+seaborn.set_context('talk')
+
+__all__ = ['Interactive', 'PredictorBaseViz']
diff --git a/flotilla/visualize/color.py b/flotilla/visualize/color.py
new file mode 100644
index 00000000..6ec019c1
--- /dev/null
+++ b/flotilla/visualize/color.py
@@ -0,0 +1,32 @@
+"""
+Convenience functions for obtaining reasonable plotting colors
+"""
+
+
+import matplotlib as mpl
+import seaborn as sns
+import brewer2mpl
+
+sns.set_palette('deep')
+deep = sns.color_palette('deep')
+
+dark2 = map(mpl.colors.rgb2hex,
+ brewer2mpl.get_map('Dark2', 'qualitative', 8).mpl_colors)
+set1 = map(mpl.colors.rgb2hex,
+ brewer2mpl.get_map('Set1', 'qualitative', 9).mpl_colors)
+red = set1[0]
+blue = set1[1]
+green = set1[2]
+purple = set1[3]
+orange = set1[4]
+yellow = set1[5]
+brown = set1[6]
+pink = set1[7]
+grey = set1[8]
+
+almost_black = '#262626'
+
+purples = sns.color_palette('Purples', 9)
+
+str_to_color = {'red': red, 'blue': blue, 'green': green, 'purple': purple,
+ 'orange': orange, 'brown': brown, 'pink': pink, 'grey': grey}
diff --git a/flotilla/visualize/decomposition.py b/flotilla/visualize/decomposition.py
new file mode 100644
index 00000000..bae338af
--- /dev/null
+++ b/flotilla/visualize/decomposition.py
@@ -0,0 +1,519 @@
+from __future__ import division
+
+from collections import defaultdict
+from itertools import cycle
+import math
+
+from matplotlib.gridspec import GridSpec, GridSpecFromSubplotSpec
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+
+from .color import deep
+from .generic import violinplot
+
+
+class DecompositionViz(object):
+ """
+ Plots the reduced space from a decomposed dataset. Does not perform any
+ reductions of its own
+ """
+
+ def __init__(self, reduced_space, components_,
+ explained_variance_ratio_,
+ feature_renamer=None, groupby=None,
+ singles=None, pooled=None, outliers=None,
+ featurewise=False,
+ order=None, violinplot_kws=None,
+ data_type='expression', label_to_color=None,
+ label_to_marker=None,
+ scale_by_variance=True, x_pc='pc_1',
+ y_pc='pc_2', n_vectors=20, distance='L1',
+ n_top_pc_features=50, max_char_width=30):
+ """Plot the results of a decomposition visualization
+
+ Parameters
+ ----------
+ reduced_space : pandas.DataFrame
+ A (n_samples, n_dimensions) DataFrame of the post-dimensionality
+ reduction data
+ components_ : pandas.DataFrame
+ A (n_features, n_dimensions) DataFrame of how much each feature
+ contributes to the components (trailing underscore to be
+ consistent with scikit-learn)
+ explained_variance_ratio_ : pandas.Series
+ A (n_dimensions,) Series of how much variance each component
+ explains. (trailing underscore to be consistent with scikit-learn)
+ feature_renamer : function, optional
+ A function which takes the name of the feature and renames it,
+ e.g. from an ENSEMBL ID to a HUGO known gene symbol. If not
+ provided, the original name is used.
+ groupby : mapping function | dict, optional
+ A mapping of the samples to a label, e.g. sample IDs to
+ phenotype, for the violinplots. If None, all samples are treated
+ the same and are colored the same.
+ singles : pandas.DataFrame, optional
+ For violinplots only. If provided and 'plot_violins' is True,
+ will plot the raw (not reduced) measurement values as violin plots.
+ pooled : pandas.DataFrame, optional
+ For violinplots only. If provided, pooled samples are plotted as
+ black dots within their label.
+ outliers : pandas.DataFrame, optional
+ For violinplots only. If provided, outlier samples are plotted as
+ a grey shadow within their label.
+ featurewise : bool, optional
+ If True, then the "samples" are features, e.g. genes instead of
+ samples, and the "features" are the samples, e.g. the cells
+ instead of the gene ids. Essentially, the transpose of the
+ original matrix. If True, then violins aren't plotted. (default
+ False)
+ order : list-like, optional
+ The order of the labels for the violinplots, e.g. if the data is
+ from a differentiation timecourse, then this would be the labels
+ of the phenotypes, in the differentiation order.
+ violinplot_kws : dict, optional
+ Any additional parameters to violinplot
+ data_type : 'expression' | 'splicing', optional
+ For violinplots only. The kind of data that was originally used
+ for the reduction. (default 'expression')
+ label_to_color : dict, optional
+ A mapping of the label, e.g. the phenotype, to the desired
+ plotting color (default None, auto-assigned with the groupby)
+ label_to_marker : dict, optional
+ A mapping of the label, e.g. the phenotype, to the desired
+ plotting symbol (default None, auto-assigned with the groupby)
+ scale_by_variance : bool, optional
+ If True, scale the x- and y-axes by their explained_variance_ratio_
+ (default True)
+ {x,y}_pc : str, optional
+ Principal component to plot on the x- and y-axis. (default "pc_1"
+ and "pc_2")
+ n_vectors : int, optional
+ Number of vectors to plot of the principal components. (default 20)
+ distance : 'L1' | 'L2', optional
+ The distance metric to use to plot the vector lengths. L1 is
+ "Cityblock", i.e. the sum of the x and y coordinates, and L2 is
+ the traditional Euclidean distance. (default "L1")
+ n_top_pc_features : int, optional
+ THe number of top features from the principal components to plot.
+ (default 50)
+ max_char_width : int, optional
+ Maximum character width of a feature name. Useful for crazy long
+ feature IDs like MISO IDs
+ """
+ self.reduced_space = reduced_space
+ self.components_ = components_
+ self.explained_variance_ratio_ = explained_variance_ratio_
+
+ self.singles = singles
+ self.pooled = pooled
+ self.outliers = outliers
+
+ self.groupby = groupby
+ self.order = order
+ self.violinplot_kws = violinplot_kws if violinplot_kws is not None \
+ else {}
+ self.data_type = data_type
+ self.label_to_color = label_to_color
+ self.label_to_marker = label_to_marker
+ self.n_vectors = n_vectors
+ self.x_pc = x_pc
+ self.y_pc = y_pc
+ self.pcs = (self.x_pc, self.y_pc)
+ self.distance = distance
+ self.n_top_pc_features = n_top_pc_features
+ self.featurewise = featurewise
+ self.feature_renamer = feature_renamer
+ self.max_char_width = max_char_width
+
+ if self.groupby is None:
+ self.groupby = dict.fromkeys(self.reduced_space.index, 'all')
+ self.grouped = self.reduced_space.groupby(self.groupby, axis=0)
+
+ if self.label_to_color is None:
+ colors = iter(sns.color_palette('husl',
+ n_colors=len(self.grouped)))
+
+ def color_factory():
+ return colors.next()
+
+ self.label_to_color = defaultdict(color_factory)
+
+ if self.label_to_marker is None:
+ markers = cycle(['o', '^', 's', 'v', '*', 'D', 'h'])
+
+ def marker_factory():
+ return markers.next()
+
+ self.label_to_marker = defaultdict(marker_factory)
+
+ if order is not None:
+ self.color_ordered = [self.label_to_color[x] for x in self.order]
+ else:
+ self.color_ordered = [self.label_to_color[x] for x in
+ self.grouped.groups]
+
+ self.loadings = self.components_.ix[[self.x_pc, self.y_pc]]
+
+ # Get the explained variance
+ if self.explained_variance_ratio_ is not None:
+ self.vars = self.explained_variance_ratio_[[self.x_pc, self.y_pc]]
+ else:
+ self.vars = pd.Series([1., 1.], index=[self.x_pc, self.y_pc])
+
+ if scale_by_variance:
+ self.loadings = self.loadings.multiply(self.vars, axis=0)
+
+ # sort features by magnitude/contribution to transformation
+ reduced_space = self.reduced_space[[self.x_pc, self.y_pc]]
+ farthest_sample = reduced_space.apply(np.linalg.norm, axis=0).max()
+ whole_space = self.loadings.apply(np.linalg.norm).max()
+ scale = .25 * farthest_sample / whole_space
+ self.loadings *= scale
+
+ ord = 2 if self.distance == 'L2' else 1
+ self.magnitudes = self.loadings.apply(np.linalg.norm, ord=ord)
+ self.magnitudes.sort(ascending=False)
+
+ self.top_features = set([])
+ self.pc_loadings_labels = {}
+ self.pc_loadings = {}
+ for pc in self.pcs:
+ x = self.components_.ix[pc].copy()
+ x.sort(ascending=True)
+ half_features = int(self.n_top_pc_features / 2)
+ if len(x) > self.n_top_pc_features:
+ a = x[:half_features]
+ b = x[-half_features:]
+ labels = np.r_[a.index, b.index]
+ self.pc_loadings[pc] = np.r_[a, b]
+ else:
+ labels = x.index
+ self.pc_loadings[pc] = x
+
+ self.pc_loadings_labels[pc] = labels
+ self.top_features.update(labels)
+
+ def plot(self, ax=None, title='', plot_violins=False,
+ show_point_labels=False,
+ show_vectors=True,
+ show_vector_labels=True,
+ markersize=10, legend=True, bokeh=False, metadata=None):
+
+ if bokeh:
+ self._plot_bokeh(metadata, title)
+ else:
+ gs_x = 14
+ gs_y = 12
+
+ if ax is None:
+ self.fig_reduced, ax = plt.subplots(1, 1, figsize=(20, 10))
+ self.gs = GridSpec(gs_x, gs_y)
+
+ else:
+ self.gs = GridSpecFromSubplotSpec(gs_x, gs_y,
+ ax.get_subplotspec())
+ self.fig_reduced = plt.gcf()
+
+ self.ax_components = plt.subplot(self.gs[:, :5])
+ self.ax_loading1 = plt.subplot(self.gs[:, 6:8])
+ self.ax_loading2 = plt.subplot(self.gs[:, 10:14])
+
+ self.plot_samples(show_point_labels=show_point_labels,
+ title=title, show_vectors=show_vectors,
+ show_vector_labels=show_vector_labels,
+ markersize=markersize, legend=legend,
+ ax=self.ax_components)
+ self.plot_loadings(pc=self.x_pc, ax=self.ax_loading1)
+ self.plot_loadings(pc=self.y_pc, ax=self.ax_loading2)
+ sns.despine()
+ self.fig_reduced.tight_layout()
+
+ singles_none = self.singles is None
+ if plot_violins and not self.featurewise and not singles_none:
+ self.plot_violins()
+ return self
+
+ def _plot_bokeh(self, metadata=None, title=''):
+ metadata = metadata if metadata is not None else pd.DataFrame(
+ index=self.reduced_space.index)
+ # Clean alias
+ import bokeh.plotting as bk
+ from bokeh.plotting import ColumnDataSource, figure, show
+ from bokeh.models import HoverTool
+
+ # Plots can be displayed inline in an IPython Notebook
+ bk.output_notebook(force=True)
+
+ # Create a set of tools to use
+ TOOLS = "pan,wheel_zoom,box_zoom,reset,hover"
+
+ x = self.reduced_space[self.x_pc]
+ y = self.reduced_space[self.y_pc]
+
+ radii = np.ones(x.shape)
+
+ colors = self.reduced_space.index.map(
+ lambda x: self.label_to_color[self.groupby[x]])
+ sample_ids = self.reduced_space.index
+
+ data = dict(
+ (col, metadata[col][self.reduced_space.index]) for
+ col in metadata)
+ tooltips = [('sample_id', '@sample_ids')]
+ tooltips.extend([(k, '@{}'.format(k)) for k in data.keys()])
+
+ data.update(dict(
+ x=x,
+ y=y,
+ colors=colors,
+ sample_ids=sample_ids
+ ))
+
+ source = ColumnDataSource(
+ data=data
+ )
+
+ p = figure(title=title, tools=TOOLS)
+
+ p.circle(x, y, radius=radii, source=source,
+ fill_color=colors, fill_alpha=0.6, line_color=None)
+ hover = p.select(dict(type=HoverTool))
+ hover.tooltips = tooltips
+
+ show(p)
+
+ def shorten(self, x):
+ if len(x) > self.max_char_width:
+ return '{}...'.format(x[:self.max_char_width])
+ else:
+ return x
+
+ def plot_samples(self, show_point_labels=True,
+ title='PCA', show_vectors=True,
+ show_vector_labels=True, markersize=10,
+ three_d=False, legend=True, ax=None):
+ """Plot PCA scatterplot
+
+ Parameters
+ ----------
+ groupby : groupby
+ How to group the samples by color/label
+ label_to_color : dict
+ Group labels to a matplotlib color E.g. if you've already chosen
+ specific colors to indicate a particular group. Otherwise will
+ auto-assign colors
+ label_to_marker : dict
+ Group labels to matplotlib marker
+ title : str
+ title of the plot
+ show_vectors : bool
+ Whether or not to draw the vectors indicating the supporting
+ principal components
+ show_vector_labels : bool
+ whether or not to draw the names of the vectors
+ show_point_labels : bool
+ Whether or not to label the scatter points
+ markersize : int
+ size of the scatter markers on the plot
+ text_group : list of str
+ Group names that you want labeled with text
+ three_d : bool
+ if you want hte plot in 3d (need to set up the axes beforehand)
+
+ Returns
+ -------
+ For each vector in data:
+ x, y, marker, distance
+ """
+ if ax is None:
+ ax = plt.gca()
+
+ # Plot the samples
+ for name, df in self.grouped:
+ color = self.label_to_color[name]
+ marker = self.label_to_marker[name]
+ x = df[self.x_pc]
+ y = df[self.y_pc]
+ ax.plot(x, y, color=color, marker=marker, linestyle='None',
+ label=name, markersize=markersize, alpha=0.75,
+ markeredgewidth=.1)
+ try:
+ if not self.pooled.empty:
+ pooled_ids = x.index.intersection(self.pooled.index)
+ pooled_x, pooled_y = x[pooled_ids], y[pooled_ids]
+ ax.plot(pooled_x, pooled_y, 'o', color=color,
+ marker=marker,
+ markeredgecolor='k', markeredgewidth=2,
+ label='{} pooled'.format(name),
+ markersize=markersize, alpha=0.75)
+ except AttributeError:
+ pass
+ try:
+ if not self.outliers.empty:
+ outlier_ids = x.index.intersection(self.outliers.index)
+ outlier_x, outlier_y = x[outlier_ids], y[outlier_ids]
+ ax.plot(outlier_x, outlier_y, 'o', color=color,
+ marker=marker,
+ markeredgecolor='lightgrey', markeredgewidth=5,
+ label='{} outlier'.format(name),
+ markersize=markersize, alpha=0.75)
+ except AttributeError:
+ pass
+ if show_point_labels:
+ for args in zip(x, y, df.index):
+ ax.text(*args)
+
+ # Plot vectors, if asked
+ if show_vectors:
+ for vector_label in self.magnitudes[:self.n_vectors].index:
+ x, y = self.loadings[vector_label]
+ ax.plot([0, x], [0, y], color='k', linewidth=1)
+ if show_vector_labels:
+ x_offset = math.copysign(5, x)
+ y_offset = math.copysign(5, y)
+ horizontalalignment = 'left' if x > 0 else 'right'
+ if self.feature_renamer is not None:
+ renamed = self.feature_renamer(vector_label)
+ else:
+ renamed = vector_label
+ ax.annotate(renamed, (x, y),
+ textcoords='offset points',
+ xytext=(x_offset, y_offset),
+ horizontalalignment=horizontalalignment)
+
+ # Label x and y axes
+ ax.set_xlabel(
+ 'Principal Component {} (Explains {:.2f}% Of Variance)'.format(
+ str(self.x_pc), 100 * self.vars[self.x_pc]))
+ ax.set_ylabel(
+ 'Principal Component {} (Explains {:.2f}% Of Variance)'.format(
+ str(self.y_pc), 100 * self.vars[self.y_pc]))
+ ax.set_title(title)
+
+ if legend:
+ ax.legend()
+ sns.despine()
+
+ def plot_loadings(self, pc='pc_1', n_features=50, ax=None):
+ loadings = self.pc_loadings[pc]
+ labels = self.pc_loadings_labels[pc]
+
+ if ax is None:
+ ax = plt.gca()
+
+ # import pdb; pdb.set_trace()
+ x = loadings
+ y = np.arange(loadings.shape[0])
+
+ ax.scatter(x, y, color=deep[0])
+ ax.set_ylim(-.5, y.max() + .5)
+
+ ax.set_yticks(np.arange(max(loadings.shape[0], n_features)))
+ ax.set_title("Component " + pc)
+
+ x_offset = max(loadings) * .05
+ ax.set_xlim(left=loadings.min() - x_offset,
+ right=loadings.max() + x_offset)
+
+ if self.feature_renamer is not None:
+ labels = map(self.feature_renamer, labels)
+ else:
+ labels = labels
+
+ labels = map(self.shorten, labels)
+ # ax.set_yticklabels(map(shorten, labels))
+ ax.set_yticklabels(labels)
+ for lab in ax.get_xticklabels():
+ lab.set_rotation(90)
+ sns.despine(ax=ax)
+
+ def plot_explained_variance(self, title="PCA explained variance"):
+ """If the reducer is a form of PCA, then plot the explained variance
+ ratio by the components.
+ """
+ # Plot the explained variance ratio
+ assert self.explained_variance_ratio_ is not None
+ import matplotlib.pyplot as plt
+ import seaborn as sns
+
+ fig, ax = plt.subplots()
+ ax.plot(self.explained_variance_ratio_, 'o-')
+
+ xticks = np.arange(len(self.explained_variance_ratio_))
+ ax.set_xticks(xticks)
+ ax.set_xticklabels(xticks + 1)
+ ax.set_xlabel('Principal component')
+ ax.set_ylabel('Fraction explained variance')
+ ax.set_title(title)
+ sns.despine()
+
+ def plot_violins(self):
+ """Make violinplots of each feature
+
+ Must be called after plot_samples because it depends on the existence
+ of the "self.magnitudes" attribute.
+ """
+ ncols = 4
+ nrows = 1
+ vector_labels = list(set(self.magnitudes[:self.n_vectors].index.union(
+ pd.Index(self.top_features))))
+ while ncols * nrows < len(vector_labels):
+ nrows += 1
+ self.fig_violins, axes = plt.subplots(nrows=nrows, ncols=ncols,
+ figsize=(4 * ncols, 4 * nrows))
+
+ if self.feature_renamer is not None:
+ renamed_vectors = map(self.feature_renamer, vector_labels)
+ else:
+ renamed_vectors = vector_labels
+ labels = [(y, x) for (y, x) in sorted(zip(renamed_vectors,
+ vector_labels))]
+
+ for (renamed, feature_id), ax in zip(labels, axes.flat):
+ singles = self.singles[feature_id] if self.singles is not None \
+ else None
+ pooled = self.pooled[feature_id] if self.pooled is not None else \
+ None
+ outliers = self.outliers[feature_id] if self.outliers is not None \
+ else None
+
+ if isinstance(feature_id, tuple):
+ feature_id = feature_id[0]
+ if len(feature_id) > 25:
+ feature_id = feature_id[:25] + '...'
+ if renamed != feature_id:
+ title = '{}\n{}'.format(feature_id, renamed)
+ else:
+ title = feature_id
+ singles.name = renamed
+ try:
+ pooled.name = renamed
+ except AttributeError:
+ pass
+ try:
+ outliers.name = renamed
+ except AttributeError:
+ pass
+ # import pdb; pdb.set_trace()
+ violinplot(singles, pooled_data=pooled, outliers=outliers,
+ groupby=self.groupby, color_ordered=self.color_ordered,
+ order=self.order, title=title,
+ ax=ax, data_type=self.data_type,
+ **self.violinplot_kws)
+
+ if self.data_type == 'splicing':
+ ax.set_ylabel('$\Psi$')
+ ax.set_ylim(0, 1)
+ elif self.data_type == 'expression':
+ ax.set_ylabel('Expression')
+ else:
+ ax.set_ylabel('')
+
+ # Clear any unused axes
+ for ax in axes.flat:
+ # Check if the plotting space is empty
+ if len(ax.collections) == 0 or len(ax.lines) == 0:
+ ax.axis('off')
+ self.fig_violins.tight_layout()
diff --git a/flotilla/visualize/expression.py b/flotilla/visualize/expression.py
new file mode 100644
index 00000000..af9a5f0e
--- /dev/null
+++ b/flotilla/visualize/expression.py
@@ -0,0 +1,38 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+from .color import blue
+from ..compute.expression import TwoWayGeneComparisonLocal
+
+
+class TwoWayScatterViz(TwoWayGeneComparisonLocal):
+ def __call__(self, **kwargs):
+ self.plot(**kwargs)
+
+ def plot(self, ax=None):
+ # co = [] # colors container
+ # results = self.result_.get(["pValue", "log2_ratio", "isSig"])
+ # for label, (pVal, logratio, isSig) in results.iterrows():
+ # if (pVal < self.p_value_cutoff) and isSig:
+ # if logratio > 0:
+ # co.append(red)
+ # elif logratio < 0:
+ # co.append(green)
+ # else:
+ # raise Exception
+ # else:
+ # co.append(blue)
+ #
+ if ax is None:
+ ax = plt.gca()
+
+ ax.set_aspect('equal')
+ vmin = np.min(np.c_[self.sample1, self.sample2])
+ ax.plot(self.sample1, self.sample2, 'o', color=blue, alpha=0.7,
+ markeredgewidth=0.1)
+ ax.set_xlabel("%s %s" % (self.sample_names[0], self.dtype))
+ ax.set_ylabel("%s %s" % (self.sample_names[1], self.dtype))
+ # ax.set_yscale('log', basey=2)
+ # ax.set_xscale('log', basex=2)
+ ax.set_xlim(xmin=max(vmin, 0))
+ ax.set_ylim(ymin=max(vmin, 0))
diff --git a/flotilla/visualize/generic.py b/flotilla/visualize/generic.py
new file mode 100644
index 00000000..68bfcbe1
--- /dev/null
+++ b/flotilla/visualize/generic.py
@@ -0,0 +1,293 @@
+import matplotlib as mpl
+from matplotlib import pyplot as plt
+import numpy as np
+import seaborn as sns
+
+
+def violinplot(data, groupby=None, color_ordered=None, ax=None,
+ pooled_data=None,
+ order=None, violinplot_kws=None, title=None,
+ label_pooled=False, outliers=None, data_type=None):
+ """
+ Parameters
+ ----------
+ data : pandas.Series
+ The main data to plot
+ groupby : dict-like
+ How to group the samples (e.g. by phenotype)
+ color_ordered : list
+
+ Returns
+ -------
+
+
+ Raises
+ ------
+ """
+ data_type = 'none' if data_type is None else data_type
+ splicing = data_type == 'splicing'
+
+ violinplot_kws = {} if violinplot_kws is None else violinplot_kws
+ violinplot_kws.setdefault('alpha', 0.75)
+
+ if ax is None:
+ ax = plt.gca()
+
+ _violinplot_single_dataset(data, groupby=groupby, color=color_ordered,
+ ax=ax, order=order,
+ violinplot_kws=violinplot_kws,
+ splicing=splicing)
+ if pooled_data is not None and groupby is not None:
+ grouped = pooled_data.groupby(groupby)
+ if order is not None:
+ for i, name in enumerate(order):
+ try:
+ subset = pooled_data.ix[grouped.groups[name]]
+ plot_pooled_dot(ax, subset, x_offset=i, label=label_pooled)
+ except KeyError:
+ pass
+ else:
+ plot_pooled_dot(ax, pooled_data)
+
+ if outliers is not None:
+ outlier_violinplot_kws = violinplot_kws
+
+ # make sure this is behind the non outlier data
+ outlier_violinplot_kws['zorder'] = -1
+ _violinplot_single_dataset(outliers, groupby=groupby,
+ color='lightgrey', ax=ax, order=order,
+ violinplot_kws=outlier_violinplot_kws,
+ splicing=splicing)
+
+ if splicing:
+ ax.set_ylim(0, 1)
+ ax.set_yticks([0, 0.5, 1])
+ ax.set_ylabel('$\Psi$')
+
+ if title is not None:
+ ax.set_title(title)
+
+ if order is not None:
+ ax.set_xlim(-.5, len(order) - .5)
+
+ if groupby is not None and order is not None:
+ sizes = data.dropna().groupby(groupby).size()
+ xticks = range(len(order))
+ xticklabels = ['{}\nn={}'.format(group, sizes[group])
+ if group in sizes else '{}\nn=0'.format(group)
+ for group in order]
+ ax.set_xticks(xticks)
+ ax.set_xticklabels(xticklabels)
+ sns.despine()
+
+
+def _violinplot_single_dataset(data, groupby=None, order=None,
+ violinplot_kws=None, color=None, ax=None,
+ splicing=False):
+ """Plot a single set of violinplot.
+
+ Separated out so real data plotting and outlier plotting works the same
+ """
+ data = data.dropna()
+ if data.empty:
+ return
+
+ single_points = data.groupby(groupby).filter(lambda x: len(x) < 2)
+ data = data.groupby(groupby).filter(lambda x: len(x) > 1)
+
+ # Check that all the groups are represented, if not, add some data out of
+ # range to the missing group
+ verified_color = color
+ if groupby is not None and order is not None:
+ verified_groups = data.groupby(groupby).size().keys()
+ verified_order = [x for x in order if x in verified_groups]
+
+ positions = [i for i, x in enumerate(order) if x in verified_groups]
+
+ single_groups = single_points.groupby(groupby).size().keys()
+ single_positions = dict((x, i) for i, x in enumerate(order) if
+ x in single_groups)
+
+ if not mpl.colors.is_color_like(color):
+ verified_color = [x for i, x in enumerate(color)
+ if order[i] in verified_groups]
+ single_color = dict((group, c) for i, (c, group) in
+ enumerate(zip(color, single_groups))
+ if group in single_groups)
+ else:
+ single_color = dict((group, color) for group in single_groups if
+ group in single_groups)
+ else:
+ verified_order = order
+ positions = None
+
+ single_positions = None
+ single_color = None
+
+ violinplot_kws = {} if violinplot_kws is None else violinplot_kws
+
+ # Add a tiny amount of random noise in case the values are all identical,
+ # Otherwise we get a LinAlg error.
+ data += np.random.uniform(0, 0.001, data.shape[0])
+
+ inner = 'points' if splicing else 'box'
+ if len(data) > 0:
+ sns.violinplot(data, groupby=groupby, bw=0.1, inner=inner,
+ color=verified_color, linewidth=0.5,
+ order=verified_order,
+ ax=ax, positions=positions, **violinplot_kws)
+
+ if single_points is not None:
+ for group, y in single_points.groupby(groupby):
+ x = single_positions[group]
+ c = single_color[group]
+ ax.scatter([x], [y], color=c, s=50)
+ ax.annotate(y.index[0], (x, y), textcoords='offset points',
+ xytext=(7, 0), fontsize=14)
+
+
+def plot_pooled_dot(ax, pooled, x_offset=0, label=False):
+ """
+ Parameters
+ ----------
+ ax : matplotlib.axes.Axes
+ Axes object to plot on
+ pooled : pandas.Series
+ Pooled data of this gene
+
+ Returns
+ -------
+
+
+ Raises
+ ------
+ """
+ pooled = pooled.dropna()
+ try:
+ xs = np.zeros(pooled.shape[0])
+ except AttributeError:
+ xs = np.zeros(1)
+ xs += x_offset
+ ax.plot(xs, pooled, 'o', color='#262626')
+
+ if label:
+ for x, y in zip(xs, pooled):
+ if np.isnan(y):
+ continue
+ ax.annotate('pooled', (x, y), textcoords='offset points',
+ xytext=(7, 0), fontsize=14)
+
+
+def nmf_space_transitions(nmf_space_positions, feature_id,
+ phenotype_to_color, phenotype_to_marker, order,
+ ax=None, xlabel=None, ylabel=None):
+ df = nmf_space_positions.ix[feature_id]
+
+ if ax is None:
+ ax = plt.gcf()
+
+ for color, s in df.groupby(phenotype_to_color, axis=0):
+ phenotype = s.index[0]
+ marker = phenotype_to_marker[phenotype]
+ ax.plot(s.pc_1, s.pc_2, color=color, marker=marker, markersize=14,
+ alpha=0.75, label=phenotype, linestyle='none')
+
+ # ax.scatter(df.ix[:, 0], df.ix[:, 1], color=color, s=100, alpha=0.75)
+ # ax.legend(points, df.index.tolist())
+ ax.set_xlim(0, nmf_space_positions.ix[:, 0].max() * 1.05)
+ ax.set_ylim(0, nmf_space_positions.ix[:, 1].max() * 1.05)
+
+ x = [df.ix[pheno, 0] for pheno in order if pheno in df.index]
+ y = [df.ix[pheno, 1] for pheno in order if pheno in df.index]
+
+ ax.plot(x, y, zorder=-1, color='#262626', alpha=0.5, linewidth=1)
+ ax.legend()
+
+ if xlabel is not None:
+ ax.set_xlabel(xlabel)
+ ax.set_xticks([])
+ if ylabel is not None:
+ ax.set_ylabel(ylabel)
+ ax.set_yticks([])
+
+
+def simple_twoway_scatter(sample1, sample2, **kwargs):
+ """Plot a two-dimensional scatterplot between two samples
+
+ Parameters
+ ----------
+ sample1 : pandas.Series
+ Data to plot on the x-axis
+ sample2 : pandas.Series
+ Data to plot on the y-axis
+ Any other keyword arguments valid for seaborn.jointplot
+
+ Returns
+ -------
+ jointgrid : seaborn.axisgrid.JointGrid
+ Returns a JointGrid instance
+
+ See Also
+ -------
+ seaborn.jointplot
+
+ """
+ joint_kws = kwargs.pop('joint_kws', {})
+
+ kind = kwargs.pop('kind', 'scatter')
+ marginal_kws = kwargs.pop('marginal_kws', {})
+ if kind == 'scatter':
+ vmin = min(sample1.min(), sample2.min())
+ vmax = max(sample1.max(), sample2.max())
+ bins = np.linspace(vmin, vmax, 50)
+ marginal_kws.setdefault('bins', bins)
+ if kind not in ('reg', 'resid'):
+ joint_kws.setdefault('alpha', 0.5)
+
+ jointgrid = sns.jointplot(sample1, sample2, joint_kws=joint_kws,
+ marginal_kws=marginal_kws, kind=kind, **kwargs)
+ xmin, xmax, ymin, ymax = jointgrid.ax_joint.axis()
+
+ xmin = max(xmin, sample1.min() - .1)
+ ymin = max(ymin, sample2.min() - .1)
+ jointgrid.ax_joint.set_xlim(xmin, xmax)
+ jointgrid.ax_joint.set_ylim(ymin, ymax)
+
+
+def barplot(data, color=None, x_order=None, ax=None, title='', **kwargs):
+ sns.barplot(data, color=color, x_order=x_order, ax=ax, **kwargs)
+ grouped = data.groupby(data)
+ ax.set_title(title)
+ sizes = grouped.size()
+ percents = sizes / sizes.sum() * 100
+ xs = ax.get_xticks()
+
+ annotate_yrange_factor = 0.025
+ ymin, ymax = ax.get_ylim()
+ yrange = ymax - ymin
+
+ # Reset ymax and ymin so there's enough room to see the annotation of
+ # the top-most
+ if ymax > 0:
+ ymax += yrange * 0.1
+ if ymin < 0:
+ ymin -= yrange * 0.1
+ ax.set_ylim(ymin, ymax)
+ yrange = ymax - ymin
+
+ offset_ = yrange * annotate_yrange_factor
+ for x, modality in zip(xs, x_order):
+ try:
+ y = sizes[modality]
+ offset = offset_ if y >= 0 else -1 * offset_
+ verticalalignment = 'bottom' if y >= 0 else 'top'
+ percent = percents[modality]
+ ax.annotate('{} ({:.1f}%)'.format(y, percent),
+ (x, y + offset),
+ verticalalignment=verticalalignment,
+ horizontalalignment='center')
+ except KeyError:
+ ax.annotate('0 (0%)',
+ (x, offset_),
+ verticalalignment='bottom',
+ horizontalalignment='center')
diff --git a/flotilla/visualize/ipython_interact.py b/flotilla/visualize/ipython_interact.py
new file mode 100644
index 00000000..56398a76
--- /dev/null
+++ b/flotilla/visualize/ipython_interact.py
@@ -0,0 +1,770 @@
+"""
+Named `ipython_interact.py` rather than just `interact.py` to differentiate
+between IPython interactive visualizations vs D3 interactive visualizations.
+"""
+import os
+import sys
+import warnings
+
+from IPython.html.widgets import interact, TextWidget, ButtonWidget
+import matplotlib.pyplot as plt
+
+from ..visualize.color import red
+from .network import NetworkerViz
+from .color import str_to_color
+from ..util import natural_sort, link_to_list
+
+default_classifier = 'ExtraTreesClassifier'
+default_regressor = 'ExtraTreesRegressor'
+default_score_coefficient = 2
+
+
+class Interactive(object):
+ """
+
+ Attributes
+ ----------
+
+
+ Methods
+ -------
+
+ """
+
+ def __init__(self, *args, **kwargs):
+ self._default_x_pc = 1
+ self._default_y_pc = 2
+
+ @staticmethod
+ def get_feature_subsets(study, data_types):
+ """Given a study and list of data types, get the relevant feature
+ subsets
+
+ Parameters
+ ----------
+ study : flotilla.Study
+ A study object which
+
+ Returns
+ -------
+
+
+ Raises
+ ------
+
+ """
+ feature_subsets = ['custom']
+
+ # datatype_to_
+
+ if 'expression' in data_types:
+ try:
+ feature_subsets.extend(study.expression.feature_subsets.keys())
+ except AttributeError:
+ pass
+ if 'splicing' in data_types:
+ try:
+ feature_subsets.extend(study.splicing.feature_subsets.keys())
+ except AttributeError:
+ pass
+
+ # Cast to "set" to get rid of duplicates, then back to list because you
+ # can't sort a set, then back to list after sorting because you get
+ # an iterator... yeah ....
+ feature_subsets = list(natural_sort(list(set(feature_subsets))))
+
+ # Make sure "variant" is first because all datasets have that
+ try:
+ feature_subsets.pop(feature_subsets.index('variant'))
+ except ValueError:
+ pass
+ feature_subsets.insert(0, 'variant')
+ return feature_subsets
+
+ @staticmethod
+ def interactive_pca(self, data_types=('expression', 'splicing'),
+ sample_subsets=None,
+ feature_subsets=None,
+ color_samples_by=None,
+ featurewise=False,
+ x_pc=(1, 10), y_pc=(1, 10),
+ show_point_labels=False,
+ list_link='', plot_violins=False,
+ scale_by_variance=True,
+ savefile='figures/last.pca.pdf'):
+
+ def do_interact(data_type='expression',
+ sample_subset=self.default_sample_subsets,
+ feature_subset=self.default_feature_subsets,
+ featurewise=False,
+ list_link='',
+ x_pc=1, y_pc=2,
+ plot_violins=False,
+ show_point_labels=False,
+ color_samples_by=self.metadata.phenotype_col,
+ bokeh=False,
+ scale_by_variance=True,
+ most_variant_features=False,
+ std_multiplier=(0, 5.0)):
+ for k, v in locals().iteritems():
+ if k == 'self':
+ continue
+ sys.stdout.write('{} : {}\n'.format(k, v))
+
+ if feature_subset != "custom" and list_link != "":
+ raise ValueError(
+ "Set feature_subset to \"custom\" to use list_link")
+
+ if feature_subset == "custom" and list_link == "":
+ raise ValueError("Use a custom list name please")
+
+ if feature_subset == 'custom':
+ feature_subset = link_to_list(list_link)
+
+ elif feature_subset not in self.default_feature_subsets[data_type]:
+ warnings.warn("This feature_subset ('{}') is not available in "
+ "this data type ('{}'). Falling back on all "
+
+ "features.".format(feature_subset, data_type))
+
+ return self.plot_pca(sample_subset=sample_subset,
+ data_type=data_type,
+ featurewise=featurewise,
+ x_pc=x_pc, y_pc=y_pc,
+ show_point_labels=show_point_labels,
+ feature_subset=feature_subset,
+ plot_violins=plot_violins,
+ color_samples_by=color_samples_by,
+ bokeh=bokeh, std_multiplier=std_multiplier,
+ scale_by_variance=scale_by_variance,
+ most_variant_features=most_variant_features)
+
+ # self.plot_study_sample_legend()
+
+ if feature_subsets is None:
+ feature_subsets = Interactive.get_feature_subsets(self, data_types)
+
+ if sample_subsets is None:
+ sample_subsets = self.default_sample_subsets
+
+ color_samples_by = self.metadata.data.columns.tolist()
+
+ gui = interact(do_interact,
+ data_type=data_types,
+ sample_subset=sample_subsets,
+ feature_subset=feature_subsets + ['custom'],
+ featurewise=featurewise,
+ x_pc=x_pc, y_pc=y_pc,
+ show_point_labels=show_point_labels,
+ list_link=list_link, plot_violins=plot_violins,
+ color_samples_by=color_samples_by,
+ scale_by_variance=scale_by_variance)
+
+ def save(w):
+ # Make the directory if it's not already there
+ filename, extension = os.path.splitext(savefile.value)
+ self.maybe_make_directory(savefile.value)
+
+ extension = extension.lstrip('.')
+
+ gui.widget.result.fig_reduced.savefig(savefile.value,
+ format=extension)
+
+ # add "violins" after the provided filename, but before the
+ # extension
+ violins_file = '{}.{}'.format("_".join([filename, 'violins']),
+ extension)
+ try:
+ gui.widget.result.fig_violins.savefig(
+ violins_file, format=extension)
+ except AttributeError:
+ pass
+
+ savefile = TextWidget(description='savefile')
+ save_widget = ButtonWidget(description='save')
+ gui.widget.children = list(gui.widget.children) + [savefile,
+ save_widget]
+ save_widget.on_click(save)
+ return gui
+
+ @staticmethod
+ def interactive_graph(self, data_types=('expression', 'splicing'),
+ sample_subsets=None,
+ feature_subsets=None,
+ featurewise=False,
+ cov_std_cut=(0.1, 3),
+ degree_cut=(0, 10),
+ n_pcs=(2, 100),
+ draw_labels=False,
+ feature_of_interest="RBFOX2",
+ weight_fun=None,
+ use_pc_1=True, use_pc_2=True, use_pc_3=True,
+ use_pc_4=True,
+ savefile='figures/last.graph.pdf'):
+
+ from IPython.html.widgets import interact
+
+ # not sure why nested fxns are required for this, but they are... i
+ # think...
+ def do_interact(data_type='expression',
+ sample_subset=self.default_sample_subsets,
+ feature_subset=self.default_feature_subsets,
+ weight_fun=NetworkerViz.weight_funs,
+ featurewise=False,
+ use_pc_1=True, use_pc_2=True, use_pc_3=True,
+ use_pc_4=True, degree_cut=1,
+ cov_std_cut=1.8, n_pcs=5,
+ feature_of_interest="RBFOX2",
+ draw_labels=False):
+
+ for k, v in locals().iteritems():
+ if k == 'self':
+ continue
+ sys.stdout.write('{} : {}\n'.format(k, v))
+
+ if data_type == 'expression':
+ assert (feature_subset in
+ self.expression.feature_subsets.keys())
+ if data_type == 'splicing':
+ assert (feature_subset in
+ self.splicing.feature_subsets.keys())
+
+ return self.plot_graph(
+ data_type=data_type, sample_subset=sample_subset,
+ feature_subset=feature_subset, featurewise=featurewise,
+ draw_labels=draw_labels, degree_cut=degree_cut,
+ cov_std_cut=cov_std_cut, n_pcs=n_pcs,
+ feature_of_interest=feature_of_interest,
+ use_pc_1=use_pc_1, use_pc_2=use_pc_2, use_pc_3=use_pc_3,
+ use_pc_4=use_pc_4, weight_function=weight_fun)
+
+ if feature_subsets is None:
+ feature_subsets = Interactive.get_feature_subsets(self, data_types)
+
+ if sample_subsets is None:
+ sample_subsets = self.default_sample_subsets
+ if weight_fun is None:
+ weight_fun = NetworkerViz.weight_funs
+
+ gui = interact(do_interact,
+ data_type=data_types,
+ sample_subset=sample_subsets,
+ feature_subset=feature_subsets,
+ featurewise=featurewise,
+ cov_std_cut=cov_std_cut,
+ degree_cut=degree_cut,
+ n_pcs=n_pcs,
+ draw_labels=draw_labels,
+ weight_fun=weight_fun,
+ feature_of_interest=feature_of_interest,
+ use_pc_1=use_pc_1, use_pc_2=use_pc_2,
+ use_pc_3=use_pc_3, use_pc_4=use_pc_4)
+
+ def save(w):
+ # Make the directory if it's not already there
+ filename, extension = os.path.splitext(savefile.value)
+ self.maybe_make_directory(savefile.value)
+ plt.gcf().savefig(savefile, format=extension.lstrip('.'))
+
+ savefile = TextWidget(description='savefile')
+ save_widget = ButtonWidget(description='save')
+ gui.widget.children = list(gui.widget.children) + [savefile,
+ save_widget]
+ save_widget.on_click(save)
+
+ return gui
+
+ @staticmethod
+ def interactive_classifier(self, data_types=('expression', 'splicing'),
+ sample_subsets=None,
+ feature_subsets=None,
+ categorical_variables=None,
+ predictor_types=None,
+ score_coefficient=(0.1, 20),
+ draw_labels=False):
+ # Get the second one, because the first one is always "all_samples"
+ categorical_variable = self.default_sample_subsets[1]
+
+ def do_interact(data_type,
+ sample_subset,
+ feature_subset,
+ predictor_type=default_classifier,
+ categorical_variable=categorical_variable,
+ score_coefficient=2,
+ plot_violins=False,
+ show_point_labels=False):
+
+ for k, v in locals().iteritems():
+ if k == 'self':
+ continue
+ sys.stdout.write('{} : {}\n'.format(k, v))
+
+ return self.plot_classifier(
+ trait=categorical_variable,
+ feature_subset=feature_subset,
+ sample_subset=sample_subset,
+ predictor_name=predictor_type,
+ score_coefficient=score_coefficient,
+ data_type=data_type,
+ plot_violins=plot_violins,
+ show_point_labels=show_point_labels)
+
+ if feature_subsets is None:
+ feature_subsets = Interactive.get_feature_subsets(self, data_types)
+ feature_subsets.insert(0, 'variant')
+ if sample_subsets is None:
+ sample_subsets = self.default_sample_subsets
+
+ if categorical_variables is None:
+ categorical_variables = [i for i in self.default_sample_subsets if
+ not i.startswith(
+ "~") and i != 'all_samples']
+
+ if predictor_types is None:
+ predictor_types = \
+ self.predictor_config_manager.predictor_configs.keys()
+
+ # self.plot_study_sample_legend()
+
+ gui = interact(do_interact,
+ data_type=data_types,
+ sample_subset=sample_subsets,
+ feature_subset=feature_subsets,
+ categorical_variable=categorical_variables,
+ score_coefficient=score_coefficient,
+ draw_labels=draw_labels,
+ predictor_type=predictor_types)
+
+ def save(w):
+ # Make the directory if it's not already there
+ filename, extension = os.path.splitext(savefile.value)
+ extension = extension.lstrip('.')
+ self.maybe_make_directory(savefile.value)
+
+ gui.widget.result.pcaviz.fig_reduced.savefig(
+ savefile.value, format=extension)
+
+ # add "violins" after the provided filename, but before the
+ # extension
+ violins_file = '{}.{}'.format("_".join([filename, 'violins']),
+ extension)
+ try:
+ gui.widget.result.pcaviz.fig_violins.savefig(
+ violins_file, format=extension)
+ except AttributeError:
+ pass
+
+ savefile = TextWidget(description='savefile')
+ save_widget = ButtonWidget(description='save')
+ gui.widget.children = list(gui.widget.children) + [savefile,
+ save_widget]
+ save_widget.on_click(save)
+ return gui
+
+ @staticmethod
+ def interactive_localZ(self):
+
+ from IPython.html.widgets import interact
+
+ def do_interact(data_type='expression', sample1='', sample2='',
+ pCut='0.01'):
+
+ for k, v in locals().iteritems():
+ if k == 'self':
+ continue
+ sys.stdout.write('{} : {}'.format(k, v))
+
+ pCut = float(pCut)
+ assert pCut > 0
+ if data_type == 'expression':
+ data_obj = self.expression
+ if data_type == 'splicing':
+ data_obj = self.splicing
+
+ try:
+ assert sample1 in data_obj.df.index
+ except:
+ sys.stdout.write("sample: {}, is not in {} DataFrame, "
+ "try a different sample ID".format(sample1,
+ data_type))
+ return
+ try:
+ assert sample2 in data_obj.df.index
+ except:
+ sys.stdout.write("sample: {}, is not in {} DataFrame, "
+ "try a different sample ID".format(sample2,
+ data_type))
+ return
+ self.localZ_result = data_obj.plot_twoway(sample1, sample2,
+ pCut=pCut).result_
+ sys.stdout.write("local_z finished, find the result in "
+ ".localZ_result_")
+
+ return interact(do_interact,
+ data_type=('expression', 'splicing'),
+ sample1='replaceme',
+ sample2='replaceme',
+ pCut='0.01')
+
+ @staticmethod
+ def interactive_plot_modalities_lavalamps(self, sample_subsets=None,
+ feature_subsets=None,
+ color=red, x_offset=0,
+ use_these_modalities=True,
+ bootstrapped=False,
+ bootstrapped_kws=None,
+ savefile=''):
+ def do_interact(sample_subset=None, feature_subset=None,
+ color=red, x_offset=0,
+ use_these_modalities=True,
+ bootstrapped=False, bootstrapped_kws=None,
+ savefile=''):
+
+ for k, v in locals().iteritems():
+ if k == 'self':
+ continue
+ sys.stdout.write('{} : {}\n'.format(k, v))
+
+ assert (feature_subset in self.splicing.feature_subsets.keys())
+ feature_ids = self.splicing.feature_subsets[feature_subset]
+
+ from sklearn.preprocessing import LabelEncoder
+
+ le = LabelEncoder()
+ n_in_this_class = len(set(
+ le.fit_transform(self.experiment_design.data[sample_subset])))
+
+ try:
+ assert n_in_this_class
+ except:
+ raise RuntimeError("this sample designator is not binary")
+
+ # TODO: cast non-boolean binary ids to boolean
+ dtype = self.experiment_design.data[sample_subset].dtype
+ try:
+ assert dtype == 'bool'
+ except:
+ raise RuntimeError("this sample designator is not boolean")
+
+ sample_ids = self.experiment_design.data[sample_subset].index[
+ self.experiment_design.data[sample_subset]]
+
+ self.splicing.plot_modalities_lavalamps(
+ sample_ids, feature_ids, color=color, x_offset=x_offset,
+ use_these_modalities=use_these_modalities,
+ bootstrapped=bootstrapped, bootstrapped_kws=bootstrapped_kws,
+ ax=None)
+ plt.tight_layout()
+
+ if feature_subsets is None:
+ feature_subsets = Interactive.get_feature_subsets(self,
+ ['splicing'])
+
+ if sample_subsets is None:
+ sample_subsets = self.default_sample_subsets
+
+ if bootstrapped_kws is None:
+ bootstrapped_kws = {}
+
+ gui = interact(do_interact,
+ sample_subset=sample_subsets,
+ feature_subset=feature_subsets,
+ color=color, x_offset=x_offset,
+ use_these_modalities=use_these_modalities,
+ bootstrapped=bootstrapped,
+ bootstrapped_kws=bootstrapped_kws,
+ savefile=savefile)
+
+ def save(w):
+ filename, extension = os.path.splitext(savefile.value)
+ self.maybe_make_directory(savefile.value)
+ gui.widget.result.savefig(savefile.value,
+ format=extension.lstrip('.'))
+
+ savefile = TextWidget(description='savefile',
+ value='figures/modalities_lavalamps.pdf')
+ save_widget = ButtonWidget(description='save')
+ gui.widget.children = list(gui.widget.children) + [savefile,
+ save_widget]
+ save_widget.on_click(save)
+
+ @staticmethod
+ def interactive_lavalamp_pooled_inconsistent(
+ self, sample_subsets=None, feature_subsets=None,
+ difference_threshold=(0.001, 1.00),
+ colors=['red', 'green', 'blue', 'purple', 'yellow'], savefile=''):
+ from IPython.html.widgets import interact
+
+ savefile = 'figures/last.lavalamp_pooled_inconsistent.pdf'
+
+ def do_interact(sample_subset=self.default_sample_subsets,
+ feature_subset=self.default_feature_subsets,
+ difference_threshold=0.1, color=red,
+ savefile=savefile):
+
+ for k, v in locals().iteritems():
+ if k == 'self':
+ continue
+ sys.stdout.write('{} : {}\n'.format(k, v))
+
+ assert (feature_subset in self.splicing.feature_subsets.keys())
+ feature_ids = self.splicing.feature_subsets[feature_subset]
+ sample_ids = self.sample_subset_to_sample_ids(sample_subset)
+
+ color = str_to_color[color]
+
+ self.splicing.plot_lavalamp_pooled_inconsistent(
+ sample_ids, feature_ids, difference_threshold, color=color)
+ plt.tight_layout()
+
+ if feature_subsets is None:
+ feature_subsets = Interactive.get_feature_subsets(
+ self, ['splicing', 'expression'])
+ if sample_subsets is None:
+ sample_subsets = self.default_sample_subsets
+
+ gui = interact(do_interact,
+ sample_subset=sample_subsets,
+ feature_subset=feature_subsets,
+ difference_threshold=difference_threshold,
+ color=colors,
+ savefile='')
+
+ def save(w):
+ filename, extension = os.path.splitext(savefile.value)
+ self.maybe_make_directory(savefile.value)
+ gui.widget.result.savefig(savefile.value,
+ format=extension.lstrip('.'))
+
+ savefile = TextWidget(description='savefile',
+ value='figures/clustermap.pdf')
+ save_widget = ButtonWidget(description='save')
+ gui.widget.children = list(gui.widget.children) + [savefile,
+ save_widget]
+ save_widget.on_click(save)
+
+ @staticmethod
+ def interactive_choose_outliers(self,
+ data_types=('expression', 'splicing'),
+ sample_subsets=None,
+ feature_subsets=None,
+ featurewise=False,
+ x_pc=(1, 3), y_pc=(1, 3),
+ show_point_labels=False,
+ kernel=('rbf', 'linear', 'poly',
+ 'sigmoid'),
+ gamma=(0, 25),
+ nu=(0.1, 9.9)):
+
+ def do_interact(data_type='expression',
+ sample_subset=self.default_sample_subset,
+ feature_subset=self.default_feature_subset,
+ x_pc=1, y_pc=2,
+ show_point_labels=False,
+ kernel='rbf',
+ gamma=16,
+ nu=.2):
+ print "transforming input gamma by 2^-(input): %f" % gamma
+ gamma = 2 ** -float(gamma)
+ print "transforming input nu by input/10: %f" % nu
+ nu = float(nu) / 10
+ kernel = str(kernel)
+ for k, v in locals().iteritems():
+ if k == 'self':
+ continue
+ sys.stdout.write('{} : {}\n'.format(k, v))
+
+ else:
+ def renamer(x):
+ return x
+
+ if feature_subset not in self.default_feature_subsets[data_type]:
+ warnings.warn("This feature_subset ('{}') is not available in "
+ "this data type ('{}'). Falling back on all "
+ "features.".format(feature_subset, data_type))
+
+ reducer, outlier_detector = self.detect_outliers(
+ data_type=data_type,
+ sample_subset=sample_subset,
+ feature_subset=feature_subset,
+ reducer=None,
+ reducer_kwargs=None,
+ outlier_detection_method=None,
+ outlier_detection_method_kwargs={'gamma': gamma,
+ 'nu': nu,
+ 'kernel': kernel})
+
+ if data_type == "expression":
+ datamodel = self.expression
+ elif data_type == "splicing":
+ datamodel = self.splicing
+ else:
+ raise ValueError('No valid data type provided')
+ datamodel.plot_outliers(reducer, outlier_detector,
+ feature_renamer=renamer,
+ show_point_labels=show_point_labels,
+ x_pc="pc_" + str(x_pc),
+ y_pc="pc_" + str(y_pc))
+
+ print "total samples:", len(outlier_detector.outliers)
+ print "outlier samples:", outlier_detector.outliers.sum()
+ print "metadata column name:", outlier_detector.title
+
+ if feature_subsets is None:
+ feature_subsets = Interactive.get_feature_subsets(self, data_types)
+
+ if sample_subsets is None:
+ sample_subsets = self.default_sample_subsets
+
+ return interact(do_interact,
+ data_type=data_types,
+ sample_subset=sample_subsets,
+ feature_subset=feature_subsets,
+ x_pc=x_pc, y_pc=y_pc,
+ show_point_labels=show_point_labels,
+ kernel=kernel,
+ nu=nu,
+ gamma=gamma)
+
+ @staticmethod
+ def interactive_reset_outliers(self):
+ """User selects from columns that start with 'outlier_' to merge
+ multiple outlier classifications"""
+ outlier_columns = dict()
+
+ for column in self.metadata.data.columns:
+ if column.startswith("outlier_"):
+ outlier_columns[column] = False
+
+ def do_interact(**columns):
+ if len(columns.keys()) == 0:
+ print "You have not specified any 'outlier_' columns in " \
+ "study.metadata.data... \n" \
+ "This function will do nothing to your data."
+ else:
+ self.metadata.set_outliers_by_merging_columns(
+ [k for (k, v) in columns.items() if v])
+
+ interact(do_interact, **outlier_columns)
+
+ @staticmethod
+ def interactive_clustermap(self):
+ def do_interact(data_type='expression',
+ sample_subset=self.default_sample_subsets,
+ feature_subset=self.default_feature_subset,
+ metric='euclidean',
+ method='median',
+ list_link='',
+ scale_fig_by_data=True,
+ fig_width='', fig_height=''):
+
+ for k, v in locals().iteritems():
+ if k == 'self':
+ continue
+ sys.stdout.write('{} : {}\n'.format(k, v))
+
+ if feature_subset != "custom" and list_link != "":
+ raise ValueError(
+ "set feature_subset to \"custom\" to use list_link")
+
+ if feature_subset == "custom" and list_link == "":
+ raise ValueError("use a custom list name please")
+
+ if feature_subset == 'custom':
+ feature_subset = link_to_list(list_link)
+ elif feature_subset not in self.default_feature_subsets[data_type]:
+ warnings.warn("This feature_subset ('{}') is not available in "
+ "this data type ('{}'). Falling back on all "
+ "features.".format(feature_subset, data_type))
+ return self.plot_clustermap(
+ sample_subset=sample_subset, feature_subset=feature_subset,
+ data_type=data_type, metric=metric, method=method,
+ scale_fig_by_data=scale_fig_by_data)
+
+ feature_subsets = Interactive.get_feature_subsets(self,
+ ['expression',
+ 'splicing'])
+ method = ('average', 'weighted', 'single', 'complete', 'ward')
+ metric = ('euclidean', 'seuclidean', 'sqeuclidean', 'chebyshev',
+ 'cosine', 'cityblock', 'mahalonobis', 'minowski', 'jaccard')
+ gui = interact(do_interact,
+ data_type=('expression', 'splicing'),
+ sample_subset=self.default_sample_subsets,
+ feature_subset=feature_subsets,
+ metric=metric,
+ method=method)
+
+ def save(w):
+ filename, extension = os.path.splitext(savefile.value)
+ self.maybe_make_directory(savefile.value)
+ gui.widget.result.savefig(savefile.value,
+ format=extension.lstrip('.'))
+
+ savefile = TextWidget(description='savefile',
+ value='figures/clustermap.pdf')
+ save_widget = ButtonWidget(description='save')
+ gui.widget.children = list(gui.widget.children) + [savefile,
+ save_widget]
+ save_widget.on_click(save)
+ return gui
+
+ @staticmethod
+ def interactive_correlations(self):
+ def do_interact(data_type='expression',
+ sample_subset=self.default_sample_subsets,
+ feature_subset=self.default_feature_subset,
+ metric='euclidean', method='average',
+ list_link='',
+ scale_fig_by_data=True,
+ fig_width='', fig_height='', featurewise=False):
+
+ for k, v in locals().iteritems():
+ if k == 'self':
+ continue
+ sys.stdout.write('{} : {}\n'.format(k, v))
+
+ if feature_subset != "custom" and list_link != "":
+ raise ValueError(
+ "set feature_subset to \"custom\" to use list_link")
+
+ if feature_subset == "custom" and list_link == "":
+ raise ValueError("use a custom list name please")
+
+ if feature_subset == 'custom':
+ feature_subset = link_to_list(list_link)
+ elif feature_subset not in self.default_feature_subsets[data_type]:
+ warnings.warn("This feature_subset ('{}') is not available in "
+ "this data type ('{}'). Falling back on all "
+ "features.".format(feature_subset, data_type))
+ return self.plot_correlations(
+ sample_subset=sample_subset, feature_subset=feature_subset,
+ data_type=data_type, scale_fig_by_data=scale_fig_by_data,
+ method=method, metric=metric, featurewise=featurewise)
+
+ feature_subsets = Interactive.get_feature_subsets(self,
+ ['expression',
+ 'splicing'])
+ method = ('average', 'weighted', 'single', 'complete', 'ward')
+ metric = ('euclidean', 'seuclidean', 'sqeuclidean', 'chebyshev',
+ 'cosine', 'cityblock', 'mahalonobis', 'minowski', 'jaccard')
+ gui = interact(do_interact,
+ data_type=('expression', 'splicing'),
+ sample_subset=self.default_sample_subsets,
+ feature_subset=feature_subsets,
+ metric=metric,
+ method=method,
+ featurewise=False)
+
+ def save(w):
+ filename, extension = os.path.splitext(savefile.value)
+ self.maybe_make_directory(savefile.value)
+ gui.widget.result.savefig(savefile.value,
+ format=extension.lstrip('.'))
+
+ savefile = TextWidget(description='savefile',
+ value='figures/correlations.pdf')
+ save_widget = ButtonWidget(description='save')
+ gui.widget.children = list(gui.widget.children) + [savefile,
+ save_widget]
+ save_widget.on_click(save)
+ return gui
diff --git a/flotilla/visualize/network.py b/flotilla/visualize/network.py
new file mode 100644
index 00000000..48724935
--- /dev/null
+++ b/flotilla/visualize/network.py
@@ -0,0 +1,370 @@
+"""
+Visualize results from :py:mod:flotilla.compute.network
+"""
+
+import sys
+
+import networkx as nx
+import numpy as np
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+from ..compute.network import Networker
+from ..util import dict_to_str
+from .color import dark2, almost_black, green, red
+
+
+class NetworkerViz(Networker):
+ # TODO: needs to be decontaminated, as it requires methods from
+ # data_object;
+ # maybe this class should move to data_model.BaseData
+ def __init__(self, DataModel):
+ self.DataModel = DataModel
+ Networker.__init__(self)
+
+ def draw_graph(self,
+ n_pcs=5,
+ use_pc_1=True, use_pc_2=True, use_pc_3=True, use_pc_4=True,
+ degree_cut=2, cov_std_cut=1.8,
+ weight_function='no_weight',
+ featurewise=False, # else feature_components
+ rpkms_not_events=False, # else event features
+ feature_of_interest='RBFOX2', draw_labels=True,
+ reduction_name=None,
+ feature_ids=None,
+ sample_ids=None,
+ graph_file='',
+ compare="",
+ sample_id_to_color=None,
+ label_to_color=None,
+ label_to_marker=None, groupby=None,
+ data_type=None):
+
+ """Draw the graph of similarities between samples or features
+
+ Parameters
+ ----------
+ feature_ids : list of str, or None
+ Feature ids to subset the data. If None, all features will be used.
+ sample_ids : list of str, or None
+ Sample ids to subset the data. If None, all features will be used.
+ x_pc : str, optional
+ Which component to use for the x-axis, default "pc_1"
+ y_pc :
+ y component for PCA, default "pc_2"
+ n_pcs : int
+ Number of components to use for cells' covariance calculation
+ cov_std_cut : float
+ Covariance cutoff for edges
+ use_pc{1-4} : bool
+ Use these pcs in cov calculation (default True)
+ degree_cut : int
+ miniumum degree for a node to be included in graph display
+ weight_function : ['arctan' | 'sq' | 'abs' | 'arctan_sq']
+ weight function (arctan (arctan cov), sq (sq cov), abs (abs cov),
+ arctan_sq (sqared arctan of cov))
+ gene_of_interest : str
+ map a gradient representing this gene's data onto nodes (ENSEMBL
+ id or gene symbol)
+
+ Returns
+ -------
+ graph : networkx.Graph
+
+ positions : (x,y) positions of nodes
+ """
+ node_color_mapper = self._default_node_color_mapper
+ node_size_mapper = self._default_node_color_mapper
+ settings = locals().copy()
+ # not pertinent to the graph, these are what we want to be able to
+ # re-apply to the same graph if it exists
+ pca_settings = dict()
+ pca_settings['sample_ids'] = sample_ids
+ pca_settings['featurewise'] = featurewise
+ pca_settings['feature_ids'] = feature_ids
+ # pca_settings['obj_id'] = reduction_name
+
+ adjacency_settings = dict((k, settings[k]) for k in
+ ['use_pc_1', 'use_pc_2', 'use_pc_3',
+ 'use_pc_4', 'n_pcs', ])
+
+ plt.figure(figsize=(10, 10))
+
+ plt.axis((-0.2, 1.2, -0.2, 1.2))
+ main_ax = plt.gca()
+ ax_pev = plt.axes([0.1, .8, .2, .15])
+ ax_cov = plt.axes([0.1, 0.1, .2, .15])
+ ax_degree = plt.axes([0.9, .8, .2, .15])
+
+ pca = self.DataModel.reduce(
+ # label_to_color=label_to_color,
+ # label_to_marker=label_to_marker,
+ # groupby=groupby,
+ **pca_settings)
+
+ try:
+ feature_id = self.DataModel.maybe_renamed_to_feature_id(
+ feature_of_interest)[0]
+ except (ValueError, KeyError, IndexError):
+ feature_id = ''
+
+ if featurewise:
+ def node_color_mapper(x):
+ if (x == feature_id):
+ return green
+ else:
+ return almost_black
+
+ def node_size_mapper(x):
+ return (pca.means.ix[x] ** 2) + 10
+
+ else:
+ if sample_id_to_color is not None:
+ def node_color_mapper(x):
+ return sample_id_to_color[x]
+
+ else:
+ def node_color_mapper(x):
+ return dark2[0]
+
+ def node_size_mapper(x):
+ return 95
+
+ ax_pev.plot(pca.explained_variance_ratio_ * 100.)
+ ax_pev.axvline(n_pcs, label='cutoff', color=green)
+ ax_pev.legend()
+ ax_pev.set_ylabel("% explained variance")
+ ax_pev.set_xlabel("component")
+ ax_pev.set_title("Explained variance from dim reduction")
+ sns.despine(ax=ax_pev)
+
+ adjacency = self.adjacency(pca.reduced_space, **adjacency_settings)
+ cov_dist = np.array(
+ [i for i in adjacency.values.ravel() if np.abs(i) > 0])
+ cov_cut = np.mean(cov_dist) + cov_std_cut * np.std(cov_dist)
+
+ graph_settings = dict(
+ (k, settings[k]) for k in ['weight_function', 'degree_cut', ])
+ graph_settings['cov_cut'] = cov_cut
+ this_graph_name = "_".join(map(dict_to_str,
+ [pca_settings, adjacency_settings,
+ graph_settings]))
+ graph_settings['name'] = this_graph_name
+
+ sns.kdeplot(cov_dist, ax=ax_cov)
+ xmin, xmax = ax_cov.get_xlim()
+ ax_cov.set_xlim(0, xmax)
+ ax_cov.axvline(cov_cut, label='cutoff', color=green)
+ ax_cov.set_title("Covariance in dim reduction space")
+ ax_cov.set_ylabel("Density")
+ ax_cov.legend()
+ sns.despine(ax=ax_cov)
+
+ graph, pos = self.graph(adjacency, **graph_settings)
+
+ nx.draw_networkx_nodes(
+ graph, pos,
+ node_color=map(node_color_mapper, graph.nodes()),
+ node_size=map(node_size_mapper, graph.nodes()),
+ ax=main_ax, alpha=0.5)
+
+ try:
+ node_color = map(lambda x: pca.X[feature_id].ix[x], graph.nodes())
+
+ nx.draw_networkx_nodes(graph, pos, node_color=node_color,
+ cmap=mpl.cm.Greys,
+ node_size=map(
+ lambda x: node_size_mapper(x) * .5,
+ graph.nodes()), ax=main_ax, alpha=1)
+ except (KeyError, ValueError):
+ pass
+
+ if featurewise:
+ namer = self.DataModel.feature_renamer
+ else:
+ def namer(x):
+ return x
+
+ labels = dict([(name, namer(name)) for name in graph.nodes()])
+ if draw_labels:
+ nx.draw_networkx_labels(graph, pos, labels=labels, ax=main_ax)
+ nx.draw_networkx_edges(graph, pos, ax=main_ax, alpha=0.1)
+ main_ax.set_axis_off()
+ degree = nx.degree(graph)
+ sns.kdeplot(np.array(degree.values()), ax=ax_degree)
+ xmin, xmax = ax_degree.get_xlim()
+ ax_degree.set_xlim(0, xmax)
+ ax_degree.set_xlabel("degree")
+ ax_degree.set_ylabel("density")
+ try:
+ ax_degree.axvline(x=degree[feature_id],
+ label=feature_of_interest,
+ color=green)
+ ax_degree.legend()
+
+ except Exception as e:
+ sys.stdout.write(str(e))
+ pass
+
+ sns.despine(ax=ax_degree)
+ if graph_file != '':
+ try:
+ nx.write_gml(graph, graph_file)
+ except Exception as e:
+ sys.stdout.write("error writing graph file:"
+ "\n{}".format(str(e)))
+
+ return graph, pos
+
+ def draw_nonreduced_graph(self,
+ degree_cut=2, cov_std_cut=1.8,
+ wt_fun='abs',
+ featurewise=False, # else feature_components
+ rpkms_not_events=False, # else event features
+ feature_of_interest='RBFOX2', draw_labels=True,
+ feature_ids=None,
+ group_id=None,
+ graph_file='',
+ compare=""):
+
+ """
+ Parameters
+ ----------
+ feature_ids : list of str, or None
+ Feature ids to subset the data. If None, all features will be used.
+ sample_ids : list of str, or None
+ Sample ids to subset the data. If None, all features will be used.
+ x_pc : str
+ x component for DataFramePCA, default "pc_1"
+ y_pc :
+ y component for DataFramePCA, default "pc_2"
+ n_pcs : int???
+ n components to use for cells' covariance calculation
+ cov_std_cut : float??
+ covariance cutoff for edges
+ use_pc{1-4} use these pcs in cov calculation (default True)
+ degree_cut : int??
+ miniumum degree for a node to be included in graph display
+ weight_function : ['arctan' | 'sq' | 'abs' | 'arctan_sq']
+ weight function (arctan (arctan cov), sq (sq cov), abs (abs cov),
+ arctan_sq (sqared arctan of cov))
+ gene_of_interest : str
+ map a gradient representing this gene's data onto nodes (ENSEMBL
+ id or gene name???)
+
+
+ Returns
+ -------
+ #TODO: Mike please fill these in
+ graph : networkx.Graph
+ ???
+ positions : ???
+ ???
+ """
+ node_color_mapper = self._default_node_color_mapper
+ node_size_mapper = self._default_node_color_mapper
+ settings = locals().copy()
+
+ adjacency_settings = dict(('non_reduced', True))
+
+ plt.figure(figsize=(10, 10))
+ plt.axis((-0.2, 1.2, -0.2, 1.2))
+ main_ax = plt.gca()
+ ax_cov = plt.axes([0.1, 0.1, .2, .15])
+ ax_degree = plt.axes([0.9, .8, .2, .15])
+
+ data = self.DataModel.df
+
+ try:
+ feature_id = self.DataModel.maybe_renamed_to_feature_id(
+ feature_of_interest)[0]
+ except (ValueError, KeyError):
+ feature_id = ''
+
+ if featurewise:
+ def node_color_mapper(x):
+ if x == feature_id:
+ return red
+ else:
+ return 'k'
+
+ def node_size_mapper(x):
+ return (data.mean().ix[x] ** 2) + 10
+
+ else:
+ def node_color_mapper(x):
+ return self.DataModel.sample_metadata.color[x]
+
+ def node_size_mapper(x):
+ return 75
+
+ adjacency_name = "_".join([dict_to_str(adjacency_settings)])
+ adjacency = self.adjacency(data, name=adjacency_name,
+ **adjacency_settings)
+ cov_dist = np.array(
+ [i for i in adjacency.values.ravel() if np.abs(i) > 0])
+ cov_cut = np.mean(cov_dist) + cov_std_cut * np.std(cov_dist)
+
+ graph_settings = dict(
+ (k, settings[k]) for k in ['wt_fun', 'degree_cut', ])
+ graph_settings['cov_cut'] = cov_cut
+ this_graph_name = "_".join(
+ map(dict_to_str, [adjacency_settings, graph_settings]))
+ graph_settings['name'] = this_graph_name
+
+ sns.kdeplot(cov_dist, ax=ax_cov)
+ ax_cov.axvline(cov_cut, label='cutoff')
+ ax_cov.set_title("covariance in original space")
+ ax_cov.set_ylabel("density")
+ ax_cov.legend()
+ sns.despine(ax=ax_cov)
+ graph, positions = self.graph(adjacency, **graph_settings)
+
+ nx.draw_networkx_nodes(graph, positions,
+ node_color=map(node_color_mapper,
+ graph.nodes()),
+ node_size=map(node_size_mapper, graph.nodes()),
+ ax=main_ax, alpha=0.5)
+ try:
+ node_color = map(lambda x: data[feature_id].ix[x],
+ graph.nodes())
+ nx.draw_networkx_nodes(graph, positions, node_color=node_color,
+ cmap=plt.cm.Greys,
+ node_size=map(
+ lambda x: node_size_mapper(x) * .5,
+ graph.nodes()), ax=main_ax, alpha=1)
+ except (KeyError, ValueError):
+ pass
+
+ def renamer(x):
+ return x
+
+ labels = dict([(name, renamer(name)) for name in graph.nodes()])
+ if draw_labels:
+ nx.draw_networkx_labels(graph, positions, labels=labels,
+ ax=main_ax)
+ nx.draw_networkx_edges(graph, positions, ax=main_ax, alpha=0.1)
+ main_ax.set_axis_off()
+ degree = nx.degree(graph)
+ sns.kdeplot(np.array(degree.values()), ax=ax_degree)
+ ax_degree.set_xlabel("degree")
+ ax_degree.set_ylabel("density")
+ try:
+ ax_degree.axvline(x=degree[feature_of_interest],
+ label=feature_of_interest)
+ ax_degree.legend()
+
+ except Exception as e:
+ sys.stdout.write(str(e))
+ pass
+
+ sns.despine(ax=ax_degree)
+ if graph_file != '':
+ try:
+ nx.write_gml(graph, graph_file)
+ except Exception as e:
+ sys.stdout.write("error writing graph file:"
+ "\n{}".format(str(e)))
+
+ return (graph, positions)
diff --git a/flotilla/visualize/predict.py b/flotilla/visualize/predict.py
new file mode 100644
index 00000000..fbedb6bf
--- /dev/null
+++ b/flotilla/visualize/predict.py
@@ -0,0 +1,120 @@
+"""
+Visualize the result of a classifcation or regression algorithm on the data.
+"""
+from matplotlib.gridspec import GridSpec, GridSpecFromSubplotSpec
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+from ..compute.predict import Classifier, Regressor, PredictorBase
+from .color import green
+from .decomposition import DecompositionViz
+
+
+class PredictorBaseViz(PredictorBase):
+ _reducer_plotting_args = {}
+
+ def plot(self, **pca_plotting_kwargs):
+ if not self.has_been_fit:
+ self.fit()
+
+ gs_x = 18
+ gs_y = 12
+
+ ax = None if 'ax' not in pca_plotting_kwargs \
+ else pca_plotting_kwargs['ax']
+
+ if ax is None:
+ fig, ax = plt.subplots(1, 1, figsize=(18, 8))
+ gs = GridSpec(gs_x, gs_y)
+ else:
+ gs = GridSpecFromSubplotSpec(gs_x, gs_y, ax.get_subplotspec())
+
+ ax_scores = plt.subplot(gs[5:10, :2])
+ ax_scores.set_xlabel("Feature Importance")
+ ax_scores.set_ylabel("Density Estimate")
+
+ if 'show_vectors' not in pca_plotting_kwargs:
+ pca_plotting_kwargs['show_vectors'] = True
+
+ ax_pca = plt.subplot(gs[:, 2:])
+ pca_plotting_kwargs['ax'] = ax_pca
+
+ self.plot_scores(ax=ax_scores)
+ pcaviz = self.do_pca(**pca_plotting_kwargs)
+ plt.tight_layout()
+
+ return pcaviz
+
+ def set_reducer_plotting_args(self, rpa):
+ self._reducer_plotting_args.update(rpa)
+
+ def do_pca(self, **plotting_kwargs):
+ # assert trait in self.traits
+ assert self.has_been_fit
+ assert self.has_been_scored
+
+ ax = plotting_kwargs.pop('ax', plt.gca())
+ local_plotting_kwargs = self._reducer_plotting_args
+ local_plotting_kwargs.update(plotting_kwargs)
+ pca = self.pca()
+
+ if self.categorical:
+ groupby = self.dataset.trait.align(self.y, join='right')[0]
+ else:
+ groupby = self.y
+
+ self.pcaviz = DecompositionViz(
+ pca.reduced_space, pca.components_,
+ pca.explained_variance_ratio_,
+ feature_renamer=self.feature_renamer,
+ groupby=groupby, singles=self.singles, pooled=self.pooled,
+ outliers=self.outliers)
+ self.pcaviz.plot(ax=ax, **local_plotting_kwargs)
+
+ def plot_scores(self, ax=None):
+
+ """
+ plot kernel density of predictor scores and draw a vertical line where
+ the cutoff was selected
+ ax - ax to plot on. if None: plt.gca()
+ """
+
+ if ax is None:
+ ax = plt.gca()
+
+ # for trait in traits:
+ sns.kdeplot(self.scores_, shade=True, ax=ax,
+ label="%s\n%d features\noob:%.2f"
+ % (self.dataset.trait_name, self.n_good_features_,
+ self.oob_score_))
+ ax.axvline(x=self.score_cutoff_, color=green)
+
+ for lab in ax.get_xticklabels():
+ lab.set_rotation(90)
+ sns.despine(ax=ax)
+
+
+class RegressorViz(Regressor, PredictorBaseViz):
+ pass
+
+
+class ClassifierViz(Classifier, PredictorBaseViz):
+ """
+ Visualize results from classification
+ """
+
+ def check_a_feature(self, feature_name, **violinplot_kwargs):
+ """Make Violin Plots for a gene/probe's value in the sets defined in
+ sets
+
+ feature_name - gene/probe id. must be in the index of self._parent.X
+ sets - list of sample ids
+ violinplot_kwargs - extra parameters for violinplot
+
+ returns a list of lists with values for feature_name in each set of
+ sets
+ """
+ sns.violinplot(self.X[feature_name], linewidth=0, groupby=self.y,
+ alpha=0.5, bw='silverman', inner='points',
+ names=None, **violinplot_kwargs)
+ sns.despine()
diff --git a/flotilla/visualize/splicing.py b/flotilla/visualize/splicing.py
new file mode 100644
index 00000000..38598735
--- /dev/null
+++ b/flotilla/visualize/splicing.py
@@ -0,0 +1,316 @@
+"""
+Splicing-specific visualization classes and methods
+"""
+
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+import numpy as np
+import seaborn as sns
+
+# from .color import red, blue, purple, grey, green
+from ..compute.splicing import get_switchy_score_order
+from ..util import as_numpy
+
+seaborn_colors = map(mpl.colors.rgb2hex, sns.color_palette('deep'))
+
+
+class _ModalityEstimatorPlotter(object):
+ def __init__(self):
+ self.fig = plt.figure(figsize=(5 * 2, 3 * 2))
+ self.ax_violin = plt.subplot2grid((3, 5), (0, 0), rowspan=3, colspan=1)
+ self.ax_loglik = plt.subplot2grid((3, 5), (0, 1), rowspan=3, colspan=3)
+ self.ax_bayesfactor = plt.subplot2grid((3, 5), (0, 4), rowspan=3,
+ colspan=1)
+
+ def plot(self, event, logliks, logsumexps, modality_colors,
+ renamed=''):
+ modality = logsumexps.idxmax()
+
+ sns.violinplot(event.dropna(), bw=0.2, ax=self.ax_violin,
+ color=modality_colors[modality])
+
+ self.ax_violin.set_ylim(0, 1)
+ self.ax_violin.set_title('Guess: {}'.format(modality))
+ self.ax_violin.set_xticks([])
+ self.ax_violin.set_yticks([0, 0.5, 1])
+ # self.ax_violin.set_xlabel(renamed)
+
+ for name, loglik in logliks.iteritems():
+ # print name,
+ self.ax_loglik.plot(loglik, 'o-', label=name,
+ color=modality_colors[name])
+ self.ax_loglik.legend(loc='best')
+ self.ax_loglik.set_title('Log likelihoods at different '
+ 'parameterizations')
+ self.ax_loglik.grid()
+ self.ax_loglik.set_xlabel('phantom', color='white')
+
+ for i, (name, height) in enumerate(logsumexps.iteritems()):
+ self.ax_bayesfactor.bar(i, height, label=name,
+ color=modality_colors[name])
+ self.ax_bayesfactor.set_title('$\log$ Bayes factors')
+ self.ax_bayesfactor.set_xticks([])
+ self.ax_bayesfactor.grid()
+ self.fig.tight_layout()
+ self.fig.text(0.5, .025, '{} ({})'.format(event.name, renamed),
+ fontsize=10, ha='center', va='bottom')
+ sns.despine()
+ return self
+
+
+class ModalitiesViz(object):
+ """Visualize results of modality assignments"""
+ modality_colors = {'bimodal': seaborn_colors[3],
+ 'excluded': seaborn_colors[0],
+ 'included': seaborn_colors[2],
+ 'middle': seaborn_colors[1],
+ # 'ambiguous': 'lightgrey',
+ 'uniform': seaborn_colors[4]}
+
+ modality_order = ['excluded', 'uniform', 'bimodal', 'middle',
+ 'included'] # , 'ambiguous']
+
+ colors = [modality_colors[modality] for modality in
+ modality_order]
+
+ def plot_reduced_space(self, binned_reduced, modality_assignments,
+ ax=None, title=None, xlabel='', ylabel=''):
+ if ax is None:
+ fig, ax = plt.subplots(figsize=(8, 8))
+
+ # For easy aliasing
+ X = binned_reduced
+
+ for modality, df in X.groupby(modality_assignments, axis=0):
+ color = self.modality_colors[modality]
+ ax.plot(df.ix[:, 0], df.ix[:, 1], 'o', color=color, alpha=0.7,
+ label=modality)
+
+ sns.despine()
+ xmax, ymax = X.max()
+ ax.set_xlim(0, 1.05 * xmax)
+ ax.set_ylim(0, 1.05 * ymax)
+ ax.set_xticks([])
+ ax.set_yticks([])
+ ax.set_xlabel(xlabel)
+ ax.set_ylabel(ylabel)
+ ax.legend()
+ if title is not None:
+ ax.set_title(title)
+
+ def bar(self, counts, phenotype_to_color=None, ax=None, percentages=True):
+ """Draw barplots grouped by modality of modality percentage per group
+
+ Parameters
+ ----------
+
+
+ Returns
+ -------
+
+
+ Raises
+ ------
+
+ """
+ if percentages:
+ counts = 100 * (counts.T / counts.T.sum()).T
+
+ # with sns.set(style='whitegrid'):
+ if ax is None:
+ ax = plt.gca()
+
+ full_width = 0.8
+ width = full_width / counts.shape[0]
+ for i, (group, series) in enumerate(counts.iterrows()):
+ left = np.arange(len(self.modality_order)) + i * width
+ height = [series[i] if i in series else 0
+ for i in self.modality_order]
+ color = phenotype_to_color[group]
+ ax.bar(left, height, width=width, color=color, label=group,
+ linewidth=.5, edgecolor='k')
+ ylabel = 'Percentage of events' if percentages else 'Number of events'
+ ax.set_ylabel(ylabel)
+ ax.set_xticks(np.arange(len(self.modality_order)) + full_width / 2)
+ ax.set_xticklabels(self.modality_order)
+ ax.set_xlabel('Splicing modality')
+ ax.set_xlim(0, len(self.modality_order))
+ ax.legend(loc='best')
+ ax.grid(axis='y', linestyle='-', linewidth=0.5)
+ sns.despine()
+
+ def event_estimation(self, event, logliks, logsumexps, renamed=''):
+ """Show the values underlying bayesian modality estimations of an event
+
+ Parameters
+ ----------
+
+
+ Returns
+ -------
+
+
+ Raises
+ ------
+ """
+ plotter = _ModalityEstimatorPlotter()
+ plotter.plot(event, logliks, logsumexps, self.modality_colors,
+ renamed=renamed)
+ return plotter
+
+
+def lavalamp(psi, color=None, x_offset=0, title='', ax=None,
+ switchy_score_psi=None, marker='o', plot_kws=None,
+ yticks=None):
+ """Make a 'lavalamp' scatter plot of many splicing events
+
+ Useful for visualizing many splicing events at once.
+
+ Parameters
+ ----------
+ psi : array
+ A (n_events, n_samples) matrix either as a numpy array or as a pandas
+ DataFrame
+ color : matplotlib color
+ Color of the scatterplot. Defaults to a dark teal
+ x_offset : numeric or None
+ How much to offset the x-values off of 1. Useful for plotting several
+ celltypes at once.
+ title : str
+ Title of the plot. Default ''
+ ax : matplotlib.Axes object
+ The axes to plot on. If not provided, will be created
+ switchy_score_psi : pandas.DataFrame
+ The psi scores to sort on for the plotting order. By default use the
+ psi provided, but sometimes you want to plot multiple psi scores on
+ the same plot, with the same events.
+ marker : str
+ A valid matplotlib marker. Default is 'd' (thin diamond)
+ plot_kws : dict
+ Keyword arguments to supply to plot()
+
+ Returns
+ -------
+ fig : matplotlib.Figure
+ A figure object for saving.
+ """
+ if psi.shape[1] == 0:
+ return
+
+ if ax is None:
+ fig, ax = plt.subplots(figsize=(16, 4))
+
+ color = seaborn_colors[0] if color is None else color
+ plot_kws = {} if plot_kws is None else plot_kws
+ plot_kws.setdefault('color', color)
+ plot_kws.setdefault('alpha', 0.2)
+ plot_kws.setdefault('markersize', 10)
+ plot_kws.setdefault('marker', marker)
+ plot_kws.setdefault('linestyle', 'None')
+ plot_kws.setdefault('markeredgecolor', '#262626')
+ plot_kws.setdefault('markeredgewidth', .1)
+
+ y = as_numpy(psi.dropna(how='all', axis=1))
+
+ if switchy_score_psi is not None:
+ switchy_score_y = as_numpy(switchy_score_psi)
+ else:
+ switchy_score_y = y
+
+ order = get_switchy_score_order(switchy_score_y)
+ y = y[:, order]
+
+ n_samples, n_events = y.shape
+ # .astype(float) is to get rid of a deprecation warning
+ x = np.vstack((np.arange(n_events) for _ in xrange(n_samples)))
+ x = x.astype(float)
+ x += x_offset
+
+ # Add one so the last value is actually included instead of cut off
+ xmax = x.max() + 1
+
+ ax.plot(x, y, **plot_kws)
+ sns.despine()
+ ax.set_ylabel('$\Psi$')
+ ax.set_xlabel('{} splicing events'.format(n_events))
+ ax.set_xticks([])
+
+ ax.set_xlim(-0.5, xmax + .5)
+ ax.set_ylim(0, 1)
+ if yticks is None:
+ ax.set_yticks([0, 0.5, 1])
+ else:
+ ax.set_yticks(yticks)
+ ax.set_title(title)
+
+
+def hist_single_vs_pooled_diff(diff_from_singles, diff_from_singles_scaled,
+ color=None, title='', nbins=50, hist_kws=None):
+ """Plot a histogram of both the original difference difference of psi
+ scores from the pooled to the singles, and the scaled difference
+
+ """
+ hist_kws = {} if hist_kws is None else hist_kws
+
+ fig, axes = plt.subplots(ncols=2, figsize=(8, 4))
+ dfs = (diff_from_singles, diff_from_singles_scaled)
+ names = ('total_diff', 'scaled_diff')
+
+ for ax, df, name in zip(axes, dfs, names):
+ vmin = df.min().min()
+ vmax = df.max().max()
+ ax.hist(df.values.flat, bins=np.linspace(vmin, vmax, nbins),
+ color=color, edgecolor='white', linewidth=0.5, **hist_kws)
+ ax.set_title(title)
+ # ax.set_title('{}, {}'.format(celltype, name))
+ ax.grid(which='y', color='white')
+ sns.despine()
+
+
+def lavalamp_pooled_inconsistent(singles, pooled, pooled_inconsistent,
+ color=None, percent=None):
+ fig, axes = plt.subplots(nrows=2, figsize=(16, 8))
+ ax_inconsistent = axes[0]
+ ax_consistent = axes[1]
+ plot_order = \
+ pooled_inconsistent.sum() / pooled_inconsistent.count().astype(float)
+ plot_order.sort()
+
+ color = seaborn_colors[0] if color is None else color
+ pooled_plot_kws = {'alpha': 0.5, 'markeredgecolor': 'k',
+ 'markerfacecolor': 'none', 'markeredgewidth': 1}
+
+ pooled = pooled.dropna(axis=1, how='all')
+
+ suffix = ' of events measured in both pooled and single'
+
+ ax_inconsistent.set_xticks([])
+ ax_consistent.set_xticks([])
+
+ try:
+ singles_values = singles.ix[:, pooled_inconsistent.columns].values
+ lavalamp(singles_values, color=color, ax=ax_inconsistent)
+ lavalamp(pooled.ix[:, pooled_inconsistent.columns], marker='o',
+ color='k',
+ switchy_score_psi=singles_values,
+ ax=ax_inconsistent, plot_kws=pooled_plot_kws)
+ title_suffix = '' if percent is None else ' ({:.1f}%){}'.format(
+ percent, suffix)
+ ax_inconsistent.set_title('Pooled splicing events inconsistent '
+ 'with singles{}'.format(title_suffix))
+ except IndexError:
+ # There are no inconsistent events
+ pass
+
+ singles = singles.dropna(axis=1, how='all')
+ consistent_events = singles.columns[
+ ~singles.columns.isin(pooled_inconsistent.columns)]
+ lavalamp(singles.ix[:, consistent_events], color=color, ax=ax_consistent)
+ lavalamp(pooled.ix[:, consistent_events], color='k', marker='o',
+ switchy_score_psi=singles.ix[:, consistent_events],
+ ax=ax_consistent, plot_kws=pooled_plot_kws)
+ title_suffix = '' if percent is None else ' ({:.1f}%){}'.format(
+ 100 - percent, suffix)
+ ax_consistent.set_title('Pooled splicing events consistent with singles{}'
+ .format(title_suffix))
+ sns.despine()
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-
-
-
-
-
-intro to flotilla
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- ![](\"img/schooner.png\")
\n",
- "\n",
- "\"exploratory data viz\" - submarine\n",
- "![](\"img/submarine.png\")
\n",
- "\n",
- "\"things for computation\" - frigate\n",
- "![](\"img/frigate.png\")
\n",
- "\n",
- "\"draw data from external sources\" - skiff\n",
- "![](\"img/skiff.png\")
\n",
- "\n",
- "\"slow-loading things\" - cargo\n",
- "![](\"img/cargo.png\")
\n",
- "\n",
- "\"database things\" - carrier\n",
- "![](\"img/carrier.png\")
\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "projects are stored in separate git repos, access them by import.\n",
- "\n",
- "
\n", - "schooner.Study takes care of organizing data from multiple sources. It inherits subclasses that have tools to interact with data via IPython widgets, which is really nice.
\n", - "schooner.ExpressionData and schooner.SplicingData do classifcation/regression and dimensionality reduction.
\n", - "All data types should be available for user interaction (and are expected to be used) via the\n", - "schooner.Study object.\n", - "
\n", - "
\n", - "##submarine is a visualization module that entirely depends on the other modules,this is mostly subclasses of the modules in frigate
\n", - "##frigate is the computational workhorse, it holds the objects that do the maths." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from flotilla import schooner, submarine, frigate \n", - "\n", - "sc_study = schooner.Study(neural_diff_project, load_cargo=True, drop_outliers=True) " - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "getting flotilla data from /Users/lovci/Projects/flotilla/flotilla/src/_cargo_commonObjects/cargo_data\n", - "dropping " - ] - }, - { - "output_type": "stream", - "stream": "stderr", - "text": [ - "importing GO...done.\n" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " set(['M_M2_07', 'M_M1_03', 'M_M2_05', 'M_M2_02', 'M_M2_01', 'P_P2_06', 'M_M2nd_21', 'S_MSA_21', 'S_MSA_05', 'S_MSA_28', 'M_M2_06', 'M_M3_11', 'M_M6_2', 'M_M6_1', 'M_M2nd_13', 'S_MSA_23', 'M_M3_3', 'M_M1_04', 'M_M3_1', 'M_M4_1', 'M_M4_2', 'M_M4_13', 'M_M4_10'])\n", - "dropping " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " set(['M_M2_07', 'M_M1_03', 'M_M2_05', 'M_M2_02', 'M_M2_01', 'P_P2_06', 'M_M2nd_21', 'M_M2nd_23', 'S_MSA_21', 'S_MSA_05', 'S_MSA_28', 'S_MSA_29', 'M_M2_06', 'M_M3_11', 'M_M6_2', 'M_M6_1', 'M_M2nd_13', 'S_MSA_19', 'S_MSA_23', 'M_M3_3', 'M_M1_04', 'M_M3_1', 'M_M4_2', 'M_M4_13', 'M_M4_10'])\n" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###what just happened?\n", - "this puts the expression and splicing data, along with metadata about the
samples into an object-oriented interface called flotilla.schooner.Study which is now accessible through the
\n", - "load_cargo=True takes >40 seconds to load the first time, it loads GO references into memory and sets up gene names for plots and such
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#Sample Metadata\n", - "\n", - "plotting requires that two columns appear in the sample metadata data frame: \"color\" and \"cell_marker\"
" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "sc_study.sample_info" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "html": [ - "
\n", - "data_type -designates whether you'd like to do PCA with either splicing or gene expression as features
\n", - "featurewise - selects whether you'd like to visualize PCA of samples or of features
\n", - "x_pc and y_pc - select the components to display for the x- and y- dimensions
\n", - "show_point_labels - draws sample/feature labels
\n", - "list_link - a local file path (full path) or a http: link to a list of line-delimited features to use for the PCA
\n", - "list_name - contains pre-loaded lists, you can set this to \"custom\" to use list_link
\n", - "savefile - a file to output\n", - "\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "sc_study.interactive_pca()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "savefile : data/last.pca.pdf\n", - "y_pc : 2\n", - "data_type : expression\n", - "featurewise : False\n", - "show_point_labels : False\n", - "x_pc : 1\n", - "list_link : \n", - "list_name : variant\n", - "group_id : ~outlier\n" - ] - }, - { - "output_type": "stream", - "stream": "stderr", - "text": [ - "/Users/lovci/venv/lib/python2.7/site-packages/matplotlib-1.3.1-py2.7-macosx-10.6-intel.egg/matplotlib/font_manager.py:1236: UserWarning: findfont: Font family ['Helvetica'] not found. Falling back to Bitstream Vera Sans\n", - " (prop.get_family(), self.defaultFamily[fontext]))\n" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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qfqoek7IIXwyCIAiC2lKEKm5me/OcUN8OHEJSK1uzRLVxTEk4UY+ZkAVBN6FV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\nBljHzGbHqIlvEUJLk2ayOD7mKDuN8KS5OzAn/jsf+D2wAyEH2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- "text": [ - ""
- ]
- }
- ],
- "prompt_number": 9
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "##interactive_graph \n",
- "\n",
- "makes a graph where layout of nodes is determined by their covariance in PCA-space\n",
- "\n",
- "group_id - a subset of samples to use
\n", - "data_type - designates whether you'd like to do PCA with either splicing or gene expression as features
\n", - "featurewise - selects whether you'd like to visualize samples or of features
\n", - "draw_labels - draws sample/feature labels on nodes
\n", - "degree_cut - minimum degree for nodes to be included in the output\n", - "cov_std_cut - minimum covariance for two nodes to have an edge\n", - "n_pcs - use the first n_pcs\n", - "feature_of_interest - feature to highlight. recommended that you paste into this box, rather than type directly\n", - "use_pc_X - de-select to exclude component X\n", - "list_name - contains pre-loaded lists and custom lists added with custom \"list_link\" in interactive_pca
\n", - "savefile - a file to output\n", - "weight_fun - a function to apply to covariances before filtering\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "sc_study.interactive_graph()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "savefile : data/last.graph.pdf\n", - "group_id : ~outlier\n", - "data_type : expression\n", - "featurewise : False\n", - "weight_fun : abs\n", - "use_pc_4 : True\n", - "use_pc_2 : True\n", - "use_pc_3 : True\n", - "use_pc_1 : True\n", - "cov_std_cut : 1.8\n", - "degree_cut : 1\n", - "feature_of_interest : RBFOX2\n", - "draw_labels : False\n", - "list_name : variant\n", - "n_pcs : 14\n", - "u'RBFOX2'" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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622sreOPj77jzuolIr/bRrk6z2j9h7TkQke7A2cA3eFH0vcDbwAvh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\nXLeYB4ALCLuUhL4I3GSM6aX+ENoybt/ERCGw1m4yx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- "text": [ - ""
- ]
- }
- ],
- "prompt_number": 17
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "##interactive_clf \n",
- "\n",
- "makes a classifier, plots PCA of samples using only important features from the classifier. (default classifier is ExtraTreesClassifier, extremely randomized forests)\n",
- "\n",
- "data_type - designates whether you'd like to do PCA with either splicing or gene expression as features
\n", - "list_name - contains pre-loaded lists and custom lists added with custom \"list_link\" in interactive_pca
\n", - "group_id - a subset of samples to use
\n", - "categorical_variable - a column in expression.sample_descriptors upon which to train a classifier
\n", - "feature_score_std_cutoff - mean + (this x std) is used to select the top features
\n", - "safefile - a file to output" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "sc_study.interactive_clf()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "savefile : data/last.clf.pdf\n", - "group_id : ~outlier\n", - "data_type : expression\n", - "categorical_variable : M_cell\n", - "list_name : variant\n", - "feature_score_std_cutoff : 2.0\n", - "Initializing predictors for M_cell" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Fitting a classifier for trait M_cell... please wait." - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Finished..." - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Scoring classifier: ExtraTreesClassifier for trait: M_cell... please wait.\n", - "Finished...\n", - "retrieve this classifier with:\n", - "prd=study.expression.get_predictor('variant', '~outlier', 'M_cell')\n", - "pca=prd('M_cell', feature_score_std_cutoff=2.000000)" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - }, - { - "output_type": "stream", - "stream": "stderr", - "text": [ - "[Parallel(n_jobs=2)]: Done 1 out of 2 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=2)]: Done 2 out of 2 | elapsed: 0.0s finished\n" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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1b1UvqRKSwKi0IcbvxvjThMQbAO6+zt2vdfcjgZWEnn3LgZy4fQLwE6AxIdGW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TsSW5lt0e1sdQ7a0XtWStkKCEVvfnLfo87y0oVyECoUJYyUcomUMlJKmWN5XtjjLzkI86wV\nLaWccmtNxb1g4MCBNjXLrJ1y86Za3qKObJFp3bo1zz77rN2adoMGDSI4OBh3d/diW1Px12jfvn2R\nuwzXqVOH+Ph4MjMzb61cCE899RQBAQF3ZHsrzNXdqPLIQ8ivj9vVKVPOEf8+dcjMzOTkyZN069aN\nhIQE9uzZY9fGy8sLX19fDh06ZMgOHz5sRNM2bdrUJu03KyuL48ePExAQcFdtAWJjYwkODiYiIoLu\n3QvWdJw3bx4LFy5k8+bNeHl52b1G0KOFc3NzAWjSpEmBdatWrYq7uztCCMqVK1fAPv//5127dnH+\n/Hmeeuqpm66ruG8x2XkOgBDCxSJfCPSSUl4HjgkhrH/ATwJrCptYSnnO8tQamVNi7DqYwIzInzlw\nPJEDxxOZEfmz+mKpUDw45D+7vgN6CiHy+i7SgHZCCLOUMktKmY3+48eXUso0AIv8MNAAsNbriELv\nelysMillppTyskWWAVy37OG6RWYGLoERqW0GzgAmIYQrUAu9G/JWIURAIXpWrgkhnAFPINH+Lbw/\nUGd56UM5CBWKUoQQYoYQonU+WWshxAPRxl5hS7t27UhKSuLYsWPk5uayYsUKBgwYUNLbUpQSTCYT\nPj4+nDt37tbKwIABA1i2bNlfXnddz/VFelgpikN5x6KdXNpxGacMJ6Y/9h7req6n7dHHeKfWNNY+\n+R++Dv6WwNMdcXNzo379+vj4+DBu3DjCwsKIiooiNzcXTdP4+eefbeYt37YcL7w1kieWd6Prgk68\n88E7nKgtCV3Tna+dVnDy3ElavNoMTdOYOXMmAQEB1K1bF4Bhw4YRERFBcnIy8fHxfPbZZ8b/v549\ne3Lx4kWWL19+27bx8fF06dKFCRMmEBYWVuBeLF26lOnTp/Pjjz9SpUoVm7G4uDj27dtHbm4u2dnZ\nzJ07l7NnzxIUFATokaIrV67kyJEjpKenM23aNAYOHAiAi4sL/fv3Z/bs2aSnp3PhwgU+//xzQkND\nbdaIjIzkqaeeuqN0dEWpxwQMsdYgBloXohOMXs5kL9DGIrN++XYBvPM4AgsghHAEJgCfFuvOb5ON\nu08XSaZQKO478p9jbdCdbWuBPnn0ZgG+wAEhxErL+eUOpAAIIXoJIXYIIWaiO9msEdVpgIdFtzhl\nWNZ1BYYDKyyvnYQQO9EjDRda1F4B5lqea+gloBqgZ3qNBGYUomflS+AYsKmQsfsOdZaXPpSDUKEo\nXYQDh/LJYigYVq94QBg0aBCRkZH89NNP1KtXj+rVq9/aSPG3oX///nz55ZdF0m3UqBHHjh3j+vXr\nt1YuRv77zs9sCNtkPH6Zaf1R/UYAgJ+fH4MGDTJSYgEyMzMJCwvDbDZTsWJF1q1bx7p164xuzFOm\nTGHixIm8/vrreHh4UKFCBUaPHs2iRYuM/yf+feviWdeDLSO3sX3MLvwer0HlFpUAKGsuS+s3W3Li\nu1jMZjMbN260SemfOHEirVu3pnbt2rRs2ZKRI0cSHBwMgLe3N+vWrWPGjBm3bbtw4ULOnDnDG2+8\ngdlsxmw24+7ubthOnjyZS5cu0bRpU2Pc2kU6NTWV5557zrjeb775hk2bNhlRhh07dmT69Ol069aN\nKlWq4Onpydtv3ygv9+GHH2I2m/Hx8aFhw4Z06dLFpp5oRkYG33zzjUovfnDRgMV5UvP2FaITCgwE\nNgBNhBC+wEagG/CERX4zZgFfSSlji2/bCoVCYWDvHFuA7ngDQEqZIqV8WUpZBz2SbiBwDvCzjK8G\nBgAV0Z151jdiq7OwuGUIIUzAIvS6h8mWfWRLKdsDPYGpQggPoLqU0lrXxWSxvyClPCGlPAZ4FqZn\ncYL2B+qgRzJOL+pNVSiKimpSolCULsqi17DISzbgXAJ7UdxlTCYTgwYNIjAwkNOnT6sv7YoCVK5c\nmUuXLpGbm2tTp84effv25euvv6Z///73YHfQ5dMCPZEMqnfyNZ6HrukO3aB7t2ACAwMBmDRp0i2b\n4YSHhxMeHm533MHRgcYjGtF4RKNCxysEeBP0YaBNxKOVMmXKMG/ePObNm1eobfv27W3SeYtqe6vr\nOnnypN2xgIAAu2taGTlyJCNHjix0zM3NjS+++MKurbOzs9HMRPHAYjfF2FI3y1tK2dny+nH0wv5z\nhRCX0OtjjbA3saUov6m4yp78FYLb+nHgeGIBmUKheCAocI5JKZOFEMewNGMUQtTI0yjpMuAEfAtE\nCyE+klImcsPXcRRoaXHgdUJ3Oha3DPT6rHuklFusmxdClLGkGaeiOxXr6WLxA9AYeEhKGSKEuCSE\nqIze5CStMD10h2eulPK6EOIKeSIX71fUWV76UBGECkXpYj8FP5wPBw6UwF4U94CaNWtSq1YtNmzY\nQJ8+fQqMl0TDENWkpHTRuXNnoqKiiqRr7YJtrVlXnPj5+eHi4mJEvZnNZmIWHSEuKp7ve2/gxGpb\nx9dPw7dyKUYvx5N1JYv9Eb+yccCPmM1m/P39mT79xg/fOTk5zJ49m0aNGmE2m/H09KR9+/ZERt7w\nQ2RmZhIeHo6npyfVqlXjo48+slnv0sFLbP2/aDaEbWLPv/5L+qX0IttGRUXRoEEDzGYznTt3Jj4+\nvki2e/bsoWPHjnh5eeHu7k6PHj1sbCdPnoyTk5NNJOHp06eN8eHDh1O/fn0cHR1trhVg0aJFNG/e\nHLPZTKVKlRg1ahRZWTd+PwoKCqJ8+fLG3A0aNLCxnzhxohFlGBYWVmiXZcUDSd7UvKrAICHEZiHE\nZvQv1r/m0d2F3pQEYDVQ2RK9YsWwFUJ0AD4Gmlvmf+seXItd2jXxYdzg1jTzr0Qz/0qMG9yadk18\nSnJLCoWi+Mh/jlmZg+4404AQIcQuIcQu9Gi6L6SUl9CbenwvhIhCL4XwpZQyC1gG7AFeAuYXt0wI\n4QP8E3jSsvdwIUQ5YLMQYht6x+UpUsp9Usq2luaRP6KnFAOMtbxeDUzKp7cZGCml/BP4WQixD4gG\n/l0cN7skUWe5QqFQ3AQhRHPLL0g7hRBLLW98l4QQLe/B2n5CCC0uLk5T3H38/Py06OhoTdM0LTY2\nVvvf//5njJlMJu306dPa4MGDtYkTJ2rp6enGIysry9A5duyYzVhOTo4xZp3bSm5urpaenq5t2rRJ\nq1mzppaRkaFlZmYa49nZ2Vp6ero2YMAAY03rfIqSJScnR/vnP/9ZZP1t27Zpa9asKfZ95P2btdJj\ndYjWbFQTzcnspJV1L6s98WU3rcfqEK3H6hCtfOXyWtt3H9F6rA7RfNpX1So1q6h1W9JZ0zRNO3ny\npLZ27VpjngEDBmitWrXSfvnlF03T9L/XnTt3akOGDDF0xo8fr7Vq1UpLTk7W4uLitJo1a2qPTnlY\n67E6RHs8sotWxqWM1uyVplqP1SFavTB/zTvA29hLYbZRUVGapmnapUuXNHd3d23p0qWapmnalClT\ntMDAwJuua7XdtGmTtn79ei0rK0tLSUnRQkNDtccee8ywnTx5sjZ06FC793TevHnali1btEceeUSL\njIy0GVuwYIFxP2JjY7XatWtrb731ljEeFBRUwMbK/PnztSpVqminTp3SMjIytL59+2qDBw+2u4/i\nIi4uThNCaEIIv7v9nqUonajPEopbUdJ/owrFg4w6gxVF4WZ/QyqCUKEoRUgpfwUEehHbY8DngJBS\n/q9EN6a4q9SuXZsWLVoYr60RfCaTialTp+Li4mI8OnfubOjVr1/fZmzJkiV214iOjsbFxYXg4GDi\n4uIoX768UTMN9FROFxcXli9fbqxZHA0vFH8dBwcHvL29uXTpUpH0AwMDiY6OLvZu1BeuXeDNHf8k\ndE1342HFvaYZT38PYtedKtQ25eQVqjz8EOU89C67tWrVMppn7N+/n5UrV7J27VpattR/CzGZTLRr\n147Fixcbc0RGRjJmzBg8PDzw9fVlxIgRxEXp0Xrn9/6Bs1c5qgdVA6Bu7zqknEzh2oVrACxZsqSA\nrXXu7777Dh8fH6PZx7hx49i/fz+nTp26pW23bt0ICQnByckJd3d3hg8fzr59N8q+aZp203+HF198\nkU6dOuHsXLCKxHPPPWfcj9q1a9OnTx+bua3zF8b69et55pln8PPzo1y5crz22musXLmSjIwMu3tR\nKBQKhUKhUPy9UQ5ChaKUIaX8U0q5WEo5VUq5xBJOrnjAOHXqlFGLLT85OTnUrFmTxYsXk5uba/PY\nvn07QAF5bm4uw4YNM8byzx0UFGTo5eTkkJuba5O2umTJkgLzDRo06C5dveJ2uZ1mJSaTiS5durBl\ny5ZbK98mN3N21e8vOPX9KbLSsguMedXzJHbNKc78eJYjR47YzLN9+3YaN26Mj4/9lJKkpCQSEhJo\n1OhGrcGGDRty5ayeNpt6Ng1zDbMx5lDGATcfV66cTSUrLZvz588XsI2JiQEgJibGZszJyQkhBDEx\nMSQlJd3UNj979+6lVatWxmuTycTq1aupUKEC/v7+fPjhh3av8Vbs2bPHZm6AsWPH4uXlRevWrdm8\nebPNWN4085ycHDIzMzl+/Pgdr69QKBQKhUKheLBRTUoUilKEEKIOMA1oCpTLM6RJKWuXzK4UCkVJ\n4+vrS3x8PJqmFalG5BNPPMGYMWPo0qVL8W1Cg31Tf8HB8cb6DQY3MF571PagYpOKnPg2loDB9W1M\nm4xsROxa3UHYbEEzfHx8mDNnDqGhoVy7dg0XFxcb/Zo1a3LlyhUyMjKQUhpyV1dX47mbmxs56XrH\n5usZ13Es52gzh6NzGa6nXycn43qhttaafGlpaQXWt45buy7bs83Lnj17mDt3Llu3bjVkzzzzDC+9\n9BIVK1bkl19+oXfv3nh7exvRikVl9uzZXLx4kXHjxhmyWbNm0bBhQ8qWLcvXX39N7969+fXXX6lb\nty7BwcFMmTKFUaNGUa1aNSIiIgBUHcJbIIQIQq8TZQ2FjQCWA75SyitCiMXAZPQi8kvQ62A5ojf6\nuCSEeBIYB2QAOeidLHdYmns8j96psq9lrmKTWfaxzrKXdOBpKWWKEGIlemH7ssAgKeUJIcTHwFPA\nWGuzESFEuGW+6xbbc0KI7sC/0IMJnpNSHrSsOxGIllIOLa77rlAoFPczQogQ9PMyA7iGXlfwVeBR\nIBO9cck4IUQyer15Z2Av8Ial4Uhl9Kwxb/TzfB76+9CHltf7pZT/J4SoCKxFf39JQj+vM+/dlSr+\nDqgIQoWidLEKvWvxq8DQPI9hJbkphUJR8rRt25bdu3cXSddkMvHII48UWb9ok8LDE1sTvKyb8ajZ\ntbqNSr0wwZlNZ8lIsv286ljWEdG3LoGz2pOcnMygQYPo168fiYmJ+Pj4EBcXZ6N/5swZkpKSyMzM\nRNM03NzcALh69aqhk5aWhqOz/jtnGecy5GTm2MyRk3GdMs5lDJ38ttY53dzcbMbyjttb1yq3EhMT\nQ+/evVm2bBnNmjW7cT/q1aNixYoAtGrVildeeYVvv/224L29CcuXL+eDDz5g06ZNNuu2bNkSZ2dn\nHBwceOaZZwgKCuI///kPoKcuDx06lMDAQPz9/Y1UZW9v79ta+2+IBiySUnaUUnZEd8DFoTvP8jIK\neFNK2QG9g2WKEKIWuvOsq8U2BPhTCGF1zj0KzAdGWroJF5sMyAKeklIGAt8A1vDvZ6WUQehOy1EW\n2RT0YvgAWIroD5ZSPgy8D4y3DL0JPAaEA9aOQmuBrrd/W4uHXQcTeOuT3bz1yW52HUwoqW0oFAqF\ngaU5yQwg2HLeDkH/7pYjpXzE8j5hfeP/VUrZSUrZFv39xnoWfwYskFI+BnQATgCngccsup5CiMbA\nZSllO8tZ/z/095n7HnW2ly6Ug1ChKF3URf+g/oOUclveR0lvTKFQlCzdu3dn/fr1Rdbv3bs3q1ev\nvos7Koi5uhtVHnkI+bX9VFYXFxfefPNNMjMzOXnyJN26dSMhIYE9e/bYtfHy8sLX15dDhw4ZssOH\nD2OurjvM3P3MRroxQG52LlfPX8Nc3Y2ybk6F2jZs2BCApk2b2qQMZ2Vlcfz4cQICAuyua7UFiI2N\nJTg4mIiICLp3v1GXsTBMJtNt1YZcu3Ytr7/+Ops2bcLPz6/Ic5tMJmbMmEFCQgLx8fF06NCBChUq\nUK9evSKv/Tcmf4jud0BPIUTez8xpQDshhFlKmSWlzAa6o3fLTAOwyA8DDdAjRgCi0LttFqtMSpkp\npbxskWWgRwIipbxukZmBSxbZH/murxJw1vI8BnjE8jzH0qnzsFVmWSOHEmDXwQRmRP7MgeOJHDie\nyIzIn9UXSYVCURr4B/CVlDIZQEp5EeiGHoGORfZzIXazgVAhhDNQT0r5vUVXk1L+T0p5Mc8Zng5c\nl1Lm/QDhhuVcv59RZ3vpQzkIFYrSRTT6h3+FQqGwwcnJCRcXF1JSUoqk7+joSKNGjThw4ECx7aEo\nzq16z/gTv+0c2XlqER7/NpaUU1fQNI2MjAzmzJmDm5sb9evXx8fHh3HjxhEWFkZUVBS5ublomkbg\nzHZoaAzbNITQNd0p37YcL7w1kieWd6Prgk6888E7+Haoxrqe64maso2y18rS72p/1j75Hx6V7WnZ\npCVbXtjGup7rGTZsGBERESQnJxMfH89nn33GgAEDAOjZsycXL15k+fLlaJrGzJkzCQgIoG7dugA3\ntY2Pj6dLly5MmDCBsLCwAvdiw4YNRlrvgQMHmDNnDk8++aQxnp2dTUZGBjk5OWRlZZGRkWHc4y1b\nthAeHs73339PQECAzbwpKSlERUWRnZ1Nbm4uq1atYsuWLYSE6MEEaWlpRr3BM2fO8NprrzF+/Pgi\npaf/zTEBQ4QQW4UQW4E26M62tUCfPHqzAF/ggBBipRDCBXAHUgCEEL2EEDuEEDPRnXNXLHZpgIdF\ntzhlWNZ1BYYDKyyvnYQQO9EjDRfZueYLgL8lkjDQMj+AoxDCGz2K0N2O7T1j4+7TRZIpFArFPaYS\nkP+Hl8rA+VvYXQAqAhWARHtKQohGQFUp5e+W122EED+jpy/vvNNNlxbU2V76UDUIFYrSxUkgSgix\nCv2Nw4ompZxSQisM//cAACAASURBVHtSKBSlhLCwML766itGjBhRJP3+/fszceJEm7TXv8J/3/kZ\nk8MNJ1PlFpV4qGVl8gZduVR2wTeoGmd+PGvIcq/nsj/iAOmX0qnoWJGmTZuybt06PDx038aUKVOo\nUaMGr7/+OidOnMDJyQkegmb/14TyFfUOv/5965KVksWWkdswOZqoE1qLyi0qAXrq7Lp163jhhRcY\nMWIEzZs3Z8WKFcb6EydOJDExkdq1a+Pk5MRrr71mdPH+K7YLFy7kzJkzvPHGG7zxxhuAHr135Yru\nv1m6dCkDBgwgKyuLypUrM2rUKKOZEEDXrl3Zvn07JpOJnTt3Mnz4cLZt20ZgYCBTp04lJSWFTp06\nGfqBgYGsX7+erKws3njjDaSUaJpG/fr1WbVqlREheOXKFUJDQ4mPj8fb25uXX36ZV1999U7/2f9O\naMBi6/utEKIDEAQsQE/djQeQUqYALwMvCyHmAgOBc0Ady/hqIcR+9HqFV7jhYLM6C4tbhhDChO4E\nfDtPJEs20F4I0QJ4Fz3tzQYpZbYQ4n30aMQD3IhIGY/uGI0Fjua7RwqFQqHQuQhUKURWFbhZKFwV\n9PP2MrqTsQCWH2nmo9eaBUBKuQ9oLYR4Db2cROQd71yhKATlIFQoShfuwAagPOBnkZlQH8gVCgVQ\np04dPv300yLrOzk5UatWLY4dO/aX00u7fNrR7lj1Tr42r5uMaESTETc6/9br50+9fv4ArOtZeJp0\neHg44eHhxuvQNbbpug6ODjQe0YjGeebNS/v27W1SgfNSpkwZ5s2bx7x584rVdtKkSUyaNKlQO4Cv\nvvrK7hjAtm3b7I7l7TKen0qVKvHLL7/YHffx8eH333+/6doKu5jyP5dSJgshjgEdAYQQNaSUVg/4\nZcAJvcZUtBDiIyllIjc+Yx8FWloceJ2AfXdBBvA2eiF8o325EKKMJUUtFdsoQJvPFVLKVcAqIcQ/\nsETCSCm3A49Z6l69Yuf+3DOC2/px4HhiAZlCoVCUMP8BfhBCzLM0h6oMbEKvJz8WQAjRupA041eB\ntVLKDCHEUSFEdynlesvZ3gL4DViG3lDqD8s8ZfKkHaei/0h0X6PO9tKHchAqFKUIKeWQkt6DQqEo\n3TRv3pz9+/fTokWLIul/77WG2RNm0WDAzR2E9hx3CsXfjCH/z969x+lYrY8f/wyN8zhFakwalSun\nHHaoKIfQFiXSYcshtXX4VruovYvwc6idiVJU6LAxSdm7b+y0fbMrhtpDpS2E6pLIYTo4jUPMON2/\nP9Z6xjNjhsEz5nS9X695eZ7rXut+1n3zWuZez1rr8tmMwW0cHzIBlxAkADqLSCgV9R7gNv9gOAB4\nX0T2AUeAZ1T1gIi8CSzBLQnuEemY3yT/cWCxz6Q8A5gOzPMPm9HA/QAiMgTo6V+fr6pPicgE4FLc\nyoV+/thgXEKS/WGxLriEJxeKyDuqmjGrJa+1ahTLoDuaZyw969QynlaNYs/UxxtjTLZU9SffX84T\nkTTgN9xWD38WkSW4LMaLgaVAExFZwNEsxmP9ae4BXhORx3H/x7wE1AGaAaNFBOAxIMrP+C6B29Ki\n15m5yrxjfXvBY5vRGJPPRKQWgKpuDL3OTthshbxqRzywfv78+cTFxZ2ouDEmn6SlpfHXv/6VJ598\nMlflu/6zCz+8v55zLz+XcueUzbHc8QYIs87mOx25HYjM7WfawGbBsXnzZtq3bw9QW1U35HNzTD6w\n3yXMiUTZZqjG5Bnrg01uHK8ftiQlxuS/DcB6v7n4hhx+1udDu4wxBVCZMmUoWbIk+/bty3WdWh3O\nZ+PHm/KwVcYYY4wxxpjCzJYYG5PPVDV8oN4G7Y0xJ3TzzTfzzjvvcMcdd+Sq/Fllz+KsMiVJ25lO\nmSql87h1xhhjjDHGmMLGBgiNKSBE5CxgDdBQVQ/kd3uMMQVXw4YNeeONN06qTq2O57Nh3kbklovz\nqFWnLpJLmI0x2RORK4D7VLWfiFTDJVYBt19ideBZVZ0mIqm4jMZVgIdUdZGIXAeMA35W1ZwzFhlj\njAFARNYCQ1X17yKyEPhUVYf5ZcDDgT8Ci4CuqrpTRKYD41X1SxEpg8uG3FFVP/fnm4tLTBIF9Mrr\n7adM8WQDhMYUEKp6yG9CWwqwAUJjzHHVrVuXb775hnr16uWqfKmYUhAEHNhzwL3OA7YfoDEFl6p+\nJiIPiUgT3IPpk8AQVW0nItVxGZGnAV/52JXAYNwD7BKgMS475xmVvDLFNrA3xhQqItIYSAJuAP7u\nw1eLSMYyDlU9IiJ/BYaJyEwf+9If/j0ui3F34HMfu0dVt4hIB1x2+Ufz/kryhvXrBZctZzSmYBkH\nTBeRy0WkVvhPfjfMGFOw3HrrrfzjH/84qTq2F6Exxd4TwGTgfFX9OBRU1a1AySxlq+AyZaKqqfmx\nuiF5ZQoJiUtZvnYry9duJSFxKckrU850M4wx5mR1B14BSotIKVx24hlAH/8aAFWdh8tYPAHXP4d0\nBUYADcPKbvEv04DDedj2PGX9esFmMwiNKVgm+j9vzBIPOPYXd2NMMVahQgUOHjxIl3c6UTI6d91D\nmaplOLT/MIf2H+KsspH/FSDrUuFIzyi0GYrGnLYfcUvUJoUHReRiIJT5qKmIfArUA5qd2eZlFpph\nkjVms02MMQVcU1UdISIfAh19bDowD/goS9lkoJOqboKMbacqq+qvIrJCROqr6hp/rCQwBHjwjFxF\nHrB+vWCzGYTGFCCqWiKHHxscNMYc48Ybb+SXL349qTq1OsTZLEJjiq/ewL+A/v5BExFJAt7ALVkD\nt8T4auAxoFe+tNIYYwop/4XLpSLyAdATt8w4UNU03GDg78PKVgY6A+tEpJUPtwXq+vptcLMRQ54F\nZqrqujy/EFMs2QxCYwoAEamIm0ZeH1gJjFDVfcetZIwp9po1a0bq07uIbXVeruuUO6cc6bsOcPjA\nYUqWsu8ejCkuRKQscB9wDXAvcDfAcZKOvAF8LiIJqpovy9k6tYxn+dqtx8SMMaYAuwn4o6omAYjI\nHI6uBHsJ+ABY5t8PA57x76cDHXz961V1va//L//nXUCUqiaeoevIE9avF2w2QGhMwfAy0AQ37bwL\nbt+fu/O1RcaYAi8qKopy55Rl3y/7KFej3HHLhi/P/bbutyxatIh77703r5tojCk4HgEmq2q6iEzC\nbaCfY8YinzxtHtBNRDYACUBjv2TuBlVNz+sGt2oUy6A7mttm9saYwqQzMD7s/WrcjGxU9ScR+RpA\nRGoD9VX1Uf8+WURuxi1PXh9Wf5ffj34i7kubJGCBqj55Bq4l4qxfL9hsgNCYgqELcKnPTDUBl0nQ\nGGNOKPbqWH6c9yN1br4413Xq1q3LlClTOHjwINHR0Scsn3Xvv6x7DRpjCj5V/WvY64PAVTmUaxf2\nekjYoY7ZFM9zrRrF2sOjMabQUNW2Wd4PxmWED73vE3b4urD4cP/yf7PUD231UCaiDc1H1q8XXLYH\noTEFQ3QoM5XfoLbI/AdgjMlbpSpEc2j/IY4cPnJS9Xr27MnMmTPzqFXGGGOMMcaYwsRmEBpTMJQU\nkdb+dRRwVth7AFT1kzPfLGNMYXDO785h67Kt1GheI9d1mjZtyrZt2/KwVcYYY4wxxpjCwgYIjSkY\nfgXCN5zdnuU9QO0z1xxjTGFStX4Vvpn+3UkNEAJ07JgvKwaPkXUJszH5QUTaAtOA0N5P44AZQJyq\n7haRqbiEYiV9ucC/7qaq20TkRmAQkAYcBkaq6qd+Y/m7gd3ALf5ckY69B7T2bVnkr+eYcj7+O+BL\nVS0hIucCb/vrrQH8W1UHishcIAb3pWUvVd0oItOAS/z1vaqqoXrGGBMRhbUf9u0IJSPZD9yqqrtE\n5O+4vrUU0FdVvxeROsAkXP/6pqpOzRKbrqrTROQ6YDhwAOjt++Fjzhepe28M2BJjYwoEVY1X1dph\nP1nf2+CgMSZHUVFRlK5cmrQdafndFGMKswCYoqrt/D58u4FNHJs07E/AYFVtg8sIvMtvNj8U6Ojr\ndgZ2iEjoIe5K3MPffSISHcmYb9O9wAuhBmb3uWHtvx/4L4Cq/hx2vR8C7/sy96hqa2Ak8HDY/fmD\nL39GBgeTV6YwbPJihk1eTPLKlDPxkcaY/FVY++EDwM2+33wH6Ovb2cvvSTjItxncoGcvVW2vqlN9\n7Lmw2DQfewxoAzzk6+d0vgLF+u3CzQYIjTHGmCIgrnUsWz6xX8SMOU1RWd7PwmXxDf+deS/QSkRi\nVPWAT/jRBXhLVfcC+PhqoB6wzNdbAFyeBzFU9ecs7c62nIg0wD1s783m2lsDC/35tvhYGnDIvw6A\nGSIyT0Tis6kfUckrU0hIXMrytVtZvnYrCYlL7WHTmOKh0PXDqpquqtt9LKPfVNVQ/xkDbBORcrhV\nYZNEJElE6otI+WxilYCd/rzLcbO3jznfiW/lmWX9duFnS4yNMcaYQix8ee7j3zzO6K6jKVEib7//\nsyXBpoiKAvr5JW4A/4d7yHsP6BFW7lngSWC5iHwJ3AlUBH4GEJHuwCPAZ77ubl9vL1DJl41kLDsx\nOZR7GJdNs214YRFpBqxU1SNhsZLAEOBBHxrgl9a1ws2AuSmHz46IeYs3ZBuzzJfGFGmFuh/2g333\nAF39+2ggCagFXAGcDdQFuuGWI4/FzequB3TPEgt9Rui+hGaHL/DnuzKHe5hvrN8u/GwGoTHGGFNE\nXHPNNSQlJeV3M4wprAJgatjSti98/HXcAx8AqrpLVR9S1YuArUAfYAsQ74/PBnoD1XAPeBV91dCg\nXaRjWa+B7MqJyMXA7rBZLuG6A+9miT0LzFTVdf66dvs/k4Hq2ZzDGGNOV6Hth0UkCpiC2/cw1bfj\noKpehRsQ/CuwC/hFVb9X1e+Ayr5+drHQZ2TwsyJD53sqF/fTmJNiA4TGGGNMEdGhQwc++uij/G5G\ntrr+s0uufozJZ1FZX/sHve+AFgAiUiuszHYgGpgL9BCR0MBZaJXOt8Bl/sHxGtzDbqRj4e0Ntf+7\nLOWWApcCzUXkA6CRiEwMq9sRtwch/hrvAqJUNTEsVsH/eQmw5zj3MCI6tYzPVcwYU+QU1n54JLBE\nVeeHGiYioTbsAWL8Fy3bROQcETkP2HOcWDURKSMiTQHN5nzHDCDmN+u3Cz8bIDTGGGOKiJIlS1Kl\nShW2bYvMtjTx8fGUK1eOmJiYjJ+BAwcybdo0SpQowdixY48pv2jRIgC2b9/O7bffTpUqVYiJiWHB\n/QtZ++7RZHvB4YB17/3Awoc+4f96/psPen3IfwYvITHxaAL39PR0+vfvT+XKlalZsyYvvvhips9b\nsGAB9erVIyYmhvbt27N58+Y8r7tkyRLatWtHlSpVqFixIjfccEOmuqNHj6Z+/fqUL1+e2NhYRo4c\nmelzV6xYwRVXXEH58uWpUaMGQ4YMyXW7+vXrR+nSpTP+LipWrEgQuAlja9as4Xe/+x2VK1emQoUK\nXH755Rl/F+ak9PN7QCUB54XFJ+D2gAqAziKSLCLJuP2opqvqNmAA8L6ILABewe2FdQB4E1gCPABM\ninQMQEQm4GbQPCci/VU1PUu5iao6W1XbqOp1wApVvd/XvQTY4OuETASa+nsx1MfeEpFPgDdwy5Tz\nVKtGsQy6ozlN6lSnSZ3qDLqjuS1TM6Z4KHT9sIjEAo8DN/q29xeR0sBHIrIQl3H5SX8df8F9ITMb\nl5E5p9gYYJG/7oRszjfqFO5tnrJ+2xhjiggRiReRYNOmTYExpvDauHFjMH78+IicKz4+Pli0aNEx\n8alTpwZnn312UK1atWD37t3Zlr/tttuCa6+9Nvj111+DIAiC9pPbBs0HXxbcMLtzcMPszkHNNrFB\npYsrBVc/2yq4YXbn4PpZ1wWtnr4i6NevX8b5nnjiiaBZs2ZBampqsGnTpuCCCy4IFixYEARBEGzb\nti2oWLFi8MYbbwRBEASjRo0KWrduned1//3vfwdz584NDhw4EOzatSvo2rVrcPXVV2fUHT9+fLBm\nzZogCIJg2bJlQdWqVYO//e1vGccbNGgQDB06NDhy5Eiwbt264LzzzgvefffdXLWrX79+wciRI7P9\nu0pNTQ02bNgQBEEQHDlyJJgwYUJQvXr1bMvmlU2bNgUiEpyJBBamYLLfJcyJ5Pe/UWOKMuuDTW4c\n79+QzSA0xhhjipDzzz+fzZs3c4L//09LVFQUl156KS1atGDcuHHZlvnqq6/o3r071au7lT7lapTj\n3BY1AEhdt4uU5J9oMfgyKl9UKeOcVetVZerUqRnnSExM5M9//jOVKlUiLi6Oe++9N+P4rFmziI2N\npU+fPgAMGjSIZcuWsX79egCmTZuWJ3WvvfZaOnfuTHR0NBUrVuSee+7hiy+OrvJ86KGHqFevHgBN\nmzalffv2mY7/8MMP9OrVi6ioKC688EKuuuoq1q1bl6t2ATn+vVaqVIkLLrgAgMOHD1OiRImM98YY\nY4wxxpyIDRAaY4wxRUzLli1ZsmRJRM6V3YBUKPbUU0/xwgsvsHPnzmPKXHnllYwdO5bXXnuNNWvW\nZDrPjjU7qFgrhjJVy+T4uTt37iQlJYWGDRtmxBo0aMCqVasAWLVqVaZj0dHRiAirVq1i586d/PTT\nT3lSN6vPPvuMZs2aZXvs4MGDLFu2LNPxG264gcTERA4dOsS3337LZ599RseOHU/YrpAJEyZQtWpV\nGjZsyNtvv33MZ1auXJmyZcuSkJDA9OnTs22XMcYYY4wxWZ114iLGGGOMKUy6dOnC8OHDadmy5Wmd\nJwgCrr/+es466+ivC2PHjiU6OhpwM+Q6dOjA6NGjGTNmTKa6kydP5rnnnuOVV17hgQce4KzKZ9Gw\nf33ObVGDw+mHKVm6ZKbyH9+zgEP7DlH2cFlUNSNevnz5jNcVKlRgzx6XG2Hv3r2UK1cu0zlCx/fu\n3ZtndcMtWbKEl156KcfM0Y8++ijnnXced911V0bsmWeeoX379jz77LMcPnyYIUOG0KRJEwB+++23\nHNsFMHDgQCZMmEBMTAwff/wxt9xyCzVr1qR169YZ5VNTU0lPT+fpp5+mR48efP3115QoYd8HF0ci\nciMwCEgDDuMya34qIq8Bh1T1f3y5EcCNuH29Pg+LlwXWA7eq6iciMh5oDJQH/qSqn53pazLGFG0i\n0ha3v15o6vw4YAYQp6q7RWQqbo++kr5c4F93U9Vtx+n37gLuxmUHvsWfK2Ix3445vi37cf3mLhGZ\ni8t0HAX0UtWNIvI2cC5QGiirqk1F5ALcvobRwHBV/XcOsauBF/znJapq5g2WjTlN9hujMcYYU8RE\nR0dTrlw5du/efVrniYqKYu7cuezcuTPjp3///plmA44aNYrJkyfz888/Z6pbpkwZhgwZwpdffklq\naipxbWvy3+e+In1XOmWqlmH/trRM5Tu8eg2d3ryW9PR0giCgQoUKgBs0C9m7d29GvEKFCpmOhR/P\ny7ohq1at4qabbuLNN9/MGOALN3r0aJKSkpgzZ07GAN2+ffto06YN9913H2lpaaSkpLBw4cKMJCjH\naxdAo0aNiImJcferQwd69+7NrFmzjvns0qVLM3LkSH7++We+/vrrY46bok9EagNDgY6q2g7oDOwQ\nkRLAOcD5YcUD4GFV/R3QQkTq+Hh/YGVYuUdVtS3QgzOQpCR5ZQrDJi9m2OTFJK9MyeuPM8YUDAEw\nRVXb+b5rN7AJNxgX7k/AYFVtg8skvOs4/V4poK+qXolL7HSfiERHMgYcAG5W1dbAO0Bf3857fGwk\n8DCAqvb07RsDvO/LDQYeBNrikp3kFHsEuB5oDvQ++dub96zvLtxsgNAYY4wpgv7whz8wc+bMPP+c\nevXqcdNNNzFqVM7J9MqVK0edHhdx5OAR9v2yn+pNqpG2M40d3x67NDmkSpUqxMXFZRrgWr16NQ0a\nNACgcePGmZbeHjhwgLVr11K/fv08rQuwbt06OnXqxLhx4+jSpcsxbX/55Zf529/+xkcffUSVKlUy\n4itXrmTTpk08+OCDlCxZknPPPZfbbruNDz74AHADgDm1KztRUVE57kl45MgRjhw5YrMHi68uuOyd\newFU9YCqrgbaAJ8AySJyZTb1vgWq+wfqy4Fk3MwXVPWQLxMDRCZVeg6SV6aQkLiU5Wu3snztVhIS\nl9qDpjHFR1SW97OAbv4LjpC9QCsRifH920Fy7vfqAct8vQW4vi2iMVVNV9XtPpYGHPJt2BIWO5zl\num7y1wYgqrpCVdOAVBGJySG2A6gClPH3oECxvrvws98ajTHGmCLo4osv5vvvvz/t8+Qm2cmIESN4\n44032LFjR0YsISGBFStWEAQBaWlprJ+7gbPKnEWFmuUpU7UMF3e/iGXjlrNt5TaCIwFBEJC6NjXT\nee+66y7GjRtHamoqmzdv5tVXX6V3b/eFebdu3fj111+ZMWMGQRAwduxY6tevz8UXX5yndTdv3kyH\nDh0YMmQIPXv2POZevPHGG4wePZoPP/yQc889N9OxCy+8kFKlSjFp0iQOHz7M1q1b+cc//pExAHii\nds2ZM4e0NDfzcuHChUyfPp2uXbsCkJSUxOrVqwE3U3Ho0KHUqFEj056GplipCOwCEJHuIvKpiIwF\nugHv+p/uYeWjRKQM8DtgI9APOGYTSxGZDcwH8nRZ27zFG3IVM8YUOVFAPxFJEpEkoAVusO093Ozl\nkGeBOGC5iPxdRMqRc78Xg5uJCG5QrZIvG8kY/nPLA/cAb4fFSgJDgFfCYtFAQ1Vdns092BN+ziyx\nF4GPcF/mzMimbr6yvrvwswFCY4wxpohq0qQJX3311Wmd47rrriMmJibj59ZbbyUqKoqoqKNf8MfH\nx9O3b9+M/fsA0tPT6dmzJzExMVSrVo2fl/5K8ycuI7q827+w7u1CnVsuZvW0b/mg14f8u+/HrJry\nDVOmTOH8893qx6FDh9K8eXMuvPBCLrvsMu677z46deoEQNWqVZkzZw4JCQnExMQwb968TEk78qru\n3/72N3788Ucee+yxjHtSsWLFjLojRoxg27ZtNG7cOOP4/fffD8A555zD22+/zdSpU4mJieGSSy6h\ndu3ajBgxIlftGjNmDDVq1CAmJoYHHniA8ePH0759ewC2bt1K9+7dqVChAjVr1mTNmjV88MEHmf6e\nTLGyBYgHUNXZuKVo1XFL8SYB44H2vmwUbk+rJGAq8BNwrar+mywzeVS1O3AZ8HSeX4ExpjgKgKlh\nS4y/8PHXcQNvAKjqLlV9SFUvArYCfci+36uGG8wL/UcdGiyMdAwRiQKm4PY9DP/G81lgpqquC4u1\nxfW52Qkf0MwaexpoBlwM9PV7xRoTMfZbozEGABGJB9bPnz+fuLi4/G6OMSYC0tLSePrpp4+7/NeY\nSNm8eXNowLK2qm7I5+YUayJSDVgEtFXVrSJyEW7myVJVHe7LjMbNQOkBJKnqJz5eE5gNbMc9hO4A\nOgBpqnpQRCoDc/y+Wlk/N54I/C4RWqYWbtAdzWnVKPaUz2kKhij71sIch4i0wfVbI/37tkAbVR0p\nIi8Bob0FA1Xd6MuMxA0SzuTYfm8ocC9u1l1b3CzqS3DJTyIWU9UEERkF7FDVF8Ku5y6gkaoOyHKd\nE3HLof/j37+C+/LmO2CeqrbJIfYf4Peq+puILAB6qOrOsPPGk4/Pc9Z3Fw7H64cti7ExxhhTRJUp\nU4Zbb701v5thjDnDfDbPAcD7IrIPOAKUBRaGFVuI2wMLwiYN+D2zWgCIyHDc4OEeEZntBwfL4ZbL\n5ZlWjWIZdEfzjKVpnVrG2wOmMcVHPz8wCPBqWHwCLiFIAHQWkT4+vge4zWcNztrvPaOqB0TkTWAJ\nbklwj0jHRCQWl0hksc+kPENVXwcmAp/75dILVPVJP9PwClW9P+zaEnDbOkQDo44TewH4VEQOAx+H\nDw4WBNZ3F372DY4xBsj/b5yMMcYUbjaD0NjvEuZEbAahMXnH+mCTG8frh20PQmOMMcYYY4wxxhhj\nijEbIDTGGGOMMcYYY4wxphizPQiNMcYYY4wpZvweX9OAH3D7E76uqn8TkRbAc8Bh4BAwEJcU4FdV\nnSoiHYAbVfVP+dJwY0yRFdYvrfehcbhkSnGqultEpgIjgJK+XOBfd/N7r94IDALScH3YSFX91CcL\nuRuXCfgWf66IxXw75vi27Adu9Xsi/h2oAZQC+qrq9z5Byc3AX1Q10V93f3++Q77uFhHpAvw/3KSu\nP6rqSv+5Q4FFqnpnpO67MSE2g9AYY4wxxpjiJwCmqOo1wDXAnSLSCpgK9FLVtrgMnQdxG+P3F5EK\nwDBgeF43LnllCsMmL2bY5MUkr0zJ648zxhQMoX6pnaq2ww3AbcINnoX7EzBYVdvg+q9dIlIbN3jW\n0dftDOwQkdDg3JW4rMD3iUh0JGPAAeBmn939HaCvb2eoLx3k2wwu4chfQhciIqWBO1T1cmAM8IQ/\nNBi4GugPjPax94COJ39b85712UWDDRAaY4wxxhhTPEUBqOp+XLbNF4FkVd3o43tV9VtVTQdeApKA\n91V1R142KnllCgmJS1m+divL124lIXGpPXAaU3xkTaAwC+gmIuFjF3uBViISo6oHVPUg0AV4S1X3\nAvj4aqAesMzXWwBcHumYqqar6nYfS8PNBERVD/lYDLDNx37Ocn3VgY3+9SrgCv/6sKoeAFaHYv4z\nDme9YfnN+uyiw5YYG2OMMcYYY37BPcT+lMPxRcB03HK6PDVv8YZsY60axeb1Rxtj8lcU0M8vNQb4\nP9xg23tAj7ByzwJPAstF5EvgTqAi8DOAiHQHHgE+83V3+3p7gUq+bCRj+M8tD9wDdPXvo3FfrNQC\nrszhmn8B6viZhK39+QFKikhVoHFYrECyPrvosBmExhhjjDHGmHNxD73n5XD8SdzStydyOG6MMacr\nAKaGLTH+wsdfxw28AaCqu1T1IVW9CNgK9AG2APH++GygN1AN16+FBthi/PtIxxCRKGAKbt/DVN+O\ng6p6FW67Apfb8gAAIABJREFUhqeyu2A/+3EMbjZiM/xMQ1xf+x5wB/BtlntkTJ6wAUJjjDGmmIqP\nj6dcuXLExMRk/AwcOJBp06ZRokQJxo4de0z5RYsWAbB9+3Zuv/12qlSpQkxMDHXq1GH06NEZZQ8f\nPsxzzz1Hw4YNiYmJoXLlylx11VUkJiZmlElPT6d///5UrlyZmjVr8uKLL2b6vAULFlCvXj1iYmJo\n3749mzdvzvO6S5YsoV27dlSpUoWKFStyww03ZKo7btw4ateuTUxMDGeffTZ9+vQhNTU143ibNm2o\nXr065cuXp06dOkycODHj2Pvvv88VV1xBpUqVqFy5Mr17985Ut1+/fpQuXTrj76JixYoEwbHPAaG/\nn/B7aczpEJGywL3Ag7hle+f7eAURqSsijYHyqjoGqCIi9fOyPZ1axucqZowpkqKyvvYDbt8BLQBE\npFZYme1ANDAX6CEi1X08tFryW+AyP4B3DW7QMdIxgJHAElWdH2qYiITasIfMswAzLaNW1f9V1VbA\nB7hZk6jqJ6p6NS5p1Oc51S0IrM8uOmyA0BhjjCmmoqKimDdvHnv27Mn4ef755wGoWrUqY8aMYc+e\nPZnKR0W530sfeOABtm/fjqqyZ88ePvzwQxo0aJBRtl+/fsycOZPExET27NnDzp07eeaZZ1i4cGFG\nmVGjRrFixQp+/PFHPv/8c5577jmSkpIANwDZvXt3nnjiCfbs2UPbtm3p1atXxOvWH3kJjz/5OC2f\nvIKu/+zCwH8/zL6r9tLqtStoNfkKlv7yBY1/3yijbvfu3VmxYgV79uzh+++/JyUlhaeffjrj+MSJ\nE/n555/57bffmDFjBo888gjffPMNAGlpaYwZM4bU1FRUle+//56HHnoo0/0dMmRIxt/F7t27M+53\nyPbt2xk9ejQNGzY85pgxp6CfiCzAzVxJVNUlQD/gLRFZCMzGPWQ/zdGZg0OAv+Zlo1o1imXQHc1p\nUqc6TepUZ9AdzW2pmjHFRz8RSRKRJDLPaJ4AXIKbQddZRJJFJBm3L+B0Vd0GDADe9/3aK7g9CQ8A\nbwJLgAeASZGOiUgs8Dhwo297f79k+CPfl07DJSdBRIYAfwb+IiJDfWyCv97ewFgfG+yvIwGXsASf\n2Xg6cK2IvBOBex0R1mcbY0wRIyLxIhJs2rQpMMYUD/Hx8cGiRYuOiU+bNi1o27Zt0Llz52DEiBHZ\nlheRYNKkSdme97///W8QHR0dbNmy5bifX7NmzWDmzJkZ759++umgT58+QRAEwauvvhrUrVs349iB\nAweCChUqBD/88EMQBEEQGxsbkbo3zO4c1O0tQVzbmsENszsf89NiSLOgRHSJbNu/bdu2oEOHDsFL\nL72U7fHPP/88qF69evDLL79ke/yll14KLrnkkoz3/fr1y3S/s9OvX7/g1VdfDdq2bRskJiYet+yZ\ntmnTpkBEAhGJz+//00z+sN8lzInk979RY4oy64NNbhzv35DNIDSmGBCREiIySUQWisjb+d0eY0zB\nkd3vCaHYU089xQsvvMDOnTuPKXPllVcyduxYXnvtNdasWZPpPJ988gmXXnopsbE5f3u8c+dOUlJS\naNiwYUasQYMGrFq1CoBVq1ZlOhYdHY2IsGrVKnbu3MlPP/10SnU7vXktKT+lMPmXl+n6zy4AxJwf\nw+6NR2dKZmqnplLpokqZYm+99RaVKlWievXqVK1alQceeCDT8euvv56yZcvStm1bJk2axDnnnJPt\nuT/77DOaNWuWKTZhwgSqVq1Kw4YNefvtzN31okWL+O677+jfv3+25zPGGGOMMeZU2QChMcVDb2CN\nqrZV1Z753RhjTMEQBAHXX389VapUyfh5/fXXM5auNm3alA4dOmTaWzBk8uTJ3HXXXbzyyis0adKE\n2rVrM2fOHAD27dtHuXLlMpW/4IILqFKlCmXLlmXTpk3s3bsXgPLly2eUqVChQsaS5r179x5zjtDx\n06l7OO0QACXLlMw4dlaZkhzef+iYa9zx7U42/N8GLr2nQab47bffzq5du9iwYQPr168/5v7861//\nYv/+/cycOZP+/fuzbt26Y8797rvv8uGHH5KQkJARGzhwIBs2bGDHjh288MIL3H///XzyyScAHDhw\ngAcffJCJEyfa0mJjjDHGGBNxZ524iDGmCOgC7BaRxbi9OF7K7wYZY/JfVFQUc+fOpXXr1pni06ZN\ny3g9atQoLr/8ch555JFMZcqUKcOQIUMYMmQI+/btIyEhgdtuu42NGzcSGxvLpk2bMpX/8ccfAShR\nogRBEBATEwPAb7/9llFm7969VKhQAXADelu2bMl0jtDxUJlTqVvyiPvV53Da4Yxjh9IOU7JM5l+J\ndv+4hy/HLKPpgCZUql2R7NSqVYshQ4YwfPhwBg8efMzxrl270rZtW95//30GDBiQEU9KSuL+++9n\n7ty5xMXFZcQbNTq612GHDh3o3bs3s2bNonXr1owZM4a2bdvSpEmTjDInWCViijERWQsMVdW/+/2v\nPlXVYX7593Dgj8AioKuq7hSR6cB4Vf1SRMoAvwIdVfVzf765uGydUUAvVd14xi/KGFPkiUhb3H59\n631oHDADiFPV3SIyFRgBlPTlAv+6m6puE5EbgUFAGnAYl1H4UxG5C7gbl3H4Fn+uiMV8O+b4tuwH\nblXVXf6azgPWAXVVdWN2/amITMPtr5gGvKKqM0VkBHAjkAq8p6oviMgg4Pf+3jT39+VotjNjTpPN\nIDSmeDgbSAFaATeJSI18bo8xppCoV68eN910E6NGjcqxTLly5Rg8eDDp6en88MMPXHvttaSkpLBk\nyZIc61SpUoW4uDi+/vrrjNjq1aszEp00btw4Y8kwuBl0a9eupX79+qdVt1SFaMqcXYbdPx5dUrxn\n0x5izq+Q8f63n37j8yeX0uDOetRolv3y4JBDhw5RokTOv04dOnSIkiWPzlb8/PPPue222/jHP/5x\nzPLirKKiojIGAefPn8/bb7/Neeedx3nnncfixYt5+OGHGThw4HHPYYofn3E4CbghLHy13zAfAFU9\ngks0MkxEWvjYl/7w73Eb8HcPq3+PqrbGZel8OA+bb4wp3gJgiqq2U9V2uAG4TbjBuHB/Agarahtc\nJuFdIlIbGIr7cqMd0BnYISKlgL6qeiUwCbhPRKIjGQMOADf7fvIdoG9YWwcAn4W9z64/DYA/+Oue\nGRYb4GMvAKhqgr+2m4GlNjhoIs0GCI0pQkTk/FDWr7CfN3H/uX6qqgHuP6iL8relxpiCIjez0EaM\nGMEbb7zBjh07MmIJCQmsWLGCIAhIS0tjwoQJVKhQgbp16xIbG8ugQYPo2bMnCxYs4MiRIwRBwNKl\nSzOd96677mLcuHGkpqayefNmXn31VXr37g1At27d+PXXX5kxYwZBEDB27Fjq16/PxRdffNp1a7WP\n44c56zn420H2b9vPjx9uIq5NTQD2b9vPkhFfUOfmi6h59bF7KE6fPp3UVPf7+A8//MDo0aPp0aMH\nAKrK/PnzOXToEEeOHGHWrFksWrSILl3cXodff/01N9xwA1OmTKFNmzbHnHvOnDmkpaUBsHDhQqZP\nn07Xrl0BmDVrFmvWrGHFihUsX76cZs2a8eSTTx534NYUW91x2TtL+wfjADcDp49/DYCqzgPq4DKD\nPhFWvytuhk7DsLKhKbmhWTkRlbwyhWGTFzNs8mKSV6ZE+vTGmMIl6z4as4BuIhI+drEXaCUiMap6\nQFUP4lZMvaWqewF8fDVQD1jm6y3AZT2OaExV01V1u4+lAYcARKQabrbgj6HrytKfhvY3CYAZIjIv\nS6KvsX4P+aZZ7smNwHvkM+u7ix5bYmxMEaKqm4B2WeMi8hhwKfAx7hf+l89w04wxBdR1112XaYbb\nddddR5cuXTLtcxcfH0/fvn155ZVXMmLp6en07NmTjRvdSsPGjRszZ84cKlVyCT1GjRpFrVq1ePTR\nR/n++++Jjo6mXr16TJkyhfPPPx+AoUOHsnXrVi688EKio6N55JFH6NSpEwBVq1Zlzpw5/M///A/3\n3nsvTZs2zZS043Tq1rnlYg7sOsD8+xYSVTKKi7rW5pzfVQdg4/zN7N+6nzWJ37Im8VvAzeRjn6v7\n8ccf8+ijj7J//36qVKnCnXfeyRNPuLGVw4cPM2jQIL777jvAJU6ZPXs2F154IQDPP/88O3fupGfP\no1vBxsfHZ8yEHDNmDH369OHIkSPUqlWL8ePH0759e8DNuAxXqlQpKlWqlLFU25gwTVV1hIh8CHT0\nsenAPOCjLGWTgU7+9wdE5Cygsqr+KiIrRKS+qq7xx0oCQ4AHI9nY5JUpJCQe/fJg+dqtDLqjOa0a\n5ZzkyBhTZEUB/fxSY4D/ww2ivQf0CCv3LPAksFxEvgTuBCoCPwOISHfgEdzEiPdwkyXADSxW8mUj\nGcN/bnngHtwXLeBmCL4E/IWwL2iy6U8H+KXLrXDLqm8CnlfVkSJyMW459VVh198NN4sy31jfXTTZ\nAKExxcNkYLaI3AbMC/vmyhhTjK1fvz7HY3fccUem9xMnTmTixIkZ74cPH87w4cOPe/7+/fsfN+Pu\nWWedxcsvv8zLL2f/ncVVV12VaRlxpOqWKFmCS+9tyKX3Njzm2CW31eGS2+rk2ObExMQcj9WrV++Y\nWZLhpkyZwpQpU3I8/p///CfHY1klJSXluqwpPvyD5KUi8gFQGlAgUNU0EUnm6N5ViEhl3BK8tSLS\nSlWTgbZAXV8/Bjc0vsZXeRaYqarHZt05DfMWb8g2Zg+ZxhRLATBVVUcBiEgbXL/0Om7p7mYAv7/f\nQ8BDIvISbob0FvwqKVWdLSLLcLOhd+MG9cD1a7vzIIaIRAFTcPsepvo+9nxVXSMikHlmZKb+VFV3\n+z+TRSQhS+x7EQkfXIwBqqnqj7m8p3nC+u6iyZYYG1MMqOpuVW2vqleo6oj8bo8xxhhj8sRNwB9V\n9TpVvQaIxW2aD24Wy/0cncUyDHgGt2fXyLD61/v6VwFXAvgN+aNUNecRcmOMiYyorK/9XnvfAS0A\nRKRWWJntQDQwF+ghItV9PDQZ6lvgMj+Adw3wRR7EwPWjS1R1vn8vrqnyAW429yTf9mP6UxGp4P+8\nBNiTJVYNKBV2vdfhZlYaE3E2g9AYY4wxJos53ebmdxOMORWdgfFh71cDjwGo6k8i8jWA38y/vqo+\n6t8ni8jNuOXJ4VOLd/kH8YnA5yKSBCxQ1Scj1eBOLeNZvnbrMTFjTLEVvsT41bD4BFxCkADoLCJ9\nfHwPcJuq7hKRAcD7IrIPOAI8o6oH/J7sS3BLgntEOiYiscDjwGKfSfktVX0NaAngsy+Hll2E96fz\nVfUp4C0/47A0bokywHMiUh8o588d0g146tRubeRY3100Zd0A1BhTTPkNcdfPnz+fuLi4/G6OMcbk\nia7/7JKrcjZAePI2b94c2jOxtqpuyOfmmHxwqr9LJK9MyViu1qllvC1RK8Kiwje4NcZE1Jl+nrO+\nu3A6Xj9sMwiNMcYYU2zYwJ8xBU+rRrH2YGmMMYWM9d1Fj+1BaIwxxhhjjDHGGGNMMWYDhMYYY4wx\nxhhjjDHGFGO2xNgYY4wxxpgiTkSqAm8DZXHPAHcDf1bVO8PKLAT+Hy4bZ2X/swGYDVwK1MVl07xP\nVb86c603xhQHPjnJNCCULGkcMAOIU9XdPtnHCFx29mm4hCUlgW6qus0nCBkEpAGHgZGq+qnPHHw3\nsBu4xZ8rYjHfjjm+LfuBW33SlLlADC73Qy9V3Sgi04BLfBtfVdW3ReQC4E1cNubhqvpvESnrr/Ec\nYLmqDhSRF4DG/t40VtWqEbjtxmSwGYTGGGOMMcYUfX2BN1S1NdAK90CbVaCqn6hqO2AAMFVV26nq\nBNyD9tVAL2Do6TQkeWUKwyYvZtjkxSSvTDmdUxljipYAmOL7nXa4AbhNuMG4cH8CBqtqG+AaXMb1\n2ri+qaOv2xnYISKlgL6qeiUwCbhPRKIjGQMOADf7/vUdXH8LcI+PjQQeDrvGP/hrfNvHBgMPAm05\nmrH4L8CbvtxAAFUd4K9tIPCvU7zHp8368KLLBgiNMcYYY4wp+vYAzUWkiqoGwG+5qJOR6VBVN/uX\noZk5pyR5ZQoJiUtZvnYry9duJSFxqT1gGmPCZc2wOgvoJiLhYxd7gVYiEqOqB1T1INAFeEtV9wL4\n+GqgHrDM11sAXB7pmKqmq+p2H0sDDvk2bMkaww0QzhCReT7rMICo6gpVTQNSRSTGX8/vRSRZRG7O\nck9u8vfljLM+vGizAUJjjDHGGGOKvum4QcIlIvIhUOMUz/M08OKpNmLe4g25ihljiqUooJ+IJIlI\nEtACN7D2HtAjrNyzQBywXET+LiLlgIrALgAR6S4in4rIWNwS392+3l6gki8byRj+c8sD9+C2cwjF\nSgJDgFd9aICqXgU8iVtCndUef86zgf8C7YHH/XlCfg/My6ZunrM+vGizAUJjjDHGGGOKOD+bZpiq\n1gU+Ah4h+2XGORKRh4F1qvppXrTRGFPsBRzd2qAd8IWPv44beANAVXep6kOqehGwFegDbAHi/fHZ\nQG+gGm4wr6KvGhosjHQMEYkCpuC2Y0gNu6ZngZmqus63bbf/Mxmons09CP+cT/2swrX4L3VEpA6w\nxceNiSgbIDTGGGOMMaaIE5G4sCV623Cb4Wddyne8+h2Bq1R15Om0o1PL+FzFjDHFVlTW137A7Tvc\njEJEpFZYme24/mwu0ENEQoNuoYSs3wKX+QG8a3CDjpGOgdtncImqzg81zCcziVLVxLBYBf/nJRyd\nibhWRJr4xCRn+0HEz4BLfb8djxsIBehOPi0vBuvDizrLYmyMMcYYY0zR1wx4TEQO4R66ewOLReQj\nf/x1jj+j8EVcIoAkYI2qPnAqjWjVKJZBdzTPWJLWqWU8rRrFnsqpjDFFUz+fzRiOLssFmIBLCBIA\nnUWkj4/vAW7zWYMHAO+LyD7gCPCMqh4QkTeBJbglwT0iHRORWFxykcU+k/IMVX0dmAh87vvN+ar6\nFPCWiFQGSnN0VmQCbhuIaGCUj40G/oFLYPKa32cR3N6EXU/57p4m68OLtlx/a2iMKdr8Jrnr58+f\nT1xcXH43xxhjTCGzefNm2rdvD1BbVTfkc3NMPrDfJcyJREVF2fOnMXnE+mCTG8frh22JsTHGGGOM\nMcYYY4wxxZgNEBpjjDHGGGOMMcYYU4zZHoTGGGOMMcYUYX4/rzlAnKruFpFpQBIwCPjZF3sMaIDb\nR+tnYI+qdhWR9cBLqvqcP9dyYPbpJisxxpiixve1HwGxqrpVRJoC/wXuBMiSrCRJVdv57PDdcIlI\nUv3PcOBRoDXQTVUX+TrvZY0ZE0k2QGiMMcYYY0zRtwm4G3iOo8lIRqvqG6ECIlI/awzYCFzpj18M\npHP8ZCbHlbwyxTa3N8YUZcuBG3GJn3oAS49XWFXHA+NFZDiQpKqfAIiIAvdmKX5vNrEzyvrwos2W\nGBtjjDHGGFO0BcAsoJuIhP/+n91G5VljR4BfRaQGcBMwO4d6J5S8MoWExKUsX7uV5Wu3kpC4lOSV\nKadyKmOMKYgC3AzCDv59A2DNSdTP6FtV9eesB7OLnUnWhxd9NkBojDHGGGNM0XcIeA83oyVkkIgk\n+Z/zcQ+nodiosHLvAd2By4HPTrUBoVknJ4oZY0whlg6ki0gL4Jv8bkwkWR9e9NkSY2OMMcYYY4qH\n14F3gM3+fdYlxkHWmLcAmI9bOnfKy4uNMaaYmAu8AtwD3J/PbTEm12wGoTHGGGOMMcWAqqYC3wEt\nfCg3S4xR1YO4JCdZBw5PSqeW8bmKGWNMITcX+FJVj7v/YC7kqo8+U6wPL/psBqExxhhjjDFFX2jm\n33jgPv96kIj0869HHVMjrJ6qPgsgIm04xVmErRrFMuiO5rbBvTGmKAtU9TdcUqhwfxGR3rj+8yGg\nsYh85I+9n/UkIjIB6AJcLyKTVfX17GJ5dxnHsj686Mu30WdjTMEiIvHA+vnz5xMXF5ffzTHGGFPI\nbN68mfbt2wPUVtUN+dwckw/sdwlzIlFRUfb8aUwesT7Y5Mbx+mFbYmyMMcYYY4wxxhhjTDFmA4TG\nGGOMMcYYY4wxxhRjtgehMcYYY4wxBZyItAWmARtxv8PfBLytqu3CyiSpajsRWQh8qqrD/JKz4cAf\ngUVAV1XdKSLTcfsRHgKmA2VUtY6IXAJMBsoA8cC3wBJgLTAMWKSqd+b19RpjipewPm69D40DZgBx\nqrpbRKYCI4CSvlzgX3dT1W0iciMwCEgDDgMjVfVTEbkLtx/gbuAWf66IxXw75vi27AduVdVdvh8O\n7dc6UlUXisgQXFbjV1V1pL/u14FLgFLAfar6lYiMAG4EUoE5qvq8iNTG9dXlgHGq+uZp33RjsrAZ\nhMYYY4wxxhR8ATBFVVvjNrS//QTlrxaR0qE3qnoE+CswTERa+NiXwPfA5cBmH/vODzr+AZinqu1U\n9QncA3DH07mA5JUpDJu8mGGTF5O8MuV0TmWMKXpCfVw73wftBjZxbLKPPwGDVbUNcA2wyw+eDQU6\n+rqdgR0iUgroq6pXApOA+0QkOpIx4ABws++b3wH6hq4ndC2qutDHXgN6ZbmeEap6tY8PDbsXA3zd\n533sCeBB4EpgiIic0cle1n8XDzZAaIwxxhhjTOEQ2li8Mu7hOScBbuZNH8IyDqvqPKAOMAH3sImq\n7lXVfcf5rFDd7bhZOackeWUKCYlLWb52K8vXbiUhcak9ZBpjssqaPGEW0E1Ewsct9gKtRCRGVQ+o\n6kFcZt+3VHUvgI+vBuoBy3y9BbgvQyIaU9V03z+Cm714yL8+IiJJIvK/IlLFt+tXsmSBV9XNYXXD\n+9ixIrJQRJr69/HAKlVNB7b6tpwR1n8XHzZAaIwxxhhjTMEXBfQTkf8CHYDEE5SfDvTOJp4MpKnq\npgi377jmLd6Qq5gxptgK9XFJIpIEtMANtr0H9Agr9ywQBywXkb+LSDmgIrALQES6i8inIjIWiOHo\nlyl7gUq+bCRj+M8tD9wDvO1D3f1sxneB/5eL638aeNG/fl5VWwD9w2LfAa1FpCrQwF/bGWH9d/Fh\nA4TGGGOMMcYUfAEwVVUvA1bhZrMcEpGSAH7pW2jmCqqahhsM/H0oJiKVcUvv1olIq1NsgzHG5IVQ\nHxdaYvyFj7+OG3gDQFV3qepDqnoRbiZdH2ALboYdqjob9+VINdxgXkVfNTRYGOkYIhIFTMHtNZjq\n2xEaSJyNG9DLkYg8DKxT1U/D66rq9xztd/8K/AV4C/gG2J7NqYw5LTZAaIwxxhhjTOEQWn73DDAA\nl0CkpY+1wj00hnsJtyF+6AFzmK87FBh5Gp9/0jq1jM9VzBhTrEVlfe0H3L7DzShERGqFldkORANz\ngR4iUt3HQ/vzfQtc5gfwrsENOkY6Bq4/XaKq80MNE5EK/uVVwA85XCMi0hG4KpS0JLyuiFTDJS9B\nVX9S1etwCaoOqup3OdzDiLP+u/iwLMbGGGOMMcYUIqq6RkTOxg0STvezBw/gZtKEl/tJRL4G8Jv4\n11fVR/37ZBG5GfgMlxG0sYh8CNytqj/iBhUzZgyKSBdchtALReQdVb3lZNrcqlEsg+5onrEsrVPL\neFo1ij35izfGFGX9fDZjgFfD4hNwCUECoLOIhPq6PcBtPmvwAOB9EdkHHAGeUdUDIvImLhP7XqBH\npGMiEgs8Diz2mZRnqOrrQJKIhPYV7A3gMyDfD1QRkWqq+ifcEuJdfln1alV9EHhOROrjMhY/7ut2\nBh7FDYg+FoF7nWvWfxcfp/wtoDGmaBGReGD9/PnziYuLy+/mGGOMKWQ2b95M+/btAWqr6oZ8bo7J\nB/a7hDmRqKgoe/40Jo9YH2xy43j9sC0xNsYYY4wxxhhjjDGmGLMBQmOMMcYYY4wxxhhjijHbg9AY\nY4wxxphCzu/b9REQq6pbRaQp8F/gTgBVTQwrm6Sq7XzmzG647J+p/mc4bp+r1kA3VV3k67yXNWaM\nMZHk+7FpwHofGgfMAOJUdbeITAVGACV9ucC/7qaq2/wegIOA0N5/I1X1U7/33924rMO3+HNFLObb\nMce3ZT9wq98XcS4u23EU0EtVN4rILb6NAE+r6rsi8jvgZR+7X1W/EpFpwCX+Wl5R1ZkiMgjo5M85\nSlXfO+2bbkwYm0FojDHGGGNM0bAcuNG/7gEsPV5hVR2vqu1wD9oPq2o7Vf0EuBd4IUvx7GInJXll\nCsMmL2bY5MUkr0w5nVMZY4qmAJji+6J2uAG4TbjBuHB/AgarahtcNuFdPhHTUKCjr9sZ2CEipYC+\nqnolMAm4zyd2ilgMlyTqZlVtDbwD9PXtvMfHRgIP+9iDuOzzrYCHfGw40BXXfw8Puxd/8Pdipo89\nq6ptcV/W/OXkb2/2rG82ITZAaIwxxhhjTOEX4GYQdvDvGwBrTqJ+xqblqvpz1oPZxU5G8soUEhKX\nsnztVpav3UpC4lJ7EDXGZCdrAoVZQDcRCR+72Au0EpEYVT2gqgeBLsBbqroXwMdXA/WAZb7eAuDy\nSMdUNV1Vt/tYGnDIt2FL1hhu0DPG/+z0sRhV3aqqv/o4uD59hojM88lHUNXQOcqF1T0t1jebcDZA\naIwxxhhjTNGQDqSLSAvgm/xuTLh5izfkKmaMKdaigH4ikiQiSUAL3MDae7hZ0SHPAnHAchH5u4iU\nAyoCuwBEpLuIfCoiY3EDbrt9vb1AJV82kjH855YH7gHeDouVBIYAr/rQy7jZ3suBiTncA4ABqnoV\n8CRuqXXofBOBr3Ooe9KsbzbhbIDQGGOMMcaYomMu8AowO78bYowxJykApoYtMf7Cx1/HDbwBoKq7\nVPUhVb0I2Ar0Abbg9lNFVWcDvYFquMG8ir5qaLAw0jFEJAqYgtv3MDXsmp4FZqrqOv/+CdzegnWB\nYTndCFXd7f9MBqqHxe8HBDfoaExE2QChMcYYY4wxRcdc4EtVPe7+g7mQdZlfTrFc6dQyPlcxY0yx\nF5Wwi9x3AAAgAElEQVT1tR9w+w43oxARqRVWZjsQjev7eohIaDAtlJD12//f3p3Hyz3dfxx/XbEv\nIaS2xtbqW7XWKkpokCrVBaWWIklpQ1GlrdKiFdLa6kdRW5VYo1VVRaMqYilBWg1q6ScUbShiicQa\nqfz+OGeSb8ad7d65mbu8n49HHrlz5vs98/l+Z+Z773zmnM8BNskJvO1IScdmt0GqMzgxIsaXAsuL\nmbQVF4kCliItZPJm/hngDUkrSloJeCPvu3T+fx1gZv55kbz928Di1U9jfXxttiKvYmxmZmZm1jvM\niYg3eH9B/yMl7UsanXMYsKGkP+f7bijvRNJZpHpeX5B0fkRc1F5bI4EN3mBVjh6+6dypaztuuSaD\nN1i1kS7MrG8YkVczhnnTcgHOIi0IMgfYSdJ+uX0msGdeNfhw4AZJbwLvAadExCxJVwATSVOCd2t2\nm6RVgaOAe/JKylfma+S5wH15uvT4iBhNWhRqEin5eXE+hhOYdy0+JP9/laTlgMWYN3ryLEkfBZYG\nzujIyS3na7MVdfhbQDPrXXLx26fGjx/PoEGDWh2OmZn1MFOnTmXo0KEAa0XE0y0Ox1rAf0tYLW1t\nbf78adZFfA22elS7DnuKsZmZmZmZmZmZWR/mBKGZmZmZmZmZmVkf5hqEZmZmZtbn5ZpXY4CnctP/\nAVcCgyJihqRLgOOBfnm7OfnnXSLipVx36mhS8fj/kVayvCsXqf8GaaXLr+S+mtaW4/hDjuUtYI+I\neC0f0yrAk8BHI+Lfkm4irbrZBuyT28aQVtR8G7ggIq6WdDywMzAduD4izpR0NLBDPjeb5vNSXKnT\nzKzDets1WNKvgZWARYFhEfFEhbZa1+ULI2JsPkdtwN+BM8oWPjFrCo8gNDMzMzNLH/IujohtI2Jb\n0oe///D+BT++BfwgIoaQVrB8TdJawLHA9nnfnYBXJJU+BG4BnAcclFehbFobMAvYPSI+DVwDDCvE\nejhwb+H2yLzdKODbhePeKx/31YW2w3PbmQARcXI+tt2BSY0mB+9+6DmOO/8ejjv/Hu5+6LlGdjWz\nvqG3XYP3iYhtSEnLb1VpO7DGdXls4di/CLyY72+Yr8NWixOEZmZmZmZJeeHu3wG7SCr+zfw6MFjS\nMhExKyLeJa3ue1VEvA6Q2x8B1gUeyPvdBmze7LaIeCciXs5tbwOzASQNJI1KeaZ0XBHxbPl2pA+a\nV0q6ORe4LzlN0u2SNi47JzsD19OAux96jpMvncTkKdOYPGUaJ186yR9Ozaw9veYaHBGla+wywEtV\n2qaW70vl6/LewNXtnKeafB22eniKsZmZmZlZ+sA1Ik9zA/gj6cPa9cBuhe1+BpwITJb0V+BrQH/g\neQBJuwLfIY3cu540CgbSh9pl87bNbCM/7lLASOBLuenbwDnAkRRGm0jqBxwDHJqbDs/T5gaTpvR9\nmTR9bZSktUlT+bYqHP8uzBv5Upeb73m63bbBG6zaSDdm1rv1qmtwHm04AVgd2KJSW26veV2W9Fng\ndtL06Yb5Omz18AhCMzMzM7OURLukML3t/tx+EelDHwAR8VpEHBYRHwamAfsBzwJr5vuvA/YFBpI+\nSPbPuy6Tbze7rVSX6mJSza3pkpYDVouIR/O2xdEmPwOujognc7wz8v93Ax8oa3uC+ZOLywADI+KZ\n6qfSzKxhveYanON4NyK2In2pMrpSW1bzugwcQPrCpuHRg2b1coLQzMzMzCxpK/85f9j7J7AZgKTV\nC9u8DCwC3ATsJqn0Qa40S+dxYJP84XE70gfeZrdBql01MSLG59tKoWocsD2pVha5uH5bsbi9pKXz\n/+sAM8vaBpKK6Zd8jjSqpyE7brlmXW1m1uf1lmswkkoxzCQnFSu01boul0YsCvg9aXTkdyV9uNqJ\nLOfrsNXDU4zNzMzMzJLi9LYLC+1nkYrRzwF2krRfbp8J7JlXrDwcuEHSm8B7wCkRMUvSFcBE0nS0\n3ZrdJmlV4CjgnryK51UR8UtgS4C88uePc7znAvdJmgCMj4jRwFV5xOFizBulc7qkjwFL5r5Lyke9\n1GXwBqty9PBN505x23HLNT2tzcza0xuuwVcClwM350TiIsDBkhYrb8vHUNd1OSI2BpA0HJhTGm1Y\nL1+HrR4enmpmAOQCuE+NHz+eQYMGtTocMzPrYaZOncrQoUMB1oqIp1scjrWA/5awWtra2vz506yL\n+Bps9ah2HfYUYzMzMzMzMzMzsz7MCUIzMzMzMzMzM7M+zDUIzfqAXOD2CuB/wB0RcVSNXczMzKwX\nyPW8hkTEKEm7A3sBb0TEcEmPA//Nm55DKvg/BngKeCYiRhT6mQIcGxG/XoDhm5n1OPm6Owb4F7AE\naSXmW4HjI+JreZsRpJqKY0l1C1cG+pGus7dJmg5MBpYHhkfE3yWNBIYBbwPDIuK5BXhY1gc4QWjW\nNxwEHBYREyXdIql/RMyouZeZmZn1dHMAJG1N+nvgUOYtPPLfiNi2tKGkIcAlETGq2IGkDYEJwBeB\nmgnCvz3+Ihfc+G/AhfDNrE+aA1wcESdIWgL4M/BYO9tAWh3+wYj4CsxbwRj4e0Rsmxc++Z6kY4Gh\nEbFVPQHc/dBzXpDEGuYpxmZ9wyvAAEn98u13WhmMmZmZLVAfB04GdieNPKnmq5ImStqn0LYrcAGw\nuKRFaz3Yedc+yOQp05g8ZRonXzqJux/yIBcz63PaACLiLdJKxYdV2O51YH1Jq+TtXy+7/zFgRWAH\nYCFJd0r6paSKuZy/Pf4iJ186yddha5hHEJr1DRcDdwPvAmMjomKC8Pnnn19gQZmZWe/h3x/dVhvw\nWeC0iJguabnCfatImpB//jowCVgXWBy4TdLNEfEysHFEHC/pT8BngD9We8B335o+3+1rx/2VNZb/\nRHOOxno0SctFxPTaW5r1Ks8DKwD/Kb8jIsZL2ggYJ+kdYL+IiMImg4FngIHAChHxaUmjgS8Dv23v\nwcbd/hDvvjlnvjZfh62k2nXYCUKzXkTSasBlZc3PArOAXYAHgWslrRERz5RtNx24Y5999hnS9ZGa\nmVkvdQfp94l1H3NI9QW3lXQfMKVw33xTjAvelHQ7sI6kF0mjW8YBiwFB5QTh9MX6r/rq1InnDyg2\nPgXcdHHnDsJ6jcOB41sdhNkCtgrwNOnLl5JFgLcAIuJ04HRJXwFGAXsDG+Xr8CzgQFKJh4l53zuB\nT7bzONOBOyZcc9L7Ps/5OmwFFa/DThCa9SIR8R/gfX/oS7oeeCUi5kh6DRhA+iaquO90SbsAy5Xv\nb2ZmVqfpHh3ULb0L7AncAny10kaSloqIN3JJks2As4F9gAMiYkLe5npJbRExp3z/9LcEH8J/S1hl\nvj5Yn5JrEB4IHAOcKmmRiHiXNDLwbEkrkX53vgO8REocAkwuqxF7b+4DYANSwnE+/jxndap4HXaC\n0KxvOA24TtIsYEpETG5vo/yhzn+4mZmZ9S5zIuJlScNIJUf+UmG7vSUdACxKKknyrKSdgJ8XtnkU\n2Jo0guV9/LeEmRkAI/LCT0sAF0XEXZLOBiZKmg3cExEPSNoC+FluWwIY2V5nEXG/pKmS7gemAntU\n2M7XYDMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzM6tHW6sDMLPuQ9I6wBXA/4A7IuKoFodU\nk6SFgF8A65JWY9y7xSHVLS8e80BEjGp1LLVIOggYTqpde05EXNrikNol6Xzg48DDEXFwq+OpRdLm\nwJmkVUYfiIhDWxxS3SQdAXypwgqo3YqkkcAw4G1gWEQ81+KQqpK0OPB7YGlgJrBLLl7erUhaBbgJ\nWDcilshtPeo9aGZmZmaJFykxs6KDgMMiYqKkWyT1j4gZrQ6qhn2BRyPim60OpBGSNgAWJyWGeoIb\nI+J8SQsD9wHdLkEo6VPAWxGxtaQzJW0WEfe3Oq4angK2jojZkq6QtH5EPNzqoGqRtBiwIT3g9Svp\nQ8DQiNiq1bE0YEfSlzQnSTom376+xTG15xVgO+A66LHvQesESRsDPwaWYd7AgzeAURHx1zr72DYi\nJkgaAPyIlGD+FzA6IqbW2cfqwNHAOoU4Ajg5Ip5eUHEU+loGWBZ4LSJmNrJvM3WHOCQtAny0FAfw\neF7B1cyaRNLawLdIqyBfDIwG+gMnRMSDneh3deDbpMEjF0TEk7n9uIg4sdOBz/9Yx0bE6Cb00/Lr\nXk/mBKGZFb0CDJDUL9/udiNW2vF5YIake4CrIuKcVgdUp28B5wIbtTqQepQ+HOVEVnd9XWwG3JZ/\nvg3YHOjWyYmIeLFw8y1gdqtiadABpCTxj1odSB12ABaSdCfwT+DAiHivxTHV8jIwIP+8LDCthbFU\nlEc1viOp1NTj3oPWaecCOxevZZJWAv5Aev7r8SNgAvBL4FrgB8BWpBkN29TZxxWkLzgnF+LYGLic\ntOLyAolD0g7AUaQVRGcCy0haATg1Im6qs48jIuKMHP/ZpC9iFgWOiYhbe1gcXyON3v57KQ5gE0mX\nR8RF9fRhZnW5jPQlyTLARGAX0ntuDDC4E/1eCYwizcA4R9LVeRbRdkCHEoSShle4ay9SYrNDmnHd\na+CxromIr3Ri/x2AY0h/9/8qIq7M7ddGxG4d7HNn4EjgVdLv5mNIg1HOKPVfDycIzazoYuBu4F1g\nbHec0taOFYDHgZHA+HzBfqHFMVUl6aPAi6RfYD2KpK+TphR2R/1JfxCQ/1+2hbE0RNJ6wCoR8Vir\nY6kljwYZEhHnSuoJCcKBwAoR8WlJo4EvA79tcUy13A2MlvQPUnKw25d7yHrse9A6pXwkcUdHFq8Q\nEWPzz7dK+nED+y4KlF8/H8vtCzKOUcA2EfF2qUHSEsDt1P+780vAGfnfiIh4QtJA4E/AJj0sjpHA\nlhEx9zWRS8PcAzhBaNY8syLiTgBJ/46IB/LPnR2tO6f0hYCkLwA/y18aLNSJPk8jJbKKmlH6rhnX\nvflIuqTCXVt2pL+C0cBnSINxjpX0K+BgYPlO9Pkj4NPAksDDgEiJ3btJid66OEFo1gdJWo30TVPR\ns8As0jdODwLXSlojIp5Z0PG1p0rMM4C7ImKOpHuBDwPdIkFYJeY3SBfxdRd4UDVUiHlqROyX6+Xt\nTPrQ0B3NICUoyP939+nxAEhaHjgP6PA3kQvYfsBVrQ6iAa+Rvk0HuBP4ZAtjqdd+wHURcaak75JK\nKVze4pjq0SPfg9YphwAX5SldJW8AjdRTbZM0AXivVNpE0tKkkQ/1GgXcLOlV5r0OB9LYCJdmxPEu\naZpzcUrfOrm9XstJGgIsGRFPAETES5Le7IFxvA58SdJtETEzv06Gkl4jZtY8L0jqFxH/A7YFyGWB\nOlsKZqakD0bEs7nvIyTtTypL1VG/Bf4UEc8XGyV9sDOB0pzrXrkhwPakKdaQzmcbsHYn+oSU0H0t\n/3yspF1JSczOJAhnRsQbwBuSflcqEybp9UY6cYLQrA+KiP+Qf3kU5UUzXsnJttdIU9y6RYKwSszf\nB9YHbgXWIy1Y0i1Uiflm0pD/5YHlJf05Iu5ZwOG1q0rMHwROJy1K0V3rzk0C9iRNbdsOGFt989bL\nf7xdARxZ/odSNyZgo7xwzQaSDoqI81sdVBX3kqZZAGwAPN26UOq2PGmaMfn/gS2MpRE97j1onZNH\nqezcyT62aaftdWDTBvoYB4yTtCw5OV348LXA4iAl83+gwrx7YAop6V+v60lTmm8qS1Q+2gPj2B34\nJjBcUulLg0lAh6bQmVn7ImLPws/v5v9nS9qxk/1+vp22i0mzzjraZ7sLmEXETzraZ9aM61650cD0\niHi52CjplE70CWnW26BCCafrJD0JnNqJPm+WtHBEzC6d4zzr5/FGOvEqxmY2l6StgJ+TRhJOiYhh\nLQ6ppvwH53XAUsDNEXF8ayOqX/5mfkhEnNDqWGpRWpl0W6C0+utnu2ORcUm/JI3MfDQiRrY6nlok\n7U16zz2Sm74fEZNaGFJD8qiQ7VodRy2SfkH6kD8V2CMiunWtR0krAtcA/UjfWu8eEd2uDmFOcN8M\nfAL4K3As8A160HvQOqdQwH42cGGhgH2ni80v6D7UhIVOCn2ViuTPiA4u9uZC+2bWWZKG55qB3b7f\nrorVGuMEoZmZmZmZNUzSXcwrYH8McHVEXCppQkS8bzR6hT4qFaw/MiLWW4B93En7C52cFRF1LXTS\nXpF8Uq3kRhYH6XQfVfruVGH9RvvoikL8ZlY/STtExJ96Qr9dGOsZEXFEd++zq/pttE9PMTYzMzMz\ns45oRgH7ZhSsb0YfzVjopBlF8jvdRzMK6zepOH9XFOI3s3bk6aQfJY88Bh5vRsKtK/rtwlg3AzbP\n/c4A7gdO7m59dlW/zejTCUIzMzMzM+uIZhSwb0bB+mb00YyFTppRJL8ZfTSjsH4z+uiKQvxmVkbS\n14BhwN+ZN/J4E0mXR0SHVwzvin67MNZzSWWybsv99gf2BpYgrajeLfrsabGamZmZmZn1SZKWlbRa\nrnHc6L5rSDpf0m2FfxdIWmsB97G/pBXaaf/CAu7jBEmDyto2yIu1mVmTSJooqa2sbSFJ93a3frsw\n1rsqtP+lO/XZVf02q0/XIDQzMzMzs6ZpRrH53tSHmVlXkvRn4BzgtoiYmb/g2A74VkQM7U79dmGs\n5wNvMf8Iuu2ApSLiG92lz+4eayP1QczMzMzMzGp5vvYmfacPSWe4j+b2YWbz2R1YF7hU0q3AmHy7\ns4sBdUW/XRJrRBwE/BpYC9gKWBP4dWcSeV3RZ3eP1SMIzczMzMysQyoUm2+kXl5v66O9IvFPRcQL\n7qNjfZhZYyRdERH79oR+uypW6xgvUmJmvZak94A1I+LfnehjBDA8IrZtQjxP577u6Gxf1hqS1gMu\niohPdfHjjCF9gBpVx7a3A5f0pSlwkpYGfkdaTfP6iNinix9vDHU+H130+CsBE4CNImJWK2Iwa08z\nis33sj46XSTefZhZEzSyQFOr++2qWK0DnCA0sx4jJ9hWJK2o9y5pJbxDImJGhV3mNOFh5zSpn5p9\nSfoUaRXFzYB+wD+BsyPisiY9freWn99hEXHnAnzME4FdSCNGRteRADoBOLWw/9PMe02W/DIivtPJ\n0Bp53TXzNQqApL1Jr8WVgfeAO0nvtf9IWhT4JTAUGAA8AxwXEdfmfdcCrgM+DIyJiG8V+h0HHBMR\nD1R57Dbge8A3gFWAV4ArgeMLybE9gKWA/hHxXtn+ewEnRcRaZe0LA88BIyLijw2ekqaf40ZExAuS\nJpDOyS9aFYdZO0YCW0bE3PeHpIWAe4B6V6PsTX2sHxFbl7Vd32CRePdhZmYt4QShmfUkc4AdI+JO\nSR8EbgaOB+ZLxkhaOCJmk5IHb3TyMa8g1XPoUpIGk47ne8DOEfG2pM2Bg4E+kSAkPb8LuvTFFOBI\nUuKlagJI0irA1sCehea5r8kuiK3ec/E5UsK8me4ifVB+SdJSwAXA6aTE3MLAI8CREfGipGHA1ZLW\njYgngGOA84CxwAOStoiIiZL2BJ6slhzMzgK2AXaPiIdywnEM8BtSMhdgEBDlycHsOuA8SUPKRuvu\nSErkNrR6Zk4QHEgLE4TZlcC5OEFo3cvrwJcklRebb+R3b2/q45FcX6+8SPxj7qPDfZiZ2QLiBKGZ\n9UgR8aykm4H1Ye504oOZlywUMFPSmhHx7zxFcAbwIWAI8AQpAfFk3n9j4AzgE6TpMBdFxNHAfsBw\nYNvC4xyaH+cDpMTF4RExR9KH8+2Pk0YA3gJ8PSJeq+OQTiWNFrygcIz3Afflx20DfgrsDywG/BkY\nGRGvSloT+FeOczSwNClJ8wBppNfapCK1++e+RgBfAx4C9iHVWTo4Isbl+9fM+22ez9lpEfHzfN/x\nwDr5HO0CvAjsm2Mt7XsuaernW8CZEXFKrX0lXQ6sDoyT9D/SiLQziydI0jb5/F4IfJs0su0HETEm\n378kcBqp+PESwOOk5N0rlU56aXRmTl7VSshtD0yqtx6VpPOAD0TE7vn2KcAmEfGZWsdS1s8A4Gpg\nE9JzPxE4ICL+kze5GbiEVOx5BOm5nZT/fws4KCJuzH0dSHptLE8amffDiLii/DEjYmrhZhspsfZM\nvu9NCqMoI+IyST/L8T0BrAGcEhEzJE0C1pT0CHAUKfFX7Zx9BPgmsGFEPJL7f0rS7sDTkrbNfRwN\ntOX2wyLikkI870j6DWmqYDFBOAy4KiLek3Qd6TW6FPAwcGBEPJRjGENKFHyIVOT5S8AI4ClgVK3n\nI0/5ngB8hnQ9mQTsFhEv5/uHAqeQ3guvk0Y7npUTkaNI7/Glc+z7R8RLOf77gY9IWiUi/lvtPJot\nQLuT3rPDc0JtBvk13xf7iIiD8myAzUjJsBmk378T3UfH+jAzswXHCUIz62naACStBuxEmmZcsgPz\nEnzt2Yt531xfSkq47SlpeVLC7bh8/6LkxGMFXwQ2ApYEbiV9oDiXNMLohxFxV04iXAP8hJRQrCgn\ntjYHjqiy2SGkRMVGwPQc/wWkEV0ln2ReUuMWYBwpmbIY8DdJnyslAUnJkbERsbykL5BGgK0dEdOA\n35K+7d+BlMS4XdKUwrTMnYHPk5ImPyWN+NpcUr/8mBcDXwBWBcZL+mdE/L7avhGxn6StSDUaq43G\n+yApqbNyPt5bJf0tIh4GziFNSf14Hvm2IZVfCx2xPmnad7lKicXvAJMlDSclcPcHNizcX+1Yyp1F\nSgQuSkoqnkc6x/D+6a9bkKY5ryDpIFKydxVJy5ESqJtExBRJK5CS3O3Kz8cNpMLyfyGNomtvu48A\ny5HqdgE8CnxW0rR8XKcAJwJnVCkHUDIUmFJKDpZExLQ8JW37iPhhTtSvUUp6t+NSUrL5kDwad1nS\n+SrVjryGdD2YTXqPXgWsV9h/L+AzETE5T6kezvznuNrzAfBVUoLwJeCPwPeBoySJVDtxRERcJ2kZ\n0lRsSEnPIaT3+CvA/5GmNe6Sz8FsSU/k+50gtG4hfwF2cum2UrH5k/pqH7mfe4F7G93PfZhZEw3r\nQf12VazWAU4QmllP0gbcKGk28DZwI2mKccnJEfF6hX3nAL+NiH8ASLqSNGIQUtLq8Yg4L99+mzRq\noJKT8uO8Lul04OvAuRHxL1IiiDyy70xSEqyWAcBCwLQq2+wNnFoaOSTpaODxnLwo+UlE/A+4Q9LL\npNFSr+bt7yAlp0oJwqkRcX6O9UZJD5CmVt1JGgE5OE/ffEzSBfnxSwnC2yNiQu73cuYlNrcCFo2I\n00qPIel8UhLz9zX2rddsYFSuETVJ0u+APSRNISVl1iuNuIqIBxvsu5ZlgfJVF4uvyZLvRcSvIuIt\nSfuREkkzgEMj4rlax0Ia0TZXfg5LifC38kjEe6rE+WRhVOBlwLlKi1y8ThoJuJ6k5/KItpcrdRIR\nfwEGSBpIGu34c8qShJKWIE3DPz0iIjefAPyKlNS+MJ+j9Umj735HSuL+JiLO4P0GUvl98CKwQv65\njSojPiPiHkkvALuSpjrvAfyzNEowIq4qHMNo4PuSBuRzPQe4JiIm521npbxeerw6no85wK8KIwp/\nQ7rGQBqxe0NEXJf7mglMzvd9nZQ4nJb3Ox6YJmnxiHg7bzOD9Do0666aUWy+N/VhZrbAFWaZdPt+\nuypW6xgnCM2sJ5kDfL7KCLNao2qKyZ23SCPrII10+1cDcRR/kT0LrAQgaRBpZNHg3Hc/UlKjlldJ\nU0wHkqYxtmdFoLga879J1/BSwoSIKD7WO8DzZbeLycTyX8ZT83GsCEyLiHfKHqu4am/xPL5Z6HcQ\nsLqkVwv39yONPqu1b72mxfyruE7NMS+f+3qywf4a8RppVcuiqq/JiLhf0r9Iz+01ZXdXOpb55NFv\nPydNcV6KlKhaqkqcc5/3iHgzJ7cWi7TQxT6k0WyXS7of+HaFEYvFY3hJ0jGkKa9zE4SSFsnHNCUi\nflDY/mXyqLc8bba0349I02RPJ9UmHF9K2BVMa+8cZCtRPXFf7jLSt9JjSaUCStPJFyVNkd6VlGwr\njcBcmvRehPnfO/Op8/ko7l+81qxC5WvNasANeXRkySzSe/zZfLs/aQSxmZmZmVlTLdTqAMzMuoFn\nmTfNrx6rFX4exLxkwMmkBMPaEbEcadRdzetsrul2LylhUckLpNpuJWuQRqBVHAFWw6Cy26uRjuMF\n4AOSFivctwZVEiYFzwOPRsSAwr/+EbFTvr/WIg/1LAIxsGzU5Go55pdJyZS16+ijo4//MKm2Zd0k\nHUJKXD5HSswVVTqW8niOJCXHNsivq8F0cDGXiPhjRGxDSlj+jTT9uB4Lk5LYwNzE3+W5rdrUkJHA\nxIh4FPgY8NdINRwfZv4pvSUTSHX2Pl5slLQi6bhvrTNeSCMbh0ragjSF/8rcPow0lXfLfD4HUWNE\nYtaM5+M5Kl9r/gsMKXv/LBkRz8LcVZjXBpo9MtbMzMzMzAlCM+szqn2A/wOwjqSDJS0saQlJm1bZ\n/ihJS+ekxRGkBQsg1SScBbyRp3R+r4H4jgIOlTQyP36bpE9KKi2+MBb4rqSVc83Ck4DrykagVVOe\nAFlN0kgASZ8HNiZNfXwC+AdwgqR+kj5GSvKMreMx7gAWzedx0XwMH5X0iUIM1bwCrFVjm0WAH0la\nKD9Hu5Cmg76TYzxb0gfyY28saelqneXne3FSAmwRSYvn5Fd7bgU2zYmaonaPK9ebO5E0rXQYaRpr\nsQZhu8dS6LPU75KkVYpLq2oeV+2YKpG0oqTP5eTvLNKU49kVtv2KpJXzzyuT6vRdW9jkAlLtxD2i\n/ZWES0m9g5lXBuApYLv8nHySdkZ75mnK5wO/lrRB7udDpLqY40vT0+sREU+TRq+OBW4pjLBdkjTV\nekZ+7keX7dre89no81HptX4V8EVJu+bXaH9JG+X7LgR+Wjjvy0v6XGHfzYAnwguUmJmZmVkXcILQ\nzHqL9kZ/zSn7uXybOQCRVrndgTTi71XSlNpdq+x3I6lu2BPAnaQFCiAlQgYDM0n1+v5QIa73iS7Q\n9J8AAAIaSURBVIi7SVMWdyONJHqNtPBJacTUuaQFIx5k3lTqYj24ekbnFbe5G1hf0iukxT32LtU+\nI63kuDHpXNxCqm04rkI/cx87ImaTzuP2pJFw00nTOgfU2jc7DThJ0quSDqtwHFNJU5P/Szof3ylM\nkT0EeJq0CM1rpOdlkQr9lFyU+9uHtLrvm8C+7W2Y6wfeRVospmicpJmFf79WWrDlclJdzIdz4vWH\npKm9pZiqHUvxXJ1BmmY6nTTSdDyVn+9q57hfPsYXSbXstictsNOeT5CmAc8kLT4yBTgMQNIawAHA\npqQaeaXj3rusj9NINRbfzLdPBD5LGml6Y+SVr9txKOl1c21+/NtJNf52r3Gc7bmUNDLzskLbGNLr\n8wXgEdJq3/VcKxp5PtrtLydAdyMlFWeSVtreKm/3E9KKyPdLmpHjGlLoZx9SEtGsO2tGsfne1IeZ\nmZmZmfVGkt6TtHqr4+gMSSMk1T0Sq7uQtI2kSjUaF1QM60ma2IR+Wn4s1nPk0Z+PFpLLZmZmZmZN\n5UVKzMzM6pRXwd6i1XFY35KnR3+s1XGYmZmZWe/lBKGZWWPqmjLczdU7PbM7aihuSVuTpnu/r5+I\n6N+ckDqspz4HZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZm\nZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZm1r39P3Lw0z3+ZrPRAAAAAElFTkSuQmCC\n", - "text": [ - ""
- ]
- }
- ],
- "prompt_number": 11
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "print \"here are the pooled samples\\n\"\n",
- "import pandas as pd\n",
- "for i in sc_study.sample_info.index[pd.Series(sc_study.sample_info['is_pooled'], dtype='bool')]:\n",
- " print \"\\t\", i"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "here are the pooled samples\n",
- "\n",
- "\tM_M2_05\n",
- "\tM_M2nd_13\n",
- "\tM_M2nd_21\n",
- "\tP_M2nd_33\n",
- "\tP_M2nd_34\n",
- "\tN_CVN_17\n",
- "\tN_CVN_35\n",
- "\tS_MSA_19\n",
- "\tS_MSA_29\n"
- ]
- }
- ],
- "prompt_number": 12
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "##interatctive_localZ\n",
- "\n",
- "runs localZ comparision on 2 RNAseq samples... works on any datatype but only really has meaning on expression.
\n", - "sample1 - name of sample on x-axis
\n", - "sample2 - name of sample on y-axis
\n", - "pCut - p-value cutoff, must be a float. recommended that you paste here, not type in this box.
\n", - "\n", - "running this updates sc_study.localZ_result with a dataFrame of the results to be parsed by the user" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "sc_study.interactive_localZ()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "sample1 : P_M2nd_34\n", - "pCut : 0.01\n", - "data_type : expression\n", - "sample2 : P_M2nd_33\n", - "localZ finished, find the result in.localZ_result_"
- ]
- },
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "\n"
- ]
- },
- {
- "metadata": {},
- "output_type": "display_data",
- "png": 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zBl6PQnnIS89wgqpibmtVayW73x0gEs/hOA6yDPFUHr9X4YU9fbzwTh+JjEFb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\nQkJCQkJCQkJCQkJCQkLCpeB/A2899g6m3/W1AAAAAElFTkSuQmCC\n",
- "text": [
- ""
- ]
- }
- ],
- "prompt_number": 13
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "sc_study.localZ_result"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "html": [
- "
\n"
+ " \n"
+ " 
\n"
+ " \n"
+ "
\n\n"
+ "\n\n"
+ "".format(self.htmlfilename,
+ self.thumbfilename,
+ self.plotfunc))
+
+
+def main(app):
+ static_dir = os.path.join(app.builder.srcdir, '_static')
+ target_dir = os.path.join(app.builder.srcdir, 'gallery')
+ image_dir = os.path.join(app.builder.srcdir, 'gallery/_images')
+ thumb_dir = os.path.join(app.builder.srcdir, "gallery_thumbs")
+ source_dir = os.path.abspath(os.path.join(app.builder.srcdir,
+ '..', 'examples'))
+ if not os.path.exists(static_dir):
+ os.makedirs(static_dir)
+
+ if not os.path.exists(target_dir):
+ os.makedirs(target_dir)
+
+ if not os.path.exists(image_dir):
+ os.makedirs(image_dir)
+
+ if not os.path.exists(thumb_dir):
+ os.makedirs(thumb_dir)
+
+ if not os.path.exists(source_dir):
+ os.makedirs(source_dir)
+
+ banner_data = []
+
+ toctree = ("\n\n"
+ ".. toctree::\n"
+ " :hidden:\n\n")
+ contents = "\n\n"
+
+ # Write individual example files
+ for filename in glob.glob(os.path.join(source_dir, "*.py")):
+ ex = ExampleGenerator(filename, target_dir)
+
+ banner_data.append({"title": ex.pagetitle,
+ "url": os.path.join('gallery', ex.htmlfilename),
+ "thumb": os.path.join(ex.thumbfilename)})
+ shutil.copyfile(filename, os.path.join(target_dir, ex.pyfilename))
+ output = RST_TEMPLATE.format(sphinx_tag=ex.sphinxtag,
+ docstring=ex.docstring,
+ end_line=ex.end_line,
+ fname=ex.pyfilename,
+ img_file=ex.pngfilename)
+ with open(os.path.join(target_dir, ex.rstfilename), 'w') as f:
+ f.write(output)
+
+ toctree += ex.toctree_entry()
+ contents += ex.contents_entry()
+
+ if len(banner_data) < 10:
+ banner_data = (4 * banner_data)[:10]
+
+ # write index file
+ index_file = os.path.join(target_dir, 'index.rst')
+ with open(index_file, 'w') as index:
+ index.write(INDEX_TEMPLATE.format(sphinx_tag="example_gallery",
+ toctree=toctree,
+ contents=contents))
+
+
+def setup(app):
+ app.connect('builder-inited', main)
diff --git a/doc/tutorial.rst b/doc/tutorial.rst
new file mode 100644
index 00000000..ccfced4b
--- /dev/null
+++ b/doc/tutorial.rst
@@ -0,0 +1,41 @@
+.. _tutorial:
+
+Flotilla tutorial
+=================
+
+Install flotilla
+----------------
+
+.. toctree::
+ :maxdepth: 2
+
+ installation
+
+Create a datapackage
+--------------------
+
+.. toctree::
+ :maxdepth: 2
+
+ tutorial/barebones_study_making
+ tutorial/advanced_study_making
+
+Plotting functions
+------------------
+
+.. toctree::
+ :maxdepth: 2
+
+ tutorial/pca
+ tutorial/classification
+ tutorial/network
+ tutorial/expression_specific
+ tutorial/splicing_specific
+
+Recreated papers
+----------------
+
+.. toctree::
+ :maxdepth: 2
+
+ tutorial/shalek2013
diff --git a/doc/tutorial/Makefile b/doc/tutorial/Makefile
new file mode 100644
index 00000000..5e573aab
--- /dev/null
+++ b/doc/tutorial/Makefile
@@ -0,0 +1,9 @@
+#!/bin/bash
+
+dir=.
+ipynbs=$(wildcard $(dir:=/*.ipynb))
+
+notebooks:
+
+ echo $(ipynbs)
+ $(foreach ipynb, $(ipynbs), ipython nbconvert --to rst "$(ipynb)";)
diff --git a/doc/whatsnew.rst b/doc/whatsnew.rst
new file mode 100644
index 00000000..15423d5c
--- /dev/null
+++ b/doc/whatsnew.rst
@@ -0,0 +1,15 @@
+.. _whatsnew:
+
+.. currentmodule:: flotilla
+
+What's new in the package
+=========================
+
+A catalog of new features, improvements, and bug-fixes in each release.
+
+.. include:: releases/v0.2.4.txt
+.. include:: releases/v0.2.3.txt
+.. include:: releases/v0.2.2.txt
+.. include:: releases/v0.2.1.txt
+.. include:: releases/v0.2.0.txt
+
diff --git a/docker/dev/.dockerignore b/docker/dev/.dockerignore
new file mode 100644
index 00000000..191381ee
--- /dev/null
+++ b/docker/dev/.dockerignore
@@ -0,0 +1 @@
+.git
\ No newline at end of file
diff --git a/docker/dev/Dockerfile b/docker/dev/Dockerfile
new file mode 100644
index 00000000..7db7febd
--- /dev/null
+++ b/docker/dev/Dockerfile
@@ -0,0 +1,44 @@
+FROM mlovci/anaconda_python
+
+MAINTAINER Michael Lovci {2}
\n" + " \n" + " \n" + "+ 2. Then start flotilla on the command line (OS X `Terminal` application): + + +
curl https://raw.githubusercontent.com/YeoLab/flotilla/master/docker/start_docker.py | python
+
+
+After the ipython notebook interface opens, test the installation with our test dataset by running the following commands in a new notebook:
+
+ import flotilla
+ study = flotilla.embark("http://sauron.ucsd.edu/flotilla_projects/neural_diff_chr22/datapackage.json")
+ study.interactive_pca()
+
+Thanks for using flotilla!
\ No newline at end of file
diff --git a/docker/master/.dockerignore b/docker/master/.dockerignore
new file mode 100644
index 00000000..191381ee
--- /dev/null
+++ b/docker/master/.dockerignore
@@ -0,0 +1 @@
+.git
\ No newline at end of file
diff --git a/docker/master/Dockerfile b/docker/master/Dockerfile
new file mode 100644
index 00000000..0045076c
--- /dev/null
+++ b/docker/master/Dockerfile
@@ -0,0 +1,32 @@
+FROM mlovci/anaconda_python
+
+MAINTAINER Michael Lovci \n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 25,
+ "text": [
+ " S1 S2 S3 S4 S5 S6 \\\n",
+ "GENE \n",
+ "XKR4 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
+ "AB338584 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
+ "B3GAT2 0.000000 0.000000 0.023441 0.000000 0.000000 0.029378 \n",
+ "NPL 72.008590 0.000000 128.062012 0.095082 0.000000 0.000000 \n",
+ "T2 0.109249 0.172009 0.000000 0.000000 0.182703 0.076012 \n",
+ "\n",
+ " S7 S8 S9 S10 ... S12 \\\n",
+ "GENE ... \n",
+ "XKR4 0.000000 0.000000 0.000000 0.000000 ... 0.000000 \n",
+ "AB338584 0.000000 0.000000 0.000000 0.000000 ... 0.000000 \n",
+ "B3GAT2 0.000000 0.055452 0.000000 0.029448 ... 0.000000 \n",
+ "NPL 112.310234 104.329122 0.119230 0.000000 ... 0.000000 \n",
+ "T2 0.078698 0.000000 0.093698 0.076583 ... 0.693459 \n",
+ "\n",
+ " S13 S14 S15 S16 S17 S18 \\\n",
+ "GENE \n",
+ "XKR4 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
+ "AB338584 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
+ "B3GAT2 0.000000 0.031654 0.000000 0.000000 0.000000 42.150208 \n",
+ "NPL 0.116802 0.104200 0.106188 0.229197 0.110582 0.000000 \n",
+ "T2 0.010137 0.081936 0.000000 0.000000 0.086879 0.068174 \n",
+ "\n",
+ " P1 P2 P3 \n",
+ "GENE \n",
+ "XKR4 0.000000 0.019906 0.000000 \n",
+ "AB338584 0.000000 0.000000 0.000000 \n",
+ "B3GAT2 0.680327 0.022996 0.110236 \n",
+ "NPL 7.109356 6.727028 14.525447 \n",
+ "T2 0.062063 0.000000 0.050605 \n",
+ "\n",
+ "[5 rows x 21 columns]"
+ ]
+ }
+ ],
+ "prompt_number": 25
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "These data are in the \"transcripts per million,\" aka TPM unit. See [this](http://haroldpimentel.wordpress.com/2014/05/08/what-the-fpkm-a-review-rna-seq-expression-units/) blog post if that sounds weird to you."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "These data are formatted with samples on the columns, and genes on the rows. But we want the opposite, with samples on the rows and genes on the columns. This follows [`scikit-learn`](http://scikit-learn.org/stable/tutorial/basic/tutorial.html#loading-an-example-dataset)'s standard of data matrices with size (`n_samples`, `n_features`) as each gene is a feature. So we will simply transpose this."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "expression = expression.T\n",
+ "expression.head()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "html": [
+ "| \n", + " | S1 | \n", + "S2 | \n", + "S3 | \n", + "S4 | \n", + "S5 | \n", + "S6 | \n", + "S7 | \n", + "S8 | \n", + "S9 | \n", + "S10 | \n", + "... | \n", + "S12 | \n", + "S13 | \n", + "S14 | \n", + "S15 | \n", + "S16 | \n", + "S17 | \n", + "S18 | \n", + "P1 | \n", + "P2 | \n", + "P3 | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| GENE | \n", + "\n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " |
| XKR4 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "... | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.019906 | \n", + "0.000000 | \n", + "
| AB338584 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "... | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "
| B3GAT2 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.023441 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.029378 | \n", + "0.000000 | \n", + "0.055452 | \n", + "0.000000 | \n", + "0.029448 | \n", + "... | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.031654 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "42.150208 | \n", + "0.680327 | \n", + "0.022996 | \n", + "0.110236 | \n", + "
| NPL | \n", + "72.008590 | \n", + "0.000000 | \n", + "128.062012 | \n", + "0.095082 | \n", + "0.000000 | \n", + "0.000000 | \n", + "112.310234 | \n", + "104.329122 | \n", + "0.119230 | \n", + "0.000000 | \n", + "... | \n", + "0.000000 | \n", + "0.116802 | \n", + "0.104200 | \n", + "0.106188 | \n", + "0.229197 | \n", + "0.110582 | \n", + "0.000000 | \n", + "7.109356 | \n", + "6.727028 | \n", + "14.525447 | \n", + "
| T2 | \n", + "0.109249 | \n", + "0.172009 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.182703 | \n", + "0.076012 | \n", + "0.078698 | \n", + "0.000000 | \n", + "0.093698 | \n", + "0.076583 | \n", + "... | \n", + "0.693459 | \n", + "0.010137 | \n", + "0.081936 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.086879 | \n", + "0.068174 | \n", + "0.062063 | \n", + "0.000000 | \n", + "0.050605 | \n", + "
5 rows \u00d7 21 columns
\n", + "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 36,
+ "text": [
+ "GENE XKR4 AB338584 B3GAT2 NPL T2 T PDE10A \\\n",
+ "S1 0 0 0.000000 72.008590 0.109249 0 0 \n",
+ "S2 0 0 0.000000 0.000000 0.172009 0 0 \n",
+ "S3 0 0 0.023441 128.062012 0.000000 0 0 \n",
+ "S4 0 0 0.000000 0.095082 0.000000 0 0 \n",
+ "S5 0 0 0.000000 0.000000 0.182703 0 0 \n",
+ "\n",
+ "GENE 1700010I14RIK 6530411M01RIK PABPC6 ... AK085062 DHX9 \\\n",
+ "S1 0 0 0 ... 0 0.774638 \n",
+ "S2 0 0 0 ... 0 0.367391 \n",
+ "S3 0 0 0 ... 0 0.249858 \n",
+ "S4 0 0 0 ... 0 0.354157 \n",
+ "S5 0 0 0 ... 0 0.039263 \n",
+ "\n",
+ "GENE RNASET2B FGFR1OP CCR6 BRP44L AK014435 AK015714 SFT2D1 \\\n",
+ "S1 23.520936 0.000000 0 460.316773 0 0.000000 39.442566 \n",
+ "S2 1.887873 0.000000 0 823.890290 0 0.000000 4.967412 \n",
+ "S3 0.313510 0.166772 0 1002.354241 0 0.000000 0.000000 \n",
+ "S4 0.000000 0.887003 0 1230.766795 0 0.000000 0.131215 \n",
+ "S5 0.000000 131.077131 0 1614.749122 0 0.242179 95.485743 \n",
+ "\n",
+ "GENE PRR18 \n",
+ "S1 0 \n",
+ "S2 0 \n",
+ "S3 0 \n",
+ "S4 0 \n",
+ "S5 0 \n",
+ "\n",
+ "[5 rows x 27723 columns]"
+ ]
+ }
+ ],
+ "prompt_number": 36
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The authors filtered the expression data based on having at least 3 single cells express genes with at TPM (transcripts per million, ) > 1. We can express this in using the [`pandas`](http://pandas.pydata.org) DataFrames easily.\n",
+ "\n",
+ "First, from reading the paper and looking at the data, I know there are 18 single cells, and there are 18 samples that start with the letter \"S.\" So I will extract the single samples from the `index` (row names) using a `lambda`, a tiny function which in this case, tells me whether or not that sample id begins with the letter \"S\"."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "singles_ids = expression.index[expression.index.map(lambda x: x.startswith('S'))]\n",
+ "print('number of single cells:', len(singles_ids))\n",
+ "singles = expression.ix[singles_ids]\n",
+ "\n",
+ "expression_filtered = expression.ix[:, singles[singles > 1].count() >= 3]\n",
+ "expression_filtered = np.log(expression_filtered + 1)\n",
+ "expression_filtered.shape"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "('number of single cells:', 18)\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 4,
+ "text": [
+ "(21, 6312)"
+ ]
+ }
+ ],
+ "prompt_number": 4
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Hmm, that's strange. The paper states that they had 6313 genes after filtering, but I get 6312. Even using \"`singles >= 1`\" doesn't help.\n",
+ "\n",
+ "(I also tried this with the expression table provided in the supplementary data as \"`SupplementaryTable2.xlsx`,\" and got the same results.)\n",
+ "\n",
+ "Now that we've taken care of importing and filtering the expression data, let's do the feature data of the expression data.\n"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Expression feature data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "\n",
+ "This is similar to the `fData` from `BioconductoR`, where there's some additional data on your features that you want to look at. They uploaded information about the features in their OTHER expression matrix, uploaded as a supplementary file, `Supplementary_Table2.xlsx`.\n",
+ "\n",
+ "Notice that this is a `csv` and not an `xlsx`. This is because Excel mangled the gene IDS that started with `201*` and assumed they were dates :(\n",
+ "\n",
+ "The workaround I did was to add another column to the sheet with the formula `=\"'\" & A1`, press `Command`-`Shift`-`End` to select the end of the rows, and then do `Ctrl`-`D` to \"fill down\" to the bottom (thanks to [this](http://superuser.com/questions/298276/excel-keyboard-shortcut-to-copy-fill-down-for-all-cells-with-non-blank-adjacent) stackoverflow post for teaching me how to Excel). Then, I saved the file as a `csv` for maximum portability and compatibility."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "expression2 = pd.read_csv('nature12172-s1/Supplementary_Table2.csv', \n",
+ " # Need to specify the index column as both the first and the last columns,\n",
+ " # Because the last column is the \"Gene Category\"\n",
+ " index_col=[0, -1], parse_dates=False, infer_datetime_format=False)\n",
+ "\n",
+ "# This was also in features x samples format, so we need to transpose\n",
+ "expression2 = expression2.T\n",
+ "expression2.head()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "html": [
+ "| GENE | \n", + "XKR4 | \n", + "AB338584 | \n", + "B3GAT2 | \n", + "NPL | \n", + "T2 | \n", + "T | \n", + "PDE10A | \n", + "1700010I14RIK | \n", + "6530411M01RIK | \n", + "PABPC6 | \n", + "... | \n", + "AK085062 | \n", + "DHX9 | \n", + "RNASET2B | \n", + "FGFR1OP | \n", + "CCR6 | \n", + "BRP44L | \n", + "AK014435 | \n", + "AK015714 | \n", + "SFT2D1 | \n", + "PRR18 | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| S1 | \n", + "0 | \n", + "0 | \n", + "0.000000 | \n", + "72.008590 | \n", + "0.109249 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "... | \n", + "0 | \n", + "0.774638 | \n", + "23.520936 | \n", + "0.000000 | \n", + "0 | \n", + "460.316773 | \n", + "0 | \n", + "0.000000 | \n", + "39.442566 | \n", + "0 | \n", + "
| S2 | \n", + "0 | \n", + "0 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.172009 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "... | \n", + "0 | \n", + "0.367391 | \n", + "1.887873 | \n", + "0.000000 | \n", + "0 | \n", + "823.890290 | \n", + "0 | \n", + "0.000000 | \n", + "4.967412 | \n", + "0 | \n", + "
| S3 | \n", + "0 | \n", + "0 | \n", + "0.023441 | \n", + "128.062012 | \n", + "0.000000 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "... | \n", + "0 | \n", + "0.249858 | \n", + "0.313510 | \n", + "0.166772 | \n", + "0 | \n", + "1002.354241 | \n", + "0 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0 | \n", + "
| S4 | \n", + "0 | \n", + "0 | \n", + "0.000000 | \n", + "0.095082 | \n", + "0.000000 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "... | \n", + "0 | \n", + "0.354157 | \n", + "0.000000 | \n", + "0.887003 | \n", + "0 | \n", + "1230.766795 | \n", + "0 | \n", + "0.000000 | \n", + "0.131215 | \n", + "0 | \n", + "
| S5 | \n", + "0 | \n", + "0 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.182703 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "... | \n", + "0 | \n", + "0.039263 | \n", + "0.000000 | \n", + "131.077131 | \n", + "0 | \n", + "1614.749122 | \n", + "0 | \n", + "0.242179 | \n", + "95.485743 | \n", + "0 | \n", + "
5 rows \u00d7 27723 columns
\n", + "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 5,
+ "text": [
+ "'GENE '0610007L01RIK '0610007P14RIK '0610007P22RIK '0610008F07RIK \\\n",
+ "Gene Category NaN NaN NaN NaN \n",
+ "S1 27.181570 0.166794 0 0 \n",
+ "S2 37.682691 0.263962 0 0 \n",
+ "S3 0.056916 78.622459 0 0 \n",
+ "S4 55.649250 0.228866 0 0 \n",
+ "S5 0.000000 0.093117 0 0 \n",
+ "\n",
+ "'GENE '0610009B22RIK '0610009D07RIK '0610009O20RIK '0610010B08RIK \\\n",
+ "Gene Category NaN NaN NaN NaN \n",
+ "S1 0.000000 178.852732 0 0.962417 \n",
+ "S2 0.207921 0.141099 0 0.000000 \n",
+ "S3 0.145680 0.396363 0 0.000000 \n",
+ "S4 0.000000 88.798158 0 0.000000 \n",
+ "S5 131.326008 155.936361 0 0.000000 \n",
+ "\n",
+ "'GENE '0610010F05RIK '0610010K06RIK ... 'ZWILCH 'ZWINT \\\n",
+ "Gene Category NaN NaN ... NaN NaN \n",
+ "S1 0.000000 143.359550 ... 0.000000 302.361227 \n",
+ "S2 0.000000 0.255617 ... 0.000000 96.033724 \n",
+ "S3 0.024692 72.775846 ... 0.000000 427.915555 \n",
+ "S4 0.000000 93.825442 ... 0.000000 9.788557 \n",
+ "S5 0.000000 0.031029 ... 0.204522 26.575760 \n",
+ "\n",
+ "'GENE 'ZXDA 'ZXDB 'ZXDC 'ZYG11A 'ZYG11B 'ZYX 'ZZEF1 \\\n",
+ "Gene Category NaN NaN NaN NaN NaN NaN NaN \n",
+ "S1 0.000000 0 0 0 0.027717 297.918756 37.685501 \n",
+ "S2 0.020459 0 0 0 0.042430 0.242888 0.000000 \n",
+ "S3 0.000000 0 0 0 0.040407 6.753530 0.132011 \n",
+ "S4 0.017787 0 0 0 0.013452 0.274689 9.724890 \n",
+ "S5 0.000000 0 0 0 1.101589 59.256094 44.430726 \n",
+ "\n",
+ "'GENE 'ZZZ3 \n",
+ "Gene Category NaN \n",
+ "S1 0.000000 \n",
+ "S2 0.000000 \n",
+ "S3 0.017615 \n",
+ "S4 0.000000 \n",
+ "S5 0.000000 \n",
+ "\n",
+ "[5 rows x 27723 columns]"
+ ]
+ }
+ ],
+ "prompt_number": 5
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we need to strip the single-quote I added to all the gene names:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "new_index, indexer = expression2.columns.reindex(map(lambda x: (x[0].lstrip(\"'\"), x[1]), expression2.columns.values))\n",
+ "expression2.columns = new_index\n",
+ "expression2.head()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "html": [
+ "| 'GENE | \n", + "'0610007L01RIK | \n", + "'0610007P14RIK | \n", + "'0610007P22RIK | \n", + "'0610008F07RIK | \n", + "'0610009B22RIK | \n", + "'0610009D07RIK | \n", + "'0610009O20RIK | \n", + "'0610010B08RIK | \n", + "'0610010F05RIK | \n", + "'0610010K06RIK | \n", + "... | \n", + "'ZWILCH | \n", + "'ZWINT | \n", + "'ZXDA | \n", + "'ZXDB | \n", + "'ZXDC | \n", + "'ZYG11A | \n", + "'ZYG11B | \n", + "'ZYX | \n", + "'ZZEF1 | \n", + "'ZZZ3 | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Gene Category | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| S1 | \n", + "27.181570 | \n", + "0.166794 | \n", + "0 | \n", + "0 | \n", + "0.000000 | \n", + "178.852732 | \n", + "0 | \n", + "0.962417 | \n", + "0.000000 | \n", + "143.359550 | \n", + "... | \n", + "0.000000 | \n", + "302.361227 | \n", + "0.000000 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0.027717 | \n", + "297.918756 | \n", + "37.685501 | \n", + "0.000000 | \n", + "
| S2 | \n", + "37.682691 | \n", + "0.263962 | \n", + "0 | \n", + "0 | \n", + "0.207921 | \n", + "0.141099 | \n", + "0 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.255617 | \n", + "... | \n", + "0.000000 | \n", + "96.033724 | \n", + "0.020459 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0.042430 | \n", + "0.242888 | \n", + "0.000000 | \n", + "0.000000 | \n", + "
| S3 | \n", + "0.056916 | \n", + "78.622459 | \n", + "0 | \n", + "0 | \n", + "0.145680 | \n", + "0.396363 | \n", + "0 | \n", + "0.000000 | \n", + "0.024692 | \n", + "72.775846 | \n", + "... | \n", + "0.000000 | \n", + "427.915555 | \n", + "0.000000 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0.040407 | \n", + "6.753530 | \n", + "0.132011 | \n", + "0.017615 | \n", + "
| S4 | \n", + "55.649250 | \n", + "0.228866 | \n", + "0 | \n", + "0 | \n", + "0.000000 | \n", + "88.798158 | \n", + "0 | \n", + "0.000000 | \n", + "0.000000 | \n", + "93.825442 | \n", + "... | \n", + "0.000000 | \n", + "9.788557 | \n", + "0.017787 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0.013452 | \n", + "0.274689 | \n", + "9.724890 | \n", + "0.000000 | \n", + "
| S5 | \n", + "0.000000 | \n", + "0.093117 | \n", + "0 | \n", + "0 | \n", + "131.326008 | \n", + "155.936361 | \n", + "0 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.031029 | \n", + "... | \n", + "0.204522 | \n", + "26.575760 | \n", + "0.000000 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "1.101589 | \n", + "59.256094 | \n", + "44.430726 | \n", + "0.000000 | \n", + "
5 rows \u00d7 27723 columns
\n", + "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 6,
+ "text": [
+ "'GENE 0610007L01RIK 0610007P14RIK 0610007P22RIK 0610008F07RIK \\\n",
+ "Gene Category NaN NaN NaN NaN \n",
+ "S1 27.181570 0.166794 0 0 \n",
+ "S2 37.682691 0.263962 0 0 \n",
+ "S3 0.056916 78.622459 0 0 \n",
+ "S4 55.649250 0.228866 0 0 \n",
+ "S5 0.000000 0.093117 0 0 \n",
+ "\n",
+ "'GENE 0610009B22RIK 0610009D07RIK 0610009O20RIK 0610010B08RIK \\\n",
+ "Gene Category NaN NaN NaN NaN \n",
+ "S1 0.000000 178.852732 0 0.962417 \n",
+ "S2 0.207921 0.141099 0 0.000000 \n",
+ "S3 0.145680 0.396363 0 0.000000 \n",
+ "S4 0.000000 88.798158 0 0.000000 \n",
+ "S5 131.326008 155.936361 0 0.000000 \n",
+ "\n",
+ "'GENE 0610010F05RIK 0610010K06RIK ... ZWILCH ZWINT \\\n",
+ "Gene Category NaN NaN ... NaN NaN \n",
+ "S1 0.000000 143.359550 ... 0.000000 302.361227 \n",
+ "S2 0.000000 0.255617 ... 0.000000 96.033724 \n",
+ "S3 0.024692 72.775846 ... 0.000000 427.915555 \n",
+ "S4 0.000000 93.825442 ... 0.000000 9.788557 \n",
+ "S5 0.000000 0.031029 ... 0.204522 26.575760 \n",
+ "\n",
+ "'GENE ZXDA ZXDB ZXDC ZYG11A ZYG11B ZYX ZZEF1 \\\n",
+ "Gene Category NaN NaN NaN NaN NaN NaN NaN \n",
+ "S1 0.000000 0 0 0 0.027717 297.918756 37.685501 \n",
+ "S2 0.020459 0 0 0 0.042430 0.242888 0.000000 \n",
+ "S3 0.000000 0 0 0 0.040407 6.753530 0.132011 \n",
+ "S4 0.017787 0 0 0 0.013452 0.274689 9.724890 \n",
+ "S5 0.000000 0 0 0 1.101589 59.256094 44.430726 \n",
+ "\n",
+ "'GENE ZZZ3 \n",
+ "Gene Category NaN \n",
+ "S1 0.000000 \n",
+ "S2 0.000000 \n",
+ "S3 0.017615 \n",
+ "S4 0.000000 \n",
+ "S5 0.000000 \n",
+ "\n",
+ "[5 rows x 27723 columns]"
+ ]
+ }
+ ],
+ "prompt_number": 6
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We want to create a `pandas.DataFrame` from the \"Gene Category\" row for our `expression_feature_data`, which we will do via:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "gene_ids, gene_category = zip(*expression2.columns.values)\n",
+ "gene_categories = pd.Series(gene_category, index=gene_ids, name='gene_category')\n",
+ "gene_categories"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 7,
+ "text": [
+ "0610007L01RIK NaN\n",
+ "0610007P14RIK NaN\n",
+ "0610007P22RIK NaN\n",
+ "0610008F07RIK NaN\n",
+ "0610009B22RIK NaN\n",
+ "0610009D07RIK NaN\n",
+ "0610009O20RIK NaN\n",
+ "0610010B08RIK NaN\n",
+ "0610010F05RIK NaN\n",
+ "0610010K06RIK NaN\n",
+ "0610010K14RIK NaN\n",
+ "0610010O12RIK NaN\n",
+ "0610011F06RIK NaN\n",
+ "0610011L14RIK NaN\n",
+ "0610012G03RIK NaN\n",
+ "...\n",
+ "ZSWIM5 NaN\n",
+ "ZSWIM6 NaN\n",
+ "ZSWIM7 NaN\n",
+ "ZUFSP LPS Response\n",
+ "ZW10 NaN\n",
+ "ZWILCH NaN\n",
+ "ZWINT NaN\n",
+ "ZXDA NaN\n",
+ "ZXDB NaN\n",
+ "ZXDC NaN\n",
+ "ZYG11A NaN\n",
+ "ZYG11B NaN\n",
+ "ZYX NaN\n",
+ "ZZEF1 NaN\n",
+ "ZZZ3 NaN\n",
+ "Name: gene_category, Length: 27723, dtype: object"
+ ]
+ }
+ ],
+ "prompt_number": 7
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "expression_feature_data = pd.DataFrame(gene_categories)\n",
+ "expression_feature_data.head()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "html": [
+ "| 'GENE | \n", + "0610007L01RIK | \n", + "0610007P14RIK | \n", + "0610007P22RIK | \n", + "0610008F07RIK | \n", + "0610009B22RIK | \n", + "0610009D07RIK | \n", + "0610009O20RIK | \n", + "0610010B08RIK | \n", + "0610010F05RIK | \n", + "0610010K06RIK | \n", + "... | \n", + "ZWILCH | \n", + "ZWINT | \n", + "ZXDA | \n", + "ZXDB | \n", + "ZXDC | \n", + "ZYG11A | \n", + "ZYG11B | \n", + "ZYX | \n", + "ZZEF1 | \n", + "ZZZ3 | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Gene Category | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| S1 | \n", + "27.181570 | \n", + "0.166794 | \n", + "0 | \n", + "0 | \n", + "0.000000 | \n", + "178.852732 | \n", + "0 | \n", + "0.962417 | \n", + "0.000000 | \n", + "143.359550 | \n", + "... | \n", + "0.000000 | \n", + "302.361227 | \n", + "0.000000 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0.027717 | \n", + "297.918756 | \n", + "37.685501 | \n", + "0.000000 | \n", + "
| S2 | \n", + "37.682691 | \n", + "0.263962 | \n", + "0 | \n", + "0 | \n", + "0.207921 | \n", + "0.141099 | \n", + "0 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.255617 | \n", + "... | \n", + "0.000000 | \n", + "96.033724 | \n", + "0.020459 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0.042430 | \n", + "0.242888 | \n", + "0.000000 | \n", + "0.000000 | \n", + "
| S3 | \n", + "0.056916 | \n", + "78.622459 | \n", + "0 | \n", + "0 | \n", + "0.145680 | \n", + "0.396363 | \n", + "0 | \n", + "0.000000 | \n", + "0.024692 | \n", + "72.775846 | \n", + "... | \n", + "0.000000 | \n", + "427.915555 | \n", + "0.000000 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0.040407 | \n", + "6.753530 | \n", + "0.132011 | \n", + "0.017615 | \n", + "
| S4 | \n", + "55.649250 | \n", + "0.228866 | \n", + "0 | \n", + "0 | \n", + "0.000000 | \n", + "88.798158 | \n", + "0 | \n", + "0.000000 | \n", + "0.000000 | \n", + "93.825442 | \n", + "... | \n", + "0.000000 | \n", + "9.788557 | \n", + "0.017787 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0.013452 | \n", + "0.274689 | \n", + "9.724890 | \n", + "0.000000 | \n", + "
| S5 | \n", + "0.000000 | \n", + "0.093117 | \n", + "0 | \n", + "0 | \n", + "131.326008 | \n", + "155.936361 | \n", + "0 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.031029 | \n", + "... | \n", + "0.204522 | \n", + "26.575760 | \n", + "0.000000 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "1.101589 | \n", + "59.256094 | \n", + "44.430726 | \n", + "0.000000 | \n", + "
5 rows \u00d7 27723 columns
\n", + "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 8,
+ "text": [
+ " gene_category\n",
+ "0610007L01RIK NaN\n",
+ "0610007P14RIK NaN\n",
+ "0610007P22RIK NaN\n",
+ "0610008F07RIK NaN\n",
+ "0610009B22RIK NaN"
+ ]
+ }
+ ],
+ "prompt_number": 8
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Splicing Data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We obtain the splicing data from this study from the supplementary information, specifically the `Supplementary_Table4.xls`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "splicing = pd.read_excel('nature12172-s1/Supplementary_Table4.xls', 'splicingTable.txt', index_col=(0,1))\n",
+ "splicing.head()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "html": [
+ "| \n", + " | gene_category | \n", + "
|---|---|
| 0610007L01RIK | \n", + "NaN | \n", + "
| 0610007P14RIK | \n", + "NaN | \n", + "
| 0610007P22RIK | \n", + "NaN | \n", + "
| 0610008F07RIK | \n", + "NaN | \n", + "
| 0610009B22RIK | \n", + "NaN | \n", + "
\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 9,
+ "text": [
+ " S1 \\\n",
+ "Event name gene \n",
+ "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- Os9 0.84 \n",
+ "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- Vta1 0.95 \n",
+ "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ Bclaf1 NaN \n",
+ "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ Bclaf1 0.02 \n",
+ "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ P4ha1 0.42 \n",
+ "\n",
+ " S2 \\\n",
+ "Event name gene \n",
+ "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- Os9 NaN \n",
+ "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- Vta1 NaN \n",
+ "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ Bclaf1 0.04 \n",
+ "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ Bclaf1 0.98 \n",
+ "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ P4ha1 NaN \n",
+ "\n",
+ " S3 \\\n",
+ "Event name gene \n",
+ "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- Os9 NaN \n",
+ "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- Vta1 NaN \n",
+ "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ Bclaf1 0.02 \n",
+ "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ Bclaf1 0.55 \n",
+ "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ P4ha1 NaN \n",
+ "\n",
+ " S4 \\\n",
+ "Event name gene \n",
+ "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- Os9 NaN \n",
+ "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- Vta1 0.84 \n",
+ "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ Bclaf1 NaN \n",
+ "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ Bclaf1 NaN \n",
+ "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ P4ha1 NaN \n",
+ "\n",
+ " S5 \\\n",
+ "Event name gene \n",
+ "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- Os9 NaN \n",
+ "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- Vta1 0.95 \n",
+ "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ Bclaf1 NaN \n",
+ "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ Bclaf1 NaN \n",
+ "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ P4ha1 0.94 \n",
+ "\n",
+ " S6 \\\n",
+ "Event name gene \n",
+ "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- Os9 0.01 \n",
+ "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- Vta1 0.91 \n",
+ "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ Bclaf1 0.14 \n",
+ "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ Bclaf1 NaN \n",
+ "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ P4ha1 NaN \n",
+ "\n",
+ " S7 \\\n",
+ "Event name gene \n",
+ "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- Os9 NaN \n",
+ "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- Vta1 0.87 \n",
+ "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ Bclaf1 NaN \n",
+ "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ Bclaf1 NaN \n",
+ "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ P4ha1 NaN \n",
+ "\n",
+ " S8 \\\n",
+ "Event name gene \n",
+ "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- Os9 NaN \n",
+ "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- Vta1 0.86 \n",
+ "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ Bclaf1 0.02 \n",
+ "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ Bclaf1 0.98 \n",
+ "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ P4ha1 0.03 \n",
+ "\n",
+ " S9 \\\n",
+ "Event name gene \n",
+ "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- Os9 NaN \n",
+ "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- Vta1 NaN \n",
+ "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ Bclaf1 NaN \n",
+ "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ Bclaf1 NaN \n",
+ "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ P4ha1 0.97 \n",
+ "\n",
+ " S10 \\\n",
+ "Event name gene \n",
+ "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- Os9 NaN \n",
+ "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- Vta1 NaN \n",
+ "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ Bclaf1 NaN \n",
+ "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ Bclaf1 NaN \n",
+ "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ P4ha1 NaN \n",
+ "\n",
+ " S11 \\\n",
+ "Event name gene \n",
+ "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- Os9 0.03 \n",
+ "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- Vta1 0.93 \n",
+ "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ Bclaf1 NaN \n",
+ "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ Bclaf1 NaN \n",
+ "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ P4ha1 NaN \n",
+ "\n",
+ " S13 \\\n",
+ "Event name gene \n",
+ "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- Os9 NaN \n",
+ "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- Vta1 NaN \n",
+ "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ Bclaf1 NaN \n",
+ "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ Bclaf1 NaN \n",
+ "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ P4ha1 NaN \n",
+ "\n",
+ " S14 \\\n",
+ "Event name gene \n",
+ "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- Os9 NaN \n",
+ "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- Vta1 NaN \n",
+ "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ Bclaf1 NaN \n",
+ "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ Bclaf1 NaN \n",
+ "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ P4ha1 NaN \n",
+ "\n",
+ " S15 \\\n",
+ "Event name gene \n",
+ "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- Os9 0.02 \n",
+ "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- Vta1 0.96 \n",
+ "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ Bclaf1 0.01 \n",
+ "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ Bclaf1 0.06 \n",
+ "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ P4ha1 NaN \n",
+ "\n",
+ " S16 \\\n",
+ "Event name gene \n",
+ "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- Os9 NaN \n",
+ "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- Vta1 NaN \n",
+ "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ Bclaf1 NaN \n",
+ "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ Bclaf1 NaN \n",
+ "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ P4ha1 NaN \n",
+ "\n",
+ " S17 \\\n",
+ "Event name gene \n",
+ "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- Os9 0.01 \n",
+ "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- Vta1 NaN \n",
+ "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ Bclaf1 NaN \n",
+ "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ Bclaf1 NaN \n",
+ "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ P4ha1 NaN \n",
+ "\n",
+ " S18 \\\n",
+ "Event name gene \n",
+ "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- Os9 NaN \n",
+ "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- Vta1 NaN \n",
+ "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ Bclaf1 NaN \n",
+ "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ Bclaf1 NaN \n",
+ "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ P4ha1 NaN \n",
+ "\n",
+ " 10,000 cell Rep1 (P1) \\\n",
+ "Event name gene \n",
+ "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- Os9 0.27 \n",
+ "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- Vta1 0.83 \n",
+ "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ Bclaf1 0.40 \n",
+ "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ Bclaf1 0.62 \n",
+ "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ P4ha1 0.43 \n",
+ "\n",
+ " 10,000 cell Rep2 (P2) \\\n",
+ "Event name gene \n",
+ "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- Os9 0.37 \n",
+ "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- Vta1 0.85 \n",
+ "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ Bclaf1 0.49 \n",
+ "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ Bclaf1 0.63 \n",
+ "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ P4ha1 0.36 \n",
+ "\n",
+ " 10,000 cell Rep3 (P3) \n",
+ "Event name gene \n",
+ "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- Os9 0.31 \n",
+ "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- Vta1 0.64 \n",
+ "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ Bclaf1 0.59 \n",
+ "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ Bclaf1 0.70 \n",
+ "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ P4ha1 0.52 "
+ ]
+ }
+ ],
+ "prompt_number": 9
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "splicing = splicing.T\n",
+ "splicing"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "html": [
+ "| \n", + " | \n", + " | S1 | \n", + "S2 | \n", + "S3 | \n", + "S4 | \n", + "S5 | \n", + "S6 | \n", + "S7 | \n", + "S8 | \n", + "S9 | \n", + "S10 | \n", + "S11 | \n", + "S13 | \n", + "S14 | \n", + "S15 | \n", + "S16 | \n", + "S17 | \n", + "S18 | \n", + "10,000 cell Rep1 (P1) | \n", + "10,000 cell Rep2 (P2) | \n", + "10,000 cell Rep3 (P3) | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Event name | \n", + "gene | \n", + "\n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " |
| chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- | \n", + "Os9 | \n", + "0.84 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.01 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.03 | \n", + "NaN | \n", + "NaN | \n", + "0.02 | \n", + "NaN | \n", + "0.01 | \n", + "NaN | \n", + "0.27 | \n", + "0.37 | \n", + "0.31 | \n", + "
| chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- | \n", + "Vta1 | \n", + "0.95 | \n", + "NaN | \n", + "NaN | \n", + "0.84 | \n", + "0.95 | \n", + "0.91 | \n", + "0.87 | \n", + "0.86 | \n", + "NaN | \n", + "NaN | \n", + "0.93 | \n", + "NaN | \n", + "NaN | \n", + "0.96 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.83 | \n", + "0.85 | \n", + "0.64 | \n", + "
| chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ | \n", + "Bclaf1 | \n", + "NaN | \n", + "0.04 | \n", + "0.02 | \n", + "NaN | \n", + "NaN | \n", + "0.14 | \n", + "NaN | \n", + "0.02 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.01 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.40 | \n", + "0.49 | \n", + "0.59 | \n", + "
| chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ | \n", + "Bclaf1 | \n", + "0.02 | \n", + "0.98 | \n", + "0.55 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.98 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.06 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.62 | \n", + "0.63 | \n", + "0.70 | \n", + "
| chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ | \n", + "P4ha1 | \n", + "0.42 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.94 | \n", + "NaN | \n", + "NaN | \n", + "0.03 | \n", + "0.97 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.43 | \n", + "0.36 | \n", + "0.52 | \n", + "
\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 10,
+ "text": [
+ "Event name chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- \\\n",
+ "gene Os9 \n",
+ "S1 0.84 \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "S6 0.01 \n",
+ "S7 NaN \n",
+ "S8 NaN \n",
+ "S9 NaN \n",
+ "S10 NaN \n",
+ "S11 0.03 \n",
+ "S13 NaN \n",
+ "S14 NaN \n",
+ "S15 0.02 \n",
+ "S16 NaN \n",
+ "S17 0.01 \n",
+ "S18 NaN \n",
+ "10,000 cell Rep1 (P1) 0.27 \n",
+ "10,000 cell Rep2 (P2) 0.37 \n",
+ "10,000 cell Rep3 (P3) 0.31 \n",
+ "\n",
+ "Event name chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- \\\n",
+ "gene Vta1 \n",
+ "S1 0.95 \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 0.84 \n",
+ "S5 0.95 \n",
+ "S6 0.91 \n",
+ "S7 0.87 \n",
+ "S8 0.86 \n",
+ "S9 NaN \n",
+ "S10 NaN \n",
+ "S11 0.93 \n",
+ "S13 NaN \n",
+ "S14 NaN \n",
+ "S15 0.96 \n",
+ "S16 NaN \n",
+ "S17 NaN \n",
+ "S18 NaN \n",
+ "10,000 cell Rep1 (P1) 0.83 \n",
+ "10,000 cell Rep2 (P2) 0.85 \n",
+ "10,000 cell Rep3 (P3) 0.64 \n",
+ "\n",
+ "Event name chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ \\\n",
+ "gene Bclaf1 \n",
+ "S1 NaN \n",
+ "S2 0.04 \n",
+ "S3 0.02 \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "S6 0.14 \n",
+ "S7 NaN \n",
+ "S8 0.02 \n",
+ "S9 NaN \n",
+ "S10 NaN \n",
+ "S11 NaN \n",
+ "S13 NaN \n",
+ "S14 NaN \n",
+ "S15 0.01 \n",
+ "S16 NaN \n",
+ "S17 NaN \n",
+ "S18 NaN \n",
+ "10,000 cell Rep1 (P1) 0.40 \n",
+ "10,000 cell Rep2 (P2) 0.49 \n",
+ "10,000 cell Rep3 (P3) 0.59 \n",
+ "\n",
+ "Event name chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ \\\n",
+ "gene Bclaf1 \n",
+ "S1 0.02 \n",
+ "S2 0.98 \n",
+ "S3 0.55 \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "S6 NaN \n",
+ "S7 NaN \n",
+ "S8 0.98 \n",
+ "S9 NaN \n",
+ "S10 NaN \n",
+ "S11 NaN \n",
+ "S13 NaN \n",
+ "S14 NaN \n",
+ "S15 0.06 \n",
+ "S16 NaN \n",
+ "S17 NaN \n",
+ "S18 NaN \n",
+ "10,000 cell Rep1 (P1) 0.62 \n",
+ "10,000 cell Rep2 (P2) 0.63 \n",
+ "10,000 cell Rep3 (P3) 0.70 \n",
+ "\n",
+ "Event name chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ \\\n",
+ "gene P4ha1 \n",
+ "S1 0.42 \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 0.94 \n",
+ "S6 NaN \n",
+ "S7 NaN \n",
+ "S8 0.03 \n",
+ "S9 0.97 \n",
+ "S10 NaN \n",
+ "S11 NaN \n",
+ "S13 NaN \n",
+ "S14 NaN \n",
+ "S15 NaN \n",
+ "S16 NaN \n",
+ "S17 NaN \n",
+ "S18 NaN \n",
+ "10,000 cell Rep1 (P1) 0.43 \n",
+ "10,000 cell Rep2 (P2) 0.36 \n",
+ "10,000 cell Rep3 (P3) 0.52 \n",
+ "\n",
+ "Event name chr10:79173370:79173665:+@chr10:79174001:79174029:+@chr10:79174239:79174726:+ \\\n",
+ "gene Bsg \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "S6 NaN \n",
+ "S7 0.62 \n",
+ "S8 NaN \n",
+ "S9 NaN \n",
+ "S10 NaN \n",
+ "S11 NaN \n",
+ "S13 NaN \n",
+ "S14 NaN \n",
+ "S15 NaN \n",
+ "S16 NaN \n",
+ "S17 NaN \n",
+ "S18 NaN \n",
+ "10,000 cell Rep1 (P1) 0.78 \n",
+ "10,000 cell Rep2 (P2) 0.72 \n",
+ "10,000 cell Rep3 (P3) 0.79 \n",
+ "\n",
+ "Event name chr10:79322526:79322700:+@chr10:79322862:79322939:+@chr10:79323569:79323862:+ \\\n",
+ "gene Ptbp1 \n",
+ "S1 0.57 \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "S6 NaN \n",
+ "S7 NaN \n",
+ "S8 NaN \n",
+ "S9 0.97 \n",
+ "S10 0.06 \n",
+ "S11 NaN \n",
+ "S13 NaN \n",
+ "S14 NaN \n",
+ "S15 NaN \n",
+ "S16 NaN \n",
+ "S17 NaN \n",
+ "S18 NaN \n",
+ "10,000 cell Rep1 (P1) NaN \n",
+ "10,000 cell Rep2 (P2) 0.47 \n",
+ "10,000 cell Rep3 (P3) NaN \n",
+ "\n",
+ "Event name chr10:87376364:87376545:+@chr10:87378043:87378094:+@chr10:87393420:87399792:+ \\\n",
+ "gene Igf1 \n",
+ "S1 0.31 \n",
+ "S2 NaN \n",
+ "S3 0.20 \n",
+ "S4 0.95 \n",
+ "S5 0.73 \n",
+ "S6 0.61 \n",
+ "S7 0.85 \n",
+ "S8 0.89 \n",
+ "S9 NaN \n",
+ "S10 0.98 \n",
+ "S11 NaN \n",
+ "S13 NaN \n",
+ "S14 NaN \n",
+ "S15 0.44 \n",
+ "S16 NaN \n",
+ "S17 NaN \n",
+ "S18 NaN \n",
+ "10,000 cell Rep1 (P1) 0.60 \n",
+ "10,000 cell Rep2 (P2) 0.60 \n",
+ "10,000 cell Rep3 (P3) 0.65 \n",
+ "\n",
+ "Event name chr10:92747514:92747722:-@chr10:92727625:92728425:-@chr10:92717434:92717556:- \\\n",
+ "gene Elk3 \n",
+ "S1 0.93 \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "S6 NaN \n",
+ "S7 0.73 \n",
+ "S8 0.82 \n",
+ "S9 0.90 \n",
+ "S10 NaN \n",
+ "S11 0.97 \n",
+ "S13 NaN \n",
+ "S14 NaN \n",
+ "S15 NaN \n",
+ "S16 NaN \n",
+ "S17 NaN \n",
+ "S18 NaN \n",
+ "10,000 cell Rep1 (P1) 0.76 \n",
+ "10,000 cell Rep2 (P2) 0.73 \n",
+ "10,000 cell Rep3 (P3) 0.42 \n",
+ "\n",
+ "Event name chr11:101438508:101438565:+@chr11:101439246:101439351:+@chr11:101441899:101443267:+ \\\n",
+ "gene Nbr1 \n",
+ "S1 0.57 \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 0.04 \n",
+ "S5 NaN \n",
+ "S6 NaN \n",
+ "S7 0.55 \n",
+ "S8 0.83 \n",
+ "S9 NaN \n",
+ "S10 NaN \n",
+ "S11 NaN \n",
+ "S13 NaN \n",
+ "S14 0.88 \n",
+ "S15 NaN \n",
+ "S16 NaN \n",
+ "S17 NaN \n",
+ "S18 NaN \n",
+ "10,000 cell Rep1 (P1) 0.52 \n",
+ "10,000 cell Rep2 (P2) 0.68 \n",
+ "10,000 cell Rep3 (P3) 0.64 \n",
+ "\n",
+ "Event name ... \\\n",
+ "gene ... \n",
+ "S1 ... \n",
+ "S2 ... \n",
+ "S3 ... \n",
+ "S4 ... \n",
+ "S5 ... \n",
+ "S6 ... \n",
+ "S7 ... \n",
+ "S8 ... \n",
+ "S9 ... \n",
+ "S10 ... \n",
+ "S11 ... \n",
+ "S13 ... \n",
+ "S14 ... \n",
+ "S15 ... \n",
+ "S16 ... \n",
+ "S17 ... \n",
+ "S18 ... \n",
+ "10,000 cell Rep1 (P1) ... \n",
+ "10,000 cell Rep2 (P2) ... \n",
+ "10,000 cell Rep3 (P3) ... \n",
+ "\n",
+ "Event name chr8:126022488:126022598:+@chr8:126023892:126024007:+@chr8:126025133:126025333:+ \\\n",
+ "gene Afg3l1 \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "S6 NaN \n",
+ "S7 NaN \n",
+ "S8 NaN \n",
+ "S9 NaN \n",
+ "S10 NaN \n",
+ "S11 NaN \n",
+ "S13 NaN \n",
+ "S14 NaN \n",
+ "S15 0.91 \n",
+ "S16 NaN \n",
+ "S17 NaN \n",
+ "S18 NaN \n",
+ "10,000 cell Rep1 (P1) 0.92 \n",
+ "10,000 cell Rep2 (P2) 0.67 \n",
+ "10,000 cell Rep3 (P3) 0.58 \n",
+ "\n",
+ "Event name chr14:51455667:51455879:-@chr14:51453589:51453752:-@chr14:51453129:51453242:- \\\n",
+ "gene Tep1 \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "S6 NaN \n",
+ "S7 NaN \n",
+ "S8 NaN \n",
+ "S9 NaN \n",
+ "S10 NaN \n",
+ "S11 NaN \n",
+ "S13 NaN \n",
+ "S14 NaN \n",
+ "S15 NaN \n",
+ "S16 0.27 \n",
+ "S17 NaN \n",
+ "S18 NaN \n",
+ "10,000 cell Rep1 (P1) NaN \n",
+ "10,000 cell Rep2 (P2) 0.15 \n",
+ "10,000 cell Rep3 (P3) 0.79 \n",
+ "\n",
+ "Event name chr17:29497858:29498102:+@chr17:29500656:29500887:+@chr17:29501856:29502226:+ \\\n",
+ "gene Fgd2 \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "S6 NaN \n",
+ "S7 NaN \n",
+ "S8 NaN \n",
+ "S9 NaN \n",
+ "S10 NaN \n",
+ "S11 NaN \n",
+ "S13 NaN \n",
+ "S14 NaN \n",
+ "S15 NaN \n",
+ "S16 0.99 \n",
+ "S17 0.96 \n",
+ "S18 NaN \n",
+ "10,000 cell Rep1 (P1) 0.81 \n",
+ "10,000 cell Rep2 (P2) 0.52 \n",
+ "10,000 cell Rep3 (P3) 0.74 \n",
+ "\n",
+ "Event name chr2:94198908:94199094:-@chr2:94182784:94182954:-@chr2:94172950:94173209:- \\\n",
+ "gene Ttc17 \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "S6 NaN \n",
+ "S7 NaN \n",
+ "S8 NaN \n",
+ "S9 NaN \n",
+ "S10 NaN \n",
+ "S11 NaN \n",
+ "S13 NaN \n",
+ "S14 NaN \n",
+ "S15 NaN \n",
+ "S16 0.99 \n",
+ "S17 NaN \n",
+ "S18 NaN \n",
+ "10,000 cell Rep1 (P1) 0.77 \n",
+ "10,000 cell Rep2 (P2) 0.67 \n",
+ "10,000 cell Rep3 (P3) 0.85 \n",
+ "\n",
+ "Event name chr9:21314438:21314697:-@chr9:21313375:21313558:-@chr9:21311823:21312835:- \\\n",
+ "gene Tmed1 \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "S6 NaN \n",
+ "S7 NaN \n",
+ "S8 NaN \n",
+ "S9 NaN \n",
+ "S10 NaN \n",
+ "S11 NaN \n",
+ "S13 NaN \n",
+ "S14 NaN \n",
+ "S15 NaN \n",
+ "S16 0.98 \n",
+ "S17 NaN \n",
+ "S18 NaN \n",
+ "10,000 cell Rep1 (P1) NaN \n",
+ "10,000 cell Rep2 (P2) 0.63 \n",
+ "10,000 cell Rep3 (P3) 0.73 \n",
+ "\n",
+ "Event name chr9:21314438:21314697:-@chr9:21313375:21313795:-@chr9:21311823:21312835:- \\\n",
+ "gene Tmed1 \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "S6 NaN \n",
+ "S7 NaN \n",
+ "S8 NaN \n",
+ "S9 NaN \n",
+ "S10 NaN \n",
+ "S11 NaN \n",
+ "S13 NaN \n",
+ "S14 NaN \n",
+ "S15 NaN \n",
+ "S16 0.98 \n",
+ "S17 NaN \n",
+ "S18 NaN \n",
+ "10,000 cell Rep1 (P1) NaN \n",
+ "10,000 cell Rep2 (P2) 0.73 \n",
+ "10,000 cell Rep3 (P3) 0.39 \n",
+ "\n",
+ "Event name chr10:79545360:79545471:-@chr10:79542698:79544127:-@chr10:79533365:79535263:- \\\n",
+ "gene Sbno2 \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "S6 NaN \n",
+ "S7 NaN \n",
+ "S8 NaN \n",
+ "S9 NaN \n",
+ "S10 NaN \n",
+ "S11 NaN \n",
+ "S13 NaN \n",
+ "S14 NaN \n",
+ "S15 NaN \n",
+ "S16 NaN \n",
+ "S17 0.99 \n",
+ "S18 NaN \n",
+ "10,000 cell Rep1 (P1) 0.84 \n",
+ "10,000 cell Rep2 (P2) 0.82 \n",
+ "10,000 cell Rep3 (P3) 0.56 \n",
+ "\n",
+ "Event name chr17:5975579:5975881:+@chr17:5985972:5986242:+@chr17:5990136:5990361:+ \\\n",
+ "gene Synj2 \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "S6 NaN \n",
+ "S7 NaN \n",
+ "S8 NaN \n",
+ "S9 NaN \n",
+ "S10 NaN \n",
+ "S11 NaN \n",
+ "S13 NaN \n",
+ "S14 NaN \n",
+ "S15 NaN \n",
+ "S16 NaN \n",
+ "S17 0.98 \n",
+ "S18 NaN \n",
+ "10,000 cell Rep1 (P1) 0.50 \n",
+ "10,000 cell Rep2 (P2) 0.90 \n",
+ "10,000 cell Rep3 (P3) NaN \n",
+ "\n",
+ "Event name chr2:29997782:29997941:+@chr2:30002172:30002382:+@chr2:30002882:30003045:+ \\\n",
+ "gene Tbc1d13 \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "S6 NaN \n",
+ "S7 NaN \n",
+ "S8 NaN \n",
+ "S9 NaN \n",
+ "S10 NaN \n",
+ "S11 NaN \n",
+ "S13 NaN \n",
+ "S14 NaN \n",
+ "S15 NaN \n",
+ "S16 NaN \n",
+ "S17 0.67 \n",
+ "S18 NaN \n",
+ "10,000 cell Rep1 (P1) 0.56 \n",
+ "10,000 cell Rep2 (P2) 0.71 \n",
+ "10,000 cell Rep3 (P3) 0.64 \n",
+ "\n",
+ "Event name chr7:119221306:119221473:+@chr7:119223686:119223745:+@chr7:119225944:119226075:+ \n",
+ "gene Usp47 \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "S6 NaN \n",
+ "S7 NaN \n",
+ "S8 NaN \n",
+ "S9 NaN \n",
+ "S10 NaN \n",
+ "S11 NaN \n",
+ "S13 NaN \n",
+ "S14 NaN \n",
+ "S15 NaN \n",
+ "S16 NaN \n",
+ "S17 0.07 \n",
+ "S18 NaN \n",
+ "10,000 cell Rep1 (P1) NaN \n",
+ "10,000 cell Rep2 (P2) 0.55 \n",
+ "10,000 cell Rep3 (P3) NaN \n",
+ "\n",
+ "[20 rows x 352 columns]"
+ ]
+ }
+ ],
+ "prompt_number": 10
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The three pooled samples aren't named consistently with the expression data, so we have to fix that."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "splicing.index[splicing.index.map(lambda x: 'P' in x)]"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 11,
+ "text": [
+ "Index([u'10,000 cell Rep1 (P1)', u'10,000 cell Rep2 (P2)', u'10,000 cell Rep3 (P3)'], dtype='object')"
+ ]
+ }
+ ],
+ "prompt_number": 11
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Since the pooled sample IDs are inconsistent with the `expression` data, we have to change them. We can get the \"P\" and the number after that using regular expressions, called `re` in the Python standard library, e.g.:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "import re\n",
+ "re.search(r'P\\d', '10,000 cell Rep1 (P1)').group()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 12,
+ "text": [
+ "'P1'"
+ ]
+ }
+ ],
+ "prompt_number": 12
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "def long_pooled_name_to_short(x):\n",
+ " if 'P' not in x:\n",
+ " return x\n",
+ " else:\n",
+ " return re.search(r'P\\d', x).group()\n",
+ "\n",
+ "\n",
+ "splicing.index.map(long_pooled_name_to_short)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 13,
+ "text": [
+ "array([u'S1', u'S2', u'S3', u'S4', u'S5', u'S6', u'S7', u'S8', u'S9',\n",
+ " u'S10', u'S11', u'S13', u'S14', u'S15', u'S16', u'S17', u'S18',\n",
+ " u'P1', u'P2', u'P3'], dtype=object)"
+ ]
+ }
+ ],
+ "prompt_number": 13
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "And now we assign this new index as our index to the `splicing` dataframe"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "splicing.index = splicing.index.map(long_pooled_name_to_short)\n",
+ "splicing.head()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "html": [
+ "| Event name | \n", + "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- | \n", + "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- | \n", + "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ | \n", + "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ | \n", + "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ | \n", + "chr10:79173370:79173665:+@chr10:79174001:79174029:+@chr10:79174239:79174726:+ | \n", + "chr10:79322526:79322700:+@chr10:79322862:79322939:+@chr10:79323569:79323862:+ | \n", + "chr10:87376364:87376545:+@chr10:87378043:87378094:+@chr10:87393420:87399792:+ | \n", + "chr10:92747514:92747722:-@chr10:92727625:92728425:-@chr10:92717434:92717556:- | \n", + "chr11:101438508:101438565:+@chr11:101439246:101439351:+@chr11:101441899:101443267:+ | \n", + "... | \n", + "chr8:126022488:126022598:+@chr8:126023892:126024007:+@chr8:126025133:126025333:+ | \n", + "chr14:51455667:51455879:-@chr14:51453589:51453752:-@chr14:51453129:51453242:- | \n", + "chr17:29497858:29498102:+@chr17:29500656:29500887:+@chr17:29501856:29502226:+ | \n", + "chr2:94198908:94199094:-@chr2:94182784:94182954:-@chr2:94172950:94173209:- | \n", + "chr9:21314438:21314697:-@chr9:21313375:21313558:-@chr9:21311823:21312835:- | \n", + "chr9:21314438:21314697:-@chr9:21313375:21313795:-@chr9:21311823:21312835:- | \n", + "chr10:79545360:79545471:-@chr10:79542698:79544127:-@chr10:79533365:79535263:- | \n", + "chr17:5975579:5975881:+@chr17:5985972:5986242:+@chr17:5990136:5990361:+ | \n", + "chr2:29997782:29997941:+@chr2:30002172:30002382:+@chr2:30002882:30003045:+ | \n", + "chr7:119221306:119221473:+@chr7:119223686:119223745:+@chr7:119225944:119226075:+ | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| gene | \n", + "Os9 | \n", + "Vta1 | \n", + "Bclaf1 | \n", + "Bclaf1 | \n", + "P4ha1 | \n", + "Bsg | \n", + "Ptbp1 | \n", + "Igf1 | \n", + "Elk3 | \n", + "Nbr1 | \n", + "... | \n", + "Afg3l1 | \n", + "Tep1 | \n", + "Fgd2 | \n", + "Ttc17 | \n", + "Tmed1 | \n", + "Tmed1 | \n", + "Sbno2 | \n", + "Synj2 | \n", + "Tbc1d13 | \n", + "Usp47 | \n", + "
| S1 | \n", + "0.84 | \n", + "0.95 | \n", + "NaN | \n", + "0.02 | \n", + "0.42 | \n", + "NaN | \n", + "0.57 | \n", + "0.31 | \n", + "0.93 | \n", + "0.57 | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| S2 | \n", + "NaN | \n", + "NaN | \n", + "0.04 | \n", + "0.98 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| S3 | \n", + "NaN | \n", + "NaN | \n", + "0.02 | \n", + "0.55 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.20 | \n", + "NaN | \n", + "NaN | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| S4 | \n", + "NaN | \n", + "0.84 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.95 | \n", + "NaN | \n", + "0.04 | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| S5 | \n", + "NaN | \n", + "0.95 | \n", + "NaN | \n", + "NaN | \n", + "0.94 | \n", + "NaN | \n", + "NaN | \n", + "0.73 | \n", + "NaN | \n", + "NaN | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| S6 | \n", + "0.01 | \n", + "0.91 | \n", + "0.14 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.61 | \n", + "NaN | \n", + "NaN | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| S7 | \n", + "NaN | \n", + "0.87 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.62 | \n", + "NaN | \n", + "0.85 | \n", + "0.73 | \n", + "0.55 | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| S8 | \n", + "NaN | \n", + "0.86 | \n", + "0.02 | \n", + "0.98 | \n", + "0.03 | \n", + "NaN | \n", + "NaN | \n", + "0.89 | \n", + "0.82 | \n", + "0.83 | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| S9 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.97 | \n", + "NaN | \n", + "0.97 | \n", + "NaN | \n", + "0.90 | \n", + "NaN | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| S10 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.06 | \n", + "0.98 | \n", + "NaN | \n", + "NaN | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| S11 | \n", + "0.03 | \n", + "0.93 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.97 | \n", + "NaN | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| S13 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| S14 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.88 | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| S15 | \n", + "0.02 | \n", + "0.96 | \n", + "0.01 | \n", + "0.06 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.44 | \n", + "NaN | \n", + "NaN | \n", + "... | \n", + "0.91 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| S16 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "... | \n", + "NaN | \n", + "0.27 | \n", + "0.99 | \n", + "0.99 | \n", + "0.98 | \n", + "0.98 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| S17 | \n", + "0.01 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "0.96 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.99 | \n", + "0.98 | \n", + "0.67 | \n", + "0.07 | \n", + "
| S18 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| 10,000 cell Rep1 (P1) | \n", + "0.27 | \n", + "0.83 | \n", + "0.40 | \n", + "0.62 | \n", + "0.43 | \n", + "0.78 | \n", + "NaN | \n", + "0.60 | \n", + "0.76 | \n", + "0.52 | \n", + "... | \n", + "0.92 | \n", + "NaN | \n", + "0.81 | \n", + "0.77 | \n", + "NaN | \n", + "NaN | \n", + "0.84 | \n", + "0.50 | \n", + "0.56 | \n", + "NaN | \n", + "
| 10,000 cell Rep2 (P2) | \n", + "0.37 | \n", + "0.85 | \n", + "0.49 | \n", + "0.63 | \n", + "0.36 | \n", + "0.72 | \n", + "0.47 | \n", + "0.60 | \n", + "0.73 | \n", + "0.68 | \n", + "... | \n", + "0.67 | \n", + "0.15 | \n", + "0.52 | \n", + "0.67 | \n", + "0.63 | \n", + "0.73 | \n", + "0.82 | \n", + "0.90 | \n", + "0.71 | \n", + "0.55 | \n", + "
| 10,000 cell Rep3 (P3) | \n", + "0.31 | \n", + "0.64 | \n", + "0.59 | \n", + "0.70 | \n", + "0.52 | \n", + "0.79 | \n", + "NaN | \n", + "0.65 | \n", + "0.42 | \n", + "0.64 | \n", + "... | \n", + "0.58 | \n", + "0.79 | \n", + "0.74 | \n", + "0.85 | \n", + "0.73 | \n", + "0.39 | \n", + "0.56 | \n", + "NaN | \n", + "0.64 | \n", + "NaN | \n", + "
20 rows \u00d7 352 columns
\n", + "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 14,
+ "text": [
+ "Event name chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- \\\n",
+ "gene Os9 \n",
+ "S1 0.84 \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "\n",
+ "Event name chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- \\\n",
+ "gene Vta1 \n",
+ "S1 0.95 \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 0.84 \n",
+ "S5 0.95 \n",
+ "\n",
+ "Event name chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ \\\n",
+ "gene Bclaf1 \n",
+ "S1 NaN \n",
+ "S2 0.04 \n",
+ "S3 0.02 \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "\n",
+ "Event name chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ \\\n",
+ "gene Bclaf1 \n",
+ "S1 0.02 \n",
+ "S2 0.98 \n",
+ "S3 0.55 \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "\n",
+ "Event name chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ \\\n",
+ "gene P4ha1 \n",
+ "S1 0.42 \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 0.94 \n",
+ "\n",
+ "Event name chr10:79173370:79173665:+@chr10:79174001:79174029:+@chr10:79174239:79174726:+ \\\n",
+ "gene Bsg \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "\n",
+ "Event name chr10:79322526:79322700:+@chr10:79322862:79322939:+@chr10:79323569:79323862:+ \\\n",
+ "gene Ptbp1 \n",
+ "S1 0.57 \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "\n",
+ "Event name chr10:87376364:87376545:+@chr10:87378043:87378094:+@chr10:87393420:87399792:+ \\\n",
+ "gene Igf1 \n",
+ "S1 0.31 \n",
+ "S2 NaN \n",
+ "S3 0.20 \n",
+ "S4 0.95 \n",
+ "S5 0.73 \n",
+ "\n",
+ "Event name chr10:92747514:92747722:-@chr10:92727625:92728425:-@chr10:92717434:92717556:- \\\n",
+ "gene Elk3 \n",
+ "S1 0.93 \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "\n",
+ "Event name chr11:101438508:101438565:+@chr11:101439246:101439351:+@chr11:101441899:101443267:+ \\\n",
+ "gene Nbr1 \n",
+ "S1 0.57 \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 0.04 \n",
+ "S5 NaN \n",
+ "\n",
+ "Event name ... \\\n",
+ "gene ... \n",
+ "S1 ... \n",
+ "S2 ... \n",
+ "S3 ... \n",
+ "S4 ... \n",
+ "S5 ... \n",
+ "\n",
+ "Event name chr8:126022488:126022598:+@chr8:126023892:126024007:+@chr8:126025133:126025333:+ \\\n",
+ "gene Afg3l1 \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "\n",
+ "Event name chr14:51455667:51455879:-@chr14:51453589:51453752:-@chr14:51453129:51453242:- \\\n",
+ "gene Tep1 \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "\n",
+ "Event name chr17:29497858:29498102:+@chr17:29500656:29500887:+@chr17:29501856:29502226:+ \\\n",
+ "gene Fgd2 \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "\n",
+ "Event name chr2:94198908:94199094:-@chr2:94182784:94182954:-@chr2:94172950:94173209:- \\\n",
+ "gene Ttc17 \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "\n",
+ "Event name chr9:21314438:21314697:-@chr9:21313375:21313558:-@chr9:21311823:21312835:- \\\n",
+ "gene Tmed1 \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "\n",
+ "Event name chr9:21314438:21314697:-@chr9:21313375:21313795:-@chr9:21311823:21312835:- \\\n",
+ "gene Tmed1 \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "\n",
+ "Event name chr10:79545360:79545471:-@chr10:79542698:79544127:-@chr10:79533365:79535263:- \\\n",
+ "gene Sbno2 \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "\n",
+ "Event name chr17:5975579:5975881:+@chr17:5985972:5986242:+@chr17:5990136:5990361:+ \\\n",
+ "gene Synj2 \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "\n",
+ "Event name chr2:29997782:29997941:+@chr2:30002172:30002382:+@chr2:30002882:30003045:+ \\\n",
+ "gene Tbc1d13 \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "\n",
+ "Event name chr7:119221306:119221473:+@chr7:119223686:119223745:+@chr7:119225944:119226075:+ \n",
+ "gene Usp47 \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "\n",
+ "[5 rows x 352 columns]"
+ ]
+ }
+ ],
+ "prompt_number": 14
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Metadata\n",
+ "\n",
+ "Now let's get into creating a metadata dataframe. We'll use the index from the `expression_filtered` data to create the minimum required column, `'phenotype'`, which has the name of the phenotype of that cell. And we'll also add the column `'pooled'` to indicate whether this sample is pooled or not."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "metadata = pd.DataFrame(index=expression_filtered.index)\n",
+ "metadata['phenotype'] = 'BDMC'\n",
+ "metadata['pooled'] = metadata.index.map(lambda x: x.startswith('P'))\n",
+ "\n",
+ "metadata"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "html": [
+ "| Event name | \n", + "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- | \n", + "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- | \n", + "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ | \n", + "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ | \n", + "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ | \n", + "chr10:79173370:79173665:+@chr10:79174001:79174029:+@chr10:79174239:79174726:+ | \n", + "chr10:79322526:79322700:+@chr10:79322862:79322939:+@chr10:79323569:79323862:+ | \n", + "chr10:87376364:87376545:+@chr10:87378043:87378094:+@chr10:87393420:87399792:+ | \n", + "chr10:92747514:92747722:-@chr10:92727625:92728425:-@chr10:92717434:92717556:- | \n", + "chr11:101438508:101438565:+@chr11:101439246:101439351:+@chr11:101441899:101443267:+ | \n", + "... | \n", + "chr8:126022488:126022598:+@chr8:126023892:126024007:+@chr8:126025133:126025333:+ | \n", + "chr14:51455667:51455879:-@chr14:51453589:51453752:-@chr14:51453129:51453242:- | \n", + "chr17:29497858:29498102:+@chr17:29500656:29500887:+@chr17:29501856:29502226:+ | \n", + "chr2:94198908:94199094:-@chr2:94182784:94182954:-@chr2:94172950:94173209:- | \n", + "chr9:21314438:21314697:-@chr9:21313375:21313558:-@chr9:21311823:21312835:- | \n", + "chr9:21314438:21314697:-@chr9:21313375:21313795:-@chr9:21311823:21312835:- | \n", + "chr10:79545360:79545471:-@chr10:79542698:79544127:-@chr10:79533365:79535263:- | \n", + "chr17:5975579:5975881:+@chr17:5985972:5986242:+@chr17:5990136:5990361:+ | \n", + "chr2:29997782:29997941:+@chr2:30002172:30002382:+@chr2:30002882:30003045:+ | \n", + "chr7:119221306:119221473:+@chr7:119223686:119223745:+@chr7:119225944:119226075:+ | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| gene | \n", + "Os9 | \n", + "Vta1 | \n", + "Bclaf1 | \n", + "Bclaf1 | \n", + "P4ha1 | \n", + "Bsg | \n", + "Ptbp1 | \n", + "Igf1 | \n", + "Elk3 | \n", + "Nbr1 | \n", + "... | \n", + "Afg3l1 | \n", + "Tep1 | \n", + "Fgd2 | \n", + "Ttc17 | \n", + "Tmed1 | \n", + "Tmed1 | \n", + "Sbno2 | \n", + "Synj2 | \n", + "Tbc1d13 | \n", + "Usp47 | \n", + "
| S1 | \n", + "0.84 | \n", + "0.95 | \n", + "NaN | \n", + "0.02 | \n", + "0.42 | \n", + "NaN | \n", + "0.57 | \n", + "0.31 | \n", + "0.93 | \n", + "0.57 | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| S2 | \n", + "NaN | \n", + "NaN | \n", + "0.04 | \n", + "0.98 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| S3 | \n", + "NaN | \n", + "NaN | \n", + "0.02 | \n", + "0.55 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.20 | \n", + "NaN | \n", + "NaN | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| S4 | \n", + "NaN | \n", + "0.84 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.95 | \n", + "NaN | \n", + "0.04 | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| S5 | \n", + "NaN | \n", + "0.95 | \n", + "NaN | \n", + "NaN | \n", + "0.94 | \n", + "NaN | \n", + "NaN | \n", + "0.73 | \n", + "NaN | \n", + "NaN | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
5 rows \u00d7 352 columns
\n", + "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 15,
+ "text": [
+ " phenotype pooled\n",
+ "S1 BDMC False\n",
+ "S2 BDMC False\n",
+ "S3 BDMC False\n",
+ "S4 BDMC False\n",
+ "S5 BDMC False\n",
+ "S6 BDMC False\n",
+ "S7 BDMC False\n",
+ "S8 BDMC False\n",
+ "S9 BDMC False\n",
+ "S10 BDMC False\n",
+ "S11 BDMC False\n",
+ "S12 BDMC False\n",
+ "S13 BDMC False\n",
+ "S14 BDMC False\n",
+ "S15 BDMC False\n",
+ "S16 BDMC False\n",
+ "S17 BDMC False\n",
+ "S18 BDMC False\n",
+ "P1 BDMC True\n",
+ "P2 BDMC True\n",
+ "P3 BDMC True"
+ ]
+ }
+ ],
+ "prompt_number": 15
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Mapping stats data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "mapping_stats = pd.read_excel('nature12172-s1/Supplementary_Table1.xls', sheetname='SuppTable1 2.txt')\n",
+ "mapping_stats"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "html": [
+ "| \n", + " | phenotype | \n", + "pooled | \n", + "
|---|---|---|
| S1 | \n", + "BDMC | \n", + "False | \n", + "
| S2 | \n", + "BDMC | \n", + "False | \n", + "
| S3 | \n", + "BDMC | \n", + "False | \n", + "
| S4 | \n", + "BDMC | \n", + "False | \n", + "
| S5 | \n", + "BDMC | \n", + "False | \n", + "
| S6 | \n", + "BDMC | \n", + "False | \n", + "
| S7 | \n", + "BDMC | \n", + "False | \n", + "
| S8 | \n", + "BDMC | \n", + "False | \n", + "
| S9 | \n", + "BDMC | \n", + "False | \n", + "
| S10 | \n", + "BDMC | \n", + "False | \n", + "
| S11 | \n", + "BDMC | \n", + "False | \n", + "
| S12 | \n", + "BDMC | \n", + "False | \n", + "
| S13 | \n", + "BDMC | \n", + "False | \n", + "
| S14 | \n", + "BDMC | \n", + "False | \n", + "
| S15 | \n", + "BDMC | \n", + "False | \n", + "
| S16 | \n", + "BDMC | \n", + "False | \n", + "
| S17 | \n", + "BDMC | \n", + "False | \n", + "
| S18 | \n", + "BDMC | \n", + "False | \n", + "
| P1 | \n", + "BDMC | \n", + "True | \n", + "
| P2 | \n", + "BDMC | \n", + "True | \n", + "
| P3 | \n", + "BDMC | \n", + "True | \n", + "
\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 16,
+ "text": [
+ " Sample PF_READS PCT_MAPPED_GENOME PCT_RIBOSOMAL_BASES \\\n",
+ "0 S1 21326048 0.706590 0.006820 \n",
+ "1 S2 27434011 0.745385 0.004111 \n",
+ "2 S3 31142391 0.722087 0.006428 \n",
+ "3 S4 26231852 0.737854 0.004959 \n",
+ "4 S5 29977214 0.746466 0.006121 \n",
+ "5 S6 24148387 0.730079 0.008794 \n",
+ "6 S7 24078116 0.730638 0.007945 \n",
+ "7 S8 25032126 0.739989 0.004133 \n",
+ "8 S9 22257682 0.747427 0.004869 \n",
+ "9 S10 29436289 0.748795 0.005499 \n",
+ "10 S11 31130278 0.741882 0.002740 \n",
+ "11 S12 21161595 0.750782 0.006837 \n",
+ "12 S13 28612833 0.733976 0.011718 \n",
+ "13 S14 26351189 0.748323 0.004106 \n",
+ "14 S15 25739575 0.748421 0.003353 \n",
+ "15 S16 26802346 0.739833 0.009370 \n",
+ "16 S17 26343522 0.749358 0.003155 \n",
+ "17 S18 25290073 0.749358 0.007465 \n",
+ "18 10k_rep1 28247826 0.688553 0.018993 \n",
+ "19 10k_rep2 39303876 0.690313 0.017328 \n",
+ "20 10k_rep3 29831281 0.710875 0.010610 \n",
+ "21 MB_SC1 13848219 0.545000 0.007000 \n",
+ "22 MB_SC2 13550218 0.458000 0.010800 \n",
+ "23 MB_SC3 26765848 0.496000 0.007900 \n",
+ "\n",
+ " MEDIAN_CV_COVERAGE MEDIAN_5PRIME_BIAS MEDIAN_3PRIME_BIAS \\\n",
+ "0 0.509939 0.092679 0.477321 \n",
+ "1 0.565732 0.056583 0.321053 \n",
+ "2 0.540341 0.079551 0.382286 \n",
+ "3 0.530978 0.067041 0.351670 \n",
+ "4 0.525598 0.066543 0.353995 \n",
+ "5 0.529650 0.072095 0.413696 \n",
+ "6 0.540913 0.051991 0.358597 \n",
+ "7 0.512725 0.058783 0.373509 \n",
+ "8 0.521622 0.063566 0.334294 \n",
+ "9 0.560454 0.036219 0.306729 \n",
+ "10 0.558882 0.049581 0.349191 \n",
+ "11 0.756339 0.013878 0.324264 \n",
+ "12 0.598687 0.035392 0.357447 \n",
+ "13 0.517518 0.070293 0.381095 \n",
+ "14 0.526238 0.050938 0.324207 \n",
+ "15 0.520287 0.071503 0.358758 \n",
+ "16 0.673195 0.024121 0.301588 \n",
+ "17 0.562382 0.048528 0.314776 \n",
+ "18 0.547000 0.056113 0.484393 \n",
+ "19 0.547621 0.055600 0.474634 \n",
+ "20 0.518053 0.066053 0.488738 \n",
+ "21 0.531495 0.127934 0.207841 \n",
+ "22 0.569271 0.102581 0.179407 \n",
+ "23 0.535192 0.141893 0.231068 \n",
+ "\n",
+ " MEDIAN_5PRIME_TO_3PRIME_BIAS \n",
+ "0 0.247741 \n",
+ "1 0.244062 \n",
+ "2 0.267367 \n",
+ "3 0.279782 \n",
+ "4 0.274252 \n",
+ "5 0.225929 \n",
+ "6 0.201984 \n",
+ "7 0.212337 \n",
+ "8 0.240641 \n",
+ "9 0.187479 \n",
+ "10 0.211787 \n",
+ "11 0.195430 \n",
+ "12 0.198566 \n",
+ "13 0.259122 \n",
+ "14 0.212366 \n",
+ "15 0.240009 \n",
+ "16 0.245854 \n",
+ "17 0.215160 \n",
+ "18 0.140333 \n",
+ "19 0.142889 \n",
+ "20 0.168180 \n",
+ "21 0.728980 \n",
+ "22 0.694747 \n",
+ "23 0.722080 "
+ ]
+ }
+ ],
+ "prompt_number": 16
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Create a `flotilla` Study!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "study = flotilla.Study(# The metadata describing phenotype and pooled samples\n",
+ " metadata, \n",
+ " \n",
+ " # A version for this data\n",
+ " version='0.1.0', \n",
+ " \n",
+ " # Dataframe of the filtered expression data\n",
+ " expression_data=expression_filtered,\n",
+ " \n",
+ " # Dataframe of the feature data of the genes\n",
+ " expression_feature_data=expression_feature_data,\n",
+ " \n",
+ " # Dataframe of the splicing data\n",
+ " splicing_data=splicing, \n",
+ " \n",
+ " # Dataframe of the mapping stats data\n",
+ " mapping_stats_data=mapping_stats, \n",
+ " \n",
+ " # Which column in \"mapping_stats\" has the number of reads\n",
+ " mapping_stats_number_mapped_col='PF_READS')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "2014-11-04 14:19:33\tInitializing Study\n",
+ "2014-11-04 14:19:33\tInitializing Predictor configuration manager for Study\n",
+ "2014-11-04 14:19:33\tPredictor ExtraTreesClassifier is of type | \n", + " | Sample | \n", + "PF_READS | \n", + "PCT_MAPPED_GENOME | \n", + "PCT_RIBOSOMAL_BASES | \n", + "MEDIAN_CV_COVERAGE | \n", + "MEDIAN_5PRIME_BIAS | \n", + "MEDIAN_3PRIME_BIAS | \n", + "MEDIAN_5PRIME_TO_3PRIME_BIAS | \n", + "
|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "S1 | \n", + "21326048 | \n", + "0.706590 | \n", + "0.006820 | \n", + "0.509939 | \n", + "0.092679 | \n", + "0.477321 | \n", + "0.247741 | \n", + "
| 1 | \n", + "S2 | \n", + "27434011 | \n", + "0.745385 | \n", + "0.004111 | \n", + "0.565732 | \n", + "0.056583 | \n", + "0.321053 | \n", + "0.244062 | \n", + "
| 2 | \n", + "S3 | \n", + "31142391 | \n", + "0.722087 | \n", + "0.006428 | \n", + "0.540341 | \n", + "0.079551 | \n", + "0.382286 | \n", + "0.267367 | \n", + "
| 3 | \n", + "S4 | \n", + "26231852 | \n", + "0.737854 | \n", + "0.004959 | \n", + "0.530978 | \n", + "0.067041 | \n", + "0.351670 | \n", + "0.279782 | \n", + "
| 4 | \n", + "S5 | \n", + "29977214 | \n", + "0.746466 | \n", + "0.006121 | \n", + "0.525598 | \n", + "0.066543 | \n", + "0.353995 | \n", + "0.274252 | \n", + "
| 5 | \n", + "S6 | \n", + "24148387 | \n", + "0.730079 | \n", + "0.008794 | \n", + "0.529650 | \n", + "0.072095 | \n", + "0.413696 | \n", + "0.225929 | \n", + "
| 6 | \n", + "S7 | \n", + "24078116 | \n", + "0.730638 | \n", + "0.007945 | \n", + "0.540913 | \n", + "0.051991 | \n", + "0.358597 | \n", + "0.201984 | \n", + "
| 7 | \n", + "S8 | \n", + "25032126 | \n", + "0.739989 | \n", + "0.004133 | \n", + "0.512725 | \n", + "0.058783 | \n", + "0.373509 | \n", + "0.212337 | \n", + "
| 8 | \n", + "S9 | \n", + "22257682 | \n", + "0.747427 | \n", + "0.004869 | \n", + "0.521622 | \n", + "0.063566 | \n", + "0.334294 | \n", + "0.240641 | \n", + "
| 9 | \n", + "S10 | \n", + "29436289 | \n", + "0.748795 | \n", + "0.005499 | \n", + "0.560454 | \n", + "0.036219 | \n", + "0.306729 | \n", + "0.187479 | \n", + "
| 10 | \n", + "S11 | \n", + "31130278 | \n", + "0.741882 | \n", + "0.002740 | \n", + "0.558882 | \n", + "0.049581 | \n", + "0.349191 | \n", + "0.211787 | \n", + "
| 11 | \n", + "S12 | \n", + "21161595 | \n", + "0.750782 | \n", + "0.006837 | \n", + "0.756339 | \n", + "0.013878 | \n", + "0.324264 | \n", + "0.195430 | \n", + "
| 12 | \n", + "S13 | \n", + "28612833 | \n", + "0.733976 | \n", + "0.011718 | \n", + "0.598687 | \n", + "0.035392 | \n", + "0.357447 | \n", + "0.198566 | \n", + "
| 13 | \n", + "S14 | \n", + "26351189 | \n", + "0.748323 | \n", + "0.004106 | \n", + "0.517518 | \n", + "0.070293 | \n", + "0.381095 | \n", + "0.259122 | \n", + "
| 14 | \n", + "S15 | \n", + "25739575 | \n", + "0.748421 | \n", + "0.003353 | \n", + "0.526238 | \n", + "0.050938 | \n", + "0.324207 | \n", + "0.212366 | \n", + "
| 15 | \n", + "S16 | \n", + "26802346 | \n", + "0.739833 | \n", + "0.009370 | \n", + "0.520287 | \n", + "0.071503 | \n", + "0.358758 | \n", + "0.240009 | \n", + "
| 16 | \n", + "S17 | \n", + "26343522 | \n", + "0.749358 | \n", + "0.003155 | \n", + "0.673195 | \n", + "0.024121 | \n", + "0.301588 | \n", + "0.245854 | \n", + "
| 17 | \n", + "S18 | \n", + "25290073 | \n", + "0.749358 | \n", + "0.007465 | \n", + "0.562382 | \n", + "0.048528 | \n", + "0.314776 | \n", + "0.215160 | \n", + "
| 18 | \n", + "10k_rep1 | \n", + "28247826 | \n", + "0.688553 | \n", + "0.018993 | \n", + "0.547000 | \n", + "0.056113 | \n", + "0.484393 | \n", + "0.140333 | \n", + "
| 19 | \n", + "10k_rep2 | \n", + "39303876 | \n", + "0.690313 | \n", + "0.017328 | \n", + "0.547621 | \n", + "0.055600 | \n", + "0.474634 | \n", + "0.142889 | \n", + "
| 20 | \n", + "10k_rep3 | \n", + "29831281 | \n", + "0.710875 | \n", + "0.010610 | \n", + "0.518053 | \n", + "0.066053 | \n", + "0.488738 | \n", + "0.168180 | \n", + "
| 21 | \n", + "MB_SC1 | \n", + "13848219 | \n", + "0.545000 | \n", + "0.007000 | \n", + "0.531495 | \n", + "0.127934 | \n", + "0.207841 | \n", + "0.728980 | \n", + "
| 22 | \n", + "MB_SC2 | \n", + "13550218 | \n", + "0.458000 | \n", + "0.010800 | \n", + "0.569271 | \n", + "0.102581 | \n", + "0.179407 | \n", + "0.694747 | \n", + "
| 23 | \n", + "MB_SC3 | \n", + "26765848 | \n", + "0.496000 | \n", + "0.007900 | \n", + "0.535192 | \n", + "0.141893 | \n", + "0.231068 | \n", + "0.722080 | \n", + "
\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 6,
+ "text": [
+ " S1 S2 S3 S4 S5 S6 \\\n",
+ "GENE \n",
+ "XKR4 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
+ "AB338584 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
+ "B3GAT2 0.000000 0.000000 0.023441 0.000000 0.000000 0.029378 \n",
+ "NPL 72.008590 0.000000 128.062012 0.095082 0.000000 0.000000 \n",
+ "T2 0.109249 0.172009 0.000000 0.000000 0.182703 0.076012 \n",
+ "\n",
+ " S7 S8 S9 S10 ... S12 \\\n",
+ "GENE ... \n",
+ "XKR4 0.000000 0.000000 0.000000 0.000000 ... 0.000000 \n",
+ "AB338584 0.000000 0.000000 0.000000 0.000000 ... 0.000000 \n",
+ "B3GAT2 0.000000 0.055452 0.000000 0.029448 ... 0.000000 \n",
+ "NPL 112.310234 104.329122 0.119230 0.000000 ... 0.000000 \n",
+ "T2 0.078698 0.000000 0.093698 0.076583 ... 0.693459 \n",
+ "\n",
+ " S13 S14 S15 S16 S17 S18 \\\n",
+ "GENE \n",
+ "XKR4 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
+ "AB338584 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
+ "B3GAT2 0.000000 0.031654 0.000000 0.000000 0.000000 42.150208 \n",
+ "NPL 0.116802 0.104200 0.106188 0.229197 0.110582 0.000000 \n",
+ "T2 0.010137 0.081936 0.000000 0.000000 0.086879 0.068174 \n",
+ "\n",
+ " P1 P2 P3 \n",
+ "GENE \n",
+ "XKR4 0.000000 0.019906 0.000000 \n",
+ "AB338584 0.000000 0.000000 0.000000 \n",
+ "B3GAT2 0.680327 0.022996 0.110236 \n",
+ "NPL 7.109356 6.727028 14.525447 \n",
+ "T2 0.062063 0.000000 0.050605 \n",
+ "\n",
+ "[5 rows x 21 columns]"
+ ]
+ }
+ ],
+ "prompt_number": 6
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "These data are in the \"transcripts per million,\" aka TPM unit. See [this](http://haroldpimentel.wordpress.com/2014/05/08/what-the-fpkm-a-review-rna-seq-expression-units/) blog post if that sounds weird to you."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "These data are formatted with samples on the columns, and genes on the rows. But we want the opposite, with samples on the rows and genes on the columns. This follows [`scikit-learn`](http://scikit-learn.org/stable/tutorial/basic/tutorial.html#loading-an-example-dataset)'s standard of data matrices with size (`n_samples`, `n_features`) as each gene is a feature. So we will simply transpose this."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "expression = expression.T\n",
+ "expression.head()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "html": [
+ "| \n", + " | S1 | \n", + "S2 | \n", + "S3 | \n", + "S4 | \n", + "S5 | \n", + "S6 | \n", + "S7 | \n", + "S8 | \n", + "S9 | \n", + "S10 | \n", + "... | \n", + "S12 | \n", + "S13 | \n", + "S14 | \n", + "S15 | \n", + "S16 | \n", + "S17 | \n", + "S18 | \n", + "P1 | \n", + "P2 | \n", + "P3 | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| GENE | \n", + "\n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " |
| XKR4 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "... | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.019906 | \n", + "0.000000 | \n", + "
| AB338584 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "... | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "
| B3GAT2 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.023441 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.029378 | \n", + "0.000000 | \n", + "0.055452 | \n", + "0.000000 | \n", + "0.029448 | \n", + "... | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.031654 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.000000 | \n", + "42.150208 | \n", + "0.680327 | \n", + "0.022996 | \n", + "0.110236 | \n", + "
| NPL | \n", + "72.008590 | \n", + "0.000000 | \n", + "128.062012 | \n", + "0.095082 | \n", + "0.000000 | \n", + "0.000000 | \n", + "112.310234 | \n", + "104.329122 | \n", + "0.119230 | \n", + "0.000000 | \n", + "... | \n", + "0.000000 | \n", + "0.116802 | \n", + "0.104200 | \n", + "0.106188 | \n", + "0.229197 | \n", + "0.110582 | \n", + "0.000000 | \n", + "7.109356 | \n", + "6.727028 | \n", + "14.525447 | \n", + "
| T2 | \n", + "0.109249 | \n", + "0.172009 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.182703 | \n", + "0.076012 | \n", + "0.078698 | \n", + "0.000000 | \n", + "0.093698 | \n", + "0.076583 | \n", + "... | \n", + "0.693459 | \n", + "0.010137 | \n", + "0.081936 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.086879 | \n", + "0.068174 | \n", + "0.062063 | \n", + "0.000000 | \n", + "0.050605 | \n", + "
5 rows \u00d7 21 columns
\n", + "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 7,
+ "text": [
+ "GENE XKR4 AB338584 B3GAT2 NPL T2 T PDE10A \\\n",
+ "S1 0 0 0.000000 72.008590 0.109249 0 0 \n",
+ "S2 0 0 0.000000 0.000000 0.172009 0 0 \n",
+ "S3 0 0 0.023441 128.062012 0.000000 0 0 \n",
+ "S4 0 0 0.000000 0.095082 0.000000 0 0 \n",
+ "S5 0 0 0.000000 0.000000 0.182703 0 0 \n",
+ "\n",
+ "GENE 1700010I14RIK 6530411M01RIK PABPC6 ... AK085062 DHX9 \\\n",
+ "S1 0 0 0 ... 0 0.774638 \n",
+ "S2 0 0 0 ... 0 0.367391 \n",
+ "S3 0 0 0 ... 0 0.249858 \n",
+ "S4 0 0 0 ... 0 0.354157 \n",
+ "S5 0 0 0 ... 0 0.039263 \n",
+ "\n",
+ "GENE RNASET2B FGFR1OP CCR6 BRP44L AK014435 AK015714 SFT2D1 \\\n",
+ "S1 23.520936 0.000000 0 460.316773 0 0.000000 39.442566 \n",
+ "S2 1.887873 0.000000 0 823.890290 0 0.000000 4.967412 \n",
+ "S3 0.313510 0.166772 0 1002.354241 0 0.000000 0.000000 \n",
+ "S4 0.000000 0.887003 0 1230.766795 0 0.000000 0.131215 \n",
+ "S5 0.000000 131.077131 0 1614.749122 0 0.242179 95.485743 \n",
+ "\n",
+ "GENE PRR18 \n",
+ "S1 0 \n",
+ "S2 0 \n",
+ "S3 0 \n",
+ "S4 0 \n",
+ "S5 0 \n",
+ "\n",
+ "[5 rows x 27723 columns]"
+ ]
+ }
+ ],
+ "prompt_number": 7
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The authors filtered the expression data based on having at least 3 single cells express genes with at TPM (transcripts per million, ) > 1. We can express this in using the [`pandas`](http://pandas.pydata.org) DataFrames easily.\n",
+ "\n",
+ "First, from reading the paper and looking at the data, I know there are 18 single cells, and there are 18 samples that start with the letter \"S.\" So I will extract the single samples from the `index` (row names) using a `lambda`, a tiny function which in this case, tells me whether or not that sample id begins with the letter \"S\"."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "singles_ids = expression.index[expression.index.map(lambda x: x.startswith('S'))]\n",
+ "print('number of single cells:', len(singles_ids))\n",
+ "singles = expression.ix[singles_ids]\n",
+ "\n",
+ "expression_filtered = expression.ix[:, singles[singles > 1].count() >= 3]\n",
+ "expression_filtered = np.log(expression_filtered + 1)\n",
+ "expression_filtered.shape"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "('number of single cells:', 18)\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 8,
+ "text": [
+ "(21, 6312)"
+ ]
+ }
+ ],
+ "prompt_number": 8
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Hmm, that's strange. The paper states that they had 6313 genes after filtering, but I get 6312. Even using \"`singles >= 1`\" doesn't help.\n",
+ "\n",
+ "(I also tried this with the expression table provided in the supplementary data as \"`SupplementaryTable2.xlsx`,\" and got the same results.)\n",
+ "\n",
+ "Now that we've taken care of importing and filtering the expression data, let's do the feature data of the expression data.\n"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Expression feature data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "\n",
+ "This is similar to the `fData` from `BioconductoR`, where there's some additional data on your features that you want to look at. They uploaded information about the features in their OTHER expression matrix, uploaded as a supplementary file, `Supplementary_Table2.xlsx`.\n",
+ "\n",
+ "Notice that this is a `csv` and not an `xlsx`. This is because Excel mangled the gene IDS that started with `201*` and assumed they were dates :(\n",
+ "\n",
+ "The workaround I did was to add another column to the sheet with the formula `=\"'\" & A1`, press `Command`-`Shift`-`End` to select the end of the rows, and then do `Ctrl`-`D` to \"fill down\" to the bottom (thanks to [this](http://superuser.com/questions/298276/excel-keyboard-shortcut-to-copy-fill-down-for-all-cells-with-non-blank-adjacent) stackoverflow post for teaching me how to Excel). Then, I saved the file as a `csv` for maximum portability and compatibility."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "expression2 = pd.read_csv('nature12172-s1/Supplementary_Table2.csv', \n",
+ " # Need to specify the index column as both the first and the last columns,\n",
+ " # Because the last column is the \"Gene Category\"\n",
+ " index_col=[0, -1], parse_dates=False, infer_datetime_format=False)\n",
+ "\n",
+ "# This was also in features x samples format, so we need to transpose\n",
+ "expression2 = expression2.T\n",
+ "expression2.head()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "html": [
+ "| GENE | \n", + "XKR4 | \n", + "AB338584 | \n", + "B3GAT2 | \n", + "NPL | \n", + "T2 | \n", + "T | \n", + "PDE10A | \n", + "1700010I14RIK | \n", + "6530411M01RIK | \n", + "PABPC6 | \n", + "... | \n", + "AK085062 | \n", + "DHX9 | \n", + "RNASET2B | \n", + "FGFR1OP | \n", + "CCR6 | \n", + "BRP44L | \n", + "AK014435 | \n", + "AK015714 | \n", + "SFT2D1 | \n", + "PRR18 | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| S1 | \n", + "0 | \n", + "0 | \n", + "0.000000 | \n", + "72.008590 | \n", + "0.109249 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "... | \n", + "0 | \n", + "0.774638 | \n", + "23.520936 | \n", + "0.000000 | \n", + "0 | \n", + "460.316773 | \n", + "0 | \n", + "0.000000 | \n", + "39.442566 | \n", + "0 | \n", + "
| S2 | \n", + "0 | \n", + "0 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.172009 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "... | \n", + "0 | \n", + "0.367391 | \n", + "1.887873 | \n", + "0.000000 | \n", + "0 | \n", + "823.890290 | \n", + "0 | \n", + "0.000000 | \n", + "4.967412 | \n", + "0 | \n", + "
| S3 | \n", + "0 | \n", + "0 | \n", + "0.023441 | \n", + "128.062012 | \n", + "0.000000 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "... | \n", + "0 | \n", + "0.249858 | \n", + "0.313510 | \n", + "0.166772 | \n", + "0 | \n", + "1002.354241 | \n", + "0 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0 | \n", + "
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5 rows \u00d7 27723 columns
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+ "prompt_number": 10,
+ "text": [
+ "'GENE '0610007L01RIK '0610007P14RIK '0610007P22RIK '0610008F07RIK \\\n",
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+ "S2 37.682691 0.263962 0 0 \n",
+ "S3 0.056916 78.622459 0 0 \n",
+ "S4 55.649250 0.228866 0 0 \n",
+ "S5 0.000000 0.093117 0 0 \n",
+ "\n",
+ "'GENE '0610009B22RIK '0610009D07RIK '0610009O20RIK '0610010B08RIK \\\n",
+ "Gene Category NaN NaN NaN NaN \n",
+ "S1 0.000000 178.852732 0 0.962417 \n",
+ "S2 0.207921 0.141099 0 0.000000 \n",
+ "S3 0.145680 0.396363 0 0.000000 \n",
+ "S4 0.000000 88.798158 0 0.000000 \n",
+ "S5 131.326008 155.936361 0 0.000000 \n",
+ "\n",
+ "'GENE '0610010F05RIK '0610010K06RIK ... 'ZWILCH 'ZWINT \\\n",
+ "Gene Category NaN NaN ... NaN NaN \n",
+ "S1 0.000000 143.359550 ... 0.000000 302.361227 \n",
+ "S2 0.000000 0.255617 ... 0.000000 96.033724 \n",
+ "S3 0.024692 72.775846 ... 0.000000 427.915555 \n",
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+ "S5 0.000000 0.031029 ... 0.204522 26.575760 \n",
+ "\n",
+ "'GENE 'ZXDA 'ZXDB 'ZXDC 'ZYG11A 'ZYG11B 'ZYX 'ZZEF1 \\\n",
+ "Gene Category NaN NaN NaN NaN NaN NaN NaN \n",
+ "S1 0.000000 0 0 0 0.027717 297.918756 37.685501 \n",
+ "S2 0.020459 0 0 0 0.042430 0.242888 0.000000 \n",
+ "S3 0.000000 0 0 0 0.040407 6.753530 0.132011 \n",
+ "S4 0.017787 0 0 0 0.013452 0.274689 9.724890 \n",
+ "S5 0.000000 0 0 0 1.101589 59.256094 44.430726 \n",
+ "\n",
+ "'GENE 'ZZZ3 \n",
+ "Gene Category NaN \n",
+ "S1 0.000000 \n",
+ "S2 0.000000 \n",
+ "S3 0.017615 \n",
+ "S4 0.000000 \n",
+ "S5 0.000000 \n",
+ "\n",
+ "[5 rows x 27723 columns]"
+ ]
+ }
+ ],
+ "prompt_number": 10
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we need to strip the single-quote I added to all the gene names:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "new_index, indexer = expression2.columns.reindex(map(lambda x: (x[0].lstrip(\"'\"), x[1]), expression2.columns.values))\n",
+ "expression2.columns = new_index\n",
+ "expression2.head()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "html": [
+ "| 'GENE | \n", + "'0610007L01RIK | \n", + "'0610007P14RIK | \n", + "'0610007P22RIK | \n", + "'0610008F07RIK | \n", + "'0610009B22RIK | \n", + "'0610009D07RIK | \n", + "'0610009O20RIK | \n", + "'0610010B08RIK | \n", + "'0610010F05RIK | \n", + "'0610010K06RIK | \n", + "... | \n", + "'ZWILCH | \n", + "'ZWINT | \n", + "'ZXDA | \n", + "'ZXDB | \n", + "'ZXDC | \n", + "'ZYG11A | \n", + "'ZYG11B | \n", + "'ZYX | \n", + "'ZZEF1 | \n", + "'ZZZ3 | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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| S2 | \n", + "37.682691 | \n", + "0.263962 | \n", + "0 | \n", + "0 | \n", + "0.207921 | \n", + "0.141099 | \n", + "0 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.255617 | \n", + "... | \n", + "0.000000 | \n", + "96.033724 | \n", + "0.020459 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0.042430 | \n", + "0.242888 | \n", + "0.000000 | \n", + "0.000000 | \n", + "
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5 rows \u00d7 27723 columns
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+ "\n",
+ " \n",
+ "
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+ "
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+ ],
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 11,
+ "text": [
+ "'GENE 0610007L01RIK 0610007P14RIK 0610007P22RIK 0610008F07RIK \\\n",
+ "Gene Category NaN NaN NaN NaN \n",
+ "S1 27.181570 0.166794 0 0 \n",
+ "S2 37.682691 0.263962 0 0 \n",
+ "S3 0.056916 78.622459 0 0 \n",
+ "S4 55.649250 0.228866 0 0 \n",
+ "S5 0.000000 0.093117 0 0 \n",
+ "\n",
+ "'GENE 0610009B22RIK 0610009D07RIK 0610009O20RIK 0610010B08RIK \\\n",
+ "Gene Category NaN NaN NaN NaN \n",
+ "S1 0.000000 178.852732 0 0.962417 \n",
+ "S2 0.207921 0.141099 0 0.000000 \n",
+ "S3 0.145680 0.396363 0 0.000000 \n",
+ "S4 0.000000 88.798158 0 0.000000 \n",
+ "S5 131.326008 155.936361 0 0.000000 \n",
+ "\n",
+ "'GENE 0610010F05RIK 0610010K06RIK ... ZWILCH ZWINT \\\n",
+ "Gene Category NaN NaN ... NaN NaN \n",
+ "S1 0.000000 143.359550 ... 0.000000 302.361227 \n",
+ "S2 0.000000 0.255617 ... 0.000000 96.033724 \n",
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+ "Gene Category NaN NaN NaN NaN NaN NaN NaN \n",
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+ "S2 0.020459 0 0 0 0.042430 0.242888 0.000000 \n",
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+ "\n",
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+ "S2 0.000000 \n",
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+ "S4 0.000000 \n",
+ "S5 0.000000 \n",
+ "\n",
+ "[5 rows x 27723 columns]"
+ ]
+ }
+ ],
+ "prompt_number": 11
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We want to create a `pandas.DataFrame` from the \"Gene Category\" row for our `expression_feature_data`, which we will do via:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "gene_ids, gene_category = zip(*expression2.columns.values)\n",
+ "gene_categories = pd.Series(gene_category, index=gene_ids, name='gene_category')\n",
+ "gene_categories"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 12,
+ "text": [
+ "0610007L01RIK NaN\n",
+ "0610007P14RIK NaN\n",
+ "0610007P22RIK NaN\n",
+ "0610008F07RIK NaN\n",
+ "0610009B22RIK NaN\n",
+ "0610009D07RIK NaN\n",
+ "0610009O20RIK NaN\n",
+ "0610010B08RIK NaN\n",
+ "0610010F05RIK NaN\n",
+ "0610010K06RIK NaN\n",
+ "0610010K14RIK NaN\n",
+ "0610010O12RIK NaN\n",
+ "0610011F06RIK NaN\n",
+ "0610011L14RIK NaN\n",
+ "0610012G03RIK NaN\n",
+ "...\n",
+ "ZSWIM5 NaN\n",
+ "ZSWIM6 NaN\n",
+ "ZSWIM7 NaN\n",
+ "ZUFSP LPS Response\n",
+ "ZW10 NaN\n",
+ "ZWILCH NaN\n",
+ "ZWINT NaN\n",
+ "ZXDA NaN\n",
+ "ZXDB NaN\n",
+ "ZXDC NaN\n",
+ "ZYG11A NaN\n",
+ "ZYG11B NaN\n",
+ "ZYX NaN\n",
+ "ZZEF1 NaN\n",
+ "ZZZ3 NaN\n",
+ "Name: gene_category, Length: 27723, dtype: object"
+ ]
+ }
+ ],
+ "prompt_number": 12
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "expression_feature_data = pd.DataFrame(gene_categories)\n",
+ "expression_feature_data.head()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "html": [
+ "| 'GENE | \n", + "0610007L01RIK | \n", + "0610007P14RIK | \n", + "0610007P22RIK | \n", + "0610008F07RIK | \n", + "0610009B22RIK | \n", + "0610009D07RIK | \n", + "0610009O20RIK | \n", + "0610010B08RIK | \n", + "0610010F05RIK | \n", + "0610010K06RIK | \n", + "... | \n", + "ZWILCH | \n", + "ZWINT | \n", + "ZXDA | \n", + "ZXDB | \n", + "ZXDC | \n", + "ZYG11A | \n", + "ZYG11B | \n", + "ZYX | \n", + "ZZEF1 | \n", + "ZZZ3 | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Gene Category | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
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5 rows \u00d7 27723 columns
\n", + "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 13,
+ "text": [
+ " gene_category\n",
+ "0610007L01RIK NaN\n",
+ "0610007P14RIK NaN\n",
+ "0610007P22RIK NaN\n",
+ "0610008F07RIK NaN\n",
+ "0610009B22RIK NaN"
+ ]
+ }
+ ],
+ "prompt_number": 13
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Splicing Data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We obtain the splicing data from this study from the supplementary information, specifically the `Supplementary_Table4.xls`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "splicing = pd.read_excel('nature12172-s1/Supplementary_Table4.xls', 'splicingTable.txt', index_col=(0,1))\n",
+ "splicing.head()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "html": [
+ "| \n", + " | gene_category | \n", + "
|---|---|
| 0610007L01RIK | \n", + "NaN | \n", + "
| 0610007P14RIK | \n", + "NaN | \n", + "
| 0610007P22RIK | \n", + "NaN | \n", + "
| 0610008F07RIK | \n", + "NaN | \n", + "
| 0610009B22RIK | \n", + "NaN | \n", + "
\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 14,
+ "text": [
+ " S1 \\\n",
+ "Event name gene \n",
+ "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- Os9 0.84 \n",
+ "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- Vta1 0.95 \n",
+ "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ Bclaf1 NaN \n",
+ "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ Bclaf1 0.02 \n",
+ "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ P4ha1 0.42 \n",
+ "\n",
+ " S2 \\\n",
+ "Event name gene \n",
+ "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- Os9 NaN \n",
+ "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- Vta1 NaN \n",
+ "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ Bclaf1 0.04 \n",
+ "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ Bclaf1 0.98 \n",
+ "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ P4ha1 NaN \n",
+ "\n",
+ " S3 \\\n",
+ "Event name gene \n",
+ "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- Os9 NaN \n",
+ "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- Vta1 NaN \n",
+ "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ Bclaf1 0.02 \n",
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+ "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ P4ha1 NaN \n",
+ "\n",
+ " S4 \\\n",
+ "Event name gene \n",
+ "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- Os9 NaN \n",
+ "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- Vta1 0.84 \n",
+ "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ Bclaf1 NaN \n",
+ "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ Bclaf1 NaN \n",
+ "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ P4ha1 NaN \n",
+ "\n",
+ " S5 \\\n",
+ "Event name gene \n",
+ "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- Os9 NaN \n",
+ "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- Vta1 0.95 \n",
+ "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ Bclaf1 NaN \n",
+ "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ Bclaf1 NaN \n",
+ "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ P4ha1 0.94 \n",
+ "\n",
+ " S6 \\\n",
+ "Event name gene \n",
+ "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- Os9 0.01 \n",
+ "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- Vta1 0.91 \n",
+ "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ Bclaf1 0.14 \n",
+ "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ Bclaf1 NaN \n",
+ "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ P4ha1 NaN \n",
+ "\n",
+ " S7 \\\n",
+ "Event name gene \n",
+ "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- Os9 NaN \n",
+ "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- Vta1 0.87 \n",
+ "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ Bclaf1 NaN \n",
+ "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ Bclaf1 NaN \n",
+ "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ P4ha1 NaN \n",
+ "\n",
+ " S8 \\\n",
+ "Event name gene \n",
+ "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- Os9 NaN \n",
+ "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- Vta1 0.86 \n",
+ "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ Bclaf1 0.02 \n",
+ "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ Bclaf1 0.98 \n",
+ "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ P4ha1 0.03 \n",
+ "\n",
+ " S9 \\\n",
+ "Event name gene \n",
+ "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- Os9 NaN \n",
+ "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- Vta1 NaN \n",
+ "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ Bclaf1 NaN \n",
+ "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ Bclaf1 NaN \n",
+ "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ P4ha1 0.97 \n",
+ "\n",
+ " S10 \\\n",
+ "Event name gene \n",
+ "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- Os9 NaN \n",
+ "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- Vta1 NaN \n",
+ "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ Bclaf1 NaN \n",
+ "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ Bclaf1 NaN \n",
+ "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ P4ha1 NaN \n",
+ "\n",
+ " S11 \\\n",
+ "Event name gene \n",
+ "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- Os9 0.03 \n",
+ "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- Vta1 0.93 \n",
+ "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ Bclaf1 NaN \n",
+ "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ Bclaf1 NaN \n",
+ "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ P4ha1 NaN \n",
+ "\n",
+ " S13 \\\n",
+ "Event name gene \n",
+ "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- Os9 NaN \n",
+ "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- Vta1 NaN \n",
+ "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ Bclaf1 NaN \n",
+ "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ Bclaf1 NaN \n",
+ "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ P4ha1 NaN \n",
+ "\n",
+ " S14 \\\n",
+ "Event name gene \n",
+ "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- Os9 NaN \n",
+ "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- Vta1 NaN \n",
+ "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ Bclaf1 NaN \n",
+ "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ Bclaf1 NaN \n",
+ "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ P4ha1 NaN \n",
+ "\n",
+ " S15 \\\n",
+ "Event name gene \n",
+ "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- Os9 0.02 \n",
+ "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- Vta1 0.96 \n",
+ "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ Bclaf1 0.01 \n",
+ "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ Bclaf1 0.06 \n",
+ "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ P4ha1 NaN \n",
+ "\n",
+ " S16 \\\n",
+ "Event name gene \n",
+ "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- Os9 NaN \n",
+ "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- Vta1 NaN \n",
+ "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ Bclaf1 NaN \n",
+ "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ Bclaf1 NaN \n",
+ "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ P4ha1 NaN \n",
+ "\n",
+ " S17 \\\n",
+ "Event name gene \n",
+ "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- Os9 0.01 \n",
+ "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- Vta1 NaN \n",
+ "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ Bclaf1 NaN \n",
+ "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ Bclaf1 NaN \n",
+ "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ P4ha1 NaN \n",
+ "\n",
+ " S18 \\\n",
+ "Event name gene \n",
+ "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- Os9 NaN \n",
+ "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- Vta1 NaN \n",
+ "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ Bclaf1 NaN \n",
+ "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ Bclaf1 NaN \n",
+ "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ P4ha1 NaN \n",
+ "\n",
+ " 10,000 cell Rep1 (P1) \\\n",
+ "Event name gene \n",
+ "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- Os9 0.27 \n",
+ "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- Vta1 0.83 \n",
+ "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ Bclaf1 0.40 \n",
+ "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ Bclaf1 0.62 \n",
+ "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ P4ha1 0.43 \n",
+ "\n",
+ " 10,000 cell Rep2 (P2) \\\n",
+ "Event name gene \n",
+ "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- Os9 0.37 \n",
+ "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- Vta1 0.85 \n",
+ "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ Bclaf1 0.49 \n",
+ "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ Bclaf1 0.63 \n",
+ "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ P4ha1 0.36 \n",
+ "\n",
+ " 10,000 cell Rep3 (P3) \n",
+ "Event name gene \n",
+ "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- Os9 0.31 \n",
+ "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- Vta1 0.64 \n",
+ "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ Bclaf1 0.59 \n",
+ "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ Bclaf1 0.70 \n",
+ "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ P4ha1 0.52 "
+ ]
+ }
+ ],
+ "prompt_number": 14
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "splicing = splicing.T\n",
+ "splicing"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "html": [
+ "| \n", + " | \n", + " | S1 | \n", + "S2 | \n", + "S3 | \n", + "S4 | \n", + "S5 | \n", + "S6 | \n", + "S7 | \n", + "S8 | \n", + "S9 | \n", + "S10 | \n", + "S11 | \n", + "S13 | \n", + "S14 | \n", + "S15 | \n", + "S16 | \n", + "S17 | \n", + "S18 | \n", + "10,000 cell Rep1 (P1) | \n", + "10,000 cell Rep2 (P2) | \n", + "10,000 cell Rep3 (P3) | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Event name | \n", + "gene | \n", + "\n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " |
| chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- | \n", + "Os9 | \n", + "0.84 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.01 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.03 | \n", + "NaN | \n", + "NaN | \n", + "0.02 | \n", + "NaN | \n", + "0.01 | \n", + "NaN | \n", + "0.27 | \n", + "0.37 | \n", + "0.31 | \n", + "
| chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- | \n", + "Vta1 | \n", + "0.95 | \n", + "NaN | \n", + "NaN | \n", + "0.84 | \n", + "0.95 | \n", + "0.91 | \n", + "0.87 | \n", + "0.86 | \n", + "NaN | \n", + "NaN | \n", + "0.93 | \n", + "NaN | \n", + "NaN | \n", + "0.96 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.83 | \n", + "0.85 | \n", + "0.64 | \n", + "
| chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ | \n", + "Bclaf1 | \n", + "NaN | \n", + "0.04 | \n", + "0.02 | \n", + "NaN | \n", + "NaN | \n", + "0.14 | \n", + "NaN | \n", + "0.02 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.01 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.40 | \n", + "0.49 | \n", + "0.59 | \n", + "
| chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ | \n", + "Bclaf1 | \n", + "0.02 | \n", + "0.98 | \n", + "0.55 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.98 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.06 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.62 | \n", + "0.63 | \n", + "0.70 | \n", + "
| chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ | \n", + "P4ha1 | \n", + "0.42 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.94 | \n", + "NaN | \n", + "NaN | \n", + "0.03 | \n", + "0.97 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.43 | \n", + "0.36 | \n", + "0.52 | \n", + "
\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 15,
+ "text": [
+ "Event name chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- \\\n",
+ "gene Os9 \n",
+ "S1 0.84 \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "S6 0.01 \n",
+ "S7 NaN \n",
+ "S8 NaN \n",
+ "S9 NaN \n",
+ "S10 NaN \n",
+ "S11 0.03 \n",
+ "S13 NaN \n",
+ "S14 NaN \n",
+ "S15 0.02 \n",
+ "S16 NaN \n",
+ "S17 0.01 \n",
+ "S18 NaN \n",
+ "10,000 cell Rep1 (P1) 0.27 \n",
+ "10,000 cell Rep2 (P2) 0.37 \n",
+ "10,000 cell Rep3 (P3) 0.31 \n",
+ "\n",
+ "Event name chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- \\\n",
+ "gene Vta1 \n",
+ "S1 0.95 \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 0.84 \n",
+ "S5 0.95 \n",
+ "S6 0.91 \n",
+ "S7 0.87 \n",
+ "S8 0.86 \n",
+ "S9 NaN \n",
+ "S10 NaN \n",
+ "S11 0.93 \n",
+ "S13 NaN \n",
+ "S14 NaN \n",
+ "S15 0.96 \n",
+ "S16 NaN \n",
+ "S17 NaN \n",
+ "S18 NaN \n",
+ "10,000 cell Rep1 (P1) 0.83 \n",
+ "10,000 cell Rep2 (P2) 0.85 \n",
+ "10,000 cell Rep3 (P3) 0.64 \n",
+ "\n",
+ "Event name chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ \\\n",
+ "gene Bclaf1 \n",
+ "S1 NaN \n",
+ "S2 0.04 \n",
+ "S3 0.02 \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "S6 0.14 \n",
+ "S7 NaN \n",
+ "S8 0.02 \n",
+ "S9 NaN \n",
+ "S10 NaN \n",
+ "S11 NaN \n",
+ "S13 NaN \n",
+ "S14 NaN \n",
+ "S15 0.01 \n",
+ "S16 NaN \n",
+ "S17 NaN \n",
+ "S18 NaN \n",
+ "10,000 cell Rep1 (P1) 0.40 \n",
+ "10,000 cell Rep2 (P2) 0.49 \n",
+ "10,000 cell Rep3 (P3) 0.59 \n",
+ "\n",
+ "Event name chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ \\\n",
+ "gene Bclaf1 \n",
+ "S1 0.02 \n",
+ "S2 0.98 \n",
+ "S3 0.55 \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "S6 NaN \n",
+ "S7 NaN \n",
+ "S8 0.98 \n",
+ "S9 NaN \n",
+ "S10 NaN \n",
+ "S11 NaN \n",
+ "S13 NaN \n",
+ "S14 NaN \n",
+ "S15 0.06 \n",
+ "S16 NaN \n",
+ "S17 NaN \n",
+ "S18 NaN \n",
+ "10,000 cell Rep1 (P1) 0.62 \n",
+ "10,000 cell Rep2 (P2) 0.63 \n",
+ "10,000 cell Rep3 (P3) 0.70 \n",
+ "\n",
+ "Event name chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ \\\n",
+ "gene P4ha1 \n",
+ "S1 0.42 \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 0.94 \n",
+ "S6 NaN \n",
+ "S7 NaN \n",
+ "S8 0.03 \n",
+ "S9 0.97 \n",
+ "S10 NaN \n",
+ "S11 NaN \n",
+ "S13 NaN \n",
+ "S14 NaN \n",
+ "S15 NaN \n",
+ "S16 NaN \n",
+ "S17 NaN \n",
+ "S18 NaN \n",
+ "10,000 cell Rep1 (P1) 0.43 \n",
+ "10,000 cell Rep2 (P2) 0.36 \n",
+ "10,000 cell Rep3 (P3) 0.52 \n",
+ "\n",
+ "Event name chr10:79173370:79173665:+@chr10:79174001:79174029:+@chr10:79174239:79174726:+ \\\n",
+ "gene Bsg \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "S6 NaN \n",
+ "S7 0.62 \n",
+ "S8 NaN \n",
+ "S9 NaN \n",
+ "S10 NaN \n",
+ "S11 NaN \n",
+ "S13 NaN \n",
+ "S14 NaN \n",
+ "S15 NaN \n",
+ "S16 NaN \n",
+ "S17 NaN \n",
+ "S18 NaN \n",
+ "10,000 cell Rep1 (P1) 0.78 \n",
+ "10,000 cell Rep2 (P2) 0.72 \n",
+ "10,000 cell Rep3 (P3) 0.79 \n",
+ "\n",
+ "Event name chr10:79322526:79322700:+@chr10:79322862:79322939:+@chr10:79323569:79323862:+ \\\n",
+ "gene Ptbp1 \n",
+ "S1 0.57 \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "S6 NaN \n",
+ "S7 NaN \n",
+ "S8 NaN \n",
+ "S9 0.97 \n",
+ "S10 0.06 \n",
+ "S11 NaN \n",
+ "S13 NaN \n",
+ "S14 NaN \n",
+ "S15 NaN \n",
+ "S16 NaN \n",
+ "S17 NaN \n",
+ "S18 NaN \n",
+ "10,000 cell Rep1 (P1) NaN \n",
+ "10,000 cell Rep2 (P2) 0.47 \n",
+ "10,000 cell Rep3 (P3) NaN \n",
+ "\n",
+ "Event name chr10:87376364:87376545:+@chr10:87378043:87378094:+@chr10:87393420:87399792:+ \\\n",
+ "gene Igf1 \n",
+ "S1 0.31 \n",
+ "S2 NaN \n",
+ "S3 0.20 \n",
+ "S4 0.95 \n",
+ "S5 0.73 \n",
+ "S6 0.61 \n",
+ "S7 0.85 \n",
+ "S8 0.89 \n",
+ "S9 NaN \n",
+ "S10 0.98 \n",
+ "S11 NaN \n",
+ "S13 NaN \n",
+ "S14 NaN \n",
+ "S15 0.44 \n",
+ "S16 NaN \n",
+ "S17 NaN \n",
+ "S18 NaN \n",
+ "10,000 cell Rep1 (P1) 0.60 \n",
+ "10,000 cell Rep2 (P2) 0.60 \n",
+ "10,000 cell Rep3 (P3) 0.65 \n",
+ "\n",
+ "Event name chr10:92747514:92747722:-@chr10:92727625:92728425:-@chr10:92717434:92717556:- \\\n",
+ "gene Elk3 \n",
+ "S1 0.93 \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "S6 NaN \n",
+ "S7 0.73 \n",
+ "S8 0.82 \n",
+ "S9 0.90 \n",
+ "S10 NaN \n",
+ "S11 0.97 \n",
+ "S13 NaN \n",
+ "S14 NaN \n",
+ "S15 NaN \n",
+ "S16 NaN \n",
+ "S17 NaN \n",
+ "S18 NaN \n",
+ "10,000 cell Rep1 (P1) 0.76 \n",
+ "10,000 cell Rep2 (P2) 0.73 \n",
+ "10,000 cell Rep3 (P3) 0.42 \n",
+ "\n",
+ "Event name chr11:101438508:101438565:+@chr11:101439246:101439351:+@chr11:101441899:101443267:+ \\\n",
+ "gene Nbr1 \n",
+ "S1 0.57 \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 0.04 \n",
+ "S5 NaN \n",
+ "S6 NaN \n",
+ "S7 0.55 \n",
+ "S8 0.83 \n",
+ "S9 NaN \n",
+ "S10 NaN \n",
+ "S11 NaN \n",
+ "S13 NaN \n",
+ "S14 0.88 \n",
+ "S15 NaN \n",
+ "S16 NaN \n",
+ "S17 NaN \n",
+ "S18 NaN \n",
+ "10,000 cell Rep1 (P1) 0.52 \n",
+ "10,000 cell Rep2 (P2) 0.68 \n",
+ "10,000 cell Rep3 (P3) 0.64 \n",
+ "\n",
+ "Event name ... \\\n",
+ "gene ... \n",
+ "S1 ... \n",
+ "S2 ... \n",
+ "S3 ... \n",
+ "S4 ... \n",
+ "S5 ... \n",
+ "S6 ... \n",
+ "S7 ... \n",
+ "S8 ... \n",
+ "S9 ... \n",
+ "S10 ... \n",
+ "S11 ... \n",
+ "S13 ... \n",
+ "S14 ... \n",
+ "S15 ... \n",
+ "S16 ... \n",
+ "S17 ... \n",
+ "S18 ... \n",
+ "10,000 cell Rep1 (P1) ... \n",
+ "10,000 cell Rep2 (P2) ... \n",
+ "10,000 cell Rep3 (P3) ... \n",
+ "\n",
+ "Event name chr8:126022488:126022598:+@chr8:126023892:126024007:+@chr8:126025133:126025333:+ \\\n",
+ "gene Afg3l1 \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "S6 NaN \n",
+ "S7 NaN \n",
+ "S8 NaN \n",
+ "S9 NaN \n",
+ "S10 NaN \n",
+ "S11 NaN \n",
+ "S13 NaN \n",
+ "S14 NaN \n",
+ "S15 0.91 \n",
+ "S16 NaN \n",
+ "S17 NaN \n",
+ "S18 NaN \n",
+ "10,000 cell Rep1 (P1) 0.92 \n",
+ "10,000 cell Rep2 (P2) 0.67 \n",
+ "10,000 cell Rep3 (P3) 0.58 \n",
+ "\n",
+ "Event name chr14:51455667:51455879:-@chr14:51453589:51453752:-@chr14:51453129:51453242:- \\\n",
+ "gene Tep1 \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "S6 NaN \n",
+ "S7 NaN \n",
+ "S8 NaN \n",
+ "S9 NaN \n",
+ "S10 NaN \n",
+ "S11 NaN \n",
+ "S13 NaN \n",
+ "S14 NaN \n",
+ "S15 NaN \n",
+ "S16 0.27 \n",
+ "S17 NaN \n",
+ "S18 NaN \n",
+ "10,000 cell Rep1 (P1) NaN \n",
+ "10,000 cell Rep2 (P2) 0.15 \n",
+ "10,000 cell Rep3 (P3) 0.79 \n",
+ "\n",
+ "Event name chr17:29497858:29498102:+@chr17:29500656:29500887:+@chr17:29501856:29502226:+ \\\n",
+ "gene Fgd2 \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "S6 NaN \n",
+ "S7 NaN \n",
+ "S8 NaN \n",
+ "S9 NaN \n",
+ "S10 NaN \n",
+ "S11 NaN \n",
+ "S13 NaN \n",
+ "S14 NaN \n",
+ "S15 NaN \n",
+ "S16 0.99 \n",
+ "S17 0.96 \n",
+ "S18 NaN \n",
+ "10,000 cell Rep1 (P1) 0.81 \n",
+ "10,000 cell Rep2 (P2) 0.52 \n",
+ "10,000 cell Rep3 (P3) 0.74 \n",
+ "\n",
+ "Event name chr2:94198908:94199094:-@chr2:94182784:94182954:-@chr2:94172950:94173209:- \\\n",
+ "gene Ttc17 \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "S6 NaN \n",
+ "S7 NaN \n",
+ "S8 NaN \n",
+ "S9 NaN \n",
+ "S10 NaN \n",
+ "S11 NaN \n",
+ "S13 NaN \n",
+ "S14 NaN \n",
+ "S15 NaN \n",
+ "S16 0.99 \n",
+ "S17 NaN \n",
+ "S18 NaN \n",
+ "10,000 cell Rep1 (P1) 0.77 \n",
+ "10,000 cell Rep2 (P2) 0.67 \n",
+ "10,000 cell Rep3 (P3) 0.85 \n",
+ "\n",
+ "Event name chr9:21314438:21314697:-@chr9:21313375:21313558:-@chr9:21311823:21312835:- \\\n",
+ "gene Tmed1 \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "S6 NaN \n",
+ "S7 NaN \n",
+ "S8 NaN \n",
+ "S9 NaN \n",
+ "S10 NaN \n",
+ "S11 NaN \n",
+ "S13 NaN \n",
+ "S14 NaN \n",
+ "S15 NaN \n",
+ "S16 0.98 \n",
+ "S17 NaN \n",
+ "S18 NaN \n",
+ "10,000 cell Rep1 (P1) NaN \n",
+ "10,000 cell Rep2 (P2) 0.63 \n",
+ "10,000 cell Rep3 (P3) 0.73 \n",
+ "\n",
+ "Event name chr9:21314438:21314697:-@chr9:21313375:21313795:-@chr9:21311823:21312835:- \\\n",
+ "gene Tmed1 \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "S6 NaN \n",
+ "S7 NaN \n",
+ "S8 NaN \n",
+ "S9 NaN \n",
+ "S10 NaN \n",
+ "S11 NaN \n",
+ "S13 NaN \n",
+ "S14 NaN \n",
+ "S15 NaN \n",
+ "S16 0.98 \n",
+ "S17 NaN \n",
+ "S18 NaN \n",
+ "10,000 cell Rep1 (P1) NaN \n",
+ "10,000 cell Rep2 (P2) 0.73 \n",
+ "10,000 cell Rep3 (P3) 0.39 \n",
+ "\n",
+ "Event name chr10:79545360:79545471:-@chr10:79542698:79544127:-@chr10:79533365:79535263:- \\\n",
+ "gene Sbno2 \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "S6 NaN \n",
+ "S7 NaN \n",
+ "S8 NaN \n",
+ "S9 NaN \n",
+ "S10 NaN \n",
+ "S11 NaN \n",
+ "S13 NaN \n",
+ "S14 NaN \n",
+ "S15 NaN \n",
+ "S16 NaN \n",
+ "S17 0.99 \n",
+ "S18 NaN \n",
+ "10,000 cell Rep1 (P1) 0.84 \n",
+ "10,000 cell Rep2 (P2) 0.82 \n",
+ "10,000 cell Rep3 (P3) 0.56 \n",
+ "\n",
+ "Event name chr17:5975579:5975881:+@chr17:5985972:5986242:+@chr17:5990136:5990361:+ \\\n",
+ "gene Synj2 \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "S6 NaN \n",
+ "S7 NaN \n",
+ "S8 NaN \n",
+ "S9 NaN \n",
+ "S10 NaN \n",
+ "S11 NaN \n",
+ "S13 NaN \n",
+ "S14 NaN \n",
+ "S15 NaN \n",
+ "S16 NaN \n",
+ "S17 0.98 \n",
+ "S18 NaN \n",
+ "10,000 cell Rep1 (P1) 0.50 \n",
+ "10,000 cell Rep2 (P2) 0.90 \n",
+ "10,000 cell Rep3 (P3) NaN \n",
+ "\n",
+ "Event name chr2:29997782:29997941:+@chr2:30002172:30002382:+@chr2:30002882:30003045:+ \\\n",
+ "gene Tbc1d13 \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "S6 NaN \n",
+ "S7 NaN \n",
+ "S8 NaN \n",
+ "S9 NaN \n",
+ "S10 NaN \n",
+ "S11 NaN \n",
+ "S13 NaN \n",
+ "S14 NaN \n",
+ "S15 NaN \n",
+ "S16 NaN \n",
+ "S17 0.67 \n",
+ "S18 NaN \n",
+ "10,000 cell Rep1 (P1) 0.56 \n",
+ "10,000 cell Rep2 (P2) 0.71 \n",
+ "10,000 cell Rep3 (P3) 0.64 \n",
+ "\n",
+ "Event name chr7:119221306:119221473:+@chr7:119223686:119223745:+@chr7:119225944:119226075:+ \n",
+ "gene Usp47 \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "S6 NaN \n",
+ "S7 NaN \n",
+ "S8 NaN \n",
+ "S9 NaN \n",
+ "S10 NaN \n",
+ "S11 NaN \n",
+ "S13 NaN \n",
+ "S14 NaN \n",
+ "S15 NaN \n",
+ "S16 NaN \n",
+ "S17 0.07 \n",
+ "S18 NaN \n",
+ "10,000 cell Rep1 (P1) NaN \n",
+ "10,000 cell Rep2 (P2) 0.55 \n",
+ "10,000 cell Rep3 (P3) NaN \n",
+ "\n",
+ "[20 rows x 352 columns]"
+ ]
+ }
+ ],
+ "prompt_number": 15
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The three pooled samples aren't named consistently with the expression data, so we have to fix that."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "splicing.index[splicing.index.map(lambda x: 'P' in x)]"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 16,
+ "text": [
+ "Index([u'10,000 cell Rep1 (P1)', u'10,000 cell Rep2 (P2)', u'10,000 cell Rep3 (P3)'], dtype='object')"
+ ]
+ }
+ ],
+ "prompt_number": 16
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Since the pooled sample IDs are inconsistent with the `expression` data, we have to change them. We can get the \"P\" and the number after that using regular expressions, called `re` in the Python standard library, e.g.:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "import re\n",
+ "re.search(r'P\\d', '10,000 cell Rep1 (P1)').group()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 17,
+ "text": [
+ "'P1'"
+ ]
+ }
+ ],
+ "prompt_number": 17
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "def long_pooled_name_to_short(x):\n",
+ " if 'P' not in x:\n",
+ " return x\n",
+ " else:\n",
+ " return re.search(r'P\\d', x).group()\n",
+ "\n",
+ "\n",
+ "splicing.index.map(long_pooled_name_to_short)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 18,
+ "text": [
+ "array([u'S1', u'S2', u'S3', u'S4', u'S5', u'S6', u'S7', u'S8', u'S9',\n",
+ " u'S10', u'S11', u'S13', u'S14', u'S15', u'S16', u'S17', u'S18',\n",
+ " u'P1', u'P2', u'P3'], dtype=object)"
+ ]
+ }
+ ],
+ "prompt_number": 18
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "And now we assign this new index as our index to the `splicing` dataframe"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "splicing.index = splicing.index.map(long_pooled_name_to_short)\n",
+ "splicing.head()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "html": [
+ "| Event name | \n", + "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- | \n", + "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- | \n", + "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ | \n", + "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ | \n", + "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ | \n", + "chr10:79173370:79173665:+@chr10:79174001:79174029:+@chr10:79174239:79174726:+ | \n", + "chr10:79322526:79322700:+@chr10:79322862:79322939:+@chr10:79323569:79323862:+ | \n", + "chr10:87376364:87376545:+@chr10:87378043:87378094:+@chr10:87393420:87399792:+ | \n", + "chr10:92747514:92747722:-@chr10:92727625:92728425:-@chr10:92717434:92717556:- | \n", + "chr11:101438508:101438565:+@chr11:101439246:101439351:+@chr11:101441899:101443267:+ | \n", + "... | \n", + "chr8:126022488:126022598:+@chr8:126023892:126024007:+@chr8:126025133:126025333:+ | \n", + "chr14:51455667:51455879:-@chr14:51453589:51453752:-@chr14:51453129:51453242:- | \n", + "chr17:29497858:29498102:+@chr17:29500656:29500887:+@chr17:29501856:29502226:+ | \n", + "chr2:94198908:94199094:-@chr2:94182784:94182954:-@chr2:94172950:94173209:- | \n", + "chr9:21314438:21314697:-@chr9:21313375:21313558:-@chr9:21311823:21312835:- | \n", + "chr9:21314438:21314697:-@chr9:21313375:21313795:-@chr9:21311823:21312835:- | \n", + "chr10:79545360:79545471:-@chr10:79542698:79544127:-@chr10:79533365:79535263:- | \n", + "chr17:5975579:5975881:+@chr17:5985972:5986242:+@chr17:5990136:5990361:+ | \n", + "chr2:29997782:29997941:+@chr2:30002172:30002382:+@chr2:30002882:30003045:+ | \n", + "chr7:119221306:119221473:+@chr7:119223686:119223745:+@chr7:119225944:119226075:+ | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| gene | \n", + "Os9 | \n", + "Vta1 | \n", + "Bclaf1 | \n", + "Bclaf1 | \n", + "P4ha1 | \n", + "Bsg | \n", + "Ptbp1 | \n", + "Igf1 | \n", + "Elk3 | \n", + "Nbr1 | \n", + "... | \n", + "Afg3l1 | \n", + "Tep1 | \n", + "Fgd2 | \n", + "Ttc17 | \n", + "Tmed1 | \n", + "Tmed1 | \n", + "Sbno2 | \n", + "Synj2 | \n", + "Tbc1d13 | \n", + "Usp47 | \n", + "
| S1 | \n", + "0.84 | \n", + "0.95 | \n", + "NaN | \n", + "0.02 | \n", + "0.42 | \n", + "NaN | \n", + "0.57 | \n", + "0.31 | \n", + "0.93 | \n", + "0.57 | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| S2 | \n", + "NaN | \n", + "NaN | \n", + "0.04 | \n", + "0.98 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| S3 | \n", + "NaN | \n", + "NaN | \n", + "0.02 | \n", + "0.55 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.20 | \n", + "NaN | \n", + "NaN | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| S4 | \n", + "NaN | \n", + "0.84 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.95 | \n", + "NaN | \n", + "0.04 | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| S5 | \n", + "NaN | \n", + "0.95 | \n", + "NaN | \n", + "NaN | \n", + "0.94 | \n", + "NaN | \n", + "NaN | \n", + "0.73 | \n", + "NaN | \n", + "NaN | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| S6 | \n", + "0.01 | \n", + "0.91 | \n", + "0.14 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.61 | \n", + "NaN | \n", + "NaN | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| S7 | \n", + "NaN | \n", + "0.87 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.62 | \n", + "NaN | \n", + "0.85 | \n", + "0.73 | \n", + "0.55 | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| S8 | \n", + "NaN | \n", + "0.86 | \n", + "0.02 | \n", + "0.98 | \n", + "0.03 | \n", + "NaN | \n", + "NaN | \n", + "0.89 | \n", + "0.82 | \n", + "0.83 | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| S9 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.97 | \n", + "NaN | \n", + "0.97 | \n", + "NaN | \n", + "0.90 | \n", + "NaN | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| S10 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.06 | \n", + "0.98 | \n", + "NaN | \n", + "NaN | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| S11 | \n", + "0.03 | \n", + "0.93 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.97 | \n", + "NaN | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| S13 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| S14 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.88 | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| S15 | \n", + "0.02 | \n", + "0.96 | \n", + "0.01 | \n", + "0.06 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.44 | \n", + "NaN | \n", + "NaN | \n", + "... | \n", + "0.91 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| S16 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "... | \n", + "NaN | \n", + "0.27 | \n", + "0.99 | \n", + "0.99 | \n", + "0.98 | \n", + "0.98 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| S17 | \n", + "0.01 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "0.96 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.99 | \n", + "0.98 | \n", + "0.67 | \n", + "0.07 | \n", + "
| S18 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| 10,000 cell Rep1 (P1) | \n", + "0.27 | \n", + "0.83 | \n", + "0.40 | \n", + "0.62 | \n", + "0.43 | \n", + "0.78 | \n", + "NaN | \n", + "0.60 | \n", + "0.76 | \n", + "0.52 | \n", + "... | \n", + "0.92 | \n", + "NaN | \n", + "0.81 | \n", + "0.77 | \n", + "NaN | \n", + "NaN | \n", + "0.84 | \n", + "0.50 | \n", + "0.56 | \n", + "NaN | \n", + "
| 10,000 cell Rep2 (P2) | \n", + "0.37 | \n", + "0.85 | \n", + "0.49 | \n", + "0.63 | \n", + "0.36 | \n", + "0.72 | \n", + "0.47 | \n", + "0.60 | \n", + "0.73 | \n", + "0.68 | \n", + "... | \n", + "0.67 | \n", + "0.15 | \n", + "0.52 | \n", + "0.67 | \n", + "0.63 | \n", + "0.73 | \n", + "0.82 | \n", + "0.90 | \n", + "0.71 | \n", + "0.55 | \n", + "
| 10,000 cell Rep3 (P3) | \n", + "0.31 | \n", + "0.64 | \n", + "0.59 | \n", + "0.70 | \n", + "0.52 | \n", + "0.79 | \n", + "NaN | \n", + "0.65 | \n", + "0.42 | \n", + "0.64 | \n", + "... | \n", + "0.58 | \n", + "0.79 | \n", + "0.74 | \n", + "0.85 | \n", + "0.73 | \n", + "0.39 | \n", + "0.56 | \n", + "NaN | \n", + "0.64 | \n", + "NaN | \n", + "
20 rows \u00d7 352 columns
\n", + "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 19,
+ "text": [
+ "Event name chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- \\\n",
+ "gene Os9 \n",
+ "S1 0.84 \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "\n",
+ "Event name chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- \\\n",
+ "gene Vta1 \n",
+ "S1 0.95 \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 0.84 \n",
+ "S5 0.95 \n",
+ "\n",
+ "Event name chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ \\\n",
+ "gene Bclaf1 \n",
+ "S1 NaN \n",
+ "S2 0.04 \n",
+ "S3 0.02 \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "\n",
+ "Event name chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ \\\n",
+ "gene Bclaf1 \n",
+ "S1 0.02 \n",
+ "S2 0.98 \n",
+ "S3 0.55 \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "\n",
+ "Event name chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ \\\n",
+ "gene P4ha1 \n",
+ "S1 0.42 \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 0.94 \n",
+ "\n",
+ "Event name chr10:79173370:79173665:+@chr10:79174001:79174029:+@chr10:79174239:79174726:+ \\\n",
+ "gene Bsg \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "\n",
+ "Event name chr10:79322526:79322700:+@chr10:79322862:79322939:+@chr10:79323569:79323862:+ \\\n",
+ "gene Ptbp1 \n",
+ "S1 0.57 \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "\n",
+ "Event name chr10:87376364:87376545:+@chr10:87378043:87378094:+@chr10:87393420:87399792:+ \\\n",
+ "gene Igf1 \n",
+ "S1 0.31 \n",
+ "S2 NaN \n",
+ "S3 0.20 \n",
+ "S4 0.95 \n",
+ "S5 0.73 \n",
+ "\n",
+ "Event name chr10:92747514:92747722:-@chr10:92727625:92728425:-@chr10:92717434:92717556:- \\\n",
+ "gene Elk3 \n",
+ "S1 0.93 \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "\n",
+ "Event name chr11:101438508:101438565:+@chr11:101439246:101439351:+@chr11:101441899:101443267:+ \\\n",
+ "gene Nbr1 \n",
+ "S1 0.57 \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 0.04 \n",
+ "S5 NaN \n",
+ "\n",
+ "Event name ... \\\n",
+ "gene ... \n",
+ "S1 ... \n",
+ "S2 ... \n",
+ "S3 ... \n",
+ "S4 ... \n",
+ "S5 ... \n",
+ "\n",
+ "Event name chr8:126022488:126022598:+@chr8:126023892:126024007:+@chr8:126025133:126025333:+ \\\n",
+ "gene Afg3l1 \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "\n",
+ "Event name chr14:51455667:51455879:-@chr14:51453589:51453752:-@chr14:51453129:51453242:- \\\n",
+ "gene Tep1 \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "\n",
+ "Event name chr17:29497858:29498102:+@chr17:29500656:29500887:+@chr17:29501856:29502226:+ \\\n",
+ "gene Fgd2 \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "\n",
+ "Event name chr2:94198908:94199094:-@chr2:94182784:94182954:-@chr2:94172950:94173209:- \\\n",
+ "gene Ttc17 \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "\n",
+ "Event name chr9:21314438:21314697:-@chr9:21313375:21313558:-@chr9:21311823:21312835:- \\\n",
+ "gene Tmed1 \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "\n",
+ "Event name chr9:21314438:21314697:-@chr9:21313375:21313795:-@chr9:21311823:21312835:- \\\n",
+ "gene Tmed1 \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "\n",
+ "Event name chr10:79545360:79545471:-@chr10:79542698:79544127:-@chr10:79533365:79535263:- \\\n",
+ "gene Sbno2 \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "\n",
+ "Event name chr17:5975579:5975881:+@chr17:5985972:5986242:+@chr17:5990136:5990361:+ \\\n",
+ "gene Synj2 \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "\n",
+ "Event name chr2:29997782:29997941:+@chr2:30002172:30002382:+@chr2:30002882:30003045:+ \\\n",
+ "gene Tbc1d13 \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "\n",
+ "Event name chr7:119221306:119221473:+@chr7:119223686:119223745:+@chr7:119225944:119226075:+ \n",
+ "gene Usp47 \n",
+ "S1 NaN \n",
+ "S2 NaN \n",
+ "S3 NaN \n",
+ "S4 NaN \n",
+ "S5 NaN \n",
+ "\n",
+ "[5 rows x 352 columns]"
+ ]
+ }
+ ],
+ "prompt_number": 19
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Metadata\n",
+ "\n",
+ "Now let's get into creating a metadata dataframe. We'll use the index from the `expression_filtered` data to create the minimum required column, `'phenotype'`, which has the name of the phenotype of that cell. And we'll also add the column `'pooled'` to indicate whether this sample is pooled or not."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "metadata = pd.DataFrame(index=expression_filtered.index)\n",
+ "metadata['phenotype'] = 'BDMC'\n",
+ "metadata['pooled'] = metadata.index.map(lambda x: x.startswith('P'))\n",
+ "\n",
+ "metadata"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "html": [
+ "| Event name | \n", + "chr10:126534988:126535177:-@chr10:126533971:126534135:-@chr10:126533686:126533798:- | \n", + "chr10:14403870:14403945:-@chr10:14395740:14395848:-@chr10:14387738:14387914:- | \n", + "chr10:20051892:20052067:+@chr10:20052202:20052363:+@chr10:20053198:20053697:+ | \n", + "chr10:20052864:20053378:+@chr10:20054305:20054451:+@chr10:20059515:20059727:+ | \n", + "chr10:58814831:58815007:+@chr10:58817088:58817158:+@chr10:58818098:58818168:+@chr10:58824609:58824708:+ | \n", + "chr10:79173370:79173665:+@chr10:79174001:79174029:+@chr10:79174239:79174726:+ | \n", + "chr10:79322526:79322700:+@chr10:79322862:79322939:+@chr10:79323569:79323862:+ | \n", + "chr10:87376364:87376545:+@chr10:87378043:87378094:+@chr10:87393420:87399792:+ | \n", + "chr10:92747514:92747722:-@chr10:92727625:92728425:-@chr10:92717434:92717556:- | \n", + "chr11:101438508:101438565:+@chr11:101439246:101439351:+@chr11:101441899:101443267:+ | \n", + "... | \n", + "chr8:126022488:126022598:+@chr8:126023892:126024007:+@chr8:126025133:126025333:+ | \n", + "chr14:51455667:51455879:-@chr14:51453589:51453752:-@chr14:51453129:51453242:- | \n", + "chr17:29497858:29498102:+@chr17:29500656:29500887:+@chr17:29501856:29502226:+ | \n", + "chr2:94198908:94199094:-@chr2:94182784:94182954:-@chr2:94172950:94173209:- | \n", + "chr9:21314438:21314697:-@chr9:21313375:21313558:-@chr9:21311823:21312835:- | \n", + "chr9:21314438:21314697:-@chr9:21313375:21313795:-@chr9:21311823:21312835:- | \n", + "chr10:79545360:79545471:-@chr10:79542698:79544127:-@chr10:79533365:79535263:- | \n", + "chr17:5975579:5975881:+@chr17:5985972:5986242:+@chr17:5990136:5990361:+ | \n", + "chr2:29997782:29997941:+@chr2:30002172:30002382:+@chr2:30002882:30003045:+ | \n", + "chr7:119221306:119221473:+@chr7:119223686:119223745:+@chr7:119225944:119226075:+ | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| gene | \n", + "Os9 | \n", + "Vta1 | \n", + "Bclaf1 | \n", + "Bclaf1 | \n", + "P4ha1 | \n", + "Bsg | \n", + "Ptbp1 | \n", + "Igf1 | \n", + "Elk3 | \n", + "Nbr1 | \n", + "... | \n", + "Afg3l1 | \n", + "Tep1 | \n", + "Fgd2 | \n", + "Ttc17 | \n", + "Tmed1 | \n", + "Tmed1 | \n", + "Sbno2 | \n", + "Synj2 | \n", + "Tbc1d13 | \n", + "Usp47 | \n", + "
| S1 | \n", + "0.84 | \n", + "0.95 | \n", + "NaN | \n", + "0.02 | \n", + "0.42 | \n", + "NaN | \n", + "0.57 | \n", + "0.31 | \n", + "0.93 | \n", + "0.57 | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| S2 | \n", + "NaN | \n", + "NaN | \n", + "0.04 | \n", + "0.98 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| S3 | \n", + "NaN | \n", + "NaN | \n", + "0.02 | \n", + "0.55 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.20 | \n", + "NaN | \n", + "NaN | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| S4 | \n", + "NaN | \n", + "0.84 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "0.95 | \n", + "NaN | \n", + "0.04 | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| S5 | \n", + "NaN | \n", + "0.95 | \n", + "NaN | \n", + "NaN | \n", + "0.94 | \n", + "NaN | \n", + "NaN | \n", + "0.73 | \n", + "NaN | \n", + "NaN | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
5 rows \u00d7 352 columns
\n", + "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 20,
+ "text": [
+ " phenotype pooled\n",
+ "S1 BDMC False\n",
+ "S2 BDMC False\n",
+ "S3 BDMC False\n",
+ "S4 BDMC False\n",
+ "S5 BDMC False\n",
+ "S6 BDMC False\n",
+ "S7 BDMC False\n",
+ "S8 BDMC False\n",
+ "S9 BDMC False\n",
+ "S10 BDMC False\n",
+ "S11 BDMC False\n",
+ "S12 BDMC False\n",
+ "S13 BDMC False\n",
+ "S14 BDMC False\n",
+ "S15 BDMC False\n",
+ "S16 BDMC False\n",
+ "S17 BDMC False\n",
+ "S18 BDMC False\n",
+ "P1 BDMC True\n",
+ "P2 BDMC True\n",
+ "P3 BDMC True"
+ ]
+ }
+ ],
+ "prompt_number": 20
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Mapping stats data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "mapping_stats = pd.read_excel('nature12172-s1/Supplementary_Table1.xls', sheetname='SuppTable1 2.txt')\n",
+ "mapping_stats"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "html": [
+ "| \n", + " | phenotype | \n", + "pooled | \n", + "
|---|---|---|
| S1 | \n", + "BDMC | \n", + "False | \n", + "
| S2 | \n", + "BDMC | \n", + "False | \n", + "
| S3 | \n", + "BDMC | \n", + "False | \n", + "
| S4 | \n", + "BDMC | \n", + "False | \n", + "
| S5 | \n", + "BDMC | \n", + "False | \n", + "
| S6 | \n", + "BDMC | \n", + "False | \n", + "
| S7 | \n", + "BDMC | \n", + "False | \n", + "
| S8 | \n", + "BDMC | \n", + "False | \n", + "
| S9 | \n", + "BDMC | \n", + "False | \n", + "
| S10 | \n", + "BDMC | \n", + "False | \n", + "
| S11 | \n", + "BDMC | \n", + "False | \n", + "
| S12 | \n", + "BDMC | \n", + "False | \n", + "
| S13 | \n", + "BDMC | \n", + "False | \n", + "
| S14 | \n", + "BDMC | \n", + "False | \n", + "
| S15 | \n", + "BDMC | \n", + "False | \n", + "
| S16 | \n", + "BDMC | \n", + "False | \n", + "
| S17 | \n", + "BDMC | \n", + "False | \n", + "
| S18 | \n", + "BDMC | \n", + "False | \n", + "
| P1 | \n", + "BDMC | \n", + "True | \n", + "
| P2 | \n", + "BDMC | \n", + "True | \n", + "
| P3 | \n", + "BDMC | \n", + "True | \n", + "
\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 21,
+ "text": [
+ " Sample PF_READS PCT_MAPPED_GENOME PCT_RIBOSOMAL_BASES \\\n",
+ "0 S1 21326048 0.706590 0.006820 \n",
+ "1 S2 27434011 0.745385 0.004111 \n",
+ "2 S3 31142391 0.722087 0.006428 \n",
+ "3 S4 26231852 0.737854 0.004959 \n",
+ "4 S5 29977214 0.746466 0.006121 \n",
+ "5 S6 24148387 0.730079 0.008794 \n",
+ "6 S7 24078116 0.730638 0.007945 \n",
+ "7 S8 25032126 0.739989 0.004133 \n",
+ "8 S9 22257682 0.747427 0.004869 \n",
+ "9 S10 29436289 0.748795 0.005499 \n",
+ "10 S11 31130278 0.741882 0.002740 \n",
+ "11 S12 21161595 0.750782 0.006837 \n",
+ "12 S13 28612833 0.733976 0.011718 \n",
+ "13 S14 26351189 0.748323 0.004106 \n",
+ "14 S15 25739575 0.748421 0.003353 \n",
+ "15 S16 26802346 0.739833 0.009370 \n",
+ "16 S17 26343522 0.749358 0.003155 \n",
+ "17 S18 25290073 0.749358 0.007465 \n",
+ "18 10k_rep1 28247826 0.688553 0.018993 \n",
+ "19 10k_rep2 39303876 0.690313 0.017328 \n",
+ "20 10k_rep3 29831281 0.710875 0.010610 \n",
+ "21 MB_SC1 13848219 0.545000 0.007000 \n",
+ "22 MB_SC2 13550218 0.458000 0.010800 \n",
+ "23 MB_SC3 26765848 0.496000 0.007900 \n",
+ "\n",
+ " MEDIAN_CV_COVERAGE MEDIAN_5PRIME_BIAS MEDIAN_3PRIME_BIAS \\\n",
+ "0 0.509939 0.092679 0.477321 \n",
+ "1 0.565732 0.056583 0.321053 \n",
+ "2 0.540341 0.079551 0.382286 \n",
+ "3 0.530978 0.067041 0.351670 \n",
+ "4 0.525598 0.066543 0.353995 \n",
+ "5 0.529650 0.072095 0.413696 \n",
+ "6 0.540913 0.051991 0.358597 \n",
+ "7 0.512725 0.058783 0.373509 \n",
+ "8 0.521622 0.063566 0.334294 \n",
+ "9 0.560454 0.036219 0.306729 \n",
+ "10 0.558882 0.049581 0.349191 \n",
+ "11 0.756339 0.013878 0.324264 \n",
+ "12 0.598687 0.035392 0.357447 \n",
+ "13 0.517518 0.070293 0.381095 \n",
+ "14 0.526238 0.050938 0.324207 \n",
+ "15 0.520287 0.071503 0.358758 \n",
+ "16 0.673195 0.024121 0.301588 \n",
+ "17 0.562382 0.048528 0.314776 \n",
+ "18 0.547000 0.056113 0.484393 \n",
+ "19 0.547621 0.055600 0.474634 \n",
+ "20 0.518053 0.066053 0.488738 \n",
+ "21 0.531495 0.127934 0.207841 \n",
+ "22 0.569271 0.102581 0.179407 \n",
+ "23 0.535192 0.141893 0.231068 \n",
+ "\n",
+ " MEDIAN_5PRIME_TO_3PRIME_BIAS \n",
+ "0 0.247741 \n",
+ "1 0.244062 \n",
+ "2 0.267367 \n",
+ "3 0.279782 \n",
+ "4 0.274252 \n",
+ "5 0.225929 \n",
+ "6 0.201984 \n",
+ "7 0.212337 \n",
+ "8 0.240641 \n",
+ "9 0.187479 \n",
+ "10 0.211787 \n",
+ "11 0.195430 \n",
+ "12 0.198566 \n",
+ "13 0.259122 \n",
+ "14 0.212366 \n",
+ "15 0.240009 \n",
+ "16 0.245854 \n",
+ "17 0.215160 \n",
+ "18 0.140333 \n",
+ "19 0.142889 \n",
+ "20 0.168180 \n",
+ "21 0.728980 \n",
+ "22 0.694747 \n",
+ "23 0.722080 "
+ ]
+ }
+ ],
+ "prompt_number": 21
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Create a `flotilla` Study!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "study = flotilla.Study(# The metadata describing phenotype and pooled samples\n",
+ " metadata, \n",
+ " \n",
+ " # A version for this data\n",
+ " version='0.1.0', \n",
+ " \n",
+ " # Dataframe of the filtered expression data\n",
+ " expression_data=expression_filtered,\n",
+ " \n",
+ " # Dataframe of the feature data of the genes\n",
+ " expression_feature_data=expression_feature_data,\n",
+ " \n",
+ " # Dataframe of the splicing data\n",
+ " splicing_data=splicing, \n",
+ " \n",
+ " # Dataframe of the mapping stats data\n",
+ " mapping_stats_data=mapping_stats, \n",
+ " \n",
+ " # Which column in \"mapping_stats\" has the number of reads\n",
+ " mapping_stats_number_mapped_col='PF_READS')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "2014-11-10 12:36:35\tInitializing Study\n",
+ "2014-11-10 12:36:35\tInitializing Predictor configuration manager for Study\n",
+ "2014-11-10 12:36:35\tPredictor ExtraTreesClassifier is of type | \n", + " | Sample | \n", + "PF_READS | \n", + "PCT_MAPPED_GENOME | \n", + "PCT_RIBOSOMAL_BASES | \n", + "MEDIAN_CV_COVERAGE | \n", + "MEDIAN_5PRIME_BIAS | \n", + "MEDIAN_3PRIME_BIAS | \n", + "MEDIAN_5PRIME_TO_3PRIME_BIAS | \n", + "
|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "S1 | \n", + "21326048 | \n", + "0.706590 | \n", + "0.006820 | \n", + "0.509939 | \n", + "0.092679 | \n", + "0.477321 | \n", + "0.247741 | \n", + "
| 1 | \n", + "S2 | \n", + "27434011 | \n", + "0.745385 | \n", + "0.004111 | \n", + "0.565732 | \n", + "0.056583 | \n", + "0.321053 | \n", + "0.244062 | \n", + "
| 2 | \n", + "S3 | \n", + "31142391 | \n", + "0.722087 | \n", + "0.006428 | \n", + "0.540341 | \n", + "0.079551 | \n", + "0.382286 | \n", + "0.267367 | \n", + "
| 3 | \n", + "S4 | \n", + "26231852 | \n", + "0.737854 | \n", + "0.004959 | \n", + "0.530978 | \n", + "0.067041 | \n", + "0.351670 | \n", + "0.279782 | \n", + "
| 4 | \n", + "S5 | \n", + "29977214 | \n", + "0.746466 | \n", + "0.006121 | \n", + "0.525598 | \n", + "0.066543 | \n", + "0.353995 | \n", + "0.274252 | \n", + "
| 5 | \n", + "S6 | \n", + "24148387 | \n", + "0.730079 | \n", + "0.008794 | \n", + "0.529650 | \n", + "0.072095 | \n", + "0.413696 | \n", + "0.225929 | \n", + "
| 6 | \n", + "S7 | \n", + "24078116 | \n", + "0.730638 | \n", + "0.007945 | \n", + "0.540913 | \n", + "0.051991 | \n", + "0.358597 | \n", + "0.201984 | \n", + "
| 7 | \n", + "S8 | \n", + "25032126 | \n", + "0.739989 | \n", + "0.004133 | \n", + "0.512725 | \n", + "0.058783 | \n", + "0.373509 | \n", + "0.212337 | \n", + "
| 8 | \n", + "S9 | \n", + "22257682 | \n", + "0.747427 | \n", + "0.004869 | \n", + "0.521622 | \n", + "0.063566 | \n", + "0.334294 | \n", + "0.240641 | \n", + "
| 9 | \n", + "S10 | \n", + "29436289 | \n", + "0.748795 | \n", + "0.005499 | \n", + "0.560454 | \n", + "0.036219 | \n", + "0.306729 | \n", + "0.187479 | \n", + "
| 10 | \n", + "S11 | \n", + "31130278 | \n", + "0.741882 | \n", + "0.002740 | \n", + "0.558882 | \n", + "0.049581 | \n", + "0.349191 | \n", + "0.211787 | \n", + "
| 11 | \n", + "S12 | \n", + "21161595 | \n", + "0.750782 | \n", + "0.006837 | \n", + "0.756339 | \n", + "0.013878 | \n", + "0.324264 | \n", + "0.195430 | \n", + "
| 12 | \n", + "S13 | \n", + "28612833 | \n", + "0.733976 | \n", + "0.011718 | \n", + "0.598687 | \n", + "0.035392 | \n", + "0.357447 | \n", + "0.198566 | \n", + "
| 13 | \n", + "S14 | \n", + "26351189 | \n", + "0.748323 | \n", + "0.004106 | \n", + "0.517518 | \n", + "0.070293 | \n", + "0.381095 | \n", + "0.259122 | \n", + "
| 14 | \n", + "S15 | \n", + "25739575 | \n", + "0.748421 | \n", + "0.003353 | \n", + "0.526238 | \n", + "0.050938 | \n", + "0.324207 | \n", + "0.212366 | \n", + "
| 15 | \n", + "S16 | \n", + "26802346 | \n", + "0.739833 | \n", + "0.009370 | \n", + "0.520287 | \n", + "0.071503 | \n", + "0.358758 | \n", + "0.240009 | \n", + "
| 16 | \n", + "S17 | \n", + "26343522 | \n", + "0.749358 | \n", + "0.003155 | \n", + "0.673195 | \n", + "0.024121 | \n", + "0.301588 | \n", + "0.245854 | \n", + "
| 17 | \n", + "S18 | \n", + "25290073 | \n", + "0.749358 | \n", + "0.007465 | \n", + "0.562382 | \n", + "0.048528 | \n", + "0.314776 | \n", + "0.215160 | \n", + "
| 18 | \n", + "10k_rep1 | \n", + "28247826 | \n", + "0.688553 | \n", + "0.018993 | \n", + "0.547000 | \n", + "0.056113 | \n", + "0.484393 | \n", + "0.140333 | \n", + "
| 19 | \n", + "10k_rep2 | \n", + "39303876 | \n", + "0.690313 | \n", + "0.017328 | \n", + "0.547621 | \n", + "0.055600 | \n", + "0.474634 | \n", + "0.142889 | \n", + "
| 20 | \n", + "10k_rep3 | \n", + "29831281 | \n", + "0.710875 | \n", + "0.010610 | \n", + "0.518053 | \n", + "0.066053 | \n", + "0.488738 | \n", + "0.168180 | \n", + "
| 21 | \n", + "MB_SC1 | \n", + "13848219 | \n", + "0.545000 | \n", + "0.007000 | \n", + "0.531495 | \n", + "0.127934 | \n", + "0.207841 | \n", + "0.728980 | \n", + "
| 22 | \n", + "MB_SC2 | \n", + "13550218 | \n", + "0.458000 | \n", + "0.010800 | \n", + "0.569271 | \n", + "0.102581 | \n", + "0.179407 | \n", + "0.694747 | \n", + "
| 23 | \n", + "MB_SC3 | \n", + "26765848 | \n", + "0.496000 | \n", + "0.007900 | \n", + "0.535192 | \n", + "0.141893 | \n", + "0.231068 | \n", + "0.722080 | \n", + "
\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 45,
+ "text": [
+ "GENE 1110018G07RIK 1110038F14RIK 1200009I06RIK 1600014C10RIK \\\n",
+ "S1 3.711442 0.000000 3.275468 0.000000 \n",
+ "S2 4.361671 0.147643 0.000000 0.000000 \n",
+ "S3 0.000000 3.737014 2.987093 0.063526 \n",
+ "S4 2.719587 0.000000 0.045823 0.000000 \n",
+ "S5 2.982073 0.000000 2.829152 0.000000 \n",
+ "\n",
+ "GENE 1810029B16RIK 2210009G21RIK 2810474O19RIK 3110001I22RIK \\\n",
+ "S1 5.609305 0 0.000000 3.828860 \n",
+ "S2 5.478071 0 3.407342 0.000000 \n",
+ "S3 5.320993 0 3.372359 0.058163 \n",
+ "S4 0.488049 0 5.127847 0.000000 \n",
+ "S5 5.093188 0 0.065122 4.635671 \n",
+ "\n",
+ "GENE 4921513D23RIK 4930523C07RIK ... ZC3H12C ZC3HAV1 ZCCHC2 \\\n",
+ "S1 1.314573 3.778275 ... 3.972904 3.509979 0.035344 \n",
+ "S2 1.531443 0.000000 ... 4.794306 4.984262 2.251330 \n",
+ "S3 1.105115 0.025043 ... 4.882749 0.807258 0.094925 \n",
+ "S4 2.303969 0.000000 ... 4.833354 4.538699 0.137427 \n",
+ "S5 1.015640 0.461296 ... 4.446634 0.157178 0.616401 \n",
+ "\n",
+ "GENE ZCCHC6 ZDHHC21 ZFP36 ZFP800 ZHX2 ZNFX1 ZUFSP \n",
+ "S1 3.042277 4.425735 4.092559 4.025124 0.779382 2.998800 0.000000 \n",
+ "S2 1.018315 4.955713 0.356008 4.297776 0.032569 3.091207 5.000843 \n",
+ "S3 0.126673 3.952273 1.956983 0.000000 0.000000 3.794063 2.928699 \n",
+ "S4 2.025546 4.193989 2.372572 0.121924 0.000000 0.230278 0.430168 \n",
+ "S5 0.000000 4.039816 0.000000 4.714087 1.565475 0.860254 4.866979 \n",
+ "\n",
+ "[5 rows x 630 columns]"
+ ]
+ }
+ ],
+ "prompt_number": 45
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "lps_response_corr = lps_response.corr()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 46
+ },
+ {
+ "cell_type": "heading",
+ "level": 4,
+ "metadata": {},
+ "source": [
+ "\"Elbow method\" for determining number of clusters"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The authors state that they used the \"Elbow method\" to determine the [number of cluster centers](http://en.wikipedia.org/wiki/Determining_the_number_of_clusters_in_a_data_set). Essentially, you try a bunch of different $k$, and see where there is a flattening out of the metric, like an elbow. There's a few different variations on which metric to use, such as using the average distance to the cluster center, or the explained variance. Let's try the distance to cluster center first, because `scikit-learn` makes it easy."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "from sklearn.cluster import KMeans\n",
+ "\n",
+ "##### cluster data into K=1..10 clusters #####\n",
+ "ks = np.arange(1, 11).astype(int)\n",
+ "\n",
+ "X = lps_response_corr.values\n",
+ "\n",
+ "kmeans = [KMeans(n_clusters=k).fit(X) for k in ks]\n",
+ "\n",
+ "# Scikit-learn makes this easy by computing the distance to the nearest center\n",
+ "dist_to_center = [km.inertia_ for km in kmeans]\n",
+ "\n",
+ "fig, ax = plt.subplots()\n",
+ "ax.plot(ks, dist_to_center, 'o-')\n",
+ "ax.set_ylabel('Sum of distance to nearest cluster center')\n",
+ "sns.despine()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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WrSznfH19LffU6XT4+/tbznl6euLh4YFOpyMlJcXqHICfnx86nU5VvFWN9EQJ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\ni4sLzMzMMG/ePJw7dw7FxcXIzMyEVCrF2LFjIZV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+ "text": [
+ "| GENE | \n", + "1110018G07RIK | \n", + "1110038F14RIK | \n", + "1200009I06RIK | \n", + "1600014C10RIK | \n", + "1810029B16RIK | \n", + "2210009G21RIK | \n", + "2810474O19RIK | \n", + "3110001I22RIK | \n", + "4921513D23RIK | \n", + "4930523C07RIK | \n", + "... | \n", + "ZC3H12C | \n", + "ZC3HAV1 | \n", + "ZCCHC2 | \n", + "ZCCHC6 | \n", + "ZDHHC21 | \n", + "ZFP36 | \n", + "ZFP800 | \n", + "ZHX2 | \n", + "ZNFX1 | \n", + "ZUFSP | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| S1 | \n", + "3.711442 | \n", + "0.000000 | \n", + "3.275468 | \n", + "0.000000 | \n", + "5.609305 | \n", + "0 | \n", + "0.000000 | \n", + "3.828860 | \n", + "1.314573 | \n", + "3.778275 | \n", + "... | \n", + "3.972904 | \n", + "3.509979 | \n", + "0.035344 | \n", + "3.042277 | \n", + "4.425735 | \n", + "4.092559 | \n", + "4.025124 | \n", + "0.779382 | \n", + "2.998800 | \n", + "0.000000 | \n", + "
| S2 | \n", + "4.361671 | \n", + "0.147643 | \n", + "0.000000 | \n", + "0.000000 | \n", + "5.478071 | \n", + "0 | \n", + "3.407342 | \n", + "0.000000 | \n", + "1.531443 | \n", + "0.000000 | \n", + "... | \n", + "4.794306 | \n", + "4.984262 | \n", + "2.251330 | \n", + "1.018315 | \n", + "4.955713 | \n", + "0.356008 | \n", + "4.297776 | \n", + "0.032569 | \n", + "3.091207 | \n", + "5.000843 | \n", + "
| S3 | \n", + "0.000000 | \n", + "3.737014 | \n", + "2.987093 | \n", + "0.063526 | \n", + "5.320993 | \n", + "0 | \n", + "3.372359 | \n", + "0.058163 | \n", + "1.105115 | \n", + "0.025043 | \n", + "... | \n", + "4.882749 | \n", + "0.807258 | \n", + "0.094925 | \n", + "0.126673 | \n", + "3.952273 | \n", + "1.956983 | \n", + "0.000000 | \n", + "0.000000 | \n", + "3.794063 | \n", + "2.928699 | \n", + "
| S4 | \n", + "2.719587 | \n", + "0.000000 | \n", + "0.045823 | \n", + "0.000000 | \n", + "0.488049 | \n", + "0 | \n", + "5.127847 | \n", + "0.000000 | \n", + "2.303969 | \n", + "0.000000 | \n", + "... | \n", + "4.833354 | \n", + "4.538699 | \n", + "0.137427 | \n", + "2.025546 | \n", + "4.193989 | \n", + "2.372572 | \n", + "0.121924 | \n", + "0.000000 | \n", + "0.230278 | \n", + "0.430168 | \n", + "
| S5 | \n", + "2.982073 | \n", + "0.000000 | \n", + "2.829152 | \n", + "0.000000 | \n", + "5.093188 | \n", + "0 | \n", + "0.065122 | \n", + "4.635671 | \n", + "1.015640 | \n", + "0.461296 | \n", + "... | \n", + "4.446634 | \n", + "0.157178 | \n", + "0.616401 | \n", + "0.000000 | \n", + "4.039816 | \n", + "0.000000 | \n", + "4.714087 | \n", + "1.565475 | \n", + "0.860254 | \n", + "4.866979 | \n", + "
5 rows \u00d7 630 columns
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+ "GENE \n",
+ "1810029B16RIK 1.000000 -0.394008 0.178867 0.271292 \n",
+ "6330409N04RIK -0.394008 1.000000 0.212542 -0.293282 \n",
+ "A130040M12RIK 0.178867 0.212542 1.000000 0.135900 \n",
+ "A630001G21RIK 0.271292 -0.293282 0.135900 1.000000 \n",
+ "AA467197 0.150375 0.119868 0.262295 0.420200 \n",
+ "\n",
+ "GENE AA467197 ACPP ACSL1 AI607873 AK035387 AK042010 \\\n",
+ "GENE \n",
+ "1810029B16RIK 0.150375 0.106683 0.182380 0.076501 0.032006 0.335728 \n",
+ "6330409N04RIK 0.119868 0.468823 0.298642 0.320296 0.158348 0.273508 \n",
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+ "AA467197 1.000000 0.277036 0.020186 0.747215 0.404682 0.342458 \n",
+ "\n",
+ "GENE ... TAPBPL TIMP1 TNFSF4 TNFSF9 TOR1AIP1 \\\n",
+ "GENE ... \n",
+ "1810029B16RIK ... -0.048557 0.029439 -0.194118 0.098728 0.228197 \n",
+ "6330409N04RIK ... -0.125377 0.027740 0.190240 -0.422914 -0.400675 \n",
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+ "A630001G21RIK ... 0.121712 -0.313992 -0.128168 0.059445 -0.177031 \n",
+ "AA467197 ... -0.023484 0.198358 0.452022 -0.467622 -0.325064 \n",
+ "\n",
+ "GENE TRIM34 TTC39C USP12 ZC3H12C ZUFSP \n",
+ "GENE \n",
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+ "\n",
+ "[5 rows x 630 columns]"
+ ]
+ }
+ ],
+ "prompt_number": 50
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The next step is to get the principal-component reduced data, using only the LPS response genes. We can do this in `flotilla` using `study.expression.reduce`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "reduced = study.expression.reduce(singles_ids, feature_ids=lps_response_genes)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 51
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can get the principal components using `reduced.components_` (similar interface as `scikit-learn`)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "reduced.components_.head()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
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| A130040M12RIK | \n", + "0.178867 | \n", + "0.212542 | \n", + "1.000000 | \n", + "0.135900 | \n", + "0.262295 | \n", + "0.591539 | \n", + "0.101211 | \n", + "0.198424 | \n", + "-0.061268 | \n", + "0.261002 | \n", + "... | \n", + "0.131664 | \n", + "0.268447 | \n", + "0.004338 | \n", + "-0.043116 | \n", + "-0.052081 | \n", + "-0.336334 | \n", + "0.322725 | \n", + "0.013706 | \n", + "0.254403 | \n", + "0.048982 | \n", + "
| A630001G21RIK | \n", + "0.271292 | \n", + "-0.293282 | \n", + "0.135900 | \n", + "1.000000 | \n", + "0.420200 | \n", + "0.317858 | \n", + "-0.312477 | \n", + "0.030636 | \n", + "0.260572 | \n", + "0.030842 | \n", + "... | \n", + "0.121712 | \n", + "-0.313992 | \n", + "-0.128168 | \n", + "0.059445 | \n", + "-0.177031 | \n", + "-0.332522 | \n", + "0.027750 | \n", + "-0.125732 | \n", + "0.071474 | \n", + "0.070166 | \n", + "
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5 rows \u00d7 630 columns
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+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 52,
+ "text": [
+ " MOV10 PPAP2B LASS6 TMCO3 CPD AK138792 TARM1 \\\n",
+ "pc_1 0.035299 0.038725 -0.006343 0.014219 0.033734 -0.079831 0.032886 \n",
+ "pc_2 0.055310 0.002925 -0.043986 -0.024020 -0.061957 -0.016327 0.002882 \n",
+ "pc_3 0.000374 0.099514 -0.039636 0.003997 -0.000575 -0.042212 -0.056827 \n",
+ "pc_4 0.022491 0.002342 0.009422 -0.034725 0.025866 -0.009656 -0.027689 \n",
+ "pc_5 -0.025743 -0.009200 -0.030187 -0.061283 0.010464 0.032668 0.012223 \n",
+ "\n",
+ " P4HA1 CD180 SMG7 ... OAS1B OAS1G AK151815 \\\n",
+ "pc_1 0.034783 0.033719 -0.048453 ... -0.022490 0.031091 -0.021397 \n",
+ "pc_2 -0.003178 0.050055 0.038601 ... 0.012240 0.052127 0.009120 \n",
+ "pc_3 0.015571 -0.039811 0.005398 ... -0.010524 -0.009277 -0.102462 \n",
+ "pc_4 -0.089803 -0.046888 0.002274 ... -0.003404 -0.070307 -0.007025 \n",
+ "pc_5 -0.047623 -0.047351 0.045909 ... -0.074817 0.044218 -0.000884 \n",
+ "\n",
+ " GTPBP2 PRPF38A SLC7A11 PCDH7 GNA13 PTPRJ ATF3 \n",
+ "pc_1 0.034917 0.001745 0.058000 0.007748 0.000767 0.016012 0.018020 \n",
+ "pc_2 0.077015 0.072064 -0.080902 -0.056607 0.068444 -0.072533 0.068088 \n",
+ "pc_3 -0.043913 -0.052513 -0.030622 0.022607 -0.002503 0.023997 -0.054205 \n",
+ "pc_4 0.003407 -0.048078 0.028099 0.032970 -0.066284 0.010371 -0.006108 \n",
+ "pc_5 -0.000597 -0.033893 -0.018108 -0.012669 -0.025833 -0.044248 -0.001995 \n",
+ "\n",
+ "[5 rows x 630 columns]"
+ ]
+ }
+ ],
+ "prompt_number": 52
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "pc_components = reduced.components_.ix[:2, lps_response_corr_ordered_by_clusters.index].T\n",
+ "pc_components.head()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "html": [
+ "| \n", + " | MOV10 | \n", + "PPAP2B | \n", + "LASS6 | \n", + "TMCO3 | \n", + "CPD | \n", + "AK138792 | \n", + "TARM1 | \n", + "P4HA1 | \n", + "CD180 | \n", + "SMG7 | \n", + "... | \n", + "OAS1B | \n", + "OAS1G | \n", + "AK151815 | \n", + "GTPBP2 | \n", + "PRPF38A | \n", + "SLC7A11 | \n", + "PCDH7 | \n", + "GNA13 | \n", + "PTPRJ | \n", + "ATF3 | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| pc_1 | \n", + "0.035299 | \n", + "0.038725 | \n", + "-0.006343 | \n", + "0.014219 | \n", + "0.033734 | \n", + "-0.079831 | \n", + "0.032886 | \n", + "0.034783 | \n", + "0.033719 | \n", + "-0.048453 | \n", + "... | \n", + "-0.022490 | \n", + "0.031091 | \n", + "-0.021397 | \n", + "0.034917 | \n", + "0.001745 | \n", + "0.058000 | \n", + "0.007748 | \n", + "0.000767 | \n", + "0.016012 | \n", + "0.018020 | \n", + "
| pc_2 | \n", + "0.055310 | \n", + "0.002925 | \n", + "-0.043986 | \n", + "-0.024020 | \n", + "-0.061957 | \n", + "-0.016327 | \n", + "0.002882 | \n", + "-0.003178 | \n", + "0.050055 | \n", + "0.038601 | \n", + "... | \n", + "0.012240 | \n", + "0.052127 | \n", + "0.009120 | \n", + "0.077015 | \n", + "0.072064 | \n", + "-0.080902 | \n", + "-0.056607 | \n", + "0.068444 | \n", + "-0.072533 | \n", + "0.068088 | \n", + "
| pc_3 | \n", + "0.000374 | \n", + "0.099514 | \n", + "-0.039636 | \n", + "0.003997 | \n", + "-0.000575 | \n", + "-0.042212 | \n", + "-0.056827 | \n", + "0.015571 | \n", + "-0.039811 | \n", + "0.005398 | \n", + "... | \n", + "-0.010524 | \n", + "-0.009277 | \n", + "-0.102462 | \n", + "-0.043913 | \n", + "-0.052513 | \n", + "-0.030622 | \n", + "0.022607 | \n", + "-0.002503 | \n", + "0.023997 | \n", + "-0.054205 | \n", + "
| pc_4 | \n", + "0.022491 | \n", + "0.002342 | \n", + "0.009422 | \n", + "-0.034725 | \n", + "0.025866 | \n", + "-0.009656 | \n", + "-0.027689 | \n", + "-0.089803 | \n", + "-0.046888 | \n", + "0.002274 | \n", + "... | \n", + "-0.003404 | \n", + "-0.070307 | \n", + "-0.007025 | \n", + "0.003407 | \n", + "-0.048078 | \n", + "0.028099 | \n", + "0.032970 | \n", + "-0.066284 | \n", + "0.010371 | \n", + "-0.006108 | \n", + "
| pc_5 | \n", + "-0.025743 | \n", + "-0.009200 | \n", + "-0.030187 | \n", + "-0.061283 | \n", + "0.010464 | \n", + "0.032668 | \n", + "0.012223 | \n", + "-0.047623 | \n", + "-0.047351 | \n", + "0.045909 | \n", + "... | \n", + "-0.074817 | \n", + "0.044218 | \n", + "-0.000884 | \n", + "-0.000597 | \n", + "-0.033893 | \n", + "-0.018108 | \n", + "-0.012669 | \n", + "-0.025833 | \n", + "-0.044248 | \n", + "-0.001995 | \n", + "
5 rows \u00d7 630 columns
\n", + "\n",
+ "\n",
+ " \n",
+ "
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+ "
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+ ],
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 53,
+ "text": [
+ " pc_1 pc_2\n",
+ "GENE \n",
+ "1810029B16RIK 0.042928 -0.005064\n",
+ "6330409N04RIK 0.033104 -0.015244\n",
+ "A130040M12RIK 0.043078 0.016664\n",
+ "A630001G21RIK 0.023375 -0.007379\n",
+ "AA467197 0.054588 0.018045"
+ ]
+ }
+ ],
+ "prompt_number": 53
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "import matplotlib as mpl\n",
+ "\n",
+ "fig = plt.figure(figsize=(12, 10))\n",
+ "gs = gridspec.GridSpec(2, 2, wspace=0.1, hspace=0.1, width_ratios=[1, .2], height_ratios=[1, .1])\n",
+ "corr_ax = fig.add_subplot(gs[0, 0])\n",
+ "corr_cbar_ax = fig.add_subplot(gs[1, 0])\n",
+ "pc_ax = fig.add_subplot(gs[0, 1:])\n",
+ "pc_cbar_ax = fig.add_subplot(gs[1:, 1:])\n",
+ "\n",
+ "sns.heatmap(lps_response_corr_ordered_by_clusters, linewidth=0, ax=corr_ax, cbar_ax=corr_cbar_ax, \n",
+ " cbar_kws=dict(orientation='horizontal'))\n",
+ "sns.heatmap(pc_components, cmap=mpl.cm.PRGn, linewidth=0, ax=pc_ax, cbar_ax=pc_cbar_ax,\n",
+ " cbar_kws=dict(orientation='horizontal'))\n",
+ "\n",
+ "corr_ax.set_xlabel('')\n",
+ "corr_ax.set_ylabel('')\n",
+ "corr_ax.set_xticks([])\n",
+ "corr_ax.set_yticks([])\n",
+ "pc_ax.set_yticks([])\n",
+ "pc_ax.set_ylabel('')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 54,
+ "text": [
+ "| \n", + " | pc_1 | \n", + "pc_2 | \n", + "
|---|---|---|
| GENE | \n", + "\n", + " | \n", + " |
| 1810029B16RIK | \n", + "0.042928 | \n", + "-0.005064 | \n", + "
| 6330409N04RIK | \n", + "0.033104 | \n", + "-0.015244 | \n", + "
| A130040M12RIK | \n", + "0.043078 | \n", + "0.016664 | \n", + "
| A630001G21RIK | \n", + "0.023375 | \n", + "-0.007379 | \n", + "
| AA467197 | \n", + "0.054588 | \n", + "0.018045 | \n", + "
\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 55,
+ "text": [
+ " Annotation Cluster PC1 Score PC2 Score\n",
+ "Gene \n",
+ "LNPEP NaN 1 0.232368 0.677266\n",
+ "TOR1AIP2 Antiv 1 -0.075934 1.485877\n",
+ "TNFSF4 NaN 1 0.497893 -0.562412\n",
+ "CFB Inflam 1 -0.394318 1.277749\n",
+ "H2-T10 NaN 1 0.514947 -0.698538"
+ ]
+ }
+ ],
+ "prompt_number": 55
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "data = lps_response_corr.ix[gene_pc_clusters.index, gene_pc_clusters.index].dropna(how='all', axis=0).dropna(how='all', axis=1)\n",
+ "\n",
+ "fig = plt.figure(figsize=(12, 10))\n",
+ "gs = gridspec.GridSpec(2, 2, wspace=0.1, hspace=0.1, width_ratios=[1, .2], height_ratios=[1, .1])\n",
+ "corr_ax = fig.add_subplot(gs[0, 0])\n",
+ "corr_cbar_ax = fig.add_subplot(gs[1, 0])\n",
+ "pc_ax = fig.add_subplot(gs[0, 1:])\n",
+ "pc_cbar_ax = fig.add_subplot(gs[1:, 1:])\n",
+ "\n",
+ "sns.heatmap(data, linewidth=0, square=True, vmin=-1, vmax=1, ax=corr_ax, cbar_ax=corr_cbar_ax, cbar_kws=dict(orientation='horizontal'))\n",
+ "sns.heatmap(gene_pc_clusters.ix[:, ['PC1 Score', 'PC2 Score']], linewidth=0, cmap=mpl.cm.PRGn,\n",
+ " ax=pc_ax, cbar_ax=pc_cbar_ax, cbar_kws=dict(orientation='horizontal'), xticklabels=False, yticklabels=False)\n",
+ "\n",
+ "corr_ax.set_xlabel('')\n",
+ "corr_ax.set_ylabel('')\n",
+ "corr_ax.set_xticks([])\n",
+ "corr_ax.set_yticks([])\n",
+ "\n",
+ "pc_ax.set_yticks([])\n",
+ "pc_ax.set_ylabel('')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 56,
+ "text": [
+ "| \n", + " | Annotation | \n", + "Cluster | \n", + "PC1 Score | \n", + "PC2 Score | \n", + "
|---|---|---|---|---|
| Gene | \n", + "\n", + " | \n", + " | \n", + " | \n", + " |
| LNPEP | \n", + "NaN | \n", + "1 | \n", + "0.232368 | \n", + "0.677266 | \n", + "
| TOR1AIP2 | \n", + "Antiv | \n", + "1 | \n", + "-0.075934 | \n", + "1.485877 | \n", + "
| TNFSF4 | \n", + "NaN | \n", + "1 | \n", + "0.497893 | \n", + "-0.562412 | \n", + "
| CFB | \n", + "Inflam | \n", + "1 | \n", + "-0.394318 | \n", + "1.277749 | \n", + "
| H2-T10 | \n", + "NaN | \n", + "1 | \n", + "0.514947 | \n", + "-0.698538 | \n", + "
\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 60,
+ "text": [
+ " Single cell average\n",
+ "GENE \n",
+ "NPL 1.075740\n",
+ "QK 2.019888\n",
+ "AK163153 1.429369\n",
+ "PARK2 0.596479\n",
+ "AGPAT4 2.021294"
+ ]
+ }
+ ],
+ "prompt_number": 60
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "data_for_correlations = pd.concat([study.expression.singles, singles_mean.T, study.expression.pooled])\n",
+ "\n",
+ "# Take the transpose of the data, because the plotting algorithm calculates correlations between columns,\n",
+ "# And we want the correlations between samples, not features\n",
+ "data_for_correlations = data_for_correlations.T\n",
+ "data_for_correlations.head()\n",
+ "\n",
+ "# %time sns.corrplot(data_for_correlations)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "html": [
+ "| \n", + " | Single cell average | \n", + "
|---|---|
| GENE | \n", + "\n", + " |
| NPL | \n", + "1.075740 | \n", + "
| QK | \n", + "2.019888 | \n", + "
| AK163153 | \n", + "1.429369 | \n", + "
| PARK2 | \n", + "0.596479 | \n", + "
| AGPAT4 | \n", + "2.021294 | \n", + "
\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 61,
+ "text": [
+ " S1 S2 S3 S4 S5 S6 \\\n",
+ "GENE \n",
+ "NPL 4.290577 0.000000 4.860293 0.090829 0.000000 0.000000 \n",
+ "QK 5.038477 4.183371 3.847854 0.066797 3.305915 0.114225 \n",
+ "AK163153 1.249363 1.947622 1.082463 1.119633 1.267464 0.901824 \n",
+ "PARK2 0.540694 0.500426 0.604097 0.418703 0.000000 0.601280 \n",
+ "AGPAT4 0.095072 5.868557 4.137252 0.066015 0.000000 4.750107 \n",
+ "\n",
+ " S7 S8 S9 S10 ... S13 \\\n",
+ "GENE ... \n",
+ "NPL 4.730129 4.657090 0.112641 0.000000 ... 0.110470 \n",
+ "QK 3.730270 2.750103 0.134389 0.760353 ... 3.395885 \n",
+ "AK163153 1.033401 0.978591 1.220720 1.035237 ... 2.103135 \n",
+ "PARK2 0.404931 0.552874 0.343271 0.844120 ... 0.755072 \n",
+ "AGPAT4 0.069345 4.130618 3.328758 0.000000 ... 0.000000 \n",
+ "\n",
+ " S14 S15 S16 S17 S18 \\\n",
+ "GENE \n",
+ "NPL 0.099121 0.100920 0.206361 0.104884 0.000000 \n",
+ "QK 2.294456 0.301120 3.547688 2.185832 0.040923 \n",
+ "AK163153 1.110511 1.202271 4.446612 1.367261 0.428320 \n",
+ "PARK2 1.109400 0.807534 0.586962 0.485122 0.091469 \n",
+ "AGPAT4 4.430612 0.000000 0.000000 4.219120 0.171028 \n",
+ "\n",
+ " Single cell average P1 P2 P3 \n",
+ "GENE \n",
+ "NPL 1.075740 2.093019 2.044724 2.742480 \n",
+ "QK 2.019888 3.869102 3.690982 3.671838 \n",
+ "AK163153 1.429369 0.605094 0.392494 0.284990 \n",
+ "PARK2 0.596479 0.815242 0.267032 0.645365 \n",
+ "AGPAT4 2.021294 2.854144 2.139655 2.806291 \n",
+ "\n",
+ "[5 rows x 22 columns]"
+ ]
+ }
+ ],
+ "prompt_number": 61
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "fig, ax = plt.subplots(figsize=(10, 10))\n",
+ "sns.corrplot(data_for_correlations, ax=ax)\n",
+ "sns.despine()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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A09OTcePGcezYMQoKCoiJieHMmTOcPn0atVrNCy+8QI8ePezW4UhZRH3Zp8dRHCJL53WI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\nzKnw22h96kvMzhJtMkw+ZLe5m1sObtf8c3+m2wBNc8ozWn6Todn+BWX72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+ "text": [
+ "| \n", + " | S1 | \n", + "S2 | \n", + "S3 | \n", + "S4 | \n", + "S5 | \n", + "S6 | \n", + "S7 | \n", + "S8 | \n", + "S9 | \n", + "S10 | \n", + "... | \n", + "S13 | \n", + "S14 | \n", + "S15 | \n", + "S16 | \n", + "S17 | \n", + "S18 | \n", + "Single cell average | \n", + "P1 | \n", + "P2 | \n", + "P3 | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| GENE | \n", + "\n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " |
| NPL | \n", + "4.290577 | \n", + "0.000000 | \n", + "4.860293 | \n", + "0.090829 | \n", + "0.000000 | \n", + "0.000000 | \n", + "4.730129 | \n", + "4.657090 | \n", + "0.112641 | \n", + "0.000000 | \n", + "... | \n", + "0.110470 | \n", + "0.099121 | \n", + "0.100920 | \n", + "0.206361 | \n", + "0.104884 | \n", + "0.000000 | \n", + "1.075740 | \n", + "2.093019 | \n", + "2.044724 | \n", + "2.742480 | \n", + "
| QK | \n", + "5.038477 | \n", + "4.183371 | \n", + "3.847854 | \n", + "0.066797 | \n", + "3.305915 | \n", + "0.114225 | \n", + "3.730270 | \n", + "2.750103 | \n", + "0.134389 | \n", + "0.760353 | \n", + "... | \n", + "3.395885 | \n", + "2.294456 | \n", + "0.301120 | \n", + "3.547688 | \n", + "2.185832 | \n", + "0.040923 | \n", + "2.019888 | \n", + "3.869102 | \n", + "3.690982 | \n", + "3.671838 | \n", + "
| AK163153 | \n", + "1.249363 | \n", + "1.947622 | \n", + "1.082463 | \n", + "1.119633 | \n", + "1.267464 | \n", + "0.901824 | \n", + "1.033401 | \n", + "0.978591 | \n", + "1.220720 | \n", + "1.035237 | \n", + "... | \n", + "2.103135 | \n", + "1.110511 | \n", + "1.202271 | \n", + "4.446612 | \n", + "1.367261 | \n", + "0.428320 | \n", + "1.429369 | \n", + "0.605094 | \n", + "0.392494 | \n", + "0.284990 | \n", + "
| PARK2 | \n", + "0.540694 | \n", + "0.500426 | \n", + "0.604097 | \n", + "0.418703 | \n", + "0.000000 | \n", + "0.601280 | \n", + "0.404931 | \n", + "0.552874 | \n", + "0.343271 | \n", + "0.844120 | \n", + "... | \n", + "0.755072 | \n", + "1.109400 | \n", + "0.807534 | \n", + "0.586962 | \n", + "0.485122 | \n", + "0.091469 | \n", + "0.596479 | \n", + "0.815242 | \n", + "0.267032 | \n", + "0.645365 | \n", + "
| AGPAT4 | \n", + "0.095072 | \n", + "5.868557 | \n", + "4.137252 | \n", + "0.066015 | \n", + "0.000000 | \n", + "4.750107 | \n", + "0.069345 | \n", + "4.130618 | \n", + "3.328758 | \n", + "0.000000 | \n", + "... | \n", + "0.000000 | \n", + "4.430612 | \n", + "0.000000 | \n", + "0.000000 | \n", + "4.219120 | \n", + "0.171028 | \n", + "2.021294 | \n", + "2.854144 | \n", + "2.139655 | \n", + "2.806291 | \n", + "
5 rows \u00d7 22 columns
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+ "\n",
+ " \n",
+ "
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+ "
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+ ],
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 63,
+ "text": [
+ " TPM Unnamed: 1 Unnamed: 2 Unnamed: 3 Unique Barcodes \\\n",
+ "GENE MB_S1 MB_S2 MB_S3 NaN MB_S1 \n",
+ "0610007L01RIK 0 0 5.595054 NaN 0 \n",
+ "0610007P14RIK 76.25091 38.77614 0.1823286 NaN 23 \n",
+ "0610007P22RIK 24.26729 50.24694 17.74422 NaN 14 \n",
+ "0610008F07RIK 0 0 0 NaN 0 \n",
+ "\n",
+ " Unnamed: 5 Unnamed: 6 \n",
+ "GENE MB_S2 MB_S3 \n",
+ "0610007L01RIK 0 0 \n",
+ "0610007P14RIK 8 0 \n",
+ "0610007P22RIK 5 6 \n",
+ "0610008F07RIK 0 0 "
+ ]
+ }
+ ],
+ "prompt_number": 63
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The first three columns are TPM calculated from the three samples that have molecular barcodes, and the last three columns are the integer counts of molecular barcodes from the three molecular barcode samples.\n",
+ "\n",
+ "Let's remove the \"Unnamed: 3\" column which is all NaNs. We'll do that with the `.dropna` method, specifying `axis=1` for columns and `how=\"all\"` to make sure only columns that have ALL NaNs are removed."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "barcoded = barcoded.dropna(how='all', axis=1)\n",
+ "barcoded.head()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "html": [
+ "| \n", + " | TPM | \n", + "Unnamed: 1 | \n", + "Unnamed: 2 | \n", + "Unnamed: 3 | \n", + "Unique Barcodes | \n", + "Unnamed: 5 | \n", + "Unnamed: 6 | \n", + "
|---|---|---|---|---|---|---|---|
| GENE | \n", + "MB_S1 | \n", + "MB_S2 | \n", + "MB_S3 | \n", + "NaN | \n", + "MB_S1 | \n", + "MB_S2 | \n", + "MB_S3 | \n", + "
| 0610007L01RIK | \n", + "0 | \n", + "0 | \n", + "5.595054 | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "
| 0610007P14RIK | \n", + "76.25091 | \n", + "38.77614 | \n", + "0.1823286 | \n", + "NaN | \n", + "23 | \n", + "8 | \n", + "0 | \n", + "
| 0610007P22RIK | \n", + "24.26729 | \n", + "50.24694 | \n", + "17.74422 | \n", + "NaN | \n", + "14 | \n", + "5 | \n", + "6 | \n", + "
| 0610008F07RIK | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "
\n",
+ "\n",
+ " \n",
+ "
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+ "
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+ ],
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 64,
+ "text": [
+ " TPM Unnamed: 1 Unnamed: 2 Unique Barcodes Unnamed: 5 \\\n",
+ "GENE MB_S1 MB_S2 MB_S3 MB_S1 MB_S2 \n",
+ "0610007L01RIK 0 0 5.595054 0 0 \n",
+ "0610007P14RIK 76.25091 38.77614 0.1823286 23 8 \n",
+ "0610007P22RIK 24.26729 50.24694 17.74422 14 5 \n",
+ "0610008F07RIK 0 0 0 0 0 \n",
+ "\n",
+ " Unnamed: 6 \n",
+ "GENE MB_S3 \n",
+ "0610007L01RIK 0 \n",
+ "0610007P14RIK 0 \n",
+ "0610007P22RIK 6 \n",
+ "0610008F07RIK 0 "
+ ]
+ }
+ ],
+ "prompt_number": 64
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Next, let's drop that pesky \"GENE\" row. Don't worry, we'll get the sample ID names back next."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "barcoded = barcoded.drop('GENE', axis=0)\n",
+ "barcoded.head()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "html": [
+ "| \n", + " | TPM | \n", + "Unnamed: 1 | \n", + "Unnamed: 2 | \n", + "Unique Barcodes | \n", + "Unnamed: 5 | \n", + "Unnamed: 6 | \n", + "
|---|---|---|---|---|---|---|
| GENE | \n", + "MB_S1 | \n", + "MB_S2 | \n", + "MB_S3 | \n", + "MB_S1 | \n", + "MB_S2 | \n", + "MB_S3 | \n", + "
| 0610007L01RIK | \n", + "0 | \n", + "0 | \n", + "5.595054 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "
| 0610007P14RIK | \n", + "76.25091 | \n", + "38.77614 | \n", + "0.1823286 | \n", + "23 | \n", + "8 | \n", + "0 | \n", + "
| 0610007P22RIK | \n", + "24.26729 | \n", + "50.24694 | \n", + "17.74422 | \n", + "14 | \n", + "5 | \n", + "6 | \n", + "
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+ "\n",
+ " \n",
+ "
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+ "
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+ ],
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 65,
+ "text": [
+ " TPM Unnamed: 1 Unnamed: 2 Unique Barcodes Unnamed: 5 \\\n",
+ "0610007L01RIK 0 0 5.595054 0 0 \n",
+ "0610007P14RIK 76.25091 38.77614 0.1823286 23 8 \n",
+ "0610007P22RIK 24.26729 50.24694 17.74422 14 5 \n",
+ "0610008F07RIK 0 0 0 0 0 \n",
+ "0610009B22RIK 67.12981 115.1393 55.98812 11 18 \n",
+ "\n",
+ " Unnamed: 6 \n",
+ "0610007L01RIK 0 \n",
+ "0610007P14RIK 0 \n",
+ "0610007P22RIK 6 \n",
+ "0610008F07RIK 0 \n",
+ "0610009B22RIK 8 "
+ ]
+ }
+ ],
+ "prompt_number": 65
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We'll create a `pandas.MultiIndex` from the tuples of `(sample_id, measurement_type)` pair."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "columns = pd.MultiIndex.from_tuples([('MB_S1', 'TPM'),\n",
+ " ('MB_S2', 'TPM'),\n",
+ " ('MB_S3', 'TPM'),\n",
+ " ('MB_S1', 'Unique Barcodes'),\n",
+ " ('MB_S2', 'Unique Barcodes'),\n",
+ " ('MB_S3', 'Unique Barcodes')])\n",
+ "barcoded.columns = columns\n",
+ "barcoded = barcoded.sort_index(axis=1)\n",
+ "barcoded.head()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "html": [
+ "| \n", + " | TPM | \n", + "Unnamed: 1 | \n", + "Unnamed: 2 | \n", + "Unique Barcodes | \n", + "Unnamed: 5 | \n", + "Unnamed: 6 | \n", + "
|---|---|---|---|---|---|---|
| 0610007L01RIK | \n", + "0 | \n", + "0 | \n", + "5.595054 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "
| 0610007P14RIK | \n", + "76.25091 | \n", + "38.77614 | \n", + "0.1823286 | \n", + "23 | \n", + "8 | \n", + "0 | \n", + "
| 0610007P22RIK | \n", + "24.26729 | \n", + "50.24694 | \n", + "17.74422 | \n", + "14 | \n", + "5 | \n", + "6 | \n", + "
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| 0610009B22RIK | \n", + "67.12981 | \n", + "115.1393 | \n", + "55.98812 | \n", + "11 | \n", + "18 | \n", + "8 | \n", + "
\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
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+ ],
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 66,
+ "text": [
+ " MB_S1 MB_S2 MB_S3 \\\n",
+ " TPM Unique Barcodes TPM Unique Barcodes TPM \n",
+ "0610007L01RIK 0 0 0 0 5.595054 \n",
+ "0610007P14RIK 76.25091 23 38.77614 8 0.1823286 \n",
+ "0610007P22RIK 24.26729 14 50.24694 5 17.74422 \n",
+ "0610008F07RIK 0 0 0 0 0 \n",
+ "0610009B22RIK 67.12981 11 115.1393 18 55.98812 \n",
+ "\n",
+ " \n",
+ " Unique Barcodes \n",
+ "0610007L01RIK 0 \n",
+ "0610007P14RIK 0 \n",
+ "0610007P22RIK 6 \n",
+ "0610008F07RIK 0 \n",
+ "0610009B22RIK 8 "
+ ]
+ }
+ ],
+ "prompt_number": 66
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For the next move, we're going to do some crazy `pandas`-fu. First we're going to transpose, then `reset_index` of the transpose. Just so you know what this looks like, it's this."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "barcoded.T.reset_index().head()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "html": [
+ "| \n", + " | MB_S1 | \n", + "MB_S2 | \n", + "MB_S3 | \n", + "|||
|---|---|---|---|---|---|---|
| \n", + " | TPM | \n", + "Unique Barcodes | \n", + "TPM | \n", + "Unique Barcodes | \n", + "TPM | \n", + "Unique Barcodes | \n", + "
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| 0610007P22RIK | \n", + "24.26729 | \n", + "14 | \n", + "50.24694 | \n", + "5 | \n", + "17.74422 | \n", + "6 | \n", + "
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\n",
+ "\n",
+ " \n",
+ "
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+ "
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+ ],
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 67,
+ "text": [
+ " level_0 level_1 0610007L01RIK 0610007P14RIK 0610007P22RIK \\\n",
+ "0 MB_S1 TPM 0 76.25091 24.26729 \n",
+ "1 MB_S1 Unique Barcodes 0 23 14 \n",
+ "2 MB_S2 TPM 0 38.77614 50.24694 \n",
+ "3 MB_S2 Unique Barcodes 0 8 5 \n",
+ "4 MB_S3 TPM 5.595054 0.1823286 17.74422 \n",
+ "\n",
+ " 0610008F07RIK 0610009B22RIK 0610009D07RIK 0610009O20RIK 0610010B08RIK \\\n",
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+ "2 0 115.1393 49.16287 0 0 \n",
+ "3 0 18 11 0 0 \n",
+ "4 0 55.98812 203.6302 0 0.4914763 \n",
+ "\n",
+ " ... ZWILCH ZWINT ZXDA ZXDB ZXDC ZYG11A ZYG11B ZYX \\\n",
+ "0 ... 0 206.8494 0 0 0 0 0.01985733 55.28996 \n",
+ "1 ... 0 33 0 0 0 0 0 6 \n",
+ "2 ... 0 48.7729 0 0 0 0 7.894789 135.1977 \n",
+ "3 ... 0 10 0 0 0 0 0 7 \n",
+ "4 ... 0 54.51386 1.120081 0 0 0 0.1238624 340.7358 \n",
+ "\n",
+ " ZZEF1 ZZZ3 \n",
+ "0 0.09482778 0 \n",
+ "1 0 0 \n",
+ "2 0 4.272594 \n",
+ "3 0 0 \n",
+ "4 0.6677646 0 \n",
+ "\n",
+ "[5 rows x 27725 columns]"
+ ]
+ }
+ ],
+ "prompt_number": 67
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Next, we're going to transform the data into a [tidy](http://vita.had.co.nz/papers/tidy-data.pdf) format, with separate columns for sample ids, measurement types, the gene that was measured, and its measurement value."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "barcoded_tidy = pd.melt(barcoded.T.reset_index(), id_vars=['level_0', 'level_1'])\n",
+ "barcoded_tidy.head()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "html": [
+ "| \n", + " | level_0 | \n", + "level_1 | \n", + "0610007L01RIK | \n", + "0610007P14RIK | \n", + "0610007P22RIK | \n", + "0610008F07RIK | \n", + "0610009B22RIK | \n", + "0610009D07RIK | \n", + "0610009O20RIK | \n", + "0610010B08RIK | \n", + "... | \n", + "ZWILCH | \n", + "ZWINT | \n", + "ZXDA | \n", + "ZXDB | \n", + "ZXDC | \n", + "ZYG11A | \n", + "ZYG11B | \n", + "ZYX | \n", + "ZZEF1 | \n", + "ZZZ3 | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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| 2 | \n", + "MB_S2 | \n", + "TPM | \n", + "0 | \n", + "38.77614 | \n", + "50.24694 | \n", + "0 | \n", + "115.1393 | \n", + "49.16287 | \n", + "0 | \n", + "0 | \n", + "... | \n", + "0 | \n", + "48.7729 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "7.894789 | \n", + "135.1977 | \n", + "0 | \n", + "4.272594 | \n", + "
| 3 | \n", + "MB_S2 | \n", + "Unique Barcodes | \n", + "0 | \n", + "8 | \n", + "5 | \n", + "0 | \n", + "18 | \n", + "11 | \n", + "0 | \n", + "0 | \n", + "... | \n", + "0 | \n", + "10 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "7 | \n", + "0 | \n", + "0 | \n", + "
| 4 | \n", + "MB_S3 | \n", + "TPM | \n", + "5.595054 | \n", + "0.1823286 | \n", + "17.74422 | \n", + "0 | \n", + "55.98812 | \n", + "203.6302 | \n", + "0 | \n", + "0.4914763 | \n", + "... | \n", + "0 | \n", + "54.51386 | \n", + "1.120081 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0.1238624 | \n", + "340.7358 | \n", + "0.6677646 | \n", + "0 | \n", + "
5 rows \u00d7 27725 columns
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+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 68,
+ "text": [
+ " level_0 level_1 variable value\n",
+ "0 MB_S1 TPM 0610007L01RIK 0\n",
+ "1 MB_S1 Unique Barcodes 0610007L01RIK 0\n",
+ "2 MB_S2 TPM 0610007L01RIK 0\n",
+ "3 MB_S2 Unique Barcodes 0610007L01RIK 0\n",
+ "4 MB_S3 TPM 0610007L01RIK 5.595054"
+ ]
+ }
+ ],
+ "prompt_number": 68
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now let's rename these columns into something more useful, instead of \"level_0\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "barcoded_tidy = barcoded_tidy.rename(columns={'level_0': 'sample_id', 'level_1': 'measurement', 'variable': 'gene_name'})\n",
+ "barcoded_tidy.head()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "html": [
+ "| \n", + " | level_0 | \n", + "level_1 | \n", + "variable | \n", + "value | \n", + "
|---|---|---|---|---|
| 0 | \n", + "MB_S1 | \n", + "TPM | \n", + "0610007L01RIK | \n", + "0 | \n", + "
| 1 | \n", + "MB_S1 | \n", + "Unique Barcodes | \n", + "0610007L01RIK | \n", + "0 | \n", + "
| 2 | \n", + "MB_S2 | \n", + "TPM | \n", + "0610007L01RIK | \n", + "0 | \n", + "
| 3 | \n", + "MB_S2 | \n", + "Unique Barcodes | \n", + "0610007L01RIK | \n", + "0 | \n", + "
| 4 | \n", + "MB_S3 | \n", + "TPM | \n", + "0610007L01RIK | \n", + "5.595054 | \n", + "
\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 69,
+ "text": [
+ " sample_id measurement gene_name value\n",
+ "0 MB_S1 TPM 0610007L01RIK 0\n",
+ "1 MB_S1 Unique Barcodes 0610007L01RIK 0\n",
+ "2 MB_S2 TPM 0610007L01RIK 0\n",
+ "3 MB_S2 Unique Barcodes 0610007L01RIK 0\n",
+ "4 MB_S3 TPM 0610007L01RIK 5.595054"
+ ]
+ }
+ ],
+ "prompt_number": 69
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Next, we're going to take some seemingly-duplicating steps, but trust me, it'll make the data easier."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "barcoded_tidy['TPM'] = barcoded_tidy.value[barcoded_tidy.measurement == 'TPM']\n",
+ "barcoded_tidy['Unique Barcodes'] = barcoded_tidy.value[barcoded_tidy.measurement == 'Unique Barcodes']"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 70
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Fill the values of the \"**TPM**\"'s forwards, since they appear first, and fill the values of the \"**Unique Barcodes**\" backwards, since they're second"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "barcoded_tidy.TPM = barcoded_tidy.TPM.ffill()\n",
+ "barcoded_tidy['Unique Barcodes'] = barcoded_tidy['Unique Barcodes'].bfill()\n",
+ "barcoded_tidy.head()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "html": [
+ "| \n", + " | sample_id | \n", + "measurement | \n", + "gene_name | \n", + "value | \n", + "
|---|---|---|---|---|
| 0 | \n", + "MB_S1 | \n", + "TPM | \n", + "0610007L01RIK | \n", + "0 | \n", + "
| 1 | \n", + "MB_S1 | \n", + "Unique Barcodes | \n", + "0610007L01RIK | \n", + "0 | \n", + "
| 2 | \n", + "MB_S2 | \n", + "TPM | \n", + "0610007L01RIK | \n", + "0 | \n", + "
| 3 | \n", + "MB_S2 | \n", + "Unique Barcodes | \n", + "0610007L01RIK | \n", + "0 | \n", + "
| 4 | \n", + "MB_S3 | \n", + "TPM | \n", + "0610007L01RIK | \n", + "5.595054 | \n", + "
\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 71,
+ "text": [
+ " sample_id measurement gene_name value TPM \\\n",
+ "0 MB_S1 TPM 0610007L01RIK 0 0.000000 \n",
+ "1 MB_S1 Unique Barcodes 0610007L01RIK 0 0.000000 \n",
+ "2 MB_S2 TPM 0610007L01RIK 0 0.000000 \n",
+ "3 MB_S2 Unique Barcodes 0610007L01RIK 0 0.000000 \n",
+ "4 MB_S3 TPM 0610007L01RIK 5.595054 5.595054 \n",
+ "\n",
+ " Unique Barcodes \n",
+ "0 0 \n",
+ "1 0 \n",
+ "2 0 \n",
+ "3 0 \n",
+ "4 0 "
+ ]
+ }
+ ],
+ "prompt_number": 71
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Drop the \"**measurement**\" column and drop duplicate rows."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "barcoded_tidy = barcoded_tidy.drop('measurement', axis=1)\n",
+ "barcoded_tidy = barcoded_tidy.drop_duplicates()\n",
+ "barcoded_tidy.head()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "html": [
+ "| \n", + " | sample_id | \n", + "measurement | \n", + "gene_name | \n", + "value | \n", + "TPM | \n", + "Unique Barcodes | \n", + "
|---|---|---|---|---|---|---|
| 0 | \n", + "MB_S1 | \n", + "TPM | \n", + "0610007L01RIK | \n", + "0 | \n", + "0.000000 | \n", + "0 | \n", + "
| 1 | \n", + "MB_S1 | \n", + "Unique Barcodes | \n", + "0610007L01RIK | \n", + "0 | \n", + "0.000000 | \n", + "0 | \n", + "
| 2 | \n", + "MB_S2 | \n", + "TPM | \n", + "0610007L01RIK | \n", + "0 | \n", + "0.000000 | \n", + "0 | \n", + "
| 3 | \n", + "MB_S2 | \n", + "Unique Barcodes | \n", + "0610007L01RIK | \n", + "0 | \n", + "0.000000 | \n", + "0 | \n", + "
| 4 | \n", + "MB_S3 | \n", + "TPM | \n", + "0610007L01RIK | \n", + "5.595054 | \n", + "5.595054 | \n", + "0 | \n", + "
\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 72,
+ "text": [
+ " sample_id gene_name value TPM Unique Barcodes\n",
+ "0 MB_S1 0610007L01RIK 0 0.000000 0\n",
+ "2 MB_S2 0610007L01RIK 0 0.000000 0\n",
+ "4 MB_S3 0610007L01RIK 5.595054 5.595054 0\n",
+ "5 MB_S3 0610007L01RIK 0 5.595054 0\n",
+ "6 MB_S1 0610007P14RIK 76.25091 76.250913 23"
+ ]
+ }
+ ],
+ "prompt_number": 72
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "barcoded_tidy['log TPM'] = np.log(barcoded_tidy.TPM)\n",
+ "barcoded_tidy['log Unique Barcodes'] = np.log(barcoded_tidy['Unique Barcodes'])"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 73
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we can use the convenient linear model plot (`lmplot`) in `seaborn` to plot these three samples together!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "sns.lmplot('log TPM', 'log Unique Barcodes', barcoded_tidy, col='sample_id')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 74,
+ "text": [
+ "| \n", + " | sample_id | \n", + "gene_name | \n", + "value | \n", + "TPM | \n", + "Unique Barcodes | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "MB_S1 | \n", + "0610007L01RIK | \n", + "0 | \n", + "0.000000 | \n", + "0 | \n", + "
| 2 | \n", + "MB_S2 | \n", + "0610007L01RIK | \n", + "0 | \n", + "0.000000 | \n", + "0 | \n", + "
| 4 | \n", + "MB_S3 | \n", + "0610007L01RIK | \n", + "5.595054 | \n", + "5.595054 | \n", + "0 | \n", + "
| 5 | \n", + "MB_S3 | \n", + "0610007L01RIK | \n", + "0 | \n", + "5.595054 | \n", + "0 | \n", + "
| 6 | \n", + "MB_S1 | \n", + "0610007P14RIK | \n", + "76.25091 | \n", + "76.250913 | \n", + "23 | \n", + "
\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 12,
+ "text": [
+ " ENSG00000003137 ENSG00000004848 ENSG00000006016 \\\n",
+ "ENSG00000003137 1.000000 -0.083809 0.119793 \n",
+ "ENSG00000004848 -0.083809 1.000000 0.535174 \n",
+ "ENSG00000006016 0.119793 0.535174 1.000000 \n",
+ "ENSG00000006116 0.076050 0.658786 0.622833 \n",
+ "ENSG00000006128 -0.035254 0.481359 0.582943 \n",
+ "\n",
+ " ENSG00000006116 ENSG00000006128 ENSG00000006377 \\\n",
+ "ENSG00000003137 0.076050 -0.035254 -0.170881 \n",
+ "ENSG00000004848 0.658786 0.481359 0.670307 \n",
+ "ENSG00000006016 0.622833 0.582943 0.415254 \n",
+ "ENSG00000006116 1.000000 0.471186 0.391800 \n",
+ "ENSG00000006128 0.471186 1.000000 0.690821 \n",
+ "\n",
+ " ENSG00000007350 ENSG00000016082 ENSG00000041353 \\\n",
+ "ENSG00000003137 0.078876 -0.164944 -0.102906 \n",
+ "ENSG00000004848 0.109242 0.187309 0.378170 \n",
+ "ENSG00000006016 0.041606 0.102551 0.418882 \n",
+ "ENSG00000006116 -0.131698 -0.074954 0.424003 \n",
+ "ENSG00000006128 -0.054401 0.427592 0.575299 \n",
+ "\n",
+ " ENSG00000041982 ... ENSG00000258283 \\\n",
+ "ENSG00000003137 -0.021128 ... -0.214898 \n",
+ "ENSG00000004848 -0.033828 ... 0.671103 \n",
+ "ENSG00000006016 0.051724 ... 0.637911 \n",
+ "ENSG00000006116 -0.297513 ... 0.673116 \n",
+ "ENSG00000006128 0.115128 ... 0.688362 \n",
+ "\n",
+ " ENSG00000258403 ENSG00000258444 ENSG00000258518 \\\n",
+ "ENSG00000003137 0.018936 -0.108965 -0.366995 \n",
+ "ENSG00000004848 0.313143 -0.001566 -0.500766 \n",
+ "ENSG00000006016 0.300402 0.243334 -0.345219 \n",
+ "ENSG00000006116 0.533787 -0.056825 -0.572178 \n",
+ "ENSG00000006128 0.404930 0.292495 -0.190202 \n",
+ "\n",
+ " ENSG00000258752 ENSG00000259190 ENSG00000259279 \\\n",
+ "ENSG00000003137 -0.136422 0.282414 0.163946 \n",
+ "ENSG00000004848 -0.091005 -0.174027 0.139083 \n",
+ "ENSG00000006016 0.053212 -0.279398 0.069264 \n",
+ "ENSG00000006116 -0.211297 0.022135 0.242858 \n",
+ "ENSG00000006128 0.151798 -0.423034 -0.093961 \n",
+ "\n",
+ " ENSG00000259373 ENSG00000259410 ENSG00000259603 \n",
+ "ENSG00000003137 0.487711 0.483485 0.088631 \n",
+ "ENSG00000004848 -0.659290 -0.580665 -0.653537 \n",
+ "ENSG00000006016 -0.309780 -0.303773 -0.267244 \n",
+ "ENSG00000006116 -0.479994 -0.401955 -0.614655 \n",
+ "ENSG00000006128 -0.293967 -0.312149 -0.171620 \n",
+ "\n",
+ "[5 rows x 564 columns]"
+ ]
+ }
+ ],
+ "prompt_number": 12
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Yay, it's here!"
+ ]
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+}
\ No newline at end of file
diff --git a/flotilla.png b/flotilla.png
new file mode 100644
index 00000000..bff6ba63
Binary files /dev/null and b/flotilla.png differ
diff --git a/flotilla/__init__.py b/flotilla/__init__.py
index 32b2eca6..c650a4d0 100644
--- a/flotilla/__init__.py
+++ b/flotilla/__init__.py
@@ -1,4 +1,57 @@
-__author__ = 'lovci'
+import os
+from .data_model import Study, ExpressionData, SplicingData, MetaData, \
+ MappingStatsData, SpikeInData, GeneOntologyData
+from .compute.predict import PredictorConfigManager, PredictorDataSetManager
+from .datapackage import make_study_datapackage, FLOTILLA_DOWNLOAD_DIR
-from .src import *
\ No newline at end of file
+
+__all__ = ['Study', 'PredictorConfigManager', 'PredictorDataSetManager',
+ 'make_study_datapackage', 'FLOTILLA_DOWNLOAD_DIR',
+ 'compute', 'data_model', 'visualize', 'Study', 'ExpressionData',
+ 'SplicingData', 'MetaData', 'SpikeInData', 'MappingStatsData',
+ 'datapackage', 'GeneOntologyData', 'go', 'util']
+
+__version__ = '0.2.6dev'
+
+# 18 cells, multiindex on the splicing data features, features already renamed
+# in the matrices
+_shalek2013 = 'https://raw.githubusercontent.com/YeoLab/shalek2013/master/' \
+ 'datapackage.json'
+
+# 250 cells, ensembl and miso ids on index, need renaming, lots of celltypes
+_test_data = 'https://raw.githubusercontent.com/YeoLab/flotilla_test_data/' \
+ 'master/datapackage.json'
+_brainspan = 'https://s3-us-west-2.amazonaws.com/flotilla/' \
+ 'brainspan_batch_corrected_for_amazon_s3/datapackage.json'
+
+
+def embark(study_name, load_species_data=True,
+ flotilla_dir=FLOTILLA_DOWNLOAD_DIR):
+ """
+ Begin your journey of data exploration.
+
+ Parameters
+ ----------
+ data_package_url : str
+ A URL to a datapackage.json file
+
+ Returns
+ -------
+ study : flotilla.Study
+ A biological study created from the data package specified
+ """
+ try:
+ try:
+ return Study.from_datapackage_file(
+ study_name, load_species_data=load_species_data)
+ except IOError:
+ pass
+ filename = os.path.abspath(os.path.expanduser(
+ '{}/{}/datapackage.json'.format(flotilla_dir,
+ study_name)))
+ return Study.from_datapackage_file(filename,
+ load_species_data=load_species_data)
+ except IOError:
+ return Study.from_datapackage_url(study_name,
+ load_species_data=load_species_data)
diff --git a/tests/__init__.py b/flotilla/compute/__init__.py
similarity index 100%
rename from tests/__init__.py
rename to flotilla/compute/__init__.py
diff --git a/flotilla/compute/decomposition.py b/flotilla/compute/decomposition.py
new file mode 100644
index 00000000..acf0fd57
--- /dev/null
+++ b/flotilla/compute/decomposition.py
@@ -0,0 +1,223 @@
+"""
+Perform various dimensionality reduction algorithms on data
+"""
+
+import sys
+
+from sklearn import decomposition
+import pandas as pd
+
+
+class DataFrameReducerBase(object):
+ """Just like scikit-learn's reducers, but with prettied up DataFrames."""
+
+ def __init__(self, df, n_components=None, **kwargs):
+ """Initialize and fit a dataframe to a decomposition algorithm
+
+ Parameters
+ ----------
+ df : pandas.DataFrame
+ A (samples, features) dataframe of data to fit to the reduction
+ algorithm
+ n_components : int
+ Number of components to calculate. If None, use as many
+ components as there are samples
+ kwargs : keyword arguments
+ Any other arguments to the reduction algorithm
+ """
+ # This magically initializes the reducer like PCA or NMF
+ if df.shape[1] <= 3:
+ raise ValueError(
+ "Too few features (n={}) to reduce".format(df.shape[1]))
+ super(DataFrameReducerBase, self).__init__(n_components=n_components,
+ **kwargs)
+ self.reduced_space = self.fit_transform(df)
+
+ @staticmethod
+ def _check_dataframe(X):
+ """Check that the input is a pandas dataframe
+
+ Parameters
+ ----------
+ X : input
+ Input to check if this is a pandas dataframe.
+
+ Raises
+ ------
+ ValueError
+ If the input is not a pandas Dataframe
+
+ """
+ try:
+ assert isinstance(X, pd.DataFrame)
+ except AssertionError:
+ sys.stdout.write("Try again as a pandas DataFrame")
+ raise ValueError('Input X was not a pandas DataFrame, '
+ 'was of type {} instead'.format(str(type(X))))
+
+ @staticmethod
+ def relabel_pcs(x):
+ """Given a list of integers, change the name to be a 1-based
+ principal component representation"""
+ return "pc_" + str(int(x) + 1)
+
+ def fit(self, X):
+ """Perform a scikit-learn fit and relabel dimensions to be
+ informative names
+
+ Parameters
+ ----------
+ X : pandas.DataFrame
+ A (n_samples, n_features) Dataframe of data to reduce
+
+ Returns
+ -------
+ self : DataFrameReducerBase
+ A instance of the data, now with components_,
+ explained_variance_, and explained_variance_ratio_ attributes
+
+ """
+ self._check_dataframe(X)
+ self.X = X
+ super(DataFrameReducerBase, self).fit(X)
+ self.components_ = pd.DataFrame(self.components_,
+ columns=self.X.columns).rename_axis(
+ self.relabel_pcs, 0)
+ try:
+ self.explained_variance_ = pd.Series(
+ self.explained_variance_).rename_axis(self.relabel_pcs, 0)
+ self.explained_variance_ratio_ = pd.Series(
+ self.explained_variance_ratio_).rename_axis(self.relabel_pcs,
+ 0)
+ except AttributeError:
+ pass
+
+ return self
+
+ def transform(self, X):
+ """Transform a matrix into the compoment space
+
+ Parameters
+ ----------
+ X : pandas.DataFrame
+ A (n_samples, n_features) sized DataFrame to transform into the
+ current compoment space
+
+ Returns
+ -------
+ component_space : pandas.DataFrame
+ A (n_samples, self.n_components) sized DataFrame transformed into
+ component space
+
+ """
+ component_space = super(DataFrameReducerBase, self).transform(X)
+ self._check_dataframe(X)
+ component_space = pd.DataFrame(component_space,
+ index=X.index).rename_axis(
+ self.relabel_pcs, 1)
+ return component_space
+
+ def fit_transform(self, X):
+ """Perform both a fit and a transform on the input data
+
+ Fit the data to the reduction algorithm, and transform the data to
+ the reduced space.
+
+ Parameters
+ ----------
+ X : pandas.DataFrame
+ A (n_samples, n_features) dataframe to both fit and transform
+
+ Returns
+ -------
+ self : DataFrameReducerBase
+ A fit and transformed instance of the object
+
+ Raises
+ ------
+ ValueError
+ If the input is not a pandas DataFrame, will not perform the fit
+ and transform
+
+ """
+ self._check_dataframe(X)
+ self.fit(X)
+ return self.transform(X)
+
+
+class DataFramePCA(DataFrameReducerBase, decomposition.PCA):
+ """Perform Principal Components Analaysis on a DataFrame"""
+ pass
+
+
+class DataFrameNMF(DataFrameReducerBase, decomposition.NMF):
+ """Perform Non-Negative Matrix Factorization on a DataFrame
+ """
+
+ def __init__(self, df, n_components=None, **kwargs):
+ kwargs.setdefault('init', 'nndsvd')
+ super(decomposition.NMF, self).__init__(n_components,
+ **kwargs)
+ self.reduced_space = self.fit_transform(df)
+
+ def fit(self, X):
+ """Override scikit-learn's fit() for our purposes
+
+ Duplicated fit code for DataFrameNMF because sklearn's NMF cheats for
+ efficiency and calls fit_transform. Method resolution order ("MRO")
+ resolves the closest (in this package)
+ _fit_transform first and so there's a recursion error:
+
+ def fit(self, X, y=None, **kwargs):
+ self._fit_transform(X, **kwargs)
+ return self
+ """
+ self._check_dataframe(X)
+ self.X = X
+ # notice this is fit_transform, not fit
+ reduced_space = super(decomposition.NMF, self).fit_transform(X)
+ self.components_ = pd.DataFrame(self.components_,
+ columns=self.X.columns).rename_axis(
+ self.relabel_pcs, 0)
+ return reduced_space
+
+
+class DataFrameICA(DataFrameReducerBase, decomposition.FastICA):
+ """Perform Independent Comopnent Analysis on a DataFrame
+ """
+ pass
+
+
+class DataFrameTSNE(DataFrameReducerBase):
+ """Perform t-Distributed Stochastic Neighbor Embedding on a DataFrame
+
+ Read more: http://homepage.tudelft.nl/19j49/t-SNE.html
+ """
+
+ def fit_transform(self, X):
+ """Perform both a fit and a transform on the input data
+
+ Fit the data to the reduction algorithm, and transform the data to
+ the reduced space.
+
+ Parameters
+ ----------
+ X : pandas.DataFrame
+ A (n_samples, n_features) dataframe to both fit and transform
+
+ Returns
+ -------
+ self : DataFrameReducerBase
+ A fit and transformed instance of the object
+
+ Raises
+ ------
+ ValueError
+ If the input is not a pandas DataFrame, will not perform the fit
+ and transform
+
+ """
+ from tsne import bh_sne
+
+ self._check_dataframe(X)
+ return pd.DataFrame(bh_sne(X), index=X.index)
diff --git a/flotilla/compute/expression.py b/flotilla/compute/expression.py
new file mode 100644
index 00000000..b7e80b2c
--- /dev/null
+++ b/flotilla/compute/expression.py
@@ -0,0 +1,191 @@
+from __future__ import division
+import itertools
+import math
+import sys
+
+import numpy as np
+from scipy import stats
+import pandas as pd
+
+
+def benjamini_hochberg(p_values, fdr=0.1):
+ """Benjamini-Hochberg correction for multiple hypothesis testing
+
+ From: http://udel.edu/~mcdonald/statmultcomp.html
+ One good technique for controlling the false discovery rate was briefly
+ mentioned by Simes (1986) and developed in detail by Benjamini and Hochberg
+ (1995). Put the individual P-values in order, from smallest to largest.
+ The smallest P-value has a rank of i=1, the next has i=2, etc. Then
+ compare each individual P-value to (i/m)Q, where m is the total number of
+ test and Q is the chosen false discovery rate. The largest P-value that
+ has P<(i/m)Q is significant, and all P-values smaller than it are also
+ significant.
+
+ Parameters
+ ----------
+ p_values : list
+ List of p-values
+ fdr : float, optional
+ Desired false-discovery rate cutoff
+
+ Returns
+ -------
+ sigs : numpy.array
+ Boolean array of whether or not the provided p-values are significant
+ given the FDR cutoff
+ """
+ nComps = len(p_values) + 0.0
+ pSorter = np.argsort(p_values)
+ pRank = np.argsort(np.argsort(p_values)) + 1
+ BHcalc = (pRank / nComps) * fdr
+ sigs = np.ndarray(shape=(nComps, ), dtype='bool')
+ issig = True
+ for (p, b, r) in itertools.izip(p_values[pSorter], BHcalc[pSorter],
+ pSorter):
+ if p > b:
+ issig = False
+ sigs[r] = issig
+ return sigs
+
+
+class TwoWayGeneComparisonLocal(object):
+ """Compare gene expression for two samples
+ """
+ def __init__(self, sample1_name, sample2_name, df, p_value_cutoff=0.001,
+ local_fraction=0.1, bonferroni=True, fdr=None,
+ dtype="RPKM"):
+ """
+
+ Plots a scatter-plot of sample1 vs sample2, taken from df.
+ Calculates differentially expressed genes with a Z-test from
+ the closest (local_fraction * 100)% points. Stores result from
+ statistical calculations in self.result_
+
+ Parameters
+ ----------
+ sample1_name : str
+ Name of the first (control) sample. Must be a row name (index) in
+ df. Plotted on the x-axis.
+ sample2_name : str
+ Name of the second (treatment) sample. Must be a row name (index)
+ in df. Plotted on the y-axis.
+ df : pandas.DataFrame
+ A samples (rows) x features (columns) pandas DataFrame of
+ expression values
+ p_value_cutoff : float, optional
+ Cutoff for the p-values. Default 0.001.
+ local_fraction : float, optional
+ What fraction of genes to use for *local* z-score calculation.
+ Default 0.1
+ bonferonni : bool, optional
+ Whether or not to use the Bonferonni correction on p-values
+ fdr : ???, optional
+ benjamini-hochberg FDR filtering - check result, proceed with
+ caution. sometimes breaks :(
+ dtype : str, optional
+ Data type
+ """
+
+ sample1 = df.ix[sample1_name]
+ sample2 = df.ix[sample2_name]
+
+ self.sample_names = (sample1.name, sample2.name)
+
+ sample1 = sample1.replace(0, np.nan).dropna()
+ sample2 = sample2.replace(0, np.nan).dropna()
+
+ sample1, sample2 = sample1.align(sample2, join='inner')
+
+ self.sample1 = sample1
+ self.sample2 = sample2
+ labels = sample1.index
+
+ self.n_genes = len(labels)
+ if bonferroni:
+ correction = self.n_genes
+ else:
+ correction = 1
+
+ local_count = int(math.ceil(self.n_genes * local_fraction))
+ self.p_value_cutoff = p_value_cutoff
+ self.upregulated_genes = set()
+ self.downregulated_genes = set()
+ self.expressed_genes = set([labels[i] for i, t in enumerate(
+ np.any(np.c_[sample1, sample2] > 1, axis=1)) if t])
+ self.log2_ratio = np.log2(sample2 / sample1)
+ self.average_expression = (sample2 + sample1) / 2.
+ self.ranks = np.argsort(np.argsort(self.average_expression))
+ self.p_values = pd.Series(index=labels)
+ self.local_mean = pd.Series(index=labels)
+ self.local_std = pd.Series(index=labels)
+ self.local_z = pd.Series(index=labels)
+ self.dtype = dtype
+
+ for g, r in itertools.izip(self.ranks.index, self.ranks):
+ if r < local_count:
+ start = 0
+ stop = local_count
+
+ elif r > self.n_genes - local_count:
+ start = self.n_genes - local_count
+ stop = self.n_genes
+
+ else:
+ start = r - int(math.floor(local_count / 2.))
+ stop = r + int(math.ceil(local_count / 2.))
+
+ local_genes = self.ranks[self.ranks.between(start, stop)].index
+ self.local_mean.ix[g] = np.mean(self.log2_ratio.ix[local_genes])
+ self.local_std.ix[g] = np.std(self.log2_ratio.ix[local_genes])
+ self.p_values.ix[g] = stats.norm.pdf(self.log2_ratio.ix[g],
+ self.local_mean.ix[g],
+ self.local_std.ix[
+ g]) * correction
+ self.local_z.ix[g] = (self.log2_ratio.ix[g] - self.local_mean.ix[
+ g]) / self.local_std.ix[g]
+
+ data = pd.DataFrame(index=labels)
+ data["rank"] = self.ranks
+ data["log2_ratio"] = self.log2_ratio
+ data["local_mean"] = self.local_mean
+ data["local_std"] = self.local_std
+ data["pValue"] = self.p_values
+
+ if fdr is None:
+ data["isSig"] = self.p_values < p_value_cutoff
+ else:
+ data["isSig"] = benjamini_hochberg(self.p_values, fdr=fdr)
+
+ data["meanExpression"] = self.average_expression
+ data["local_z"] = self.local_z
+ data[self.sample_names[0]] = sample1
+ data[self.sample_names[1]] = sample2
+
+ self.result_ = data
+
+ for label, (pVal, logratio, isSig) in data.get(
+ ["pValue", "log2_ratio", "isSig"]).iterrows():
+ if (pVal < p_value_cutoff) and isSig:
+ if logratio > 0:
+ self.upregulated_genes.add(label)
+ elif logratio < 0:
+ self.downregulated_genes.add(label)
+ else:
+ raise ValueError
+
+ def gstats(self):
+ """Write general statistics of the two-way comparison to standard output
+ """
+ sys.stdout.write(
+ "I used a p-value cutoff of {:.2e}\n".format(self.p_value_cutoff))
+ sys.stdout.write("\tThere are {} up-regulated genes in {} vs {}\n"
+ .format(len(self.upregulated_genes),
+ self.sample_names[1],
+ self.sample_names[0]))
+ sys.stdout.write("\tThere are {} down-regulated genes in %s vs %s"
+ .format(len(self.downregulated_genes),
+ self.sample_names[1],
+ self.sample_names[0]))
+ sys.stdout.write("There are {} expressed genes in both {} and {}"
+ .format(len(self.expressed_genes),
+ *self.sample_names))
diff --git a/flotilla/compute/generic.py b/flotilla/compute/generic.py
new file mode 100644
index 00000000..76a537e0
--- /dev/null
+++ b/flotilla/compute/generic.py
@@ -0,0 +1,489 @@
+import sys
+
+import numpy as np
+import pandas as pd
+from sklearn.ensemble import ExtraTreesRegressor, GradientBoostingRegressor
+from scipy import stats
+
+from ..util import timeout, TimeoutError
+
+
+def get_regressor(x, y, n_estimators=1500, n_tries=5,
+ verbose=False):
+ """Calculate an ExtraTreesRegressor on predictor and target variables
+
+ Parameters
+ ----------
+ x : numpy.array
+ Predictor vector
+ y : numpy.array
+ Target vector
+ n_estimators : int, optional
+ Number of estimators to use
+ n_tries : int, optional
+ Number of attempts to calculate regression
+ verbose : bool, optional
+ If True, output progress statements
+
+ Returns
+ -------
+ classifier : sklearn.ensemble.ExtraTreesRegressor
+ The classifier with the highest out of bag scores of all the
+ attempted "tries"
+ oob_scores : numpy.array
+ Out of bag scores of the classifier
+ """
+ if verbose:
+ sys.stderr.write('Getting regressor\n')
+ clfs = []
+ oob_scores = []
+
+ for i in range(n_tries):
+ if verbose:
+ sys.stderr.write('%d.' % i)
+
+ clf = ExtraTreesRegressor(n_estimators=n_estimators, oob_score=True,
+ bootstrap=True, max_features='sqrt',
+ n_jobs=1, random_state=i).fit(x, y)
+ clfs.append(clf)
+ oob_scores.append(clf.oob_score_)
+ clf = clfs[np.argmax(oob_scores)]
+ clf.feature_importances = pd.Series(clf.feature_importances_,
+ index=x.columns)
+
+ return clf, oob_scores
+
+
+def get_boosting_regressor(x, y, verbose=False):
+ """Calculate a GradientBoostingRegressor on predictor and target variables
+
+ Parameters
+ ----------
+ x : numpy.array
+ Predictor variable
+ y : numpy.array
+ Target variable
+ verbose : bool, optional
+ If True, output status messages
+
+ Returns
+ -------
+ classifier : sklearn.ensemble.GradientBoostingRegressor
+ A fitted classifier of the predictor and target variable
+ """
+ if verbose:
+ sys.stderr.write('Getting boosting regressor\n')
+
+ clf = GradientBoostingRegressor(n_estimators=50, subsample=0.6,
+ max_features=100,
+ verbose=0, learning_rate=0.1,
+ random_state=0).fit(x, y)
+
+ clf.feature_importances = pd.Series(clf.feature_importances_,
+ index=x.columns)
+ if verbose:
+ sys.stderr.write('Finished boosting regressor\n')
+
+ return clf
+
+
+def get_unstarted_events(mongodb):
+ """
+ get events that have not been started yet.
+ generator sets started to True before returning an event
+
+ Parameters
+ ----------
+ mongodb : pymongo.Database
+ A MongoDB database object
+ """
+ go_on = True
+ while go_on:
+ event = mongodb['list'].find_one({"started": False})
+
+ if event is None:
+ go_on = False
+ else:
+ event['started'] = True
+ mongodb['list'].save(event)
+ yield event
+
+
+@timeout(5) # because these sometimes hang
+def get_slope(x, y):
+ """Get the linear regression slope of x and y
+
+ Parameters
+ ----------
+ x : numpy.array
+ X-values of data
+ y : numpy.array
+ Y-values of data
+
+ Returns
+ -------
+ slope : float
+ Scipy.stats.linregress slope
+
+ """
+ return stats.linregress(x, y)[0]
+
+
+@timeout(5) # because these sometimes hang
+def do_r(s_1, s_2, method=stats.pearsonr, min_items=12):
+ """Calculate correlation ("R-value") between two vectors
+
+ Parameters
+ ----------
+ s_1 : pandas.Series
+ Predictor vector
+ s_2 : pandas.Series
+ Target vector
+ method : function, optional
+ Which correlation method to use. (default scipy.stats.pearsonr)
+ min_items : int, optional
+ Minimum number of items occuring in both s_1 and s_2 (default 12)
+
+ Returns
+ -------
+ r_value : float
+ R-value of the correlation, i.e. how correlated the two inputs are
+ p_value : float
+ p-value of the correlation, i.e. how likely this correlation would
+ happen given the null hypothesis that the two are not correlated
+
+ Notes
+ -----
+ If too few items overlap, return (np.nan, np.nan)
+ """
+ s_1, s_2 = s_1.dropna().align(s_2.dropna(), join='inner')
+ if len(s_1) <= min_items:
+ return np.nan, np.nan
+ return method(s_1, s_2)
+
+
+@timeout(10) # because these sometimes hang
+def get_robust_values(x, y):
+ """Calculate robust linear regression
+
+ Parameters
+ ----------
+ x : numpy.array
+ Predictor vector
+ y : numpy.array
+ Target vector
+
+ Returns
+ -------
+ intercept : float
+ Intercept of the fitted line
+ slope : float
+ Slope of the fitted line
+ t_statistic : float
+ T-statistic of the fit
+ p_value : float
+ p-value of the fit
+ """
+ import statsmodels.api as sm
+
+ r = sm.RLM(y, sm.add_constant(x), missing='drop').fit()
+ results = r.params[0], r.params[1], r.tvalues[0], r.pvalues[0]
+ return results
+
+
+@timeout(5)
+def get_dcor(x, y):
+ """Calculate distance correlation between two vectors
+
+ Uses the distance correlation package from:
+ https://github.com/andrewdyates/dcor
+
+ Parameters
+ ----------
+ x : numpy.array
+ 1-dimensional array (aka a vector) of the independent, predictor
+ variable
+ y : numpy.array
+ 1-dimensional array (aka a vector) of the dependent, target variable
+
+ Returns
+ -------
+ dc : float
+ Distance covariance
+ dr : float
+ Distance correlation
+ dvx : float
+ Distance variance on x
+ dvy : float
+ Distance variance on y
+ """
+ # cython version of dcor
+ try:
+ import dcor_cpy as dcor
+ except ImportError as e:
+ sys.stderr.write("Please install dcor_cpy.")
+ raise e
+
+ dc, dr, dvx, dvy = dcor.dcov_all(x, y)
+ return dc, dr, dvx, dvy
+
+
+@timeout(100)
+def apply_calc_rs(X, y, method=stats.pearsonr):
+ """Apply R calculation method on each column of X versus the values of y
+
+ Parameters
+ ----------
+ X : pandas.DataFrame
+ A (n_samples, n_features) sized DataFrame, assumed to be of
+ log-normal expression values
+ y : pandas.Series
+ A (n_samples,) sized Series, assumed to be of percent spliced-in
+ alternative splicing scores
+ method : function, optional
+ Which correlation method to use on each feature in X versus the
+ values in y
+
+ Returns
+ -------
+ r_coefficients : pandas.Series
+ Correlation coefficients
+ p_values : pandas.Series
+ Correlation significances (smaller is better)
+
+ See Also
+ --------
+ do_r
+ This is the underlying function which calculates correlation
+ """
+ out_R = pd.Series(index=X.columns, name=y.name)
+ out_P = pd.Series(index=X.columns, name=y.name)
+ for this_id, data in X.iteritems():
+ x = pd.Series(data, name=this_id)
+ try:
+ r, p = do_r(x, y, method=method)
+
+ except TimeoutError:
+ sys.stderr.write(
+ "%s r timeout event:%s, gene:%s\n" % (method, y.name, x.name))
+ r, p = np.nan, np.nan
+ out_R.ix[this_id] = r
+ out_P.ix[this_id] = p
+ return out_R, out_P
+
+
+@timeout(220)
+def apply_calc_robust(X, y, verbose=False):
+ """Calculate robust regression between the columns of X and y
+
+ Parameters
+ ----------
+ X : pandas.DataFrame
+ A (n_samples, n_features) Dataframe of the predictor variable
+ y : pandas.DataFrame
+ A (n_samples, m_features) DataFrame of the response variable
+ verbose : bool, optional
+ If True, output status messages as the calculation is happening
+
+ Returns
+ -------
+ out_I : pandas.Series
+ Intercept of regressions
+ out_S : pandas.Series
+ Slope of regressions
+ out_T : pandas.Series
+ t-statistic of regressions
+ out_P : pandas.Series
+ p-values of regressions
+
+ See Also
+ --------
+ get_robust_values
+ This is the underlying function which calculates the slope,
+ intercept, t-value, and p-value of the fit
+ """
+ if verbose:
+ sys.stderr.write("getting robust regression\n")
+ out_I = pd.Series(index=X.columns, name=y.name) # intercept
+ out_S = pd.Series(index=X.columns, name=y.name) # slope
+ out_T = pd.Series(index=X.columns, name=y.name) # t-value
+ out_P = pd.Series(index=X.columns, name=y.name) # p-value
+
+ for this_id, data in X.iteritems():
+ x = pd.Series(data, name=this_id)
+ try:
+ i, s, t, p = get_robust_values(x, y)
+ except TimeoutError:
+ sys.stderr.write(
+ "robust timeout event:%s, gene:%s\n" % (y.name, x.name))
+ i, s, t, p = np.nan, np.nan, np.nan, np.nan
+ out_I.ix[this_id] = i
+ out_S.ix[this_id] = s
+ out_T.ix[this_id] = t
+ out_P.ix[this_id] = p
+ return out_I, out_S, out_T, out_P
+
+
+@timeout(50)
+def apply_calc_slope(X, y, verbose=False):
+ """X and y are dataframes, returns slope, t-value and p-value of robust
+ regression
+
+ Parameters
+ ----------
+ X : pandas.DataFrame
+ A (n_samples, n_features) Dataframe of predictor variable values
+ y : pandas.DataFrame
+ A (n_samples, m_features) Dataframe of response variable values
+ verbose : bool, optional
+ If True, output status messages
+
+ Returns
+ -------
+ slope : pandas.Series
+ Slopes of the linear regression
+
+ See Also
+ --------
+ get_slope
+ This is the underlying function which calculates the slope
+ """
+ if verbose:
+ sys.stderr.write("getting slope\n")
+
+ out_S = pd.Series(index=X.columns, name=y.name)
+
+ for this_id, data in X.iteritems():
+ x = pd.Series(data, name=this_id)
+ try:
+ s = get_slope(x, y)
+ except TimeoutError:
+ sys.stderr.write(
+ "linregress timeout event:%s, gene:%s\n" % (y.name, x.name))
+ s = np.nan
+ out_S.ix[this_id] = s
+
+ return out_S
+
+
+@timeout(50)
+def apply_dcor(X, y, verbose=False):
+ """Calcualte distance correlation between the columns of two dataframes
+
+ Parameters
+ ----------
+ X : pandas.DataFrame
+ A (n_samples, n_features) Dataframe of predictor variable values
+ y : pandas.DataFrame
+ A (n_samples, m_features) Dataframe of response variable values
+ verbose : bool, optional
+ If True, output status messages
+
+ Returns
+ -------
+ dc : pandas.Series
+ Distance covariance
+ dr : pandas.Series
+ Distance correlation
+ dvx : pandas.Series
+ Distance variance of x
+ dvy : pandas.Series
+ Distance variance of y
+
+ See Also
+ --------
+ get_dcor
+ This is the underlying function that gets called to calculate the
+ distance correlation
+ """
+ if verbose:
+ sys.stderr.write("getting dcor\n")
+
+ out_DC = pd.Series(index=X.columns, name=y.name)
+ out_DR = pd.Series(index=X.columns, name=y.name)
+ out_DVX = pd.Series(index=X.columns, name=y.name)
+ out_DVY = pd.Series(index=X.columns, name=y.name)
+
+ for this_id, data in X.iteritems():
+ x = pd.Series(data, name=this_id)
+ try:
+ dc, dr, dvx, dvy = get_dcor(*map(np.array, [x, y]))
+
+ except TimeoutError:
+ sys.stderr.write("dcor timeout event:%s, gene:%s\n" % (y.name,
+ x.name))
+ dc, dr, dvx, dvy = [np.nan] * 4
+ out_DC.ix[this_id] = dc
+ out_DR.ix[this_id] = dr
+ out_DVX.ix[this_id] = dvx
+ out_DVY.ix[this_id] = dvy
+ return out_DC, out_DR, out_DVX, out_DVY
+
+
+def dropna_mean(x):
+ """Drop NA values and return the mean
+ """
+ return x.dropna().mean()
+
+
+def spearmanr_series(x, y):
+ """Calculate spearman r (with p-values) between two pandas series
+
+ Parameters
+ ----------
+ x : pandas.Series
+ One of the two series you'd like to correlate
+ y : pandas.Series
+ The other series you'd like to correlate
+
+ Returns
+ -------
+ r_value : float
+ The R-value of the correlation. 1 for perfect positive correlation,
+ and -1 for perfect negative correlation
+ p_value : float
+ The p-value of the correlation.
+ """
+ x, y = x.dropna().align(y.dropna(), 'inner')
+ return stats.spearmanr(x, y)
+
+
+def spearmanr_dataframe(A, B, axis=0):
+ """Calculate spearman correlations between dataframes A and B
+
+ Parameters
+ ----------
+ A : pandas.DataFrame
+ A n_samples x n_features1 dataframe. Must have the same number of rows
+ as "B"
+ B : pandas.DataFrame
+ A n_samples x n_features2 Dataframe. Must have the same number of rows
+ as "A"
+ axis : int
+ Which axis to compare. If 0, calculate correlations between all the
+ columns of A vs te columns of B. If 1, calculate between rows.
+ (default 0)
+
+ Returns
+ -------
+ correlations : pandas.DataFrame
+ A n_features2 x n_features1 DataFrame of (spearman_r, spearman_p)
+ tuples
+
+ Notes
+ -----
+ Use "applymap" to get just the R- and p-values of the resulting dataframe
+
+ >>> import pandas as pd
+ >>> import numpy as np
+ >>> A = pd.DataFrame(np.random.randn(100).reshape(5, 20))
+ >>> B = pd.DataFrame(np.random.randn(55).reshape(5, 11))
+ >>> correls = spearmanr_dataframe(A, B)
+ >>> correls.shape
+ (11, 20)
+ >>> spearman_r = correls.applymap(lambda x: x[0])
+ >>> spearman_p = correls.applymap(lambda x: x[1])
+ """
+ return A.apply(lambda x: B.apply(lambda y: spearmanr_series(x, y),
+ axis=axis),
+ axis=axis)
diff --git a/flotilla/compute/infotheory.py b/flotilla/compute/infotheory.py
new file mode 100644
index 00000000..66ce30dd
--- /dev/null
+++ b/flotilla/compute/infotheory.py
@@ -0,0 +1,265 @@
+"""
+Information-theoretic calculations
+"""
+
+import numpy as np
+import pandas as pd
+from sklearn import cross_validation
+
+EPSILON = 100 * np.finfo(float).eps
+
+
+def bin_range_strings(bins):
+ """Given a list of bins, make a list of strings of those bin ranges
+
+ Parameters
+ ----------
+ bins : list_like
+ List of anything, usually values of bin edges
+
+ Returns
+ -------
+ bin_ranges : list
+ List of bin ranges
+
+ >>> bin_range_strings((0, 0.5, 1))
+ ['0-0.5', '0.5-1']
+ """
+ return ['{}-{}'.format(i, j) for i, j in zip(bins, bins[1:])]
+
+
+def _check_prob_dist(x):
+ if np.any(x < 0):
+ raise ValueError('Each column of the input dataframes must be '
+ '**non-negative** probability distributions')
+ try:
+ if np.any(np.abs(x.sum() - np.ones(x.shape[1])) > EPSILON):
+ raise ValueError('Each column of the input dataframe must be '
+ 'probability distributions that **sum to 1**')
+ except IndexError:
+ if np.any(np.abs(x.sum() - 1) > EPSILON):
+ raise ValueError('Each column of the input dataframe must be '
+ 'probability distributions that **sum to 1**')
+
+
+def binify(df, bins):
+ """Makes a histogram of each column the provided binsize
+
+ Parameters
+ ----------
+ data : pandas.DataFrame
+ A samples x features dataframe. Each feature (column) will be binned
+ into the provided bins
+ bins : iterable
+ Bins you would like to use for this data. Must include the final bin
+ value, e.g. (0, 0.5, 1) for the two bins (0, 0.5) and (0.5, 1).
+ nbins = len(bins) - 1
+
+ Returns
+ -------
+ binned : pandas.DataFrame
+ An nbins x features DataFrame of each column binned across rows
+ """
+ if bins is None:
+ raise ValueError('Must specify "bins"')
+ binned = df.apply(lambda x: pd.Series(np.histogram(x, bins=bins)[0]))
+ binned.index = bin_range_strings(bins)
+
+ # Normalize so each column sums to 1
+ binned = binned / binned.sum().astype(float)
+ return binned
+
+
+def kld(p, q):
+ """Kullback-Leiber divergence of two probability distributions pandas
+ dataframes, p and q
+
+ Parameters
+ ----------
+ p : pandas.DataFrame
+ An nbins x features DataFrame, or (nbins,) Series
+ q : pandas.DataFrame
+ An nbins x features DataFrame, or (nbins,) Series
+
+ Returns
+ -------
+ kld : pandas.Series
+ Kullback-Lieber divergence of the common columns between the
+ dataframe. E.g. between 1st column in p and 1st column in q, and 2nd
+ column in p and 2nd column in q.
+
+ Raises
+ ------
+ ValueError
+ If the data provided is not a probability distribution, i.e. it has
+ negative values or its columns do not sum to 1, raise ValueError
+
+ Notes
+ -----
+ The input to this function must be probability distributions, not raw
+ values. Otherwise, the output makes no sense.
+ """
+ try:
+ _check_prob_dist(p)
+ _check_prob_dist(q)
+ except ValueError:
+ return np.nan
+ # If one of them is zero, then the other should be considered to be 0.
+ # In this problem formulation, log0 = 0
+ p = p.replace(0, np.nan)
+ q = q.replace(0, np.nan)
+
+ return (np.log2(p / q) * p).sum(axis=0)
+
+
+def jsd(p, q):
+ """Finds the per-column JSD betwen dataframes p and q
+
+ Jensen-Shannon divergence of two probability distrubutions pandas
+ dataframes, p and q. These distributions are usually created by running
+ binify() on the dataframe.
+
+ Parameters
+ ----------
+ p : pandas.DataFrame
+ An nbins x features DataFrame.
+ q : pandas.DataFrame
+ An nbins x features DataFrame.
+
+ Returns
+ -------
+ jsd : pandas.Series
+ Jensen-Shannon divergence of each column with the same names between
+ p and q
+
+ Raises
+ ------
+ ValueError
+ If the data provided is not a probability distribution, i.e. it has
+ negative values or its columns do not sum to 1, raise ValueError
+ """
+ try:
+ _check_prob_dist(p)
+ _check_prob_dist(q)
+ except ValueError:
+ return np.nan
+ weight = 0.5
+ m = weight * (p + q)
+
+ result = weight * kld(p, m) + (1 - weight) * kld(q, m)
+ return result
+
+
+def entropy(binned, base=2):
+ """Find the entropy of each column of a dataframe
+
+ Parameters
+ ----------
+ binned : pandas.DataFrame
+ A nbins x features DataFrame of probability distributions, where each
+ column sums to 1
+ base : numeric
+ The log-base of the entropy. Default is 2, so the resulting entropy
+ is in bits.
+
+ Returns
+ -------
+ entropy : pandas.Seires
+ Entropy values for each column of the dataframe.
+
+ Raises
+ ------
+ ValueError
+ If the data provided is not a probability distribution, i.e. it has
+ negative values or its columns do not sum to 1, raise ValueError
+ """
+ try:
+ _check_prob_dist(binned)
+ except ValueError:
+ np.nan
+ return -((np.log(binned) / np.log(base)) * binned).sum(axis=0)
+
+
+def binify_and_jsd(df1, df2, pair, bins):
+ binned1 = binify(df1, bins=bins).dropna(how='all', axis=1)
+ binned2 = binify(df2, bins=bins).dropna(how='all', axis=1)
+
+ binned1, binned2 = binned1.align(binned2, axis=1, join='inner')
+
+ series = np.sqrt(jsd(binned1, binned2))
+ series.name = pair
+ return series
+
+
+def cross_phenotype_jsd(data, groupby, bins, n_iter=100):
+ """Jensen-Shannon divergence of features across phenotypes
+
+ Parameters
+ ----------
+ data : pandas.DataFrame
+ A (n_samples, n_features) Dataframe
+ groupby : mappable
+ A samples to phenotypes mapping
+ n_iter : int
+ Number of bootstrap resampling iterations to perform for the
+ within-group comparisons
+ n_bins : int
+ Number of bins to binify the singles data on
+
+ Returns
+ -------
+ jsd_df : pandas.DataFrame
+ A (n_features, n_phenotypes^2) dataframe of the JSD between each
+ feature between and within phenotypes
+ """
+ grouped = data.groupby(groupby)
+ jsds = []
+
+ seen = set([])
+
+ for phenotype1, df1 in grouped:
+ for phenotype2, df2 in grouped:
+ pair = tuple(sorted([phenotype1, phenotype2]))
+ if pair in seen:
+ continue
+ seen.add(pair)
+
+ if phenotype1 == phenotype2:
+ seriess = []
+ bs = cross_validation.Bootstrap(df1.shape[0], n_iter=n_iter,
+ train_size=0.5)
+ for i, (ind1, ind2) in enumerate(bs):
+ df1_subset = df1.iloc[ind1, :]
+ df2_subset = df2.iloc[ind2, :]
+ seriess.append(
+ binify_and_jsd(df1_subset, df2_subset, None, bins))
+ series = pd.concat(seriess, axis=1, names=None).mean(axis=1)
+ series.name = pair
+ jsds.append(series)
+ else:
+ series = binify_and_jsd(df1, df2, pair, bins)
+ jsds.append(series)
+ return pd.concat(jsds, axis=1)
+
+
+def jsd_df_to_2d(jsd_df):
+ """Transform a tall JSD dataframe to a square matrix of mean JSDs
+
+ Parameters
+ ----------
+ jsd_df : pandas.DataFrame
+ A (n_features, n_phenotypes^2) dataframe of the JSD between each
+ feature between and within phenotypes
+
+ Returns
+ -------
+ jsd_2d : pandas.DataFrame
+ A (n_phenotypes, n_phenotypes) symmetric dataframe of the mean JSD
+ between and within phenotypes
+ """
+ jsd_2d = jsd_df.mean().reset_index()
+ jsd_2d = jsd_2d.rename(
+ columns={'level_0': 'phenotype1', 'level_1': 'phenotype2', 0: 'jsd'})
+ jsd_2d = jsd_2d.pivot(index='phenotype1', columns='phenotype2',
+ values='jsd')
+ return jsd_2d + np.tril(jsd_2d.T, -1)
diff --git a/flotilla/compute/network.py b/flotilla/compute/network.py
new file mode 100644
index 00000000..b1032e8f
--- /dev/null
+++ b/flotilla/compute/network.py
@@ -0,0 +1,174 @@
+"""
+Compute networks (the kind with nodes and edges) on data. Visualize with
+:py:mod:flotilla.visualize.network
+"""
+
+import networkx as nx
+import numpy as np
+import pandas as pd
+
+from ..util import memoize
+from ..visualize.color import dark2
+
+
+class Networker(object):
+ """Networks (the kind with nodes and edges), aka a graph
+
+ Calculate the edges based on similarity between rows of PCA-reduced data
+ """
+ weight_funs = ['no_weight', 'sq', 'arctan', 'arctan_sq']
+
+ def __init__(self):
+ """Construct a Networker object with default node colors (dark teal)
+ and sizes (all nodes at 300)
+ """
+ self._default_node_color_mapper = lambda x: dark2[0]
+ self._default_node_size_mapper = lambda x: 300
+
+ def get_weight_fun(self, fun_name='no_weight'):
+ """Given a string, return the function
+
+ Used to obtain functions that perform common transforms on distance
+
+ Parameters
+ ----------
+ fun_name : 'no_weight' | 'sq' | 'arctan' | 'arctan_sq', optional
+ Name of the function to obtain (default 'no_weight')
+
+ Returns
+ -------
+ func : function
+ A function which transforms a number in the indicated way
+
+ Raises
+ ------
+ ValueError
+ If `fun_name` is not one of the ones indicated above
+ """
+ def _noweight(x):
+ return x
+
+ def _arctan_sq(x):
+ return np.arctan(x) ** 2
+
+ if fun_name == 'no_weight':
+ wt = _noweight
+ elif fun_name == 'sq':
+ wt = np.square
+ elif fun_name == 'arctan':
+ wt = np.arctan
+ elif fun_name == 'arctan_sq':
+ wt = _arctan_sq
+ else:
+ raise ValueError
+ return wt
+
+ @memoize
+ def adjacency(self, data, use_pc_1=True, use_pc_2=True,
+ use_pc_3=True, use_pc_4=True, n_pcs=5):
+ """Calculate the adjacency graph, i.e. connectedness between nodes
+
+ Parameters
+ ----------
+ data : pandas.DataFrame
+ A (n_nodes, n_pcs) sized dataframe of reduced data
+ use_pc1 : bool, optional
+ If True, use the first principal component of reduced data
+ (default True)
+ use_pc2 : bool, optional
+ If True, use the second principal component of reduced data
+ (default True)
+ use_pc3 : bool, optional
+ If True, use the third principal component of reduced data
+ (default True)
+ use_pc4 : bool, optional
+ If True, use the fourth principal component of reduced data
+ (default True)
+ n_pcs : int, optional
+ Total number of principal components to use (default 5)
+
+ Returns
+ -------
+ adjacency : pandas.DataFrame
+ A lower triangular matrix of the edge weights between the rows of
+ the data
+ """
+ total_pcs = data.shape[1]
+ use_cols = np.ones(total_pcs, dtype='bool')
+ use_cols[n_pcs:] = False
+ use_cols = use_cols * np.array(
+ [use_pc_1, use_pc_2, use_pc_3, use_pc_4] + [True, ] * (
+ total_pcs - 4))
+ subset = data.loc[:, use_cols]
+ cov = np.cov(subset)
+ nrow, ncol = subset.shape
+ return pd.DataFrame(np.tril(cov * - (np.identity(nrow) - 1)),
+ index=subset.index, columns=data.index)
+
+ @memoize
+ def graph(self, adjacency, cov_cut=0,
+ node_color_mapper=None,
+ node_size_mapper=None,
+ degree_cut=2,
+ weight_function='no_weight', name=None):
+ """Create a graph based on the adjacency matrix and other inputs
+
+ Parameters
+ ----------
+ adjacency : pandas.DataFrame
+ A (n_nodes, n_nodes) square dataframe of edge weights between all
+ nodes in the graph
+ cov_cut : float, optional
+ Minimum covariance between two nodes for their edge to be plotted.
+ (default 0)
+ node_color_mapper : function, optional
+ Function to recolor the nodes for plotting, based on the node name.
+ If None, defaults to a dark teal. (default None)
+ node_size_mapper : function, optional
+ Function to resize the nodes for plotting, based on the node name.
+ If None, defaults to the same size for all nodes. (default None)
+ degree_cut : int
+ Minimum number of edges a node must have for it to be drawn on the
+ graph
+ weight_function : 'no_weight' | 'sq' | 'arctan' | 'arctan_sq', optional
+ Weight function of the edges. The lower the weight, the farther
+ away two nodes are drawn from each other.
+ name : str, optional (default=None)
+ For memoization purposes, not used in the function.
+
+ Returns
+ -------
+ graph : networkx.Graph
+ The graph created with all these parameters
+ positions : dict
+ A {node_name : [x, y]} mapping of all nodes and their x, y
+ positions
+ """
+ if node_color_mapper is None:
+ node_color_mapper = self._default_node_color_mapper
+ if node_size_mapper is None:
+ node_size_mapper = self._default_node_size_mapper
+
+ weight = self.get_weight_fun(weight_function)
+ graph = nx.Graph()
+ for node_label in adjacency.index:
+ node_color = node_color_mapper(node_label)
+ node_size = node_size_mapper(node_label)
+ graph.add_node(node_label, node_size=node_size,
+ node_color=node_color)
+ for cell1, others in adjacency.iterrows():
+ for cell2, value in others.iteritems():
+ if value > cov_cut:
+ # cast to floats because write_gml doesn't like numpy
+ # dtypes
+ graph.add_edge(cell1, cell2, weight=float(weight(value)),
+ inv_weight=float(1 / weight(value)),
+ alpha=0.05)
+
+ graph.remove_nodes_from(
+ [k for k, v in graph.degree().iteritems() if v <= degree_cut])
+
+ # TODO: can we output this as a (nodes, (x, y)) DataFrame instead?
+ positions = nx.spring_layout(graph)
+
+ return graph, positions
diff --git a/flotilla/compute/outlier.py b/flotilla/compute/outlier.py
new file mode 100644
index 00000000..4d975e2a
--- /dev/null
+++ b/flotilla/compute/outlier.py
@@ -0,0 +1,72 @@
+"""
+Detect outlier samples in data
+"""
+
+import sklearn
+import pandas as pd
+
+
+class OutlierDetection(object):
+
+ """Construct an outlier detection object
+
+ Parameters
+ ----------
+ X : pandas.DataFrame
+ A (n_samples, n_features) dataframe, where the outliers will be
+ detected from the rows (the samples)
+ method : sklearn classifier, optional
+ If None, defaults to OneClassSVM. The method class must have both
+ method.fit() and method.predict() methods
+ nu : float, optional (default 0.1)
+ An upper bound on the fraction of training errors and a lower
+ bound of the fraction of support vectors. Should be in the
+ interval (0, 1]. By default 0.5 will be taken.
+ kernel : str, optional (default='rbf')
+ The kernel to be used by the outlier detection algorihthm
+ gamma : float, optional (default=0.1)
+ Kernel coefficient for 'rbf', 'poly' and 'sigmoid'. If gamma is
+ 0.0 then 1/n_features will be used instead.
+ random_state : int, optional (default=0)
+ Random state of the method, for reproducibility.
+ kwargs : other keyword arguments, optional
+ All other keyword arguments are passed to method()
+ """
+ def __init__(self, X, method=None, nu=0.1, kernel='rbf', gamma=0.1,
+ random_state=0, **kwargs):
+ if method is None:
+ method = sklearn.svm.OneClassSVM
+
+ print kernel
+
+ kwargs.update(dict(nu=nu, kernel=kernel, gamma=gamma,
+ random_state=random_state))
+ self.kwargs = kwargs
+ self.outlier_detector = method(**kwargs)
+ self.X = X
+ self.outlier_detector.fit(self.X)
+
+ def predict(self, X=None):
+
+ """Predict which samples are outliers
+
+ Parameters
+ ----------
+ X : pandas.DataFrame, optional (default None)
+ A (n_samples, n_features) Dataframe. If None, predict outliers of
+ the original input data, where the new data has the same number of
+ features as the original data. Otherwise, use the original input
+ data to detect outliers on this new data.
+
+ Returns
+ -------
+ outliers : pandas.Series
+ A boolean
+ """
+ X = X if X is not None else self.X
+ self.outliers = pd.Series(
+ self.outlier_detector.predict(X.fillna(0)) == -1,
+ index=X.index)
+ # TODO: Since you can run this on self.X OR new X, then "self.outliers"
+ # can change and not be consistent....... this is a problem
+ return self.outliers
diff --git a/flotilla/compute/predict.py b/flotilla/compute/predict.py
new file mode 100644
index 00000000..c2ca75bb
--- /dev/null
+++ b/flotilla/compute/predict.py
@@ -0,0 +1,1057 @@
+"""
+Compute predictors on data, e.g. classify or regress on features/samples
+"""
+import sys
+import warnings
+from collections import defaultdict
+import math
+
+import numpy as np
+import pandas as pd
+from sklearn.ensemble import ExtraTreesClassifier, ExtraTreesRegressor, \
+ GradientBoostingClassifier, GradientBoostingRegressor
+from sklearn.preprocessing import LabelEncoder
+import pandas.util.testing as pdt
+
+from ..util import memoize, timestamp
+from .decomposition import DataFramePCA, DataFrameNMF
+
+
+CLASSIFIER = 'ExtraTreesClassifier'
+REGRESSOR = 'ExtraTreesRegressor'
+SCORE_COEFFICIENT = 2
+
+
+def default_predictor_scoring_fun(cls):
+ """Return scores of how important a feature is to the prediction
+
+ Most predictors score output coefficients in the variable
+ cls.feature_importances_ and others may use another name for scores, so
+ this function bridges the gap
+
+ Parameters
+ ----------
+ cls : sklearn predictor
+ A scikit-learn prediction class, such as ExtraTreesClassifier or
+ ExtraTreesRegressor
+
+ Returns
+ -------
+ scores : pandas.Series
+ A (n_features,) size series of how important each feature was to the
+ classification (bigger is better)
+ """
+ return cls.feature_importances_
+
+
+def default_score_cutoff_fun(arr, std_multiplier=SCORE_COEFFICIENT):
+ """Calculate a minimum score cutoff for the best features
+
+ By default, this function calculates: :math:`f(x) = mean(x) + 2 * std(x)`
+
+ Parameters
+ ----------
+ arr : numpy.ndarray
+ A numpy array of scores
+ std_multiplier : float, optional (default=2)
+ What to multiply the standard deviation by. E.g. if you want only
+ features that are 6 standard deviations away, set this to 6.
+
+ Returns
+ -------
+ cutoff : float
+ Minimum score of "best" features, given these parameters
+ """
+ return np.mean(arr) + std_multiplier * np.std(arr)
+
+
+class PredictorConfig(object):
+ """A configuration for a predictor, names and tracks/sets parameters
+
+ Dynamically configures some args for predictor based on n_features
+ (if this attribute exists)
+ set general parameters with __init__
+ yield instances, set by your parameters, with __call__
+ """
+
+ def __init__(self, predictor_name, obj,
+ predictor_scoring_fun=default_predictor_scoring_fun,
+ score_cutoff_fun=default_score_cutoff_fun,
+ n_features_dependent_kwargs=None,
+ **kwargs):
+ """Construct a predictor configuration
+
+ Parameters
+ ----------
+ predictor_name : str
+ A name for this predictor
+ obj : sklearn predictor
+ A scikit-learn predictor, eg sklearn.ensemble.ExtraTreesClassifier
+ predictor_scoring_fun : function, optional
+ A function which returns the scores of a predictor. May be
+ different for different predictor objects
+ score_cutoff_fun : function, optional
+ A function which returns the minimum "good" score of a predictor
+ n_features_dependent_kwargs : dict, optional (default None)
+ A dictionary of (key, function) arguments for the classifier, for
+ keyword arguments that are dependent on the dataset input size
+ kwargs : other keyword arguments, optional
+ All other keyword arguments are passed along to the predictor
+ """
+ if n_features_dependent_kwargs is None:
+ n_features_dependent_kwargs = {}
+ self.n_features_dependent_kwargs = n_features_dependent_kwargs
+ self.constant_kwargs = kwargs
+ self.predictor_scoring_fun = predictor_scoring_fun
+ self.score_cutoff_fun = score_cutoff_fun
+ self.predictor_name = predictor_name
+ sys.stdout.write(
+ "{}\tPredictor {} is of type {}\n".format(timestamp(),
+ self.predictor_name,
+ obj))
+ self._parent = obj
+ self.__doc__ = obj.__doc__
+ sys.stdout.write(
+ "{}\tAdded {} to default predictors\n".format(timestamp(),
+ self.predictor_name))
+
+ @memoize
+ def parameters(self, n_features):
+ """Given a number of features, return the appropriately scaled keyword
+ arguments
+
+ Parameters
+ ----------
+ n_features : int
+ Number of features in the data to scale appropriate keyword
+ arguments to the predictor object
+
+ """
+ kwargs = {}
+ for parameter, setter in self.n_features_dependent_kwargs.items():
+ kwargs[parameter] = setter(n_features)
+
+ for parameter, value in self.constant_kwargs.items():
+ kwargs[parameter] = value
+
+ return kwargs
+
+ def __call__(self, n_features):
+ """Initialize a predictor with this number of features
+
+ Parameters
+ ----------
+ n_features : int
+ The number of features in the data
+
+ Returns
+ -------
+ predictor : sklearn predictor
+ A scikit-learn predictor object inialized with keyword arguments
+ specified in __init__, and the
+ :py:attr:`n_feature_dependent_kwargs` scaled to this number of
+ features
+ """
+ parameters = self.parameters(n_features)
+
+ sys.stdout.write(
+ "{} Configuring predictor type: {} with {} features".format(
+ timestamp(), self.predictor_name, n_features))
+
+ predictor = self._parent(**parameters)
+ predictor.score_cutoff_fun = self.score_cutoff_fun
+ predictor.predictor_scoring_fun = self.predictor_scoring_fun
+ predictor.has_been_fit = False
+ predictor.has_been_scored = False
+ predictor._score_coefficient = SCORE_COEFFICIENT
+ return predictor
+
+
+class PredictorConfigScalers(object):
+ """Scale parameters specified in the keyword arugments based on the
+ dataset size
+ """
+
+ _default_coef = 2.5
+ _default_nfeatures = 500
+
+ @staticmethod
+ def max_feature_scaler(n_features=_default_nfeatures, coef=_default_coef):
+ """Scale the maximum number of features per estimator
+
+ # TODO: @mlovci what are the principles behind this scaler? to see each
+ feature "x" number of times?
+
+ Parameters
+ ----------
+ n_features : int, optional (default 500)
+ Number of features in the data
+ coef : float, optional (default 2.5)
+ # TODO: What does this do?
+
+ Returns
+ -------
+ n_features : int
+ Maximum number of features per estimator
+
+ Raises
+ ------
+ ValueError
+ If n_features is None
+ """
+ if n_features is None:
+ raise ValueError
+ return int(math.ceil(np.sqrt(np.sqrt(n_features) ** (coef + .3))))
+
+ @staticmethod
+ def n_estimators_scaler(n_features=_default_nfeatures, coef=_default_coef):
+ """Scale the number of estimators based on the input features
+
+ # TODO: @mlovci what are the principles behind this scaler? to see each
+ feature "x" number of times?
+
+ Parameters
+ ----------
+ n_features : int, optional (default 500)
+ Number of features in the data
+ coef : float, optional (default 2.5)
+ # @mlovci TODO: What does this do?
+
+ Returns
+ -------
+ n_estimators : int
+ Number of estimators to use
+
+ Raises
+ ------
+ ValueError
+ If n_features is None
+ """
+ if n_features is None:
+ raise ValueError
+ return int(math.ceil((n_features / 50.) * coef))
+
+ @staticmethod
+ def n_jobs_scaler(n_features=_default_nfeatures):
+ """Scale the number of jobs based on how many features are in the data
+
+ # TODO: @mlovci what are the principles behind this scaler? to see each
+ feature "x" number of times?
+
+ Parameters
+ ----------
+ n_features : int
+ Number of features in the data
+
+ Returns
+ -------
+ n_jobs : int
+ Number of jobs to use
+
+ Raises
+ ------
+ ValueError
+ If n_features is None
+ """
+ if n_features is None:
+ raise ValueError
+
+ return int(min(4, math.ceil(n_features / 2000.)))
+
+
+class ConfigOptimizer(object):
+ """choose the coef that makes some result most likely at all n_features
+ (or some other function of the dataset)
+ """
+
+ @staticmethod
+ def objective_average_times_seen(
+ n_features, coef=PredictorConfigScalers._default_coef,
+ max_feature_scaler=PredictorConfigScalers.max_feature_scaler,
+ n_estimators_scaler=PredictorConfigScalers.n_estimators_scaler):
+ """I have no idea what this does. @mlovci
+
+ Parameters
+ ----------
+ n_features : int
+ ???
+ coef : float
+ ???
+ max_feature_scaler : function
+ ???
+ n_estimators_scaler : function
+ ???
+
+ Returns
+ -------
+ ???
+ """
+ return ((n_features / max_feature_scaler(n_features, coef)) *
+ n_estimators_scaler(n_features, coef)) / float(n_features)
+
+
+class PredictorConfigManager(object):
+ """Manage several predictor configurations
+
+ A container for predictor configurations, includes several built-ins
+ @mlovci: built-ins such as ........ ?
+ What is predictor_config vs new_predictor_config? Why are they separate?
+
+ Attributes
+ ----------
+ predictor_config :
+
+ predictor_configs :
+
+ builtin_predictor_configs :
+
+ Methods
+ -------
+ new_predictor_config
+ Create a new predictor configuration
+
+
+ >>> pcm = PredictorConfigManager()
+ >>> # add a new type of predictor
+ >>> pcm.new_predictor_config(ExtraTreesClassifier, 'ExtraTreesClassifier',
+ ... n_features_dependent_kwargs=
+ ... {'max_features': PredictorConfigScalers.max_feature_scaler,
+ ... 'n_estimators': PredictorConfigScalers.n_estimators_scaler,
+ ... 'n_jobs': PredictorConfigScalers.n_jobs_scaler},
+ ... bootstrap=True, random_state=0,
+ ... oob_score=True,
+ ... verbose=True})
+ """
+
+ def __init__(self):
+ """Construct a predictor configuration manager with
+ ExtraTreesClassifier, ExtraTreesRegressor, GradientBoostingClassifier,
+ and GradientBoostingRegressor as default predictors.
+ """
+
+ constant_extratrees_kwargs = {'bootstrap': True,
+ 'random_state': 0,
+ 'oob_score': True,
+ 'verbose': True}
+
+ self.predictor_config(
+ 'ExtraTreesClassifier', obj=ExtraTreesClassifier,
+ n_features_dependent_kwargs={
+ 'max_features': PredictorConfigScalers.max_feature_scaler,
+ 'n_estimators': PredictorConfigScalers.n_estimators_scaler,
+ 'n_jobs': PredictorConfigScalers.n_jobs_scaler},
+ **constant_extratrees_kwargs)
+
+ self.predictor_config(
+ 'ExtraTreesRegressor', obj=ExtraTreesRegressor,
+ n_features_dependent_kwargs={
+ 'max_features': PredictorConfigScalers.max_feature_scaler,
+ 'n_estimators': PredictorConfigScalers.n_estimators_scaler,
+ 'n_jobs': PredictorConfigScalers.n_jobs_scaler},
+ **constant_extratrees_kwargs)
+
+ constant_boosting_kwargs = {'n_estimators': 80, 'max_features': 1000,
+ 'learning_rate': 0.2, 'subsample': 0.6, }
+
+ self.predictor_config('GradientBoostingClassifier',
+ obj=GradientBoostingClassifier,
+ **constant_boosting_kwargs)
+
+ self.predictor_config('GradientBoostingRegressor',
+ obj=GradientBoostingRegressor,
+ **constant_boosting_kwargs)
+
+ @property
+ def builtin_predictor_configs(self):
+ """Names of the predictor configurations
+ """
+ return self.predictor_configs.keys()
+
+ @property
+ def predictor_configs(self):
+ """Dict of predictor configurations
+ """
+ if not hasattr(self, '_predictors'):
+ self._predictors = {}
+
+ return self._predictors
+
+ def predictor_config(self, name, **kwargs):
+ """Create a new predictor configuration, added to
+ :py:attr:`.predictors`
+
+ Parameters
+ ----------
+ name : str
+ Name of the predictor
+ kwargs : other keyword arguments, optional
+ All other keyword arguments are passed to
+ :py:meth:`predictor_configs`
+
+ Returns
+ -------
+ predictor : sklearn predictor
+ An initalized scikit-learn predictor
+ """
+ predictor = self.new_predictor_config(name, **kwargs)
+ if name in self.predictor_configs and \
+ self.predictor_configs[name] != predictor:
+ sys.stderr.write(
+ "WARNING: over-writing predictor named: {}".format(name))
+ self.predictor_configs[name] = predictor
+ return predictor
+
+ @memoize
+ def new_predictor_config(self, name, obj=None,
+ predictor_scoring_fun=None,
+ score_cutoff_fun=None,
+ n_features_dependent_kwargs=None,
+ **kwargs):
+ """Create a new predictor configuration
+
+ Parameters
+ ----------
+ name : str
+ Name of the predictor configuration
+ obj : sklearn predictor object, optional (default=None)
+ @mlovci: what is the point of setting the default to None if it's
+ not really allowed?
+ predictor_scoring_fun : function, optional (default=None)
+ If None, get feature scores from obj.feature_importances_
+ score_cutoff_fun : function, optional (default=None)
+ If None, get the cutoff for important features with by taking
+ features with scores that are 2 standard deviations away from the
+ mean score
+ n_features_dependent_kwargs : dict, optional (default=None)
+ A (key, function) dictionary of keyword argument names and
+ functions which scale their values based on the dataset input size
+ kwargs : other keyword arguments
+ All other keyword arguments are passed to
+ :py:class:`PredictorConfig`
+
+ Returns
+ -------
+ predictorconfig : PredictorConfig
+ A predictor configuration
+
+ Raises
+ ------
+ ValueError
+ If `obj` is None and any of the other keyword arguments are None
+ KeyError
+ If `obj` is None and "name" is not already in
+ :py:attr:`.predictor_configs`
+ """
+ if obj is None:
+ # If obj is None, then this is probably just a "name" and you can't
+ # change any of the parameters
+ n_features_dependent_kwargs = None if \
+ n_features_dependent_kwargs == {} else \
+ n_features_dependent_kwargs
+ kwargs = None if kwargs == {} else kwargs
+ args = [predictor_scoring_fun, score_cutoff_fun,
+ n_features_dependent_kwargs, kwargs]
+ if any([i is not None for i in args]):
+ # if obj is None, you'd better not be asking to set parameters
+ # on it.
+ raise ValueError
+
+ try:
+ return self.predictor_configs[name]
+ except KeyError:
+ raise KeyError("No such predictor: {}".format(name))
+
+ if predictor_scoring_fun is None:
+ predictor_scoring_fun = default_predictor_scoring_fun
+
+ if score_cutoff_fun is None:
+ score_cutoff_fun = default_score_cutoff_fun
+
+ if n_features_dependent_kwargs is not None:
+ if type(n_features_dependent_kwargs) is not dict:
+ raise TypeError
+ else:
+ n_features_dependent_kwargs = {}
+
+ return PredictorConfig(
+ name, obj, predictor_scoring_fun=predictor_scoring_fun,
+ score_cutoff_fun=score_cutoff_fun,
+ n_features_dependent_kwargs=n_features_dependent_kwargs,
+ **kwargs)
+
+
+class PredictorDataSet(object):
+ def __init__(self, data, trait,
+ data_name="MyDataset",
+ categorical_trait=False,
+ predictor_config_manager=None):
+ """Store a (n_samples, n_features) matrix and (n_samples,) trait pair
+
+ In scikit-learn parlance, store an X (data of independent variables)
+ and y (target prediction) pair
+
+ Parameters
+ ----------
+ data : pandas.DataFrame
+ A (n_samples, n_features) datafarme
+ trait : pandas.Series
+
+
+
+ Returns
+ -------
+
+
+ Raises
+ ------
+
+ data - X
+ trait - y
+ data_name - name to store this dataset, to be used with trait.name
+ categorical_trait - is y categorical?
+ """
+
+ if not isinstance(trait, pd.Series):
+ raise TypeError("Traits must be pandas.Series objects")
+
+ self.dataset_name = (data_name, trait.name)
+ self.data_name = data_name
+ self._data = data
+ self.trait = trait
+ self.trait_name = self.trait.name
+ self.categorical_trait = categorical_trait
+
+ if categorical_trait:
+
+ if len(self.traitset) > 2:
+ warnings.warn("WARNING: trait {} has >2 categories".format(
+ self.trait_name))
+
+ # categorical encoder
+ le = LabelEncoder().fit(self.traitset)
+
+ # categorical encoding
+ self._y = pd.Series(data=le.transform(self.trait),
+ index=trait.index,
+ name=self.trait.name)
+
+ else:
+ self._y = trait
+
+ self.predictor_config_manager = predictor_config_manager \
+ if predictor_config_manager is not None \
+ else PredictorConfigManager()
+
+ self.n_features = self.X.shape[1]
+ self._predictors = defaultdict(dict)
+
+ @property
+ def X(self):
+ """(n_samples, n_features) matrix"""
+ return self._data.align(self._y, axis=0,
+ join='inner')[0]
+
+ @property
+ def y(self):
+ """(n_samples,) vector of traits"""
+ return self._data.align(self._y, axis=0,
+ join='inner')[1]
+
+ @property
+ def traitset(self):
+ """All unique values in :py:attr:`self.trait`"""
+ return self.trait.groupby(self.trait).groups.keys()
+
+ @property
+ def predictors(self):
+ """dict of PredictorConfig instances
+
+ The idea here is to keep the predictors tied to their datasets
+ """
+ if hasattr(self, '_predictors'):
+ return self._predictors
+
+ @memoize
+ def predictor(self, name, **kwargs):
+ """A single, initialized PredictorConfig instance
+
+ Parameters
+ ----------
+ name : str
+ Name of the predictor to retrieve or initialize
+ kwargs : other keyword arguments
+ All other keyword arguments are passed to
+ :py:class:`PredictorConfig`
+
+ Returns
+ -------
+ predictorconfig : PredictorConfig
+ An initialized scikit-learn classifier or regressor
+ """
+ predictor = self.predictor_config_manager.predictor_config(name,
+ **kwargs)
+ initialized = predictor(self.n_features)
+ self.predictors[name] = initialized
+ return initialized
+
+ def check_if_equal(self, data, trait, categorical_trait):
+ """Check if this is the same as another dataset.
+
+ Parameters
+ ----------
+ data : pandas.DataFrame
+ Input data of another dataset
+ trait : pandas.Series
+ Response variable of another dataset
+ categorical_trait : bool
+ Whether or not ``trait`` is categorical
+
+ Raises
+ ------
+ AssertionError
+ If datasets are not the same
+ """
+ pdt.assert_frame_equal(data, self._data)
+ pdt.assert_series_equal(trait, self.trait)
+ pdt.assert_equal(categorical_trait, self.categorical_trait)
+
+
+class PredictorDataSetManager(object):
+ """A collection of PredictorDataSet instances.
+
+ Parameters
+ ----------
+ predictor_config_manager : PredictorConfigManager, optional (default None)
+ A predictor configuration manager. If None, instantiate a new one.
+
+ Attributes
+ ----------
+ datasets : dict
+ Dict of dicts of {data: {trait: {categorical: dataset}}}. For
+ convenient retrieval of predictors
+ """
+
+ def __init__(self, predictor_config_manager=None):
+ self.predictor_config_manager = predictor_config_manager \
+ if predictor_config_manager is not None \
+ else PredictorConfigManager()
+
+ @property
+ def datasets(self):
+ """3-layer deep dict of {data: {trait: {categorical: dataset}}}
+ """
+ if not hasattr(self, '_datasets'):
+ # 3 layer deep (data, trait, categorical?)
+ # will almost always be either categorical true or false, rarely
+ # both
+ self._datasets = defaultdict(lambda: defaultdict(dict))
+ return self._datasets
+
+ def dataset(self, data_name, trait_name, categorical_trait=False,
+ **kwargs):
+ """???? @mlovci please fill in
+
+ Parameters
+ ----------
+ data_name : str
+ Name of this data
+ trait_name : str
+ Name of this trait
+ categorical_trait : bool, optional (default=False)
+ If True, then this trait is treated as a categorical, rather than a
+ sequential trait
+
+ Returns
+ -------
+ dataset : PredictorDataSet
+ ???
+ """
+ kwargs['categorical_trait'] = categorical_trait
+ dataset = self.new_dataset(data_name, trait_name, **kwargs)
+
+ if data_name in self.datasets:
+ if trait_name in self.datasets[data_name]:
+ if categorical_trait in self.datasets[data_name][
+ trait_name] and \
+ self.datasets[data_name][trait_name][
+ categorical_trait] != dataset:
+ sys.stderr.write(
+ "WARNING: over-writing dataset named: {}".format(
+ (data_name,
+ trait_name,
+ categorical_trait)))
+ self.datasets[data_name][trait_name][
+ categorical_trait] = dataset
+ else:
+ self.datasets[data_name][trait_name][
+ categorical_trait] = dataset
+ else:
+ self.datasets[data_name][trait_name][
+ categorical_trait] = dataset
+ else:
+ self.datasets[data_name][trait_name][categorical_trait] = dataset
+
+ return dataset
+
+ @memoize
+ def new_dataset(self, data_name, trait_name,
+ categorical_trait=False,
+ data=None, trait=None,
+ predictor_config_manager=None):
+ """??? Difference betwen this and ``dataset``??? @mlovci
+
+ Parameters
+ ----------
+ data_name : str
+ Name of this data
+ trait_name : str
+ Name of this trait
+ categorical_trait : bool, optional (default=False)
+ If True, then this trait is treated as a categorical, rather than a
+ sequential trait
+ data : pandas.DataFrame, optional (default=None)
+ ??? WHy is this optional!?!??!?!
+ trait : pandas.Series, optional (default=None)
+ ???? Why is this optional!?!?!?
+ predictor_config_manager : PredictorConfigManager (default=None)
+
+ Returns
+ -------
+ dataset : PredictorDataSet
+ ???
+ """
+
+ if data is None:
+ # try to get this dataset by key in the dictionary
+ args = np.array([data, trait, predictor_config_manager])
+ if np.any([i is not None for i in args]):
+ # if data is None, you'd better not be asking to set other
+ # parameters
+ raise Exception
+ try:
+ return self.datasets[data_name][trait_name][categorical_trait]
+ except KeyError:
+ raise KeyError("No such dataset: {}".format(
+ (data_name, trait_name, categorical_trait)))
+
+ if trait is None:
+ raise Exception
+
+ if trait_name != trait.name:
+ raise ValueError
+
+ if data_name is None:
+ data_name = "MyData"
+
+ predictor_config_manager = predictor_config_manager \
+ if predictor_config_manager is not None \
+ else self.predictor_config_manager
+
+ return PredictorDataSet(
+ data, trait, data_name, categorical_trait=categorical_trait,
+ predictor_config_manager=predictor_config_manager)
+
+
+class PredictorBase(object):
+ def __init__(self, predictor_name, data_name, trait_name,
+ X_data=None,
+ trait=None,
+ predictor_obj=None,
+ predictor_scoring_fun=None,
+ score_cutoff_fun=None,
+ n_features_dependent_kwargs=None,
+ constant_kwargs=None,
+ is_categorical_trait=None,
+ predictor_dataset_manager=None,
+ predictor_config_manager=None,
+ feature_renamer=None,
+ groupby=None, color=None, pooled=None, order=None,
+ violinplot_kws=None, data_type=None,
+ label_to_color=None, label_to_marker=None,
+ singles=None, outliers=None):
+ """A dataset-predictor pair from PredictorDatasetManager
+
+ One datset, one predictor, from dataset manager.
+
+
+ Parameters
+ ----------
+ predictor_name : str
+ Name for predictor
+ data_name : str
+ Name for this (subset of the) data
+ trait_name : str
+ Name for this trait
+ X_data : pandas.DataFrame, optional
+ Samples-by-features (row x col) dataset to train the predictor on
+ trait : pandas.Series, optional
+ A variable you want to predict using X_data. Indexed like X_data.
+ predictor_obj : sklearn predictor, optional
+ A scikit-learn predictor that implements fit and score on
+ (X_data,trait) Default ExtraTreesClassifier
+ predictor_scoring_fun : function, optional
+ Function to get the feature scores for a scikit-learn classifier.
+ This can be different for different classifiers, e.g. for a
+ classifier named "x" it could be x.scores_, for other it's
+ x.feature_importances_. Default: lambda x: x.feature_importances_
+ score_cutoff_fun : function, optional
+ Function to cut off insignificant scores
+ Default: lambda scores: np.mean(x) + 2 * np.std(x)
+ n_features_dependent_kwargs : dict, optional
+ kwargs to the predictor that depend on n_features
+ Default: {}
+ constant_kwargs : dict, optional
+ kwargs to the predictor that are constant, i.e.:
+ {'n_estimators': 100, 'bootstrap': True, 'max_features': 'auto',
+ 'random_state': 0, 'oob_score': True, 'n_jobs': 2, 'verbose': True}
+ """
+
+ self.predictor_name = predictor_name
+ self.data_name = data_name
+ self.trait_name = trait_name
+
+ self.feature_renamer = feature_renamer
+ self.groupby = groupby
+ self.color = color
+ self.pooled = pooled
+ self.singles = singles
+ self.outliers = outliers
+ self.order = order
+ self.violinplot_kws = violinplot_kws
+ self.data_type = data_type
+ self.label_to_color = label_to_color
+ self.label_to_marker = label_to_marker
+
+ if trait is not None:
+ trait = trait.copy()
+ trait.name = trait_name
+
+ if predictor_dataset_manager is None:
+ if predictor_config_manager is None:
+ self.predictor_config_manager = PredictorConfigManager()
+ else:
+ self.predictor_config_manager = predictor_config_manager
+
+ self.predictor_data_manager = PredictorDataSetManager(
+ self.predictor_config_manager)
+ else:
+ self.predictor_data_manager = predictor_dataset_manager
+
+ # load all args and kwargs into instance attributes
+
+ self._data = X_data
+ self.trait = trait
+ self.predictor_obj = predictor_obj
+ self.predictor_scoring_fun = predictor_scoring_fun
+ self.score_cutoff_fun = score_cutoff_fun
+ self.constant_kwargs = {} if constant_kwargs is None \
+ else constant_kwargs
+ self.n_features_dependent_kwargs = {} \
+ if n_features_dependent_kwargs is None else \
+ n_features_dependent_kwargs
+ self.categorical_trait = is_categorical_trait if \
+ is_categorical_trait is not None else False
+
+ self.__doc__ = '{}\n\n{}\n\n{}\n\n'.format(self.__doc__,
+ self.dataset.__doc__,
+ self.predictor.__doc__)
+
+ @property
+ def dataset(self):
+ """Thin reference to `dataset`"""
+ return self.predictor_data_manager.dataset(
+ self.data_name, self.trait_name, data=self._data, trait=self.trait,
+ categorical_trait=self.categorical_trait)
+
+ @property
+ def X(self):
+ """Predictive variables, aligned with target.
+
+ Thin reference to `dataset.X`
+ """
+ return self.dataset.X
+
+ @property
+ def y(self):
+ """Target variable, aligned with predictive variables
+
+ Thin reference to `dataset.y`
+ """
+ return self.dataset.y
+
+ @property
+ def predictor(self):
+ """Thin reference to ``dataset.predictor``"""
+ return self.dataset.predictor(
+ self.predictor_name, obj=self.predictor_obj,
+ predictor_scoring_fun=self.predictor_scoring_fun,
+ score_cutoff_fun=self.score_cutoff_fun,
+ n_features_dependent_kwargs=self.n_features_dependent_kwargs,
+ **self.constant_kwargs)
+
+ def fit(self):
+ """Fit predictor to the dataset"""
+ sys.stdout.write(
+ "Fitting a predictor for X:{}, y:{}, method:{}... please wait.\n"
+ .format(self.dataset.data_name,
+ self.dataset.trait_name,
+ self.predictor_name))
+
+ self.predictor.fit(self.dataset.X, self.dataset.y)
+ self.has_been_fit = True
+ sys.stdout.write("\tFinished.\n")
+ # Collect scores from predictor, rename innate scores variable to
+ # self.scores_
+ scores = self.predictor.predictor_scoring_fun(self.predictor)
+ self.scores_ = pd.Series(index=self.X.columns, data=scores)
+ self.has_been_scored = True
+
+ @memoize
+ def predict(self, other):
+ """Predict
+
+ Parameters
+ ----------
+ other : pandas.DataFrame
+ Given a (m_samples, n_features) dataframe, predict the response
+
+ Returns
+ -------
+ prediction : pandas.Series
+ (m_samples,) sized series of prediction of response
+
+ Raises
+ ------
+ TypeError
+ If ``other`` is not a pandas DataFrame
+ """
+ if not isinstance(other, pd.DataFrame):
+ raise TypeError("please predict on a DataFrame")
+ other_aligned, _ = other.align(self.X, axis=1, join='right').fillna(0)
+ sys.stderr.write("predicting value, there are \
+ {} common and {} not-common features.".format(
+ len(set(other.columns) and self.X.columns),
+ len(other.columns and not self.X.columns)))
+ return pd.Series(self.predictor.predict(other_aligned.values),
+ index=other.index)
+
+ @property
+ def oob_score_(self):
+ """Thin reference to `predictor.oob_score_`"""
+ return self.predictor.oob_score_
+
+ @property
+ def has_been_fit(self):
+ """Thin reference to `predictor.has_been_fit`"""
+ return self.predictor.has_been_fit
+
+ @has_been_fit.setter
+ def has_been_fit(self, value):
+ """Set whether the predictor has been fit"""
+ self.predictor.has_been_fit = value
+
+ @property
+ def has_been_scored(self):
+ """Thin reference to :py:attr:`.predictor.has_been_scored`"""
+ return self.predictor.has_been_scored
+
+ @has_been_scored.setter
+ def has_been_scored(self, value):
+ """Set whether the predictor has been scored"""
+ self.predictor.has_been_scored = value
+
+ @property
+ def score_coefficient(self):
+ """Thin reference to ``predictor._score_coefficient``"""
+ return self.predictor._score_coefficient
+
+ @score_coefficient.setter
+ def score_coefficient(self, value):
+ """Set the predictor's score coefficient"""
+ self.predictor._score_coefficient = value
+
+ @property
+ def scores_(self):
+ """Scores of these features' importances in this predictor"""
+ return self.predictor.scores_
+
+ @scores_.setter
+ def scores_(self, value):
+ """Set the predictor scores
+
+ If zero important features found, raise a warning
+ """
+ self.predictor.scores_ = value
+ if self.n_good_features_ <= 1:
+ sys.stderr.write("cutoff: %.4f\n" % self.score_cutoff_)
+ UserWarning("These classifier settings produced <= 1 important "
+ "feature, consider reducing score_coefficient. "
+ "DataFramePCA will fail with this error: "
+ "\"ValueError: failed to create intent("
+ "cache|hide)|optional array-- must have defined "
+ "dimensions but got (0,)\"\n")
+
+ @property
+ def score_cutoff_(self):
+ """Get the minimum score of the 'good' features"""
+ return self.predictor.score_cutoff_fun(self.scores_,
+ self.score_coefficient)
+
+ @property
+ def important_features_(self):
+ """Get all features with scores greater than ``score_cutoff_``"""
+ return self.scores_ > self.score_cutoff_
+
+ @property
+ def subset_(self):
+ """Get the subset of the data with only important features"""
+ return self.X.ix[:, self.important_features_]
+
+ @property
+ def n_good_features_(self):
+ """Get the number of good features"""
+ return np.sum(self.important_features_)
+
+ @memoize
+ def pca(self):
+ """Perform PCA on the top-performing features"""
+ return DataFramePCA(self.subset_)
+
+ @memoize
+ def nmf(self):
+ """Perform NMF on the top-performing features"""
+ return DataFrameNMF(self.subset_)
+
+
+class Regressor(PredictorBase):
+
+ categorical = False
+
+ __doc__ = "Regressor for continuous response variables.\n" + \
+ PredictorBase.__init__.__doc__
+
+ def __init__(self, data_name, trait_name,
+ predictor_name=None,
+ *args, **kwargs):
+ if predictor_name is None:
+ predictor_name = REGRESSOR
+ kwargs['is_categorical_trait'] = False
+ super(Regressor, self).__init__(predictor_name, data_name, trait_name,
+ *args, **kwargs)
+
+
+class Classifier(PredictorBase):
+
+ categorical = True
+
+ __doc__ = "Classifier for categorical response variables.\n" + \
+ PredictorBase.__init__.__doc__
+
+ def __init__(self, data_name, trait_name,
+ predictor_name=None,
+ *args, **kwargs):
+ if predictor_name is None:
+ predictor_name = CLASSIFIER
+ kwargs['is_categorical_trait'] = True
+ super(Classifier, self).__init__(predictor_name, data_name, trait_name,
+ *args, **kwargs)
diff --git a/flotilla/compute/splicing.py b/flotilla/compute/splicing.py
new file mode 100644
index 00000000..a7fc3e9c
--- /dev/null
+++ b/flotilla/compute/splicing.py
@@ -0,0 +1,187 @@
+"""
+Calculate modalities of splicing events.
+"""
+
+from collections import Iterable
+
+import numpy as np
+import pandas as pd
+from scipy import stats
+from scipy.misc import logsumexp
+
+
+MODALITIES_NAMES = ['excluded', 'middle', 'included', 'bimodal',
+ 'uniform']
+
+
+class ModalityModel(object):
+ """Object to model modalities from beta distributions"""
+
+ def __init__(self, alphas, betas):
+ if not isinstance(alphas, Iterable) and not isinstance(betas,
+ Iterable):
+ alphas = [alphas]
+ betas = [betas]
+
+ self.alphas = alphas if isinstance(alphas, Iterable) else np.ones(
+ len(betas)) * alphas
+ self.betas = betas if isinstance(betas, Iterable) else np.ones(
+ len(alphas)) * betas
+
+ self.rvs = [stats.beta(a, b) for a, b in
+ zip(self.alphas, self.betas)]
+ self.scores = np.arange(len(self.rvs)).astype(float) + .1
+ self.scores = self.scores / self.scores.max()
+ self.prob_parameters = self.scores / self.scores.sum()
+
+ def __eq__(self, other):
+ return np.all(self.alphas == other.alphas) \
+ and np.all(self.betas == other.betas) \
+ and np.all(self.prob_parameters == other.prob_parameters)
+
+ def __ne__(self, other):
+ return not self.__eq__(other)
+
+ def logliks(self, x):
+ x = x.copy()
+ x[x == 0] = 0.001
+ x[x == 1] = 0.999
+
+ return np.array([np.log(prob) + rv.logpdf(x[np.isfinite(x)]).sum()
+ for prob, rv in
+ zip(self.prob_parameters, self.rvs)])
+
+ def logsumexp_logliks(self, x):
+ return logsumexp(self.logliks(x))
+
+
+class ModalityEstimator(object):
+ """Use Bayesian methods to estimate modalities of splicing events"""
+
+ # colors = dict(
+ # zip(['excluded', 'middle', 'included', 'bimodal', 'uniform'],
+ # sns.color_palette('deep', n_colors=5)))
+
+ def __init__(self, step, vmax, logbf_thresh=3):
+ """Initialize an object with models to estimate splicing modality
+
+ Parameters
+ ----------
+ step : float
+ Distance between parameter values
+ vmax : float
+ Maximum parameter value
+ logbf_thresh : float
+ Minimum threshold at which the bayes factor difference is defined
+ to be significant
+ """
+ self.step = step
+ self.vmax = vmax
+ self.logbf_thresh = logbf_thresh
+
+ self.parameters = np.arange(2, self.vmax + self.step,
+ self.step).astype(float)
+ self.exclusion_model = ModalityModel(1, self.parameters)
+ self.inclusion_model = ModalityModel(self.parameters, 1)
+ self.middle_model = ModalityModel(self.parameters, self.parameters)
+ self.bimodal_model = ModalityModel(1 / self.parameters,
+ 1 / self.parameters)
+
+ self.models = {'included': self.inclusion_model,
+ 'excluded': self.exclusion_model,
+ 'bimodal': self.bimodal_model,
+ 'middle': self.middle_model}
+
+ def _loglik(self, event):
+ """Calculate log-likelihoods of an event, given the modality models"""
+ return dict((name, m.logliks(event))
+ for name, m in self.models.iteritems())
+
+ def _logsumexp(self, logliks):
+ """Calculate logsumexps of each modality's loglikelihood"""
+ logsumexps = pd.Series(dict((name, logsumexp(loglik))
+ for name, loglik in logliks.iteritems()))
+ logsumexps['uniform'] = self.logbf_thresh
+ return logsumexps
+
+ def _guess_modality(self, logsumexps):
+ """Guess the most likely modality.
+
+ If no modalilites have logsumexp'd logliks greater than the log Bayes
+ factor threshold, then they are assigned the 'uniform' modality,
+ which is the null hypothesis
+ """
+ return logsumexps.idxmax()
+
+ def fit_transform(self, data):
+ """Get the modality assignments of each splicing event in the data
+
+ Parameters
+ ----------
+ data : pandas.DataFrame
+ A (n_samples, n_events) dataframe of splicing events' PSI scores.
+ Must be psi scores which range from 0 to 1
+
+ Returns
+ -------
+ modality_assignments : pandas.Series
+ A (n_events,) series of the estimated modality for each splicing
+ event
+
+ Raises
+ ------
+ AssertionError
+ If ``data`` does not fall only between 0 and 1.
+ """
+ assert np.all(data.values.flat[np.isfinite(data.values.flat)] <= 1)
+ assert np.all(data.values.flat[np.isfinite(data.values.flat)] >= 0)
+
+ logsumexp_logliks = data.apply(lambda x:
+ pd.Series({k: v.logsumexp_logliks(x)
+ for k, v in
+ self.models.iteritems()}),
+ axis=0)
+ logsumexp_logliks.ix['uniform'] = self.logbf_thresh
+ return logsumexp_logliks.idxmax()
+
+
+def switchy_score(array):
+ """Transform a 1D array of data scores to a vector of "switchy scores"
+
+ Calculates std deviation and mean of sine- and cosine-transformed
+ versions of the array. Better than sorting by just the mean which doesn't
+ push the really lowly variant events to the ends.
+
+ Parameters
+ ----------
+ array : numpy.array
+ A 1-D numpy array or something that could be cast as such (like a list)
+
+ Returns
+ -------
+ switchy_score : float
+ The "switchy score" of the study_data which can then be compared to
+ other splicing event study_data
+
+ """
+ array = np.array(array)
+ variance = 1 - np.std(np.sin(array[~np.isnan(array)] * np.pi))
+ mean_value = -np.mean(np.cos(array[~np.isnan(array)] * np.pi))
+ return variance * mean_value
+
+
+def get_switchy_score_order(x):
+ """Apply switchy scores to a 2D array of data scores
+
+ Parameters
+ ----------
+ x : numpy.array
+ A 2-D numpy array in the shape [n_events, n_samples]
+
+ Returns
+ -------
+ score_order : numpy.array
+ A 1-D array of the ordered indices, in switchy score order
+ """
+ switchy_scores = np.apply_along_axis(switchy_score, axis=0, arr=x)
+ return np.argsort(switchy_scores)
diff --git a/flotilla/src/_schooner_data_model/README b/flotilla/data_model/README.md
similarity index 64%
rename from flotilla/src/_schooner_data_model/README
rename to flotilla/data_model/README.md
index d28d0b28..9d481d3e 100644
--- a/flotilla/src/_schooner_data_model/README
+++ b/flotilla/data_model/README.md
@@ -1,6 +1,9 @@
data models
-Data - base data model class for splicing and expression data
+BaseData - base data model class for splicing and expression data
+
SplicingData - data model for splicing
+
ExpressionData - data model for expression
-Study - integrates SplicingData and ExpressionData data types and calls analyses
\ No newline at end of file
+
+Study - integrates SplicingData and ExpressionData data types and calls analyses
diff --git a/flotilla/data_model/__init__.py b/flotilla/data_model/__init__.py
new file mode 100644
index 00000000..dac99eaa
--- /dev/null
+++ b/flotilla/data_model/__init__.py
@@ -0,0 +1,11 @@
+from .expression import ExpressionData, SpikeInData
+from .gene_ontology import GeneOntologyData
+from .metadata import MetaData
+from .quality_control import MappingStatsData
+from .splicing import SplicingData
+from .study import Study
+
+__author__ = 'olga'
+
+__all__ = ['Study', 'ExpressionData', 'SpikeInData', 'GeneOntologyData',
+ 'MetaData', 'MappingStatsData', 'SplicingData']
diff --git a/flotilla/data_model/base.py b/flotilla/data_model/base.py
new file mode 100644
index 00000000..d768365c
--- /dev/null
+++ b/flotilla/data_model/base.py
@@ -0,0 +1,1484 @@
+"""
+Base data class for all data types. All data types in flotilla inherit from
+this, or a child object (like ExpressionData).
+"""
+import sys
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+from sklearn.preprocessing import StandardScaler
+from scipy.cluster.vq import whiten
+
+from ..compute.decomposition import DataFramePCA, DataFrameNMF
+from ..compute.infotheory import binify, cross_phenotype_jsd, jsd_df_to_2d
+from ..compute.predict import PredictorConfigManager, \
+ PredictorDataSetManager, CLASSIFIER
+from ..visualize.decomposition import DecompositionViz
+from ..visualize.generic import violinplot, nmf_space_transitions, \
+ simple_twoway_scatter
+from ..visualize.network import NetworkerViz
+from ..visualize.predict import ClassifierViz
+from ..util import memoize, cached_property
+from ..compute.outlier import OutlierDetection
+
+MINIMUM_FEATURE_SUBSET = 20
+
+
+class BaseData(object):
+ """Base class for biological data measurements.
+
+ All data types in flotilla inherit from this, and have all functionality
+ described here
+
+ Attributes
+ ----------
+ data : pandas.DataFrame
+ A (n_samples, m_features) sized DataFrame of filtered input data, with
+ features with too few samples (``minimum_samples``) detected at
+ ``thresh`` removed. Compared to :py:attr:`.data_original`,
+ ``m_features <= n_features`
+ data_type : str
+ String indicating what kind of data this is, e.g. "splicing" or
+ "expression"
+ data_original : pandas.DataFrame
+ A (n_samples, n_features) sized DataFrame of all input data, before
+ removing features for having too few samples
+ feature_data : pandas.DataFrame
+ A (k_features, n_features_about_features) sized DataFrame of features
+ about the feature data. Notice that this DataFrame does not need to be
+ the same size as the data, but must at least include all the features
+ from :py:attr:`data`. Compared to :py:attr:`.data`,
+ ``k_features >= m_features``
+ feature_subsets : dict
+ Dict of {"subset_name" : list_of_feature_ids} for feature subsets
+ specified as either boolean columns in ``feature_data``. All columns in
+ ``feature_ignore_subset_cols`` are ignored
+ predictor_config_manager : PredictorConfigManager
+ Manage different combinations of predictor on different data subtypes
+ variant : pandas.Index
+ Genes whose variance among all cells is 2 standard deviations away
+ from the mean variance
+
+
+ Methods
+ -------
+ feature_renamer
+ If ``feature_rename_col`` is specified in :py:meth:`BaseData.__init__`,
+ this will rename the feature ID to a new name. If
+ ``feature_rename_col`` is not specified, then this will return the
+ original id
+ maybe_renamed_to_feature_id
+ Convert a weird feature ID to your known gene names
+
+ """
+
+ def __init__(self, data, thresh=-np.inf,
+ minimum_samples=0,
+ feature_data=None,
+ feature_rename_col=None,
+ feature_ignore_subset_cols=None,
+ technical_outliers=None,
+ outliers=None,
+ pooled=None,
+ predictor_config_manager=None,
+ data_type=None):
+ """Abstract base class for biological measurements
+
+ Parameters
+ ----------
+ data : pandas.DataFrame
+ A samples x features (samples on rows, features on columns)
+ dataframe with some kind of measurements of cells,
+ e.g. gene expression values such as TPM, RPKM or FPKM, alternative
+ splicing "Percent-spliced-in" (PSI) values, or RNA editing scores.
+ Note: If the columns are a multi-index, the "level 0" is assumed to
+ be the unique, crazy ID like 'ENSG00000100320', and "level 1" is
+ assumed to be the convenient gene name like "RBFOX2"
+ thresh : float, optional (default=-np.inf)
+ Minimum value to accept for this data.
+ minimum_samples : int, optional (default=0)
+ Minimum number of samples with values greater than ``thresh``.
+ E.g., for use with "at least 3 single cells expressing the gene at
+ greater than 1 TPM."
+ feature_data : pandas.DataFrame, optional (default=None)
+ A features x attributes dataframe of metadata about the features,
+ e.g. annotating whether the gene is a housekeeping gene
+ feature_rename_col : str, optional (default=None)
+ Which column in the feature_data to use to rename feature IDs
+ from a crazy ID to a common gene symbol, e.g. to transform
+ 'ENSG00000100320' into 'RBFOX2'
+ feature_ignore_subset_cols : list-like (default=None)
+ Columns in the feature data to ignore when making subsets,
+ e.g. "gene_name" shouldn't be used to create subsets, since it's
+ just a small number of them.
+ technical_outliers : list-like, optional (default=None)
+ List of sample IDs which should be completely ignored because
+ they didn't pass the technical quality control
+ outliers : list-like, optional (default=None)
+ List of sample IDs which should be marked as outliers for
+ plotting and interpretation purposes
+ pooled : list-like, optional (default=None)
+ List of sample IDs which should be marked as pooled for plotting
+ and interpretation purposes.
+ predictor_config_manager : PredictorConfigManager, optional
+ (default=None)
+ Object used to organize inputs to
+ :py:class:`compute.predict.Regressor` and
+ :py:class:`compute.predict.Classifier`. If None, one is initialized
+ for this instance.
+ data_type : str, optional (default=None)
+ A string indicating what kind of data this is, e.g. "expression" or
+ "splicing"
+
+ Notes
+ -----
+ Any cells not marked as "technical_outliers", "outliers" or "pooled"
+ are considered as single-cell samples.
+
+ """
+ if isinstance(data.index, pd.MultiIndex) \
+ or isinstance(data.columns, pd.MultiIndex):
+ raise ValueError('flotilla does not currently support '
+ 'multi-indexed dataframes')
+
+ self.data = data
+ self.data_original = data.copy()
+ self.thresh = thresh if thresh is not None else -np.inf
+ self.minimum_samples = minimum_samples \
+ if minimum_samples is not None else 0
+ self.data_type = data_type
+ self.technical_outliers = technical_outliers
+
+ if self.technical_outliers is not None and len(
+ self.technical_outliers) > 0:
+ outlier_ids = ", ".join(self.technical_outliers)
+ sys.stderr.write(
+ "Removing technical outliers from consideration "
+ "in {0}:\n\t{1}\n".format(self.data_type, outlier_ids))
+ good_samples = ~self.data.index.isin(self.technical_outliers)
+ self.data = self.data.ix[good_samples]
+
+ self.pooled_samples = pooled if pooled is not None else []
+ self.outlier_samples = outliers if outliers is not None else []
+ self.single_samples = self.data.index[~self.data.index.isin(
+ self.pooled_samples)]
+
+ if self.thresh > -np.inf or self.minimum_samples > 0:
+ # self.data_original = self.data.copy()
+ if not self.singles.empty:
+ self.data = self._threshold(self.data, self.singles)
+ else:
+ self.data = self._threshold(self.data)
+
+ self.feature_data = feature_data
+ self.feature_ignore_subset_cols = []\
+ if feature_ignore_subset_cols is \
+ None else feature_ignore_subset_cols
+ # if self.feature_data is None:
+ # self.feature_data = pd.DataFrame(index=self.data.columns)
+ self.feature_rename_col = feature_rename_col
+ self.default_feature_sets = []
+
+ if self.feature_data is not None and self.feature_rename_col is not \
+ None:
+ self.feature_renamer = \
+ lambda x: self._shortener(x, renamer=self._feature_renamer)
+ else:
+ self.feature_renamer = self._shortener
+
+ if predictor_config_manager is None:
+ self.predictor_config_manager = PredictorConfigManager()
+ else:
+ self.predictor_config_manager = predictor_config_manager
+
+ self.predictor_dataset_manager = PredictorDataSetManager(
+ self.predictor_config_manager)
+
+ self.networks = NetworkerViz(self)
+
+ def _threshold(self, data, other=None):
+ """Only take features with expression greater than the threshold,
+ in at least the minimum number of samples.
+
+ Parameters
+ ----------
+ data : pandas.DataFrame
+ The data to filter, make smaller
+ other : pandas.DataFrame, optional
+ If provided, use this DataFrame to filter data. E.g. use the
+ genes expressed in only single cells to filter the whole dataset.
+
+ Returns
+ -------
+ filtered : pandas.DataFrame
+ "data" filtered with expression values at least self.thresh
+ in least self.minimum_samples
+ """
+ if other is None:
+ other = data
+ samples_per_feature = other[other > self.thresh].count()
+ features = samples_per_feature >= self.minimum_samples
+ filtered = data.ix[:, features]
+ return filtered
+
+ def _feature_renamer(self, x):
+ """Rename a feature from a crazy ID like 'ENSG00000100320' to 'RBFOX2'
+ """
+ if x in self.feature_renamer_series.index:
+ rename = self.feature_renamer_series[x]
+ if isinstance(rename, pd.Series):
+ return rename.values[0]
+ else:
+ return rename
+ else:
+ if type(x) == str:
+ return x
+ else:
+ return '_'.join(x)
+
+ @staticmethod
+ def _shortener(x, renamer=None, max_char_len=20):
+ """Shorten a feature ID to minimize the amount of messy text on plots
+
+ Parameters
+ ----------
+ x : str
+ A feature ID
+ renamer : function, optional (default=None)
+ A function to rename feature IDs to known gene symbols
+ max_char_len : int, optional (default=20)
+ Maximum length of the feature ids
+
+ Returns
+ -------
+ shortened : str
+ A potentially renamed, shortened string
+ """
+ if renamer is not None:
+ renamed = renamer(x)
+ else:
+ renamed = x
+
+ if isinstance(renamed, float):
+ return renamed
+ elif len(renamed) > max_char_len:
+ return '{}...'.format(renamed[:max_char_len])
+ else:
+ return renamed
+
+ @property
+ def singles(self):
+ """Data from only the single cells"""
+ return self.data.ix[self.single_samples]
+
+ @property
+ def pooled(self):
+ """Data from only the pooled samples"""
+ return self.data.ix[self.pooled_samples]
+
+ @property
+ def outliers(self):
+ """Data from only the outlier samples"""
+ return self.data.ix[self.outlier_samples]
+
+ @property
+ def feature_renamer_series(self):
+ """A pandas Series of the original feature ids to the renamed ids"""
+ if self.feature_data is not None:
+ if self.feature_rename_col is not None:
+ return self.feature_data[self.feature_rename_col].dropna()
+ else:
+ return pd.Series(self.feature_data.index,
+ index=self.feature_data.index)
+ else:
+ return pd.Series(self.data_original.columns.values,
+ index=self.data_original.columns)
+
+ def maybe_renamed_to_feature_id(self, feature_id):
+ """To be able to give a simple gene name, e.g. "RBFOX2" and get the
+ official ENSG ids or MISO ids
+
+ Parameters
+ ----------
+ feature_id : str
+ The name of a feature ID. Could be either a common gene name, as in
+ what the crazy IDs are :py:meth:`.feature_renamer` to, or
+
+ Returns
+ -------
+ feature_id : str or list-like
+ Valid Feature ID(s) that can be used to subset self.data
+ """
+ if feature_id in self.feature_renamer_series.values:
+ feature_ids = self.feature_renamer_series[
+ self.feature_renamer_series ==
+ feature_id].index
+ return self.data.columns.intersection(feature_ids)
+ elif feature_id in self.data.columns:
+ return feature_id
+ else:
+ raise ValueError('{} is not a valid feature identifier (it may '
+ 'not have been measured in this dataset!)'
+ .format(feature_id))
+
+ @property
+ def _var_cut(self):
+ """Variance cutoff, 2 std devs away from mean variance"""
+ return self.data.var().dropna().mean() + 2 * self.data.var() \
+ .dropna().std()
+
+ @property
+ def variant(self):
+ """Features whose variance is 2 std devs away from mean variance"""
+ return self.data.columns[self.data.var() > self._var_cut]
+
+ # def drop_outliers(self, data, outliers):
+ # # assert 'outlier' din self.experiment_design_data.columns
+ # outliers = set(outliers).intersection(data.index)
+ # sys.stdout.write("Dropping {}\n".format(outliers))
+ # data = data.drop(outliers)
+ # outlier_data = data.ix[outliers]
+ # return data, outlier_data
+
+ @property
+ def feature_subsets(self):
+ """Dict of feature subset names to their list of feature ids"""
+ feature_subsets = subsets_from_metadata(
+ self.feature_data, MINIMUM_FEATURE_SUBSET, 'features',
+ ignore=self.feature_ignore_subset_cols)
+ feature_subsets['variant'] = self.variant
+ return feature_subsets
+
+ def feature_subset_to_feature_ids(self, feature_subset, rename=True):
+ """Convert a feature subset name to a list of feature ids"""
+ feature_ids = pd.Index([])
+ if feature_subset is not None:
+ try:
+ if feature_subset in self.feature_subsets:
+ feature_ids = self.feature_subsets[feature_subset]
+ elif feature_subset.startswith('all'):
+ feature_ids = self.data.columns
+ except TypeError:
+ if not isinstance(feature_subset, str):
+ feature_ids = feature_subset
+ n_custom = self.feature_data.columns.map(
+ lambda x: x.startswith('custom')).sum()
+ self.feature_data['custom_{}'.format(n_custom + 1)] = \
+ self.feature_data.index.isin(feature_ids)
+ else:
+ raise ValueError(
+ "There are no {} features in this data: "
+ "{}".format(feature_subset, self))
+ if rename:
+ feature_ids = map(self.feature_renamer, feature_ids)
+ else:
+ feature_ids = self.data.columns
+ return feature_ids
+
+ def jsd_df(self, groupby=None, n_iter=100, n_bins=10):
+ """Jensen-Shannon divergence of features across phenotypes
+
+ Parameters
+ ----------
+ groupby : mappable
+ A samples to phenotypes mapping
+ n_iter : int
+ Number of bootstrap resampling iterations to perform for the
+ within-group comparisons
+ n_bins : int
+ Number of bins to binify the singles data on
+
+ Returns
+ -------
+ jsd_df : pandas.DataFrame
+ A (n_features, n_phenotypes^2) dataframe of the JSD between each
+ feature between and within phenotypes
+ """
+ bins = np.linspace(self.singles.min().min(), self.singles.max().max(),
+ n_bins)
+ return cross_phenotype_jsd(self.singles, groupby=groupby,
+ bins=bins, n_iter=n_iter)
+
+ def jsd_2d(self, groupby=None, n_iter=100, n_bins=10):
+ """Mean Jensen-Shannon divergence of features across phenotypes
+
+ Parameters
+ ----------
+ groupby : mappable
+ A samples to phenotypes mapping
+ n_iter : int
+ Number of bootstrap resampling iterations to perform for the
+ within-group comparisons
+ n_bins : int
+ Number of bins to binify the singles data on
+
+ Returns
+ -------
+ jsd_2d : pandas.DataFrame
+ A (n_phenotypes, n_phenotypes) symmetric dataframe of the mean JSD
+ between and within phenotypes
+ """
+ return jsd_df_to_2d(self.jsd_df(groupby=groupby, n_iter=n_iter,
+ n_bins=n_bins))
+
+ def plot_classifier(self, trait, sample_ids=None, feature_ids=None,
+ predictor_name=None, standardize=True,
+ score_coefficient=None, data_name=None, groupby=None,
+ label_to_color=None, label_to_marker=None, order=None,
+ color=None, **plotting_kwargs):
+ """Classify samples on boolean or categorical traits
+
+ Parameters
+ ----------
+ trait : pandas.Series
+ A (n_samples,) series of categorical features. Must have the same
+ index as :py:attr:`.data`
+ sample_ids : list-like, optional (default=None)
+ Which samples to use to classify
+ feature_ids : list-like, optional (default=None)
+ Which features to use
+ predictor_name : str
+ Name of the predictor to use, in
+ :py:attr:`.predictor_config_manager`
+ standardize : bool, optional (default=True)
+ If True, mean-center the data so the mean of all features is 0,
+ and divide by the standard deviation so the standard deviation of
+ all features is 1. This allows us to compare lowly expressed
+ features and highly expressed features on the same playing field
+ data_name : str, optional (default=None)
+ Name for this subset of the data
+ groupby : mappable, optional (default=None)
+ Map each sample id to a group, such as a phenotype label
+ label_to_color : dict, optional (default=None)
+ For each phenotype label, assign a color
+ label_to_marker : dict, optional (default=None)
+ For each phenotype label, assign a plotting marker symbol/shape
+ order : list, optional (default=None)
+ For violinplots, the order of the phenotype groups
+ color : list, optional (default=None)
+ For violinplots, the colors of the phenotypes in their order
+ plotting_kwargs : other keyword arguments
+ All other keyword arguments are passed to
+ :py:meth:`.Classifier.__call__`, which passes them to
+ :py:meth:`DecomopsitionViz.__call__`
+
+ Returns
+ -------
+ cv : ClassifierViz
+ Visualziation of the classifier
+ """
+ # print trait
+ plotting_kwargs = {} if plotting_kwargs is None else plotting_kwargs
+
+ # local_plotting_args = self.pca_plotting_args.copy()
+ # local_plotting_args.update(plotting_kwargs)
+ if predictor_name is None:
+ predictor_name = CLASSIFIER
+
+ classifier = self.classify(trait, sample_ids=sample_ids,
+ feature_ids=feature_ids,
+ data_name=data_name,
+ standardize=standardize,
+ predictor_name=predictor_name,
+ groupby=groupby,
+ label_to_marker=label_to_marker,
+ label_to_color=label_to_color, order=order,
+ color=color)
+
+ if score_coefficient is not None:
+ classifier.score_coefficient = score_coefficient
+ classifier.plot(**plotting_kwargs)
+ return classifier
+
+ def plot_dimensionality_reduction(self, x_pc=1, y_pc=2, sample_ids=None,
+ feature_ids=None, featurewise=False,
+ reducer=None, plot_violins=False,
+ groupby=None, label_to_color=None,
+ label_to_marker=None, order=None,
+ reduce_kwargs=None, title='',
+ most_variant_features=False,
+ std_multiplier=2,
+ scale_by_variance=True,
+ **plotting_kwargs):
+ """Principal component-like analysis of measurements
+
+ Parameters
+ ----------
+ x_pc : int, optional
+ Which principal component to plot on the x-axis (default 1)
+ y_pc : int, optional
+ Which principal component to plot on the y-axis (default 2)
+ sample_ids : list, optional
+ If None, plot all the samples. If a list of strings, must be
+ valid sample ids of the data. (default None)
+ feature_ids : list, optional
+ If None, plot all the features. If a list of strings, perform and
+ plot dimensionality reduction on only these feature ids
+ featurewise : bool, optional
+ If True, the features are reduced on the samples, and the plotted
+ points are features, not samples. (default False)
+ reducer : :py:class:`.DataFrameReducerBase`, optional
+ Which decomposition object to use. Must be a child of
+ :py:class:`.DataFrameReducerBase` as this has built-in
+ compatibility with pandas.DataFrames.
+ (default=:py:class:`.DataFramePCA`)
+ plot_violins : bool, optional
+ If True, plot the violinplots of the top features. This
+ can take a long time, so to save time you can turn it off if you
+ just want a quick look at the PCA. (default False)
+ groupby : mappable, optional
+ Map each sample id to a group, such as a phenotype label
+ (default None)
+ label_to_color : dict, optional
+ For each phenotype label, assign a color
+ (default None)
+ label_to_marker : dict, optional
+ For each phenotype label, assign a plotting marker symbol/shape
+ (default None)
+ order : list, optional
+ For violinplots, the order of the phenotype groups
+ (default None)
+ reduce_kwargs : dict, optional
+ Keyword arguments to the reducer (default None)
+ title : str, optional
+ Title of the reduced space plot (default '')
+ most_variant_features : bool, optional
+ If True, then only take the most variant of the provided features.
+ The most variant are determined by taking the features whose
+ variance is ``std_multiplier``standard deviations away from the
+ mean feature variance (default False)
+ std_multiplier : float, optional
+ If ``most_variant_features`` is True, then use this as a cutoff
+ for the minimum variance of a feature to be included (default 2)
+ scale_by_variance : bool, optional
+ If True, then scale the x- and y-axes by the explained variance
+ ratio of the principal component dimensions. Only valid for PCA
+ and its variations, not for NMF or tSNE. (default True)
+ plotting_kwargs : other keyword arguments
+ All other keyword arguments are passed to
+ :py:meth:`DecomopsitionViz.plot`
+
+ Returns
+ -------
+ viz : :py:class:`.DecompositionViz`
+ Object with plotted dimensionality reduction
+ """
+ reduce_kwargs = {} if reduce_kwargs is None else reduce_kwargs
+
+ reduced = self.reduce(sample_ids, feature_ids,
+ featurewise=featurewise,
+ reducer=reducer,
+ most_variant_features=most_variant_features,
+ std_multiplier=std_multiplier,
+ **reduce_kwargs)
+
+ visualized = DecompositionViz(reduced.reduced_space,
+ reduced.components_,
+ reduced.explained_variance_ratio_,
+ singles=self.singles,
+ pooled=self.pooled,
+ outliers=self.outliers,
+ feature_renamer=self.feature_renamer,
+ featurewise=featurewise,
+ label_to_color=label_to_color,
+ label_to_marker=label_to_marker,
+ groupby=groupby, order=order,
+ data_type=self.data_type,
+ scale_by_variance=scale_by_variance,
+ x_pc="pc_" + str(x_pc),
+ y_pc="pc_" + str(y_pc))
+ # pca(show_vectors=True,
+ # **plotting_kwargs)
+ return visualized.plot(title=title,
+ plot_violins=plot_violins, **plotting_kwargs)
+
+ def plot_pca(self, **kwargs):
+ """Call ``plot_dimensionality_reduction`` with PCA specifically"""
+ return self.plot_dimensionality_reduction(reducer=DataFramePCA,
+ **kwargs)
+
+ def _subset(self, data, sample_ids=None, feature_ids=None,
+ require_min_samples=True):
+ """Smartly subset the data given sample and feature ids
+
+ Take only a subset of the data, and require at least the minimum
+ samples observed to be not NA for each feature.
+
+ Parameters
+ ----------
+ data : pandas.DataFrame
+ Data to subset
+ sample_ids : list-like, optional (default=None)
+ Which samples to use. If None, use all.
+ feature_ids : list-like, optional (default=None)
+ Which features to use. If None, use all.
+ require_min_samples : bool, optional (default=True)
+ If True, then require `minimum_samples` for each feature
+
+ Returns
+ -------
+ subset : pandas.DataFrame
+ The subset of data with only these sample ids and feature ides
+ """
+ if feature_ids is None:
+ feature_ids = data.columns
+ else:
+ feature_ids = pd.Index(set(feature_ids).intersection(data.columns))
+ if sample_ids is None:
+ sample_ids = data.index
+ else:
+ sample_ids = pd.Index(set(sample_ids).intersection(data.index))
+
+ if len(sample_ids) == 1:
+ sample_ids = sample_ids[0]
+
+ if len(feature_ids) == 1:
+ feature_ids = feature_ids[0]
+ single_feature = True
+ else:
+ single_feature = False
+
+ subset = data.ix[sample_ids, :].ix[:, feature_ids]
+ # subset = subset.T.ix[feature_ids].T
+
+ if require_min_samples and not single_feature:
+ subset = subset.ix[:, subset.count() >= self.minimum_samples]
+
+ if subset.empty:
+ raise ValueError('This data subset is empty. Please double-check '
+ 'that the gene ids are for the correct species!')
+ return subset
+
+ def _subset_singles_and_pooled(self, sample_ids=None,
+ feature_ids=None, data=None,
+ require_min_samples=True):
+ """Subset singles and pooled, taking only features that appear in both
+
+ Parameters
+ ----------
+ sample_ids : list-like, optional (default=None)
+ List of samples to use. If None, use all. If none of the sample ids
+ overlap with pooled samples, will assume you want all the pooled
+ samples
+ feature_ids : list-like, optional (default=None)
+ List of feature ids to use. If None, use all
+ data : pandas.DataFrame, optional (default=None)
+ If provided, use this ``data`` instead of this instance's
+ py:attr:`BaseData.data`. Convenient for when you filtered based on
+ some other criteria, e.g. for splicing events with expression
+ greater than some threshold
+ require_min_samples : bool
+ If True, then require the study-default minimum number of samples,
+ but only for singles.
+
+ Returns
+ -------
+ singles : pandas.DataFrame
+ DataFrame of only single-cell samples, with only features that
+ appear in both these single cell and pooled samples
+ pooled : pandas.DataFrame
+ DataFrame of only pooled samples, with only features that appear
+ in both these single cell and pooled samples
+ """
+ # singles_ids = self.data.index.intersection(sample_ids)
+ # pooled_ids = self.pooled.index.intersection(sample_ids)
+ # import pdb; pdb.set_trace()
+ if data is None:
+ singles = self._subset(self.singles, sample_ids, feature_ids,
+ require_min_samples=require_min_samples)
+ else:
+ sample_ids = data.index.intersection(self.singles.index)
+ singles = self._subset(data, sample_ids,
+ require_min_samples=require_min_samples)
+
+ try:
+ # If the sample ids don't overlap with the pooled sample, assume
+ # you want all the pooled samples
+ n_pooled_sample_ids = sum(self.pooled.index.isin(sample_ids))
+
+ if sample_ids is not None and n_pooled_sample_ids > 0:
+ pooled_sample_ids = sample_ids
+ else:
+ pooled_sample_ids = None
+
+ if data is None:
+ pooled = self._subset(self.pooled, pooled_sample_ids,
+ feature_ids,
+ require_min_samples=False)
+ else:
+ sample_ids = data.index.intersection(self.pooled.index)
+ pooled = self._subset(data, sample_ids,
+ require_min_samples=False)
+
+ if feature_ids is None or len(feature_ids) > 1:
+ # These are DataFrames
+ singles, pooled = singles.align(pooled, axis=1, join='inner')
+ else:
+ # These are Seriessssss
+ singles = singles.dropna()
+ pooled = pooled.dropna()
+ except (AttributeError, ValueError):
+ pooled = None
+
+ return singles, pooled
+
+ # def _subset_ids_or_data(self, sample_ids, feature_ids, data,
+ # singles=False):
+ # if data is None:
+ # if singles:
+ # data = self.singles
+ # else:
+ # data = self.data
+ # return self._subset(data, sample_ids, feature_ids,
+ # require_min_samples=False)
+ # else:
+ # if feature_ids is not None and sample_ids is not None:
+ # raise ValueError('Can only specify `sample_ids` and '
+ # '`feature_ids` or `data`, but not both.')
+ # else:
+ # return data
+
+ def _subset_and_standardize(self, data, sample_ids=None,
+ feature_ids=None,
+ standardize=True, return_means=False,
+ rename=False):
+
+ """Grab a subset of the provided data and standardize/remove NAs
+
+ Take only the sample ids and feature ids from this data, require
+ at least some minimum samples, and standardize data using
+ scikit-learn. Will also fill na values with the mean of the feature
+ (column)
+
+ Parameters
+ ----------
+ data : pandas.DataFrame
+ The data you want to standardize
+ sample_ids : list-like, optional (default=None)
+ If None, all sample ids will be used, else only the sample ids
+ specified
+ feature_ids : list-like, optional (default=None)
+ If None, all features will be used, else only the features
+ specified
+ standardize : bool, optional (default=True)
+ If True, "whiten" (make all variables uncorrelated) and
+ mean-center via sklearn.preprocessing.StandardScaler
+ return_means : bool, optional (default=False)
+ If True, return a tuple of (subset, means), otherwise just return
+ the subset
+ rename : bool, optional (default=False)
+ Whether or not to rename the feature ids using ``feature_renamer``
+
+ Returns
+ -------
+ subset : pandas.DataFrame
+ Subset of the dataframe with the requested samples and features,
+ and standardized as described
+ means : pandas.DataFrame
+ (Only if return_means=True) Mean values of the features (columns).
+ """
+ # fill na with mean for each event
+ subset = self._subset(data, sample_ids, feature_ids)
+ subset = subset.dropna(how='all', axis=1).dropna(how='all', axis=0)
+ means = subset.mean()
+ subset = subset.fillna(means).fillna(0)
+
+ if rename:
+ means = means.rename_axis(self.feature_renamer)
+ subset = subset.rename_axis(self.feature_renamer, 1)
+
+ # whiten, mean-center
+ if standardize:
+ data = StandardScaler().fit_transform(subset)
+ else:
+ data = subset
+
+ # "data" is a matrix so need to transform it back into a convenient
+ # dataframe
+ subset = pd.DataFrame(data, index=subset.index,
+ columns=subset.columns)
+ if return_means:
+ return subset, means
+ else:
+ return subset
+
+ def _standardize(self, data):
+ """Rescale data so mean is 0 and variance is 1"""
+ data = StandardScaler().fit_transform(data)
+ return pd.DataFrame(data, index=data.index, columns=data.columns)
+
+ def detect_outliers(self,
+ sample_ids=None, feature_ids=None,
+ featurewise=False,
+ reducer=None,
+ standardize=True,
+ reducer_kwargs=None,
+ bins=None,
+ outlier_detection_method=None,
+ outlier_detection_method_kwargs=None):
+
+ default_reducer_args = {"n_components": 2}
+
+ if reducer_kwargs is None:
+ reducer_kwargs = default_reducer_args
+ else:
+ default_reducer_args.update(reducer_kwargs)
+ reducer_kwargs = default_reducer_args
+
+ reducer = self.reduce(sample_ids, feature_ids,
+ featurewise, reducer,
+ standardize, reducer_kwargs,
+ bins)
+
+ outlier_detector = OutlierDetection(reducer.reduced_space,
+ method=outlier_detection_method,
+ **outlier_detection_method_kwargs)
+
+ return reducer, outlier_detector
+
+ def plot_outliers(self, reducer, outlier_detector, **pca_args):
+ show_point_labels = pca_args['show_point_labels']
+ del pca_args['show_point_labels']
+ dv = DecompositionViz(reducer.reduced_space,
+ reducer.components_,
+ reducer.explained_variance_ratio_,
+ groupby=outlier_detector.outliers)
+
+ dv.plot(show_point_labels=show_point_labels)
+
+ def reduce(self, sample_ids=None, feature_ids=None,
+ featurewise=False,
+ reducer=DataFramePCA,
+ standardize=True,
+ reducer_kwargs=None, bins=None,
+ most_variant_features=False, std_multiplier=2,
+ cosine_transform=False):
+ """Make and memoize a reduced dimensionality representation of data
+
+ Parameters
+ ----------
+ data : pandas.DataFrame
+ samples x features data to reduce
+ sample_ids : None or list of strings
+ If None, all sample ids will be used, else only the sample ids
+ specified
+ feature_ids : None or list of strings
+ If None, all features will be used, else only the features
+ specified
+ featurewise : bool
+ Whether or not to use the features as the "samples", e.g. if you
+ want to reduce the features in to "sample-space" instead of
+ reducing the samples into "feature-space"
+ standardize : bool
+ Whether or not to "whiten" (make all variables uncorrelated) and
+ mean-center via sklearn.preprocessing.StandardScaler
+ title : str
+ Title of the plot
+ reducer_kwargs : dict
+ Any additional arguments to send to the reducer
+
+ Returns
+ -------
+ reducer_object : flotilla.compute.reduce.ReducerViz
+ A ready-to-plot object containing the reduced space
+ """
+ reducer_kwargs = {} if reducer_kwargs is None else reducer_kwargs
+ subset, means = self._subset_and_standardize(self.data, sample_ids,
+ feature_ids,
+ standardize=standardize,
+ return_means=True)
+
+ if most_variant_features:
+ var = subset.var()
+ ind = var >= (var.mean() + std_multiplier * var.std())
+ subset = subset.ix[:, ind]
+ means = means[ind]
+
+ if bins is not None:
+ subset = self.binify(subset, bins)
+
+ if featurewise:
+ subset = subset.T
+
+ # Compute dimensionality reduction
+ reducer_object = reducer(subset, **reducer_kwargs)
+ reducer_object.means = means
+ return reducer_object
+
+ def classify(self, trait, sample_ids, feature_ids,
+ standardize=True,
+ data_name='expression',
+ predictor_name='ExtraTreesClassifier',
+ predictor_obj=None,
+ predictor_scoring_fun=None,
+ score_cutoff_fun=None,
+ n_features_dependent_kwargs=None,
+ constant_kwargs=None,
+ plotting_kwargs=None,
+ color=None, groupby=None, label_to_color=None,
+ label_to_marker=None, order=None, bins=None):
+ """Make and memoize a predictor on a categorical trait (associated
+ with samples) subset of genes
+
+ Parameters
+ ----------
+ trait : pandas.Series
+ samples x categorical feature
+ sample_ids : None or list of strings
+ If None, all sample ids will be used, else only the sample ids
+ specified
+ feature_ids : None or list of strings
+ If None, all features will be used, else only the features
+ specified
+ standardize : bool
+ Whether or not to "whiten" (make all variables uncorrelated) and
+ mean-center and make unit-variance all the data via sklearn
+ .preprocessing.StandardScaler
+ predictor : flotilla.visualize.predict classifier
+ Must inherit from flotilla.visualize.PredictorBaseViz. Default is
+ flotilla.visualize.predict.ClassifierViz
+ predictor_kwargs : dict or None
+ Additional 'keyword arguments' to supply to the predictor class
+ predictor_scoring_fun : function
+ Function to get the feature scores for a scikit-learn classifier.
+ This can be different for different classifiers, e.g. for a
+ classifier named "x" it could be x.scores_, for other it's
+ x.feature_importances_. Default: lambda x: x.feature_importances_
+ score_cutoff_fun : function
+ Function to cut off insignificant scores
+ Default: lambda scores: np.mean(x) + 2 * np.std(x)
+
+ Returns
+ -------
+ predictor : flotilla.compute.predict.PredictorBaseViz
+ A ready-to-plot object containing the predictions
+ """
+ subset = self._subset_and_standardize(self.data, sample_ids,
+ feature_ids, standardize)
+ # subset.rename_axis(self.feature_renamer, 1, inplace=True)
+ plotting_kwargs = {} if plotting_kwargs is None else plotting_kwargs
+
+ classifier = ClassifierViz(
+ data_name, trait.name, predictor_name=predictor_name,
+ X_data=subset, trait=trait, predictor_obj=predictor_obj,
+ predictor_scoring_fun=predictor_scoring_fun,
+ score_cutoff_fun=score_cutoff_fun,
+ n_features_dependent_kwargs=n_features_dependent_kwargs,
+ constant_kwargs=constant_kwargs,
+ predictor_dataset_manager=self.predictor_dataset_manager,
+ data_type=self.data_type, color=color,
+ groupby=groupby, label_to_color=label_to_color,
+ label_to_marker=label_to_marker, order=order,
+ feature_renamer=self.feature_renamer,
+ singles=self.singles, pooled=self.pooled, outliers=self.outliers,
+ **plotting_kwargs)
+ return classifier
+
+ def _calculate_linkage(self, data, sample_ids, feature_ids,
+ metric='euclidean',
+ linkage_method='median', standardize=True,
+ require_min_samples=True):
+
+ subset = self._subset_and_standardize(data, sample_ids,
+ feature_ids,
+ standardize=standardize)
+ row_linkage, col_linkage = self.clusterer(subset, metric,
+ linkage_method)
+ return subset, row_linkage, col_linkage
+
+ def binify(self, data, bins=None):
+ return binify(data, bins).dropna(how='all', axis=1)
+
+ def _violinplot(self, feature_id, sample_ids=None,
+ phenotype_groupby=None,
+ phenotype_order=None, ax=None, color=None,
+ label_pooled=False):
+ """For compatiblity across data types, can specify _violinplot
+ """
+ sample_ids = self.data.index if sample_ids is None else sample_ids
+ singles, pooled = self._subset_singles_and_pooled(sample_ids,
+ feature_ids=[
+ feature_id])
+ outliers = None
+ try:
+ outliers_in_data = self.outliers.index.intersection(sample_ids)
+ if len(outliers_in_data) > 0:
+ outliers = self._subset(self.outliers,
+ feature_ids=[feature_id])
+ except AttributeError:
+ pass
+
+ renamed = self.feature_renamer(feature_id)
+ # if isinstance(self.data.columns, pd.MultiIndex):
+ # feature_id, renamed = feature_id
+ # else:
+ # renamed = self.feature_renamer(feature_id)
+ title = '{}\n{}'.format(renamed, ':'.join(
+ feature_id.split('@')[0].split(':')[:2]))
+
+ violinplot(singles, groupby=phenotype_groupby, color_ordered=color,
+ pooled_data=pooled, order=phenotype_order,
+ title=title, data_type=self.data_type, ax=ax,
+ label_pooled=label_pooled, outliers=outliers)
+
+ @staticmethod
+ def _thresh_int(df, n):
+ return n
+
+ @staticmethod
+ def _thresh_float(df, f):
+ return f * df.shape[0]
+
+ @cached_property()
+ def nmf(self):
+ data = self._subset(self.data)
+ return DataFrameNMF(self.binify(data).T, n_components=2)
+
+ @memoize
+ def binned_nmf_reduced(self, sample_ids=None, feature_ids=None,
+ data=None):
+ if data is None:
+ data = self._subset(self.data, sample_ids, feature_ids,
+ require_min_samples=False)
+ binned = self.binify(data)
+ reduced = self.nmf.transform(binned.T)
+ return reduced
+
+ def plot_feature(self, feature_id, sample_ids=None,
+ phenotype_groupby=None,
+ phenotype_order=None, color=None,
+ phenotype_to_color=None,
+ phenotype_to_marker=None, nmf_xlabel=None,
+ nmf_ylabel=None,
+ nmf_space=False, fig=None, axesgrid=None):
+ """
+ Plot the violinplot of a feature. Have the option to show NMF movement
+ """
+ feature_ids = self.maybe_renamed_to_feature_id(feature_id)
+ if phenotype_groupby is None:
+ phenotype_groupby = pd.Series('all', index=self.data.index)
+
+ if not isinstance(feature_ids, pd.Index):
+ feature_ids = [feature_id]
+
+ if fig is None and axesgrid is None:
+ nrows = len(feature_ids)
+ ncols = 2 if nmf_space else 1
+ figsize = 4 * ncols, 4 * nrows
+
+ fig, axesgrid = plt.subplots(nrows=nrows, ncols=ncols,
+ figsize=figsize)
+ if nrows == 1:
+ axesgrid = [axesgrid]
+
+ for feature_id, axes in zip(feature_ids, axesgrid):
+ if not nmf_space:
+ axes = [axes]
+ # if self.data_type == 'expression':
+ # axes = [axes]
+
+ self._violinplot(feature_id, sample_ids=sample_ids,
+ phenotype_groupby=phenotype_groupby,
+ phenotype_order=phenotype_order, ax=axes[0],
+ color=color)
+
+ if nmf_space:
+ try:
+ self.plot_nmf_space_transitions(
+ feature_id, groupby=phenotype_groupby,
+ phenotype_to_color=phenotype_to_color,
+ phenotype_to_marker=phenotype_to_marker,
+ order=phenotype_order, ax=axes[1],
+ xlabel=nmf_xlabel, ylabel=nmf_ylabel)
+ except KeyError:
+ continue
+ sns.despine()
+ fig.tight_layout()
+
+ def nmf_space_positions(self, groupby, n=0.5):
+ """Calculate NMF-space position of splicing events in phenotype groups
+
+ Parameters
+ ----------
+ groupby : mappable
+ A sample id to phenotype mapping
+ n : int or float
+ If int, then this is the absolute number of cells that are minimum
+ required to calculate modalities. If a float, then require this
+ fraction of samples to calculate modalities, e.g. if 0.6, then at
+ least 60% of samples must have an event detected for modality
+ detection
+
+ Returns
+ -------
+ df : pandas.DataFrame
+ A (n_events, n_groups) dataframe of NMF positions
+ """
+ grouped = self.singles.groupby(groupby)
+ if isinstance(n, int):
+ thresh = self._thresh_int
+ elif isinstance(n, float):
+ thresh = self._thresh_float
+
+ at_least_n_per_group_per_event = pd.concat(
+ [df.dropna(thresh=thresh(df, n), axis=1) for name, df in grouped])
+ # at_least_n_per_group_per_event = grouped.transform(
+ # lambda x: x if x.count() >= n else pd.Series(np.nan,
+ # index=x.index))
+ df = at_least_n_per_group_per_event.groupby(groupby).apply(
+ lambda x: self.binned_nmf_reduced(data=x))
+ df = df.swaplevel(0, 1)
+ df = df.sort_index()
+ return df
+
+ def plot_nmf_space_transitions(self, feature_id, groupby,
+ phenotype_to_color,
+ phenotype_to_marker, order, ax=None,
+ xlabel=None, ylabel=None):
+ nmf_space_positions = self.nmf_space_positions(groupby)
+
+ nmf_space_transitions(nmf_space_positions, feature_id,
+ phenotype_to_color,
+ phenotype_to_marker, order,
+ ax, xlabel, ylabel)
+
+ @staticmethod
+ def transition_distances(positions, transitions):
+ """Get NMF distance of features between phenotype transitions
+
+ Parameters
+ ----------
+ positions : pandas.DataFrame
+ A ((n_features, phenotypes), 2) MultiIndex dataframe of the NMF
+ positions of splicing events for different phenotypes
+ transitions : list of 2-string tuples
+ List of (phenotype1, phenotype2) transitions
+
+ Returns
+ -------
+ transitions : pandas.DataFrame
+ A (n_features, n_transitions) DataFrame of the NMF distances
+ of features between different phenotypes
+ """
+ positions_phenotype = positions.copy()
+ positions_phenotype.index = positions_phenotype.index.droplevel(0)
+ distances = pd.Series(index=transitions)
+ for transition in transitions:
+ try:
+ phenotype1, phenotype2 = transition
+ norm = np.linalg.norm(positions_phenotype.ix[phenotype2] -
+ positions_phenotype.ix[phenotype1])
+ # print phenotype1, phenotype2, norm
+ distances[transition] = norm
+ except KeyError:
+ pass
+ return distances
+
+ def nmf_space_transitions(self, groupby, phenotype_transitions, n=0.5):
+ """Get distance in NMF space of different splicing events
+
+ Parameters
+ ----------
+ groupby : mappable
+ A sample id to phenotype mapping
+ phenotype_transitions : list of str pairs
+ Which phenotype follows from one to the next, for calculating
+ distances between
+ n : int or float
+ If int, then this is the absolute number of cells that are minimum
+ required to calculate modalities. If a float, then require this
+ fraction of samples to calculate modalities, e.g. if 0.6, then at
+ least 60% of samples must have an event detected for modality
+ detection
+
+ Returns
+ -------
+ nmf_space_transitions : pandas.DataFrame
+ A (n_events, n_phenotype_transitions) sized DataFrame of the
+ distances of these events in NMF space
+ """
+ nmf_space_positions = self.nmf_space_positions(groupby, n=n)
+
+ # Take only splicing events that have at least two phenotypes
+ nmf_space_positions = nmf_space_positions.groupby(
+ level=0, axis=0).filter(lambda x: len(x) > 1)
+
+ nmf_space_transitions = nmf_space_positions.groupby(
+ level=0, axis=0, as_index=True, group_keys=False).apply(
+ self.transition_distances, transitions=phenotype_transitions)
+
+ # Remove any events that didn't have phenotype pairs from
+ # the transitions
+ nmf_space_transitions = nmf_space_transitions.dropna(how='all',
+ axis=0)
+ return nmf_space_transitions
+
+ def big_nmf_space_transitions(self, groupby, phenotype_transitions, n=0.5):
+ """Get features whose change in NMF space between phenotypes is large
+
+ Parameters
+ ----------
+ groupby : mappable
+ A sample id to phenotype group mapping
+ phenotype_transitions : list of length-2 tuples of str
+ List of ('phenotype1', 'phenotype2') transitions whose change in
+ distribution you are interested in
+ n : int
+ Minimum number of samples per phenotype, per event
+
+ Returns
+ -------
+ big_transitions : pandas.DataFrame
+ A (n_events, n_transitions) dataframe of the NMF distances between
+ splicing events
+ """
+ nmf_space_transitions = self.nmf_space_transitions(
+ groupby, phenotype_transitions, n=n)
+
+ # get the mean and standard dev of the whole array
+ n = nmf_space_transitions.count().sum()
+ mean = nmf_space_transitions.sum().sum() / n
+ std = np.sqrt(np.square(nmf_space_transitions - mean).sum().sum() / n)
+
+ big_transitions = nmf_space_transitions[
+ nmf_space_transitions > (mean + std)].dropna(how='all')
+ return big_transitions
+
+ def plot_big_nmf_space_transitions(self, phenotype_groupby,
+ phenotype_transitions,
+ phenotype_order, color,
+ phenotype_to_color,
+ phenotype_to_marker, n=0.5):
+ """Violinplots and NMF transitions of features different in phenotypes
+
+ Plot violinplots and NMF-space transitions of features that have large
+ NMF-space transitions between different phenotypes
+
+ Parameters
+ ----------
+ n : int
+ Minimum number of samples per phenotype, per event
+
+
+ Returns
+ -------
+
+
+ Raises
+ ------
+ """
+ big_transitions = self.big_nmf_space_transitions(phenotype_groupby,
+ phenotype_transitions,
+ n=n)
+ nrows = big_transitions.shape[0]
+ ncols = 2
+ figsize = 4 * ncols, 4 * nrows
+
+ fig, axesgrid = plt.subplots(nrows=nrows, ncols=ncols,
+ figsize=figsize)
+ if nrows == 1:
+ axesgrid = [axesgrid]
+ for feature_id in big_transitions.index:
+ self.plot_feature(feature_id, phenotype_groupby=phenotype_groupby,
+ phenotype_order=phenotype_order, color=color,
+ phenotype_to_color=phenotype_to_color,
+ phenotype_to_marker=phenotype_to_marker,
+ nmf_space=True, fig=fig, axesgrid=axesgrid)
+
+ def plot_two_samples(self, sample1, sample2, fillna=None,
+ **kwargs):
+ """
+ Parameters
+ ----------
+ sample1 : str
+ Name of the sample to plot on the x-axis
+ sample2 : str
+ Name of the sample to plot on the y-axis
+ fillna : float
+ Value to replace NAs with
+ Any other keyword arguments valid for seaborn.jointplot
+
+ Returns
+ -------
+ jointgrid : seaborn.axisgrid.JointGrid
+ Returns a JointGrid instance
+
+ See Also
+ -------
+ seaborn.jointplot
+
+ """
+ x = self.data.ix[sample1]
+ y = self.data.ix[sample2]
+
+ if fillna is not None:
+ x = x.fillna(fillna)
+ y = y.fillna(fillna)
+ return simple_twoway_scatter(x, y, **kwargs)
+
+ def plot_two_features(self, feature1, feature2, groupby=None,
+ label_to_color=None, fillna=None, **kwargs):
+ """Plot the values of two features
+ """
+ feature1s = self.maybe_renamed_to_feature_id(feature1)
+ feature2s = self.maybe_renamed_to_feature_id(feature2)
+ if isinstance(feature1s, str):
+ feature1s = [feature1s]
+ if isinstance(feature2s, str):
+ feature2s = [feature2s]
+
+ for f1 in feature1s:
+ for f2 in feature2s:
+ x = self.data.ix[:, f1].copy()
+ y = self.data.ix[:, f2].copy()
+
+ if fillna is not None:
+ x = x.fillna(fillna)
+ y = y.fillna(fillna)
+
+ x.name = feature1
+ y.name = feature2
+ x, y = x.align(y, 'inner')
+
+ joint_kws = {}
+ if groupby is not None:
+ if label_to_color is not None:
+ joint_kws['color'] = [label_to_color[groupby[i]]
+ for i in x.index]
+ simple_twoway_scatter(x, y, joint_kws=joint_kws, **kwargs)
+
+ @staticmethod
+ def _figsizer(shape, multiplier=0.25):
+ """Scale a heatmap figure based on the dataframe shape"""
+ return tuple(reversed(map(lambda x: min(x * multiplier, 40),
+ shape)))
+
+ def plot_clustermap(self, sample_ids=None, feature_ids=None, data=None,
+ feature_colors=None, sample_id_to_color=None,
+ metric='euclidean', method='average',
+ norm_features=True,
+ scale_fig_by_data=True, **kwargs):
+ # data = self._subset_ids_or_data(sample_ids, feature_ids, data)
+ if data is None:
+ data = self._subset(self.data, sample_ids, feature_ids,
+ require_min_samples=False)
+ # Get a mask of what values are NA, then replace them because
+ # clustering doesn't work if there's NAs
+ data = data.dropna(how='all', axis=1).dropna(how='all', axis=0)
+ mask = data.isnull()
+
+ if norm_features:
+ # Try to get close to 0 center, unit variance for each feature
+ x = data
+ x[x <= 0] = np.min(x[x > 0]).min()
+ z = whiten(x.apply(lambda x: np.log2(x / x.mean(axis=0)), 0))
+ data = z
+
+ data = data.fillna(data.mean())
+ if sample_id_to_color is not None:
+ sample_colors = [sample_id_to_color[i] for i in data.index]
+
+ col_colors = feature_colors
+ row_colors = sample_colors
+ data.columns = data.columns.map(self.feature_renamer)
+
+ if scale_fig_by_data:
+ figsize = self._figsizer(data.shape)
+ kwargs.pop('figsize')
+ else:
+ figsize = kwargs.pop('figsize', None)
+
+ return sns.clustermap(data, linewidth=0, col_colors=col_colors,
+ row_colors=row_colors, metric=metric,
+ method=method, figsize=figsize, mask=mask,
+ **kwargs)
+
+ def plot_correlations(self, sample_ids=None, feature_ids=None, data=None,
+ featurewise=False, sample_id_to_color=None,
+ metric='euclidean', method='average',
+ scale_fig_by_data=True, **kwargs):
+ if data is None:
+ data = self._subset(self.data, sample_ids, feature_ids,
+ require_min_samples=False)
+
+ if sample_id_to_color is not None and not featurewise:
+ colors = [sample_id_to_color[x] for x in data.index]
+ else:
+ colors = None
+
+ if not featurewise:
+ data = data.T
+ corr = data.corr()
+ corr = corr.dropna(how='all', axis=0).dropna(how='all', axis=1)
+
+ # Get a mask of what values are NA, then replace them because
+ # clustering doesn't work if there's NAs
+ mask = corr.isnull()
+ corr = corr.fillna(data.mean())
+
+ if featurewise:
+ corr.index = corr.index.map(self.feature_renamer)
+ corr.columns = corr.columns.map(self.feature_renamer)
+
+ if scale_fig_by_data:
+ figsize = self._figsizer(corr.shape)
+ kwargs.pop('figsize')
+ else:
+ figsize = kwargs.pop('figsize', None)
+
+ return sns.clustermap(corr, linewidth=0, col_colors=colors,
+ row_colors=colors, figsize=figsize,
+ method=method, metric=metric, mask=mask,
+ **kwargs)
+
+
+def subsets_from_metadata(metadata, minimum, subset_type, ignore=None):
+ """Get subsets from metadata, including boolean and categorical columns
+
+ Parameters
+ ----------
+ metadata : pandas.DataFrame
+ The dataframe whose columns to use to create subsets of the rows
+ minimum : int
+ Minimum number of rows required for a column or group in the column
+ to be included
+ subset_type : str
+ The name of the kind of subset. e.g. "samples" or "features"
+ ignore : list-like
+ List of columns to ignore
+
+ Returns
+ -------
+ subsets : dict
+ A name: row_ids mapping of which samples correspond to which group
+ """
+ subsets = {}
+ ignore = () if ignore is None else ignore
+ if metadata is not None:
+ for col in metadata:
+ if col in ignore:
+ continue
+ if metadata[col].dtype == bool:
+ sample_subset = metadata.index[metadata[col]]
+ subsets[col] = sample_subset
+ else:
+ grouped = metadata.groupby(col)
+ sizes = grouped.size()
+ filtered_sizes = sizes[sizes >= minimum]
+ for group in filtered_sizes.keys():
+ if isinstance(group, bool):
+ continue
+ name = '{}: {}'.format(col, group)
+ subsets[name] = grouped.groups[group]
+ for sample_subset in subsets.keys():
+ name = 'not ({})'.format(sample_subset)
+ if 'False' in name or 'True' in name:
+ continue
+ if name not in subsets:
+ in_features = metadata.index.isin(subsets[
+ sample_subset])
+ subsets[name] = metadata.index[~in_features]
+ subsets['all {}'.format(subset_type)] = metadata.index
+ return subsets
diff --git a/flotilla/data_model/expression.py b/flotilla/data_model/expression.py
new file mode 100644
index 00000000..ae44ff05
--- /dev/null
+++ b/flotilla/data_model/expression.py
@@ -0,0 +1,177 @@
+"""
+Data types related to gene expression, e.g. from RNA-Seq or microarrays.
+Included SpikeIn data.
+"""
+import sys
+
+import numpy as np
+
+from .base import BaseData
+from ..util import memoize, timestamp
+
+EXPRESSION_THRESH = -np.inf
+
+
+class ExpressionData(BaseData):
+ def __init__(self, data,
+ feature_data=None, thresh=EXPRESSION_THRESH,
+ feature_rename_col=None, feature_ignore_subset_cols=None,
+ outliers=None, log_base=None,
+ pooled=None, plus_one=False, minimum_samples=0,
+ technical_outliers=None, predictor_config_manager=None):
+ """Object for holding and operating on expression data
+
+
+ """
+ sys.stdout.write("{}\tInitializing expression\n".format(timestamp()))
+
+ super(ExpressionData, self).__init__(
+ data, feature_data=feature_data,
+ feature_rename_col=feature_rename_col,
+ feature_ignore_subset_cols=feature_ignore_subset_cols,
+ thresh=thresh,
+ outliers=outliers, pooled=pooled, minimum_samples=minimum_samples,
+ predictor_config_manager=predictor_config_manager,
+ technical_outliers=technical_outliers, data_type='expression')
+ self.thresh_original = thresh
+ self.plus_one = plus_one
+
+ if plus_one:
+ self.data += 1
+ self.thresh = self.thresh_original + 1
+ # self.original_data = self.data
+ # import pdb; pdb.set_trace()
+ # self.data = self._threshold(data, thresh)
+ self.log_base = log_base
+
+ if self.log_base is not None:
+ self.data = np.divide(np.log(self.data), np.log(self.log_base))
+
+ self.feature_data = feature_data
+
+ sys.stdout.write("{}\tDone initializing expression\n".format(
+ timestamp()))
+
+ def _calculate_linkage(self, sample_ids, feature_ids, metric='euclidean',
+ linkage_method='average', standardize=True):
+ return super(ExpressionData, self)._calculate_linkage(
+ self.data, sample_ids=sample_ids, feature_ids=feature_ids,
+ standardize=standardize, metric=metric,
+ linkage_method=linkage_method)
+
+ @memoize
+ def binify(self, data):
+ data = self._subset(data, require_min_samples=False)
+ data = (data - data.min()) / (data.max() - data.min())
+ # vmax = data.abs().max().max()
+ # vmin = -vmax
+ # bins = np.linspace(vmin, vmax, 10)
+ bins = np.arange(0, 1.1, .1)
+ # print 'bins:', bins
+ return super(ExpressionData, self).binify(data, bins)
+
+
+class SpikeInData(ExpressionData):
+ """Class for Spikein data and associated functions
+ Attributes
+ ----------
+
+
+ Methods
+ -------
+
+ """
+
+ def __init__(self, data, feature_data=None,
+ predictor_config_manager=None,
+ technical_outliers=None):
+ """Constructor for
+
+ Parameters
+ ----------
+ data, experiment_design_data
+
+ Returns
+ -------
+
+
+ Raises
+ ------
+
+ """
+ super(SpikeInData, self).__init__(
+ data, feature_data,
+ technical_outliers=technical_outliers,
+ predictor_config_manager=predictor_config_manager)
+
+# def spikeins_violinplot(self):
+# import matplotlib.pyplot as plt
+# import seaborn as sns
+# import numpy as np
+#
+# fig, axes = plt.subplots(nrows=5, figsize=(16, 20), sharex=True,
+# sharey=True)
+# ercc_concentrations = \
+# ercc_controls_analysis.mix1_molecules_per_ul.copy()
+# ercc_concentrations.sort()
+#
+# for ax, (celltype, celltype_df) in \
+# zip(axes.flat, tpm.ix[spikeins].groupby(
+# sample_id_to_celltype_, axis=1)):
+# print celltype
+# # fig, ax = plt.subplots(figsize=(16, 4))
+# x_so_far = 0
+# # ax.set_yscale('log')
+# xticklabels = []
+# for spikein_type, spikein_df in celltype_df.groupby(
+# spikein_to_type):
+# # print spikein_df.shape
+# df = spikein_df.T + np.random.uniform(0, 0.01,
+# size=spikein_df.T.shape)
+# df = np.log2(df)
+# if spikein_type == 'ERCC':
+# df = df[ercc_concentrations.index]
+# xticklabels.extend(df.columns.tolist())
+# color = 'husl' if spikein_type == 'ERCC' else 'Greys_d'
+# sns.violinplot(df, ax=ax,
+# positions=np.arange(df.shape[1])+x_so_far,
+# linewidth=0, inner='none', color=color)
+#
+# x_so_far += df.shape[1]
+#
+# ax.set_title(celltype)
+# ax.set_xticks(np.arange(x_so_far))
+# ax.set_xticklabels(xticklabels, rotation=90, fontsize=8)
+# ax.set_ylabel('$\\log_2$ TPM')
+#
+# xmin, xmax = -0.5, x_so_far - 0.5
+#
+# ax.hlines(0, xmin, xmax)
+# ax.set_xlim(xmin, xmax)
+# sns.despine()
+#
+# def samples_violinplot():
+# fig, axes = plt.subplots(nrows=3, figsize=(16, 6))
+#
+# for ax, (spikein_type, df) in zip(axes,
+# tpm.groupby(spikein_to_type,
+# axis=0)):
+# print spikein_type, df.shape
+# if df.shape[0] > 1:
+# sns.violinplot(np.log2(df + 1), ax=ax, linewidth=0.1)
+# ax.set_xticks([])
+# ax.set_xlabel('')
+#
+# else:
+# x = np.arange(df.shape[1])
+# ax.bar(np.arange(df.shape[1]),
+# np.log2(df.ix[spikein_type]),
+# color=green)
+# ax.set_xticks(x + 0.4)
+# ax.set_xticklabels(df.columns, rotation=60)
+# sns.despine()
+#
+# ax.set_title(spikein_type)
+# ax.set_xlim(0, tpm.shape[1])
+# ax.set_ylabel('$\\log_2$ TPM')
+# sns.despine()
diff --git a/flotilla/data_model/gene_ontology.py b/flotilla/data_model/gene_ontology.py
new file mode 100644
index 00000000..b8ca7694
--- /dev/null
+++ b/flotilla/data_model/gene_ontology.py
@@ -0,0 +1,170 @@
+from collections import defaultdict, Iterable
+import sys
+import warnings
+
+import pandas as pd
+from scipy.stats import hypergeom
+
+from ..util import timestamp
+
+
+class GeneOntologyData(object):
+
+ domains = frozenset(['biological_process', 'molecular_function',
+ 'cellular_component'])
+
+ def __init__(self, data):
+ """Object to calculate enrichment of Gene Ontology terms
+
+ Acceptable Gene Ontology tables can be downloaded from ENSEMBL's
+ BioMart tool: http://www.ensembl.org/biomart
+
+ 1. Choose "Ensembl Genes ##" (## = version number, for me it's 78)
+ 2. Click "Attributes"
+ 3. Expand "EXTERNAL"
+ 4. Check the boxes for 'GO Term Accession', 'Ensembl Gene ID',
+ 'GO Term Name', and 'GO domain'
+
+ Parameters
+ ----------
+ data : pandas.DataFrame
+ A dataframe with at least the following columns:
+ 'GO Term Accession', 'Ensembl Gene ID', 'GO Term Name', 'GO domain'
+ """
+
+ self.data = data.dropna()
+
+ # Need "data_original" to be consistent with other datatypes
+ self.data_original = self.data
+ sys.stdout.write('{}\tBuilding Gene Ontology '
+ 'database...\n'.format(timestamp()))
+ self.ontology = defaultdict(dict)
+ for go, df in data.groupby('GO Term Accession'):
+ self.ontology[go]['genes'] = set(df['Ensembl Gene ID'])
+ self.ontology[go]['name'] = df['GO Term Name'].values[0]
+ self.ontology[go]['domain'] = df['GO domain'].values[0]
+ self.ontology[go]['n_genes'] = len(self.ontology[go]['genes'])
+ sys.stdout.write('{}\t\tDone.'.format(timestamp()))
+
+ self.all_genes = self.data['Ensembl Gene ID'].unique()
+
+ def enrichment(self, features_of_interest, background=None,
+ p_value_cutoff=1000000, cross_reference=None,
+ min_feature_size=3, min_background_size=5,
+ domain=None):
+ """Bonferroni-corrected hypergeometric p-values of GO enrichment
+
+ Calculates hypergeometric enrichment of the features of interest, in
+ each GO category.
+
+ Parameters
+ ----------
+ features_of_interest : list-like
+ List of features. Must match the identifiers in the ontology
+ database exactly, i.e. if your ontology database is ENSEMBL ids,
+ then you can only provide those and not common names like "RBFOX2"
+ background : list-like, optional
+ Background genes to use. It is best to use a relevant background
+ such as all expressed genes. If None, defaults to all genes.
+ p_value_cutoff : float, optional
+ Maximum accepted Bonferroni-corrected p-value
+ cross_reference : dict-like, optional
+ A mapping of gene ids to gene symbols, e.g. a pandas Series of
+ ENSEMBL genes e.g. ENSG00000139675 to gene symbols e.g HNRNPA1L2
+ min_feature_size : int, optional
+ Minimum number of features of interest overlapping in a GO Term,
+ to calculate enrichment
+ min_background_size : int, optional
+ Minimum number of features in the background overlapping a GO Term
+ domain : str or list, optional
+ Only calculate GO enrichment for a particular GO category or
+ subset of categories. Valid domains:
+ 'biological_process', 'molecular_function', 'cellular_component'
+
+ Returns
+ -------
+ enrichment_df : pandas.DataFrame
+ A (n_go_categories, columns) DataFrame of the enrichment scores
+
+ Raises
+ ------
+ ValueError
+ If features of interest and background do not overlap, or invalid
+ GO domains are given
+ """
+ cross_reference = {} if cross_reference is None else cross_reference
+ background = self.all_genes if background is None else background
+ if len(set(background) & set(features_of_interest)) == 0:
+ raise ValueError('Features of interest and background do not '
+ 'overlap! Not calculating GO enrichment')
+ if len(set(features_of_interest) & set(self.all_genes)) == 0:
+ raise ValueError('Features of interest do not overlap with GO term'
+ 'gene ids. Not calculating GO enrichment.')
+ domains = self.domains
+ valid_domains = ",".join("'{}'".format(x) for x in self.domains)
+
+ if isinstance(domain, str):
+ if domain not in self.domains:
+ raise ValueError(
+ "'{}' is not a valid GO domain. "
+ "Only {} are acceptable".format(domain, valid_domains))
+ domains = frozenset([domain])
+ elif isinstance(domain, Iterable):
+ if len(set(domain) & self.domains) == 0:
+ raise ValueError(
+ "'{}' are not a valid GO domains. "
+ "Only {} are acceptable".format(
+ ",".join("'{}'".format(x) for x in domain),
+ valid_domains))
+ domains = frozenset(domain)
+
+ n_all_genes = len(background)
+ n_features_of_interest = len(features_of_interest)
+ enrichment = defaultdict(dict)
+
+ for go_term, go_genes in self.ontology.items():
+ if go_genes['domain'] not in domains:
+ continue
+
+ features_in_go = go_genes['genes'].intersection(
+ features_of_interest)
+ background_in_go = go_genes['genes'].intersection(background)
+ too_few_features = len(features_in_go) < min_feature_size
+ too_few_background = len(background_in_go) < min_background_size
+ if too_few_features or too_few_background:
+ continue
+
+ # Survival function is more accurate on small p-values
+ p_value = hypergeom.sf(len(features_in_go), n_all_genes,
+ len(background_in_go),
+ n_features_of_interest)
+ p_value = 0 if p_value < 0 else p_value
+ symbols = [cross_reference[f] if f in cross_reference else f for f
+ in features_in_go]
+ enrichment['p_value'][go_term] = p_value
+ enrichment['n_features_of_interest_in_go_term'][go_term] = len(
+ features_in_go)
+ enrichment['n_background_in_go_term'][go_term] = len(
+ background_in_go)
+ enrichment['n_features_total_in_go_term'][go_term] = len(
+ go_genes['genes'])
+ enrichment['features_of_interest_in_go_term'][
+ go_term] = ','.join(features_in_go)
+ enrichment['features_of_interest_in_go_term_gene_symbols'][
+ go_term] = ','.join(symbols)
+ enrichment['go_domain'][go_term] = go_genes['domain']
+ enrichment['go_name'][go_term] = go_genes['name']
+ enrichment_df = pd.DataFrame(enrichment)
+
+ if enrichment_df.empty:
+ warnings.warn('No GO categories enriched in provided features')
+ return
+
+ # Bonferonni correction
+ enrichment_df['bonferonni_corrected_p_value'] = \
+ enrichment_df.p_value * enrichment_df.shape[0]
+ ind = enrichment_df['bonferonni_corrected_p_value'] < p_value_cutoff
+ enrichment_df = enrichment_df.ix[ind]
+ enrichment_df = enrichment_df.sort(columns=['p_value'])
+
+ return enrichment_df
diff --git a/flotilla/data_model/metadata.py b/flotilla/data_model/metadata.py
new file mode 100644
index 00000000..35ef1f04
--- /dev/null
+++ b/flotilla/data_model/metadata.py
@@ -0,0 +1,192 @@
+from collections import defaultdict
+import sys
+import warnings
+
+import matplotlib as mpl
+import pandas as pd
+import seaborn as sns
+
+from .base import BaseData, subsets_from_metadata
+from ..visualize.color import str_to_color
+
+
+POOLED_COL = 'pooled'
+PHENOTYPE_COL = 'phenotype'
+MINIMUM_SAMPLE_SUBSET = 10
+OUTLIER_COL = 'outlier'
+
+
+class MetaData(BaseData):
+ def __init__(self, data, phenotype_order=None, phenotype_to_color=None,
+ phenotype_to_marker=None,
+ phenotype_col=PHENOTYPE_COL,
+ pooled_col=POOLED_COL,
+ outlier_col=OUTLIER_COL,
+ predictor_config_manager=None,
+ minimum_sample_subset=MINIMUM_SAMPLE_SUBSET):
+ super(MetaData, self).__init__(
+ data, outliers=None,
+ predictor_config_manager=predictor_config_manager)
+ self.data_original = self.data
+
+ self.phenotype_col = phenotype_col if phenotype_col is not None else \
+ self._default_phenotype_col
+ self.phenotype_order = phenotype_order
+ self.phenotype_to_color = phenotype_to_color
+ self.pooled_col = pooled_col
+ self.minimum_sample_subset = minimum_sample_subset
+ self.outlier_col = outlier_col
+
+ phenotypes_not_in_order = set(self.unique_phenotypes).difference(
+ set(self.phenotype_order))
+
+ if len(phenotypes_not_in_order) > 0:
+ self.phenotype_order.extend(phenotypes_not_in_order)
+
+ if self.phenotype_col not in self.data:
+ sys.stderr.write('The required column name "{}" does not exist in '
+ 'the sample metadata. All samples will be '
+ 'treated as the same phenotype. You may also '
+ 'specify "phenotype_col" in the metadata section '
+ 'of the '
+ 'datapackage.\n'.format(self.phenotype_col))
+ self.data[self.phenotype_col] = 'phenotype'
+ self.phenotype_order = None
+ self.phenotype_to_color = None
+
+ # Convert color strings to non-default matplotlib colors
+ if self.phenotype_to_color is not None:
+ # colors = iter(self._colors)
+ for phenotype, color in self.phenotype_to_color.iteritems():
+ try:
+ color = str_to_color[color]
+ except KeyError:
+ pass
+ self._phenotype_to_color[phenotype] = color
+
+ self.phenotype_to_marker = phenotype_to_marker
+ if self.phenotype_to_marker is not None:
+ for phenotype in self.unique_phenotypes:
+ try:
+ marker = self.phenotype_to_marker[phenotype]
+ except KeyError:
+ sys.stderr.write(
+ '{} does not have marker style, '
+ 'falling back on "o" (circle)'.format(phenotype))
+ marker = 'o'
+ if marker not in mpl.markers.MarkerStyle.filled_markers:
+ sys.stderr.write(
+ '{} is not a valid matplotlib marker style, '
+ 'falling back on "o" (circle)'.format(marker))
+ marker = 'o'
+ self.phenotype_to_marker[phenotype] = marker
+
+ @property
+ def sample_id_to_phenotype(self):
+ return self.data[self.phenotype_col]
+
+ @property
+ def unique_phenotypes(self):
+ return self.sample_id_to_phenotype.unique()
+
+ @property
+ def n_phenotypes(self):
+ return len(self.unique_phenotypes)
+
+ @property
+ def _default_phenotype_order(self):
+ return list(sorted(self.unique_phenotypes))
+
+ @property
+ def phenotype_order(self):
+ if len(set(self._phenotype_order) & set(self.unique_phenotypes)) > 0:
+ return [v for v in self._phenotype_order if
+ v in self.unique_phenotypes]
+ else:
+ return self._default_phenotype_order
+
+ @phenotype_order.setter
+ def phenotype_order(self, value):
+ if value is not None:
+ self._phenotype_order = value
+ else:
+ self._phenotype_order = self._default_phenotype_order
+
+ @property
+ def phenotype_transitions(self):
+ return zip(self.phenotype_order[:-1], self.phenotype_order[1:])
+
+ @property
+ def _colors(self):
+ return map(mpl.colors.rgb2hex,
+ sns.color_palette('husl', n_colors=self.n_phenotypes))
+
+ @property
+ def _default_phenotype_to_color(self):
+ colors = iter(self._colors)
+
+ def color_factory():
+ return colors.next()
+
+ return defaultdict(color_factory)
+
+ @property
+ def phenotype_to_color(self):
+ _default_phenotype_to_color = self._default_phenotype_to_color
+ all_phenotypes = self._phenotype_to_color.keys()
+ all_phenotypes.extend(self.phenotype_order)
+ return dict((k, self._phenotype_to_color[k])
+ if k in self._phenotype_to_color else
+ (k, _default_phenotype_to_color[k])
+ for k in all_phenotypes)
+
+ @phenotype_to_color.setter
+ def phenotype_to_color(self, value):
+ if value is not None:
+ self._phenotype_to_color = value
+ else:
+ sys.stderr.write('No phenotype to color mapping was provided, '
+ 'falling back on reasonable defaults.\n')
+ self._phenotype_to_color = self._default_phenotype_to_color
+
+ @property
+ def phenotype_to_marker(self):
+ _default_phenotype_to_marker = defaultdict(lambda: 'o')
+ all_phenotypes = self._phenotype_to_marker.keys()
+ all_phenotypes.extend(self.phenotype_order)
+ return dict((k, self._phenotype_to_marker[k])
+ if k in self._phenotype_to_marker else
+ (k, _default_phenotype_to_marker[k])
+ for k in all_phenotypes)
+
+ @phenotype_to_marker.setter
+ def phenotype_to_marker(self, value):
+ if value is not None:
+ self._phenotype_to_marker = value
+ else:
+ sys.stderr.write('No phenotype to marker (matplotlib plotting '
+ 'symbol) was provided, so each phenotype will be '
+ 'plotted as a circle in visualizations.\n')
+ self._phenotype_to_marker = dict.fromkeys(self.unique_phenotypes,
+ 'o')
+
+ @property
+ def phenotype_color_order(self):
+ return [self.phenotype_to_color[p] for p in self.phenotype_order]
+
+ @property
+ def sample_id_to_color(self):
+ return pd.Series(
+ dict((sample_id, self.phenotype_to_color[p])
+ for sample_id, p in
+ self.sample_id_to_phenotype.iteritems()))
+
+ @property
+ def sample_subsets(self):
+ return subsets_from_metadata(self.data, self.minimum_sample_subset,
+ 'samples')
+
+ @property
+ def phenotype_series(self):
+ warnings.warn('MetaData.phenotype_series will be deprecated in 0.3.0')
+ return self.data[self.phenotype_col]
diff --git a/flotilla/data_model/quality_control.py b/flotilla/data_model/quality_control.py
new file mode 100644
index 00000000..bef5b2c8
--- /dev/null
+++ b/flotilla/data_model/quality_control.py
@@ -0,0 +1,46 @@
+from .base import BaseData
+
+
+MIN_READS = 5e5
+
+
+class MappingStatsData(BaseData):
+ """Constructor for mapping statistics data from STAR
+
+ Attributes
+ ----------
+
+
+ Methods
+ -------
+
+ """
+
+ def __init__(self, data, number_mapped_col, min_reads=MIN_READS,
+ predictor_config_manager=None):
+ """Constructor for MappingStatsData
+
+ Parameters
+ ----------
+ data, sample_descriptors
+
+ Returns
+ -------
+
+
+ Raises
+ ------
+
+ """
+ super(MappingStatsData, self).__init__(
+ data, predictor_config_manager=predictor_config_manager)
+ self.number_mapped_col = number_mapped_col
+ self.min_reads = min_reads
+
+ @property
+ def too_few_mapped(self):
+ return self.number_mapped.index[self.number_mapped < self.min_reads]
+
+ @property
+ def number_mapped(self):
+ return self.data[self.number_mapped_col]
diff --git a/flotilla/data_model/splicing.py b/flotilla/data_model/splicing.py
new file mode 100644
index 00000000..7238bf55
--- /dev/null
+++ b/flotilla/data_model/splicing.py
@@ -0,0 +1,802 @@
+from collections import Iterable
+import sys
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+from .base import BaseData
+from ..compute.splicing import ModalityEstimator
+from ..visualize.splicing import ModalitiesViz
+from ..util import memoize, timestamp
+from ..visualize.splicing import lavalamp, hist_single_vs_pooled_diff, \
+ lavalamp_pooled_inconsistent
+
+
+FRACTION_DIFF_THRESH = 0.1
+
+
+class SplicingData(BaseData):
+ binned_reducer = None
+ raw_reducer = None
+
+ n_components = 2
+ _binsize = 0.1
+
+ included_label = 'included >>'
+ excluded_label = 'excluded >>'
+
+ def __init__(self, data,
+ feature_data=None, binsize=0.1, outliers=None,
+ feature_rename_col=None,
+ feature_ignore_subset_cols=None,
+ excluded_max=0.2, included_min=0.8,
+ pooled=None, predictor_config_manager=None,
+ technical_outliers=None, minimum_samples=0,
+ feature_expression_id_col=None):
+ """Instantiate a object for percent spliced in (PSI) scores
+
+ Parameters
+ ----------
+ data : pandas.DataFrame
+ A [n_events, n_samples] dataframe of data events
+ n_components : int
+ Number of components to use in the reducer
+ binsize : float
+ Value between 0 and 1, the bin size for binning the study_data
+ scores
+ excluded_max : float
+ Maximum value for the "excluded" bin of psi scores. Default 0.2.
+ included_max : float
+ Minimum value for the "included" bin of psi scores. Default 0.8.
+
+ Notes
+ -----
+ 'thresh' from BaseData is not used.
+ """
+ sys.stdout.write("{}\tInitializing splicing\n".format(timestamp()))
+
+ super(SplicingData, self).__init__(
+ data, feature_data=feature_data,
+ feature_rename_col=feature_rename_col,
+ feature_ignore_subset_cols=feature_ignore_subset_cols,
+ outliers=outliers, pooled=pooled,
+ technical_outliers=technical_outliers,
+ predictor_config_manager=predictor_config_manager,
+ minimum_samples=minimum_samples, data_type='splicing')
+ sys.stdout.write(
+ "{}\tDone initializing splicing\n".format(timestamp()))
+
+ self.feature_expression_id_col = feature_expression_id_col \
+ if feature_expression_id_col is not None \
+ else self.feature_rename_col
+
+ self.binsize = binsize
+ self.excluded_max = excluded_max
+ self.included_min = included_min
+
+ self.bins = np.arange(0, 1 + self.binsize, self.binsize)
+
+ self.modality_estimator = ModalityEstimator(step=2., vmax=20.)
+ # self.modalities_calculator = Modalities(excluded_max=excluded_max,
+ # included_min=included_min)
+ self.modality_visualizer = ModalitiesViz()
+
+ @memoize
+ def modality_assignments(self, sample_ids=None, feature_ids=None,
+ data=None, groupby=None, min_samples=0.5):
+ """Assigned modalities for these samples and features.
+
+ Parameters
+ ----------
+ sample_ids : list of str, optional
+ Which samples to use. If None, use all. Default None.
+ feature_ids : list of str, optional
+ Which features to use. If None, use all. Default None.
+ data : pandas.DataFrame, optional
+ If provided, use this dataframe instead of the sample_ids and
+ feature_ids provided
+
+ Returns
+ -------
+ modality_assignments : pandas.Series
+ The modality assignments of each feature given these samples
+ """
+ if data is None:
+ data = self._subset(self.singles, sample_ids, feature_ids,
+ require_min_samples=False)
+ else:
+ if feature_ids is not None and sample_ids is not None:
+ raise ValueError('Can only specify `sample_ids` and '
+ '`feature_ids` or `data`, but not both.')
+ if groupby is None:
+ groupby = pd.Series('all', index=data.index)
+
+ grouped = data.groupby(groupby)
+ if isinstance(min_samples, int):
+ thresh = self._thresh_int
+ elif isinstance(min_samples, float):
+ thresh = self._thresh_float
+ else:
+ raise TypeError('Threshold for minimum samples for modality '
+ 'detection can only be int or float, '
+ 'not {}'.format(type(min_samples)))
+ data = pd.concat([df.dropna(thresh=thresh(df, min_samples), axis=1)
+ for name, df in grouped])
+ assignments = data.groupby(groupby).apply(
+ self.modality_estimator.fit_transform)
+ return assignments
+
+ @memoize
+ def modality_counts(self, sample_ids=None, feature_ids=None, data=None,
+ groupby=None, min_samples=0.5):
+ """Count the number of each modalities of these samples and features
+
+ Parameters
+ ----------
+ sample_ids : list of str
+ Which samples to use. If None, use all. Default None.
+ feature_ids : list of str
+ Which features to use. If None, use all. Default None.
+ data : pandas.DataFrame, optional
+ If provided, use this dataframe instead of the sample_ids and
+ feature_ids provided
+
+ Returns
+ -------
+ modalities_counts : pandas.Series
+ The number of events detected in each modality
+ """
+ assignments = self.modality_assignments(sample_ids, feature_ids, data,
+ groupby, min_samples)
+ counts = assignments.apply(lambda x: x.groupby(x).size(), axis=1)
+ return counts
+
+ def binify(self, data):
+ return super(SplicingData, self).binify(data, self.bins)
+
+ def plot_modalities_reduced(self, sample_ids=None, feature_ids=None,
+ data=None, ax=None, title=None):
+ """Plot events modality assignments in NMF space
+
+ This will calculate modalities on all samples provided, without
+ grouping them by celltype. This is because each NMF axis can only show
+ one set of sample ids' modalties.
+
+ Parameters
+ ----------
+ sample_ids : list of str
+ Which samples to use. If None, use all. Default None.
+ feature_ids : list of str
+ Which features to use. If None, use all. Default None.
+ data : pandas.DataFrame, optional
+ If provided, use this dataframe instead of the sample_ids and
+ feature_ids provided
+ ax : matplotlib.axes.Axes object
+ Axes to plot on. If none, gets current axes
+ title : str
+ Title of the reduced space plot
+ """
+ groupby = pd.Series('all', self.data.index)
+ modality_assignments = self.modality_assignments(sample_ids,
+ feature_ids,
+ data, groupby)
+ modality_assignments = pd.Series(modality_assignments.values[0],
+ index=modality_assignments.columns)
+
+ self.modality_visualizer.plot_reduced_space(
+ self.binned_nmf_reduced(sample_ids, feature_ids),
+ modality_assignments, ax=ax, title=title,
+ xlabel=self._nmf_space_xlabel(groupby),
+ ylabel=self._nmf_space_ylabel(groupby))
+
+ def plot_modalities_bars(self, sample_ids=None, feature_ids=None,
+ data=None, groupby=None, phenotype_to_color=None,
+ percentages=False, ax=None):
+ """Make grouped barplots of the number of modalities per group
+
+ Parameters
+ ----------
+ sample_ids : None or list of str
+ Which samples to use. If None, use all
+ feature_ids : None or list of str
+ Which features to use. If None, use all
+ color : None or matplotlib color
+ Which color to use for plotting the lavalamps of these features
+ and samples
+ x_offset : numeric
+ How much to offset the x-axis of each event. Useful if you want
+ to plot the same event, but in several iterations with different
+ celltypes or colors
+ """
+
+ counts = self.modality_counts(
+ sample_ids, feature_ids, data=data, groupby=groupby)
+
+ # make sure this is always a dataframe
+ if isinstance(counts, pd.Series):
+ counts = pd.DataFrame([counts.values],
+ index=counts.name,
+ columns=counts.index)
+ return self.modality_visualizer.bar(counts, phenotype_to_color,
+ percentages=percentages, ax=ax)
+
+ def plot_modalities_lavalamps(self, sample_ids=None, feature_ids=None,
+ data=None, groupby=None,
+ phenotype_to_color=None):
+ """Plot "lavalamp" scatterplot of each event
+
+ Parameters
+ ----------
+ sample_ids : None or list of str
+ Which samples to use. If None, use all
+ feature_ids : None or list of str
+ Which features to use. If None, use all
+ color : None or matplotlib color
+ Which color to use for plotting the lavalamps of these features
+ and samples
+ x_offset : numeric
+ How much to offset the x-axis of each event. Useful if you want
+ to plot the same event, but in several iterations with different
+ celltypes or colors
+ """
+ if groupby is None:
+ groupby = pd.Series('all', index=self.data.index)
+
+ assignments = self.modality_assignments(
+ sample_ids, feature_ids, data=data, groupby=groupby)
+
+ # make sure this is always a dataframe
+ if isinstance(assignments, pd.Series):
+ assignments = pd.DataFrame([assignments.values],
+ index=assignments.name,
+ columns=assignments.index)
+
+ grouped = self.singles.groupby(groupby)
+ nrows = assignments.groupby(
+ level=0, axis=0).apply(
+ lambda x: np.unique(x.values)).apply(lambda x: len(x)).sum()
+ figsize = 10, nrows * 4
+ fig, axes = plt.subplots(nrows=nrows, figsize=figsize)
+ axes_iter = axes.flat
+
+ yticks = [0, self.excluded_max, self.included_min, 1]
+ for phenotype, modalities in assignments.iterrows():
+ color = phenotype_to_color[phenotype]
+ sample_ids = grouped.groups[phenotype]
+ for modality, s in modalities.groupby(modalities):
+ ax = axes_iter.next()
+ psi = self.data.ix[sample_ids, s.index]
+ # if modality == 'excluded': import pdb; pdb.set_trace()
+ lavalamp(psi, color=color, ax=ax, yticks=yticks)
+ ax.set_title('{} {}'.format(phenotype, modality))
+ sns.despine()
+ fig.tight_layout()
+
+ def plot_event_modality_estimation(self, event_id, sample_ids=None,
+ data=None,
+ groupby=None, min_samples=0.5):
+ if data is None:
+ data = self._subset(self.singles, sample_ids,
+ require_min_samples=False)
+ else:
+ if sample_ids is not None:
+ raise ValueError(
+ 'Can only specify `sample_ids` or `data`, but not both.')
+ if groupby is None:
+ groupby = pd.Series('all', index=data.index)
+
+ grouped = data.groupby(groupby)
+ if isinstance(min_samples, int):
+ thresh = self._thresh_int
+ elif isinstance(min_samples, float):
+ thresh = self._thresh_float
+ else:
+ raise TypeError('Threshold for minimum samples for modality '
+ 'detection can only be int or float, '
+ 'not {}'.format(type(min_samples)))
+ data = pd.concat([df.dropna(thresh=thresh(df, min_samples), axis=1)
+ for name, df in grouped])
+ event = data[event_id]
+ renamed = self.feature_renamer(event_id)
+ logliks = self.modality_estimator._loglik(event)
+ logsumexps = self.modality_estimator._logsumexp(logliks)
+ self.modality_visualizer.event_estimation(event, logliks, logsumexps,
+ renamed=renamed)
+
+ @memoize
+ def _is_nmf_space_x_axis_excluded(self, phenotype_groupby):
+ nmf_space_positions = self.nmf_space_positions(phenotype_groupby)
+
+ # Get the correct included/excluded labeling for the x and y axes
+ event, phenotype = nmf_space_positions.pc_1.argmax()
+ top_pc1_samples = self.data.groupby(phenotype_groupby).groups[
+ phenotype]
+
+ data = self._subset(self.data, sample_ids=top_pc1_samples)
+ binned = self.binify(data)
+ return bool(binned[event][0])
+
+ def _nmf_space_xlabel(self, phenotype_groupby):
+ if self._is_nmf_space_x_axis_excluded(phenotype_groupby):
+ return self.excluded_label
+ else:
+ return self.included_label
+
+ def _nmf_space_ylabel(self, phenotype_groupby):
+ if self._is_nmf_space_x_axis_excluded(phenotype_groupby):
+ return self.included_label
+ else:
+ return self.excluded_label
+
+ def plot_feature(self, feature_id, sample_ids=None,
+ phenotype_groupby=None,
+ phenotype_order=None, color=None,
+ phenotype_to_color=None,
+ phenotype_to_marker=None, nmf_xlabel=None,
+ nmf_ylabel=None,
+ nmf_space=False, fig=None, axesgrid=None):
+ if nmf_space:
+ nmf_xlabel = self._nmf_space_xlabel(phenotype_groupby)
+ nmf_ylabel = self._nmf_space_xlabel(phenotype_groupby)
+ else:
+ nmf_ylabel = None
+ nmf_xlabel = None
+
+ super(SplicingData, self).plot_feature(feature_id, sample_ids,
+ phenotype_groupby,
+ phenotype_order, color,
+ phenotype_to_color,
+ phenotype_to_marker, nmf_xlabel,
+ nmf_ylabel, nmf_space=nmf_space,
+ fig=fig, axesgrid=axesgrid)
+
+ @memoize
+ def pooled_inconsistent(self, data, feature_ids=None,
+ fraction_diff_thresh=FRACTION_DIFF_THRESH):
+ """Return splicing events which pooled samples are consistently
+ different from the single cells.
+
+ Parameters
+ ----------
+ singles_ids : list-like
+ List of sample ids of single cells (in the main ".data" DataFrame)
+ pooled_ids : list-like
+ List of sample ids of pooled cells (in the other ".pooled"
+ DataFrame)
+ feature_ids : None or list-like
+ List of feature ids. If None, use all
+ fraction_diff_thresh : float
+
+
+ Returns
+ -------
+ large_diff : pandas.DataFrame
+ All splicing events which have a scaled difference larger than
+ the fraction diff thresh
+ """
+ # singles = self._subset(self.data, singles_ids, feature_ids)
+ singles, pooled, not_measured_in_pooled, diff_from_singles = \
+ self._diff_from_singles(data, feature_ids, scaled=True)
+
+ try:
+ ind = diff_from_singles.abs() >= fraction_diff_thresh
+ large_diff = diff_from_singles[ind].dropna(axis=1, how='all')
+ except AttributeError:
+ large_diff = None
+ return singles, pooled, not_measured_in_pooled, large_diff
+
+ @memoize
+ def _diff_from_singles(self, data,
+ feature_ids=None, scaled=True, dropna=True):
+ """Calculate the difference between pooled and singles' psis
+
+ Parameters
+ ----------
+ data : pandas.DataFrame
+ A (n_samples, n_features) DataFrame
+ feature_ids : list-like
+ Subset of the features you want
+ scaled : bool
+ If True, then take the average difference between each pooled
+ sample and all singles. If False, then get the summed difference
+ dropna : bool
+ If True, remove events which were not measured in the pooled
+ samples
+
+ Returns
+ -------
+ singles : pandas.DataFrame
+ Subset of the data that's only the single-cell samples
+ pooled : pandas.DataFrame
+ Subset of the data that's only the pooled samples
+ not_measured_in_pooled : list-like
+ List of features not measured in the pooled samples
+ diff_from_singles : pandas.DataFrame
+ A (n_pooled, n_features) Dataframe of the summed (or scaled if
+ scaled=True)
+ """
+ singles, pooled = self._subset_singles_and_pooled(
+ feature_ids, data=data, require_min_samples=False)
+ if pooled is None:
+ not_measured_in_pooled = None
+ diff_from_singles = None
+ return singles, pooled, not_measured_in_pooled, diff_from_singles
+
+ # Make sure "pooled" is always a dataframe
+ if isinstance(pooled, pd.Series):
+ pooled = pd.DataFrame([pooled.values], columns=pooled.index,
+ index=[pooled.name])
+ pooled = pooled.dropna(how='all', axis=1)
+ not_measured_in_pooled = singles.columns.diff(pooled.columns)
+ singles, pooled = singles.align(pooled, axis=1, join='inner')
+ # import pdb; pdb.set_trace()
+
+ diff_from_singles = pooled.apply(
+ lambda x: (singles - x.values).abs().sum(), axis=1)
+
+ if scaled:
+ diff_from_singles = \
+ diff_from_singles / singles.count().astype(float)
+ if dropna:
+ diff_from_singles = diff_from_singles.dropna(axis=1, how='all')
+ return singles, pooled, not_measured_in_pooled, diff_from_singles
+
+ def plot_lavalamp(self, phenotype_to_color, sample_ids=None,
+ feature_ids=None,
+ data=None, groupby=None, order=None):
+ if data is None:
+ data = self._subset(self.data, sample_ids, feature_ids,
+ require_min_samples=False)
+ else:
+ if feature_ids is not None and sample_ids is not None:
+ raise ValueError('Can only specify `sample_ids` and '
+ '`feature_ids` or `data`, but not both.')
+
+ if groupby is None:
+ groupby = pd.Series('all', index=self.singles.index)
+ grouped = data.groupby(groupby)
+
+ nrows = len(grouped.groups)
+ figsize = 12, nrows * 4
+ fig, axes = plt.subplots(nrows=len(grouped.groups), figsize=figsize,
+ sharex=False)
+ if not isinstance(axes, Iterable):
+ axes = [axes]
+
+ if order is None:
+ order = grouped.groups.keys()
+
+ for ax, name in zip(axes, order):
+ try:
+ color = phenotype_to_color[name]
+ except KeyError:
+ color = None
+ samples = grouped.groups[name]
+ psi = data.ix[samples]
+ lavalamp(psi, color=color, ax=ax)
+ ax.set_title(name)
+ sns.despine()
+ fig.tight_layout()
+
+ def plot_lavalamp_pooled_inconsistent(
+ self, data, feature_ids=None,
+ fraction_diff_thresh=FRACTION_DIFF_THRESH, color=None):
+ """
+
+ Parameters
+ ----------
+
+
+ Returns
+ -------
+
+
+ Raises
+ ------
+
+ """
+ singles, pooled, not_measured_in_pooled, pooled_inconsistent = \
+ self.pooled_inconsistent(data, feature_ids,
+ fraction_diff_thresh)
+ percent = self._divide_inconsistent_and_pooled(pooled,
+ pooled_inconsistent)
+ lavalamp_pooled_inconsistent(singles, pooled, pooled_inconsistent,
+ color=color, percent=percent)
+
+ def plot_hist_single_vs_pooled_diff(self, data, feature_ids=None,
+ color=None, title='',
+ hist_kws=None):
+ """Plot histogram of distances between singles and pooled"""
+ singles, pooled, not_measured_in_pooled, diff_from_singles = \
+ self._diff_from_singles(data, feature_ids)
+ singles, pooled, not_measured_in_pooled, diff_from_singles_scaled = \
+ self._diff_from_singles(data, feature_ids, scaled=True)
+ hist_single_vs_pooled_diff(diff_from_singles,
+ diff_from_singles_scaled, color=color,
+ title=title, hist_kws=hist_kws)
+
+ @staticmethod
+ def _divide_inconsistent_and_pooled(pooled, pooled_inconsistent):
+ """The percent of events with pooled psi different from singles"""
+ if pooled_inconsistent is None:
+ return np.nan
+ if pooled_inconsistent.shape[1] == 0:
+ return 0.0
+ try:
+ return pooled_inconsistent.shape[1] / float(pooled.shape[1]) * 100
+ except ZeroDivisionError:
+ return 100.0
+
+ def _calculate_linkage(self, sample_ids, feature_ids,
+ metric='euclidean', linkage_method='median',
+ bins=None, standardize=False):
+ if bins is not None:
+ data = self.binify(bins)
+ else:
+ data = self.data
+ return super(SplicingData, self)._calculate_linkage(
+ data, sample_ids=sample_ids, feature_ids=feature_ids,
+ standardize=standardize, metric=metric,
+ linkage_method=linkage_method)
+
+ def _subset_and_standardize(self, data, sample_ids=None,
+ feature_ids=None,
+ standardize=True, return_means=False,
+ rename=False):
+ """Grab a subset of the provided data and standardize/remove NAs
+
+ Take only the sample ids and feature ids from this data, require
+ at least some minimum samples. Standardization is performed by
+ replacing ``NA``s with the value 0.5. Then, all values for
+ that event are transformed with :math:`\arccos`/:math:`\cos^{-1}`/arc
+ cosine so that all values range from :math:`-\pi` to :math:`+\pi` and
+ are centered around :math:`0`. As much of single-cell alternative
+ splicing data is near-0 or near-1, this spreads out the values near 0
+ and 1, and squishes the values near 0.5.
+
+ Parameters
+ ----------
+ data : pandas.DataFrame
+ Dataframe to subset
+ sample_ids : list-like, optional (default=None)
+ If None, all sample ids will be used, else only the sample ids
+ specified
+ feature_ids : list-like, optional (default=None)
+ If None, all features will be used, else only the features
+ specified
+ standardize : bool, optional (default=True)
+ If True, replaced NAs with 0.5 and perform an arccosine transform
+ to 0-center the splicing data.
+ return_means : bool, optional (default=False)
+ If True, return a tuple of (subset, means), otherwise just return
+ the subset
+ rename : bool, optional (default=False)
+ Whether or not to rename the feature ids using ``feature_renamer``
+
+ Returns
+ -------
+ subset : pandas.DataFrame
+ Subset of the dataframe with the requested samples and features,
+ and standardized as described
+ means : pandas.DataFrame
+ (Only if return_means=True) Mean values of the features (columns).
+
+ """
+ subset = self._subset(self.data, sample_ids, feature_ids)
+ subset = subset.dropna(how='all', axis=1).dropna(how='all', axis=0)
+
+ # This is splicing data ranging from 0 to 1, so fill na with 0.5
+ # and perform an arc-cosine transform to make the data range from
+ # -pi to pi
+ if standardize:
+ subset = subset.fillna(0.5)
+ subset = -2 * np.arccos(subset * 2 - 1) + np.pi
+ means = subset.mean()
+
+ if rename:
+ means = means.rename_axis(self.feature_renamer)
+ subset = subset.rename_axis(self.feature_renamer, 1)
+
+ if return_means:
+ return subset, means
+ else:
+ return subset
+
+ def plot_two_features(self, feature1, feature2, groupby=None,
+ label_to_color=None, fillna=None, **kwargs):
+ xlim = kwargs.pop('xlim', (0, 1))
+ ylim = kwargs.pop('ylim', (0, 1))
+ return super(SplicingData, self).plot_two_features(
+ feature1, feature2, groupby=groupby, label_to_color=label_to_color,
+ xlim=xlim, ylim=ylim, **kwargs)
+
+ def plot_two_samples(self, sample1, sample2, fillna=None, **kwargs):
+ xlim = kwargs.pop('xlim', (0, 1))
+ ylim = kwargs.pop('ylim', (0, 1))
+ return super(SplicingData, self).plot_two_samples(
+ sample1, sample2, xlim=xlim, ylim=ylim, **kwargs)
+
+
+# class SpliceJunctionData(SplicingData):
+# """Class for splice junction information from SJ.out.tab files from STAR
+#
+# Attributes
+# ----------
+#
+#
+# Methods
+# -------
+#
+# """
+#
+# def __init__(self, df, phenotype_data):
+# """Constructor for SpliceJunctionData
+#
+# Parameters
+# ----------
+# data, experiment_design_data
+#
+# Returns
+# -------
+#
+#
+# Raises
+# ------
+#
+# """
+# super(SpliceJunctionData).__init__()
+# pass
+#
+#
+# class DownsampledSplicingData(BaseData):
+# binned_reducer = None
+# raw_reducer = None
+#
+# n_components = 2
+# _binsize = 0.1
+# _var_cut = 0.2
+#
+# def __init__(self, df, sample_descriptors):
+# """Instantiate an object of downsampled splicing data
+#
+# Parameters
+# ----------
+# df : pandas.DataFrame
+# A "tall" dataframe of all miso summary events, with the usual
+# MISO summary columns, and these are required: 'splice_type',
+# 'probability', 'iteration.' Where "probability" indicates the
+# randomly sampling probability from the bam file used to generate
+# these reads, and "iteration" indicates the integer iteration
+# performed, e.g. if multiple resamplings were performed.
+# experiment_design_data: pandas.DataFrame
+#
+# Notes
+# -----
+# Warning: this data is usually HUGE (we're taking like 10GB raw .tsv
+# files) so make sure you have the available memory for dealing with
+# these.
+#
+# """
+# super(DownsampledSplicingData, self).__init__(sample_descriptors)
+#
+# self.sample_descriptors, splicing = \
+# self.sample_descriptors.align(df, join='inner', axis=0)
+#
+# self.df = df
+#
+# @property
+# def shared_events(self):
+# """
+# Parameters
+# ----------
+#
+# Returns
+# -------
+# event_count_df : pandas.DataFrame
+# Splicing events on the rows, splice types and probability as
+# column MultiIndex. Values are the number of iterations which
+# share this splicing event at that probability and splice type.
+# """
+#
+# if not hasattr(self, '_shared_events'):
+# shared_events = {}
+#
+# for (splice_type, probability), df in self.df.groupby(
+# ['splice_type', 'probability']):
+# event_count = collections.Counter(df.event_name)
+# shared_events[(splice_type, probability)] = pd.Series(
+# event_count)
+#
+# self._shared_events = pd.DataFrame(shared_events)
+# self._shared_events.columns = pd.MultiIndex.from_tuples(
+# self._shared_events_df.columns.tolist())
+# else:
+# return self._shared_events
+#
+# def shared_events_barplot(self, figure_dir='./'):
+# """PLot a "histogram" via colored bars of the number of events shared
+# by different iterations at a particular sampling probability
+#
+# Parameters
+# ----------
+# figure_dir : str
+# Where to save the pdf figures created
+# """
+# figure_dir = figure_dir.rstrip('/')
+# colors = purples + ['#262626']
+#
+# for splice_type, df in self.shared_events.groupby(level=0, axis=1):
+# print splice_type, df.dropna(how='all').shape
+#
+# fig, ax = plt.subplots(figsize=(16, 4))
+#
+# count_values = np.unique(df.values)
+# count_values = count_values[np.isfinite(count_values)]
+#
+# height_so_far = np.zeros(df.shape[1])
+# left = np.arange(df.shape[1])
+#
+# for count, color in zip(count_values, colors):
+# height = df[df == count].count()
+# ax.bar(left, height, bottom=height_so_far, color=color,
+# label=str(int(count)))
+# height_so_far += height
+# ymax = max(height_so_far)
+# ax.set_ylim(0, ymax)
+#
+# legend = ax.legend(loc='center left', bbox_to_anchor=(1, 0.5),
+# title='Iterations sharing event')
+# ax.set_title(splice_type)
+# ax.set_xlabel('Percent downsampled')
+# ax.set_ylabel('number of events')
+# sns.despine()
+# fig.tight_layout()
+# filename = '{}/downsampled_shared_events_{}.pdf'.format(
+# figure_dir, splice_type)
+# fig.savefig(filename, bbox_extra_artists=(legend,),
+# bbox_inches='tight', format="pdf")
+#
+# def shared_events_percentage(self, min_iter_shared=5, figure_dir='./'):
+# """Plot the percentage of all events detected at that iteration,
+# shared by at least 'min_iter_shared'
+#
+# Parameters
+# ----------
+# min_iter_shared : int
+# Minimum number of iterations sharing an event
+# figure_dir : str
+# Where to save the pdf figures created
+# """
+# figure_dir = figure_dir.rstrip('/')
+# sns.set(style='whitegrid', context='talk')
+#
+# for splice_type, df in self.shared_events.groupby(level=0, axis=1):
+# df = df.dropna()
+#
+# fig, ax = plt.subplots(figsize=(16, 4))
+#
+# left = np.arange(df.shape[1])
+# num_greater_than = df[df >= min_iter_shared].count()
+# percent_greater_than = num_greater_than / df.shape[0]
+#
+# ax.plot(left, percent_greater_than,
+# label='Shared with at least {} iter'.format(
+# min_iter_shared))
+#
+# ax.set_xticks(np.arange(0, 101, 10))
+#
+# legend = ax.legend(loc='center left', bbox_to_anchor=(1, 0.5),
+# title='Iterations sharing event')
+#
+# ax.set_title(splice_type)
+# ax.set_xlabel('Percent downsampled')
+# ax.set_ylabel('Percent of events')
+# sns.despine()
+# fig.tight_layout()
+# fig.savefig(
+# '{}/downsampled_shared_events_{}_min_iter_shared{}.pdf'
+# .format(figure_dir, splice_type, min_iter_shared),
+# bbox_extra_artists=(legend,), bbox_inches='tight',
+# format="pdf")
diff --git a/flotilla/data_model/study.py b/flotilla/data_model/study.py
new file mode 100644
index 00000000..638e693b
--- /dev/null
+++ b/flotilla/data_model/study.py
@@ -0,0 +1,1930 @@
+"""
+Data models for "studies" studies include attributes about the data and are
+heavier in terms of data load
+"""
+import inspect
+import json
+import os
+import sys
+import warnings
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import semantic_version
+import seaborn as sns
+
+from .metadata import MetaData, PHENOTYPE_COL, POOLED_COL, OUTLIER_COL
+from .expression import ExpressionData, SpikeInData
+from .gene_ontology import GeneOntologyData
+from .quality_control import MappingStatsData, MIN_READS
+from .splicing import SplicingData, FRACTION_DIFF_THRESH
+from .supplemental import SupplementalData
+from ..compute.predict import PredictorConfigManager
+from ..datapackage import datapackage_url_to_dict, \
+ check_if_already_downloaded, make_study_datapackage
+from ..visualize.color import blue
+from ..visualize.ipython_interact import Interactive
+from ..datapackage import FLOTILLA_DOWNLOAD_DIR
+from ..util import load_csv, load_json, load_tsv, load_gzip_pickle_df, \
+ load_pickle_df, timestamp, cached_property
+
+
+SPECIES_DATA_PACKAGE_BASE_URL = 'https://s3-us-west-2.amazonaws.com/' \
+ 'flotilla-projects'
+DATAPACKAGE_RESOURCE_COMMON_KWS = ('url', 'path', 'format', 'compression',
+ 'name')
+
+
+def _is_absolute_path(location):
+ return location.startswith('http') or location.startswith('/')
+
+
+class Study(object):
+ """A biological study, with associated metadata, expression, and splicing
+ data.
+ """
+ default_feature_set_ids = []
+
+ # Data types with enough data that we'd probably reduce them, and even
+ # then we might want to take subsets. E.g. most variant genes for
+ # expression. But we don't expect to do this for spikein or mapping_stats
+ # data
+ _subsetable_data_types = ['expression', 'splicing']
+
+ initializers = {'metadata_data': MetaData,
+ 'expression_data': ExpressionData,
+ 'splicing_data': SplicingData,
+ 'mapping_stats_data': MappingStatsData,
+ 'spikein_data': SpikeInData}
+
+ readers = {'tsv': load_tsv,
+ 'csv': load_csv,
+ 'json': load_json,
+ 'pickle_df': load_pickle_df,
+ 'gzip_pickle_df': load_gzip_pickle_df}
+
+ _default_reducer_kwargs = {'whiten': False,
+ 'show_point_labels': False,
+ 'show_vectors': False}
+ _common_id = 'common_id'
+ _sample_id = 'sample_id'
+ _event_name = 'event_name'
+
+ _default_plot_kwargs = {'marker': 'o', 'color': blue}
+
+ def __init__(self, sample_metadata, version='0.1.0', expression_data=None,
+ expression_feature_data=None,
+ expression_feature_rename_col=None,
+ expression_feature_ignore_subset_cols=None,
+ expression_log_base=None,
+ expression_thresh=-np.inf,
+ expression_plus_one=False,
+ splicing_data=None,
+ splicing_feature_data=None,
+ splicing_feature_rename_col=None,
+ splicing_feature_ignore_subset_cols=None,
+ splicing_feature_expression_id_col=None,
+ mapping_stats_data=None,
+ mapping_stats_number_mapped_col=None,
+ mapping_stats_min_reads=MIN_READS,
+ spikein_data=None,
+ spikein_feature_data=None,
+ drop_outliers=True, species=None,
+ gene_ontology_data=None,
+ predictor_config_manager=None,
+ metadata_pooled_col=POOLED_COL,
+ metadata_minimum_samples=0,
+ metadata_phenotype_col=PHENOTYPE_COL,
+ metadata_phenotype_order=None,
+ metadata_phenotype_to_color=None,
+ metadata_phenotype_to_marker=None,
+ metadata_outlier_col=OUTLIER_COL,
+ license=None, title=None, sources=None,
+ default_sample_subset="all_samples",
+ default_feature_subset="variant",
+ supplemental_data=None):
+ """Construct a biological study
+
+ This class only accepts data, no filenames. All data must already
+ have been read in and exist as Python objects.
+
+ Parameters
+ ----------
+ sample_metadata : pandas.DataFrame
+ The only required parameter. Samples as the index, with features as
+ columns. Required column: "phenotype". If there is a boolean
+ column "pooled", this will be used to separate pooled from single
+ cells. Similarly, the column "outliers" will also be used to
+ separate outlier cells from the rest.
+ version : str
+ A string describing the semantic version of the data. Must be in:
+ major.minor.patch format, as the "patch" number will be increased
+ if you change something in the study and then study.save() it.
+ (default "0.1.0")
+ expression_data : pandas.DataFrame
+ Samples x feature dataframe of gene expression measurements,
+ e.g. from an RNA-Seq or a microarray experiment. Assumed to be
+ log-transformed, i.e. you took the log of it. (default None)
+ expression_feature_data : pandas.DatFrame
+ Features x annotations dataframe describing other parameters
+ of the gene expression features, e.g. mapping Ensembl IDs to gene
+ symbols or gene biotypes. (default None)
+ expression_feature_rename_col : str
+ A column name in the expression_feature_data dataframe that you'd
+ like to rename the expression features to, in the plots. For
+ example, if your gene IDs are Ensembl IDs, but you want to plot
+ UCSC IDs, make sure the column you want, e.g. "ucsc_id" is in your
+ dataframe and specify that. (default "gene_name")
+ expression_log_base : float
+ If you want to log-transform your expression data (and it's not
+ already log-transformed), use this number as the base of the
+ transform. E.g. expression_log_base=10 will take the log10 of
+ your data. (default None)
+ thresh : float
+ Minimum (non log-transformed) expression value. (default -inf)
+ expression_plus_one : bool
+ Whether or not to add 1 to the expression data. (default False)
+ splicing_data : pandas.DataFrame
+ Samples x feature dataframe of percent spliced in scores, e.g. as
+ measured by the program MISO. Assumed that these values only fall
+ between 0 and 1.
+ splicing_feature_data : pandas.DataFrame
+ features x other_features dataframe describing other parameters
+ of the splicing features, e.g. mapping MISO IDs to Ensembl IDs or
+ gene symbols or transcript types
+ splicing_feature_rename_col : str
+ A column name in the splicing_feature_data dataframe that you'd
+ like to rename the splicing features to, in the plots. For
+ example, if your splicing IDs are MISO IDs, but you want to plot
+ Ensembl IDs, make sure the column you want, e.g. "ensembl_id" is
+ in your dataframe and specify that. Default "gene_name".
+ splicing_feature_expression_id_col : str
+ A column name in the splicing_feature_data dataframe that
+ corresponds to the row names of the expression data
+ mapping_stats_data : pandas.DataFrame
+ Samples x feature dataframe of mapping stats measurements.
+ Currently, this
+ mapping_stats_number_mapped_col : str
+ A column name in the mapping_stats_data which specifies the
+ number of (uniquely or not) mapped reads. Default "Uniquely
+ mapped reads number"
+ spikein_data : pandas.DataFrame
+ samples x features DataFrame of spike-in expression values
+ spikein_feature_data : pandas.DataFrame
+ Features x other_features dataframe, e.g. of the molecular
+ concentration of particular spikein transcripts
+ drop_outliers : bool
+ Whether or not to drop samples indicated as outliers in the
+ sample_metadata from the other data, i.e. with a column
+ named 'outlier' in sample_metadata, then remove those
+ samples from expression_data for further analysis
+ species : str
+ Name of the species and genome version, e.g. 'hg19' or 'mm10'.
+ gene_ontology_data : pandas.DataFrame
+ Gene ids x ontology categories dataframe used for GO analysis.
+ metadata_pooled_col : str
+ Column in metadata_data which specifies as a boolean
+ whether or not this sample was pooled.
+ supplemental_data : dict
+ str: dataframe mapping of the attribute name, and the pandas
+ dataframe
+
+ Note
+ ----
+ This function explicitly specifies ALL the instance variables (except
+ those that are marked by the @property decorator), because,
+ as described [1], "If you write initialization functions separate from
+ __init__ then experienced developers will certainly see your code as a
+ kid's playground."
+
+ [1] http://stackoverflow.com/q/12513185/1628971
+ """
+ sys.stdout.write("{}\tInitializing Study\n".format(timestamp()))
+ sys.stdout.write("{}\tInitializing Predictor configuration manager "
+ "for Study\n".format(timestamp()))
+ self.predictor_config_manager = predictor_config_manager \
+ if predictor_config_manager is not None \
+ else PredictorConfigManager()
+ # self.predictor_config_manager = None
+
+ self.species = species
+ if gene_ontology_data is not None:
+ self.gene_ontology = GeneOntologyData(gene_ontology_data)
+
+ self.license = license
+ self.title = title
+ self.sources = sources
+ self.version = version
+
+ sys.stdout.write('{}\tLoading metadata\n'.format(timestamp()))
+ self.metadata = MetaData(
+ sample_metadata, metadata_phenotype_order,
+ metadata_phenotype_to_color,
+ metadata_phenotype_to_marker, pooled_col=metadata_pooled_col,
+ outlier_col=metadata_outlier_col,
+ phenotype_col=metadata_phenotype_col,
+ predictor_config_manager=self.predictor_config_manager)
+
+ self.default_feature_subset = default_feature_subset
+ self.default_sample_subset = default_sample_subset
+
+ if self.metadata.outlier_col in self.metadata.data and drop_outliers:
+ outliers = self.metadata.data.index[
+ self.metadata.data[self.metadata.outlier_col].astype(bool)]
+ else:
+ outliers = None
+ self.metadata.data[self.metadata.outlier_col] = False
+
+ # Get pooled samples
+ pooled = None
+ if self.metadata.pooled_col is not None:
+ if self.metadata.pooled_col in self.metadata.data:
+ try:
+ pooled = self.metadata.data.index[
+ self.metadata.data[
+ self.metadata.pooled_col].astype(bool)]
+ except KeyError:
+ pooled = None
+ self.pooled = pooled
+
+ if mapping_stats_data is not None:
+ self.mapping_stats = MappingStatsData(
+ mapping_stats_data,
+ number_mapped_col=mapping_stats_number_mapped_col,
+ predictor_config_manager=self.predictor_config_manager,
+ min_reads=mapping_stats_min_reads)
+ self.technical_outliers = self.mapping_stats.too_few_mapped
+ if len(self.technical_outliers) > 0:
+ outliers_ids = ', '.join(self.technical_outliers)
+ sys.stderr.write(
+ 'Samples had too few mapped reads (<{:.1e} reads)'
+ ':\n\t{}\n'.format(mapping_stats_min_reads, outliers_ids))
+ else:
+ self.technical_outliers = None
+ self.mapping_stats = None
+ feature_data_none = expression_feature_data is None or \
+ splicing_feature_data is None
+
+ if self.species is not None and feature_data_none:
+ sys.stdout.write('{}\tLoading species metadata from '
+ '~/flotilla_packages\n'.format(timestamp()))
+ species_kws = self.load_species_data(self.species, self.readers)
+ expression_feature_data = species_kws.pop(
+ 'expression_feature_data',
+ None)
+ expression_feature_rename_col = species_kws.pop(
+ 'expression_feature_rename_col', None)
+ splicing_feature_data = species_kws.pop('splicing_feature_data',
+ None)
+ splicing_feature_rename_col = species_kws.pop(
+ 'splicing_feature_rename_col', None)
+
+ if expression_feature_data is None:
+ expression_feature_data = species_kws.pop(
+ 'expression_feature_data',
+ None)
+
+ if expression_feature_rename_col is None:
+ expression_feature_rename_col = species_kws.pop(
+ 'expression_feature_rename_col', None)
+
+ if splicing_feature_data is None:
+ splicing_feature_data = species_kws.pop(
+ 'splicing_feature_data',
+ None)
+
+ if splicing_feature_rename_col is None:
+ splicing_feature_rename_col = species_kws.pop(
+ 'splicing_feature_rename_col', None)
+
+ if expression_data is not None:
+ sys.stdout.write(
+ "{}\tLoading expression data\n".format(timestamp()))
+ feature_ignore_subset_cols = expression_feature_ignore_subset_cols
+ self.expression = ExpressionData(
+ expression_data,
+ feature_data=expression_feature_data,
+ thresh=expression_thresh,
+ feature_rename_col=expression_feature_rename_col,
+ outliers=outliers, plus_one=expression_plus_one,
+ log_base=expression_log_base, pooled=pooled,
+ predictor_config_manager=self.predictor_config_manager,
+ technical_outliers=self.technical_outliers,
+ minimum_samples=metadata_minimum_samples,
+ feature_ignore_subset_cols=feature_ignore_subset_cols)
+ self.default_feature_set_ids.extend(self.expression.feature_subsets
+ .keys())
+ else:
+ self.expression = None
+ if splicing_data is not None:
+ sys.stdout.write("{}\tLoading splicing data\n".format(
+ timestamp()))
+ self.splicing = SplicingData(
+ splicing_data, feature_data=splicing_feature_data,
+ feature_rename_col=splicing_feature_rename_col,
+ outliers=outliers, pooled=pooled,
+ predictor_config_manager=self.predictor_config_manager,
+ technical_outliers=self.technical_outliers,
+ minimum_samples=metadata_minimum_samples,
+ feature_ignore_subset_cols=splicing_feature_ignore_subset_cols,
+ feature_expression_id_col=splicing_feature_expression_id_col)
+ else:
+ self.splicing = None
+
+ if spikein_data is not None:
+ self.spikein = SpikeInData(
+ spikein_data, feature_data=spikein_feature_data,
+ technical_outliers=self.technical_outliers,
+ predictor_config_manager=self.predictor_config_manager)
+ else:
+ self.spikein = None
+ self.supplemental = SupplementalData(supplemental_data)
+ sys.stdout.write("{}\tSuccessfully initialized a Study "
+ "object!\n".format(timestamp()))
+
+ def __setattr__(self, key, value):
+ """Check if the attribute already exists and warns on overwrite.
+ """
+ if hasattr(self, key):
+ warnings.warn('Over-writing attribute {}'.format(key))
+ super(Study, self).__setattr__(key, value)
+
+ @property
+ def phenotype_col(self):
+ return self.metadata.phenotype_col
+
+ @property
+ def phenotype_order(self):
+ return self.metadata.phenotype_order
+
+ @property
+ def phenotype_to_color(self):
+ return self.metadata.phenotype_to_color
+
+ @property
+ def phenotype_to_marker(self):
+ return self.metadata.phenotype_to_marker
+
+ @property
+ def sample_id_to_phenotype(self):
+ return self.metadata.sample_id_to_phenotype
+
+ @property
+ def sample_id_to_color(self):
+ return self.metadata.sample_id_to_color
+
+ @property
+ def phenotype_transitions(self):
+ return self.metadata.phenotype_transitions
+
+ @property
+ def phenotype_color_ordered(self):
+ return self.metadata.phenotype_color_order
+
+ @property
+ def default_sample_subsets(self):
+ # move default_sample_subset to the front of the list, sort the rest
+ sorted_sample_subsets = list(sorted(list(set(
+ self.metadata.sample_subsets.keys()).difference(
+ set(self.default_sample_subset)))))
+ sorted_sample_subsets.insert(0, self.default_sample_subset)
+ return sorted_sample_subsets
+
+ @property
+ def default_feature_subsets(self):
+ feature_subsets = {}
+ for name in self._subsetable_data_types:
+ try:
+ data_type = getattr(self, name)
+ feature_subsets[name] = data_type.feature_subsets
+ except AttributeError:
+ continue
+ return feature_subsets
+
+ @classmethod
+ def from_datapackage_url(
+ cls, datapackage_url,
+ load_species_data=True,
+ species_datapackage_base_url=SPECIES_DATA_PACKAGE_BASE_URL):
+ """Create a study from a url of a datapackage.json file
+
+ Parameters
+ ----------
+ datapackage_url : str
+ HTTP url of a datapackage.json file, following the specification
+ described here: http://dataprotocols.org/data-packages/ and
+ requiring the following data resources: metadata,
+ expression, splicing
+ species_data_pacakge_base_url : str
+ Base URL to fetch species-specific _ and splicing event
+ metadata frnm.
+ Default 'https://s3-us-west-2.amazonaws.com/flotilla-projects/'
+
+ Returns
+ -------
+ study : Study
+ A "study" object containing the data described in the
+ datapackage_url file
+
+ Raises
+ ------
+ AttributeError
+ If the datapackage.json file does not contain the required
+ resources of metadata, expression, and splicing.
+ """
+ datapackage = datapackage_url_to_dict(datapackage_url)
+ datapackage_dir = '{}/{}'.format(FLOTILLA_DOWNLOAD_DIR,
+ datapackage['name'])
+ return cls.from_datapackage(
+ datapackage, load_species_data=load_species_data,
+ datapackage_dir=datapackage_dir,
+ species_datapackage_base_url=species_datapackage_base_url)
+
+ @classmethod
+ def from_datapackage_file(
+ cls, datapackage_filename,
+ load_species_data=True,
+ species_datapackage_base_url=SPECIES_DATA_PACKAGE_BASE_URL):
+ with open(datapackage_filename) as f:
+ sys.stdout.write('{}\tReading datapackage from {}\n'.format(
+ timestamp(), datapackage_filename))
+ datapackage = json.load(f)
+ datapackage_dir = os.path.dirname(datapackage_filename)
+ return cls.from_datapackage(
+ datapackage, datapackage_dir=datapackage_dir,
+ load_species_data=load_species_data,
+ species_datapackage_base_url=species_datapackage_base_url)
+
+ @staticmethod
+ def _filename_from_resource(resource, datapackage_dir,
+ datapackage_name):
+ if 'url' in resource:
+ resource_url = resource['url']
+ if not _is_absolute_path(resource_url):
+ resource_url = '{}/{}'.format(datapackage_dir,
+ resource_url)
+ filename = check_if_already_downloaded(resource_url,
+ datapackage_name)
+ return filename
+ elif 'path' in resource:
+ if resource['path'].startswith('http'):
+ filename = check_if_already_downloaded(resource['path'],
+ datapackage_name)
+ else:
+ filename = resource['path']
+ if not _is_absolute_path(filename):
+ filename = '{}/{}'.format(datapackage_dir,
+ filename)
+
+ # Test if the file exists, if not, then add the datapackage
+ # file
+ if not os.path.exists(filename):
+ filename = os.path.join(datapackage_dir, filename)
+ return filename
+ else:
+ return None
+
+ @classmethod
+ def from_datapackage(
+ cls, datapackage, datapackage_dir='./',
+ load_species_data=True,
+ species_datapackage_base_url=SPECIES_DATA_PACKAGE_BASE_URL):
+ """Create a study object from a datapackage dictionary
+
+ Parameters
+ ----------
+ datapackage : dict
+
+
+ Returns
+ -------
+ study : flotilla.Study
+ Study object
+ """
+ sys.stdout.write('{}\tParsing datapackage to create a Study '
+ 'object\n'.format(timestamp()))
+ dfs = {}
+ kwargs = {}
+ supplemental_data = {}
+ datapackage_name = datapackage['name']
+
+ for resource in datapackage['resources']:
+ filename = cls._filename_from_resource(resource, datapackage_dir,
+ datapackage_name)
+ if filename is None:
+ # This is supplemental data
+ for supplemental in resource[u'resources']:
+ filename = cls._filename_from_resource(supplemental,
+ datapackage_dir,
+ datapackage_name)
+ name = supplemental['name']
+
+ reader = cls.readers[supplemental['format']]
+ compression = None if 'compression' not in \
+ supplemental else \
+ supplemental['compression']
+ header = supplemental.pop('header', 0)
+ index_col = supplemental.pop('index_col', 0)
+ df = reader(filename, compression=compression,
+ header=header, index_col=index_col)
+ supplemental_data[name] = df
+ else:
+
+ name = resource['name']
+
+ reader = cls.readers[resource['format']]
+ compression = None if 'compression' not in resource else \
+ resource['compression']
+ header = resource.pop('header', 0)
+ index_col = resource.pop('index_col', 0)
+
+ dfs[name] = reader(filename, compression=compression,
+ header=header, index_col=index_col)
+
+ for key in set(resource.keys()).difference(
+ DATAPACKAGE_RESOURCE_COMMON_KWS):
+ kwargs['{}_{}'.format(name, key)] = resource[key]
+
+ species_kws = {}
+ species = None if 'species' not in datapackage else datapackage[
+ 'species']
+ if load_species_data and species is not None:
+ species_kws = cls.load_species_data(species, cls.readers,
+ species_datapackage_base_url)
+
+ try:
+ sample_metadata = dfs.pop('metadata')
+ except KeyError:
+ raise AttributeError('The datapackage.json file is required to '
+ 'have the "metadata" resource')
+ dfs = dict(('{}_data'.format(k), v) for k, v in dfs.iteritems())
+
+ nones = [k for k, v in kwargs.iteritems() if v is None]
+ for key in nones:
+ kwargs.pop(key)
+ kwargs.update(species_kws)
+ kwargs.update(dfs)
+
+ license = None if 'license' not in datapackage else datapackage[
+ 'license']
+ title = None if 'title' not in datapackage else datapackage[
+ 'title']
+ sources = None if 'sources' not in datapackage else datapackage[
+ 'sources']
+ version = None if 'datapackage_version' not in datapackage else \
+ datapackage['datapackage_version']
+ if not semantic_version.validate(version):
+ raise ValueError(
+ '{} is not a valid version string. Please use semantic '
+ 'versioning, with major.minor.patch, e.g. 0.1.2 is a valid '
+ 'version string'.format(version))
+ study = Study(sample_metadata=sample_metadata, species=species,
+ license=license, title=title, sources=sources,
+ version=version, supplemental_data=supplemental_data,
+ **kwargs)
+ return study
+
+ @staticmethod
+ def load_species_data(
+ species, readers,
+ species_datapackage_base_url=SPECIES_DATA_PACKAGE_BASE_URL):
+ dfs = {}
+
+ try:
+ species_data_url = '{}/{}/datapackage.json'.format(
+ species_datapackage_base_url, species)
+ species_datapackage = datapackage_url_to_dict(
+ species_data_url)
+
+ for resource in species_datapackage['resources']:
+ if 'url' in resource:
+ resource_url = resource['url']
+ filename = check_if_already_downloaded(resource_url,
+ species)
+ else:
+ filename = resource['path']
+
+ reader = readers[resource['format']]
+
+ compression = None if 'compression' not in resource else \
+ resource['compression']
+ name = resource['name']
+ dfs[name] = reader(filename,
+ compression=compression)
+ other_keys = set(resource.keys()).difference(
+ DATAPACKAGE_RESOURCE_COMMON_KWS)
+ name_no_data = name.rstrip('_data')
+ for key in other_keys:
+ new_key = '{}_{}'.format(name_no_data, key)
+ dfs[new_key] = resource[key]
+ except (IOError, ValueError) as e:
+ sys.stderr.write('Error loading species {} data:'
+ ' {}'.format(species, e))
+ return dfs
+
+ def detect_outliers(self, data_type='expression',
+ sample_subset=None, feature_subset=None,
+ featurewise=False,
+ reducer=None,
+ standardize=None,
+ reducer_kwargs=None,
+ bins=None,
+ outlier_detection_method=None,
+ outlier_detection_method_kwargs=None):
+
+ if sample_subset is None:
+ sample_subset = self.default_sample_subset
+
+ sample_ids = self.sample_subset_to_sample_ids(sample_subset)
+
+ if feature_subset is None:
+ feature_subset = self.default_feature_subset
+
+ feature_ids = self.feature_subset_to_feature_ids(data_type,
+ feature_subset,
+ rename=False)
+ if data_type == "expression":
+ datamodel = self.expression
+ elif data_type == "splicing":
+ datamodel = self.splicing
+ else:
+ raise TypeError('{} not a supported data type'.format(data_type))
+
+ reducer, outlier_detector = datamodel.detect_outliers(
+ sample_ids=sample_ids, feature_ids=feature_ids,
+ featurewise=featurewise, reducer=reducer, standardize=standardize,
+ reducer_kwargs=reducer_kwargs, bins=bins,
+ outlier_detection_method=outlier_detection_method,
+ outlier_detection_method_kwargs=outlier_detection_method_kwargs)
+
+ outlier_detector.predict(reducer.reduced_space)
+ outlier_detector.title = "_".join(
+ ['outlier', data_type, sample_subset, feature_subset])
+ print "setting outlier type:\"{}\" in metadata".format(
+ outlier_detector.title)
+ if outlier_detector.title not in self.metadata.data:
+ self.metadata.data[outlier_detector.title] = False
+
+ self.metadata.data[outlier_detector.title].update(
+ outlier_detector.outliers)
+ return reducer, outlier_detector
+
+ def drop_outliers(self):
+ """Assign samples marked as "outlier" in metadata, to other datas"""
+ outliers = self.metadata.data['outlier'][
+ self.metadata.data['outlier']].index
+ self.expression.outlier_samples = outliers
+ self.splicing.outlier_samples = outliers
+
+ @staticmethod
+ def maybe_make_directory(filename):
+ # Make the directory if it's not already there
+ try:
+ directory = os.path.abspath(os.path.dirname(filename))
+ os.makedirs(os.path.abspath(directory))
+ except OSError:
+ pass
+
+ def feature_subset_to_feature_ids(self, data_type, feature_subset=None,
+ rename=False):
+ """Given a name of a feature subset, get the associated feature ids
+
+ Parameters
+ ----------
+ data_type : str
+ A string describing the data type, e.g. "expression"
+ feature_subset : str
+ A string describing the subset of data type (must be already
+ calculated)
+
+ Returns
+ -------
+ feature_ids : list of strings
+ List of features ids from the specified datatype
+ """
+ if 'expression'.startswith(data_type):
+ return self.expression.feature_subset_to_feature_ids(
+ feature_subset, rename)
+ elif 'splicing'.startswith(data_type):
+ return self.splicing.feature_subset_to_feature_ids(
+ feature_subset, rename)
+
+ def sample_subset_to_sample_ids(self, phenotype_subset=None):
+
+ """Convert a string naming a subset of phenotypes in the data into
+ sample ids
+
+ Parameters
+ ----------
+ phenotype_subset : str
+ A valid string describing a boolean phenotype described in the
+ metadata data
+
+ Returns
+ -------
+ sample_ids : list of strings
+ List of sample ids in the data
+ """
+
+ # IF this is a list of IDs
+
+ try:
+ return self.metadata.sample_subsets[phenotype_subset]
+ except (KeyError, TypeError):
+ pass
+
+ try:
+ ind = self.metadata.sample_id_to_phenotype == phenotype_subset
+ if ind.sum() > 0:
+ return self.metadata.sample_id_to_phenotype.index[ind]
+
+ if phenotype_subset is None or 'all_samples'.startswith(
+ phenotype_subset):
+ sample_ind = np.ones(self.metadata.data.shape[0],
+ dtype=bool)
+ elif phenotype_subset.startswith("~"):
+ sample_ind = ~pd.Series(
+ self.metadata.data[phenotype_subset.lstrip("~")],
+ dtype='bool')
+
+ else:
+ sample_ind = pd.Series(
+ self.metadata.data[phenotype_subset], dtype='bool')
+ sample_ids = self.metadata.data.index[sample_ind]
+ return sample_ids
+ except (AttributeError, ValueError):
+ return phenotype_subset
+
+ def plot_pca(self, data_type='expression', x_pc=1, y_pc=2,
+ sample_subset=None, feature_subset=None,
+ title='', featurewise=False, plot_violins=False,
+ show_point_labels=False, reduce_kwargs=None,
+ color_samples_by=None, bokeh=False,
+ most_variant_features=False, std_multiplier=2,
+ scale_by_variance=True,
+ **kwargs):
+ """Performs DataFramePCA on both expression and splicing study_data
+
+ Parameters
+ ----------
+ data_type : str
+ One of the names of the data types, e.g. "expression" or
+ "splicing" (default "expression")
+ x_pc : int, optional
+ Which principal component to plot on the x-axis (default 1)
+ y_pc : int, optional
+ Which principal component to plot on the y-axis (default 2)
+ sample_subset : str or None
+ Which subset of the samples to use, based on some phenotype
+ column in the experiment design data. If None, all samples are
+ used. (default None)
+ feature_subset : str or None
+ Which subset of the features to used, based on some feature type
+ in the expression data (e.g. "variant"). If None, all features
+ are used. (default None)
+ title : str, optional
+ Title of the reduced space plot (default '')
+ featurewise : bool, optional
+ If True, the features are reduced on the samples, and the plotted
+ points are features, not samples. (default False)
+ plot_violins : bool
+ Whether or not to make the violinplots of the top features. This
+ can take a long time, so to save time you can turn it off if you
+ just want a quick look at the PCA. (default False)
+ show_point_labels : bool, optional
+ Whether or not to show the labels of the points. If this is
+ samplewise (default), then this labels the samples. If this is
+ featurewise, then this labels the features. (default False)
+ reduce_kwargs : dict, optional
+ Keyword arguments to the reducer (default None)
+ color_samples_by : str, optional
+ Instead of coloring the samples by their phenotype, color them by
+ this column in the metadata. (default None)
+ bokeh : bool, optional
+ If True, plot a javascripty/interactive bokeh plot instead of a
+ static printable figure (default False)
+ most_variant_features : bool, optional
+ If True, then only take the most variant of the provided features.
+ The most variant are determined by taking the features whose
+ variance is ``std_multiplier``standard deviations away from the
+ mean feature variance (default False)
+ std_multiplier : float, optional
+ If ``most_variant_features`` is True, then use this as a cutoff
+ for the minimum variance of a feature to be included (default 2)
+ scale_by_variance : bool, optional
+ If True, then scale the x- and y-axes by the explained variance
+ ratio of the principal component dimensions. Only valid for PCA
+ and its variations, not for NMF or tSNE. (default True)
+ kwargs : other keyword arguments
+ All other keyword arguments are passed to
+ :py:meth:`DecomopsitionViz.plot`
+ """
+
+ sample_subset = self.default_sample_subset \
+ if sample_subset is None else sample_subset
+ feature_subset = self.default_feature_subset \
+ if feature_subset is None else feature_subset
+
+ sample_ids = self.sample_subset_to_sample_ids(sample_subset)
+ feature_ids = self.feature_subset_to_feature_ids(data_type,
+ feature_subset,
+ rename=False)
+
+ label_to_color = None
+ label_to_marker = None
+ groupby = None
+ order = None
+ color_samples_by_phenotype = color_samples_by \
+ == self.metadata.phenotype_col
+ if not featurewise:
+ if color_samples_by is None or color_samples_by_phenotype:
+ label_to_color = self.phenotype_to_color
+ label_to_marker = self.phenotype_to_marker
+ groupby = self.sample_id_to_phenotype
+ order = self.phenotype_order
+ else:
+ groupby = self.metadata.data[color_samples_by]
+
+ if "expression".startswith(data_type):
+ reducer = self.expression.plot_pca(
+ x_pc=x_pc, y_pc=y_pc, sample_ids=sample_ids,
+ feature_ids=feature_ids,
+ label_to_color=label_to_color,
+ label_to_marker=label_to_marker, groupby=groupby,
+ order=order, std_multiplier=std_multiplier,
+ featurewise=featurewise, show_point_labels=show_point_labels,
+ title=title, reduce_kwargs=reduce_kwargs,
+ plot_violins=plot_violins, metadata=self.metadata.data,
+ bokeh=bokeh, most_variant_features=most_variant_features,
+ scale_by_variance=scale_by_variance,
+ **kwargs)
+
+ elif "splicing".startswith(data_type):
+ reducer = self.splicing.plot_pca(
+ x_pc=x_pc, y_pc=y_pc, sample_ids=sample_ids,
+ feature_ids=feature_ids,
+ label_to_color=label_to_color,
+ label_to_marker=label_to_marker, groupby=groupby,
+ order=order, std_multiplier=std_multiplier,
+ featurewise=featurewise, show_point_labels=show_point_labels,
+ title=title, reduce_kwargs=reduce_kwargs,
+ plot_violins=plot_violins, metadata=self.metadata.data,
+ bokeh=bokeh, most_variant_features=most_variant_features,
+ scale_by_variance=scale_by_variance,
+ **kwargs)
+ else:
+ raise ValueError('The data type {} does not exist in this study'
+ .format(data_type))
+ return reducer
+
+ def plot_graph(self, data_type='expression', sample_subset=None,
+ feature_subset=None, featurewise=False,
+ **kwargs):
+ """Plot the graph (network) of these data
+
+ Parameters
+ ----------
+ data_type : str
+ One of the names of the data types, e.g. "expression" or "splicing"
+ sample_subset : str or None
+ Which subset of the samples to use, based on some phenotype
+ column in the experiment design data. If None, all samples are
+ used.
+ feature_subset : str or None
+ Which subset of the features to used, based on some feature type
+ in the expression data (e.g. "variant"). If None, all features
+ are used.
+ """
+ sample_ids = self.sample_subset_to_sample_ids(sample_subset)
+ feature_ids = self.feature_subset_to_feature_ids(data_type,
+ feature_subset,
+ rename=False)
+
+ if not featurewise:
+ label_to_color = self.phenotype_to_color
+ label_to_marker = self.phenotype_to_marker
+ groupby = self.sample_id_to_phenotype
+ else:
+ label_to_color = None
+ label_to_marker = None
+ groupby = None
+
+ if data_type == "expression":
+ return self.expression.networks.draw_graph(
+ sample_ids=sample_ids, feature_ids=feature_ids,
+ sample_id_to_color=self.sample_id_to_color,
+ label_to_color=label_to_color,
+ label_to_marker=label_to_marker, groupby=groupby,
+ featurewise=featurewise,
+ **kwargs)
+ elif data_type == "splicing":
+ return self.splicing.networks.draw_graph(
+ sample_ids=sample_ids, feature_ids=feature_ids,
+ sample_id_to_color=self.sample_id_to_color,
+ label_to_color=label_to_color,
+ label_to_marker=label_to_marker, groupby=groupby,
+ featurewise=featurewise,
+ **kwargs)
+
+ def plot_classifier(self, trait, sample_subset=None,
+ feature_subset='all_genes',
+ data_type='expression', title='',
+ show_point_labels=False,
+ **kwargs):
+ """Plot a predictor for the specified data type and trait(s)
+
+ Parameters
+ ----------
+ data_type : str
+ One of the names of the data types, e.g. "expression" or "splicing"
+ trait : str
+ Column name in the metadata data that you would like
+ to classify on
+
+ Returns
+ -------
+
+
+ """
+ try:
+ trait_data = self.metadata.data[trait]
+ except KeyError:
+ trait_ids = self.sample_subset_to_sample_ids(trait)
+ trait_data = self.metadata.data.index.isin(trait_ids)
+
+ if isinstance(trait_data.dtype, bool):
+ all_true = np.all(trait_data)
+ all_false = np.all(~trait_data)
+ too_few_categories = False
+ else:
+ all_false = False
+ all_true = False
+ too_few_categories = len(set(trait_data)) <= 1
+ nothing_to_classify = all_true or all_false or too_few_categories
+
+ if nothing_to_classify:
+ raise ValueError("With the trait '{}', all samples are True "
+ "(or all samples are "
+ "False) or all are the same, cannot classify"
+ " when all samples are the same".format(trait))
+ trait_data = pd.Series(trait_data, name=trait,
+ index=self.metadata.data.index)
+
+ sample_ids = self.sample_subset_to_sample_ids(sample_subset)
+ feature_ids = self.feature_subset_to_feature_ids(data_type,
+ feature_subset,
+ rename=False)
+ feature_subset = 'none' if feature_subset is None else feature_subset
+ sample_subset = 'none' if sample_subset is None else sample_subset
+ data_name = '_'.join([sample_subset, feature_subset])
+
+ label_to_color = self.phenotype_to_color
+ label_to_marker = self.phenotype_to_marker
+ groupby = self.sample_id_to_phenotype
+
+ order = self.phenotype_order
+ color = self.phenotype_color_ordered
+
+ if data_type == "expression":
+ return self.expression.plot_classifier(
+ data_name=data_name, trait=trait_data,
+ sample_ids=sample_ids, feature_ids=feature_ids,
+ label_to_color=label_to_color,
+ label_to_marker=label_to_marker, groupby=groupby,
+ show_point_labels=show_point_labels, title=title,
+ order=order, color=color,
+ **kwargs)
+ elif data_type == "splicing":
+ return self.splicing.plot_classifier(
+ data_name=data_name, trait=trait_data,
+ sample_ids=sample_ids, feature_ids=feature_ids,
+ label_to_color=label_to_color,
+ label_to_marker=label_to_marker, groupby=groupby,
+ show_point_labels=show_point_labels, title=title,
+ order=order, color=color,
+ **kwargs)
+
+ def modality_assignments(self, sample_subset=None, feature_subset=None,
+ expression_thresh=-np.inf, min_samples=0.5):
+ """Get modality assignments of splicing data
+
+ Parameters
+ ----------
+ sample_subset : str or None, optional
+ Which subset of the samples to use, based on some phenotype
+ column in the experiment design data. If None, all samples are
+ used.
+ feature_subset : str or None, optional
+ Which subset of the features to used, based on some feature type
+ in the expression data (e.g. "variant"). If None, all features
+ are used.
+ expression_thresh : float, optional
+ Minimum expression value, of the original input. E.g. if the
+ original input is already log-transformed, then this threshold is
+ on the log values.
+
+ Returns
+ -------
+ modalities : pandas.DataFrame
+ A (n_phenotypes, n_events) shaped DataFrame of the assigned
+ modality
+ """
+ min_expression = self.expression.data.min().min()
+ if expression_thresh > -np.inf and expression_thresh > min_expression:
+ data = self.filter_splicing_on_expression(
+ expression_thresh=expression_thresh,
+ sample_subset=sample_subset)
+ sample_ids = None
+ feature_ids = None
+ else:
+ sample_ids = self.sample_subset_to_sample_ids(sample_subset)
+ feature_ids = self.feature_subset_to_feature_ids(
+ 'splicing', feature_subset, rename=False)
+ data = None
+
+ return self.splicing.modality_assignments(
+ sample_ids, feature_ids, data=data,
+ groupby=self.sample_id_to_phenotype, min_samples=min_samples)
+
+ def modality_counts(self, sample_subset=None, feature_subset=None,
+ expression_thresh=-np.inf, min_samples=0.5):
+ """Get number of splicing events in modality categories
+
+ Parameters
+ ----------
+ sample_subset : str or None, optional
+ Which subset of the samples to use, based on some phenotype
+ column in the experiment design data. If None, all samples are
+ used.
+ feature_subset : str or None, optional
+ Which subset of the features to used, based on some feature type
+ in the expression data (e.g. "variant"). If None, all features
+ are used.
+ expression_thresh : float, optional
+ Minimum expression value, of the original input. E.g. if the
+ original input is already log-transformed, then this threshold is
+ on the log values.
+
+ Returns
+ -------
+ modalities : pandas.DataFrame
+ A (n_phenotypes, n_modalities) shaped DataFrame of the number of
+ events assigned to each modality
+ """
+ min_expression = self.expression.data.min().min()
+ if expression_thresh > -np.inf and expression_thresh > min_expression:
+ data = self.filter_splicing_on_expression(
+ expression_thresh=expression_thresh,
+ sample_subset=sample_subset)
+ sample_ids = None
+ feature_ids = None
+ else:
+ sample_ids = self.sample_subset_to_sample_ids(sample_subset)
+ feature_ids = self.feature_subset_to_feature_ids(
+ 'splicing', feature_subset, rename=False)
+ data = None
+
+ return self.splicing.modality_assignments(
+ sample_ids, feature_ids, data=data,
+ groupby=self.sample_id_to_phenotype, min_samples=min_samples)
+
+ def plot_modalities_bars(self, sample_subset=None, feature_subset=None,
+ expression_thresh=-np.inf, percentages=True):
+ """Make grouped barplots of the number of modalities per phenotype
+
+ Parameters
+ ----------
+ sample_subset : str or None
+ Which subset of the samples to use, based on some phenotype
+ column in the experiment design data. If None, all samples are
+ used.
+ feature_subset : str or None
+ Which subset of the features to used, based on some feature type
+ in the expression data (e.g. "variant"). If None, all features
+ are used.
+ expression_thresh : float
+ If greater than -inf, then filter on splicing events in genes
+ with expression at least this value
+ percentages : bool
+ If True, plot percentages instead of counts
+ """
+ if expression_thresh > -np.inf:
+ data = self.filter_splicing_on_expression(
+ expression_thresh=expression_thresh,
+ sample_subset=sample_subset)
+ sample_ids = None
+ feature_ids = None
+ else:
+ sample_ids = self.sample_subset_to_sample_ids(sample_subset)
+ feature_ids = self.feature_subset_to_feature_ids(
+ 'splicing', feature_subset, rename=False)
+ data = None
+
+ self.splicing.plot_modalities_bars(sample_ids, feature_ids, data,
+ self.sample_id_to_phenotype,
+ self.phenotype_to_color,
+ percentages=percentages)
+
+ def plot_modalities_reduced(self, sample_subset=None, feature_subset=None,
+ expression_thresh=-np.inf):
+ """Plot splicing events with modality assignments in NMF space
+
+ This will plot a separate NMF space for each celltype in the data, as
+ well as one for all samples.
+
+ Parameters
+ ----------
+ sample_subset : str or None
+ Which subset of the samples to use, based on some phenotype
+ column in the experiment design data. If None, all samples are
+ used.
+ feature_subset : str or None
+ Which subset of the features to used, based on some feature type
+ in the expression data (e.g. "variant"). If None, all features
+ are used.
+ expression_thresh : float
+ If greater than -inf, then filter on splicing events in genes
+ with expression at least this value
+ """
+ if expression_thresh > -np.inf:
+ data = self.filter_splicing_on_expression(
+ expression_thresh=expression_thresh,
+ sample_subset=sample_subset)
+ sample_ids = None
+ feature_ids = None
+ else:
+ sample_ids = self.sample_subset_to_sample_ids(sample_subset)
+ feature_ids = self.feature_subset_to_feature_ids(
+ 'splicing', feature_subset, rename=False)
+ data = None
+
+ grouped = self.sample_id_to_phenotype.groupby(
+ self.sample_id_to_phenotype)
+
+ # Account for bar plot and plot of the reduced space of ALL samples
+ n = grouped.ngroups + 1
+ groups = ['all']
+ fig, axes = plt.subplots(ncols=n, figsize=(n * 4, 4))
+ all_ax = axes[0]
+ self.splicing.plot_modalities_reduced(sample_ids, feature_ids,
+ data=data,
+ ax=all_ax, title='all samples')
+ axes = axes[1:]
+ for i, ((celltype, series), ax) in enumerate(zip(grouped, axes)):
+ groups.append(celltype)
+ # sys.stdout.write('\n---- {} ----\n'.format(celltype))
+ samples = series.index.intersection(sample_ids)
+ # legend = i == 0
+ self.splicing.plot_modalities_reduced(samples, feature_ids,
+ data=data,
+ ax=ax, title=celltype)
+
+ def celltype_sizes(self, data_type='splicing'):
+ if data_type == 'expression':
+ self.expression.data.groupby(self.sample_id_to_phenotype,
+ axis=0).size()
+ if data_type == 'splicing':
+ self.splicing.data.groupby(self.sample_id_to_phenotype,
+ axis=0).size()
+
+ @property
+ def celltype_event_counts(self):
+ """Number of cells that detected each event, per celltype
+ """
+ return self.splicing.data.groupby(
+ self.sample_id_to_phenotype, axis=0).apply(
+ lambda x: x.groupby(level=0, axis=0).transform(
+ lambda x: x.count()).sum()).replace(0, np.nan)
+
+ def unique_celltype_event_counts(self, n=1):
+ celltype_event_counts = self.celltype_event_counts
+ return celltype_event_counts[celltype_event_counts <= n]
+
+ def percent_unique_celltype_events(self, n=1):
+ n_unique = self.unique_celltype_event_counts(n).sum(axis=1)
+ n_total = self.celltype_event_counts.sum(axis=1).astype(float)
+ return n_unique / n_total * 100
+
+ # @property
+ # def celltype_modalities(self):
+ # """Return modality assignments of each celltype
+ # """
+ # return self.splicing.data.groupby(
+ # self.sample_id_to_phenotype, axis=0).apply(
+ # lambda x: self.splicing.modalities(x.index))
+
+ def plot_modalities_lavalamps(self, sample_subset=None,
+ feature_subset=None,
+ expression_thresh=-np.inf):
+ """Plot each modality in each celltype on a separate axes
+
+ Parameters
+ ----------
+ sample_subset : str or None
+ Which subset of the samples to use, based on some phenotype
+ column in the experiment design data. If None, all samples are
+ used.
+ feature_subset : str or None
+ Which subset of the features to used, based on some feature type
+ in the expression data (e.g. "variant"). If None, all features
+ are used.
+ expression_thresh : float
+ If greater than -inf, then filter on splicing events in genes
+ with expression at least this value
+ """
+ if expression_thresh > -np.inf:
+ data = self.filter_splicing_on_expression(
+ expression_thresh=expression_thresh,
+ sample_subset=sample_subset)
+ sample_ids = None
+ feature_ids = None
+ else:
+ sample_ids = self.sample_subset_to_sample_ids(sample_subset)
+ feature_ids = self.feature_subset_to_feature_ids(
+ 'splicing', feature_subset, rename=False)
+ data = None
+
+ self.splicing.plot_modalities_lavalamps(
+ groupby=self.sample_id_to_phenotype,
+ phenotype_to_color=self.phenotype_to_color,
+ sample_ids=sample_ids, data=data, feature_ids=feature_ids)
+
+ def plot_event_modality_estimation(self, event_id, sample_subset=None,
+ expression_thresh=-np.inf):
+ if expression_thresh > -np.inf:
+ data = self.filter_splicing_on_expression(
+ expression_thresh=expression_thresh,
+ sample_subset=sample_subset)
+ sample_ids = None
+ else:
+ sample_ids = self.sample_subset_to_sample_ids(sample_subset)
+ data = None
+
+ self.splicing.plot_event_modality_estimation(
+ event_id, groupby=self.sample_id_to_phenotype,
+ sample_ids=sample_ids, data=data)
+
+ def plot_event(self, feature_id, sample_subset=None, nmf_space=False):
+ """Plot the violinplot and NMF transitions of a splicing event
+ """
+ sample_ids = self.sample_subset_to_sample_ids(sample_subset)
+ self.splicing.plot_feature(
+ feature_id, sample_ids,
+ phenotype_groupby=self.sample_id_to_phenotype,
+ phenotype_order=self.phenotype_order,
+ color=self.phenotype_color_ordered,
+ phenotype_to_color=self.phenotype_to_color,
+ phenotype_to_marker=self.phenotype_to_marker, nmf_space=nmf_space)
+
+ def plot_gene(self, feature_id, sample_subset=None, nmf_space=False):
+ sample_ids = self.sample_subset_to_sample_ids(sample_subset)
+ self.expression.plot_feature(
+ feature_id, sample_ids,
+ phenotype_groupby=self.sample_id_to_phenotype,
+ phenotype_order=self.phenotype_order,
+ color=self.phenotype_color_ordered,
+ phenotype_to_color=self.phenotype_to_color,
+ phenotype_to_marker=self.phenotype_to_marker, nmf_space=nmf_space)
+
+ def plot_lavalamp_pooled_inconsistent(
+ self, sample_subset=None, feature_subset=None,
+ fraction_diff_thresh=FRACTION_DIFF_THRESH,
+ expression_thresh=-np.inf):
+ # grouped_ids = self.splicing.data.groupby(self.sample_id_to_color,
+ # axis=0)
+ celltype_groups = self.metadata.data.groupby(
+ self.sample_id_to_phenotype, axis=0)
+
+ if sample_subset is not None:
+ # Only plotting one sample_subset
+ celltype_samples = set(celltype_groups.groups[sample_subset])
+ else:
+ # Plotting all the celltypes
+ celltype_samples = self.sample_subset_to_sample_ids(sample_subset)
+
+ feature_ids = self.feature_subset_to_feature_ids(
+ 'splicing', feature_subset=feature_subset)
+
+ celltype_and_sample_ids = celltype_groups.groups.iteritems()
+ for i, (phenotype, sample_ids) in enumerate(celltype_and_sample_ids):
+ # import pdb; pdb.set_trace()
+
+ # Assumes all samples of a sample_subset have the same color...
+ # probably wrong
+ color = self.phenotype_to_color[phenotype]
+ sample_ids = celltype_samples.intersection(sample_ids)
+ if len(sample_ids) == 0:
+ continue
+ data = self.filter_splicing_on_expression(expression_thresh)
+ data = data.ix[sample_ids, :]
+ self.splicing.plot_lavalamp_pooled_inconsistent(
+ data, feature_ids, fraction_diff_thresh, color=color)
+
+ def percent_pooled_inconsistent(
+ self, sample_subset=None, feature_subset=None,
+ fraction_diff_thresh=FRACTION_DIFF_THRESH,
+ expression_thresh=-np.inf):
+ celltype_groups = self.metadata.data.groupby(
+ self.sample_id_to_phenotype, axis=0)
+
+ if sample_subset is not None:
+ # Only plotting one sample_subset
+ celltype_samples = set(celltype_groups.groups[sample_subset])
+ else:
+ # Plotting all the celltypes
+ celltype_samples = self.sample_subset_to_sample_ids(sample_subset)
+
+ feature_ids = self.feature_subset_to_feature_ids(
+ 'splicing', feature_subset=feature_subset)
+
+ celltype_and_sample_ids = celltype_groups.groups.iteritems()
+ index = pd.MultiIndex.from_product([celltype_groups.groups.keys(),
+ ['n_events', 'percent']])
+ percents = pd.Series(index=index)
+ for i, (phenotype, sample_ids) in enumerate(celltype_and_sample_ids):
+ # import pdb; pdb.set_trace()
+
+ # Assumes all samples of a sample_subset have the same color...
+ # probably wrong
+ sample_ids = celltype_samples.intersection(sample_ids)
+ if len(sample_ids) == 0:
+ continue
+ data = self.filter_splicing_on_expression(expression_thresh)
+ data = data.ix[sample_ids, :]
+ if not data.empty:
+ singles, pooled, not_measured_in_pooled, pooled_inconsistent \
+ = self.splicing.pooled_inconsistent(data, feature_ids,
+ fraction_diff_thresh)
+ percent = self.splicing._divide_inconsistent_and_pooled(
+ pooled, pooled_inconsistent)
+ else:
+ percent = np.nan
+ percents[phenotype, 'percent'] = percent
+ percents[phenotype, 'n_events'] = data.shape[1]
+ return percents
+
+ def expression_vs_inconsistent_splicing(self, bins=None):
+ """Percentage of events inconsistent with pooled at expression threshs
+
+ Parameters
+ ----------
+ bins : list-like
+ List of expression cutoffs
+
+ Returns
+ -------
+ expression_vs_inconsistent : pd.DataFrame
+ A (len(bins), n_phenotypes) dataframe of the percentage of events
+ in single cells that are inconsistent with pooled
+ """
+
+ if bins is None:
+ emin = int(np.floor(self.expression.data_original.min().min()))
+ emax = int(np.ceil(self.expression.data_original.max().max()))
+ bins = np.arange(emin, emax)
+
+ expression_vs_inconsistent = pd.Series(bins).apply(
+ lambda x: self.percent_pooled_inconsistent(expression_thresh=x))
+ return expression_vs_inconsistent
+
+ def plot_expression_vs_inconsistent_splicing(self, bins=None):
+
+ expression_vs_inconsistent = self.expression_vs_inconsistent_splicing(
+ bins=bins)
+
+ fig, axes = plt.subplots(nrows=2, figsize=(6, 6))
+
+ # Plot the percent inconsistent
+ ax = axes[0]
+ for phenotype in self.phenotype_order:
+ s = expression_vs_inconsistent[(phenotype, 'percent')]
+ color = self.phenotype_to_color[phenotype]
+ ax.plot(s, 'o-', color=color)
+ ax.set_xlabel('Expression threshold')
+ ax.set_ylabel('Percent events inconsistent with pooled')
+ ymin, ymax = ax.get_ylim()
+ ax.set_ylim(0, ymax)
+
+ # Plot number of events at each cutoff
+ ax = axes[1]
+ for phenotype in self.phenotype_order:
+ s = expression_vs_inconsistent[(phenotype, 'n_events')]
+ color = self.phenotype_to_color[phenotype]
+ ax.plot(s, 'o-', color=color)
+ ax.set_xlabel('Expression threshold')
+ ax.set_ylabel('Number of events')
+ ymin, ymax = ax.get_ylim()
+ ax.set_ylim(0, ymax)
+ ax.legend()
+
+ sns.despine()
+
+ def plot_clustermap(self, sample_subset=None, feature_subset=None,
+ data_type='expression', metric='euclidean',
+ method='average', figsize=None,
+ scale_fig_by_data=True, **kwargs):
+ """Visualize hierarchical relationships within samples and features
+
+ Parameters
+ ----------
+
+
+ Returns
+ -------
+
+
+ Raises
+ ------
+ """
+
+ if feature_subset is None:
+ feature_subset = self.default_feature_subset
+
+ sample_ids = self.sample_subset_to_sample_ids(sample_subset)
+ feature_ids = self.feature_subset_to_feature_ids(data_type,
+ feature_subset,
+ rename=False)
+
+ if data_type == "expression":
+ return self.expression.plot_clustermap(
+ sample_ids=sample_ids, feature_ids=feature_ids, method=method,
+ metric=metric, sample_id_to_color=self.sample_id_to_color,
+ figsize=figsize, scale_fig_by_data=scale_fig_by_data,
+ **kwargs)
+ elif data_type == "splicing":
+ return self.splicing.plot_clustermap(
+ sample_ids=sample_ids, feature_ids=feature_ids, method=method,
+ metric=metric, sample_id_to_color=self.sample_id_to_color,
+ figsize=figsize, scale_fig_by_data=scale_fig_by_data,
+ **kwargs)
+
+ def plot_correlations(self, sample_subset=None, feature_subset=None,
+ data_type='expression', metric='euclidean',
+ method='average', figsize=None, featurewise=False,
+ scale_fig_by_data=True, **kwargs):
+ """Visualize clustered correlations of samples across features
+
+ Parameters
+ ----------
+
+
+ Returns
+ -------
+
+
+ Raises
+ ------
+
+ """
+
+ if feature_subset is None:
+ feature_subset = self.default_feature_subset
+
+ sample_ids = self.sample_subset_to_sample_ids(sample_subset)
+ feature_ids = self.feature_subset_to_feature_ids(data_type,
+ feature_subset,
+ rename=False)
+
+ if figsize is not None and scale_fig_by_data:
+ raise ValueError('If "scale_fig_by_data" is true, then cannot '
+ 'also specify "figsize"')
+
+ if data_type == "expression":
+ return self.expression.plot_correlations(
+ sample_ids=sample_ids, feature_ids=feature_ids,
+ sample_id_to_color=self.sample_id_to_color,
+ figsize=figsize, scale_fig_by_data=scale_fig_by_data,
+ metric=metric, method=method, featurewise=featurewise,
+ **kwargs)
+ elif data_type == "splicing":
+ return self.splicing.plot_correlations(
+ sample_ids=sample_ids, feature_ids=feature_ids, method=method,
+ metric=metric, sample_id_to_color=self.sample_id_to_color,
+ figsize=figsize, scale_fig_by_data=scale_fig_by_data,
+ featurewise=featurewise, **kwargs)
+
+ def plot_lavalamps(self, sample_subset=None, feature_subset=None,
+ expression_thresh=-np.inf):
+ if expression_thresh > -np.inf:
+ data = self.filter_splicing_on_expression(
+ expression_thresh=expression_thresh,
+ sample_subset=sample_subset)
+ sample_ids = None
+ feature_ids = None
+ else:
+ sample_ids = self.sample_subset_to_sample_ids(sample_subset)
+ feature_ids = self.feature_subset_to_feature_ids(
+ 'splicing', feature_subset, rename=False)
+ data = None
+
+ self.splicing.plot_lavalamp(sample_ids=sample_ids,
+ feature_ids=feature_ids, data=data,
+ groupby=self.sample_id_to_phenotype,
+ phenotype_to_color=self.phenotype_to_color,
+ order=self.phenotype_order)
+
+ def plot_big_nmf_space_transitions(self, data_type='expression', n=5):
+ if data_type == 'expression':
+ self.expression.plot_big_nmf_space_transitions(
+ self.sample_id_to_phenotype, self.phenotype_transitions,
+ self.phenotype_order, self.phenotype_color_ordered,
+ self.phenotype_to_color, self.phenotype_to_marker, n=n)
+ if data_type == 'splicing':
+ self.splicing.plot_big_nmf_space_transitions(
+ self.sample_id_to_phenotype, self.phenotype_transitions,
+ self.phenotype_order, self.phenotype_color_ordered,
+ self.phenotype_to_color, self.phenotype_to_marker, n=n)
+
+ def plot_two_samples(self, sample1, sample2, data_type='expression',
+ **kwargs):
+ """Plot a scatterplot of two samples' data
+
+ Parameters
+ ----------
+ sample1 : str
+ Name of the sample to plot on the x-axis
+ sample2 : str
+ Name of the sample to plot on the y-axis
+ data_type : "expression" | "splicing"
+ Type of data to plot. Default "expression"
+ Any other keyword arguments valid for seaborn.jointplot
+
+ Returns
+ -------
+ jointgrid : seaborn.axisgrid.JointGrid
+ Returns a JointGrid instance
+
+ See Also
+ -------
+ seaborn.jointplot
+
+ """
+ if data_type == 'expression':
+ return self.expression.plot_two_samples(sample1, sample2, **kwargs)
+ elif data_type == 'splicing':
+ return self.splicing.plot_two_samples(sample1, sample2, **kwargs)
+
+ def plot_two_features(self, feature1, feature2, data_type='expression',
+ **kwargs):
+ """Make a scatterplot of two features' data
+
+ Parameters
+ ----------
+ feature1 : str
+ Name of the feature to plot on the x-axis. If you have a
+ feature_data dataframe for this data type, will attempt to map
+ the common name, e.g. "RBFOX2" back to the crazy name,
+ e.g. "ENSG00000100320"
+ feature2 : str
+ Name of the feature to plot on the y-axis. If you have a
+ feature_data dataframe for this data type, will attempt to map
+ the common name, e.g. "RBFOX2" back to the crazy name,
+ e.g. "ENSG00000100320"
+
+ Returns
+ -------
+
+
+ Raises
+ ------
+ """
+ if data_type == 'expression':
+ self.expression.plot_two_features(
+ feature1, feature2, groupby=self.sample_id_to_phenotype,
+ label_to_color=self.phenotype_to_color, **kwargs)
+ if data_type == 'splicing':
+ self.splicing.plot_two_features(
+ feature1, feature2, groupby=self.sample_id_to_phenotype,
+ label_to_color=self.phenotype_to_color, **kwargs)
+
+ def nmf_space_positions(self, data_type='splicing'):
+ if data_type == 'splicing':
+ return self.splicing.nmf_space_positions(
+ self.sample_id_to_phenotype)
+
+ def nmf_space_transitions(self, phenotype_transitions='all',
+ data_type='splicing', n=0.5):
+ """The change in NMF space of splicing events across phenotypes
+
+ Parameters
+ ----------
+ phenotype_transitions : list of length-2 tuples of str
+ List of ('phenotype1', 'phenotype2') transitions whose change in
+ distribution you are interested in
+ data_type : 'splicing' | 'expression'
+ Which data type to calculate this on. (default='splicing')
+ n : int
+ Minimum number of samples per phenotype, per event
+
+ Returns
+ -------
+ big_transitions : pandas.DataFrame
+ A (n_events, n_transitions) dataframe of the NMF distances between
+ splicing events
+ """
+ if phenotype_transitions == 'all':
+ phenotype_transitions = self.phenotype_transitions
+ if data_type == 'splicing':
+ return self.splicing.nmf_space_transitions(
+ self.sample_id_to_phenotype, phenotype_transitions, n=n)
+
+ def big_nmf_space_transitions(self, phenotype_transitions='all',
+ data_type='splicing', n=0.5):
+ """Splicing events whose change in NMF space is large
+
+ By large, we mean that difference is 2 standard deviations away from
+ the mean
+
+ Parameters
+ ----------
+ phenotype_transitions : list of length-2 tuples of str
+ List of ('phenotype1', 'phenotype2') transitions whose change in
+ distribution you are interested in
+ data_type : 'splicing' | 'expression'
+ Which data type to calculate this on. (default='splicing')
+ n : int
+ Minimum number of samples per phenotype, per event
+
+ Returns
+ -------
+ big_transitions : pandas.DataFrame
+ A (n_events, n_transitions) dataframe of the NMF distances between
+ splicing events
+ """
+ if phenotype_transitions == 'all':
+ phenotype_transitions = self.phenotype_transitions
+ if data_type == 'splicing':
+ return self.splicing.big_nmf_space_transitions(
+ self.sample_id_to_phenotype, phenotype_transitions, n=n)
+
+ def save(self, study_name, flotilla_dir=FLOTILLA_DOWNLOAD_DIR):
+
+ metadata = self.metadata.data_original
+
+ metadata_kws = {'pooled_col': self.metadata.pooled_col,
+ 'phenotype_col': self.metadata.phenotype_col,
+ 'phenotype_order': self.metadata.phenotype_order,
+ 'phenotype_to_color':
+ self.metadata.phenotype_to_color,
+ 'phenotype_to_marker':
+ self.metadata.phenotype_to_marker,
+ 'minimum_samples': self.metadata.minimum_samples,
+ 'outlier_col': self.metadata.outlier_col}
+
+ try:
+ expression = self.expression.data_original
+ expression_kws = {
+ 'log_base': self.expression.log_base,
+ 'thresh': self.expression.thresh_original,
+ 'plus_one': self.expression.plus_one}
+ except AttributeError:
+ expression = None
+ expression_kws = None
+
+ try:
+ expression_feature_data = self.expression.feature_data
+ expression_feature_kws = {
+ 'rename_col': self.expression.feature_rename_col,
+ 'ignore_subset_cols':
+ self.expression.feature_ignore_subset_cols}
+ except AttributeError:
+ expression_feature_data = None
+ expression_feature_kws = None
+
+ try:
+ splicing = self.splicing.data_original
+ splicing_kws = {}
+ except AttributeError:
+ splicing = None
+ splicing_kws = None
+
+ try:
+ splicing_feature_data = self.splicing.feature_data
+ splicing_feature_kws = \
+ {'rename_col': self.splicing.feature_rename_col,
+ 'ignore_subset_cols':
+ self.splicing.feature_ignore_subset_cols,
+ 'expression_id_col': self.splicing.feature_expression_id_col}
+ except AttributeError:
+ splicing_feature_data = None
+ splicing_feature_kws = None
+
+ try:
+ spikein = self.spikein.data_original
+ except AttributeError:
+ spikein = None
+
+ try:
+ gene_ontology = self.gene_ontology.data
+ except AttributeError:
+ gene_ontology = None
+
+ try:
+ mapping_stats = self.mapping_stats.data_original
+ mapping_stats_kws = {
+ 'number_mapped_col': self.mapping_stats.number_mapped_col,
+ 'min_reads': self.mapping_stats.min_reads}
+ except AttributeError:
+ mapping_stats = None
+ mapping_stats_kws = None
+
+ supplemental_attributes = inspect.getmembers(self.supplemental,
+ lambda a: not
+ (inspect.isroutine(a)))
+ supplemental_attributes = [a for a in supplemental_attributes
+ if not(a[0].startswith('__') and
+ a[0].endswith('__'))]
+ supplemental_kws = {}
+ for supplemental_name, df in supplemental_attributes:
+ supplemental_kws[supplemental_name] = df
+
+ # Increase the version number
+ version = semantic_version.Version(self.version)
+ version.patch = version.patch + 1
+ version = str(version)
+
+ return make_study_datapackage(
+ study_name, metadata, expression, splicing,
+ spikein, mapping_stats, metadata_kws=metadata_kws,
+ expression_kws=expression_kws, splicing_kws=splicing_kws,
+ mapping_stats_kws=mapping_stats_kws,
+ expression_feature_kws=expression_feature_kws,
+ expression_feature_data=expression_feature_data,
+ splicing_feature_data=splicing_feature_data,
+ splicing_feature_kws=splicing_feature_kws, species=self.species,
+ license=self.license, title=self.title, sources=self.sources,
+ version=version, flotilla_dir=flotilla_dir,
+ gene_ontology=gene_ontology, supplemental_kws=supplemental_kws)
+
+ @staticmethod
+ def _maybe_get_axis_name(df, axis=0, alt_name=None):
+ if alt_name is None:
+ alt_name = 'columns' if axis == 1 else 'index'
+ axis = df.columns if axis == 1 else df.index
+ if isinstance(axis, pd.MultiIndex):
+ name = axis.names
+ else:
+ name = axis.name
+ name = alt_name if name is None else name
+ return name
+
+ @cached_property()
+ def tidy_splicing_with_expression(self):
+ """A tall 'tidy' dataframe of samples with expression and splicing
+
+ :return:
+ :rtype:
+ """
+ # Establish common strings
+
+ splicing_common_id = self.splicing.feature_data[
+ self.splicing.feature_expression_id_col]
+
+ # Tidify splicing
+ splicing = self.splicing.data
+ splicing_index_name = self._maybe_get_axis_name(splicing, axis=0)
+ splicing_columns_name = self._maybe_get_axis_name(splicing, axis=1)
+
+ splicing_tidy = pd.melt(splicing.reset_index(),
+ id_vars=splicing_index_name,
+ value_name='psi',
+ var_name=splicing_columns_name)
+ rename_columns = {}
+ if splicing_index_name == 'index':
+ rename_columns[splicing_index_name] = self._sample_id
+ if splicing_columns_name == 'columns':
+ rename_columns[splicing_columns_name] = self._event_name
+ splicing_columns_name = self._event_name
+ splicing_tidy = splicing_tidy.rename(columns=rename_columns)
+
+ # Create a column of the common id on which to join splicing
+ # and expression
+ splicing_names = splicing_tidy[splicing_columns_name]
+ if isinstance(splicing_names, pd.Series):
+ splicing_tidy[self._common_id] = splicing_tidy[
+ splicing_columns_name].map(splicing_common_id)
+ else:
+ # Splicing ids are a multi-index, so the feature renamer will get
+ # the name of the feature.
+ splicing_tidy[self._common_id] = [
+ self.splicing.feature_renamer(x)
+ for x in splicing_names.itertuples(index=False)]
+
+ splicing_tidy = splicing_tidy.dropna()
+
+ # Tidify expression
+ expression = self.expression.data_original
+ expression_index_name = self._maybe_get_axis_name(expression, axis=0)
+
+ expression_tidy = pd.melt(expression.reset_index(),
+ id_vars=expression_index_name,
+ value_name='expression',
+ var_name=self._common_id)
+ # This will only do anything if there is a column named "index" so
+ # no need to check anything
+ expression_tidy = expression_tidy.rename(
+ columns={'index': self._sample_id})
+ expression_tidy = expression_tidy.dropna()
+
+ splicing_tidy.set_index([self._sample_id, self._common_id],
+ inplace=True)
+ expression_tidy.set_index([self._sample_id, self._common_id],
+ inplace=True)
+ return splicing_tidy.join(expression_tidy, how='inner').reset_index()
+
+ def filter_splicing_on_expression(self, expression_thresh,
+ sample_subset=None):
+ """Filter splicing events on expression values
+
+ Parameters
+ ----------
+ expression_thresh : float
+ Minimum expression value, of the original input. E.g. if the
+ original input is already log-transformed, then this threshold is
+ on the log values.
+
+ Returns
+ -------
+ psi : pandas.DataFrame
+ A (n_samples, n_features)
+
+ """
+ min_expression = self.expression.data_original.min().min()
+ if expression_thresh > -np.inf \
+ and expression_thresh > min_expression:
+ columns = self._maybe_get_axis_name(self.splicing.data, axis=1,
+ alt_name=self._event_name)
+ index = self._maybe_get_axis_name(self.splicing.data, axis=0,
+ alt_name=self._sample_id)
+
+ sample_ids = self.sample_subset_to_sample_ids(sample_subset)
+ splicing_with_expression = \
+ self.tidy_splicing_with_expression.ix[
+ self.tidy_splicing_with_expression.sample_id.isin(
+ sample_ids)]
+ ind = splicing_with_expression.expression >= expression_thresh
+ splicing_high_expression = splicing_with_expression.ix[ind]
+ splicing_high_expression = \
+ splicing_high_expression.reset_index().dropna()
+
+ if isinstance(columns, list) or isinstance(index, list):
+ filtered_psi = splicing_high_expression.pivot_table(
+ columns=columns, index=index, values='psi')
+ else:
+ filtered_psi = splicing_high_expression.pivot(
+ columns=columns, index=index, values='psi')
+ return filtered_psi
+ else:
+ return self.splicing.data
+
+ def go_enrichment(self, feature_ids, background=None, domain=None,
+ p_value_cutoff=1000000, min_feature_size=3,
+ min_background_size=5):
+ """Calculate gene ontology enrichment of provided features
+
+ Parameters
+ ----------
+ feature_ids : list-like
+ Features to calculate gene ontology enrichment on
+ background : list-like, optional
+ Features to use as the background
+ domain : str or list, optional
+ Only calculate GO enrichment for a particular GO category or
+ subset of categories. Valid domains:
+ 'biological_process', 'molecular_function', 'cellular_component'
+ p_value_cutoff : float, optional
+ Maximum accepted Bonferroni-corrected p-value
+ min_feature_size : int, optional
+ Minimum number of features of interest overlapping in a GO Term,
+ to calculate enrichment
+ min_background_size : int, optional
+ Minimum number of features in the background overlapping a GO Term
+ Returns
+ -------
+ enrichment : pandas.DataFrame
+ A (go_categories, columns) dataframe showing the GO
+ enrichment categories that were enriched in the features
+ """
+ if background is None:
+ warnings.warn('No background provided, defaulting to all '
+ 'expressed genes')
+ background = self.expression.data.columns
+ return self.gene_ontology.enrichment(
+ feature_ids, background=background,
+ cross_reference=self.expression.feature_renamer_series,
+ domain=domain, p_value_cutoff=p_value_cutoff,
+ min_feature_size=min_feature_size,
+ min_background_size=min_background_size)
+
+# Add interactive visualizations
+Study.interactive_classifier = Interactive.interactive_classifier
+Study.interactive_graph = Interactive.interactive_graph
+Study.interactive_pca = Interactive.interactive_pca
+# Study.interactive_localZ = Interactive.interactive_localZ
+Study.interactive_lavalamp_pooled_inconsistent = \
+ Interactive.interactive_lavalamp_pooled_inconsistent
+Study.interactive_choose_outliers = Interactive.interactive_choose_outliers
+Study.interactive_reset_outliers = Interactive.interactive_reset_outliers
+Study.interactive_clustermap = Interactive.interactive_clustermap
+Study.interactive_correlations = Interactive.interactive_correlations
diff --git a/flotilla/data_model/supplemental.py b/flotilla/data_model/supplemental.py
new file mode 100644
index 00000000..92552f10
--- /dev/null
+++ b/flotilla/data_model/supplemental.py
@@ -0,0 +1,18 @@
+
+
+class SupplementalData(object):
+
+ def __init__(self, name_to_df=None):
+ """Container for holding any arbitrary pandas dataframes
+
+ All attributes of type pandas.DataFrame will be saved with the study.
+
+ Parameters
+ ----------
+ name_to_df : dict
+ Mapping of a string of the name of the desired attribute, to the
+ pandas dataframe
+ """
+ if name_to_df is not None:
+ for name, df in name_to_df.items():
+ self.__setattr__(name, df)
diff --git a/flotilla/datapackage.py b/flotilla/datapackage.py
new file mode 100644
index 00000000..53ccbf23
--- /dev/null
+++ b/flotilla/datapackage.py
@@ -0,0 +1,195 @@
+"""
+Functions to deal with creation and loading of datapackages
+"""
+
+import gzip
+import json
+import os
+import string
+import sys
+import urllib2
+
+import matplotlib as mpl
+
+
+FLOTILLA_DOWNLOAD_DIR = os.path.expanduser('~/flotilla_projects')
+
+
+def datapackage_url_to_dict(datapackage_url):
+ filename = check_if_already_downloaded(datapackage_url)
+
+ with open(filename) as f:
+ datapackage = json.load(f)
+ return datapackage
+
+
+def check_if_already_downloaded(url,
+ datapackage_name=None,
+ download_dir=FLOTILLA_DOWNLOAD_DIR):
+ """If a url filename has already been downloaded, don't download it again.
+
+ Parameters
+ ----------
+ url : str
+ HTTP url of a file you want to downlaod
+
+ Returns
+ -------
+ filename : str
+ Location of the file on your system
+ """
+ try:
+ os.mkdir(download_dir)
+ sys.stdout.write('Creating a directory for saving your flotilla '
+ 'projects: {}\n'.format(download_dir))
+ except OSError:
+ pass
+
+ if datapackage_name is None:
+ req = urllib2.Request(url)
+ opener = urllib2.build_opener()
+ opened_url = opener.open(req)
+ datapackage = json.loads(opened_url.read())
+ datapackage_name = datapackage['name']
+
+ package_dir = '{}/{}'.format(download_dir, datapackage_name)
+
+ try:
+ os.mkdir(package_dir)
+ sys.stdout.write('Creating a directory for saving the data for this '
+ 'project: {}\n'.format(package_dir))
+ except OSError:
+ pass
+ basename = url.rsplit('/', 1)[-1]
+ filename = os.path.expanduser(os.path.join(package_dir, basename))
+
+ if not os.path.isfile(filename):
+ sys.stdout.write('{} has not been downloaded before.\n\tDownloading '
+ 'now to {}\n'.format(url, filename))
+ req = urllib2.Request(url)
+ opener = urllib2.build_opener()
+ opened_url = opener.open(req)
+ with open(filename, 'w') as f:
+ f.write(opened_url.read())
+ return filename
+
+
+def make_study_datapackage(study_name, metadata,
+ expression_data=None,
+ splicing_data=None,
+ spikein_data=None,
+ mapping_stats_data=None,
+ title='',
+ sources='', license=None, species=None,
+ flotilla_dir=FLOTILLA_DOWNLOAD_DIR,
+ metadata_kws=None,
+ expression_kws=None,
+ splicing_kws=None,
+ spikein_kws=None,
+ mapping_stats_kws=None,
+ version=None,
+ expression_feature_kws=None,
+ expression_feature_data=None,
+ splicing_feature_data=None,
+ splicing_feature_kws=None,
+ gene_ontology=None,
+ supplemental_kws=None,
+ host="https://s3-us-west-2.amazonaws.com/",
+ host_destination='flotilla-projects/'):
+ """Example code for making a datapackage for a Study"""
+ if ' ' in study_name:
+ raise ValueError("Datapackage name cannot have any spaces")
+ if set(string.uppercase) & set(study_name):
+ raise ValueError("Datapackage can only contain lowercase letters")
+
+ datapackage_dir = '{}/{}'.format(flotilla_dir, study_name)
+ try:
+ os.makedirs(datapackage_dir)
+ except OSError:
+ pass
+
+ supplemental_kws = {} if supplemental_kws is None else supplemental_kws
+
+ datapackage = {'name': study_name, 'title': title, 'sources': sources,
+ 'licenses': license, 'datapackage_version': version}
+
+ if species is not None:
+ datapackage['species'] = species
+
+ resources = {'metadata': (metadata, metadata_kws),
+ 'expression': (expression_data, expression_kws),
+ 'splicing': (splicing_data, splicing_kws),
+ 'spikein': (spikein_data, spikein_kws),
+ 'mapping_stats': (mapping_stats_data, mapping_stats_kws),
+ 'expression_feature': (expression_feature_data,
+ expression_feature_kws),
+ 'splicing_feature': (splicing_feature_data,
+ splicing_feature_kws),
+ 'gene_ontology': (gene_ontology, {})}
+
+ datapackage['resources'] = []
+ for resource_name, (data, kws) in resources.items():
+ if data is None:
+ continue
+
+ datapackage['resources'].append({'name': resource_name})
+ resource = datapackage['resources'][-1]
+
+ basename = '{}.csv.gz'.format(resource_name)
+ data_filename = '{}/{}'.format(datapackage_dir, basename)
+ with gzip.open(data_filename, 'wb') as f:
+ data.to_csv(f)
+
+ # if isinstance(data.columns, pd.MultiIndex):
+ # resource['header'] = range(len(data.columns.levels))
+ # if isinstance(data.index, pd.MultiIndex):
+ # resource['index_col'] = range(len(data.index.levels))
+ # try:
+ # # TODO: only transmit data if it has been updated
+ # subprocess.call(
+ # "scp {} {}:{}{}.".format(data_filename, host, host_destination,
+ # name), shell=True)
+ # except Exception as e:
+ # sys.stderr.write("error sending data to host: {}".format(e))
+
+ resource['path'] = basename
+ resource['compression'] = 'gzip'
+ resource['format'] = 'csv'
+ if kws is not None:
+ for key, value in kws.iteritems():
+ if key == 'phenotype_to_color':
+ value = dict((k, mpl.colors.rgb2hex(v))
+ if isinstance(v, tuple) else
+ (k, v)
+ for k, v in value.iteritems())
+ resource[key] = value
+
+ datapackage['resources'].append({'name': 'supplemental'})
+ supplemental = datapackage['resources'][-1]
+ supplemental['resources'] = []
+ for supplemental_name, data in supplemental_kws.items():
+ resource = {}
+
+ basename = '{}.csv.gz'.format(supplemental_name)
+ data_filename = '{}/{}'.format(datapackage_dir, basename)
+ with gzip.open(data_filename, 'wb') as f:
+ data.to_csv(f)
+
+ resource['name'] = supplemental_name
+ resource['path'] = basename
+ resource['compression'] = 'gzip'
+ resource['format'] = 'csv'
+ supplemental['resources'].append(resource)
+
+ filename = '{}/datapackage.json'.format(datapackage_dir)
+ with open(filename, 'w') as f:
+ json.dump(datapackage, f, indent=2)
+ sys.stdout.write('Wrote datapackage to {}\n'.format(filename))
+
+
+def name_to_resource(datapackage, name):
+ """Get resource with specified name in the datapackage"""
+ for resource in datapackage['resources']:
+ if resource['name'] == name:
+ return resource
+ raise ValueError('No resource named {} in this datapackage'.format(name))
diff --git a/flotilla/setup.py b/flotilla/setup.py
new file mode 120000
index 00000000..f8f80fc2
--- /dev/null
+++ b/flotilla/setup.py
@@ -0,0 +1 @@
+../setup.py
\ No newline at end of file
diff --git a/flotilla/src/__init__.py b/flotilla/src/__init__.py
deleted file mode 100644
index 918d8fa8..00000000
--- a/flotilla/src/__init__.py
+++ /dev/null
@@ -1,8 +0,0 @@
-__author__ = 'lovci'
-
-import _cargo_commonObjects as cargo
-import _frigate_compute as frigate
-import _carrier_DBconnection as carrier
-import _barge_utils as barge
-import _schooner_data_model as schooner
-import _submaraine_viz as submarine
\ No newline at end of file
diff --git a/flotilla/src/_barge_utils.py b/flotilla/src/_barge_utils.py
deleted file mode 100644
index 9c2908c9..00000000
--- a/flotilla/src/_barge_utils.py
+++ /dev/null
@@ -1,67 +0,0 @@
-__author__ = 'lovci'
-
-"""
-
-general utilities
-
-"""
-
-from functools import wraps
-import errno
-import os
-import signal
-import sys
-import subprocess
-
-###http://stackoverflow.com/questions/2281850/timeout-function-if-it-takes-too-long-to-finish###
-
-
-class TimeoutError(Exception):
- pass
-
-
-def timeout(seconds=10, error_message=os.strerror(errno.ETIME)):
- def decorator(func):
- def _handle_timeout(signum, frame):
- raise TimeoutError(error_message)
-
- def wrapper(*args, **kwargs):
- signal.signal(signal.SIGALRM, _handle_timeout)
- signal.alarm(seconds)
- try:
- result = func(*args, **kwargs)
- finally:
- signal.alarm(0)
- return result
-
- return wraps(func)(wrapper)
-
- return decorator
-###http://stackoverflow.com/questions/2281850/timeout-function-if-it-takes-too-long-to-finish###
-
-
-def serve_ipython():
- try:
-
- assert len(sys.argv) == 2
- path = sys.argv[1]
- assert os.path.exists(sys.argv[1])
-
- except:
- raise ValueError("specify a notebook directory as the first and only argument")
-
- c = subprocess.Popen(['ipython', 'notebook', '--script', '--notebook-dir', path])
- try:
- c.wait()
- except KeyboardInterrupt:
- c.terminate()
-
-
-def dict_to_str(dic):
- """join dictionary study_data into a string with that study_data"""
- return "_".join([k + ":" + str(v) for (k, v) in dic.items()])
-
-
-#def path_to_this_file():
-#
-# return os.path.join(os.path.dirname(__file__))
\ No newline at end of file
diff --git a/flotilla/src/_cargo_commonObjects/__init__.py b/flotilla/src/_cargo_commonObjects/__init__.py
deleted file mode 100644
index daa86b6f..00000000
--- a/flotilla/src/_cargo_commonObjects/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-__author__ = 'lovci'
-from _cargo_commonObjects import Cargo
-cargo = Cargo()
diff --git a/flotilla/src/_cargo_commonObjects/_cargo_commonObjects.py b/flotilla/src/_cargo_commonObjects/_cargo_commonObjects.py
deleted file mode 100644
index 2a9c15f4..00000000
--- a/flotilla/src/_cargo_commonObjects/_cargo_commonObjects.py
+++ /dev/null
@@ -1,115 +0,0 @@
-__author__ = 'lovci'
-
-"""
-
-object library
-
-commonly used data objects for genomes
-
-"""
-
-import sys
-
-import pandas as pd
-import os
-
-from .._skiff_external_sources import link_to_list, GO
-from .cargo_data import go_file_name, data_path
-
-
-class Cargo(object):
- """long loading and memory-intensive object management"""
-
- go = {}
- gene_lists = {}
-
- def get_species_cargo(self, species):
- """
- retrieve all cargo for a given species
- >>> cargo = Cargo()
- >>> hg19Cargo = cargo.get_species_cargo('hg19')
- """
- self.get_go(species)
- self.get_lists(species)
- return self
-
- def get_go(self, species):
- """
- initialize gene ontology object, takes > 30 seconds
- """
- try:
- assert(species in ["hg19", "mm9", "ce10"])
- except AssertionError:
- raise NotImplementedError("what is this %s you speak of?" % species)
-
- if species in self.go.keys():
- return self.go[species]
- else:
- GOFile = os.path.join(data_path, species, go_file_name)
- sys.stderr.write("importing GO...")
- self.go[species] = GO(GOFile)
- sys.stderr.write("done.\n")
-
-
- def get_lists(self, species):
- """
-
- get gene lists from cargo_data for a given species
-
- """
- try:
- assert(species == "hg19")
- except AssertionError:
- raise NotImplementedError("only hg19 is allowed at this point")
-
- try:
- rbps = pd.read_pickle(os.path.join(data_path, species, "gene_lists", "rbps.df"))
- confident_rbps = pd.read_pickle(os.path.join(data_path, species, "gene_lists", "confident_rpbs.df"))
- splicing_genes = pd.read_pickle(os.path.join(data_path, species, "gene_lists", "splicing_genes.df"))
- tfs = pd.read_pickle(os.path.join(data_path, species, "gene_lists", "tfs.df"))
-
-
- except:
- raise
- sys.stderr.write("rebuilding gene list objects from text and databases...\n")
-
- rbps_file = os.path.join(data_path, "rbps_list")
- confident_rbp_file = os.path.join(data_path, "all_pfam_defined_rbps_uniq.txt")
-
- rbps = pd.read_table(rbps_file).set_index("Ensembl_ID")
- rbps.to_pickle(os.path.join(data_path, species, "rbps.df"))
-
- with open(confident_rbp_file, 'r') as f:
- confident_rbps = set(map(str.strip, f.readlines()))
- rbps = rbps.ix[pd.Series(rbps.index.unique()).dropna()]
-
- confident_rbps = rbps.select(lambda x: rbps.GeneSymbol[x] in confident_rbps, 0)
-
- confident_rbps.to_pickle(os.path.join(data_path, species, "confident_rpbs.df"))
-
- splicing_genes = set(self.go[species].GO['GO:0008380']['genes']) | \
- set(self.go[species].GO['GO:0000381']['genes']) | \
- set(self.go[species].GO['GO:0006397']['genes'])
- splicing_genes = rbps.select(lambda x: x in splicing_genes)
-
- splicing_genes.to_pickle(os.path.join(data_path, species, "splicing_genes.df"))
-
- tfs = link_to_list("http://www.bioguo.org/AnimalTFDB/download/gene_list_of_Homo_sapiens.txt")
-
- with open(os.path.join(data_path, species, "gene_list_of_Homo_sapiens.txt"), 'r') as f:
- xx = f.readlines()
- tfs = pd.Series(map(lambda x: go.geneNames(x.strip()), xx), index= map(str.strip, xx))
- tfs.to_pickle(os.path.join(data_path, species, "tfs.df"))
-
- self.gene_lists = dict([('confident_rbps', confident_rbps),
- ('rbps', rbps),
- ('splicing_genes', splicing_genes),
- ('tfs', tfs)
- ])
-
-
- def get_conservation(self, species, interval):
- raise NotImplementedError
-
- def get_sequence(self, species, interval):
- raise NotImplementedError
diff --git a/flotilla/src/_cargo_commonObjects/cargo_data b/flotilla/src/_cargo_commonObjects/cargo_data
deleted file mode 160000
index 582b64c5..00000000
--- a/flotilla/src/_cargo_commonObjects/cargo_data
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit 582b64c595283ae5635a0f9bc1c2f5243232f5e1
diff --git a/flotilla/src/_carrier_DBconnection.py b/flotilla/src/_carrier_DBconnection.py
deleted file mode 100644
index c8bd2b53..00000000
--- a/flotilla/src/_carrier_DBconnection.py
+++ /dev/null
@@ -1,220 +0,0 @@
-__author__ = 'lovci'
-
-"""
-
-handles database connection, performs operations as a slave if run as a script.
-
-"""
-
-
-
-#!grep def slave.py
-import random
-
-from ._frigate_compute import *
-from ._cargo_commonObjects import *
-#from ..flotilla.neural_diff_project.src.project_params import letters, min_cells, mongoHost, mongoPort
-#from ..neural_diff_project.loaders import load_transcriptome_data
-mongoHost = ''
-mongoPort = ''
-min_cells = ''
-
-def fill_database(mongodb, predictor, target, verbose=False):
- """
- mongodb - mongo database connection object
- predictor - X values, i.e. RPKMs. Rows == cell ids, cols == gene ids
- target - Y values, i.e. PSIs. Rows == cell ids, cols == exon ids
- """
-
- nRand = 10
- if verbose:
- sys.stderr.write("processing...\n")
- event_i = 0
- for splice_event in get_unstarted_events(mongodb):
- event_i += 1
- E = splice_event['eventId']
- if verbose:
- sys.stderr.write(E)
-
- X = predictor.copy()
- y = target.copy()
-
- y = y.ix[E].dropna()
- X, y = X.align(y, join='inner', axis=0)
- basic={'eventId':E, '_id':splice_event['_id']}
-
- #objects = basic.copy()
- series = basic.copy()
- lists = basic.copy()
- values = basic.copy()
-
- for let in letters + ['all']:
- if verbose:
- sys.stderr.write("%s started\n" % let)
-
- if len(let) == 1:
- XX, yy = [i.ix[[j for j in i.index if j.startswith(let)]] for i in [X,y]]
-
- elif len(let)==2:
- XX, yy = [i.ix[[j for j in i.index if j.startswith(let[0]) or \
- j.startswith(let[1])]] for i in [X,y]]
- else:
- assert let == "all"
- XX, yy = X, y
-
- values['nCells_' + let] = len(yy)
- if verbose:
- print let, E, len(yy)
- if len(yy) <= min_cells:
- continue
-
- series['yy_' + let] = yy.to_json()
-
- regressor, oob_score_dist = get_regressor(XX,yy, verbose=verbose)
- series['importances_' + let] = regressor.feature_importances.to_json()
- values['clf_randomstate_' + let] = regressor.random_state
-
- boosting_regressor = get_boosting_regressor(XX,yy, verbose=verbose)
- values['boosting_clf_randomstate_' + let] = boosting_regressor.random_state
- series['boosting_importances_' + let] = regressor.feature_importances.to_json()
-
- dcor_DC, dcor_DR, dcor_DVX, dcor_DVY = apply_dcor(XX, yy)
- series['dcor_DC_' + let] = dcor_DC.to_json()
- series['dcor_DR_' + let] = dcor_DR.to_json()
- series['dcor_DVX_' + let] = dcor_DVX.to_json()
- series['dcor_DVY_' + let] = dcor_DVY.to_json()
-
- lists['scoreDist_' + let] = oob_score_dist
-
- values['score_' + let] = float(regressor.oob_score_)
-
- if np.mean(np.array(oob_score_dist)) > .05: #arbitrary... this is actually a pretty bad score.
- #don't waste time on random calculation if the real calcuations are crappy
- for i in range(nRand):
- if verbose:
- sys.stderr.write('shuffle\n')
- rand_yy = pd.Series(random.sample(yy, len(yy)), index=yy.index)
- regressor, oob_score_dist = get_regressor(XX, rand_yy, n_tries=3, verbose=verbose)
- lists['rand_%d_scoreDist_' % i + let] = oob_score_dist
-
- for method, method_name in [(stats.pearsonr, 'pearson'),
- (stats.spearmanr, 'spearman'), ]:
- if verbose:
- sys.stderr.write("trying method %s\n" % method_name)
- try:
- r, p = apply_calc_rs(XX, yy, method=method)
- except TimeoutError:
- sys.stderr.write("r caculation with method %s timed out on event %s\n" %(method_name, E))
- continue
-
- series[method_name + "_corr_r_" + let] = r.to_json()
- series[method_name + "_corr_p_" + let] = p.to_json()
-
- if verbose:
- sys.stderr.write("%s finished\n" % let)
-
- try:
- robust_intercept, robust_slope, robust_t, robust_p = apply_calc_robust(XX, yy)
- series["robust_intercept_" + let] = robust_intercept.to_json()
- series["robust_slope_" + let] = robust_slope.to_json()
- series["robust_t_" + let] = robust_t.to_json()
- series["robust_p_" + let] = robust_p.to_json()
-
- except TimeoutError:
- sys.stderr.write("robust regression timed out on event %s\n" % E)
- continue
- try:
- slope = apply_calc_slope(XX, yy)
- series["slope_" + let] = slope.to_json()
-
- except TimeoutError:
- sys.stderr.write("lingregress timed out on event %s\n" % E)
- continue
- if verbose:
- sys.stderr.write("saving %s\n" % E)
- splice_event['finished'] = True
- mongodb['list'].save(splice_event)
- mongodb['values'].save(values)
- mongodb['series'].save(series)
- mongodb['lists'].save(lists)
-
- sys.stderr.write("database is full, checked %d events\n" % (event_i+1))
-
-
-def get_mongo_db(db, mongoHost=mongoHost, mongoPort=mongoPort):
- from pymongo import MongoClient
- #ssh = subprocess.Popen(["ssh", "-L", ("%s:localhost:%s" %(mongoPort, mongoPort)), mongoHost, "-N"])
- c = MongoClient(mongoHost, port=mongoPort)
- sys.stderr.write('connected to database on %s:%d\n' % (mongoHost, mongoPort))
- return c, c[db]
-
-def load_event_list(mongo, target, target_type="SE"):
-
- mongo['list'].drop()
- _ = [mongo['list'].save({"eventId":ev, "started":False, "finished":False,
- 'splice_type':target_type}) for ev in target.index]
-
-def reset_event_list(mongodb):
- mongodb['list'].update({}, {'$set':{'started':False, 'finished':False}}, multi=True)
-
-def reset_event_data(mongodb):
- mongodb.drop_collection('values')
- mongodb.drop_collection('lists')
- mongodb.drop_collection('series')
-
-def poll(mongodb):
- #must be refactored for new "letters", i.e. celltypes
-
- list_size = mongodb['list'].count()
- started_size = mongodb['list'].find({'started':True}).count()
- finished_size = mongodb['list'].find({'finished':True}).count()
-
- total_count = mongodb['values'].count()
-# all_regressor_count = mongodb['values'].find({'score_all':{'$exists':True}}).count()
-# N_regressor_count = mongodb['values'].find({'score_N':{'$exists':True}}).count()
-# M_regressor_count = mongodb['values'].find({'score_M':{'$exists':True}}).count()
-# P_regressor_count = mongodb['values'].find({'score_P':{'$exists':True}}).count()
-# S_regressor_count = mongodb['values'].find({'score_S':{'$exists':True}}).count()
-# PN_regressor_count = mongodb['values'].find({'score_PN':{'$exists':True}}).count()
-# NM_regressor_count = mongodb['values'].find({'score_NM':{'$exists':True}}).count()
-# MS_regressor_count = mongodb['values'].find({'score_MS':{'$exists':True}}).count()
-
- print "%d events in whole transcriptome" %(list_size)
- print "%d (%.2f %%) events started" %(started_size, 100*started_size/list_size)
- print "%d (%.2f %%) events finished" %(finished_size, 100*finished_size/list_size)
- print "%d events examined" %(total_count)
-# print "%d (%.2f%%) with P regressors" %(P_regressor_count, 100*P_regressor_count/total_count)
-# print "%d (%.2f%%) with N regressors" %(N_regressor_count, 100*N_regressor_count/total_count)
-# print "%d (%.2f%%) with M regressors" %(M_regressor_count, 100*M_regressor_count/total_count)
-# print "%d (%.2f%%) with S regressors" %(S_regressor_count, 100*S_regressor_count/total_count)
-# print "%d (%.2f%%) with PN regressors" %(PN_regressor_count, 100*PN_regressor_count/total_count)
-# print "%d (%.2f%%) with NM regressors" %(NM_regressor_count, 100*NM_regressor_count/total_count)
-# print "%d (%.2f%%) with MS regressors" %(MS_regressor_count, 100*MS_regressor_count/total_count)
-# print "%d (%.2f%%) with combined regressors" %(all_regressor_count, 100*all_regressor_count/total_count)
-
-
-def begin(db = 'events'):
- (SEpsis, rpkm) = load_transcriptome_data()
-
- rbpRpkms = rpkms.ix[pd.Series(rbps.index).dropna()].fillna(0)
-
- mongo_con, mongodb = get_mongo_db(db)
- sys.stderr.write("finished loading raw study_data\n")
- return (SEpsis, rbpRpkms, mongodb)
-
-
-# load_event_list(mongodb, SEpsis, target_type="SE")
-
-# if __name__ == "__main__":
-# reset_event_list(mongodb)
-# reset_event_data(mongodb)
-
-
-if __name__ == "__main__":
- (SEpsis, rpkm, mongodb) = begin(db='misoevents')
- fill_database(mongodb, rpkm, SEpsis, verbose=True)
- poll(mongodb)
-
-#mongodb.values.find_one({'nCells_all':{'$gt':10}})
-
-# mongodb.list.update({'started':True, 'finished':False}, {"$set":{"started":False}}, multi=True)
\ No newline at end of file
diff --git a/flotilla/src/_frigate_compute.py b/flotilla/src/_frigate_compute.py
deleted file mode 100644
index c9762967..00000000
--- a/flotilla/src/_frigate_compute.py
+++ /dev/null
@@ -1,862 +0,0 @@
-from __future__ import division
-__author__ = 'lovci, obot'
-
-
-"""
-
-metrics, math for study_data analysis
-
-
-"""
-
-# -*- coding: utf-8 -*-
-# | \n", + " | ENSG00000003137 | \n", + "ENSG00000004848 | \n", + "ENSG00000006016 | \n", + "ENSG00000006116 | \n", + "ENSG00000006128 | \n", + "ENSG00000006377 | \n", + "ENSG00000007350 | \n", + "ENSG00000016082 | \n", + "ENSG00000041353 | \n", + "ENSG00000041982 | \n", + "... | \n", + "ENSG00000258283 | \n", + "ENSG00000258403 | \n", + "ENSG00000258444 | \n", + "ENSG00000258518 | \n", + "ENSG00000258752 | \n", + "ENSG00000259190 | \n", + "ENSG00000259279 | \n", + "ENSG00000259373 | \n", + "ENSG00000259410 | \n", + "ENSG00000259603 | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ENSG00000003137 | \n", + "1.000000 | \n", + "-0.083809 | \n", + "0.119793 | \n", + "0.076050 | \n", + "-0.035254 | \n", + "-0.170881 | \n", + "0.078876 | \n", + "-0.164944 | \n", + "-0.102906 | \n", + "-0.021128 | \n", + "... | \n", + "-0.214898 | \n", + "0.018936 | \n", + "-0.108965 | \n", + "-0.366995 | \n", + "-0.136422 | \n", + "0.282414 | \n", + "0.163946 | \n", + "0.487711 | \n", + "0.483485 | \n", + "0.088631 | \n", + "
| ENSG00000004848 | \n", + "-0.083809 | \n", + "1.000000 | \n", + "0.535174 | \n", + "0.658786 | \n", + "0.481359 | \n", + "0.670307 | \n", + "0.109242 | \n", + "0.187309 | \n", + "0.378170 | \n", + "-0.033828 | \n", + "... | \n", + "0.671103 | \n", + "0.313143 | \n", + "-0.001566 | \n", + "-0.500766 | \n", + "-0.091005 | \n", + "-0.174027 | \n", + "0.139083 | \n", + "-0.659290 | \n", + "-0.580665 | \n", + "-0.653537 | \n", + "
| ENSG00000006016 | \n", + "0.119793 | \n", + "0.535174 | \n", + "1.000000 | \n", + "0.622833 | \n", + "0.582943 | \n", + "0.415254 | \n", + "0.041606 | \n", + "0.102551 | \n", + "0.418882 | \n", + "0.051724 | \n", + "... | \n", + "0.637911 | \n", + "0.300402 | \n", + "0.243334 | \n", + "-0.345219 | \n", + "0.053212 | \n", + "-0.279398 | \n", + "0.069264 | \n", + "-0.309780 | \n", + "-0.303773 | \n", + "-0.267244 | \n", + "
| ENSG00000006116 | \n", + "0.076050 | \n", + "0.658786 | \n", + "0.622833 | \n", + "1.000000 | \n", + "0.471186 | \n", + "0.391800 | \n", + "-0.131698 | \n", + "-0.074954 | \n", + "0.424003 | \n", + "-0.297513 | \n", + "... | \n", + "0.673116 | \n", + "0.533787 | \n", + "-0.056825 | \n", + "-0.572178 | \n", + "-0.211297 | \n", + "0.022135 | \n", + "0.242858 | \n", + "-0.479994 | \n", + "-0.401955 | \n", + "-0.614655 | \n", + "
| ENSG00000006128 | \n", + "-0.035254 | \n", + "0.481359 | \n", + "0.582943 | \n", + "0.471186 | \n", + "1.000000 | \n", + "0.690821 | \n", + "-0.054401 | \n", + "0.427592 | \n", + "0.575299 | \n", + "0.115128 | \n", + "... | \n", + "0.688362 | \n", + "0.404930 | \n", + "0.292495 | \n", + "-0.190202 | \n", + "0.151798 | \n", + "-0.423034 | \n", + "-0.093961 | \n", + "-0.293967 | \n", + "-0.312149 | \n", + "-0.171620 | \n", + "
5 rows \u00d7 564 columns
\n", + "
-
-
-
diff --git a/intro to flotilla.ipynb b/intro to flotilla.ipynb
deleted file mode 100644
index a2de5b5e..00000000
--- a/intro to flotilla.ipynb
+++ /dev/null
@@ -1,8156 +0,0 @@
-{
- "metadata": {
- "name": "",
- "signature": "sha256:79bcc1745b911e02917526ecbdc395bb5e6648c7ce5e7ddbb270ef56afd449ac"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
- {
- "cells": [
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "%load_ext autoreload\n",
- "%autoreload 2\n",
- "%matplotlib inline"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 1
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#FLOTILLA INTRODUCTION"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Flotilla is a container for software to do genomics things. It is sub-divided into thematic parts that are named after boats.\n",
- "\n",
- "\"object-oriented magic\" - schooner\n",
- "
-
-
-
-
-
-
-
-
-In [1]:
-
-
-
-
-
-
-
-%load_ext autoreload
-%autoreload 2
-%matplotlib inline
-
-
-
-
-
-
-
-
-FLOTILLA INTRODUCTION
-
-
-
-
-
-
-
-
-Flotilla is a container for software to do genomics things. It is sub-divided into thematic parts that are named after boats.
-"object-oriented magic" - schooner 
"exploratory data viz" - submarine 
"things for computation" - frigate 
"draw data from external sources" - skiff 
"slow-loading things" - cargo 
"database things" - carrier 
-
-
-
-
-
-
-
-projects are stored in separate git repos, access them by import.
-
-neural_diff_project is yan's data, but projects can come with readme's
that make the stderr show messages about the data (like below)
-
-
-
-
-
-
-
-
-users import prjoects using:
-import projectnamewhere someone has already installed projectname into the python path.
-This tutorial uses neural_diff_project as an example.
-
-
-
-
-
-
-In [3]:
-
-
-
-
-
-
-
-import pandas as pd
-
-
-
-
-
-
-In [4]:
-
-
-
-
-
-
-
-import neural_diff_project
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-STDERR provides messages about the methods used to make the data.
-
-
-
-
-
-
-
-schooner is an object-oriented user interface.
-
-schooner is an object-oriented user interface.
-schooner.Study takes care of organizing data from multiple sources. It inherits subclasses that have tools to interact with data via IPython widgets, which is really nice.
schooner.ExpressionData and schooner.SplicingData do classifcation/regression and dimensionality reduction.
All data types should be available for user interaction (and are expected to be used) via the schooner.Study object.
##submarine is a visualization module that entirely depends on the other modules,this is mostly subclasses of the modules in frigate
##frigate is the computational workhorse, it holds the objects that do the maths.
-
-
-
-
-
-In [5]:
-
-
-
-
-
-
-
-from flotilla import schooner, submarine, frigate
-
-sc_study = schooner.Study(neural_diff_project, load_cargo=True, drop_outliers=True)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-what just happened?
-this puts the expression and splicing data, along with metadata about the
samples into an object-oriented interface called flotilla.schooner.Study which is now accessible through the sc_study variable
load_cargo=True takes >40 seconds to load the first time, it loads GO references into memory and sets up gene names for plots and such
-
-
-
-
-
-
-
-Sample Metadata
-plotting requires that two columns appear in the sample metadata data frame: "color" and "cell_marker"
-
-
-
-
-
-In [6]:
-
-
-
-
-
-
-
-sc_study.sample_info
-
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-
-
-
-
-
-
-
-
- Out[6]:
-
-
-
-
-
-
-
-
-
-
-
-Raw data for gene expression, TPM:
-
-
-
-
-
-
-In [7]:
-
-
-
-
-
-
-
-sc_study.expression.expression_df
-
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-
-
-
-
-
-
-
-
- Out[7]:
-
-
-
-
-
-
-
-
-
-
-
-Raw data for splicing, PSI:
-
-
-
-
-
-
-In [8]:
-
-
-
-
-
-
-
-sc_study.splicing.splicing_df
-
-
-
-
-
-
-
-
-
-
- Out[8]:
-
-
-
-
-
-
-
-
-
-
-parameters established in
-
-parameters established in neural_cell_diff poulate the lists of interactive objects
-
-
-
-
-
-
-
-
-interactive_pca
-runs PCA
-group_id is a subset of samples to use
data_type -designates whether you'd like to do PCA with either splicing or gene expression as features
featurewise - selects whether you'd like to visualize PCA of samples or of features
x_pc and y_pc - select the components to display for the x- and y- dimensions
show_point_labels - draws sample/feature labels
list_link - a local file path (full path) or a http: link to a list of line-delimited features to use for the PCA
list_name - contains pre-loaded lists, you can set this to "custom" to use list_link
savefile - a file to output
-
-
-
-
-
-In [9]:
-
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-
-
-sc_study.interactive_pca()
-
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-
-
-
-interactive_graph
-makes a graph where layout of nodes is determined by their covariance in PCA-space
-group_id - a subset of samples to use
data_type - designates whether you'd like to do PCA with either splicing or gene expression as features
featurewise - selects whether you'd like to visualize samples or of features
draw_labels - draws sample/feature labels on nodes
degree_cut - minimum degree for nodes to be included in the output cov_std_cut - minimum covariance for two nodes to have an edge n_pcs - use the first n_pcs feature_of_interest - feature to highlight. recommended that you paste into this box, rather than type directly use_pc_X - de-select to exclude component X list_name - contains pre-loaded lists and custom lists added with custom "list_link" in interactive_pca
savefile - a file to output weight_fun - a function to apply to covariances before filtering
-
-
-
-
-
-In [17]:
-
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-sc_study.interactive_graph()
-
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-
-
-interactive_clf
-makes a classifier, plots PCA of samples using only important features from the classifier. (default classifier is ExtraTreesClassifier, extremely randomized forests)
-data_type - designates whether you'd like to do PCA with either splicing or gene expression as features
list_name - contains pre-loaded lists and custom lists added with custom "list_link" in interactive_pca
group_id - a subset of samples to use
categorical_variable - a column in expression.sample_descriptors upon which to train a classifier
feature_score_std_cutoff - mean + (this x std) is used to select the top features
safefile - a file to output
-
-
-
-
-
-In [11]:
-
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-
-
-sc_study.interactive_clf()
-
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-In [12]:
-
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-
-
-
-
-print "here are the pooled samples\n"
-import pandas as pd
-for i in sc_study.sample_info.index[pd.Series(sc_study.sample_info['is_pooled'], dtype='bool')]:
- print "\t", i
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-interatctive_localZ
-runs localZ comparision on 2 RNAseq samples... works on any datatype but only really has meaning on expression.
sample1 - name of sample on x-axis
sample2 - name of sample on y-axis
pCut - p-value cutoff, must be a float. recommended that you paste here, not type in this box.
running this updates sc_study.localZ_result with a dataFrame of the results to be parsed by the user
-
-
-
-
-
-
-In [13]:
-
-
-
-
-
-
-
-sc_study.interactive_localZ()
-
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-In [16]:
-
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-
-sc_study.localZ_result
-
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-
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-
- Out[16]:
-
-
-
-
-
-
-
-
-
-In []:
-
-
-
-
-
-
-
-
-\n",
- "\n",
- "\"exploratory data viz\" - submarine\n",
- "\n",
- "\n",
- "\"things for computation\" - frigate\n",
- "\n",
- "\n",
- "\"draw data from external sources\" - skiff\n",
- "\n",
- "\n",
- "\"slow-loading things\" - cargo\n",
- "\n",
- "\n",
- "\"database things\" - carrier\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "projects are stored in separate git repos, access them by import.\n",
- "\n",
- "neural_diff_project is yan's data, but projects can come with readme's
that make the stderr show messages about the data (like below)
import projectname\n", - "schooner.Study takes care of organizing data from multiple sources. It inherits subclasses that have tools to interact with data via IPython widgets, which is really nice.
\n", - "schooner.ExpressionData and schooner.SplicingData do classifcation/regression and dimensionality reduction.
\n", - "All data types should be available for user interaction (and are expected to be used) via the\n", - "schooner.Study object.\n", - "
\n", - "
\n", - "##submarine is a visualization module that entirely depends on the other modules,this is mostly subclasses of the modules in frigate
\n", - "##frigate is the computational workhorse, it holds the objects that do the maths." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from flotilla import schooner, submarine, frigate \n", - "\n", - "sc_study = schooner.Study(neural_diff_project, load_cargo=True, drop_outliers=True) " - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "getting flotilla data from /Users/lovci/Projects/flotilla/flotilla/src/_cargo_commonObjects/cargo_data\n", - "dropping " - ] - }, - { - "output_type": "stream", - "stream": "stderr", - "text": [ - "importing GO...done.\n" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " set(['M_M2_07', 'M_M1_03', 'M_M2_05', 'M_M2_02', 'M_M2_01', 'P_P2_06', 'M_M2nd_21', 'S_MSA_21', 'S_MSA_05', 'S_MSA_28', 'M_M2_06', 'M_M3_11', 'M_M6_2', 'M_M6_1', 'M_M2nd_13', 'S_MSA_23', 'M_M3_3', 'M_M1_04', 'M_M3_1', 'M_M4_1', 'M_M4_2', 'M_M4_13', 'M_M4_10'])\n", - "dropping " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " set(['M_M2_07', 'M_M1_03', 'M_M2_05', 'M_M2_02', 'M_M2_01', 'P_P2_06', 'M_M2nd_21', 'M_M2nd_23', 'S_MSA_21', 'S_MSA_05', 'S_MSA_28', 'S_MSA_29', 'M_M2_06', 'M_M3_11', 'M_M6_2', 'M_M6_1', 'M_M2nd_13', 'S_MSA_19', 'S_MSA_23', 'M_M3_3', 'M_M1_04', 'M_M3_1', 'M_M4_2', 'M_M4_13', 'M_M4_10'])\n" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###what just happened?\n", - "this puts the expression and splicing data, along with metadata about the
samples into an object-oriented interface called flotilla.schooner.Study which is now accessible through the
sc_study variable\n", - "load_cargo=True takes >40 seconds to load the first time, it loads GO references into memory and sets up gene names for plots and such
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#Sample Metadata\n", - "\n", - "plotting requires that two columns appear in the sample metadata data frame: \"color\" and \"cell_marker\"
" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "sc_study.sample_info" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "html": [ - "
\n",
- "\n",
- " \n",
- "
\n",
- "
"
- ],
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 6,
- "text": [
- " R1_fastq.gz_md5sum R2_fastq.gz_md5sum \\\n",
- "id \n",
- "M_M1_01 \n",
- "M_M1_02 \n",
- "M_M1_03 \n",
- "M_M1_04 \n",
- "M_M1_05 \n",
- "M_M1_06 \n",
- "M_M1_07 \n",
- "M_M1_08 \n",
- "M_M1_09 \n",
- "M_M1_10 \n",
- "M_M1_11 \n",
- "M_M1_12 \n",
- "M_M2_01 \n",
- "M_M2_02 \n",
- "M_M2_03 \n",
- "M_M2_04 \n",
- "M_M2_05 \n",
- "M_M2_06 \n",
- "M_M2_07 \n",
- "M_M2_08 \n",
- "M_M2_09 \n",
- "M_M2_10 \n",
- "M_M2_11 \n",
- "M_M2_12 \n",
- "M_M2nd_01 \n",
- "M_M2nd_02 \n",
- "M_M2nd_03 \n",
- "M_M2nd_04 \n",
- "M_M2nd_05 \n",
- "M_M2nd_06 \n",
- "M_M2nd_07 \n",
- "M_M2nd_08 \n",
- "M_M2nd_09 \n",
- "M_M2nd_10 \n",
- "M_M2nd_11 \n",
- "M_M2nd_12 \n",
- "M_M2nd_13 \n",
- "M_M2nd_14 \n",
- "M_M2nd_15 \n",
- "M_M2nd_16 \n",
- "M_M2nd_17 \n",
- "M_M2nd_18 \n",
- "M_M2nd_19 \n",
- "M_M2nd_20 \n",
- "M_M2nd_21 \n",
- "M_M2nd_22 \n",
- "M_M2nd_23 \n",
- "M_M2nd_24 \n",
- "M_M2nd_25 \n",
- "M_M2nd_26 \n",
- "M_M2nd_27 \n",
- "M_M2nd_28 \n",
- "M_M2nd_29 \n",
- "M_M2nd_30 \n",
- "M_M2nd_31 \n",
- "M_M2nd_32 \n",
- "P_M2nd_33 \n",
- "P_M2nd_34 \n",
- "M_M3_1 \n",
- "M_M3_10 \n",
- " ... ... \n",
- "\n",
- " bam_location batch \\\n",
- "id \n",
- "M_M1_01 /oasis/tscc/scratch/ppliu/single_cell/single_c... round_1 \n",
- "M_M1_02 /oasis/tscc/scratch/ppliu/single_cell/single_c... round_1 \n",
- "M_M1_03 /oasis/tscc/scratch/ppliu/single_cell/single_c... round_1 \n",
- "M_M1_04 /oasis/tscc/scratch/ppliu/single_cell/single_c... round_1 \n",
- "M_M1_05 /oasis/tscc/scratch/ppliu/single_cell/single_c... round_1 \n",
- "M_M1_06 /oasis/tscc/scratch/ppliu/single_cell/single_c... round_1 \n",
- "M_M1_07 /oasis/tscc/scratch/ppliu/single_cell/single_c... round_1 \n",
- "M_M1_08 /oasis/tscc/scratch/ppliu/single_cell/single_c... round_1 \n",
- "M_M1_09 /oasis/tscc/scratch/ppliu/single_cell/single_c... round_1 \n",
- "M_M1_10 /oasis/tscc/scratch/ppliu/single_cell/single_c... round_1 \n",
- "M_M1_11 /oasis/tscc/scratch/ppliu/single_cell/single_c... round_1 \n",
- "M_M1_12 /oasis/tscc/scratch/ppliu/single_cell/single_c... round_1 \n",
- "M_M2_01 /oasis/tscc/scratch/ppliu/single_cell/single_c... round_1 \n",
- "M_M2_02 /oasis/tscc/scratch/ppliu/single_cell/single_c... round_1 \n",
- "M_M2_03 /oasis/tscc/scratch/ppliu/single_cell/single_c... round_1 \n",
- "M_M2_04 /oasis/tscc/scratch/ppliu/single_cell/single_c... round_1 \n",
- "M_M2_05 /oasis/tscc/scratch/ppliu/single_cell/single_c... round_1 \n",
- "M_M2_06 /oasis/tscc/scratch/ppliu/single_cell/single_c... round_1 \n",
- "M_M2_07 /oasis/tscc/scratch/ppliu/single_cell/single_c... round_1 \n",
- "M_M2_08 /oasis/tscc/scratch/ppliu/single_cell/single_c... round_1 \n",
- "M_M2_09 /oasis/tscc/scratch/ppliu/single_cell/single_c... round_1 \n",
- "M_M2_10 /oasis/tscc/scratch/ppliu/single_cell/single_c... round_1 \n",
- "M_M2_11 /oasis/tscc/scratch/ppliu/single_cell/single_c... round_1 \n",
- "M_M2_12 /oasis/tscc/scratch/ppliu/single_cell/single_c... round_1 \n",
- "M_M2nd_01 /oasis/tscc/scratch/ppliu/single_cell_stressed... round_4 \n",
- "M_M2nd_02 /oasis/tscc/scratch/ppliu/single_cell_stressed... round_4 \n",
- "M_M2nd_03 /oasis/tscc/scratch/ppliu/single_cell_stressed... round_4 \n",
- "M_M2nd_04 /oasis/tscc/scratch/ppliu/single_cell_stressed... round_4 \n",
- "M_M2nd_05 /oasis/tscc/scratch/ppliu/single_cell_stressed... round_4 \n",
- "M_M2nd_06 /oasis/tscc/scratch/ppliu/single_cell_stressed... round_4 \n",
- "M_M2nd_07 /oasis/tscc/scratch/ppliu/single_cell_stressed... round_4 \n",
- "M_M2nd_08 /oasis/tscc/scratch/ppliu/single_cell_stressed... round_4 \n",
- "M_M2nd_09 /oasis/tscc/scratch/ppliu/single_cell_stressed... round_4 \n",
- "M_M2nd_10 /oasis/tscc/scratch/ppliu/single_cell_stressed... round_4 \n",
- "M_M2nd_11 /oasis/tscc/scratch/ppliu/single_cell_stressed... round_4 \n",
- "M_M2nd_12 /oasis/tscc/scratch/ppliu/single_cell_stressed... round_4 \n",
- "M_M2nd_13 /oasis/tscc/scratch/ppliu/single_cell_stressed... round_4 \n",
- "M_M2nd_14 /oasis/tscc/scratch/ppliu/single_cell_stressed... round_4 \n",
- "M_M2nd_15 /oasis/tscc/scratch/ppliu/single_cell_stressed... round_4 \n",
- "M_M2nd_16 /oasis/tscc/scratch/ppliu/single_cell_stressed... round_4 \n",
- "M_M2nd_17 /oasis/tscc/scratch/ppliu/single_cell_stressed... round_4 \n",
- "M_M2nd_18 /oasis/tscc/scratch/ppliu/single_cell_stressed... round_4 \n",
- "M_M2nd_19 /oasis/tscc/scratch/ppliu/single_cell_stressed... round_4 \n",
- "M_M2nd_20 /oasis/tscc/scratch/ppliu/single_cell_stressed... round_4 \n",
- "M_M2nd_21 /oasis/tscc/scratch/ppliu/single_cell_stressed... round_4 \n",
- "M_M2nd_22 /oasis/tscc/scratch/ppliu/single_cell_stressed... round_4 \n",
- "M_M2nd_23 /oasis/tscc/scratch/ppliu/single_cell_stressed... round_4 \n",
- "M_M2nd_24 /oasis/tscc/scratch/ppliu/single_cell_stressed... round_4 \n",
- "M_M2nd_25 /oasis/tscc/scratch/ppliu/single_cell_stressed... round_4 \n",
- "M_M2nd_26 /oasis/tscc/scratch/ppliu/single_cell_stressed... round_4 \n",
- "M_M2nd_27 /oasis/tscc/scratch/ppliu/single_cell_stressed... round_4 \n",
- "M_M2nd_28 /oasis/tscc/scratch/ppliu/single_cell_stressed... round_4 \n",
- "M_M2nd_29 /oasis/tscc/scratch/ppliu/single_cell_stressed... round_4 \n",
- "M_M2nd_30 /oasis/tscc/scratch/ppliu/single_cell_stressed... round_4 \n",
- "M_M2nd_31 /oasis/tscc/scratch/ppliu/single_cell_stressed... round_4 \n",
- "M_M2nd_32 /oasis/tscc/scratch/ppliu/single_cell_stressed... round_4 \n",
- "P_M2nd_33 /oasis/tscc/scratch/ppliu/single_cell_stressed... round_4 \n",
- "P_M2nd_34 /oasis/tscc/scratch/ppliu/single_cell_stressed... round_4 \n",
- "M_M3_1 /oasis/tscc/scratch/ppliu/single_cell/single_c... round_2 \n",
- "M_M3_10 /oasis/tscc/scratch/ppliu/single_cell/single_c... round_2 \n",
- " ... ... \n",
- "\n",
- " cell_type is_craig? is_pooled is_split_of outlier M_cell \\\n",
- "id \n",
- "M_M1_01 M TRUE 0 NaN 0 True \n",
- "M_M1_02 M TRUE 0 NaN 0 True \n",
- "M_M1_03 M TRUE 0 NaN 1 True \n",
- "M_M1_04 M TRUE 0 NaN 1 True \n",
- "M_M1_05 M TRUE 0 NaN 0 True \n",
- "M_M1_06 M TRUE 0 NaN 0 True \n",
- "M_M1_07 M TRUE 0 NaN 0 True \n",
- "M_M1_08 M TRUE 0 NaN 0 True \n",
- "M_M1_09 M TRUE 0 NaN 0 True \n",
- "M_M1_10 M TRUE 0 NaN 0 True \n",
- "M_M1_11 M TRUE 0 NaN 0 True \n",
- "M_M1_12 M TRUE 0 NaN 0 True \n",
- "M_M2_01 M TRUE 0 NaN 1 True \n",
- "M_M2_02 M TRUE 0 NaN 1 True \n",
- "M_M2_03 M TRUE 0 NaN 0 True \n",
- "M_M2_04 M TRUE 0 NaN 0 True \n",
- "M_M2_05 M TRUE 1 NaN 1 True \n",
- "M_M2_06 M TRUE 0 NaN 1 True \n",
- "M_M2_07 M TRUE 0 NaN 1 True \n",
- "M_M2_08 M TRUE 0 NaN 0 True \n",
- "M_M2_09 M TRUE 0 NaN 0 True \n",
- "M_M2_10 M TRUE 0 NaN 0 True \n",
- "M_M2_11 M TRUE 0 NaN 0 True \n",
- "M_M2_12 M TRUE 0 NaN 0 True \n",
- "M_M2nd_01 M TRUE 0 NaN 0 True \n",
- "M_M2nd_02 M TRUE 0 NaN 0 True \n",
- "M_M2nd_03 M TRUE 0 NaN 0 True \n",
- "M_M2nd_04 M TRUE 0 NaN 0 True \n",
- "M_M2nd_05 M TRUE 0 NaN 0 True \n",
- "M_M2nd_06 M TRUE 0 NaN 0 True \n",
- "M_M2nd_07 M TRUE 0 NaN 0 True \n",
- "M_M2nd_08 M TRUE 0 NaN 0 True \n",
- "M_M2nd_09 M TRUE 0 NaN 0 True \n",
- "M_M2nd_10 M TRUE 0 NaN 0 True \n",
- "M_M2nd_11 M TRUE 0 NaN 0 True \n",
- "M_M2nd_12 M TRUE 0 NaN 0 True \n",
- "M_M2nd_13 M TRUE 1 NaN 1 True \n",
- "M_M2nd_14 M TRUE 0 NaN 0 True \n",
- "M_M2nd_15 M TRUE 0 NaN 0 True \n",
- "M_M2nd_16 M TRUE 0 NaN 0 True \n",
- "M_M2nd_17 M TRUE 0 NaN 0 True \n",
- "M_M2nd_18 M TRUE 0 NaN 0 True \n",
- "M_M2nd_19 M TRUE 0 NaN 0 True \n",
- "M_M2nd_20 M TRUE 0 NaN 0 True \n",
- "M_M2nd_21 M TRUE 1 NaN 1 True \n",
- "M_M2nd_22 M TRUE 0 NaN 0 True \n",
- "M_M2nd_23 M TRUE 0 NaN 1 True \n",
- "M_M2nd_24 M TRUE 0 NaN 0 True \n",
- "M_M2nd_25 M TRUE 0 NaN 0 True \n",
- "M_M2nd_26 M TRUE 0 NaN 0 True \n",
- "M_M2nd_27 M TRUE 0 NaN 0 True \n",
- "M_M2nd_28 M TRUE 0 NaN 0 True \n",
- "M_M2nd_29 M TRUE 0 NaN 0 True \n",
- "M_M2nd_30 M TRUE 0 NaN 0 True \n",
- "M_M2nd_31 M TRUE 0 NaN 0 True \n",
- "M_M2nd_32 M TRUE 0 NaN 0 True \n",
- "P_M2nd_33 P TRUE 1 NaN 0 False \n",
- "P_M2nd_34 P TRUE 1 NaN 0 False \n",
- "M_M3_1 M TRUE 0 NaN 1 True \n",
- "M_M3_10 M TRUE 0 NaN 0 True \n",
- " ... ... ... ... ... ... \n",
- "\n",
- " neuron_cell N_cell P_cell S_cell cell_color cell_marker \\\n",
- "id \n",
- "M_M1_01 True False False False #e41a1c D \n",
- "M_M1_02 True False False False #e41a1c D \n",
- "M_M1_03 True False False False #e41a1c D \n",
- "M_M1_04 True False False False #e41a1c D \n",
- "M_M1_05 True False False False #e41a1c D \n",
- "M_M1_06 True False False False #e41a1c D \n",
- "M_M1_07 True False False False #e41a1c D \n",
- "M_M1_08 True False False False #e41a1c D \n",
- "M_M1_09 True False False False #e41a1c D \n",
- "M_M1_10 True False False False #e41a1c D \n",
- "M_M1_11 True False False False #e41a1c D \n",
- "M_M1_12 True False False False #e41a1c D \n",
- "M_M2_01 True False False False #e41a1c D \n",
- "M_M2_02 True False False False #e41a1c D \n",
- "M_M2_03 True False False False #e41a1c D \n",
- "M_M2_04 True False False False #e41a1c D \n",
- "M_M2_05 True False False False #e41a1c D \n",
- "M_M2_06 True False False False #e41a1c D \n",
- "M_M2_07 True False False False #e41a1c D \n",
- "M_M2_08 True False False False #e41a1c D \n",
- "M_M2_09 True False False False #e41a1c D \n",
- "M_M2_10 True False False False #e41a1c D \n",
- "M_M2_11 True False False False #e41a1c D \n",
- "M_M2_12 True False False False #e41a1c D \n",
- "M_M2nd_01 True False False False #e41a1c D \n",
- "M_M2nd_02 True False False False #e41a1c D \n",
- "M_M2nd_03 True False False False #e41a1c D \n",
- "M_M2nd_04 True False False False #e41a1c D \n",
- "M_M2nd_05 True False False False #e41a1c D \n",
- "M_M2nd_06 True False False False #e41a1c D \n",
- "M_M2nd_07 True False False False #e41a1c D \n",
- "M_M2nd_08 True False False False #e41a1c D \n",
- "M_M2nd_09 True False False False #e41a1c D \n",
- "M_M2nd_10 True False False False #e41a1c D \n",
- "M_M2nd_11 True False False False #e41a1c D \n",
- "M_M2nd_12 True False False False #e41a1c D \n",
- "M_M2nd_13 True False False False #e41a1c D \n",
- "M_M2nd_14 True False False False #e41a1c D \n",
- "M_M2nd_15 True False False False #e41a1c D \n",
- "M_M2nd_16 True False False False #e41a1c D \n",
- "M_M2nd_17 True False False False #e41a1c D \n",
- "M_M2nd_18 True False False False #e41a1c D \n",
- "M_M2nd_19 True False False False #e41a1c D \n",
- "M_M2nd_20 True False False False #e41a1c D \n",
- "M_M2nd_21 True False False False #e41a1c D \n",
- "M_M2nd_22 True False False False #e41a1c D \n",
- "M_M2nd_23 True False False False #e41a1c D \n",
- "M_M2nd_24 True False False False #e41a1c D \n",
- "M_M2nd_25 True False False False #e41a1c D \n",
- "M_M2nd_26 True False False False #e41a1c D \n",
- "M_M2nd_27 True False False False #e41a1c D \n",
- "M_M2nd_28 True False False False #e41a1c D \n",
- "M_M2nd_29 True False False False #e41a1c D \n",
- "M_M2nd_30 True False False False #e41a1c D \n",
- "M_M2nd_31 True False False False #e41a1c D \n",
- "M_M2nd_32 True False False False #e41a1c D \n",
- "P_M2nd_33 False False True False #377eb8 o \n",
- "P_M2nd_34 False False True False #377eb8 o \n",
- "M_M3_1 True False False False #e41a1c D \n",
- "M_M3_10 True False False False #e41a1c D \n",
- " ... ... ... ... ... ... \n",
- "\n",
- " original_name any_cell color marker \n",
- "id \n",
- "M_M1_01 M1_01 True #e41a1c D \n",
- "M_M1_02 M1_02 True #e41a1c D \n",
- "M_M1_03 M1_03 True #e41a1c D \n",
- "M_M1_04 M1_04 True #e41a1c D \n",
- "M_M1_05 M1_05 True #e41a1c D \n",
- "M_M1_06 M1_06 True #e41a1c D \n",
- "M_M1_07 M1_07 True #e41a1c D \n",
- "M_M1_08 M1_08 True #e41a1c D \n",
- "M_M1_09 M1_09 True #e41a1c D \n",
- "M_M1_10 M1_10 True #e41a1c D \n",
- "M_M1_11 M1_11 True #e41a1c D \n",
- "M_M1_12 M1_12 True #e41a1c D \n",
- "M_M2_01 M2_01 True #e41a1c D \n",
- "M_M2_02 M2_02 True #e41a1c D \n",
- "M_M2_03 M2_03 True #e41a1c D \n",
- "M_M2_04 M2_04 True #e41a1c D \n",
- "M_M2_05 M2_05 True #e41a1c D \n",
- "M_M2_06 M2_06 True #e41a1c D \n",
- "M_M2_07 M2_07 True #e41a1c D \n",
- "M_M2_08 M2_08 True #e41a1c D \n",
- "M_M2_09 M2_09 True #e41a1c D \n",
- "M_M2_10 M2_10 True #e41a1c D \n",
- "M_M2_11 M2_11 True #e41a1c D \n",
- "M_M2_12 M2_12 True #e41a1c D \n",
- "M_M2nd_01 M2nd_01 True #e41a1c D \n",
- "M_M2nd_02 M2nd_02 True #e41a1c D \n",
- "M_M2nd_03 M2nd_03 True #e41a1c D \n",
- "M_M2nd_04 M2nd_04 True #e41a1c D \n",
- "M_M2nd_05 M2nd_05 True #e41a1c D \n",
- "M_M2nd_06 M2nd_06 True #e41a1c D \n",
- "M_M2nd_07 M2nd_07 True #e41a1c D \n",
- "M_M2nd_08 M2nd_08 True #e41a1c D \n",
- "M_M2nd_09 M2nd_09 True #e41a1c D \n",
- "M_M2nd_10 M2nd_10 True #e41a1c D \n",
- "M_M2nd_11 M2nd_11 True #e41a1c D \n",
- "M_M2nd_12 M2nd_12 True #e41a1c D \n",
- "M_M2nd_13 M2nd_13 True #e41a1c D \n",
- "M_M2nd_14 M2nd_14 True #e41a1c D \n",
- "M_M2nd_15 M2nd_15 True #e41a1c D \n",
- "M_M2nd_16 M2nd_16 True #e41a1c D \n",
- "M_M2nd_17 M2nd_17 True #e41a1c D \n",
- "M_M2nd_18 M2nd_18 True #e41a1c D \n",
- "M_M2nd_19 M2nd_19 True #e41a1c D \n",
- "M_M2nd_20 M2nd_20 True #e41a1c D \n",
- "M_M2nd_21 M2nd_21 True #e41a1c D \n",
- "M_M2nd_22 M2nd_22 True #e41a1c D \n",
- "M_M2nd_23 M2nd_23 True #e41a1c D \n",
- "M_M2nd_24 M2nd_24 True #e41a1c D \n",
- "M_M2nd_25 M2nd_25 True #e41a1c D \n",
- "M_M2nd_26 M2nd_26 True #e41a1c D \n",
- "M_M2nd_27 M2nd_27 True #e41a1c D \n",
- "M_M2nd_28 M2nd_28 True #e41a1c D \n",
- "M_M2nd_29 M2nd_29 True #e41a1c D \n",
- "M_M2nd_30 M2nd_30 True #e41a1c D \n",
- "M_M2nd_31 M2nd_31 True #e41a1c D \n",
- "M_M2nd_32 M2nd_32 True #e41a1c D \n",
- "P_M2nd_33 M2nd_33 True #377eb8 o \n",
- "P_M2nd_34 M2nd_34 True #377eb8 o \n",
- "M_M3_1 M3_1 True #e41a1c D \n",
- "M_M3_10 M3_10 True #e41a1c D \n",
- " ... ... ... ... \n",
- "\n",
- "[295 rows x 20 columns]"
- ]
- }
- ],
- "prompt_number": 6
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#Raw data for gene expression, TPM:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "sc_study.expression.expression_df"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "html": [
- "| \n", - " | R1_fastq.gz_md5sum | \n", - "R2_fastq.gz_md5sum | \n", - "bam_location | \n", - "batch | \n", - "cell_type | \n", - "is_craig? | \n", - "is_pooled | \n", - "is_split_of | \n", - "outlier | \n", - "M_cell | \n", - "neuron_cell | \n", - "N_cell | \n", - "P_cell | \n", - "S_cell | \n", - "cell_color | \n", - "cell_marker | \n", - "original_name | \n", - "any_cell | \n", - "color | \n", - "marker | \n", - "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | \n", - "\n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " |
| M_M1_01 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell/single_c... | \n", - "round_1 | \n", - "M | \n", - "TRUE | \n", - "0 | \n", - "NaN | \n", - "0 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M1_01 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
| M_M1_02 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell/single_c... | \n", - "round_1 | \n", - "M | \n", - "TRUE | \n", - "0 | \n", - "NaN | \n", - "0 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M1_02 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
| M_M1_03 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell/single_c... | \n", - "round_1 | \n", - "M | \n", - "TRUE | \n", - "0 | \n", - "NaN | \n", - "1 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M1_03 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
| M_M1_04 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell/single_c... | \n", - "round_1 | \n", - "M | \n", - "TRUE | \n", - "0 | \n", - "NaN | \n", - "1 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M1_04 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
| M_M1_05 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell/single_c... | \n", - "round_1 | \n", - "M | \n", - "TRUE | \n", - "0 | \n", - "NaN | \n", - "0 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M1_05 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
| M_M1_06 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell/single_c... | \n", - "round_1 | \n", - "M | \n", - "TRUE | \n", - "0 | \n", - "NaN | \n", - "0 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M1_06 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
| M_M1_07 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell/single_c... | \n", - "round_1 | \n", - "M | \n", - "TRUE | \n", - "0 | \n", - "NaN | \n", - "0 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M1_07 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
| M_M1_08 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell/single_c... | \n", - "round_1 | \n", - "M | \n", - "TRUE | \n", - "0 | \n", - "NaN | \n", - "0 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M1_08 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
| M_M1_09 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell/single_c... | \n", - "round_1 | \n", - "M | \n", - "TRUE | \n", - "0 | \n", - "NaN | \n", - "0 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M1_09 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
| M_M1_10 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell/single_c... | \n", - "round_1 | \n", - "M | \n", - "TRUE | \n", - "0 | \n", - "NaN | \n", - "0 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M1_10 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
| M_M1_11 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell/single_c... | \n", - "round_1 | \n", - "M | \n", - "TRUE | \n", - "0 | \n", - "NaN | \n", - "0 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M1_11 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
| M_M1_12 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell/single_c... | \n", - "round_1 | \n", - "M | \n", - "TRUE | \n", - "0 | \n", - "NaN | \n", - "0 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M1_12 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
| M_M2_01 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell/single_c... | \n", - "round_1 | \n", - "M | \n", - "TRUE | \n", - "0 | \n", - "NaN | \n", - "1 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M2_01 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
| M_M2_02 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell/single_c... | \n", - "round_1 | \n", - "M | \n", - "TRUE | \n", - "0 | \n", - "NaN | \n", - "1 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M2_02 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
| M_M2_03 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell/single_c... | \n", - "round_1 | \n", - "M | \n", - "TRUE | \n", - "0 | \n", - "NaN | \n", - "0 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M2_03 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
| M_M2_04 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell/single_c... | \n", - "round_1 | \n", - "M | \n", - "TRUE | \n", - "0 | \n", - "NaN | \n", - "0 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M2_04 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
| M_M2_05 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell/single_c... | \n", - "round_1 | \n", - "M | \n", - "TRUE | \n", - "1 | \n", - "NaN | \n", - "1 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M2_05 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
| M_M2_06 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell/single_c... | \n", - "round_1 | \n", - "M | \n", - "TRUE | \n", - "0 | \n", - "NaN | \n", - "1 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M2_06 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
| M_M2_07 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell/single_c... | \n", - "round_1 | \n", - "M | \n", - "TRUE | \n", - "0 | \n", - "NaN | \n", - "1 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M2_07 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
| M_M2_08 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell/single_c... | \n", - "round_1 | \n", - "M | \n", - "TRUE | \n", - "0 | \n", - "NaN | \n", - "0 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M2_08 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
| M_M2_09 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell/single_c... | \n", - "round_1 | \n", - "M | \n", - "TRUE | \n", - "0 | \n", - "NaN | \n", - "0 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M2_09 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
| M_M2_10 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell/single_c... | \n", - "round_1 | \n", - "M | \n", - "TRUE | \n", - "0 | \n", - "NaN | \n", - "0 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M2_10 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
| M_M2_11 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell/single_c... | \n", - "round_1 | \n", - "M | \n", - "TRUE | \n", - "0 | \n", - "NaN | \n", - "0 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M2_11 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
| M_M2_12 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell/single_c... | \n", - "round_1 | \n", - "M | \n", - "TRUE | \n", - "0 | \n", - "NaN | \n", - "0 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M2_12 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
| M_M2nd_01 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell_stressed... | \n", - "round_4 | \n", - "M | \n", - "TRUE | \n", - "0 | \n", - "NaN | \n", - "0 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M2nd_01 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
| M_M2nd_02 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell_stressed... | \n", - "round_4 | \n", - "M | \n", - "TRUE | \n", - "0 | \n", - "NaN | \n", - "0 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M2nd_02 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
| M_M2nd_03 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell_stressed... | \n", - "round_4 | \n", - "M | \n", - "TRUE | \n", - "0 | \n", - "NaN | \n", - "0 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M2nd_03 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
| M_M2nd_04 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell_stressed... | \n", - "round_4 | \n", - "M | \n", - "TRUE | \n", - "0 | \n", - "NaN | \n", - "0 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M2nd_04 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
| M_M2nd_05 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell_stressed... | \n", - "round_4 | \n", - "M | \n", - "TRUE | \n", - "0 | \n", - "NaN | \n", - "0 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M2nd_05 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
| M_M2nd_06 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell_stressed... | \n", - "round_4 | \n", - "M | \n", - "TRUE | \n", - "0 | \n", - "NaN | \n", - "0 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M2nd_06 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
| M_M2nd_07 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell_stressed... | \n", - "round_4 | \n", - "M | \n", - "TRUE | \n", - "0 | \n", - "NaN | \n", - "0 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M2nd_07 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
| M_M2nd_08 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell_stressed... | \n", - "round_4 | \n", - "M | \n", - "TRUE | \n", - "0 | \n", - "NaN | \n", - "0 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M2nd_08 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
| M_M2nd_09 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell_stressed... | \n", - "round_4 | \n", - "M | \n", - "TRUE | \n", - "0 | \n", - "NaN | \n", - "0 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M2nd_09 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
| M_M2nd_10 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell_stressed... | \n", - "round_4 | \n", - "M | \n", - "TRUE | \n", - "0 | \n", - "NaN | \n", - "0 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M2nd_10 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
| M_M2nd_11 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell_stressed... | \n", - "round_4 | \n", - "M | \n", - "TRUE | \n", - "0 | \n", - "NaN | \n", - "0 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M2nd_11 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
| M_M2nd_12 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell_stressed... | \n", - "round_4 | \n", - "M | \n", - "TRUE | \n", - "0 | \n", - "NaN | \n", - "0 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M2nd_12 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
| M_M2nd_13 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell_stressed... | \n", - "round_4 | \n", - "M | \n", - "TRUE | \n", - "1 | \n", - "NaN | \n", - "1 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M2nd_13 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
| M_M2nd_14 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell_stressed... | \n", - "round_4 | \n", - "M | \n", - "TRUE | \n", - "0 | \n", - "NaN | \n", - "0 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M2nd_14 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
| M_M2nd_15 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell_stressed... | \n", - "round_4 | \n", - "M | \n", - "TRUE | \n", - "0 | \n", - "NaN | \n", - "0 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M2nd_15 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
| M_M2nd_16 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell_stressed... | \n", - "round_4 | \n", - "M | \n", - "TRUE | \n", - "0 | \n", - "NaN | \n", - "0 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M2nd_16 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
| M_M2nd_17 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell_stressed... | \n", - "round_4 | \n", - "M | \n", - "TRUE | \n", - "0 | \n", - "NaN | \n", - "0 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M2nd_17 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
| M_M2nd_18 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell_stressed... | \n", - "round_4 | \n", - "M | \n", - "TRUE | \n", - "0 | \n", - "NaN | \n", - "0 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M2nd_18 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
| M_M2nd_19 | \n", - "\n", - " | \n", - " | /oasis/tscc/scratch/ppliu/single_cell_stressed... | \n", - "round_4 | \n", - "M | \n", - "TRUE | \n", - "0 | \n", - "NaN | \n", - "0 | \n", - "True | \n", - "True | \n", - "False | \n", - "False | \n", - "False | \n", - "#e41a1c | \n", - "D | \n", - "M2nd_19 | \n", - "True | \n", - "#e41a1c | \n", - "D | \n", - "
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- " ... ... ... ... \n",
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- "gene_ID ENSG00000254673 ENSG00000106992 ENSG00000167074 ENSG00000143479 \\\n",
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- "S_MSA_06 1.824732 0.803690 0.000000 0.000000 \n",
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- " ... ... ... ... \n",
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- "| gene_ID | \n", - "ENSG00000237851 | \n", - "ENSG00000257527 | \n", - "ENSG00000225193 | \n", - "ENSG00000271402 | \n", - "ENSG00000254681 | \n", - "ENSG00000228477 | \n", - "ENSG00000159733 | \n", - "ENSG00000220785 | \n", - "ENSG00000125409 | \n", - "ENSG00000034063 | \n", - "ENSG00000237472 | \n", - "ENSG00000140575 | \n", - "ENSG00000254673 | \n", - "ENSG00000106992 | \n", - "ENSG00000167074 | \n", - "ENSG00000143479 | \n", - "ENSG00000225118 | \n", - "ENSG00000119514 | \n", - "ENSG00000223861 | \n", - "ENSG00000108510 | \n", - "\n", - " |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | \n", - "\n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | |
| S_MSA_10 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.441947 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.786171 | \n", - "... | \n", - "
| P_P9_1 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.292186 | \n", - "0.000000 | \n", - "0.919445 | \n", - "0.000000 | \n", - "0.233416 | \n", - "0.000000 | \n", - "0.853646 | \n", - "0.000000 | \n", - "1.355984 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.163835 | \n", - "0.184955 | \n", - "0.000000 | \n", - "... | \n", - "
| P_P4_14 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.275686 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "1.414262 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.227558 | \n", - "0.906800 | \n", - "... | \n", - "
| P_P9_2 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.247384 | \n", - "0.000000 | \n", - "1.001820 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "1.614222 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "... | \n", - "
| P_P4_12 | \n", - "0.000000 | \n", - "0 | \n", - "0.203649 | \n", - "0 | \n", - "0.000000 | \n", - "0.403581 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "1.378214 | \n", - "0.000000 | \n", - "1.595169 | \n", - "0.823780 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.186982 | \n", - "0.375245 | \n", - "... | \n", - "
| P_P4_13 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.245697 | \n", - "0.000000 | \n", - "0.816606 | \n", - "0.000000 | \n", - "1.047981 | \n", - "0.000000 | \n", - "1.116706 | \n", - "0.000000 | \n", - "0.923456 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.162517 | \n", - "0.204347 | \n", - "0.784076 | \n", - "... | \n", - "
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| M_M2nd_25 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "2.435548 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.867796 | \n", - "... | \n", - "
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| M_M2nd_22 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.196529 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "... | \n", - "
| M_M2nd_29 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.619188 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.183506 | \n", - "... | \n", - "
| M_M2nd_28 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.285564 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "... | \n", - "
| S_MSA_06 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "1.824732 | \n", - "0.803690 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "1.166301 | \n", - "... | \n", - "
| S_MSA_07 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "1.745239 | \n", - "0.951727 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "... | \n", - "
| S_MSA_04 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "1.590292 | \n", - "1.039193 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.648695 | \n", - "1.933000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "1.142070 | \n", - "... | \n", - "
| S_MSA_02 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "2.505317 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "... | \n", - "
| S_MSA_03 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "1.996689 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "... | \n", - "
| M_M1_10 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "1.006625 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "... | \n", - "
| S_MSA_01 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "2.003808 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.999177 | \n", - "... | \n", - "
| S_MSA_08 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.289902 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "1.016311 | \n", - "... | \n", - "
| S_MSA_09 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.220417 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.498623 | \n", - "... | \n", - "
| S_MSA_17 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "... | \n", - "
| S_MSA_16 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.755249 | \n", - "0.360332 | \n", - "0.341251 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "1.761553 | \n", - "... | \n", - "
| P_P9_3 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.292664 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.906811 | \n", - "0.000000 | \n", - "1.301994 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.263340 | \n", - "0.224821 | \n", - "0.000000 | \n", - "... | \n", - "
| S_MSA_12 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "1.895404 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.766319 | \n", - "... | \n", - "
| M_M3_14 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "1.687273 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.522418 | \n", - "... | \n", - "
| M_M3_13 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.742259 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.657571 | \n", - "0.998822 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "1.024732 | \n", - "... | \n", - "
| M_M3_12 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.304830 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "1.904700 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "... | \n", - "
| M_M6_3 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.202844 | \n", - "0.814383 | \n", - "... | \n", - "
| P_P7_1 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.705456 | \n", - "0.000000 | \n", - "0.842115 | \n", - "0.000000 | \n", - "1.230105 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "... | \n", - "
| M_M1_11 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.150106 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.223456 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "1.447192 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.746604 | \n", - "... | \n", - "
| P_P7_6 | \n", - "0.361340 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.649421 | \n", - "0.233953 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "1.086616 | \n", - "0.000000 | \n", - "1.104646 | \n", - "0.666705 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.553757 | \n", - "... | \n", - "
| M_M6_4 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.372945 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "1.305338 | \n", - "... | \n", - "
| M_M1_12 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.755837 | \n", - "1.357737 | \n", - "0.000000 | \n", - "1.436146 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "... | \n", - "
| S_MSA_18 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "1.063519 | \n", - "1.193236 | \n", - "1.007837 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "1.861963 | \n", - "... | \n", - "
| M_M2nd_32 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.462564 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.695564 | \n", - "... | \n", - "
| M_M4_9 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "... | \n", - "
| M_M2nd_30 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "... | \n", - "
| M_M2nd_31 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "... | \n", - "
| M_M4_4 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.319653 | \n", - "0.000000 | \n", - "0.763680 | \n", - "0.000000 | \n", - "0.549987 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "1.474814 | \n", - "... | \n", - "
| M_M4_5 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.804178 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.737072 | \n", - "1.262246 | \n", - "0.644757 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.594551 | \n", - "... | \n", - "
| S_MSA_13 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "1.640449 | \n", - "0.000000 | \n", - "0.826850 | \n", - "0.479066 | \n", - "1.808761 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "... | \n", - "
| M_M4_7 | \n", - "1.103224 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.163784 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "1.715476 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "... | \n", - "
| S_MSA_15 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "1.697166 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "1.228550 | \n", - "... | \n", - "
| M_M4_3 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.868814 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.578687 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.410289 | \n", - "... | \n", - "
| P_P1_02 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.501463 | \n", - "0.361731 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "1.432888 | \n", - "0.000000 | \n", - "1.435808 | \n", - "0.000000 | \n", - "1.331503 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.183074 | \n", - "0.199741 | \n", - "0.000000 | \n", - "... | \n", - "
| P_P1_03 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0 | \n", - "0.000000 | \n", - "0.181594 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0.000000 | \n", - "1.023256 | \n", - "0.000000 | \n", - "1.198007 | \n", - "0.978182 | \n", - "1.062759 | \n", - "0.000000 | \n", - "0.000000 | \n", - "0 | \n", - "0.229616 | \n", - "0.000000 | \n", - "0.375111 | \n", - "... | \n", - "
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- "id \n",
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- " ... \n",
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- " ... \n",
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- "event_name chr10:101379802:101380221:-@chr10:101373453:101373681:-@chr10:101372300:101372356:-@chr10:101370642:101371215:- \\\n",
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- " ... \n",
- "\n",
- "event_name chr10:101419619:101419721:+@chr10:101420057:101420239:+@chr10:101421203:101421385:+ \\\n",
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- " ... \n",
- "\n",
- "event_name \n",
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- "N_CVN_01 ... \n",
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- "N_CVN_05 ... \n",
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- "N_CVN_22 ... \n",
- "N_CVN_23 ... \n",
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- "N_CVN_27 ... \n",
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- "N_CVN_30 ... \n",
- "N_CVN_31 ... \n",
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- "M_M2_09 ... \n",
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- "\n",
- "[223 rows x 56830 columns]"
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- "##parameters established in | event_name | \n", - "chr10:100148111:100148265:-@chr10:100147622:100147841:-@chr10:100146958:100147064:- | \n", - "chr10:100174766:100174978:-@chr10:100174489:100174591:-@chr10:100173705:100174052:- | \n", - "chr10:100185575:100185742:-@chr10:100185441|100185477:100185298:- | \n", - "chr10:100186972:100187021:-@chr10:100185575:100185742:-@chr10:100185298:100185477:- | \n", - "chr10:100190328:100190427:-@chr10:100189548:100189646:-@chr10:100189330:100189399:- | \n", - "chr10:100193697:100193848:-@chr10:100190888:100191048:-@chr10:100190328:100190427:- | \n", - "chr10:100193740:100193848:-@chr10:100190888:100191048:-@chr10:100190328:100190427:- | \n", - "chr10:100193848:100193697|100193740:-@chr10:100190328:100190427:- | \n", - "chr10:100193848:100193697|100193740:-@chr10:100190888:100191048:- | \n", - "chr10:100195392:100195529:-@chr10:100195029:100195171:-@chr10:100193740:100193848:- | \n", - "chr10:100206564:100206704:-@chr10:100205161|100205170:100205057:- | \n", - "chr10:101124712:101124759:+@chr10:101128375:101128437:+@chr10:101136812:101136975:+ | \n", - "chr10:101147577:101147760:+@chr10:101147909:101148058:+@chr10:101150063:101150164:+ | \n", - "chr10:101180562:101180370|101180381:-@chr10:101166483:101166606:- | \n", - "chr10:101373453:101373681:-@chr10:101372300:101372356:-@chr10:101370642:101371215:- | \n", - "chr10:101373681:101373173|101373453:-@chr10:101372300:101372356:- | \n", - "chr10:101379802:101380221:-@chr10:101373173:101373681:-@chr10:101372300:101372356:- | \n", - "chr10:101379802:101380221:-@chr10:101373453:101373681:-@chr10:101372300:101372356:- | \n", - "chr10:101379802:101380221:-@chr10:101373453:101373681:-@chr10:101372300:101372356:-@chr10:101370642:101371215:- | \n", - "chr10:101419619:101419721:+@chr10:101420057:101420239:+@chr10:101421203:101421385:+ | \n", - "\n", - " |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | \n", - "\n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | |
| N_CVN_01 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "0.05 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "0.05 | \n", - "... | \n", - "
| N_CVN_02 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| N_CVN_03 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "0.02 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| N_CVN_04 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| N_CVN_05 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "0.02 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| N_CVN_06 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| N_CVN_07 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "0.11 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| N_CVN_08 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| N_CVN_09 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| N_CVN_10 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| N_CVN_11 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| N_CVN_12 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| N_CVN_13 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "0.02 | \n", - "0.99 | \n", - "0.99 | \n", - "0.99 | \n", - "NaN | \n", - "... | \n", - "
| N_CVN_14 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| N_CVN_15 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| N_CVN_16 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| N_CVN_17 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "0.93 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "0.94 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "0.04 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| N_CVN_18 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| N_CVN_19 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "0.01 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "0.05 | \n", - "... | \n", - "
| N_CVN_20 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| N_CVN_21 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| N_CVN_22 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "0.96 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| N_CVN_23 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "0.09 | \n", - "NaN | \n", - "... | \n", - "
| N_CVN_24 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| N_CVN_25 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| N_CVN_26 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| N_CVN_27 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| N_CVN_28 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "0.05 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| N_CVN_29 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| N_CVN_30 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "0.06 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| N_CVN_31 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "0.06 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "0.04 | \n", - "NaN | \n", - "... | \n", - "
| N_CVN_32 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "0.08 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| N_CVN_33 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| N_CVN_34 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| N_CVN_35 | \n", - "0.91 | \n", - "0.08 | \n", - "NaN | \n", - "NaN | \n", - "0.75 | \n", - "0.95 | \n", - "0.95 | \n", - "NaN | \n", - "0.05 | \n", - "0.96 | \n", - "0.91 | \n", - "NaN | \n", - "NaN | \n", - "0.03 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| M_M1_01 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| M_M1_02 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "0.10 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| M_M1_05 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| M_M1_06 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "0.05 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| M_M1_07 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "0.04 | \n", - "... | \n", - "
| M_M1_08 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "0.98 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| M_M1_09 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| M_M1_10 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "0.06 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| M_M1_11 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| M_M1_12 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| M_M2_03 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "0.04 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| M_M2_04 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| M_M2_08 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| M_M2_09 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| M_M2_10 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "0.06 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| M_M2_11 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| M_M2_12 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "0.04 | \n", - "0.98 | \n", - "0.04 | \n", - "1.00 | \n", - "1.00 | \n", - "1.00 | \n", - "NaN | \n", - "... | \n", - "
| M_M2nd_01 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| M_M2nd_02 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| M_M2nd_03 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| M_M2nd_04 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| M_M2nd_05 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| M_M2nd_06 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| M_M2nd_07 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| M_M2nd_08 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "
| \n", - " | ... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "
223 rows \u00d7 56830 columns
\n", - "neural_cell_diff poulate the lists of interactive objects"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "##interactive_pca \n",
- "\n",
- "runs PCA\n",
- "\n",
- "group_id is a subset of samples to use\n", - "data_type -designates whether you'd like to do PCA with either splicing or gene expression as features
\n", - "featurewise - selects whether you'd like to visualize PCA of samples or of features
\n", - "x_pc and y_pc - select the components to display for the x- and y- dimensions
\n", - "show_point_labels - draws sample/feature labels
\n", - "list_link - a local file path (full path) or a http: link to a list of line-delimited features to use for the PCA
\n", - "list_name - contains pre-loaded lists, you can set this to \"custom\" to use list_link
\n", - "savefile - a file to output\n", - "\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "sc_study.interactive_pca()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "savefile : data/last.pca.pdf\n", - "y_pc : 2\n", - "data_type : expression\n", - "featurewise : False\n", - "show_point_labels : False\n", - "x_pc : 1\n", - "list_link : \n", - "list_name : variant\n", - "group_id : ~outlier\n" - ] - }, - { - "output_type": "stream", - "stream": "stderr", - "text": [ - "/Users/lovci/venv/lib/python2.7/site-packages/matplotlib-1.3.1-py2.7-macosx-10.6-intel.egg/matplotlib/font_manager.py:1236: UserWarning: findfont: Font family ['Helvetica'] not found. Falling back to Bitstream Vera Sans\n", - " (prop.get_family(), self.defaultFamily[fontext]))\n" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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qfqoek7IIXwyCIAiC2lKEKm5me/OcUN8OHEJSK1uzRLVxTEk4UY+ZkAVBN6FV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\nBljHzGbHqIlvEUJLk2ayOD7mKDuN8KS5OzAn/jsf+D2wAyEH2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- "text": [ - "
\n", - "data_type - designates whether you'd like to do PCA with either splicing or gene expression as features
\n", - "featurewise - selects whether you'd like to visualize samples or of features
\n", - "draw_labels - draws sample/feature labels on nodes
\n", - "degree_cut - minimum degree for nodes to be included in the output\n", - "cov_std_cut - minimum covariance for two nodes to have an edge\n", - "n_pcs - use the first n_pcs\n", - "feature_of_interest - feature to highlight. recommended that you paste into this box, rather than type directly\n", - "use_pc_X - de-select to exclude component X\n", - "list_name - contains pre-loaded lists and custom lists added with custom \"list_link\" in interactive_pca
\n", - "savefile - a file to output\n", - "weight_fun - a function to apply to covariances before filtering\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "sc_study.interactive_graph()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "savefile : data/last.graph.pdf\n", - "group_id : ~outlier\n", - "data_type : expression\n", - "featurewise : False\n", - "weight_fun : abs\n", - "use_pc_4 : True\n", - "use_pc_2 : True\n", - "use_pc_3 : True\n", - "use_pc_1 : True\n", - "cov_std_cut : 1.8\n", - "degree_cut : 1\n", - "feature_of_interest : RBFOX2\n", - "draw_labels : False\n", - "list_name : variant\n", - "n_pcs : 14\n", - "u'RBFOX2'" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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622sreOPj77jzuolIr/bRrk6z2j9h7TkQke7A2cA3eFH0vcDbwAvh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\nXLeYB4ALCLuUhL4I3GSM6aX+ENoybt/ERCGw1m4yx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- "text": [ - "
\n", - "list_name - contains pre-loaded lists and custom lists added with custom \"list_link\" in interactive_pca
\n", - "group_id - a subset of samples to use
\n", - "categorical_variable - a column in expression.sample_descriptors upon which to train a classifier
\n", - "feature_score_std_cutoff - mean + (this x std) is used to select the top features
\n", - "safefile - a file to output" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "sc_study.interactive_clf()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "savefile : data/last.clf.pdf\n", - "group_id : ~outlier\n", - "data_type : expression\n", - "categorical_variable : M_cell\n", - "list_name : variant\n", - "feature_score_std_cutoff : 2.0\n", - "Initializing predictors for M_cell" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Fitting a classifier for trait M_cell... please wait." - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Finished..." - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Scoring classifier: ExtraTreesClassifier for trait: M_cell... please wait.\n", - "Finished...\n", - "retrieve this classifier with:\n", - "prd=study.expression.get_predictor('variant', '~outlier', 'M_cell')\n", - "pca=prd('M_cell', feature_score_std_cutoff=2.000000)" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - }, - { - "output_type": "stream", - "stream": "stderr", - "text": [ - "[Parallel(n_jobs=2)]: Done 1 out of 2 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=2)]: Done 2 out of 2 | elapsed: 0.0s finished\n" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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1b1UvqRKSwKi0IcbvxvjThMQbAO6+zt2vdfcjgZWEnn3LgZy4fQLwE6AxIdGW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TsSW5lt0e1sdQ7a0XtWStkKCEVvfnLfo87y0oVyECoUJYyUcomUMlJKmWN5XtjjLzkI86wV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- "text": [ - "
\n", - "sample1 - name of sample on x-axis
\n", - "sample2 - name of sample on y-axis
\n", - "pCut - p-value cutoff, must be a float. recommended that you paste here, not type in this box.
\n", - "\n", - "running this updates sc_study.localZ_result with a dataFrame of the results to be parsed by the user" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "sc_study.interactive_localZ()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "sample1 : P_M2nd_34\n", - "pCut : 0.01\n", - "data_type : expression\n", - "sample2 : P_M2nd_33\n", - "localZ finished, find the result in
\n",
- "\n",
- " \n",
- "
\n",
- "
"
- ],
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 16,
- "text": [
- " rank log2Ratio localMean localStd pValue isSig \\\n",
- "gene_ID \n",
- "ENSG00000220785 418 0.204153 -0.074472 0.629507 6369.501096 False \n",
- "ENSG00000034063 5721 0.098194 -0.163993 0.397732 8947.335245 False \n",
- "ENSG00000140575 7505 -0.349715 -0.121749 0.238659 11742.023421 False \n",
- "ENSG00000254673 6532 -0.900368 -0.150624 0.321584 907.925895 False \n",
- "ENSG00000106992 2003 -0.883352 -0.220418 0.744643 3995.680485 False \n",
- "ENSG00000119514 6008 -0.780213 -0.164083 0.373755 3040.597395 False \n",
- "ENSG00000108510 4846 -0.156939 -0.189505 0.490895 8988.795810 False \n",
- "ENSG00000085832 7127 -0.019240 -0.134601 0.257175 15549.754435 False \n",
- "ENSG00000261118 253 0.327599 -0.074472 0.629507 5728.755750 False \n",
- "ENSG00000121690 6367 -0.386748 -0.155941 0.332717 10448.994746 False \n",
- "ENSG00000081842 1429 0.563782 -0.162371 0.770549 3681.283862 False \n",
- "ENSG00000039560 8100 -0.060417 -0.117451 0.203581 20886.459956 False \n",
- "ENSG00000111775 10976 -0.024313 -0.033637 0.074049 59249.689743 False \n",
- "ENSG00000167881 5217 -0.381570 -0.190366 0.446589 9035.112509 False \n",
- "ENSG00000197372 7712 0.082133 -0.119618 0.228731 13103.265292 False \n",
- "ENSG00000181555 6239 -0.223808 -0.150975 0.357327 12121.544331 False \n",
- "ENSG00000214940 6901 0.017082 -0.143739 0.279193 13418.104711 False \n",
- "ENSG00000137312 3653 -1.193034 -0.204439 0.638103 2087.122890 False \n",
- "ENSG00000139579 8516 -0.265572 -0.108458 0.169503 16978.673405 False \n",
- "ENSG00000105677 9680 -0.060817 -0.074243 0.117345 37440.141139 False \n",
- "ENSG00000172071 3523 0.224018 -0.207007 0.656297 5431.056295 False \n",
- "ENSG00000176148 2672 -0.425260 -0.190788 0.733344 5729.801563 False \n",
- "ENSG00000111817 7402 -0.096741 -0.119975 0.243966 18044.566347 False \n",
- "ENSG00000138686 5220 -0.389329 -0.190201 0.446598 8965.170785 False \n",
- "ENSG00000172613 700 -0.332567 -0.074472 0.629507 6458.678339 False \n",
- "ENSG00000188636 3472 0.811145 -0.190448 0.669769 2158.296640 False \n",
- "ENSG00000233476 11064 0.044297 -0.033637 0.074049 34324.094111 False \n",
- "ENSG00000270113 7292 0.330735 -0.124059 0.251199 3418.563223 False \n",
- "ENSG00000064490 7558 -0.088767 -0.123126 0.234250 18676.486684 False \n",
- "ENSG00000099991 1906 -0.290516 -0.222295 0.756502 5821.970993 False \n",
- "ENSG00000115414 2409 -0.472082 -0.194566 0.737359 5587.374508 False \n",
- "ENSG00000135686 1790 -0.416170 -0.214163 0.756222 5642.889786 False \n",
- "ENSG00000053770 8897 0.054141 -0.094844 0.148840 18003.388473 False \n",
- "ENSG00000260345 3018 -0.938587 -0.188596 0.712045 3566.402007 False \n",
- "ENSG00000269131 7911 -0.048574 -0.120155 0.215955 19383.134262 False \n",
- "ENSG00000213976 1772 -1.281339 -0.216706 0.754254 2165.186904 False \n",
- "ENSG00000179091 6776 -0.238272 -0.144335 0.296207 14197.473307 False \n",
- "ENSG00000142546 3134 -0.678246 -0.192976 0.712042 4923.596504 False \n",
- "ENSG00000109171 2889 -1.035209 -0.185275 0.717005 3054.896830 False \n",
- "ENSG00000143190 4165 -0.344100 -0.187924 0.581364 7337.144011 False \n",
- "ENSG00000179988 3040 0.207785 -0.191473 0.711499 5309.997263 False \n",
- "ENSG00000120306 9125 -0.104952 -0.088536 0.139157 31558.583472 False \n",
- "ENSG00000152022 3263 1.439525 -0.192271 0.696531 408.226530 False \n",
- "ENSG00000138771 2320 -0.220497 -0.205345 0.737924 5991.600352 False \n",
- "ENSG00000179163 2879 -0.845813 -0.184478 0.719659 4028.491567 False \n",
- "ENSG00000236714 4783 -0.652258 -0.190004 0.509777 5750.663892 False \n",
- "ENSG00000198901 7823 -0.064297 -0.118845 0.226257 18985.527055 False \n",
- "ENSG00000025770 2994 -1.348303 -0.182646 0.712977 1629.850920 False \n",
- "ENSG00000040633 3133 0.799237 -0.193581 0.711746 2348.576044 False \n",
- "ENSG00000117222 10043 0.106058 -0.033637 0.074049 10076.393494 False \n",
- "ENSG00000164715 3582 -0.086822 -0.206028 0.658133 6610.097841 False \n",
- "ENSG00000162585 207 -0.479314 -0.074472 0.629507 5712.621467 False \n",
- "ENSG00000260257 10465 -0.050353 -0.033637 0.074049 58218.712458 False \n",
- "ENSG00000091490 4000 -0.997151 -0.182881 0.613937 2989.093643 False \n",
- "ENSG00000258016 8751 -0.047766 -0.100084 0.158395 26436.992479 False \n",
- "ENSG00000123636 6033 -0.138440 -0.158209 0.370987 11903.379143 False \n",
- "ENSG00000253900 2950 0.700587 -0.182789 0.716054 2885.450147 False \n",
- "ENSG00000263986 10193 0.014580 -0.033637 0.074049 48312.564384 False \n",
- "ENSG00000196826 6706 -0.058049 -0.145785 0.301443 14061.948733 False \n",
- "ENSG00000092036 7867 -0.247923 -0.118681 0.219516 16939.805973 False \n",
- " ... ... ... ... ... ... \n",
- "\n",
- " meanExpression localZ P_M2nd_34 P_M2nd_33 \n",
- "gene_ID \n",
- "ENSG00000220785 0.231022 0.442609 0.214704 0.247341 \n",
- "ENSG00000034063 0.751718 0.659206 0.726145 0.777290 \n",
- "ENSG00000140575 1.000505 -0.955195 1.121178 0.879832 \n",
- "ENSG00000254673 0.854851 -2.331414 1.113268 0.596434 \n",
- "ENSG00000106992 0.383386 -0.890271 0.497224 0.269548 \n",
- "ENSG00000119514 0.785056 -1.648488 0.992310 0.577803 \n",
- "ENSG00000108510 0.655248 0.066340 0.690852 0.619643 \n",
- "ENSG00000085832 0.943139 0.448568 0.949428 0.936850 \n",
- "ENSG00000261118 0.211900 0.638708 0.187944 0.235856 \n",
- "ENSG00000121690 0.834459 -0.693704 0.945642 0.723276 \n",
- "ENSG00000081842 0.332429 0.942385 0.268289 0.396569 \n",
- "ENSG00000039560 1.103766 0.280153 1.126874 1.080657 \n",
- "ENSG00000111775 2.714205 0.125915 2.737075 2.691335 \n",
- "ENSG00000167881 0.692883 -0.428144 0.783980 0.601785 \n",
- "ENSG00000197372 1.037649 0.882047 1.008121 1.067178 \n",
- "ENSG00000181555 0.814942 -0.203826 0.878027 0.751856 \n",
- "ENSG00000214940 0.907391 0.576021 0.902019 0.912762 \n",
- "ENSG00000137312 0.533189 -1.549273 0.741890 0.324489 \n",
- "ENSG00000139579 1.184834 -0.926910 1.293579 1.076088 \n",
- "ENSG00000105677 1.497486 0.114422 1.529044 1.465927 \n",
- "ENSG00000172071 0.520914 0.656753 0.480552 0.561276 \n",
- "ENSG00000176148 0.444230 -0.319730 0.509232 0.379227 \n",
- "ENSG00000111817 0.987444 0.095235 1.020539 0.954350 \n",
- "ENSG00000138686 0.693184 -0.445876 0.786152 0.600215 \n",
- "ENSG00000172613 0.263312 -0.409994 0.293528 0.233097 \n",
- "ENSG00000188636 0.517093 1.495431 0.375439 0.658746 \n",
- "ENSG00000233476 3.384901 1.052464 3.332939 3.436862 \n",
- "ENSG00000270113 0.970942 1.810494 0.860133 1.081751 \n",
- "ENSG00000064490 1.012329 0.146678 1.043463 0.981196 \n",
- "ENSG00000099991 0.374703 -0.090180 0.412303 0.337103 \n",
- "ENSG00000115414 0.422787 -0.376366 0.491349 0.354225 \n",
- "ENSG00000135686 0.365201 -0.267127 0.417513 0.312889 \n",
- "ENSG00000053770 1.272073 1.000976 1.248207 1.295940 \n",
- "ENSG00000260345 0.474555 -1.053292 0.623699 0.325411 \n",
- "ENSG00000269131 1.070272 0.331463 1.088288 1.052256 \n",
- "ENSG00000213976 0.363171 -1.411506 0.514621 0.211722 \n",
- "ENSG00000179091 0.887847 -0.317132 0.960999 0.814696 \n",
- "ENSG00000142546 0.485694 -0.681519 0.597804 0.373583 \n",
- "ENSG00000109171 0.463707 -1.185395 0.623285 0.304129 \n",
- "ENSG00000143190 0.584367 -0.268638 0.653728 0.515006 \n",
- "ENSG00000179988 0.475729 0.561150 0.441529 0.509928 \n",
- "ENSG00000120306 1.325617 -0.117967 1.373813 1.277420 \n",
- "ENSG00000152022 0.496298 2.342746 0.267379 0.725217 \n",
- "ENSG00000138771 0.414207 -0.020533 0.445799 0.382616 \n",
- "ENSG00000179163 0.462953 -0.918956 0.594903 0.331002 \n",
- "ENSG00000236714 0.648718 -0.906776 0.792917 0.504520 \n",
- "ENSG00000198901 1.054674 0.241087 1.078172 1.031176 \n",
- "ENSG00000025770 0.472816 -1.634914 0.678965 0.266666 \n",
- "ENSG00000040633 0.485659 1.394904 0.354472 0.616845 \n",
- "ENSG00000117222 1.644935 1.886527 1.584499 1.705370 \n",
- "ENSG00000164715 0.526679 0.181127 0.542522 0.510836 \n",
- "ENSG00000162585 0.203370 -0.643109 0.236846 0.169894 \n",
- "ENSG00000260257 1.900572 -0.225748 1.933735 1.867408 \n",
- "ENSG00000091490 0.566992 -1.326308 0.755491 0.378492 \n",
- "ENSG00000258016 1.238020 0.330304 1.258512 1.217527 \n",
- "ENSG00000123636 0.787534 0.053285 0.825291 0.749777 \n",
- "ENSG00000253900 0.469012 1.233672 0.357320 0.580704 \n",
- "ENSG00000263986 1.717582 0.651147 1.708903 1.726260 \n",
- "ENSG00000196826 0.878815 0.291054 0.896493 0.861137 \n",
- "ENSG00000092036 1.060964 -0.588761 1.151903 0.970026 \n",
- " ... ... ... ... \n",
- "\n",
- "[11085 rows x 10 columns]"
- ]
- }
- ],
- "prompt_number": 16
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [],
- "language": "python",
- "metadata": {},
- "outputs": []
- }
- ],
- "metadata": {}
- }
- ]
-}
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 00000000..4a98cb89
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,28 @@
+# These are all in a specific order! If you add another package as
+# a depencency, please make sure that its dependencies come before it (above)
+# in the list. E.g. "numpy" and "scipy" must precede "matplotlib"
+setuptools
+numpy >= 1.8.0
+scipy >= 0.14
+matplotlib >= 1.3.1
+scikit-learn >= 0.13.0
+gspread
+brewer2mpl
+pymongo >= 2.7
+ipython >= 2.0.0
+husl
+patsy >= 0.2.1
+pandas >= 0.13.1
+statsmodels >= 0.5.0
+seaborn >= 0.5
+networkx
+tornado >= 3.2.1
+pyzmq
+#'dcor_cpy' #needs to be built with extutils
+six
+pytest-cov
+python-coveralls
+jinja2
+#fastcluster
+semantic_version
+joblib >= 0.8.4
diff --git a/setup.cfg b/setup.cfg
new file mode 100644
index 00000000..224a7795
--- /dev/null
+++ b/setup.cfg
@@ -0,0 +1,2 @@
+[metadata]
+description-file = README.md
\ No newline at end of file
diff --git a/setup.py b/setup.py
index 1b7945c3..b9ae01d8 100644
--- a/setup.py
+++ b/setup.py
@@ -1,40 +1,53 @@
from setuptools import setup
from setuptools import find_packages
+version = "0.2.6dev"
+
setup(
name='flotilla',
packages=find_packages(),
- url='',
- license='',
- author='lovci',
- author_email='mlovci@ucsd.edu',
- description='',
- include_package_data=True,
- #package_dir={'flotilla': 'flotilla'},
- package_data={
- 'flotilla': ['src/_cargo_commonObjects/cargo_data/*/*txt.gz',
- 'src/_cargo_commonObjects/cargo_data/*/gene_lists/*']
- },
- entry_points={
- 'console_scripts': ['metrics_runner.py = flotilla.src.carrier',
- 'serve_flotilla_notebook = flotilla.src._barge_utils:serve_ipython']},
- install_requires=['setuptools',
- 'numpy >= 1.8.1 ',
- 'scipy >= 0.14.0',
- 'matplotlib >= 1.3.1',
- 'scikit-learn >= 0.13.0',
- 'gspread',
- 'brewer2mpl',
- 'pymongo >= 2.7',
- 'ipython >= 2.0.0',
- 'husl',
- 'seaborn >= 0.3.1',
- 'patsy >= 0.2.1',
- 'statsmodels >= 0.5.0',
- 'pandas >= 0.13.1',
- 'networkx',
- 'tornado >= 3.2.1',
- #'dcor_cpy' #needs to be built with extutils
- ],
- version = "0.0.3"
+ url='http://github.com/YeoLab/flotilla',
+ license='3-Clause BSD',
+ author='olgabot',
+ author_email='obotvinn@ucsd.edu',
+ description='Embark on a journey of single-cell data exploration.',
+ # These are all in a specific order! If you add another package as
+ # a depencency, please make sure that its dependencies come before it
+ # (above) in the list. E.g. "numpy" and "scipy" must precede "matplotlib"
+ install_requires=["setuptools",
+ "numpy >= 1.8.0",
+ "scipy >= 0.14",
+ "matplotlib >= 1.3.1",
+ "scikit-learn >= 0.13.0",
+ "gspread",
+ "brewer2mpl",
+ "pymongo >= 2.7",
+ "ipython >= 2.0.0",
+ "husl",
+ "patsy >= 0.2.1",
+ "pandas >= 0.13.1",
+ "statsmodels >= 0.5.0",
+ "seaborn >= 0.3",
+ "networkx",
+ "tornado >= 3.2.1",
+ "pyzmq",
+ # "dcor_cpy' #needs to be built with extutils",
+ "six",
+ "pytest-cov",
+ "python-coveralls",
+ "jinja2",
+ # "fastcluster",
+ "semantic_version",
+ "joblib >= 0.8.4"],
+ version=version,
+ classifiers=['License :: OSI Approved :: BSD License',
+ 'Topic :: Scientific/Engineering :: Bio-Informatics',
+ 'Topic :: Scientific/Engineering :: Visualization',
+ 'Topic :: Scientific/Engineering :: Medical Science Apps.',
+ 'Programming Language :: Python :: 2.7',
+ 'Topic :: Multimedia :: Graphics',
+ 'Operating System :: POSIX',
+ 'Operating System :: Unix',
+ 'Operating System :: MacOS',
+ 'Operating System :: Microsoft :: Windows']
)
diff --git a/talks.md b/talks.md
new file mode 100644
index 00000000..e10e2c22
--- /dev/null
+++ b/talks.md
@@ -0,0 +1,3 @@
+# A list of talks with slides and/or notebooks
+
+- November 23rd, 2014 - [PyData NYC 2014](http://nbviewer.ipython.org/github/olgabot/pydata2014biodata/blob/master/presentation.ipynb)
diff --git a/testing/matplotlibrc b/testing/matplotlibrc
new file mode 100644
index 00000000..13468274
--- /dev/null
+++ b/testing/matplotlibrc
@@ -0,0 +1 @@
+backend : Agg
| \n", - " | rank | \n", - "log2Ratio | \n", - "localMean | \n", - "localStd | \n", - "pValue | \n", - "isSig | \n", - "meanExpression | \n", - "localZ | \n", - "P_M2nd_34 | \n", - "P_M2nd_33 | \n", - "
|---|---|---|---|---|---|---|---|---|---|---|
| gene_ID | \n", - "\n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " | \n", - " |
| ENSG00000220785 | \n", - "418 | \n", - "0.204153 | \n", - "-0.074472 | \n", - "0.629507 | \n", - "6369.501096 | \n", - "False | \n", - "0.231022 | \n", - "0.442609 | \n", - "0.214704 | \n", - "0.247341 | \n", - "
| ENSG00000034063 | \n", - "5721 | \n", - "0.098194 | \n", - "-0.163993 | \n", - "0.397732 | \n", - "8947.335245 | \n", - "False | \n", - "0.751718 | \n", - "0.659206 | \n", - "0.726145 | \n", - "0.777290 | \n", - "
| ENSG00000140575 | \n", - "7505 | \n", - "-0.349715 | \n", - "-0.121749 | \n", - "0.238659 | \n", - "11742.023421 | \n", - "False | \n", - "1.000505 | \n", - "-0.955195 | \n", - "1.121178 | \n", - "0.879832 | \n", - "
| ENSG00000254673 | \n", - "6532 | \n", - "-0.900368 | \n", - "-0.150624 | \n", - "0.321584 | \n", - "907.925895 | \n", - "False | \n", - "0.854851 | \n", - "-2.331414 | \n", - "1.113268 | \n", - "0.596434 | \n", - "
| ENSG00000106992 | \n", - "2003 | \n", - "-0.883352 | \n", - "-0.220418 | \n", - "0.744643 | \n", - "3995.680485 | \n", - "False | \n", - "0.383386 | \n", - "-0.890271 | \n", - "0.497224 | \n", - "0.269548 | \n", - "
| ENSG00000119514 | \n", - "6008 | \n", - "-0.780213 | \n", - "-0.164083 | \n", - "0.373755 | \n", - "3040.597395 | \n", - "False | \n", - "0.785056 | \n", - "-1.648488 | \n", - "0.992310 | \n", - "0.577803 | \n", - "
| ENSG00000108510 | \n", - "4846 | \n", - "-0.156939 | \n", - "-0.189505 | \n", - "0.490895 | \n", - "8988.795810 | \n", - "False | \n", - "0.655248 | \n", - "0.066340 | \n", - "0.690852 | \n", - "0.619643 | \n", - "
| ENSG00000085832 | \n", - "7127 | \n", - "-0.019240 | \n", - "-0.134601 | \n", - "0.257175 | \n", - "15549.754435 | \n", - "False | \n", - "0.943139 | \n", - "0.448568 | \n", - "0.949428 | \n", - "0.936850 | \n", - "
| ENSG00000261118 | \n", - "253 | \n", - "0.327599 | \n", - "-0.074472 | \n", - "0.629507 | \n", - "5728.755750 | \n", - "False | \n", - "0.211900 | \n", - "0.638708 | \n", - "0.187944 | \n", - "0.235856 | \n", - "
| ENSG00000121690 | \n", - "6367 | \n", - "-0.386748 | \n", - "-0.155941 | \n", - "0.332717 | \n", - "10448.994746 | \n", - "False | \n", - "0.834459 | \n", - "-0.693704 | \n", - "0.945642 | \n", - "0.723276 | \n", - "
| ENSG00000081842 | \n", - "1429 | \n", - "0.563782 | \n", - "-0.162371 | \n", - "0.770549 | \n", - "3681.283862 | \n", - "False | \n", - "0.332429 | \n", - "0.942385 | \n", - "0.268289 | \n", - "0.396569 | \n", - "
| ENSG00000039560 | \n", - "8100 | \n", - "-0.060417 | \n", - "-0.117451 | \n", - "0.203581 | \n", - "20886.459956 | \n", - "False | \n", - "1.103766 | \n", - "0.280153 | \n", - "1.126874 | \n", - "1.080657 | \n", - "
| ENSG00000111775 | \n", - "10976 | \n", - "-0.024313 | \n", - "-0.033637 | \n", - "0.074049 | \n", - "59249.689743 | \n", - "False | \n", - "2.714205 | \n", - "0.125915 | \n", - "2.737075 | \n", - "2.691335 | \n", - "
| ENSG00000167881 | \n", - "5217 | \n", - "-0.381570 | \n", - "-0.190366 | \n", - "0.446589 | \n", - "9035.112509 | \n", - "False | \n", - "0.692883 | \n", - "-0.428144 | \n", - "0.783980 | \n", - "0.601785 | \n", - "
| ENSG00000197372 | \n", - "7712 | \n", - "0.082133 | \n", - "-0.119618 | \n", - "0.228731 | \n", - "13103.265292 | \n", - "False | \n", - "1.037649 | \n", - "0.882047 | \n", - "1.008121 | \n", - "1.067178 | \n", - "
| ENSG00000181555 | \n", - "6239 | \n", - "-0.223808 | \n", - "-0.150975 | \n", - "0.357327 | \n", - "12121.544331 | \n", - "False | \n", - "0.814942 | \n", - "-0.203826 | \n", - "0.878027 | \n", - "0.751856 | \n", - "
| ENSG00000214940 | \n", - "6901 | \n", - "0.017082 | \n", - "-0.143739 | \n", - "0.279193 | \n", - "13418.104711 | \n", - "False | \n", - "0.907391 | \n", - "0.576021 | \n", - "0.902019 | \n", - "0.912762 | \n", - "
| ENSG00000137312 | \n", - "3653 | \n", - "-1.193034 | \n", - "-0.204439 | \n", - "0.638103 | \n", - "2087.122890 | \n", - "False | \n", - "0.533189 | \n", - "-1.549273 | \n", - "0.741890 | \n", - "0.324489 | \n", - "
| ENSG00000139579 | \n", - "8516 | \n", - "-0.265572 | \n", - "-0.108458 | \n", - "0.169503 | \n", - "16978.673405 | \n", - "False | \n", - "1.184834 | \n", - "-0.926910 | \n", - "1.293579 | \n", - "1.076088 | \n", - "
| ENSG00000105677 | \n", - "9680 | \n", - "-0.060817 | \n", - "-0.074243 | \n", - "0.117345 | \n", - "37440.141139 | \n", - "False | \n", - "1.497486 | \n", - "0.114422 | \n", - "1.529044 | \n", - "1.465927 | \n", - "
| ENSG00000172071 | \n", - "3523 | \n", - "0.224018 | \n", - "-0.207007 | \n", - "0.656297 | \n", - "5431.056295 | \n", - "False | \n", - "0.520914 | \n", - "0.656753 | \n", - "0.480552 | \n", - "0.561276 | \n", - "
| ENSG00000176148 | \n", - "2672 | \n", - "-0.425260 | \n", - "-0.190788 | \n", - "0.733344 | \n", - "5729.801563 | \n", - "False | \n", - "0.444230 | \n", - "-0.319730 | \n", - "0.509232 | \n", - "0.379227 | \n", - "
| ENSG00000111817 | \n", - "7402 | \n", - "-0.096741 | \n", - "-0.119975 | \n", - "0.243966 | \n", - "18044.566347 | \n", - "False | \n", - "0.987444 | \n", - "0.095235 | \n", - "1.020539 | \n", - "0.954350 | \n", - "
| ENSG00000138686 | \n", - "5220 | \n", - "-0.389329 | \n", - "-0.190201 | \n", - "0.446598 | \n", - "8965.170785 | \n", - "False | \n", - "0.693184 | \n", - "-0.445876 | \n", - "0.786152 | \n", - "0.600215 | \n", - "
| ENSG00000172613 | \n", - "700 | \n", - "-0.332567 | \n", - "-0.074472 | \n", - "0.629507 | \n", - "6458.678339 | \n", - "False | \n", - "0.263312 | \n", - "-0.409994 | \n", - "0.293528 | \n", - "0.233097 | \n", - "
| ENSG00000188636 | \n", - "3472 | \n", - "0.811145 | \n", - "-0.190448 | \n", - "0.669769 | \n", - "2158.296640 | \n", - "False | \n", - "0.517093 | \n", - "1.495431 | \n", - "0.375439 | \n", - "0.658746 | \n", - "
| ENSG00000233476 | \n", - "11064 | \n", - "0.044297 | \n", - "-0.033637 | \n", - "0.074049 | \n", - "34324.094111 | \n", - "False | \n", - "3.384901 | \n", - "1.052464 | \n", - "3.332939 | \n", - "3.436862 | \n", - "
| ENSG00000270113 | \n", - "7292 | \n", - "0.330735 | \n", - "-0.124059 | \n", - "0.251199 | \n", - "3418.563223 | \n", - "False | \n", - "0.970942 | \n", - "1.810494 | \n", - "0.860133 | \n", - "1.081751 | \n", - "
| ENSG00000064490 | \n", - "7558 | \n", - "-0.088767 | \n", - "-0.123126 | \n", - "0.234250 | \n", - "18676.486684 | \n", - "False | \n", - "1.012329 | \n", - "0.146678 | \n", - "1.043463 | \n", - "0.981196 | \n", - "
| ENSG00000099991 | \n", - "1906 | \n", - "-0.290516 | \n", - "-0.222295 | \n", - "0.756502 | \n", - "5821.970993 | \n", - "False | \n", - "0.374703 | \n", - "-0.090180 | \n", - "0.412303 | \n", - "0.337103 | \n", - "
| ENSG00000115414 | \n", - "2409 | \n", - "-0.472082 | \n", - "-0.194566 | \n", - "0.737359 | \n", - "5587.374508 | \n", - "False | \n", - "0.422787 | \n", - "-0.376366 | \n", - "0.491349 | \n", - "0.354225 | \n", - "
| ENSG00000135686 | \n", - "1790 | \n", - "-0.416170 | \n", - "-0.214163 | \n", - "0.756222 | \n", - "5642.889786 | \n", - "False | \n", - "0.365201 | \n", - "-0.267127 | \n", - "0.417513 | \n", - "0.312889 | \n", - "
| ENSG00000053770 | \n", - "8897 | \n", - "0.054141 | \n", - "-0.094844 | \n", - "0.148840 | \n", - "18003.388473 | \n", - "False | \n", - "1.272073 | \n", - "1.000976 | \n", - "1.248207 | \n", - "1.295940 | \n", - "
| ENSG00000260345 | \n", - "3018 | \n", - "-0.938587 | \n", - "-0.188596 | \n", - "0.712045 | \n", - "3566.402007 | \n", - "False | \n", - "0.474555 | \n", - "-1.053292 | \n", - "0.623699 | \n", - "0.325411 | \n", - "
| ENSG00000269131 | \n", - "7911 | \n", - "-0.048574 | \n", - "-0.120155 | \n", - "0.215955 | \n", - "19383.134262 | \n", - "False | \n", - "1.070272 | \n", - "0.331463 | \n", - "1.088288 | \n", - "1.052256 | \n", - "
| ENSG00000213976 | \n", - "1772 | \n", - "-1.281339 | \n", - "-0.216706 | \n", - "0.754254 | \n", - "2165.186904 | \n", - "False | \n", - "0.363171 | \n", - "-1.411506 | \n", - "0.514621 | \n", - "0.211722 | \n", - "
| ENSG00000179091 | \n", - "6776 | \n", - "-0.238272 | \n", - "-0.144335 | \n", - "0.296207 | \n", - "14197.473307 | \n", - "False | \n", - "0.887847 | \n", - "-0.317132 | \n", - "0.960999 | \n", - "0.814696 | \n", - "
| ENSG00000142546 | \n", - "3134 | \n", - "-0.678246 | \n", - "-0.192976 | \n", - "0.712042 | \n", - "4923.596504 | \n", - "False | \n", - "0.485694 | \n", - "-0.681519 | \n", - "0.597804 | \n", - "0.373583 | \n", - "
| ENSG00000109171 | \n", - "2889 | \n", - "-1.035209 | \n", - "-0.185275 | \n", - "0.717005 | \n", - "3054.896830 | \n", - "False | \n", - "0.463707 | \n", - "-1.185395 | \n", - "0.623285 | \n", - "0.304129 | \n", - "
| ENSG00000143190 | \n", - "4165 | \n", - "-0.344100 | \n", - "-0.187924 | \n", - "0.581364 | \n", - "7337.144011 | \n", - "False | \n", - "0.584367 | \n", - "-0.268638 | \n", - "0.653728 | \n", - "0.515006 | \n", - "
| ENSG00000179988 | \n", - "3040 | \n", - "0.207785 | \n", - "-0.191473 | \n", - "0.711499 | \n", - "5309.997263 | \n", - "False | \n", - "0.475729 | \n", - "0.561150 | \n", - "0.441529 | \n", - "0.509928 | \n", - "
| ENSG00000120306 | \n", - "9125 | \n", - "-0.104952 | \n", - "-0.088536 | \n", - "0.139157 | \n", - "31558.583472 | \n", - "False | \n", - "1.325617 | \n", - "-0.117967 | \n", - "1.373813 | \n", - "1.277420 | \n", - "
| ENSG00000152022 | \n", - "3263 | \n", - "1.439525 | \n", - "-0.192271 | \n", - "0.696531 | \n", - "408.226530 | \n", - "False | \n", - "0.496298 | \n", - "2.342746 | \n", - "0.267379 | \n", - "0.725217 | \n", - "
| ENSG00000138771 | \n", - "2320 | \n", - "-0.220497 | \n", - "-0.205345 | \n", - "0.737924 | \n", - "5991.600352 | \n", - "False | \n", - "0.414207 | \n", - "-0.020533 | \n", - "0.445799 | \n", - "0.382616 | \n", - "
| ENSG00000179163 | \n", - "2879 | \n", - "-0.845813 | \n", - "-0.184478 | \n", - "0.719659 | \n", - "4028.491567 | \n", - "False | \n", - "0.462953 | \n", - "-0.918956 | \n", - "0.594903 | \n", - "0.331002 | \n", - "
| ENSG00000236714 | \n", - "4783 | \n", - "-0.652258 | \n", - "-0.190004 | \n", - "0.509777 | \n", - "5750.663892 | \n", - "False | \n", - "0.648718 | \n", - "-0.906776 | \n", - "0.792917 | \n", - "0.504520 | \n", - "
| ENSG00000198901 | \n", - "7823 | \n", - "-0.064297 | \n", - "-0.118845 | \n", - "0.226257 | \n", - "18985.527055 | \n", - "False | \n", - "1.054674 | \n", - "0.241087 | \n", - "1.078172 | \n", - "1.031176 | \n", - "
| ENSG00000025770 | \n", - "2994 | \n", - "-1.348303 | \n", - "-0.182646 | \n", - "0.712977 | \n", - "1629.850920 | \n", - "False | \n", - "0.472816 | \n", - "-1.634914 | \n", - "0.678965 | \n", - "0.266666 | \n", - "
| ENSG00000040633 | \n", - "3133 | \n", - "0.799237 | \n", - "-0.193581 | \n", - "0.711746 | \n", - "2348.576044 | \n", - "False | \n", - "0.485659 | \n", - "1.394904 | \n", - "0.354472 | \n", - "0.616845 | \n", - "
| ENSG00000117222 | \n", - "10043 | \n", - "0.106058 | \n", - "-0.033637 | \n", - "0.074049 | \n", - "10076.393494 | \n", - "False | \n", - "1.644935 | \n", - "1.886527 | \n", - "1.584499 | \n", - "1.705370 | \n", - "
| ENSG00000164715 | \n", - "3582 | \n", - "-0.086822 | \n", - "-0.206028 | \n", - "0.658133 | \n", - "6610.097841 | \n", - "False | \n", - "0.526679 | \n", - "0.181127 | \n", - "0.542522 | \n", - "0.510836 | \n", - "
| ENSG00000162585 | \n", - "207 | \n", - "-0.479314 | \n", - "-0.074472 | \n", - "0.629507 | \n", - "5712.621467 | \n", - "False | \n", - "0.203370 | \n", - "-0.643109 | \n", - "0.236846 | \n", - "0.169894 | \n", - "
| ENSG00000260257 | \n", - "10465 | \n", - "-0.050353 | \n", - "-0.033637 | \n", - "0.074049 | \n", - "58218.712458 | \n", - "False | \n", - "1.900572 | \n", - "-0.225748 | \n", - "1.933735 | \n", - "1.867408 | \n", - "
| ENSG00000091490 | \n", - "4000 | \n", - "-0.997151 | \n", - "-0.182881 | \n", - "0.613937 | \n", - "2989.093643 | \n", - "False | \n", - "0.566992 | \n", - "-1.326308 | \n", - "0.755491 | \n", - "0.378492 | \n", - "
| ENSG00000258016 | \n", - "8751 | \n", - "-0.047766 | \n", - "-0.100084 | \n", - "0.158395 | \n", - "26436.992479 | \n", - "False | \n", - "1.238020 | \n", - "0.330304 | \n", - "1.258512 | \n", - "1.217527 | \n", - "
| ENSG00000123636 | \n", - "6033 | \n", - "-0.138440 | \n", - "-0.158209 | \n", - "0.370987 | \n", - "11903.379143 | \n", - "False | \n", - "0.787534 | \n", - "0.053285 | \n", - "0.825291 | \n", - "0.749777 | \n", - "
| ENSG00000253900 | \n", - "2950 | \n", - "0.700587 | \n", - "-0.182789 | \n", - "0.716054 | \n", - "2885.450147 | \n", - "False | \n", - "0.469012 | \n", - "1.233672 | \n", - "0.357320 | \n", - "0.580704 | \n", - "
| ENSG00000263986 | \n", - "10193 | \n", - "0.014580 | \n", - "-0.033637 | \n", - "0.074049 | \n", - "48312.564384 | \n", - "False | \n", - "1.717582 | \n", - "0.651147 | \n", - "1.708903 | \n", - "1.726260 | \n", - "
| ENSG00000196826 | \n", - "6706 | \n", - "-0.058049 | \n", - "-0.145785 | \n", - "0.301443 | \n", - "14061.948733 | \n", - "False | \n", - "0.878815 | \n", - "0.291054 | \n", - "0.896493 | \n", - "0.861137 | \n", - "
| ENSG00000092036 | \n", - "7867 | \n", - "-0.247923 | \n", - "-0.118681 | \n", - "0.219516 | \n", - "16939.805973 | \n", - "False | \n", - "1.060964 | \n", - "-0.588761 | \n", - "1.151903 | \n", - "0.970026 | \n", - "
| \n", - " | ... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "
11085 rows \u00d7 10 columns
\n", - "